vllm.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import copy
import getpass
import hashlib
import json
import os
import tempfile
import threading
import time
from contextlib import contextmanager
from dataclasses import replace
from datetime import datetime
from functools import lru_cache
from pathlib import Path
from typing import TYPE_CHECKING, Any, TypeVar

import torch
from pydantic import ConfigDict, Field, model_validator
from pydantic.dataclasses import dataclass

import vllm.envs as envs
from vllm.logger import enable_trace_function_call, init_logger
from vllm.transformers_utils.runai_utils import is_runai_obj_uri
from vllm.utils import random_uuid

from .cache import CacheConfig
from .compilation import CompilationConfig, CompilationMode, CUDAGraphMode
from .device import DeviceConfig
from .ec_transfer import ECTransferConfig
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
from .scheduler import SchedulerConfig
from .speculative import SpeculativeConfig
from .structured_outputs import StructuredOutputsConfig
from .utils import SupportsHash, config

if TYPE_CHECKING:
    from transformers import PretrainedConfig

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

    QuantizationConfig = Any

    KVCacheConfig = Any

logger = init_logger(__name__)


@config
@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
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
    model_config: ModelConfig = Field(default=None)
    """Model configuration."""
    cache_config: CacheConfig = Field(default_factory=CacheConfig)
    """Cache configuration."""
    parallel_config: ParallelConfig = Field(default_factory=ParallelConfig)
    """Parallel configuration."""
    scheduler_config: SchedulerConfig = Field(default_factory=SchedulerConfig)
    """Scheduler configuration."""
    device_config: DeviceConfig = Field(default_factory=DeviceConfig)
    """Device configuration."""
    load_config: LoadConfig = Field(default_factory=LoadConfig)
    """Load configuration."""
    lora_config: LoRAConfig | None = None
    """LoRA configuration."""
    speculative_config: SpeculativeConfig | None = None
    """Speculative decoding configuration."""
    structured_outputs_config: StructuredOutputsConfig = Field(
        default_factory=StructuredOutputsConfig
    )
    """Structured outputs configuration."""
    observability_config: ObservabilityConfig = Field(
        default_factory=ObservabilityConfig
    )
    """Observability configuration."""
    quant_config: QuantizationConfig | None = None
    """Quantization configuration."""
    compilation_config: CompilationConfig = Field(default_factory=CompilationConfig)
    """`torch.compile` and cudagraph capture configuration for the model.

    As a shorthand, one can append compilation arguments via 
    -0.parameter=arguement such as `-O.mode=3` (same as `-O='{"mode":3}'`).

    You can specify the full compilation config like so:
    `{"mode": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}`
    """
    kv_transfer_config: KVTransferConfig | None = None
    """The configurations for distributed KV cache transfer."""
    kv_events_config: KVEventsConfig | None = None
    """The configurations for event publishing."""
    ec_transfer_config: ECTransferConfig | None = None
    """The configurations for distributed EC cache transfer."""
    # some opaque config, only used to provide additional information
    # for the hash computation, mainly used for testing, debugging or out of
    # tree config registration.
    additional_config: dict | SupportsHash = Field(default_factory=dict)
    """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."""

    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__

        vllm_factors.append(__version__)
        if self.model_config:
            vllm_factors.append(self.model_config.compute_hash())
        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")
        if self.lora_config:
            vllm_factors.append(self.lora_config.compute_hash())
            # LoRA creates static buffers based on max_num_batched_tokens.
            # The tensor sizes and strides get captured in the torch.compile
            # graph explicitly.
            vllm_factors.append(str(self.scheduler_config.max_num_batched_tokens))
        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())
        else:
            vllm_factors.append("None")
        vllm_factors.append(self.observability_config.compute_hash())
        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")
        if self.ec_transfer_config:
            vllm_factors.append(self.ec_transfer_config.compute_hash())
        else:
            vllm_factors.append("None")
        if self.additional_config:
            if isinstance(additional_config := self.additional_config, dict):
                additional_config_hash = hashlib.md5(
                    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)

        hash_str = hashlib.md5(
            str(factors).encode(), usedforsecurity=False
        ).hexdigest()[:10]
        return hash_str

    def pad_for_cudagraph(self, batch_size: int) -> int:
        # if batch_size > self.compilation_config.max_cudagraph_capture_size,
        # it should raise an IndexError.
        # the caller should make sure the batch_size is within the range,
        # i.e., batch_size <= self.compilation_config.max_cudagraph_capture_size
        return self.compilation_config.bs_to_padded_graph_size[batch_size]

    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)

    @staticmethod
    def _get_quantization_config(
        model_config: ModelConfig, load_config: LoadConfig
    ) -> QuantizationConfig | None:
        """Get the quantization config."""
        from vllm.platforms import current_platform

        if model_config.quantization is not None:
            from vllm.model_executor.model_loader.weight_utils import get_quant_config

            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()}. "
                        f"Current capability: {capability}."
                    )
            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: "
                    f"{supported_dtypes}"
                )
            quant_config.maybe_update_config(model_config.model)
            return quant_config
        return None

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

        # For some reason, the _ version of this modifies the model_config
        # object, so using deepcopy to avoid this problem.
        return VllmConfig._get_quantization_config(
            copy.deepcopy(model_config), load_config
        )

    def with_hf_config(
        self,
        hf_config: PretrainedConfig,
        architectures: list[str] | None = None,
    ) -> "VllmConfig":
        if architectures is not None:
            hf_config = copy.deepcopy(hf_config)
            hf_config.architectures = architectures

        model_config = copy.deepcopy(self.model_config)
        model_config.hf_config = hf_config

        return replace(self, model_config=model_config)

    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.
        """
        if (kv_offloading_backend := self.cache_config.kv_offloading_backend) is None:
            return

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

        if (kv_offloading_size := self.cache_config.kv_offloading_size) is None:
            raise ValueError(
                "You must set kv_offloading_size when kv_offloading_backend is set."
            )
        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"
            kv_bytes_per_rank = kv_offloading_size * (1 << 30) / num_kv_ranks

            # NOTE(ApostaC): the actual calculation for num_cpu_blocks should be
            # done after the model's KV cache is initialized
            self.kv_transfer_config.kv_connector_extra_config.update(
                {"kv_bytes_per_rank": kv_bytes_per_rank, "num_cpu_blocks": 0}
            )
        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"

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

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

        self.try_verify_and_update_config()

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

        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(
                self.model_config, self.load_config
            )

        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.
            if self.parallel_config.pipeline_parallel_size > 1:
                raise ValueError(
                    "Async scheduling is not yet compatible with "
                    "pipeline_parallel_size > 1."
                )
            if self.speculative_config is not None:
                raise ValueError(
                    "Async scheduling is not yet compatible with speculative decoding."
                )
            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.
            # NOTE: we won't reach here until async scheduling is enabled by default.
            if (
                self.parallel_config.pipeline_parallel_size > 1
                or self.speculative_config is not None
            ):
                logger.warning(
                    "Async scheduling is not yet supported with speculative decoding "
                    " or pipeline_parallel_size > 1 and will be disabled."
                )
                self.scheduler_config.async_scheduling = False
            elif not executor_supports_async_sched:
                logger.warning(
                    "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,
                )
                self.scheduler_config.async_scheduling = False
            else:
                self.scheduler_config.async_scheduling = True

        from vllm.platforms import current_platform

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

        # If the user does not explicitly set a compilation mode, then
        # we use the default mode. The default mode depends on other
        # settings (see the below code).
        if self.compilation_config.mode is None:
            if self.model_config is not None and not self.model_config.enforce_eager:
                self.compilation_config.mode = CompilationMode.VLLM_COMPILE
            else:
                self.compilation_config.mode = CompilationMode.NONE

        # If user does not set custom ops via none or all set it here based on
        # compilation mode and backend.
        if all(s not in self.compilation_config.custom_ops for s in ("all", "none")):
            if (
                self.compilation_config.backend == "inductor"
                and self.compilation_config.mode != CompilationMode.NONE
            ):
                self.compilation_config.custom_ops.append("none")
            else:
                self.compilation_config.custom_ops.append("all")

        # async tp is built on top of sequence parallelism
        # and requires it to be enabled.
        if self.compilation_config.pass_config.enable_async_tp:
            self.compilation_config.pass_config.enable_sequence_parallelism = True
        if self.compilation_config.pass_config.enable_sequence_parallelism:
            self.compilation_config.custom_ops.append("+rms_norm")

        if current_platform.support_static_graph_mode():
            # if cudagraph_mode is not explicitly set by users, set default
            # value
            if self.compilation_config.cudagraph_mode is None:
                if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
                    # default to full and piecewise for most models
                    self.compilation_config.cudagraph_mode = (
                        CUDAGraphMode.FULL_AND_PIECEWISE
                    )
                else:
                    self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE

            # if cudagraph_mode has full cudagraphs, we need to check support
            if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
                # decode context parallel does not support full cudagraphs
                if self.parallel_config.decode_context_parallel_size > 1:
                    logger.warning_once(
                        "Decode context parallel (DCP) is enabled, which is "
                        "incompatible with full CUDA graphs. "
                        "Overriding cudagraph_mode to PIECEWISE."
                    )
                    self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
                elif self.model_config is not None:
                    if self.model_config.pooler_config is not None:
                        logger.warning_once(
                            "Pooling models do not support full cudagraphs. "
                            "Overriding cudagraph_mode to PIECEWISE."
                        )
                        self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
                    elif self.model_config.is_encoder_decoder:
                        logger.warning_once(
                            "Encoder-decoder models do not support full cudagraphs. "
                            "Overriding cudagraph_mode to PIECEWISE."
                        )
                        self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
                    elif (
                        current_platform.is_cuda()
                        and current_platform.is_device_capability(100)
                        and self.model_config.max_model_len > 131072
                        and not self.model_config.use_mla
                    ):
                        # Refer to vllm/utils/flashinfer.py::use_trtllm_attention()
                        logger.warning_once(
                            "NVIDIA Blackwell TRTLLM attention cannot support "
                            "max_model_len >= 131072 (found "
                            f"{self.model_config.max_model_len}), causing dynamic "
                            "dispatching that breaks full cudagraphs. "
                            "Overriding cudagraph_mode to PIECEWISE."
                        )
                        self.compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE

            # disable cudagraph when enforce eager execution
            if self.model_config is not None and self.model_config.enforce_eager:
                logger.info("Cudagraph is disabled under eager mode")
                self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
                # override related settings when enforce eager
                self.compilation_config.max_cudagraph_capture_size = 0
                self.compilation_config.cudagraph_capture_sizes = []
            else:
                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:
            if (
                self.speculative_config is not None
                and self.speculative_config.use_eagle()
            ):
                raise ValueError(
                    "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 "
                    "for prompt tokens."
                )

            logger.warning_once(
                "--kv-sharing-fast-prefill requires changes on model side for "
                "correctness and to realize prefill savings. "
            )

        disable_chunked_prefill_reasons: list[str] = []

        if self.model_config:
            if self.model_config.pooler_config:
                pooling_type = self.model_config.pooler_config.pooling_type
                if pooling_type is None or pooling_type.lower() != "last":
                    disable_chunked_prefill_reasons.append(
                        'Only "last" pooling supports chunked '
                        "prefill and prefix caching; disabling both."
                    )
                if not getattr(self.model_config.hf_config, "is_causal", True):
                    disable_chunked_prefill_reasons.append(
                        "Only models using causal attention support chunked "
                        "prefill and prefix caching; disabling both."
                    )
            elif self.model_config.is_encoder_decoder:
                from vllm.multimodal import MULTIMODAL_REGISTRY

                self.scheduler_config.max_num_encoder_input_tokens = (
                    MULTIMODAL_REGISTRY.get_encdec_max_encoder_len(self.model_config)
                )
                logger.debug(
                    "Encoder-decoder model detected: setting "
                    "`max_num_encoder_input_tokens` to encoder length (%s)",
                    self.scheduler_config.max_num_encoder_input_tokens,
                )
                if (
                    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'."
                    )

        # Final off-switch for CP/APC:
        # Disable for (a) collected blockers, (b) encoder–decoder, or
        # (c) explicit CP=False when APC wasn't requested.
        # Do NOT disable merely because the resolved CP flag is False.
        apc_requested = (
            self.cache_config is not None and self.cache_config.enable_prefix_caching
        )
        if (
            disable_chunked_prefill_reasons
            or (self.model_config is not None and self.model_config.is_encoder_decoder)
            or (
                self.scheduler_config.enable_chunked_prefill is False
                and not apc_requested
            )
        ):
            for reason in disable_chunked_prefill_reasons:
                logger.info(reason)
            self.scheduler_config.chunked_prefill_enabled = False
            self.scheduler_config.long_prefill_token_threshold = 0

            if self.cache_config is not None:
                self.cache_config.enable_prefix_caching = False

        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
        ):
            logger.warning(
                "KV cache events are on, but prefix caching is not enabled."
                "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(
                "KV cache events are disabled,"
                "but the scheduler is configured to publish them."
                "Modify KVEventsConfig.enable_kv_cache_events"
                "to True to enable."
            )
        current_platform.check_and_update_config(self)

        # If DCP, ensure the block size is right.
        if self.parallel_config.decode_context_parallel_size > 1:
            assert (
                self.parallel_config.dcp_kv_cache_interleave_size
                <= self.cache_config.block_size
                and self.cache_config.block_size
                % self.parallel_config.dcp_kv_cache_interleave_size
                == 0
            ), (
                f"Block_size({self.cache_config.block_size}) should be greater "
                "than or equal to and divisible by dcp_kv_cache_interleave_size "
                f"({self.parallel_config.dcp_kv_cache_interleave_size})."
            )

        assert (
            self.parallel_config.dcp_kv_cache_interleave_size == 1
            or self.speculative_config is None
        ), "MTP with dcp_kv_cache_interleave_size > 1 is not supported now."

        # Do this after all the updates to compilation_config.mode
        if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
            self.compilation_config.set_splitting_ops_for_v1()

        # final check of cudagraph mode after all possible updates
        if current_platform.is_cuda_alike():
            if (
                self.compilation_config.cudagraph_mode.has_full_cudagraphs()
                and self.model_config is not None
                and not self.model_config.disable_cascade_attn
                and not self.compilation_config.cudagraph_mode.has_piecewise_cudagraphs()  # noqa: E501
            ):
                logger.warning_once(
                    "No piecewise cudagraph for executing cascade attention."
                    " Will fall back to eager execution if a batch runs "
                    "into cascade attentions"
                )

            if self.compilation_config.cudagraph_mode.requires_piecewise_compilation():
                assert self.compilation_config.mode == CompilationMode.VLLM_COMPILE, (
                    "Compilation mode should be CompilationMode.VLLM_COMPILE "
                    "when cudagraph_mode piecewise cudagraphs is used, "
                    f"cudagraph_mode={self.compilation_config.cudagraph_mode}"
                )

            # final migrate the deprecated flags
            self.compilation_config.use_cudagraph = (
                self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
            )
            self.compilation_config.full_cuda_graph = (
                self.compilation_config.cudagraph_mode.has_full_cudagraphs()
            )

        if self.parallel_config.enable_dbo:
            a2a_backend = self.parallel_config.all2all_backend
            assert a2a_backend in ["deepep_low_latency", "deepep_high_throughput"], (
                "Microbatching currently only supports the deepep_low_latency and "
                f"deepep_high_throughput all2all backend. {a2a_backend} is not "
                "supported. To fix use --all2all-backend=deepep_low_latency or "
                "--all2all-backend=deepep_high_throughput and install the DeepEP"
                " kernels."
            )

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

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

        if not self.scheduler_config.disable_hybrid_kv_cache_manager:
            # 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.
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
            if self.kv_transfer_config is not None:
                # NOTE(Kuntai): turn HMA off for connector for now.
                # TODO(Kuntai): have a more elegent solution to check and
                # turn off HMA for connector that does not support HMA.
                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"
                    " of `SupportsHMA` defined in kv_connector/v1/base.py."
                )
                self.scheduler_config.disable_hybrid_kv_cache_manager = True
            if self.kv_events_config is not None:
                # Hybrid KV cache manager is not compatible with KV events.
                self.scheduler_config.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.
                    self.scheduler_config.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.
                    self.scheduler_config.disable_hybrid_kv_cache_manager = True

        if self.compilation_config.debug_dump_path:
            self.compilation_config.debug_dump_path = (
                self.compilation_config.debug_dump_path.absolute().expanduser()
            )
        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"
                    " by VLLM_DEBUG_DUMP_PATH to %s",
                    env_path,
                )
            self.compilation_config.debug_dump_path = env_path

        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")

        # Handle the KV connector configs
        self._post_init_kv_transfer_config()

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

        return [
            size
            for size in possible_sizes
            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)
        # 1, 2, 4, then multiples of 8 up to 256 and then multiples of 16
        # up to max_graph_size
        cuda_graph_sizes = [1, 2, 4] + list(range(8, 256, 8)) + list(
            range(256, max_graph_size + 1, 16))

        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
        will be the final sizes to capture cudagraph (in ascending order).

        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.
        """

        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:
                max_cudagraph_capture_size = min(
                    self.scheduler_config.max_num_seqs * 2, 512
                )
            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))
                cudagraph_capture_sizes = dedup_sizes
                # sort to make sure the sizes are in ascending order
                cudagraph_capture_sizes.sort()
            else:
                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)
                    )

            if (
                self.parallel_config.tensor_parallel_size > 1
                and self.compilation_config.pass_config.enable_sequence_parallelism
            ):
                cudagraph_capture_sizes = self.update_sizes_for_sequence_parallelism(
                    cudagraph_capture_sizes
                )

            # 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()

    def recalculate_max_model_len(self, max_model_len: int):
        # Can only be called in try_verify_and_update_config
        model_config = self.model_config
        max_model_len = model_config.get_and_verify_max_len(max_model_len)
        self.model_config.max_model_len = max_model_len
        self.scheduler_config.max_model_len = max_model_len

    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 (
            MODELS_CONFIG_MAP,
            HybridAttentionMambaModelConfig,
        )

        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.
            from vllm.model_executor.models.adapters import SequenceClassificationConfig

            SequenceClassificationConfig.verify_and_update_config(self)

        if hasattr(self.model_config, "model_weights") and is_runai_obj_uri(
            self.model_config.model_weights
        ):
            if self.load_config.load_format == "auto":
                logger.info(
                    "Detected Run:ai model config. "
                    "Overriding `load_format` to 'runai_streamer'"
                )
                self.load_config.load_format = "runai_streamer"
            elif self.load_config.load_format not in (
                "runai_streamer",
                "runai_streamer_sharded",
            ):
                raise ValueError(
                    f"To load a model from S3, 'load_format' "
                    f"must be 'runai_streamer' or 'runai_streamer_sharded', "
                    f"but got '{self.load_config.load_format}'. "
                    f"Model: {self.model_config.model}"
                )

    def compile_debug_dump_path(self) -> Path | None:
        """Returns a rank-aware path for dumping
        torch.compile debug information.
        """
        if self.compilation_config.debug_dump_path is None:
            return None
        tp_rank = self.parallel_config.rank
        dp_rank = self.parallel_config.data_parallel_rank
        data_parallel_size = self.parallel_config.data_parallel_size
        append_path = (
            f"rank_{tp_rank}"
            if data_parallel_size == 1
            else f"rank_{tp_rank}_dp_{dp_rank}"
        )
        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}, "
            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}, "
            f"revision={self.model_config.revision}, "
            f"tokenizer_revision={self.model_config.tokenizer_revision}, "
            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}, "
            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}, "
            f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, "  # noqa
            f"pooler_config={self.model_config.pooler_config!r}, "
            f"compilation_config={self.compilation_config!r}"
        )

    @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


_current_vllm_config: VllmConfig | None = None
_current_prefix: str | None = None


@contextmanager
def set_current_vllm_config(
    vllm_config: VllmConfig, check_compile=False, prefix: str | None = None
):
    """
    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

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

        if (
            check_compile
            and vllm_config.compilation_config.mode == CompilationMode.VLLM_COMPILE
            and compilation_counter.num_models_seen == num_models_seen
        ):
            # 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.",
                vllm_config.model_config.model,
            )
    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:
        # in ci, usually when we test custom ops/modules directly,
        # we don't set the vllm config. In that case, we set a default
        # config.
        logger.warning("Current vLLM config is not set.")
        return VllmConfig()
    return _current_vllm_config


T = TypeVar("T")


def get_layers_from_vllm_config(
    vllm_config: VllmConfig,
    layer_type: type[T],
    layer_names: list[str] | None = None,
) -> dict[str, T]:
    """
    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:
        layer_names = list(vllm_config.compilation_config.static_forward_context.keys())

    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)
    }