gpu_worker.py 43.7 KB
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# 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 gc
import os
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from collections.abc import Callable
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from contextlib import AbstractContextManager, nullcontext
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from datetime import timedelta
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from types import NoneType
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from typing import TYPE_CHECKING, Any
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import numpy as np
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import torch
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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 CUDAGraphMode, VllmConfig, set_current_vllm_config
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from vllm.config.compilation import CompilationMode
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from vllm.distributed import (
    ensure_model_parallel_initialized,
    init_distributed_environment,
    set_custom_all_reduce,
)
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from vllm.distributed.ec_transfer import ensure_ec_transfer_initialized
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from vllm.distributed.eplb.eplb_utils import override_envs_for_eplb
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from vllm.distributed.kv_transfer import (
    ensure_kv_transfer_initialized,
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    ensure_kv_transfer_shutdown,
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    get_kv_transfer_group,
    has_kv_transfer_group,
)
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from vllm.distributed.parallel_state import (
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    Handle,
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    get_pp_group,
    get_tp_group,
)
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from vllm.distributed.weight_transfer import WeightTransferEngineFactory
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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.warmup.kernel_warmup import kernel_warmup
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from vllm.platforms import current_platform
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from vllm.profiler.wrapper import CudaProfilerWrapper, TorchProfilerWrapper
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import SupportedTask
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from vllm.tracing import instrument
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from vllm.utils.mem_constants import GiB_bytes
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from vllm.utils.mem_utils import MemorySnapshot, format_gib, memory_profiling
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from vllm.utils.torch_utils import set_random_seed
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from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
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from vllm.v1.outputs import (
    AsyncModelRunnerOutput,
    DraftTokenIds,
    ModelRunnerOutput,
)
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from vllm.v1.utils import compute_iteration_details, report_usage_stats
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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.worker.workspace import init_workspace_manager
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from ...model_executor.model_loader import TensorizerLoader
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from .gpu.warmup import warmup_kernels
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from .utils import request_memory

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logger = init_logger(__name__)

if TYPE_CHECKING:
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    from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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    from vllm.v1.worker.gpu_model_runner import GPUModelRunner
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class AsyncIntermediateTensors(IntermediateTensors):
    """IntermediateTensors with lazy comm synchronization"""

    def __init__(
        self,
        tensors: dict[str, torch.Tensor],
        comm_handles: list[Handle] | None = None,
        comm_postprocess: list[Callable[[], None]] | None = None,
    ) -> None:
        super().__init__(tensors)
        self._comm_handles = comm_handles
        self._comm_postprocess = comm_postprocess
        self._comm_waited = False

    def wait_for_comm(self) -> None:
        if self._comm_waited:
            return
        if self._comm_handles:
            for handle in self._comm_handles:
                handle.wait()
        if self._comm_postprocess:
            for fn in self._comm_postprocess:
                fn()
        self._comm_waited = True

    def __getattribute__(self, name: str):
        # ensure `.tensors` is ready before use
        if name == "tensors" and not object.__getattribute__(self, "_comm_waited"):
            object.__getattribute__(self, "wait_for_comm")()
        return object.__getattribute__(self, name)


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class Worker(WorkerBase):
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    def __init__(
        self,
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        vllm_config: VllmConfig,
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        local_rank: int,
        rank: int,
        distributed_init_method: str,
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        is_driver_worker: bool = False,
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    ):
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        super().__init__(
            vllm_config=vllm_config,
            local_rank=local_rank,
            rank=rank,
            distributed_init_method=distributed_init_method,
            is_driver_worker=is_driver_worker,
        )
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        # configure float32 matmul precision according to vLLM env.
        precision = envs.VLLM_FLOAT32_MATMUL_PRECISION
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        torch.set_float32_matmul_precision(precision)
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        from vllm.distributed.elastic_ep.elastic_execute import ElasticEPScalingExecutor

        self.elastic_ep_executor = ElasticEPScalingExecutor(self)

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        # Buffers saved before sleep
        self._sleep_saved_buffers: dict[str, torch.Tensor] = {}

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        # Weight transfer engine (initialized on-demand)
        self.weight_transfer_engine = (
            WeightTransferEngineFactory.create_engine(
                self.vllm_config.weight_transfer_config,
                self.vllm_config.parallel_config,
            )
            if self.vllm_config.weight_transfer_config is not None
            else None
        )

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        # Torch/CUDA profiler. Enabled and configured through profiler_config.
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        # Profiler wrapper is created lazily in profile() when start is called,
        # so we have all the information needed for proper trace naming.
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        self.profiler: Any | None = None
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        self.profiler_config = vllm_config.profiler_config

        # Only validate profiler config is valid, don't instantiate yet
        if self.profiler_config.profiler not in ("torch", "cuda", None):
            raise ValueError(f"Unknown profiler type: {self.profiler_config.profiler}")
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        self.use_v2_model_runner = envs.VLLM_USE_V2_MODEL_RUNNER
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        # pending non-blocking PP send work from the previous iteration
        self._pp_send_work: list[Handle] = []
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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
        if level == 2:
            model = self.model_runner.model
            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()
        freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
        used_bytes = total - free_bytes_after_sleep
        assert freed_bytes >= 0, "Memory usage increased after sleeping."
        logger.info(
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            "Sleep mode freed %s GiB memory, %s GiB memory is still in use.",
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            format_gib(freed_bytes),
            format_gib(used_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
        if len(self._sleep_saved_buffers):
            model = self.model_runner.model
            for name, buffer in model.named_buffers():
                if name in self._sleep_saved_buffers:
                    buffer.data.copy_(self._sleep_saved_buffers[name].data)
            self._sleep_saved_buffers = {}

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        # If the KV cache has just been woken up,
        # the internal state of cache_engine must be reset,
        # especially the FP8 scaling factor.
        if (
            (tags is None or "kv_cache" in tags)
            and self.cache_config.cache_dtype.startswith("fp8")
            and hasattr(self.model_runner, "init_fp8_kv_scales")
        ):
            self.model_runner.init_fp8_kv_scales()

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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:
            from vllm.device_allocator.cumem import CuMemAllocator

            allocator = CuMemAllocator.get_instance()
            if tag == "weights":
                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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            return allocator.use_memory_pool(tag=tag)
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        else:
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            return nullcontext()
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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
        self.cache_config.num_cpu_blocks = num_cpu_blocks

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    @instrument(span_name="Init device")
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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.
            os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
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            parallel_config = self.parallel_config
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            if (
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                parallel_config.distributed_executor_backend
                not in ("ray", "external_launcher")
                and parallel_config.data_parallel_backend != "ray"
                and parallel_config.nnodes_within_dp == 1
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            ):
                # Use local DP rank if available, otherwise use global DP rank.
                dp_local_rank = self.parallel_config.data_parallel_rank_local
                if dp_local_rank is None:
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                    dp_local_rank = self.parallel_config.data_parallel_index
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                tp_pp_world_size = (
                    self.parallel_config.pipeline_parallel_size
                    * self.parallel_config.tensor_parallel_size
                )

                # DP_LOCAL_RANK * TP_PP_WORLD_SIZE + TP_LOCAL_RANK
                self.local_rank += dp_local_rank * tp_pp_world_size
                assert self.local_rank < torch.cuda.device_count(), (
                    f"DP adjusted local rank {self.local_rank} is out of bounds. "
                )
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                visible_device_count = (
                    torch.cuda.device_count() if torch.cuda.is_available() else 0
                )
                assert self.parallel_config.local_world_size <= visible_device_count, (
                    f"local_world_size ({self.parallel_config.local_world_size}) must "
                    f"be less than or equal to the number of visible devices "
                    f"({visible_device_count})."
                )
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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
            # memory snapshot
            # This ensures NCCL buffers are allocated before we measure
            # available memory
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            init_worker_distributed_environment(
                self.vllm_config,
                self.rank,
                self.distributed_init_method,
                self.local_rank,
                current_platform.dist_backend,
            )
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            if self.use_v2_model_runner:
                logger.info_once("Using V2 Model Runner", scope="local")

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            # Set random seed.
            set_random_seed(self.model_config.seed)

            # Now take memory snapshot after NCCL is initialized
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            gc.collect()
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            torch.accelerator.empty_cache()
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            # take current memory snapshot
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            self.init_snapshot = init_snapshot = MemorySnapshot(device=self.device)
            self.requested_memory = request_memory(init_snapshot, self.cache_config)
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            logger.debug("worker init memory snapshot: %r", self.init_snapshot)
            logger.debug(
                "worker requested memory: %sGiB", format_gib(self.requested_memory)
            )
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        else:
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            raise RuntimeError(f"Not support device type: {self.device_config.device}")
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        # Initialize workspace manager
        num_ubatches = 2 if self.vllm_config.parallel_config.enable_dbo else 1
        init_workspace_manager(self.device, num_ubatches)

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        # Construct the model runner
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        if self.use_v2_model_runner:
            from vllm.v1.worker.gpu.model_runner import (
                GPUModelRunner as GPUModelRunnerV2,
            )

            # HACK(woosuk): This is a temporary fix to avoid type errors.
            self.model_runner: GPUModelRunner = GPUModelRunnerV2(  # type: ignore
                self.vllm_config, self.device
            )
        else:
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            from vllm.v1.worker.gpu_model_runner import (
                GPUModelRunner as GPUModelRunnerV1,
            )

            self.model_runner = GPUModelRunnerV1(self.vllm_config, self.device)
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        if self.rank == 0:
            # If usage stat is enabled, collect relevant info.
            report_usage_stats(self.vllm_config)

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    # FIXME(youkaichao & ywang96): Use TorchDispatchMode instead of memory pool
    # to hijack tensor allocation.
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    def load_model(self) -> None:
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        dummy_weights = os.environ.get("VLLM_ELASTIC_EP_SCALE_UP_LAUNCH") == "1"
        if dummy_weights:
            (
                expanded_physical_to_logical,
                num_logical_experts,
                old_num_physical_experts,
            ) = self.elastic_ep_executor.receive_expert_mapping()
            num_physical_experts = expanded_physical_to_logical.shape[1]
            self.parallel_config.eplb_config.num_redundant_experts = (
                num_physical_experts - num_logical_experts
            )

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        with (
            self._maybe_get_memory_pool_context(tag="weights"),
            set_current_vllm_config(self.vllm_config),
        ):
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            self.model_runner.load_model(load_dummy_weights=dummy_weights)

        if dummy_weights:
            self.model_runner.setup_eplb_from_mapping(
                expanded_physical_to_logical, old_num_physical_experts
            )
            self.model_runner.eep_eplb_suppressed = True
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    def update_config(self, overrides: dict[str, Any]) -> None:
        self.model_runner.update_config(overrides)

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    def reload_weights(self, *args, **kwargs) -> None:
        self.model_runner.reload_weights(*args, **kwargs)
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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:
            You may limit the usage of GPU memory
            by adjusting the `gpu_memory_utilization` parameter.
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        """
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        if kv_cache_memory_bytes := self.cache_config.kv_cache_memory_bytes:
            # still need a profile run which compiles the model for
            # max_num_batched_tokens
            self.model_runner.profile_run()

            msg = (
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                f"Initial free memory {format_gib(self.init_snapshot.free_memory)} "
                f"GiB, reserved {format_gib(kv_cache_memory_bytes)} 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 "
                "config when you want manual control of KV cache memory "
                "size. If OOM'ed, check the difference of initial free "
                "memory between the current run and the previous run "
                "where kv_cache_memory_bytes is suggested and update it "
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                "correspondingly."
            )
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            logger.info(msg)
            return kv_cache_memory_bytes

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        # Execute a forward pass with dummy inputs to profile the memory usage
        # of the model.
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        with memory_profiling(
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            self.init_snapshot,
            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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            profile_torch_peak = current_platform.memory_stats(self.device).get(
                "allocated_bytes.all.peak", 0
            )

            # Profile CUDA graph memory if graphs will be captured.
            cudagraph_memory_estimate = 0
            if not self.model_config.enforce_eager:
                cudagraph_memory_estimate = self.model_runner.profile_cudagraph_memory()

        # Use the pre-cudagraph torch peak to avoid double-counting.
        profile_result.torch_peak_increase = (
            profile_torch_peak - profile_result.before_profile.torch_peak
        )
        profile_result.non_kv_cache_memory = (
            profile_result.non_torch_increase
            + profile_result.torch_peak_increase
            + profile_result.weights_memory
        )

        cudagraph_memory_estimate_applied = (
            cudagraph_memory_estimate
            if envs.VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS
            else 0
        )

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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 + cudagraph_memory_estimate_applied
        )
        self.cudagraph_memory_estimate = cudagraph_memory_estimate
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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
        # 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 {format_gib(self.init_snapshot.free_memory)} GiB, "
            f"current free memory {format_gib(free_gpu_memory)} GiB. "
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            "This happens when other processes sharing the same container "
            "release GPU memory while vLLM is profiling during initialization. "
            "To fix this, ensure consistent GPU memory allocation or "
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            "isolate vLLM in its own container."
        )
        self.available_kv_cache_memory_bytes = (
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            self.requested_memory
            - profile_result.non_kv_cache_memory
            - cudagraph_memory_estimate_applied
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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: %s GiB; Requested memory: %f (util), %s GiB",
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            format_gib(self.init_snapshot.free_memory),
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            self.cache_config.gpu_memory_utilization,
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            format_gib(self.requested_memory),
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        )
        logger.debug(
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            "Free memory after profiling: %s GiB (total), %s GiB (within requested)",
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            format_gib(free_gpu_memory),
            format_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: %s GiB",
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            format_gib(self.available_kv_cache_memory_bytes),
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            scope="local",
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        )
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        if cudagraph_memory_estimate > 0:
            total_mem = self.init_snapshot.total_memory
            current_util = self.cache_config.gpu_memory_utilization
            cg_util_delta = cudagraph_memory_estimate / total_mem
            if envs.VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS:
                equiv_util = round(current_util - cg_util_delta, 4)
                suggested_util = min(
                    round(current_util + cg_util_delta, 4),
                    1.0,
                )
                logger.info(
                    "CUDA graph memory profiling is enabled "
                    "(VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1). "
                    "This will become the default in v0.19. "
                    "The current --gpu-memory-utilization=%.4f is equivalent "
                    "to --gpu-memory-utilization=%.4f without CUDA graph "
                    "memory profiling. To maintain the same effective KV "
                    "cache size as before, increase "
                    "--gpu-memory-utilization to %.4f.",
                    current_util,
                    equiv_util,
                    suggested_util,
                )
            else:
                suggested_util = min(
                    round(current_util + cg_util_delta, 4),
                    1.0,
                )
                logger.info(
                    "In v0.19, CUDA graph memory profiling will be enabled "
                    "by default (VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1), "
                    "which more accurately accounts for CUDA graph memory "
                    "during KV cache allocation. To try it now, set "
                    "VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=1 and increase "
                    "--gpu-memory-utilization from %.4f to %.4f to maintain "
                    "the same effective KV cache size.",
                    current_util,
                    suggested_util,
                )

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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:
        """Get KV connector metadata from this worker if available."""

        if not has_kv_transfer_group():
            return None

        connector = get_kv_transfer_group()
        # Return None for connectors that don't need to exchange handshake
        # metadata across workers.
        if (metadata := connector.get_handshake_metadata()) is None:
            return None

        tp_rank = get_tp_group().rank_in_group
        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 update_max_model_len(self, max_model_len: int) -> None:
        """Update max_model_len after auto-fit to GPU memory.

        This is called when max_model_len=-1 is used and the engine
        automatically determines the maximum context length that fits
        in GPU memory. Workers need to update their cached max_model_len
        to match the engine's decision.
        """
        self.model_config.max_model_len = max_model_len
        if self.model_runner is not None:
532
            self.model_runner.update_max_model_len(max_model_len)
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        logger.debug("Updated max_model_len to %d", max_model_len)

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    @instrument(span_name="Allocate KV cache")
536
    def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None:
537
        """Allocate GPU KV cache with the specified kv_cache_config."""
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        # Update local config with adjusted num blocks after profiling,
        # so that it's available to the warmup stage.
        self.cache_config.num_gpu_blocks = kv_cache_config.num_blocks

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        # Init kv cache connector here, because it requires
        # `kv_cache_config`.
        # NOTE(Kuntai): This need to be done before `initialize_kv_cache`,
        # because `initialize_kv_cache` will inject kv cache groups not
        # 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)
549

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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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            with allocator.use_memory_pool(tag="kv_cache"):
                self.model_runner.initialize_kv_cache(kv_cache_config)
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        else:
            self.model_runner.initialize_kv_cache(kv_cache_config)
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        # Build KV-zero metadata outside the CuMem pool so the bookkeeping
        # GPU tensors (seg_addrs, block-id buffers) use the standard PyTorch
        # allocator and are not discarded during sleep/wake cycles.
        if kv_cache_config.needs_kv_cache_zeroing and hasattr(
            self.model_runner, "_init_kv_zero_meta"
        ):
            self.model_runner._init_kv_zero_meta()

567
    @instrument(span_name="Warmup (GPU)")
568
    def compile_or_warm_up_model(self) -> float:
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        warmup_sizes: list[int] = []
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        if self.vllm_config.compilation_config.mode == CompilationMode.VLLM_COMPILE:
            # warm up sizes that are not in cudagraph capture sizes,
            # but users still want to compile for better performance,
            # e.g. for the max-num-batched token size in chunked prefill.
            compile_sizes = self.vllm_config.compilation_config.compile_sizes
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            warmup_sizes = compile_sizes.copy() if compile_sizes is not None else []  # type: ignore[assignment]
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            cg_capture_sizes: list[int] = []

            if self.vllm_config.compilation_config.cudagraph_mode != CUDAGraphMode.NONE:
                cg_sizes = self.vllm_config.compilation_config.cudagraph_capture_sizes
                cg_capture_sizes = [] if cg_sizes is None else cg_sizes
                warmup_sizes = [x for x in warmup_sizes if x not in cg_capture_sizes]

            compile_ranges = self.vllm_config.compilation_config.get_compile_ranges()
            # For each compile_range, if none of the batch sizes
            # in warmup_sizes or cudagraph_capture_sizes are in the range,
            # add the end of the range to ensure compilation/warmup.
            all_sizes = set(cg_capture_sizes)
            all_sizes.update([x for x in warmup_sizes if isinstance(x, int)])
            for compile_range in compile_ranges:
                if not any(x in compile_range for x in all_sizes):
                    warmup_sizes.append(compile_range.end)

594
        # 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):
            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)
598
        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
        # cuda graph capture.
        kernel_warmup(self)

604
        cuda_graph_memory_bytes = 0
605
        if not self.model_config.enforce_eager:
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            cuda_graph_memory_bytes = self.model_runner.capture_model()

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        # Compare actual vs estimated CUDA graph memory (if we did profiling)
        if (
            hasattr(self, "cudagraph_memory_estimate")
            and self.cudagraph_memory_estimate > 0
        ):
            GiB = lambda b: round(b / GiB_bytes, 2)
            diff = abs(cuda_graph_memory_bytes - self.cudagraph_memory_estimate)
            logger.info(
                "CUDA graph pool memory: %s GiB (actual), %s GiB (estimated), "
                "difference: %s GiB (%.1f%%).",
                GiB(cuda_graph_memory_bytes),
                GiB(self.cudagraph_memory_estimate),
                GiB(diff),
                100 * diff / max(cuda_graph_memory_bytes, 1),
            )

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        if self.cache_config.kv_cache_memory_bytes is None and hasattr(
            self, "peak_activation_memory"
        ):
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            # Suggests optimal kv cache memory size if we rely on
            # memory_profiling to guess the kv cache memory size which
            # provides peak_activation_memory and a few other memory
            # consumption. `memory_profiling` does not consider
            # CUDAGraph memory size and may not utilize all gpu memory.
            # Users may want fine-grained control to specify kv cache
            # memory size.

            # empirically observed that the memory profiling may
            # slightly underestimate the memory consumption.
            # So leave a small buffer (=150MiB) to avoid OOM.
            redundancy_buffer_memory = 150 * (1 << 20)
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            non_kv_cache_memory = (
                self.model_runner.model_memory_usage
                + self.peak_activation_memory
                + self.non_torch_memory
                + cuda_graph_memory_bytes
            )
645
            kv_cache_memory_bytes_to_gpu_limit = (
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                self.init_snapshot.free_memory
                - non_kv_cache_memory
                - redundancy_buffer_memory
            )
650
            kv_cache_memory_bytes_to_requested_limit = (
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                int(self.requested_memory)
                - non_kv_cache_memory
                - redundancy_buffer_memory
            )
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            msg = (
                f"Free memory on device "
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                f"({format_gib(self.init_snapshot.free_memory)}/"
                f"{format_gib(self.init_snapshot.total_memory)} GiB) on startup. "
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                f"Desired GPU memory utilization is "
                f"({self.cache_config.gpu_memory_utilization}, "
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                f"{format_gib(self.requested_memory)} GiB). "
                f"Actual usage is {format_gib(self.model_runner.model_memory_usage)} "
                f"GiB for weight, {format_gib(self.peak_activation_memory)} GiB "
                f"for peak activation, {format_gib(self.non_torch_memory)} GiB "
                f"for non-torch memory, and {format_gib(cuda_graph_memory_bytes)} "
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                f"GiB for CUDAGraph memory. Replace gpu_memory_utilization "
                f"config with `--kv-cache-memory="
669
                f"{kv_cache_memory_bytes_to_requested_limit}` "
670
                f"({format_gib(kv_cache_memory_bytes_to_requested_limit)} GiB) to fit "
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                f"into requested memory, or `--kv-cache-memory="
                f"{kv_cache_memory_bytes_to_gpu_limit}` "
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                f"({format_gib(kv_cache_memory_bytes_to_gpu_limit)} GiB) to fully "
674
                f"utilize gpu memory. Current kv cache memory in use is "
675
                f"{format_gib(self.available_kv_cache_memory_bytes)} GiB."
676
            )
677

678
            logger.debug(msg)
679

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        if self.use_v2_model_runner:
            # V2: Run full execute_model + sample_tokens to JIT compile triton kernels.
682
            warmup_kernels(self.model_runner, self.execute_model, self.sample_tokens)
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        elif get_pp_group().is_last_rank:
            # V1: 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
688
            # memory buffers from being cleared by `torch.accelerator.empty_cache`.
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            max_num_reqs = min(
                self.scheduler_config.max_num_seqs,
                self.scheduler_config.max_num_batched_tokens,
            )
693

694
            # We skip EPLB here since we don't want to record dummy metrics
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            hidden_states, last_hidden_states = self.model_runner._dummy_run(
                num_tokens=max_num_reqs,
                skip_eplb=True,
698
                cudagraph_runtime_mode=CUDAGraphMode.NONE,
699
            )
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            if self.model_runner.is_pooling_model:
                self.model_runner._dummy_pooler_run(hidden_states)
            else:
703
                self.model_runner._dummy_sampler_run(hidden_states=last_hidden_states)
704

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        # 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)

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        return self.compilation_config.compilation_time

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

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

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

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    def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        return self.model_runner.get_supported_tasks()
722

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    def get_encoder_timing_stats(self) -> dict[str, dict[str, float | int]]:
        """Get encoder timing stats from model runner."""
        return self.model_runner.get_encoder_timing_stats()

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    def annotate_profile(self, scheduler_output):
        # add trace annotation so that we can easily distinguish
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        # context/generation request numbers in each iteration.
        # A context request is a request that has not yet generated any tokens
731
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733
        if not self.profiler:
            return nullcontext()

734
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        self.profiler.step()

736
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        iteration_details = compute_iteration_details(scheduler_output)

        annotation = "".join(
            [
                "execute_context_",
                str(iteration_details.num_ctx_requests),
                "(",
                str(iteration_details.num_ctx_tokens),
                ")_generation_",
                str(iteration_details.num_generation_requests),
                "(",
                str(iteration_details.num_generation_tokens),
                ")",
            ]
750
        )
751
        return self.profiler.annotate_context_manager(annotation)
752

753
754
    @torch.inference_mode()
    def sample_tokens(
755
        self, grammar_output: "GrammarOutput | None"
756
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758
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput:
        return self.model_runner.sample_tokens(grammar_output)

759
760
    @torch.inference_mode()
    def execute_model(
761
        self, scheduler_output: "SchedulerOutput"
762
    ) -> ModelRunnerOutput | AsyncModelRunnerOutput | None:
763
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768
        # ensure any previous non-blocking PP sends are complete
        if self._pp_send_work:
            for handle in self._pp_send_work:
                handle.wait()
            self._pp_send_work = []

769
        intermediate_tensors = None
770
        forward_pass = scheduler_output.total_num_scheduled_tokens > 0
771
        num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
772
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        all_gather_tensors = {}
        compilation_config = self.vllm_config.compilation_config
        parallel_config = self.vllm_config.parallel_config

        if (
            parallel_config.pipeline_parallel_size > 1
778
            and compilation_config.pass_config.enable_sp
779
780
781
            and forward_pass
        ):
            # currently only supported by V1 GPUModelRunner
782
            assert not self.use_v2_model_runner
783
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789
            num_scheduled_tokens_np = np.array(
                list(scheduler_output.num_scheduled_tokens.values()),
                dtype=np.int32,
            )
            # TODO(lucas): This is pretty gross; ideally we should only ever call
            # `_determine_batch_execution_and_padding` once (will get called again
            # in `execute_model`) but this requires a larger refactor of PP.
790
            _, batch_desc, _, _, _ = (
791
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                self.model_runner._determine_batch_execution_and_padding(
                    num_tokens=num_scheduled_tokens,
                    num_reqs=len(num_scheduled_tokens_np),
                    num_scheduled_tokens_np=num_scheduled_tokens_np,
                    max_num_scheduled_tokens=num_scheduled_tokens_np.max(),
                    use_cascade_attn=False,  # TODO(lucas): Handle cascade attention
                )
798
            )
799
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804
            all_gather_tensors = {
                "residual": not is_residual_scattered_for_sp(
                    self.vllm_config, batch_desc.num_tokens
                )
            }

805
        if forward_pass and not get_pp_group().is_first_rank:
806
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810
            tensor_dict, comm_handles, comm_postprocess = (
                get_pp_group().irecv_tensor_dict(
                    all_gather_group=get_tp_group(),
                    all_gather_tensors=all_gather_tensors,
                )
811
            )
812
            assert tensor_dict is not None
813
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817
            intermediate_tensors = AsyncIntermediateTensors(
                tensor_dict,
                comm_handles=comm_handles,
                comm_postprocess=comm_postprocess,
            )
818

819
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821
822
        with self.annotate_profile(scheduler_output):
            output = self.model_runner.execute_model(
                scheduler_output, intermediate_tensors
            )
823
824
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828
            if (
                self.use_v2_model_runner
                and self.model_runner.is_pooling_model
                and output is None
            ):
                output = self.model_runner.pool()  # type: ignore
829
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831
            if isinstance(
                output, ModelRunnerOutput | AsyncModelRunnerOutput | NoneType
            ):
832
                return output
833

834
        assert isinstance(output, IntermediateTensors)
835
        parallel_config = self.vllm_config.parallel_config
836
        assert (
837
            parallel_config.distributed_executor_backend != "external_launcher"
838
839
            and not get_pp_group().is_last_rank
        )
840

841
842
        # launch non-blocking send of intermediate tensors
        self._pp_send_work = get_pp_group().isend_tensor_dict(
843
844
845
846
            output.tensors,
            all_gather_group=get_tp_group(),
            all_gather_tensors=all_gather_tensors,
        )
847

848
        return None
849

850
    def take_draft_token_ids(self) -> DraftTokenIds | None:
851
852
        return self.model_runner.take_draft_token_ids()

853
854
855
    def profile(self, is_start: bool = True, profile_prefix: str | None = None):
        # Check if profiling is enabled
        if self.profiler_config is None or self.profiler_config.profiler is None:
856
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861
            raise RuntimeError(
                "Profiling is not enabled. Please set --profiler-config to enable "
                "profiling. Example: "
                "'--profiler-config.profiler=torch --profiler-config.torch_profiler_dir"
                "=YOUR_DIR_PATH_TO_DUMP_TRACE'"
            )
862

863
        if is_start:
864
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869
870
871
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875
876
            # Generate the trace name by combining prefix with comprehensive rank suffix
            from vllm.distributed.utils import get_worker_rank_suffix

            rank_suffix = get_worker_rank_suffix(global_rank=self.rank)

            # Build the full trace name
            if profile_prefix:
                trace_name = f"{profile_prefix}_{rank_suffix}"
            else:
                trace_name = rank_suffix

            # Create the profiler wrapper only on the first start call
            if self.profiler is None:
877
878
                profiler_type = self.profiler_config.profiler
                if profiler_type == "torch":
879
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886
887
                    self.profiler = TorchProfilerWrapper(
                        self.profiler_config,
                        worker_name=trace_name,
                        local_rank=self.local_rank,
                        activities=["CPU", "CUDA"],
                    )
                    logger.debug(
                        "Starting torch profiler with trace name: %s", trace_name
                    )
888
                elif profiler_type == "cuda":
889
890
                    self.profiler = CudaProfilerWrapper(self.profiler_config)
                    logger.debug("Starting CUDA profiler")
891
                else:
892
893
894
895
896
897
898
899
                    # Config validation should prevent this code being reached
                    raise ValueError(
                        f"Invalid profiler value of {self.profiler_config.profiler}"
                    )

            # If profiler already initialized, restart profiling but keep
            # the original trace name from the first initialization.
            self.profiler.start()
900
        else:
901
902
903
            if self.profiler is None:
                logger.warning("Profiler was not started, nothing to stop.")
                return
904
905
            self.profiler.stop()

906
    def execute_dummy_batch(self) -> None:
907
        self.model_runner._dummy_run(1, uniform_decode=True)
908

909
910
911
    def add_lora(self, lora_request: LoRARequest) -> bool:
        return self.model_runner.add_lora(lora_request)

912
913
914
    def remove_lora(self, lora_id: int) -> bool:
        return self.model_runner.remove_lora(lora_id)

915
    def list_loras(self) -> set[int]:
916
917
918
919
920
        return self.model_runner.list_loras()

    def pin_lora(self, lora_id: int) -> bool:
        return self.model_runner.pin_lora(lora_id)

921
922
923
924
    def check_health(self) -> None:
        # worker will always be healthy as long as it's running.
        return

925
926
927
    def save_sharded_state(
        self,
        path: str,
928
929
        pattern: str | None = None,
        max_size: int | None = None,
930
    ) -> None:
931
        from vllm.model_executor.model_loader import ShardedStateLoader
932

933
934
935
936
937
938
939
        ShardedStateLoader.save_model(
            self.model_runner.model,
            path,
            pattern=pattern,
            max_size=max_size,
        )

940
941
942
    def save_tensorized_model(self, tensorizer_config: "TensorizerConfig") -> None:
        TensorizerLoader.save_model(
            self.get_model(),
943
            tensorizer_config=tensorizer_config,
944
            model_config=self.model_config,
945
        )
946

947
948
949
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1003
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1009
    def init_weight_transfer_engine(self, init_info: dict) -> None:
        """
        Initialize weight transfer mechanism.
        For NCCL backend, this creates a process group with the trainer.

        Args:
            init_info: Dictionary containing backend-specific initialization info
        """
        if self.weight_transfer_engine is None:
            raise RuntimeError(
                "Weight transfer not configured. "
                "Please set weight_transfer_config to enable weight transfer."
            )
        # Parse dict into backend-specific typed dataclass
        typed_init_info = self.weight_transfer_engine.parse_init_info(init_info)
        self.weight_transfer_engine.init_transfer_engine(typed_init_info)

    def update_weights(self, update_info: dict) -> None:
        """
        Batched weight update from the trainer.

        Args:
            update_info: Dictionary containing backend-specific update info
        """
        if self.weight_transfer_engine is None:
            raise RuntimeError(
                "Weight transfer not configured. "
                "Please set weight_transfer_config to enable weight transfer."
            )

        # Parse dict into backend-specific typed dataclass
        typed_update_info = self.weight_transfer_engine.parse_update_info(update_info)

        model = self.model_runner.model

        if typed_update_info.is_checkpoint_format:
            from vllm.model_executor.model_loader.reload import (
                finalize_layerwise_reload,
                initialize_layerwise_reload,
            )

            # Use layerwise reload pattern for checkpoint format weights
            with torch.device(self.device):
                initialize_layerwise_reload(model)
                self.weight_transfer_engine.receive_weights(
                    typed_update_info,
                    load_weights=model.load_weights,
                )
                finalize_layerwise_reload(model, self.model_config)
        else:
            # Weights are already in kernel format, copy directly
            def load_weights_direct(
                weights: list[tuple[str, torch.Tensor]],
            ) -> None:
                for name, weight in weights:
                    param = model.get_parameter(name)
                    param.copy_(weight)

            self.weight_transfer_engine.receive_weights(
                typed_update_info,
                load_weights=load_weights_direct,
            )

1010
    def shutdown(self) -> None:
1011
1012
1013
        # has_kv_transfer_group can be None during interpreter shutdown.
        if ensure_kv_transfer_shutdown is not None:
            ensure_kv_transfer_shutdown()
1014
1015
        if self.profiler is not None:
            self.profiler.shutdown()
1016

1017
1018
1019
        if weight_transfer_engine := getattr(self, "weight_transfer_engine", None):
            weight_transfer_engine.shutdown()

1020
1021
1022
    def elastic_ep_execute(self, execute_method: str, *args, **kwargs):
        return self.elastic_ep_executor.execute(execute_method, *args, **kwargs)

1023
1024

def init_worker_distributed_environment(
1025
    vllm_config: VllmConfig,
1026
    rank: int,
1027
    distributed_init_method: str | None = None,
1028
    local_rank: int = -1,
1029
    backend: str = "nccl",
1030
1031
) -> None:
    """Initialize the distributed environment."""
1032
    attention_config = vllm_config.attention_config
1033
    parallel_config = vllm_config.parallel_config
1034
1035
    from vllm.model_executor.layers.batch_invariant import init_batch_invariance

1036
    init_batch_invariance(attention_config.backend)
1037
    override_envs_for_eplb(parallel_config)
1038
1039
    set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)

1040
    init_method = distributed_init_method or "env://"
1041
1042
1043
1044
1045

    timeout = None
    if parallel_config.distributed_timeout_seconds is not None:
        timeout = timedelta(seconds=parallel_config.distributed_timeout_seconds)

1046
    init_distributed_environment(
1047
1048
1049
1050
1051
1052
        parallel_config.world_size,
        rank,
        init_method,
        local_rank,
        backend,
        timeout,
1053
    )
1054

1055
1056
1057
    ensure_model_parallel_initialized(
        parallel_config.tensor_parallel_size,
        parallel_config.pipeline_parallel_size,
1058
        parallel_config.prefill_context_parallel_size,
1059
1060
        parallel_config.decode_context_parallel_size,
    )
1061
1062
1063
1064

    # Init ec connector here before KV caches caches init
    # NOTE: We do not init KV caches for Encoder-only instance in EPD disagg mode
    ensure_ec_transfer_initialized(vllm_config)