vllm.py 65.7 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, 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 .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
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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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."""


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:
    """Enable if either SiLU+Mul or quant FP8 custom op is active;
    otherwise Inductor handles fusion."""
    return cfg.compilation_config.is_custom_op_enabled(
        "silu_and_mul"
    ) or cfg.compilation_config.is_custom_op_enabled("quant_fp8")


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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."""

    return (
        envs.VLLM_ROCM_USE_AITER
        and envs.VLLM_ROCM_USE_AITER_RMSNORM
        and envs.VLLM_ROCM_USE_AITER_TRITON_GEMM
        and cfg.model_config.get_hidden_size() == 2880
    )


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OPTIMIZATION_LEVEL_00 = {
    "compilation_config": {
        "pass_config": {
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            "eliminate_noops": False,
            "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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        },
        "cudagraph_mode": CUDAGraphMode.NONE,
        "use_inductor_graph_partition": False,
    },
}
OPTIMIZATION_LEVEL_01 = {
    "compilation_config": {
        "pass_config": {
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            "eliminate_noops": True,
            "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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        },
        "cudagraph_mode": CUDAGraphMode.PIECEWISE,
        "use_inductor_graph_partition": False,
    },
}
OPTIMIZATION_LEVEL_02 = {
    "compilation_config": {
        "pass_config": {
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            "eliminate_noops": True,
            "fuse_norm_quant": enable_norm_fusion,
            "fuse_act_quant": enable_act_fusion,
            "fuse_allreduce_rms": False,
            "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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        },
        "cudagraph_mode": CUDAGraphMode.FULL_AND_PIECEWISE,
        "use_inductor_graph_partition": False,
    },
}
OPTIMIZATION_LEVEL_03 = {
    "compilation_config": {
        "pass_config": {
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            "eliminate_noops": True,
            "fuse_norm_quant": enable_norm_fusion,
            "fuse_act_quant": enable_act_fusion,
            "fuse_allreduce_rms": False,
            "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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        },
        "cudagraph_mode": CUDAGraphMode.FULL_AND_PIECEWISE,
        "use_inductor_graph_partition": False,
    },
}

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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    attention_config: AttentionConfig = Field(default_factory=AttentionConfig)
    """Attention 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
    performance. -02 is used by defult. See  OptimizationLevel for full
    description."""
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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.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 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

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        model_config.hf_config = hf_config
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        model_config.model_arch_config = model_config.get_model_arch_config()
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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:
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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(
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                {"cpu_bytes_to_use": kv_offloading_size * (1 << 30)}
555
556
557
558
559
560
561
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563
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565
566
            )
        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"

567
    def __post_init__(self):
568
        """Verify configs are valid & consistent with each other."""
569

570
571
572
        # To give each torch profile run a unique instance name.
        self.instance_id = f"{time.time_ns()}"

573
574
575
576
        self.try_verify_and_update_config()

        if self.model_config is not None:
            self.model_config.verify_with_parallel_config(self.parallel_config)
577
            self.model_config.verify_dual_chunk_attention_config(self.load_config)
578

579
580
            self.parallel_config.is_moe_model = self.model_config.is_moe

581
582
583
584
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586
587
        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(
588
589
                self.model_config, self.load_config
            )
590

591
592
593
594
595
596
597
598
599
        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.
600
601
            # Currently, async scheduling only support eagle speculative
            # decoding.
602
            if self.speculative_config is not None:
603
604
605
                if self.speculative_config.method not in get_args(EagleModelTypes):
                    raise ValueError(
                        "Currently, async scheduling is only supported "
606
                        "with EAGLE/MTP kind of speculative decoding."
607
608
609
                    )
                if self.speculative_config.disable_padded_drafter_batch:
                    raise ValueError(
610
611
                        "Async scheduling is not compatible with "
                        "disable_padded_drafter_batch=True."
612
                    )
613
614
615
616
617
618
            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}`."
                )
619
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621
622
623
            if self.cache_config.mamba_cache_mode != "none":
                raise ValueError(
                    "Currently, async scheduling is not compatible with "
                    "prefix caching for Mamba models."
                )
624
625
        elif self.scheduler_config.async_scheduling is None:
            # Enable async scheduling unless there is an incompatible option.
626
            if (
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638
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640
641
642
643
644
645
                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",
                )
646
                self.scheduler_config.async_scheduling = False
647
            elif not executor_supports_async_sched:
648
                logger.warning_once(
649
650
651
652
                    "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,
653
                    scope="local",
654
655
                )
                self.scheduler_config.async_scheduling = False
656
657
658
659
660
661
662
            elif self.cache_config.mamba_cache_mode != "none":
                logger.warning_once(
                    "Async scheduling is not compatible with "
                    "prefix caching for Mamba models and will be disabled.",
                    scope="local",
                )
                self.scheduler_config.async_scheduling = False
663
664
665
            else:
                self.scheduler_config.async_scheduling = True

666
667
668
669
670
        logger.info_once(
            "Asynchronous scheduling is %s.",
            "enabled" if self.scheduler_config.async_scheduling else "disabled",
        )

671
672
        if self.parallel_config.disable_nccl_for_dp_synchronization is None:
            if self.scheduler_config.async_scheduling:
673
674
675
676
677
678
679
680
                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",
                    )
681
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683
684
                self.parallel_config.disable_nccl_for_dp_synchronization = True
            else:
                self.parallel_config.disable_nccl_for_dp_synchronization = False

685
        from vllm.platforms import current_platform
686
687
688

        if (
            self.model_config is not None
689
            and self.scheduler_config.enable_chunked_prefill
690
691
692
            and self.model_config.dtype == torch.float32
            and current_platform.get_device_capability() == (7, 5)
        ):
693
694
695
            logger.warning_once(
                "Turing devices tensor cores do not support float32 matmul. "
                "To workaround this limitation, vLLM will set 'ieee' input "
696
697
                "precision for chunked prefill triton kernels."
            )
698

699
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702
703
704
705
706
707
708
709
710
711
        if (
            self.optimization_level > OptimizationLevel.O0
            and self.model_config is not None
            and self.model_config.enforce_eager
        ):
            logger.warning("Enforce eager set, overriding optimization level to -O0")
            self.optimization_level = OptimizationLevel.O0

        if self.compilation_config.backend == "eager" or (
            self.compilation_config.mode is not None
            and self.compilation_config.mode != CompilationMode.VLLM_COMPILE
        ):
            logger.warning(
712
713
714
                "Inductor compilation was disabled by user settings, "
                "optimizations settings that are only active during "
                "inductor compilation will be ignored."
715
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721
722
723
724
725
726
727
728
729
730
731
732
733
            )

        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")

734
        if self.compilation_config.mode is None:
735
            if self.optimization_level > OptimizationLevel.O0:
736
                self.compilation_config.mode = CompilationMode.VLLM_COMPILE
737
            else:
738
                self.compilation_config.mode = CompilationMode.NONE
739
740
741
742

        if all(s not in self.compilation_config.custom_ops for s in ("all", "none")):
            if (
                self.compilation_config.backend == "inductor"
743
                and self.compilation_config.mode != CompilationMode.NONE
744
745
746
747
            ):
                self.compilation_config.custom_ops.append("none")
            else:
                self.compilation_config.custom_ops.append("all")
748

749
750
        default_config = OPTIMIZATION_LEVEL_TO_CONFIG[self.optimization_level]
        self._apply_optimization_level_defaults(default_config)
751

752
        if (
753
            self.compilation_config.cudagraph_mode.requires_piecewise_compilation()
754
755
756
757
758
759
760
761
762
763
            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

764
765
        # async tp is built on top of sequence parallelism
        # and requires it to be enabled.
766
767
768
        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:
769
770
771
772
773
774
            if "-rms_norm" in self.compilation_config.custom_ops:
                logger.warning(
                    "RMS norm force disabled, sequence parallelism might break"
                )
            else:
                self.compilation_config.custom_ops.append("+rms_norm")
775
776

        if current_platform.support_static_graph_mode():
777
            # if cudagraph_mode has full cudagraphs, we need to check support
778
779
780
781
782
            if model_config := self.model_config:
                if (
                    self.compilation_config.cudagraph_mode.has_full_cudagraphs()
                    and model_config.pooler_config is not None
                ):
783
                    logger.warning_once(
784
                        "Pooling models do not support full cudagraphs. "
785
786
787
                        "Overriding cudagraph_mode to PIECEWISE."
                    )
                    self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
788
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791
792
793
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795
796
797
798
799
                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
800
                    )
801
802

            # disable cudagraph when enforce eager execution
803
            if self.model_config is not None and self.model_config.enforce_eager:
804
805
                logger.info("Cudagraph is disabled under eager mode")
                self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
806
807
808
                # override related settings when enforce eager
                self.compilation_config.max_cudagraph_capture_size = 0
                self.compilation_config.cudagraph_capture_sizes = []
809
            else:
810
811
812
813
814
815
816
                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:
817
818
819
820
            if (
                self.speculative_config is not None
                and self.speculative_config.use_eagle()
            ):
821
                raise ValueError(
822
823
824
                    "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 "
825
826
                    "for prompt tokens."
                )
827
828
829

            logger.warning_once(
                "--kv-sharing-fast-prefill requires changes on model side for "
830
                "correctness and to realize prefill savings."
831
            )
832
833
        # TODO: Move after https://github.com/vllm-project/vllm/pull/26847 lands
        self._set_compile_ranges()
834

835
836
837
838
839
840
841
842
843
844
        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'."
845
            )
846

847
848
849
850
851
        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
        ):
852
            logger.warning(
853
                "KV cache events are on, but prefix caching is not enabled. "
854
855
856
857
858
859
860
861
                "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(
862
863
864
                "KV cache events are disabled, "
                "but the scheduler is configured to publish them. "
                "Modify KVEventsConfig.enable_kv_cache_events "
865
866
                "to True to enable."
            )
867
868
        current_platform.check_and_update_config(self)

869
870
        # If DCP, ensure the block size is right.
        if self.parallel_config.decode_context_parallel_size > 1:
871
872
873
874
875
876
877
878
879
880
881
882
            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."
                )
883
            assert (
884
                self.parallel_config.cp_kv_cache_interleave_size
885
886
                <= self.cache_config.block_size
                and self.cache_config.block_size
887
                % self.parallel_config.cp_kv_cache_interleave_size
888
889
890
                == 0
            ), (
                f"Block_size({self.cache_config.block_size}) should be greater "
891
892
                "than or equal to and divisible by cp_kv_cache_interleave_size "
                f"({self.parallel_config.cp_kv_cache_interleave_size})."
893
            )
894

895
        # Do this after all the updates to compilation_config.mode
896
897
898
899
900
        effective_dp_size = (
            self.parallel_config.data_parallel_size
            if self.model_config is None or self.model_config.is_moe
            else 1
        )
901
902
        self.compilation_config.set_splitting_ops_for_v1(
            all2all_backend=self.parallel_config.all2all_backend,
903
            data_parallel_size=effective_dp_size,
904
        )
905

906
        if self.compilation_config.pass_config.enable_sp:
907
908
909
910
911
            # 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
912
913
914
915
916
917
918
            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,
                )

919
920
921
922
923
924
925
926
927
928
929
930
931
932
            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(
933
                        "Sequence parallelism not supported with "
934
935
936
937
938
                        "native rms_norm when using %s, "
                        "this will likely lead to an error.",
                        regime,
                    )

939
        # final check of cudagraph mode after all possible updates
940
        if current_platform.is_cuda_alike():
941
942
943
944
            if (
                self.compilation_config.cudagraph_mode.has_full_cudagraphs()
                and self.model_config is not None
                and not self.model_config.disable_cascade_attn
945
                and not self.compilation_config.cudagraph_mode.has_piecewise_cudagraphs()  # noqa: E501
946
            ):
947
948
949
                logger.warning_once(
                    "No piecewise cudagraph for executing cascade attention."
                    " Will fall back to eager execution if a batch runs "
950
                    "into cascade attentions."
951
952
953
                )

            if self.compilation_config.cudagraph_mode.requires_piecewise_compilation():
954
955
                assert self.compilation_config.mode == CompilationMode.VLLM_COMPILE, (
                    "Compilation mode should be CompilationMode.VLLM_COMPILE "
956
                    "when cudagraph_mode piecewise cudagraphs is used, "
957
                    f"cudagraph_mode={self.compilation_config.cudagraph_mode}"
958
                )
959
960
961
962
963
964
965
966
967
968
969
970
        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",
            )
971

972
        if self.parallel_config.use_ubatching:
973
            a2a_backend = self.parallel_config.all2all_backend
974
975
976
977
            assert a2a_backend in [
                "deepep_low_latency",
                "deepep_high_throughput",
            ], (
978
979
                "Microbatching currently only supports the deepep_low_latency and "
                f"deepep_high_throughput all2all backend. {a2a_backend} is not "
980
981
982
                "supported. To fix use --all2all-backend=deepep_low_latency or "
                "--all2all-backend=deepep_high_throughput and install the DeepEP"
                " kernels."
983
            )
984
985
986

            if not self.model_config.disable_cascade_attn:
                self.model_config.disable_cascade_attn = True
987
                logger.warning_once("Disabling cascade attention when DBO is enabled.")
988
989
990
991

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

992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
        # 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.
1031
            if self.kv_transfer_config is not None:
1032
1033
                # NOTE(Kuntai): turn HMA off for connector unless specifically enabled.
                need_disable_hybrid_kv_cache_manager = True
1034
1035
1036
1037
1038
1039
1040
                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"
1041
1042
                    " of `SupportsHMA` defined in kv_connector/v1/base.py and"
                    " use --no-disable-hybrid-kv-cache-manager to start vLLM."
1043
                )
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
            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
1061

1062
1063
1064
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        if self.cache_config.mamba_cache_mode == "align":
            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"
            )
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        if self.compilation_config.debug_dump_path:
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            self.compilation_config.debug_dump_path = (
1075
                self.compilation_config.debug_dump_path.absolute().expanduser()
1076
            )
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        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"
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                    " by VLLM_DEBUG_DUMP_PATH to %s",
                    env_path,
                )
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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
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            if "-quant_fp8" not in custom_ops:
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                custom_ops.append("+quant_fp8")

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        # Handle the KV connector configs
        self._post_init_kv_transfer_config()

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    def update_sizes_for_sequence_parallelism(self, possible_sizes: list) -> list:
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        # remove the sizes that not multiple of tp_size when
        # enable sequence parallelism
        removed_sizes = [
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            size
            for size in possible_sizes
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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 "
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                "sequence parallelism is enabled",
                removed_sizes,
                self.parallel_config.tensor_parallel_size,
            )
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        return [
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            size
            for size in possible_sizes
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            if size % self.parallel_config.tensor_parallel_size == 0
        ]

    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)
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        # 1, 2, 4, then multiples of 8 up to 256 and then multiples of 16
        # up to max_graph_size
1139
        cudagraph_capture_sizes = [1, 2, 4] + list(range(8, 256, 8)) + list(
1140
            range(256, max_graph_size + 1, 16))
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        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
1143
        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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        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:
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                decode_query_len = 1
                if (
                    self.speculative_config
                    and self.speculative_config.num_speculative_tokens
                ):
                    decode_query_len += self.speculative_config.num_speculative_tokens
1186
                max_cudagraph_capture_size = min(
1187
                    self.scheduler_config.max_num_seqs * decode_query_len * 2, 512
1188
                )
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            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))
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                cudagraph_capture_sizes = [
                    i for i in dedup_sizes if i <= max_num_tokens
                ]
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                # sort to make sure the sizes are in ascending order
                cudagraph_capture_sizes.sort()
1210
            else:
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                cudagraph_capture_sizes = [
                    i for i in [1, 2, 4] if i <= max_cudagraph_capture_size
                ]
                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)
                    )

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            if (
                self.parallel_config.tensor_parallel_size > 1
1227
                and self.compilation_config.pass_config.enable_sp
1228
            ):
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                cudagraph_capture_sizes = self.update_sizes_for_sequence_parallelism(
                    cudagraph_capture_sizes
1231
                )
1232

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            # 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()
1283

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    def _set_compile_ranges(self):
        """
        Set the compile ranges for the compilation config.
        """
        compilation_config = self.compilation_config
        computed_compile_ranges_split_points = []

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        # The upper bound of the compile ranges is the max_num_batched_tokens.
        # For speculative decoding with draft model, the compile range must be extended
        # by 1 for each sequence.
        compile_range_end = self.scheduler_config.max_num_batched_tokens
        if compile_range_end is not None:
            do_extend: bool = (
                self.speculative_config is not None
                and self.speculative_config.uses_draft_model()
            )
            if do_extend:
                compile_range_end += self.scheduler_config.max_num_seqs

            computed_compile_ranges_split_points.append(compile_range_end)
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1313

        # 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
                )
1314
                if compile_range_end is not None and max_token_num < compile_range_end:
1315
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                    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."
                    )

        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}"
1326
                if compile_range_end is not None and x < compile_range_end and x > 1:
1327
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                    computed_compile_ranges_split_points.append(x)
        compilation_config.compile_ranges_split_points = sorted(
            computed_compile_ranges_split_points
        )

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    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 (
1346
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1349
            MODELS_CONFIG_MAP,
            HybridAttentionMambaModelConfig,
        )

1350
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        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.
1359
1360
            from vllm.model_executor.models.adapters import SequenceClassificationConfig

1361
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1363
            SequenceClassificationConfig.verify_and_update_config(self)

        if hasattr(self.model_config, "model_weights") and is_runai_obj_uri(
1364
1365
            self.model_config.model_weights
        ):
1366
            if self.load_config.load_format == "auto":
1367
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1369
1370
                logger.info(
                    "Detected Run:ai model config. "
                    "Overriding `load_format` to 'runai_streamer'"
                )
1371
                self.load_config.load_format = "runai_streamer"
1372
1373
1374
1375
            elif self.load_config.load_format not in (
                "runai_streamer",
                "runai_streamer_sharded",
            ):
1376
1377
                raise ValueError(
                    f"To load a model from S3, 'load_format' "
1378
                    f"must be 'runai_streamer' or 'runai_streamer_sharded', "
1379
1380
1381
                    f"but got '{self.load_config.load_format}'. "
                    f"Model: {self.model_config.model}"
                )
1382

1383
    def compile_debug_dump_path(self) -> Path | None:
1384
        """Returns a rank-aware path for dumping
1385
1386
1387
1388
1389
        torch.compile debug information.
        """
        if self.compilation_config.debug_dump_path is None:
            return None
        tp_rank = self.parallel_config.rank
1390
1391
        dp_rank = self.parallel_config.data_parallel_index
        append_path = f"rank_{tp_rank}_dp_{dp_rank}"
1392
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1398
        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}, "
1399
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1401
            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}, "
1402
            f"revision={self.model_config.revision}, "
1403
            f"tokenizer_revision={self.model_config.tokenizer_revision}, "
1404
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1406
1407
1408
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1410
1411
1412
1413
1414
            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}, "
1415
            f"enable_return_routed_experts={self.model_config.enable_return_routed_experts}, "  # noqa
1416
1417
1418
1419
1420
1421
1422
            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}, "
1423
            f"enable_chunked_prefill={self.scheduler_config.enable_chunked_prefill}, "  # noqa
1424
            f"pooler_config={self.model_config.pooler_config!r}, "
1425
1426
            f"compilation_config={self.compilation_config!r}"
        )
1427

1428
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1438
1439
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1441
    @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

1442

1443
1444
_current_vllm_config: VllmConfig | None = None
_current_prefix: str | None = None
1445
1446
1447


@contextmanager
1448
def set_current_vllm_config(
1449
    vllm_config: VllmConfig, check_compile=False, prefix: str | None = None
1450
):
1451
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1461
    """
    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
1462

1463
1464
    num_models_seen = compilation_counter.num_models_seen
    try:
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1469
        # 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()

1479
1480
        if (
            check_compile
1481
            and vllm_config.compilation_config.mode == CompilationMode.VLLM_COMPILE
1482
1483
            and compilation_counter.num_models_seen == num_models_seen
        ):
1484
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1492
            # 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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1506
1507
1508
1509
    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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1520
1521
        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:
1522
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1524
1525
1526
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1528
    return _current_vllm_config


T = TypeVar("T")


def get_layers_from_vllm_config(
1529
1530
    vllm_config: VllmConfig,
    layer_type: type[T],
1531
    layer_names: list[str] | None = None,
1532
) -> dict[str, T]:
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1542
    """
    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:
1543
        layer_names = list(vllm_config.compilation_config.static_forward_context.keys())
1544
1545
1546
1547
1548
1549
1550
1551

    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)
    }