vllm.py 72.3 KB
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import copy
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import getpass
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import json
import os
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import tempfile
import threading
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import time
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from contextlib import contextmanager
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from dataclasses import is_dataclass
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from datetime import datetime
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from enum import IntEnum
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from functools import lru_cache
from pathlib import Path
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from typing import TYPE_CHECKING, Any, Literal, TypeVar, get_args
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import torch
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from pydantic import ConfigDict, Field, model_validator
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import vllm.envs as envs
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from vllm.logger import enable_trace_function_call, init_logger
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from vllm.transformers_utils.runai_utils import is_runai_obj_uri
from vllm.utils import random_uuid
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from vllm.utils.hashing import safe_hash
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from .attention import AttentionConfig
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from .cache import CacheConfig
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from .compilation import CompilationConfig, CompilationMode, CUDAGraphMode
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from .device import DeviceConfig
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from .ec_transfer import ECTransferConfig
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from .kernel import KernelConfig
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from .kv_events import KVEventsConfig
from .kv_transfer import KVTransferConfig
from .load import LoadConfig
from .lora import LoRAConfig
from .model import ModelConfig
from .observability import ObservabilityConfig
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from .offload import OffloadConfig
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from .parallel import ParallelConfig
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from .profiler import ProfilerConfig
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from .scheduler import SchedulerConfig
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from .speculative import EagleModelTypes, SpeculativeConfig
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from .structured_outputs import StructuredOutputsConfig
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from .utils import SupportsHash, config, replace
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from .weight_transfer import WeightTransferConfig
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if TYPE_CHECKING:
    from transformers import PretrainedConfig

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    from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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    from vllm.v1.kv_cache_interface import KVCacheConfig
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else:
    PretrainedConfig = Any

    QuantizationConfig = Any

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    KVCacheConfig = Any

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logger = init_logger(__name__)


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class OptimizationLevel(IntEnum):
    """Optimization level enum."""

    O0 = 0
    """O0 : No optimization. no compilation, no cudagraphs, no other
    optimization, just starting up immediately"""
    O1 = 1
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    """O1: Quick optimizations. Dynamo+Inductor compilation and Piecewise
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    cudagraphs"""
    O2 = 2
    """O2: Full optimizations. -O1 as well as Full and Piecewise cudagraphs."""
    O3 = 3
    """O3: Currently the same as -O2s."""


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PerformanceMode = Literal["balanced", "interactivity", "throughput"]

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IS_QUANTIZED = False
IS_DENSE = False
# The optimizations that depend on these properties currently set to False
# in all cases.
# if model_config is not None:
#     IS_QUANTIZED = lambda c: c.model_config.is_quantized()
#     IS_DENSE = lambda c: not c.model_config.is_model_moe()
# See https://github.com/vllm-project/vllm/issues/25689.


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def enable_norm_fusion(cfg: "VllmConfig") -> bool:
    """Enable if either RMS norm or quant FP8 custom op is active;
    otherwise Inductor handles fusion."""

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    return cfg.compilation_config.is_custom_op_enabled(
        "rms_norm"
    ) or cfg.compilation_config.is_custom_op_enabled("quant_fp8")


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def enable_act_fusion(cfg: "VllmConfig") -> bool:
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    """
    Enable if either SiLU+Mul or quant FP8 custom op is active;
    otherwise Inductor handles fusion.
    Also enable for FP4 models as FP4 quant is always custom so Inductor cannot fuse it.
    """
    return (
        cfg.compilation_config.is_custom_op_enabled("silu_and_mul")
        or cfg.compilation_config.is_custom_op_enabled("quant_fp8")
        or (cfg.model_config is not None and cfg.model_config.is_nvfp4_quantized())
    )
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def enable_allreduce_rms_fusion(cfg: "VllmConfig") -> bool:
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    """Enable if TP > 1 and Hopper/Blackwell and flashinfer installed."""
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    from vllm.platforms import current_platform
    from vllm.utils.flashinfer import has_flashinfer

    return (
        cfg.parallel_config.tensor_parallel_size > 1
        and current_platform.is_cuda()
        and has_flashinfer()
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        and (
            current_platform.is_device_capability(100)
            or current_platform.is_device_capability(90)
        )
        # tp-dp combination broken:
        # https://github.com/vllm-project/vllm/issues/34458
        and cfg.parallel_config.data_parallel_size == 1
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    )


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def enable_rope_kvcache_fusion(cfg: "VllmConfig") -> bool:
    """Enable if rotary embedding custom op is active and
    use_inductor_graph_partition is enabled.
    """
    from vllm._aiter_ops import rocm_aiter_ops

    return (
        rocm_aiter_ops.is_enabled()
        and cfg.compilation_config.is_custom_op_enabled("rotary_embedding")
        and cfg.compilation_config.use_inductor_graph_partition
    )


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def enable_norm_pad_fusion(cfg: "VllmConfig") -> bool:
    """Enable if using AITER RMSNorm and AITER Triton GEMMs
    and hidden size is 2880 i.e. gpt-oss; otherwise Inductor handles fusion."""
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    from vllm._aiter_ops import rocm_aiter_ops
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    return (
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        rocm_aiter_ops.is_rmsnorm_enabled()
        and not rocm_aiter_ops.is_triton_gemm_enabled()
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        and cfg.model_config is not None
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        and cfg.model_config.get_hidden_size() == 2880
    )


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OPTIMIZATION_LEVEL_00 = {
    "compilation_config": {
        "pass_config": {
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            "fuse_norm_quant": False,
            "fuse_act_quant": False,
            "fuse_allreduce_rms": False,
            "fuse_attn_quant": False,
            "enable_sp": False,
            "fuse_gemm_comms": False,
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            "fuse_act_padding": False,
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            "fuse_rope_kvcache": False,
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        },
        "cudagraph_mode": CUDAGraphMode.NONE,
        "use_inductor_graph_partition": False,
    },
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    "kernel_config": {
        "enable_flashinfer_autotune": False,
    },
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}
OPTIMIZATION_LEVEL_01 = {
    "compilation_config": {
        "pass_config": {
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            "fuse_norm_quant": enable_norm_fusion,
            "fuse_act_quant": enable_act_fusion,
            "fuse_allreduce_rms": False,
            "fuse_attn_quant": False,
            "enable_sp": False,
            "fuse_gemm_comms": False,
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            "fuse_act_padding": enable_norm_pad_fusion,
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            "fuse_rope_kvcache": enable_rope_kvcache_fusion,
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        },
        "cudagraph_mode": CUDAGraphMode.PIECEWISE,
        "use_inductor_graph_partition": False,
    },
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    "kernel_config": {
        "enable_flashinfer_autotune": True,
    },
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}
OPTIMIZATION_LEVEL_02 = {
    "compilation_config": {
        "pass_config": {
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            "fuse_norm_quant": enable_norm_fusion,
            "fuse_act_quant": enable_act_fusion,
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            "fuse_allreduce_rms": enable_allreduce_rms_fusion,
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            "fuse_attn_quant": IS_QUANTIZED,
            "enable_sp": IS_DENSE,
            "fuse_gemm_comms": IS_DENSE,
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            "fuse_act_padding": enable_norm_pad_fusion,
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            "fuse_rope_kvcache": enable_rope_kvcache_fusion,
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        },
        "cudagraph_mode": CUDAGraphMode.FULL_AND_PIECEWISE,
        "use_inductor_graph_partition": False,
    },
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    "kernel_config": {
        "enable_flashinfer_autotune": True,
    },
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}
OPTIMIZATION_LEVEL_03 = {
    "compilation_config": {
        "pass_config": {
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            "fuse_norm_quant": enable_norm_fusion,
            "fuse_act_quant": enable_act_fusion,
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            "fuse_allreduce_rms": enable_allreduce_rms_fusion,
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            "fuse_attn_quant": IS_QUANTIZED,
            "enable_sp": IS_DENSE,
            "fuse_gemm_comms": IS_DENSE,
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            "fuse_act_padding": enable_norm_pad_fusion,
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            "fuse_rope_kvcache": enable_rope_kvcache_fusion,
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        },
        "cudagraph_mode": CUDAGraphMode.FULL_AND_PIECEWISE,
        "use_inductor_graph_partition": False,
    },
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    "kernel_config": {
        "enable_flashinfer_autotune": True,
    },
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}

OPTIMIZATION_LEVEL_TO_CONFIG = {
    OptimizationLevel.O0: OPTIMIZATION_LEVEL_00,
    OptimizationLevel.O1: OPTIMIZATION_LEVEL_01,
    OptimizationLevel.O2: OPTIMIZATION_LEVEL_02,
    OptimizationLevel.O3: OPTIMIZATION_LEVEL_03,
}


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@config(config=ConfigDict(arbitrary_types_allowed=True))
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class VllmConfig:
    """Dataclass which contains all vllm-related configuration. This
    simplifies passing around the distinct configurations in the codebase.
    """

    # TODO: use default_factory once default constructing ModelConfig doesn't
    # try to download a model
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    model_config: ModelConfig = Field(default=None)
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    """Model configuration."""
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    cache_config: CacheConfig = Field(default_factory=CacheConfig)
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    """Cache configuration."""
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    parallel_config: ParallelConfig = Field(default_factory=ParallelConfig)
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    """Parallel configuration."""
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    scheduler_config: SchedulerConfig = Field(
        default_factory=SchedulerConfig.default_factory,
    )
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    """Scheduler configuration."""
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    device_config: DeviceConfig = Field(default_factory=DeviceConfig)
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    """Device configuration."""
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    load_config: LoadConfig = Field(default_factory=LoadConfig)
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    """Load configuration."""
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    offload_config: OffloadConfig = Field(default_factory=OffloadConfig)
    """Model weight offloading configuration."""
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    attention_config: AttentionConfig = Field(default_factory=AttentionConfig)
    """Attention configuration."""
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    kernel_config: KernelConfig = Field(default_factory=KernelConfig)
    """Kernel configuration."""
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    lora_config: LoRAConfig | None = None
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    """LoRA configuration."""
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    speculative_config: SpeculativeConfig | None = None
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    """Speculative decoding configuration."""
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    structured_outputs_config: StructuredOutputsConfig = Field(
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        default_factory=StructuredOutputsConfig
    )
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    """Structured outputs configuration."""
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    observability_config: ObservabilityConfig = Field(
        default_factory=ObservabilityConfig
    )
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    """Observability configuration."""
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    quant_config: QuantizationConfig | None = None
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    """Quantization configuration."""
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    compilation_config: CompilationConfig = Field(default_factory=CompilationConfig)
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    """`torch.compile` and cudagraph capture configuration for the model.

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    As a shorthand, one can append compilation arguments via
    -cc.parameter=argument such as `-cc.mode=3` (same as `-cc='{"mode":3}'`).
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    You can specify the full compilation config like so:
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    `{"mode": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}`
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    """
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    profiler_config: ProfilerConfig = Field(default_factory=ProfilerConfig)
    """Profiling configuration."""
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    kv_transfer_config: KVTransferConfig | None = None
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    """The configurations for distributed KV cache transfer."""
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    kv_events_config: KVEventsConfig | None = None
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    """The configurations for event publishing."""
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    ec_transfer_config: ECTransferConfig | None = None
    """The configurations for distributed EC cache transfer."""
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    # some opaque config, only used to provide additional information
    # for the hash computation, mainly used for testing, debugging or out of
    # tree config registration.
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    additional_config: dict | SupportsHash = Field(default_factory=dict)
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    """Additional config for specified platform. Different platforms may
    support different configs. Make sure the configs are valid for the platform
    you are using. Contents must be hashable."""
    instance_id: str = ""
    """The ID of the vLLM instance."""
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    optimization_level: OptimizationLevel = OptimizationLevel.O2
    """The optimization level. These levels trade startup time cost for
    performance, with -O0 having the best startup time and -O3 having the best
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    performance. -O2 is used by default. See OptimizationLevel for full
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    description."""
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    performance_mode: PerformanceMode = "balanced"
    """Performance mode for runtime behavior, 'balanced' is the default.
    'interactivity' favors low end-to-end per-request latency at small batch
    sizes (fine-grained CUDA graphs, latency-oriented kernels).
    'throughput' favors aggregate tokens/sec at high concurrency (larger CUDA
    graphs, more aggressive batching, throughput-oriented kernels)."""

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    weight_transfer_config: WeightTransferConfig | None = None
    """The configurations for weight transfer during RL training."""

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    def compute_hash(self) -> str:
        """
        WARNING: Whenever a new field is added to this config,
        ensure that it is included in the factors list if
        it affects the computation graph.

        Provide a hash that uniquely identifies all the configs
        that affect the structure of the computation
        graph from input ids/embeddings to the final hidden states,
        excluding anything before input ids/embeddings and after
        the final hidden states.
        """
        factors: list[Any] = []

        # summarize vllm config
        vllm_factors: list[Any] = []
        from vllm import __version__
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        vllm_factors.append(__version__)
        if self.model_config:
            vllm_factors.append(self.model_config.compute_hash())
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            if (
                self.compilation_config
                and getattr(self.compilation_config, "compile_mm_encoder", False)
                and self.model_config.multimodal_config
            ):
                vllm_factors.append(self.model_config.multimodal_config.compute_hash())
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        else:
            vllm_factors.append("None")
        if self.cache_config:
            vllm_factors.append(self.cache_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.parallel_config:
            vllm_factors.append(self.parallel_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.scheduler_config:
            vllm_factors.append(self.scheduler_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.device_config:
            vllm_factors.append(self.device_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.load_config:
            vllm_factors.append(self.load_config.compute_hash())
        else:
            vllm_factors.append("None")
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        if self.offload_config:
            vllm_factors.append(self.offload_config.compute_hash())
        else:
            vllm_factors.append("None")
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        if self.attention_config:
            vllm_factors.append(self.attention_config.compute_hash())
        else:
            vllm_factors.append("None")
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        if self.lora_config:
            vllm_factors.append(self.lora_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.speculative_config:
            vllm_factors.append(self.speculative_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.structured_outputs_config:
            vllm_factors.append(self.structured_outputs_config.compute_hash())
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        if self.profiler_config:
            vllm_factors.append(self.profiler_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        vllm_factors.append(self.observability_config.compute_hash())
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        if self.quant_config:
            pass  # should be captured by model_config.quantization
        if self.compilation_config:
            vllm_factors.append(self.compilation_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.kv_transfer_config:
            vllm_factors.append(self.kv_transfer_config.compute_hash())
        else:
            vllm_factors.append("None")
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        if self.ec_transfer_config:
            vllm_factors.append(self.ec_transfer_config.compute_hash())
        else:
            vllm_factors.append("None")
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        if self.additional_config:
            if isinstance(additional_config := self.additional_config, dict):
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                additional_config_hash = safe_hash(
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                    json.dumps(additional_config, sort_keys=True).encode(),
                    usedforsecurity=False,
                ).hexdigest()
            else:
                additional_config_hash = additional_config.compute_hash()
            vllm_factors.append(additional_config_hash)
        else:
            vllm_factors.append("None")
        factors.append(vllm_factors)

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        hash_str = safe_hash(str(factors).encode(), usedforsecurity=False).hexdigest()[
            :10
        ]
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        return hash_str

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    @property
    def num_speculative_tokens(self) -> int:
        if (
            self.speculative_config is not None
            and self.speculative_config.num_speculative_tokens is not None
        ):
            return self.speculative_config.num_speculative_tokens
        return 0

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    @property
    def needs_dp_coordinator(self) -> bool:
        """
        Determine if the DPCoordinator process is needed.

        The DPCoordinator is needed in two cases:
        1. For MoE models with DP > 1: to handle wave coordination
           (even in external LB mode, since wave coordination runs in the coordinator)
        2. For non-MoE models in internal/hybrid LB mode: to collect and publish
           queue stats for load balancing across DP ranks

        Returns:
            True if DPCoordinator process is needed, False otherwise.
        """

        # For non-MoE models, only need coordinator in internal/hybrid LB mode
        # (for stats collection).
        return self.parallel_config.data_parallel_size > 1 and (
            self.model_config is None
            or self.model_config.is_moe
            or not self.parallel_config.data_parallel_external_lb
        )

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    def enable_trace_function_call_for_thread(self) -> None:
        """
        Set up function tracing for the current thread,
        if enabled via the `VLLM_TRACE_FUNCTION` environment variable.
        """
        if envs.VLLM_TRACE_FUNCTION:
            tmp_dir = tempfile.gettempdir()
            # add username to tmp_dir to avoid permission issues
            tmp_dir = os.path.join(tmp_dir, getpass.getuser())
            filename = (
                f"VLLM_TRACE_FUNCTION_for_process_{os.getpid()}"
                f"_thread_{threading.get_ident()}_at_{datetime.now()}.log"
            ).replace(" ", "_")
            log_path = os.path.join(
                tmp_dir,
                "vllm",
                f"vllm-instance-{self.instance_id}",
                filename,
            )
            os.makedirs(os.path.dirname(log_path), exist_ok=True)
            enable_trace_function_call(log_path)

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    @staticmethod
    def _get_quantization_config(
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        model_config: ModelConfig, load_config: LoadConfig
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    ) -> QuantizationConfig | None:
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        """Get the quantization config."""
        from vllm.platforms import current_platform
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        if model_config.quantization is not None:
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            from vllm.model_executor.model_loader.weight_utils import get_quant_config

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            quant_config = get_quant_config(model_config, load_config)
            capability_tuple = current_platform.get_device_capability()

            if capability_tuple is not None:
                capability = capability_tuple.to_int()
                if capability < quant_config.get_min_capability():
                    raise ValueError(
                        f"The quantization method {model_config.quantization} "
                        "is not supported for the current GPU. Minimum "
                        f"capability: {quant_config.get_min_capability()}. "
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                        f"Current capability: {capability}."
                    )
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            supported_dtypes = quant_config.get_supported_act_dtypes()
            if model_config.dtype not in supported_dtypes:
                raise ValueError(
                    f"{model_config.dtype} is not supported for quantization "
                    f"method {model_config.quantization}. Supported dtypes: "
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                    f"{supported_dtypes}"
                )
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            quant_config.maybe_update_config(model_config.model)
            return quant_config
        return None

    @staticmethod
    def get_quantization_config(
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        model_config: ModelConfig, load_config: LoadConfig
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    ) -> QuantizationConfig | None:
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        import copy

        # For some reason, the _ version of this modifies the model_config
        # object, so using deepcopy to avoid this problem.
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        return VllmConfig._get_quantization_config(
            copy.deepcopy(model_config), load_config
        )
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    def with_hf_config(
        self,
        hf_config: PretrainedConfig,
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        architectures: list[str] | None = None,
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    ) -> "VllmConfig":
        if architectures is not None:
            hf_config = copy.deepcopy(hf_config)
            hf_config.architectures = architectures

        model_config = copy.deepcopy(self.model_config)
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        if (
            model_config.is_multimodal_model
            and hasattr(model_config.hf_config, "tie_word_embeddings")
            and not hasattr(hf_config.get_text_config(), "tie_word_embeddings")
        ):
            # In Transformers v5, tie_word_embeddings belongs to the config of the class
            # that can see both layers to be tied. For example:
            #
            # SomeVLModel:
            #   self.language_model = SomeLanguageModel()
            #   self.vision_model = SomeVisionModel()
            #
            # SomeVLModelForMultimodalLM:
            #   self.model = SomeVLModel()
            #   self.lm_head = nn.Linear()
            #
            # Therefore, tie_word_embeddings is defined in SomeVLModelForMultimodalLM's
            # config and is not present in SomeVLModel's config. In vLLM, the lm_head
            # belongs to the language_model, so we must ensure that tie_word_embeddings
            # is set in the language_model's config.
            tie_word_embeddings = model_config.hf_config.tie_word_embeddings
            hf_config.get_text_config().tie_word_embeddings = tie_word_embeddings

564
        model_config.hf_config = hf_config
565
        model_config.model_arch_config = model_config.get_model_arch_config()
566
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        return replace(self, model_config=model_config)

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    def _set_config_default(self, config_obj: Any, key: str, value: Any) -> None:
        """Set config attribute to default if not already set by user.

        Args:
            config_obj: Configuration object to update.
            key: Attribute name.
            value: Default value (static or callable).
        """
        if getattr(config_obj, key) is None:
            # Some config values are known before initialization and are
            # hard coded.
            # Other values depend on the user given configuration, so they are
            # implemented with lambda functions and decided at run time.
            setattr(config_obj, key, value(self) if callable(value) else value)

    def _apply_optimization_level_defaults(self, defaults: dict[str, Any]) -> None:
        """Apply optimization level defaults using self as root.

        Recursively applies values from defaults into nested config objects.
        Only fields present in defaults are overwritten.

        If the user configuration does not specify a value for a default field
        and if the default field is still None after all user selections are
        applied, then default values will be applied to the field. User speciied
        fields will not be overridden by the default.

        Args:
            defaults: Dictionary of default values to apply.
        """

        def apply_recursive(config_obj: Any, config_defaults: dict[str, Any]) -> None:
            """Recursively apply defaults to config_obj, using self as root."""
            for key, value in config_defaults.items():
                if not hasattr(config_obj, key):
                    continue

                current = getattr(config_obj, key)
                if isinstance(value, dict) and is_dataclass(current):
                    apply_recursive(current, value)
                else:
                    self._set_config_default(config_obj, key, value)

        apply_recursive(self, defaults)

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    def _post_init_kv_transfer_config(self) -> None:
        """Update KVTransferConfig based on top-level configs in VllmConfig.

        Right now, this function reads the offloading settings from
        CacheConfig and configures the KVTransferConfig accordingly.
        """
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        # KV offloading is only activated when kv_offloading_size is set.
        if (kv_offloading_size := self.cache_config.kv_offloading_size) is None:
621
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            return

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        kv_offloading_backend = self.cache_config.kv_offloading_backend

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        # If no KVTransferConfig is provided, create a default one.
        if self.kv_transfer_config is None:
            self.kv_transfer_config = KVTransferConfig()
        num_kv_ranks = (
            self.parallel_config.tensor_parallel_size
            * self.parallel_config.pipeline_parallel_size
        )

        if kv_offloading_backend == "native":
            self.kv_transfer_config.kv_connector = "OffloadingConnector"
            self.kv_transfer_config.kv_connector_extra_config.update(
636
                {"cpu_bytes_to_use": kv_offloading_size * (1 << 30)}
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            )
        elif kv_offloading_backend == "lmcache":
            self.kv_transfer_config.kv_connector = "LMCacheConnectorV1"
            kv_gb_per_rank = kv_offloading_size / num_kv_ranks
            self.kv_transfer_config.kv_connector_extra_config = {
                "lmcache.local_cpu": True,
                "lmcache.max_local_cpu_size": kv_gb_per_rank,
            }

        # This is the same for all backends
        self.kv_transfer_config.kv_role = "kv_both"

649
    def __post_init__(self):
650
        """Verify configs are valid & consistent with each other."""
651

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        # To give each torch profile run a unique instance name.
        self.instance_id = f"{time.time_ns()}"

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        if self.performance_mode != "balanced":
            logger.info_once(
                "Performance mode set to '%s'.", self.performance_mode, scope="local"
            )

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        self.try_verify_and_update_config()

        if self.model_config is not None:
            self.model_config.verify_with_parallel_config(self.parallel_config)
664
            self.model_config.verify_dual_chunk_attention_config(self.load_config)
665

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            self.parallel_config.is_moe_model = self.model_config.is_moe

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        self.cache_config.verify_with_parallel_config(self.parallel_config)

        if self.lora_config is not None:
            self.lora_config.verify_with_model_config(self.model_config)

        if self.quant_config is None and self.model_config is not None:
            self.quant_config = VllmConfig._get_quantization_config(
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                self.model_config, self.load_config
            )
677

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        executor_backend = self.parallel_config.distributed_executor_backend
        executor_supports_async_sched = executor_backend in (
            "mp",
            "uni",
            "external_launcher",
        )

        if self.scheduler_config.async_scheduling:
            # Async scheduling explicitly enabled, hard fail any incompatibilities.
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            # Currently, async scheduling only support eagle speculative
            # decoding.
689
            if self.speculative_config is not None:
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                if (
                    self.speculative_config.method not in get_args(EagleModelTypes)
                    and self.speculative_config.method != "draft_model"
                ):
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                    raise ValueError(
                        "Currently, async scheduling is only supported "
696
                        "with EAGLE/MTP/Draft Model kind of speculative decoding."
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699
                    )
                if self.speculative_config.disable_padded_drafter_batch:
                    raise ValueError(
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                        "Async scheduling is not compatible with "
                        "disable_padded_drafter_batch=True."
702
                    )
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            if not executor_supports_async_sched:
                raise ValueError(
                    "Currently, async scheduling only supports `mp`, `uni`, or "
                    "`external_launcher` distributed executor backend, but you chose "
                    f"`{executor_backend}`."
                )
        elif self.scheduler_config.async_scheduling is None:
            # Enable async scheduling unless there is an incompatible option.
711
            if (
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                self.speculative_config is not None
                and self.speculative_config.method not in get_args(EagleModelTypes)
            ):
                logger.warning_once(
                    "Async scheduling not supported with %s-based "
                    "speculative decoding and will be disabled.",
                    self.speculative_config.method,
                    scope="local",
                )
                self.scheduler_config.async_scheduling = False
            elif (
                self.speculative_config is not None
                and self.speculative_config.disable_padded_drafter_batch
            ):
                logger.warning_once(
                    "Async scheduling is not compatible with "
                    "disable_padded_drafter_batch=True and will be disabled.",
                    scope="local",
                )
731
                self.scheduler_config.async_scheduling = False
732
            elif not executor_supports_async_sched:
733
                logger.warning_once(
734
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736
737
                    "Async scheduling will be disabled because it is not supported "
                    "with the `%s` distributed executor backend (only `mp`, `uni`, and "
                    "`external_launcher` are supported).",
                    executor_backend,
738
                    scope="local",
739
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742
743
                )
                self.scheduler_config.async_scheduling = False
            else:
                self.scheduler_config.async_scheduling = True

744
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747
748
        logger.info_once(
            "Asynchronous scheduling is %s.",
            "enabled" if self.scheduler_config.async_scheduling else "disabled",
        )

749
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        if self.parallel_config.disable_nccl_for_dp_synchronization is None:
            if self.scheduler_config.async_scheduling:
751
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757
758
                if self.parallel_config.data_parallel_size > 1 and (
                    self.model_config is None or self.model_config.is_moe
                ):
                    logger.info_once(
                        "Disabling NCCL for DP synchronization "
                        "when using async scheduling.",
                        scope="local",
                    )
759
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762
                self.parallel_config.disable_nccl_for_dp_synchronization = True
            else:
                self.parallel_config.disable_nccl_for_dp_synchronization = False

763
        from vllm.platforms import current_platform
764
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766

        if (
            self.model_config is not None
767
            and self.scheduler_config.enable_chunked_prefill
768
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            and self.model_config.dtype == torch.float32
            and current_platform.get_device_capability() == (7, 5)
        ):
771
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773
            logger.warning_once(
                "Turing devices tensor cores do not support float32 matmul. "
                "To workaround this limitation, vLLM will set 'ieee' input "
774
775
                "precision for chunked prefill triton kernels."
            )
776

777
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783
        if self.model_config is not None and self.model_config.enforce_eager:
            logger.warning(
                "Enforce eager set, disabling torch.compile and CUDAGraphs. "
                "This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none"
            )
            self.compilation_config.mode = CompilationMode.NONE
            self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
784
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786
787
788
789

        if self.compilation_config.backend == "eager" or (
            self.compilation_config.mode is not None
            and self.compilation_config.mode != CompilationMode.VLLM_COMPILE
        ):
            logger.warning(
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                "Inductor compilation was disabled by user settings, "
                "optimizations settings that are only active during "
                "inductor compilation will be ignored."
793
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800
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802
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804
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811
            )

        def has_blocked_weights():
            if self.quant_config is not None:
                if hasattr(self.quant_config, "weight_block_size"):
                    return self.quant_config.weight_block_size is not None
                elif hasattr(self.quant_config, "has_blocked_weights"):
                    return self.quant_config.has_blocked_weights()
            return False

        # Enable quant_fp8 CUDA ops (TODO disable in follow up)
        # On H100 the CUDA kernel is faster than
        # native implementation
        # https://github.com/vllm-project/vllm/issues/25094
        if has_blocked_weights():
            custom_ops = self.compilation_config.custom_ops
            if "-quant_fp8" not in custom_ops:
                custom_ops.append("+quant_fp8")

812
        if self.compilation_config.mode is None:
813
            if self.optimization_level > OptimizationLevel.O0:
814
                self.compilation_config.mode = CompilationMode.VLLM_COMPILE
815
            else:
816
                self.compilation_config.mode = CompilationMode.NONE
817
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819
820

        if all(s not in self.compilation_config.custom_ops for s in ("all", "none")):
            if (
                self.compilation_config.backend == "inductor"
821
                and self.compilation_config.mode != CompilationMode.NONE
822
823
824
825
            ):
                self.compilation_config.custom_ops.append("none")
            else:
                self.compilation_config.custom_ops.append("all")
826

827
828
        default_config = OPTIMIZATION_LEVEL_TO_CONFIG[self.optimization_level]
        self._apply_optimization_level_defaults(default_config)
829
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831
832
833
        if self.kernel_config.enable_flashinfer_autotune is None:
            raise ValueError(
                "KernelConfig.enable_flashinfer_autotune must be set after applying "
                "optimization level defaults."
            )
834

835
        if (
836
            self.compilation_config.cudagraph_mode.requires_piecewise_compilation()
837
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846
            and self.compilation_config.mode != CompilationMode.VLLM_COMPILE
        ):
            logger.info(
                "Cudagraph mode %s is not compatible with compilation mode %s."
                "Overriding to NONE.",
                self.compilation_config.cudagraph_mode,
                self.compilation_config.mode,
            )
            self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE

847
848
        # async tp is built on top of sequence parallelism
        # and requires it to be enabled.
849
850
851
        if self.compilation_config.pass_config.fuse_gemm_comms:
            self.compilation_config.pass_config.enable_sp = True
        if self.compilation_config.pass_config.enable_sp:
852
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855
856
857
            if self.parallel_config.tensor_parallel_size == 1:
                logger.warning("Sequence Parallelism requires TP>1, disabling")
                self.compilation_config.pass_config.enable_sp = False
                self.compilation_config.pass_config.fuse_gemm_comms = False

            elif "-rms_norm" in self.compilation_config.custom_ops:
858
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861
862
                logger.warning(
                    "RMS norm force disabled, sequence parallelism might break"
                )
            else:
                self.compilation_config.custom_ops.append("+rms_norm")
863

864
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866
867
868
869
870
871
        if self.compilation_config.fast_moe_cold_start is None:
            # resolve default behavior: try to be as safe as possible
            # this config is unsafe if any spec decoding draft model has a MOE.
            # We'll conservatively turn it off if we see spec decoding.
            self.compilation_config.fast_moe_cold_start = (
                self.speculative_config is None
            )

872
873
        self._set_max_num_scheduled_tokens()

874
        if current_platform.support_static_graph_mode():
875
            # if cudagraph_mode has full cudagraphs, we need to check support
876
877
878
879
880
            if model_config := self.model_config:
                if (
                    self.compilation_config.cudagraph_mode.has_full_cudagraphs()
                    and model_config.pooler_config is not None
                ):
881
                    logger.warning_once(
882
                        "Pooling models do not support full cudagraphs. "
883
884
885
                        "Overriding cudagraph_mode to PIECEWISE."
                    )
                    self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
886
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888
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890
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893
894
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896
897
                elif (
                    model_config.is_encoder_decoder
                    and self.compilation_config.cudagraph_mode
                    not in (CUDAGraphMode.NONE, CUDAGraphMode.FULL_DECODE_ONLY)
                ):
                    logger.info_once(
                        "Encoder-decoder models do not support %s. "
                        "Overriding cudagraph_mode to FULL_DECODE_ONLY.",
                        self.compilation_config.cudagraph_mode.name,
                    )
                    self.compilation_config.cudagraph_mode = (
                        CUDAGraphMode.FULL_DECODE_ONLY
898
                    )
899
900

            # disable cudagraph when enforce eager execution
901
            if self.model_config is not None and self.model_config.enforce_eager:
902
903
                logger.info("Cudagraph is disabled under eager mode")
                self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
904
905
906
                # override related settings when enforce eager
                self.compilation_config.max_cudagraph_capture_size = 0
                self.compilation_config.cudagraph_capture_sizes = []
907
            else:
908
909
910
911
912
913
914
                self.compilation_config.cudagraph_num_of_warmups = 1

            self._set_cudagraph_sizes()
        else:
            self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE

        if self.cache_config.kv_sharing_fast_prefill:
915
916
917
918
            if (
                self.speculative_config is not None
                and self.speculative_config.use_eagle()
            ):
919
                raise ValueError(
920
921
922
                    "Fast prefill optimization for KV sharing is not "
                    "compatible with EAGLE as EAGLE requires correct logits "
                    "for all tokens while fast prefill gives incorrect logits "
923
924
                    "for prompt tokens."
                )
925
926
927

            logger.warning_once(
                "--kv-sharing-fast-prefill requires changes on model side for "
928
                "correctness and to realize prefill savings."
929
            )
930
931
        # TODO: Move after https://github.com/vllm-project/vllm/pull/26847 lands
        self._set_compile_ranges()
932

933
934
935
936
937
938
939
940
941
942
        if (
            self.model_config
            and self.model_config.architecture == "WhisperForConditionalGeneration"
            and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") != "spawn"
        ):
            logger.warning(
                "Whisper is known to have issues with "
                "forked workers. If startup is hanging, "
                "try setting 'VLLM_WORKER_MULTIPROC_METHOD' "
                "to 'spawn'."
943
            )
944

945
946
947
948
949
        if (
            self.kv_events_config is not None
            and self.kv_events_config.enable_kv_cache_events
            and not self.cache_config.enable_prefix_caching
        ):
950
            logger.warning(
951
                "KV cache events are on, but prefix caching is not enabled. "
952
953
954
955
956
957
958
959
                "Use --enable-prefix-caching to enable."
            )
        if (
            self.kv_events_config is not None
            and self.kv_events_config.publisher != "null"
            and not self.kv_events_config.enable_kv_cache_events
        ):
            logger.warning(
960
961
962
                "KV cache events are disabled, "
                "but the scheduler is configured to publish them. "
                "Modify KVEventsConfig.enable_kv_cache_events "
963
964
                "to True to enable."
            )
965
966
        current_platform.check_and_update_config(self)

967
968
969
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971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
        # If DCP, ensure the block size is right.
        if self.parallel_config.decode_context_parallel_size > 1:
            if self.parallel_config.dcp_kv_cache_interleave_size > 1 and (
                self.parallel_config.cp_kv_cache_interleave_size
                != self.parallel_config.dcp_kv_cache_interleave_size
            ):
                self.parallel_config.cp_kv_cache_interleave_size = (
                    self.parallel_config.dcp_kv_cache_interleave_size
                )
                logger.warning_once(
                    "cp_kv_cache_interleave_size is overridden by dcp_kv_cache"
                    "_interleave_size. And dcp-kv-cache-interleave-size will be "
                    "deprecated when PCP is fully supported."
                )
            assert (
                self.parallel_config.cp_kv_cache_interleave_size
                <= self.cache_config.block_size
                and self.cache_config.block_size
                % self.parallel_config.cp_kv_cache_interleave_size
                == 0
            ), (
                f"Block_size({self.cache_config.block_size}) should be greater "
                "than or equal to and divisible by cp_kv_cache_interleave_size "
                f"({self.parallel_config.cp_kv_cache_interleave_size})."
            )

993
        # Do this after all the updates to compilation_config.mode
994
995
996
997
998
        effective_dp_size = (
            self.parallel_config.data_parallel_size
            if self.model_config is None or self.model_config.is_moe
            else 1
        )
999
1000
        self.compilation_config.set_splitting_ops_for_v1(
            all2all_backend=self.parallel_config.all2all_backend,
1001
            data_parallel_size=effective_dp_size,
1002
        )
1003

1004
        if self.compilation_config.pass_config.enable_sp:
1005
1006
1007
1008
1009
            # With pipeline parallelism or dynamo partitioning,
            # native rms norm tracing errors due to incorrect residual shape.
            # Use custom rms norm to unblock. In the future,
            # the pass will operate on higher-level IR to avoid the issue.
            # TODO: https://github.com/vllm-project/vllm/issues/27894
1010
1011
1012
1013
1014
1015
1016
            if self.compilation_config.mode != CompilationMode.VLLM_COMPILE:
                logger.warning(
                    "Sequence parallelism is enabled, but running in wrong "
                    "vllm compile mode: %s.",
                    self.compilation_config.mode,
                )

1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
            is_fullgraph = (
                self.compilation_config.use_inductor_graph_partition
                or len(self.compilation_config.splitting_ops) == 0
            )
            if self.parallel_config.pipeline_parallel_size > 1 or not is_fullgraph:
                if "-rms_norm" not in self.compilation_config.custom_ops:
                    self.compilation_config.custom_ops.append("+rms_norm")
                else:
                    regime = (
                        "Dynamo partition"
                        if not is_fullgraph
                        else "pipeline parallelism"
                    )
                    logger.warning_once(
1031
                        "Sequence parallelism not supported with "
1032
1033
1034
1035
1036
                        "native rms_norm when using %s, "
                        "this will likely lead to an error.",
                        regime,
                    )

1037
        # final check of cudagraph mode after all possible updates
1038
        if current_platform.is_cuda_alike():
1039
1040
1041
1042
            if (
                self.compilation_config.cudagraph_mode.has_full_cudagraphs()
                and self.model_config is not None
                and not self.model_config.disable_cascade_attn
1043
                and not self.compilation_config.cudagraph_mode.has_piecewise_cudagraphs()  # noqa: E501
1044
            ):
1045
1046
1047
                logger.warning_once(
                    "No piecewise cudagraph for executing cascade attention."
                    " Will fall back to eager execution if a batch runs "
1048
                    "into cascade attentions."
1049
1050
1051
                )

            if self.compilation_config.cudagraph_mode.requires_piecewise_compilation():
1052
1053
                assert self.compilation_config.mode == CompilationMode.VLLM_COMPILE, (
                    "Compilation mode should be CompilationMode.VLLM_COMPILE "
1054
                    "when cudagraph_mode piecewise cudagraphs is used, "
1055
                    f"cudagraph_mode={self.compilation_config.cudagraph_mode}"
1056
                )
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
        from vllm.model_executor.layers.batch_invariant import vllm_is_batch_invariant

        if (
            self.model_config
            and vllm_is_batch_invariant()
            and not self.model_config.disable_cascade_attn
        ):
            self.model_config.disable_cascade_attn = True
            logger.warning_once(
                "Disabling cascade attention when VLLM_BATCH_INVARIANT is enabled.",
                scope="local",
            )
1069

1070
        if self.parallel_config.use_ubatching:
1071
            a2a_backend = self.parallel_config.all2all_backend
1072
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1075
            assert a2a_backend in [
                "deepep_low_latency",
                "deepep_high_throughput",
            ], (
1076
1077
                "Microbatching currently only supports the deepep_low_latency and "
                f"deepep_high_throughput all2all backend. {a2a_backend} is not "
1078
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1080
                "supported. To fix use --all2all-backend=deepep_low_latency or "
                "--all2all-backend=deepep_high_throughput and install the DeepEP"
                " kernels."
1081
            )
1082
1083
1084

            if not self.model_config.disable_cascade_attn:
                self.model_config.disable_cascade_attn = True
1085
                logger.warning_once("Disabling cascade attention when DBO is enabled.")
1086
1087
1088
1089

        if not self.instance_id:
            self.instance_id = random_uuid()[:5]

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1128
        # Hybrid KV cache manager (HMA) runtime rules:
        # - Explicit enable (--no-disable-kv-cache-manager): error if runtime
        #   disables it
        # - No preference: auto-disable for unsupported features (e.g. kv connector)
        # - Explicit disable (--disable-kv-cache-manager): always respect it
        need_disable_hybrid_kv_cache_manager = False
        # logger should only print warning message for hybrid models. As we
        # can't know whether the model is hybrid or not now, so we don't log
        # warning message here and will log it later.
        if not current_platform.support_hybrid_kv_cache():
            # Hybrid KV cache manager is not supported on non-GPU platforms.
            need_disable_hybrid_kv_cache_manager = True
        if self.kv_events_config is not None:
            # Hybrid KV cache manager is not compatible with KV events.
            need_disable_hybrid_kv_cache_manager = True
        if (
            self.model_config is not None
            and self.model_config.attention_chunk_size is not None
        ):
            if (
                self.speculative_config is not None
                and self.speculative_config.use_eagle()
            ):
                # Hybrid KV cache manager is not yet supported with chunked
                # local attention + eagle.
                need_disable_hybrid_kv_cache_manager = True
            elif not envs.VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE:
                logger.warning(
                    "There is a latency regression when using chunked local"
                    " attention with the hybrid KV cache manager. Disabling"
                    " it, by default. To enable it, set the environment "
                    "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE=1."
                )
                # Hybrid KV cache manager is not yet supported with chunked
                # local attention.
                need_disable_hybrid_kv_cache_manager = True

        if self.scheduler_config.disable_hybrid_kv_cache_manager is None:
            # Default to disable HMA, but only if the user didn't express a preference.
1129
            if self.kv_transfer_config is not None:
1130
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                # NOTE(Kuntai): turn HMA off for connector unless specifically enabled.
                need_disable_hybrid_kv_cache_manager = True
1132
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                logger.warning(
                    "Turning off hybrid kv cache manager because "
                    "`--kv-transfer-config` is set. This will reduce the "
                    "performance of vLLM on LLMs with sliding window attention "
                    "or Mamba attention. If you are a developer of kv connector"
                    ", please consider supporting hybrid kv cache manager for "
                    "your connector by making sure your connector is a subclass"
1139
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                    " of `SupportsHMA` defined in kv_connector/v1/base.py and"
                    " use --no-disable-hybrid-kv-cache-manager to start vLLM."
1141
                )
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            self.scheduler_config.disable_hybrid_kv_cache_manager = (
                need_disable_hybrid_kv_cache_manager
            )
        elif (
            self.scheduler_config.disable_hybrid_kv_cache_manager is False
            and need_disable_hybrid_kv_cache_manager
        ):
            raise ValueError(
                "Hybrid KV cache manager was explicitly enabled but is not "
                "supported in this configuration. Consider omitting the "
                "--no-disable-hybrid-kv-cache-manager flag to let vLLM decide"
                " automatically."
            )

        if self.scheduler_config.disable_hybrid_kv_cache_manager is None:
            # Default to enable HMA if not explicitly disabled by user or logic above.
            self.scheduler_config.disable_hybrid_kv_cache_manager = False
1159

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        if self.cache_config.mamba_cache_mode == "align":
            assert (
                self.cache_config.block_size
                <= self.scheduler_config.max_num_batched_tokens
            ), (
                "In Mamba cache align mode, block_size "
                f"({self.cache_config.block_size}) must be <= "
                "max_num_batched_tokens "
                f"({self.scheduler_config.max_num_batched_tokens})."
            )
            if self.scheduler_config.long_prefill_token_threshold > 0:
                assert (
                    self.scheduler_config.long_prefill_token_threshold
                    >= self.cache_config.block_size
                )
            assert not self.scheduler_config.disable_chunked_mm_input, (
                "Chunked MM input is required because we need the flexibility to "
                "schedule a multiple of block_size tokens even if they are in the "
                "middle of a mm input"
            )
1180
        if self.compilation_config.debug_dump_path:
1181
            self.compilation_config.debug_dump_path = (
1182
                self.compilation_config.debug_dump_path.absolute().expanduser()
1183
            )
1184
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1188
        if envs.VLLM_DEBUG_DUMP_PATH is not None:
            env_path = Path(envs.VLLM_DEBUG_DUMP_PATH).absolute().expanduser()
            if self.compilation_config.debug_dump_path:
                logger.warning(
                    "Config-specified debug dump path is overridden"
1189
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                    " by VLLM_DEBUG_DUMP_PATH to %s",
                    env_path,
                )
1192
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            self.compilation_config.debug_dump_path = env_path

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        def has_blocked_weights():
            if self.quant_config is not None:
                if hasattr(self.quant_config, "weight_block_size"):
                    return self.quant_config.weight_block_size is not None
                elif hasattr(self.quant_config, "has_blocked_weights"):
                    return self.quant_config.has_blocked_weights()
            return False

        # Enable quant_fp8 CUDA ops (TODO disable in follow up)
        # On H100 the CUDA kernel is faster than
        # native implementation
        # https://github.com/vllm-project/vllm/issues/25094
        if has_blocked_weights():
            custom_ops = self.compilation_config.custom_ops
1208
            if "-quant_fp8" not in custom_ops:
1209
1210
                custom_ops.append("+quant_fp8")

1211
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1213
        # Handle the KV connector configs
        self._post_init_kv_transfer_config()

1214
    def update_sizes_for_sequence_parallelism(self, possible_sizes: list) -> list:
1215
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1217
        # remove the sizes that not multiple of tp_size when
        # enable sequence parallelism
        removed_sizes = [
1218
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            size
            for size in possible_sizes
1220
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            if size % self.parallel_config.tensor_parallel_size != 0
        ]
        if removed_sizes:
            logger.warning(
                "Batch sizes %s are removed because they are not "
                "multiple of tp_size %d when "
1226
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                "sequence parallelism is enabled",
                removed_sizes,
                self.parallel_config.tensor_parallel_size,
            )
1230
1231

        return [
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            size
            for size in possible_sizes
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            if size % self.parallel_config.tensor_parallel_size == 0
        ]

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    def _set_max_num_scheduled_tokens(self):
        """
        In most cases, the scheduler may schedule a batch with as many tokens as the
        worker is configured to handle. However for some speculative decoding methods,
        the drafter model may insert additional slots into the batch when drafting.
        To account for this, we need to decrease the max_num_scheduled_tokens by an
        upper bound on the number of slots that can be added.
        """
        if self.speculative_config is not None:
            scheduled_token_delta = (
                self.speculative_config.max_num_new_slots_for_drafting
                * self.scheduler_config.max_num_seqs
            )
            max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
            if self.scheduler_config.max_num_scheduled_tokens is None:
                self.scheduler_config.max_num_scheduled_tokens = (
                    max_num_batched_tokens - scheduled_token_delta
                )

            max_num_scheduled_tokens = self.scheduler_config.max_num_scheduled_tokens
            if max_num_batched_tokens < max_num_scheduled_tokens + (
                self.speculative_config.max_num_new_slots_for_drafting
                * self.scheduler_config.max_num_seqs
            ):
                raise ValueError(
                    f"VllmConfig received max_num_scheduled_tokens but it does not have"
                    " enough slots to support the speculative decoding settings."
                    f" It should be greater by at least {scheduled_token_delta}, but"
                    f" got {max_num_batched_tokens=} and {max_num_scheduled_tokens=}."
                )

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    def _set_cudagraph_sizes(self):
        """
        vLLM defines the default candidate list of batch sizes for CUDA graph
        capture as:

        ```python
        max_graph_size = min(max_num_seqs * 2, 512)
1275
1276
        # 1, 2, 4, then multiples of 8 up to 256 and then multiples of 16
        # up to max_graph_size
1277
        cudagraph_capture_sizes = [1, 2, 4] + list(range(8, 256, 8)) + list(
1278
            range(256, max_graph_size + 1, 16))
1279
1280

        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
1281
        will be the final sizes to capture cudagraph (in ascending order).
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        These sizes are used to capture and reuse CUDA graphs for
        performance-critical paths (e.g., decoding). Capturing enables
        significantly faster kernel dispatch by avoiding Python overhead. The
        list is then filtered based on `max_num_batched_tokens` (e.g., 8192 on
        most GPUs), which controls the total allowed number of tokens in a
        batch. Since each sequence may have a variable number of tokens, the
        maximum usable batch size will depend on actual sequence lengths.

        Example:
            With `max_num_batched_tokens = 8192`, and typical sequences
            averaging ~32 tokens, most practical batch sizes fall below 256.
            However, the system will still allow capture sizes up to 512 if
            shape and memory permit.

        Note:
            If users explicitly specify cudagraph capture sizes in the
            compilation config, those will override this default logic.
            At runtime:

            - If batch size <= one of the `cudagraph_capture_sizes`, the closest
            padded CUDA graph will be used.
            - If batch size > largest `cudagraph_capture_sizes`, cudagraph will
            not be used.
        """

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1317
        if (
            self.model_config is not None
            and not self.model_config.enforce_eager
            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
        ):
            # determine the initial max_cudagraph_capture_size
            max_cudagraph_capture_size = (
                self.compilation_config.max_cudagraph_capture_size
            )
            if max_cudagraph_capture_size is None:
1318
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1323
                decode_query_len = 1
                if (
                    self.speculative_config
                    and self.speculative_config.num_speculative_tokens
                ):
                    decode_query_len += self.speculative_config.num_speculative_tokens
1324
                max_cudagraph_capture_size = min(
1325
                    self.scheduler_config.max_num_seqs * decode_query_len * 2, 512
1326
                )
1327
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1342
            max_num_tokens = self.scheduler_config.max_num_batched_tokens
            max_cudagraph_capture_size = min(max_num_tokens, max_cudagraph_capture_size)

            assert max_cudagraph_capture_size >= 1, (
                "Maximum cudagraph size should be greater than or equal to 1 "
                "when using cuda graph."
            )

            # determine the cudagraph_capture_sizes
            if self.compilation_config.cudagraph_capture_sizes is not None:
                assert len(self.compilation_config.cudagraph_capture_sizes) > 0, (
                    "cudagraph_capture_sizes should contain at least one element "
                    "when using cuda graph."
                )
                # de-duplicate the sizes provided by the config
                dedup_sizes = list(set(self.compilation_config.cudagraph_capture_sizes))
1343
1344
1345
                cudagraph_capture_sizes = [
                    i for i in dedup_sizes if i <= max_num_tokens
                ]
1346
1347
                # sort to make sure the sizes are in ascending order
                cudagraph_capture_sizes.sort()
1348
            else:
1349
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1352
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1355
1356
1357
                if self.performance_mode == "interactivity":
                    # Fine-grained CUDA graphs at small batch sizes
                    # for minimal padding overhead
                    interactivity_max = min(max_cudagraph_capture_size, 32)
                    cudagraph_capture_sizes = list(range(1, interactivity_max + 1))
                else:
                    cudagraph_capture_sizes = [
                        i for i in [1, 2, 4] if i <= max_cudagraph_capture_size
                    ]
1358
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1362
1363
1364
1365
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1367
                if max_cudagraph_capture_size >= 8:
                    # Step size 8 for small batch sizes, up to 256(not included)
                    cudagraph_capture_sizes += list(
                        range(8, min(max_cudagraph_capture_size + 1, 256), 8)
                    )
                if max_cudagraph_capture_size >= 256:
                    # Step size 16 for larger batch sizes
                    cudagraph_capture_sizes += list(
                        range(256, max_cudagraph_capture_size + 1, 16)
                    )
1368
1369
                # de-duplicate and sort the sizes
                cudagraph_capture_sizes = sorted(set(cudagraph_capture_sizes))
1370

1371
1372
            if (
                self.parallel_config.tensor_parallel_size > 1
1373
                and self.compilation_config.pass_config.enable_sp
1374
            ):
1375
1376
                cudagraph_capture_sizes = self.update_sizes_for_sequence_parallelism(
                    cudagraph_capture_sizes
1377
                )
1378

1379
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1425
1426
1427
1428
            # user-specific compilation_config.max_cudagraph_capture_size get
            # truncated to valid_max_size when they are inconsistent.
            valid_max_size = (
                cudagraph_capture_sizes[-1] if cudagraph_capture_sizes else 0
            )
            if (
                self.compilation_config.max_cudagraph_capture_size is not None
                and self.compilation_config.max_cudagraph_capture_size != valid_max_size
            ):
                # raise error only when both two flags are user-specified
                # and they are inconsistent with each other
                if self.compilation_config.cudagraph_capture_sizes is not None:
                    raise ValueError(
                        "customized max_cudagraph_capture_size"
                        f"(={self.compilation_config.max_cudagraph_capture_size}) "
                        "should be consistent with the max value of "
                        f"cudagraph_capture_sizes(={valid_max_size})"
                    )

                logger.warning(
                    "Truncating max_cudagraph_capture_size to %d",
                    valid_max_size,
                )
            # always set the final max_cudagraph_capture_size
            self.compilation_config.max_cudagraph_capture_size = valid_max_size

            if self.compilation_config.cudagraph_capture_sizes is not None and len(
                cudagraph_capture_sizes
            ) < len(self.compilation_config.cudagraph_capture_sizes):
                # If users have specified capture sizes, we only need to
                # compare the lens before and after modification since the modified
                # list is only the subset of the original list.
                logger.warning(
                    (
                        "cudagraph_capture_sizes specified in compilation_config"
                        " %s is overridden by config %s"
                    ),
                    self.compilation_config.cudagraph_capture_sizes,
                    cudagraph_capture_sizes,
                )
            # always write back the final sizes
            self.compilation_config.cudagraph_capture_sizes = cudagraph_capture_sizes

        else:
            # no cudagraph in use
            self.compilation_config.max_cudagraph_capture_size = 0
            self.compilation_config.cudagraph_capture_sizes = []

        # complete the remaining process.
        self.compilation_config.post_init_cudagraph_sizes()
1429

1430
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1432
1433
1434
1435
1436
    def _set_compile_ranges(self):
        """
        Set the compile ranges for the compilation config.
        """
        compilation_config = self.compilation_config
        computed_compile_ranges_split_points = []

1437
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1439
1440
        # The upper bound of the compile ranges is the max_num_batched_tokens.
        compile_range_end = self.scheduler_config.max_num_batched_tokens
        if compile_range_end is not None:
            computed_compile_ranges_split_points.append(compile_range_end)
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450

        # Add the compile ranges for flashinfer
        if compilation_config.pass_config.fuse_allreduce_rms:
            tp_size = self.parallel_config.tensor_parallel_size
            max_size = compilation_config.pass_config.flashinfer_max_size(tp_size)
            if max_size is not None:
                max_token_num = max_size // (
                    self.model_config.get_hidden_size()
                    * self.model_config.dtype.itemsize
                )
1451
                if compile_range_end is not None and max_token_num < compile_range_end:
1452
1453
1454
1455
1456
1457
1458
                    computed_compile_ranges_split_points.append(max_token_num)
                else:
                    logger.debug(
                        "Max num batched tokens below allreduce-rms fusion threshold, "
                        "allreduce-rms fusion will be enabled for all num_tokens."
                    )

1459
1460
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1462
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1464
1465
1466
1467
1468
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1470
1471
1472
        if compilation_config.pass_config.fuse_rope_kvcache:
            max_token_num = (
                compilation_config.pass_config.rope_kvcache_fusion_max_token_num
            )
            if max_token_num is not None:
                if compile_range_end is not None and max_token_num < compile_range_end:
                    computed_compile_ranges_split_points.append(max_token_num)
                else:
                    logger.debug(
                        "Max num batched tokens below rope+kvcache fusion threshold, "
                        "rope+kvcache fusion enabled for num_tokens <= %d.",
                        compile_range_end,
                    )

1473
1474
1475
1476
        if compilation_config.compile_ranges_split_points is not None:
            for x in compilation_config.compile_ranges_split_points:
                assert isinstance(x, int)
                assert x > 0, f"Invalid compile range split point: {x}"
1477
                if compile_range_end is not None and x < compile_range_end and x > 1:
1478
1479
1480
1481
1482
                    computed_compile_ranges_split_points.append(x)
        compilation_config.compile_ranges_split_points = sorted(
            computed_compile_ranges_split_points
        )

1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
    def try_verify_and_update_config(self):
        if self.model_config is None:
            return

        # Avoid running try_verify_and_update_config multiple times
        if getattr(self.model_config, "config_updated", False):
            return
        self.model_config.config_updated = True

        architecture = self.model_config.architecture
        if architecture is None:
            return

        from vllm.model_executor.models.config import (
1497
1498
1499
1500
            MODELS_CONFIG_MAP,
            HybridAttentionMambaModelConfig,
        )

1501
1502
1503
1504
1505
1506
1507
1508
1509
        cls = MODELS_CONFIG_MAP.get(architecture, None)
        if cls is not None:
            cls.verify_and_update_config(self)

        if self.model_config.is_hybrid:
            HybridAttentionMambaModelConfig.verify_and_update_config(self)

        if self.model_config.convert_type == "classify":
            # Maybe convert ForCausalLM into ForSequenceClassification model.
1510
1511
            from vllm.model_executor.models.adapters import SequenceClassificationConfig

1512
1513
1514
            SequenceClassificationConfig.verify_and_update_config(self)

        if hasattr(self.model_config, "model_weights") and is_runai_obj_uri(
1515
1516
            self.model_config.model_weights
        ):
1517
            if self.load_config.load_format == "auto":
1518
1519
1520
1521
                logger.info(
                    "Detected Run:ai model config. "
                    "Overriding `load_format` to 'runai_streamer'"
                )
1522
                self.load_config.load_format = "runai_streamer"
1523
1524
1525
1526
            elif self.load_config.load_format not in (
                "runai_streamer",
                "runai_streamer_sharded",
            ):
1527
1528
                raise ValueError(
                    f"To load a model from S3, 'load_format' "
1529
                    f"must be 'runai_streamer' or 'runai_streamer_sharded', "
1530
1531
1532
                    f"but got '{self.load_config.load_format}'. "
                    f"Model: {self.model_config.model}"
                )
1533

1534
    def compile_debug_dump_path(self) -> Path | None:
1535
        """Returns a rank-aware path for dumping
1536
1537
1538
1539
1540
        torch.compile debug information.
        """
        if self.compilation_config.debug_dump_path is None:
            return None
        tp_rank = self.parallel_config.rank
1541
1542
        dp_rank = self.parallel_config.data_parallel_index
        append_path = f"rank_{tp_rank}_dp_{dp_rank}"
1543
1544
1545
1546
1547
1548
1549
        path = self.compilation_config.debug_dump_path / append_path
        return path

    def __str__(self):
        return (
            f"model={self.model_config.model!r}, "
            f"speculative_config={self.speculative_config!r}, "
1550
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            f"tokenizer={self.model_config.tokenizer!r}, "
            f"skip_tokenizer_init={self.model_config.skip_tokenizer_init}, "
            f"tokenizer_mode={self.model_config.tokenizer_mode}, "
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            f"revision={self.model_config.revision}, "
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            f"tokenizer_revision={self.model_config.tokenizer_revision}, "
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            f"trust_remote_code={self.model_config.trust_remote_code}, "
            f"dtype={self.model_config.dtype}, "
            f"max_seq_len={self.model_config.max_model_len}, "
            f"download_dir={self.load_config.download_dir!r}, "
            f"load_format={self.load_config.load_format}, "
            f"tensor_parallel_size={self.parallel_config.tensor_parallel_size}, "  # noqa
            f"pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, "  # noqa
            f"data_parallel_size={self.parallel_config.data_parallel_size}, "  # noqa
            f"disable_custom_all_reduce={self.parallel_config.disable_custom_all_reduce}, "  # noqa
            f"quantization={self.model_config.quantization}, "
            f"enforce_eager={self.model_config.enforce_eager}, "
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            f"enable_return_routed_experts={self.model_config.enable_return_routed_experts}, "  # noqa
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            f"kv_cache_dtype={self.cache_config.cache_dtype}, "
            f"device_config={self.device_config.device}, "
            f"structured_outputs_config={self.structured_outputs_config!r}, "
            f"observability_config={self.observability_config!r}, "
            f"seed={self.model_config.seed}, "
            f"served_model_name={self.model_config.served_model_name}, "
            f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
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            f"enable_chunked_prefill={self.scheduler_config.enable_chunked_prefill}, "  # noqa
1575
            f"pooler_config={self.model_config.pooler_config!r}, "
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            f"compilation_config={self.compilation_config!r}"
        )
1578

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    @model_validator(mode="after")
    def validate_mamba_block_size(self) -> "VllmConfig":
        if self.model_config is None:
            return self
        mamba_block_size_is_set = (
            self.cache_config.mamba_block_size is not None
            and self.cache_config.mamba_block_size != self.model_config.max_model_len
        )
        if mamba_block_size_is_set and not self.cache_config.enable_prefix_caching:
            raise ValueError(
                "--mamba-block-size can only be set with --enable-prefix-caching"
            )
        return self

1593

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_current_vllm_config: VllmConfig | None = None
_current_prefix: str | None = None
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@contextmanager
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def set_current_vllm_config(
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    vllm_config: VllmConfig, check_compile=False, prefix: str | None = None
1601
):
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    """
    Temporarily set the current vLLM config.
    Used during model initialization.
    We save the current vLLM config in a global variable,
    so that all modules can access it, e.g. custom ops
    can access the vLLM config to determine how to dispatch.
    """
    global _current_vllm_config, _current_prefix
    old_vllm_config = _current_vllm_config
    old_prefix = _current_prefix
    from vllm.compilation.counter import compilation_counter
1613

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    num_models_seen = compilation_counter.num_models_seen
    try:
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        # Clear the compilation config cache when context changes.
        # This is needed since the old config may have been accessed
        # and cached before the new config is set.
        get_cached_compilation_config.cache_clear()

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        _current_vllm_config = vllm_config
        _current_prefix = prefix
        yield
    except Exception:
        raise
    else:
        if check_compile:
            vllm_config.compilation_config.custom_op_log_check()

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        if (
            check_compile
1632
            and vllm_config.compilation_config.mode == CompilationMode.VLLM_COMPILE
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            and compilation_counter.num_models_seen == num_models_seen
        ):
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            # If the model supports compilation,
            # compilation_counter.num_models_seen should be increased
            # by at least 1.
            # If it is not increased, it means the model does not support
            # compilation (does not have @support_torch_compile decorator).
            logger.warning(
                "`torch.compile` is turned on, but the model %s"
                " does not support it. Please open an issue on GitHub"
                " if you want it to be supported.",
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                vllm_config.model_config.model,
            )
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    finally:
        _current_vllm_config = old_vllm_config
        _current_prefix = old_prefix
        # Clear the compilation config cache when context changes
        get_cached_compilation_config.cache_clear()


@lru_cache(maxsize=1)
def get_cached_compilation_config():
    """Cache config to avoid repeated calls to get_current_vllm_config()"""
    return get_current_vllm_config().compilation_config


def get_current_vllm_config() -> VllmConfig:
    if _current_vllm_config is None:
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        raise AssertionError(
            "Current vLLM config is not set. This typically means "
            "get_current_vllm_config() was called outside of a "
            "set_current_vllm_config() context, or a CustomOp was instantiated "
            "at module import time or model forward time when config is not set. "
            "For tests that directly test custom ops/modules, use the "
            "'default_vllm_config' pytest fixture from tests/conftest.py."
        )
    return _current_vllm_config


def get_current_vllm_config_or_none() -> VllmConfig | None:
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    return _current_vllm_config


T = TypeVar("T")


def get_layers_from_vllm_config(
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    vllm_config: VllmConfig,
    layer_type: type[T],
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    layer_names: list[str] | None = None,
1683
) -> dict[str, T]:
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    """
    Get layers from the vLLM config.

    Args:
        vllm_config: The vLLM config.
        layer_type: The type of the layer to get.
        layer_names: The names of the layers to get. If None, return all layers.
    """

    if layer_names is None:
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        layer_names = list(vllm_config.compilation_config.static_forward_context.keys())
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    forward_context = vllm_config.compilation_config.static_forward_context

    return {
        layer_name: forward_context[layer_name]
        for layer_name in layer_names
        if isinstance(forward_context[layer_name], layer_type)
    }