config.py 174 KB
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# SPDX-License-Identifier: Apache-2.0

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import ast
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import copy
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import enum
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import hashlib
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import inspect
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import json
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import sys
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import textwrap
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import warnings
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from collections import Counter
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from contextlib import contextmanager
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from dataclasses import (MISSING, dataclass, field, fields, is_dataclass,
                         replace)
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from importlib.util import find_spec
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from pathlib import Path
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from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Final, Literal,
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                    Optional, Protocol, TypeVar, Union, get_args)
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import torch
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from pydantic import BaseModel, Field, PrivateAttr
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from torch.distributed import ProcessGroup, ReduceOp
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from transformers import PretrainedConfig
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import vllm.envs as envs
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from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization import (QUANTIZATION_METHODS,
                                                     get_quantization_config)
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from vllm.model_executor.models import ModelRegistry
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from vllm.platforms import CpuArchEnum, current_platform
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from vllm.sampling_params import GuidedDecodingParams
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from vllm.tracing import is_otel_available, otel_import_error_traceback
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from vllm.transformers_utils.config import (
    ConfigFormat, get_config, get_hf_image_processor_config,
    get_hf_text_config, get_pooling_config,
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    get_sentence_transformer_tokenizer_config, is_encoder_decoder,
    try_get_generation_config, uses_mrope)
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from vllm.transformers_utils.s3_utils import S3Model
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from vllm.transformers_utils.utils import is_s3, maybe_model_redirect
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from vllm.utils import (GiB_bytes, LayerBlockType, cuda_device_count_stateless,
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                        get_cpu_memory, get_open_port, is_torch_equal_or_newer,
                        random_uuid, resolve_obj_by_qualname)
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if TYPE_CHECKING:
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    from _typeshed import DataclassInstance
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    from ray.util.placement_group import PlacementGroup

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    from vllm.executor.executor_base import ExecutorBase
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    from vllm.model_executor.layers.quantization.base_config import (
        QuantizationConfig)
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    from vllm.model_executor.model_loader.loader import BaseModelLoader
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    from vllm.transformers_utils.tokenizer_group.base_tokenizer_group import (
        BaseTokenizerGroup)
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    Config = TypeVar("Config", bound=DataclassInstance)
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else:
    QuantizationConfig = None
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    Config = TypeVar("Config")
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logger = init_logger(__name__)

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# This value is chosen to have a balance between ITL and TTFT. Note it is
# not optimized for throughput.
_DEFAULT_MAX_NUM_BATCHED_TOKENS = 2048
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_POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
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_MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120
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TaskOption = Literal["auto", "generate", "embedding", "embed", "classify",
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                     "score", "reward", "transcription"]
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_ResolvedTask = Literal["generate", "embed", "classify", "score", "reward",
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                        "draft", "transcription"]
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RunnerType = Literal["generate", "pooling", "draft", "transcription"]
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_RUNNER_TASKS: dict[RunnerType, list[_ResolvedTask]] = {
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    "generate": ["generate"],
    "pooling": ["embed", "classify", "score", "reward"],
    "draft": ["draft"],
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    "transcription": ["transcription"],
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}

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_TASK_RUNNER: dict[_ResolvedTask, RunnerType] = {
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    task: runner
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    for runner, tasks in _RUNNER_TASKS.items()
    for task in tasks
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}
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HfOverrides = Union[dict[str, Any], Callable[[PretrainedConfig],
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                                             PretrainedConfig]]

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class SupportsHash(Protocol):

    def compute_hash(self) -> str:
        ...


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class SupportsMetricsInfo(Protocol):

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    def metrics_info(self) -> dict[str, str]:
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        ...


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class ModelImpl(str, enum.Enum):
    AUTO = "auto"
    VLLM = "vllm"
    TRANSFORMERS = "transformers"


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def get_attr_docs(cls: type[Any]) -> dict[str, str]:
    """
    Get any docstrings placed after attribute assignments in a class body.

    https://davidism.com/mit-license/
    """

    def pairwise(iterable):
        """
        Manually implement https://docs.python.org/3/library/itertools.html#itertools.pairwise
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        Can be removed when Python 3.9 support is dropped.
        """
        iterator = iter(iterable)
        a = next(iterator, None)

        for b in iterator:
            yield a, b
            a = b

    cls_node = ast.parse(textwrap.dedent(inspect.getsource(cls))).body[0]

    if not isinstance(cls_node, ast.ClassDef):
        raise TypeError("Given object was not a class.")

    out = {}

    # Consider each pair of nodes.
    for a, b in pairwise(cls_node.body):
        # Must be an assignment then a constant string.
        if (not isinstance(a, (ast.Assign, ast.AnnAssign))
                or not isinstance(b, ast.Expr)
                or not isinstance(b.value, ast.Constant)
                or not isinstance(b.value.value, str)):
            continue

        doc = inspect.cleandoc(b.value.value)

        # An assignment can have multiple targets (a = b = v), but an
        # annotated assignment only has one target.
        targets = a.targets if isinstance(a, ast.Assign) else [a.target]

        for target in targets:
            # Must be assigning to a plain name.
            if not isinstance(target, ast.Name):
                continue

            out[target.id] = doc

    return out


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def config(cls: type[Config]) -> type[Config]:
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    """
    A decorator that ensures all fields in a dataclass have default values
    and that each field has a docstring.
    """
    if not is_dataclass(cls):
        raise TypeError("The decorated class must be a dataclass.")
    attr_docs = get_attr_docs(cls)
    for f in fields(cls):
        if f.init and f.default is MISSING and f.default_factory is MISSING:
            raise ValueError(
                f"Field '{f.name}' in {cls.__name__} must have a default value."
            )
        if f.name not in attr_docs:
            raise ValueError(
                f"Field '{f.name}' in {cls.__name__} must have a docstring.")
    return cls


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def get_field(cls: type[Config], name: str) -> Field:
    """Get the default factory field of a dataclass by name. Used for getting
    default factory fields in `EngineArgs`."""
    if not is_dataclass(cls):
        raise TypeError("The given class is not a dataclass.")
    cls_fields = {f.name: f for f in fields(cls)}
    if name not in cls_fields:
        raise ValueError(f"Field '{name}' not found in {cls.__name__}.")
    named_field: Field = cls_fields.get(name)
    if (default_factory := named_field.default_factory) is not MISSING:
        return field(default_factory=default_factory)
    if (default := named_field.default) is not MISSING:
        return field(default=default)
    raise ValueError(
        f"{cls.__name__}.{name} must have a default value or default factory.")


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class ModelConfig:
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    """Configuration for the model.

    Args:
        model: Name or path of the huggingface model to use.
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            It is also used as the content for `model_name` tag in metrics
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            output when `served_model_name` is not specified.
        task: The task to use the model for. Each vLLM instance only supports
            one task, even if the same model can be used for multiple tasks.
            When the model only supports one task, "auto" can be used to select
            it; otherwise, you must specify explicitly which task to use.
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        tokenizer: Name or path of the huggingface tokenizer to use.
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        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
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            available, "slow" will always use the slow tokenizer,
            "mistral" will always use the tokenizer from `mistral_common`, and
            "custom" will use --tokenizer to select the preregistered tokenizer.
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        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
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        allowed_local_media_path: Allowing API requests to read local images or
            videos from directories specified by the server file system.
            This is a security risk. Should only be enabled in trusted
            environments.
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        dtype: Data type for model weights and activations. The "auto" option
            will use FP16 precision for FP32 and FP16 models, and BF16 precision
            for BF16 models.
        seed: Random seed for reproducibility.
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        revision: The specific model version to use. It can be a branch name,
            a tag name, or a commit id. If unspecified, will use the default
            version.
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        code_revision: The specific revision to use for the model code on
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            Hugging Face Hub. It can be a branch name, a tag name, or a
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            commit id. If unspecified, will use the default version.
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        tokenizer_revision: The specific tokenizer version to use. It can be a
            branch name, a tag name, or a commit id. If unspecified, will use
            the default version.
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        max_model_len: Maximum length of a sequence (including prompt and
            output). If None, will be derived from the model.
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        spec_target_max_model_len: Specify the the maximum length for spec
            decoding draft models.
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        quantization: Quantization method that was used to quantize the model
            weights. If None, we assume the model weights are not quantized.
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        enforce_eager: Whether to enforce eager execution. If True, we will
            disable CUDA graph and always execute the model in eager mode.
            If False, we will use CUDA graph and eager execution in hybrid.
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            If None, the user did not specify, so default to False.
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        max_seq_len_to_capture: Maximum sequence len covered by CUDA graphs.
            When a sequence has context length larger than this, we fall back
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            to eager mode. Additionally for encoder-decoder models, if the
            sequence length of the encoder input is larger than this, we fall
            back to the eager mode.
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        max_logprobs: Maximum number of log probabilities. Defaults to 20.
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        disable_sliding_window: Whether to disable sliding window. If True,
            we will disable the sliding window functionality of the model.
            If the model does not support sliding window, this argument is
            ignored.
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        skip_tokenizer_init: If true, skip initialization of tokenizer and
            detokenizer.
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        served_model_name: The model name used in metrics tag `model_name`,
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            matches the model name exposed via the APIs. If multiple model
            names provided, the first name will be used. If not specified,
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            the model name will be the same as `model`.
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        limit_mm_per_prompt: Maximum number of data items per modality
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            per prompt. Only applicable for multimodal models.
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        use_async_output_proc: Whether to use async output processor.
            Defaults to True.
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        config_format: The config format which shall be loaded.
            Defaults to 'auto' which defaults to 'hf'.
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        hf_token: The token to use as HTTP bearer authorization for remote files
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            . If `True`, will use the token generated when running
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            `huggingface-cli login` (stored in `~/.huggingface`).
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        hf_overrides: If a dictionary, contains arguments to be forwarded to the
            HuggingFace config. If a callable, it is called to update the
            HuggingFace config.
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        mm_processor_kwargs: Arguments to be forwarded to the model's processor
            for multi-modal data, e.g., image processor.
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        disable_mm_preprocessor_cache: If true, then disables caching of the
            multi-modal preprocessor/mapper. (not recommended)
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        override_neuron_config: Initialize non default neuron config or
            override default neuron config that are specific to Neuron devices,
            this argument will be used to configure the neuron config that
            can not be gathered from the vllm arguments.
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        override_pooler_config: Initialize non default pooling config or
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            override default pooling config for the pooling model.
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        logits_processor_pattern: Optional regex pattern specifying valid
            logits processor qualified names that can be passed with the
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            `logits_processors` extra completion argument. Defaults to None,
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            which allows no processors.
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        generation_config: Configuration parameter file for generation.
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        model_impl: Which implementation of the model to use:
            "auto" will try to use the vLLM implementation if it exists and
                fall back to the Transformers implementation if no vLLM
                implementation is available.
            "vllm" will use the vLLM model implementation.
            "transformers" will use the Transformers model implementation.
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        override_generation_config: Override the generation config with the
            given config.
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    """
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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.
        """
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        factors: list[Any] = []
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        factors.append(self.model)
        factors.append(self.dtype)
        factors.append(self.quantization)
        factors.append(self.revision)
        factors.append(self.code_revision)
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        factors.append(self.max_model_len)
        factors.append(self.max_logprobs)
        factors.append(self.disable_sliding_window)
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        factors.append(self.trust_remote_code)
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        factors.append(self.mm_processor_kwargs)
        factors.append(self.generation_config)
        factors.append(self.model_impl)
        factors.append(self.override_generation_config)
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        factors.append(self.rope_scaling)
        factors.append(self.rope_theta)
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        # hf_config can control how the model looks!
        factors.append(self.hf_config.to_json_string())
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        return hashlib.sha256(str(factors).encode()).hexdigest()

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    def __init__(
        self,
        model: str,
        task: Union[TaskOption, Literal["draft"]],
        tokenizer: str,
        tokenizer_mode: str,
        trust_remote_code: bool,
        dtype: Union[str, torch.dtype],
        seed: int,
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        hf_config_path: Optional[str] = None,
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        allowed_local_media_path: str = "",
        revision: Optional[str] = None,
        code_revision: Optional[str] = None,
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        rope_scaling: Optional[dict[str, Any]] = None,
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        rope_theta: Optional[float] = None,
        tokenizer_revision: Optional[str] = None,
        max_model_len: Optional[int] = None,
        spec_target_max_model_len: Optional[int] = None,
        quantization: Optional[str] = None,
        enforce_eager: Optional[bool] = None,
        max_seq_len_to_capture: Optional[int] = None,
        max_logprobs: int = 20,
        disable_sliding_window: bool = False,
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        disable_cascade_attn: bool = False,
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        skip_tokenizer_init: bool = False,
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        served_model_name: Optional[Union[str, list[str]]] = None,
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        limit_mm_per_prompt: Optional[dict[str, int]] = None,
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        use_async_output_proc: bool = True,
        config_format: ConfigFormat = ConfigFormat.AUTO,
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        hf_token: Optional[Union[bool, str]] = None,
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        hf_overrides: Optional[HfOverrides] = None,
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        mm_processor_kwargs: Optional[dict[str, Any]] = None,
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        disable_mm_preprocessor_cache: bool = False,
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        override_neuron_config: Optional[dict[str, Any]] = None,
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        override_pooler_config: Optional["PoolerConfig"] = None,
        logits_processor_pattern: Optional[str] = None,
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        generation_config: str = "auto",
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        enable_sleep_mode: bool = False,
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        override_generation_config: Optional[dict[str, Any]] = None,
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        model_impl: Union[str, ModelImpl] = ModelImpl.AUTO,
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    ) -> None:
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        self.model = maybe_model_redirect(model)
        self.tokenizer = maybe_model_redirect(tokenizer)

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        self.hf_config_path = hf_config_path
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        if isinstance(hf_config_path, str):
            self.hf_config_path = maybe_model_redirect(hf_config_path)

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        self.tokenizer_mode = tokenizer_mode
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        self.trust_remote_code = trust_remote_code
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        self.allowed_local_media_path = allowed_local_media_path
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        self.seed = seed
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        self.revision = revision
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        self.code_revision = code_revision
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        self.rope_scaling = rope_scaling
        self.rope_theta = rope_theta
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        self.model_impl = model_impl
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        if hf_overrides is None:
            hf_overrides = {}
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        if callable(hf_overrides):
            hf_overrides_kw = {}
            hf_overrides_fn = hf_overrides
        else:
            hf_overrides_kw = hf_overrides
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            hf_overrides_fn = None
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        if rope_scaling is not None:
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            hf_override: dict[str, Any] = {"rope_scaling": rope_scaling}
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            hf_overrides_kw.update(hf_override)
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            hf_overrides_str = json.dumps(hf_overrides)
            msg = (
                "`--rope-scaling` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
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            warnings.warn(DeprecationWarning(msg), stacklevel=2)
        if rope_theta is not None:
            hf_override = {"rope_theta": rope_theta}
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            hf_overrides_kw.update(hf_override)
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            hf_overrides_str = json.dumps(hf_overrides)
            msg = (
                "`--rope-theta` will be removed in a future release. "
                f"'Please instead use `--hf-overrides '{hf_overrides_str}'`")
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            warnings.warn(DeprecationWarning(msg), stacklevel=2)

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        self.maybe_pull_model_tokenizer_for_s3(model, tokenizer)

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        if (backend := envs.VLLM_ATTENTION_BACKEND
            ) and backend == "FLASHINFER" and find_spec("flashinfer") is None:
            raise ValueError(
                "VLLM_ATTENTION_BACKEND is set to FLASHINFER, but flashinfer "
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                "module was not found. See "
                "https://github.com/vllm-project/vllm/blob/main/docker/Dockerfile "  # noqa: E501
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                "for instructions on how to install it.")

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        # The tokenizer version is consistent with the model version by default.
        if tokenizer_revision is None:
            self.tokenizer_revision = revision
        else:
            self.tokenizer_revision = tokenizer_revision
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        self.quantization = quantization
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        self.enforce_eager = enforce_eager
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        self.max_seq_len_to_capture = max_seq_len_to_capture
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        self.max_logprobs = max_logprobs
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        self.disable_sliding_window = disable_sliding_window
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        self.disable_cascade_attn = disable_cascade_attn
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        self.skip_tokenizer_init = skip_tokenizer_init
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        self.enable_sleep_mode = enable_sleep_mode

        from vllm.platforms import current_platform

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        if (self.enable_sleep_mode
                and not current_platform.is_sleep_mode_available()):
            raise ValueError(
                "Sleep mode is not supported on current platform.")
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        hf_config = get_config(self.hf_config_path or self.model,
                               trust_remote_code, revision, code_revision,
                               config_format)
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        if hf_overrides_kw:
            logger.info("Overriding HF config with %s", hf_overrides_kw)
            hf_config.update(hf_overrides_kw)
        if hf_overrides_fn:
            logger.info("Overriding HF config with %s", hf_overrides_fn)
            hf_config = hf_overrides_fn(hf_config)

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        self.hf_config = hf_config

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        self.hf_text_config = get_hf_text_config(self.hf_config)
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        self.attention_chunk_size = getattr(self.hf_text_config,
                                            "attention_chunk_size", None)
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        self.encoder_config = self._get_encoder_config()
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        self.hf_image_processor_config = get_hf_image_processor_config(
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            self.model, hf_token=hf_token, revision=revision)
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        self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
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        self.use_async_output_proc = use_async_output_proc
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        self.mm_processor_kwargs = mm_processor_kwargs
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        self.disable_mm_preprocessor_cache = disable_mm_preprocessor_cache
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        # Set enforce_eager to False if the value is unset.
        if self.enforce_eager is None:
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            self.enforce_eager = False

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        interleaved_attn_models = ["gemma2", "gemma3_text", "cohere2"]
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        sliding_window = getattr(self.hf_text_config, "sliding_window", None)
        has_interleaved_attention = (sliding_window is not None) and (
            isinstance(sliding_window, list) or
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            (self.hf_text_config.model_type in interleaved_attn_models))
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        if (not self.disable_sliding_window and has_interleaved_attention):
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            if (backend :=
                    envs.VLLM_ATTENTION_BACKEND) in ("XFORMERS", "FLASHINFER"):
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                sliding_window_len_min = get_min_sliding_window(
                    self.hf_text_config.sliding_window)
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                logger.warning_once(
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                    f"{self.hf_text_config.model_type} has interleaved "
                    "attention, which is currently not supported by the "
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                    f"{backend} backend. Disabling sliding window and capping "
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                    "the max length to the sliding window size "
                    f"({sliding_window_len_min}).")
                self.disable_sliding_window = True
            else:
                # for a model with interleaved attention,
                # the scheduler and the model treat it as full attention
                # (i.e., not dropping any tokens outside the window).
                # only the attention layer itself is aware of the sliding
                # window, and use the window size to compute the attention.
                self.hf_text_config.interleaved_sliding_window = sliding_window
                delattr(self.hf_text_config, "sliding_window")
                sliding_window = None
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        self.max_model_len = _get_and_verify_max_len(
            hf_config=self.hf_text_config,
            max_model_len=max_model_len,
            disable_sliding_window=self.disable_sliding_window,
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            sliding_window_len=self.get_hf_config_sliding_window(),
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            spec_target_max_model_len=spec_target_max_model_len,
            encoder_config=self.encoder_config)
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        self.served_model_name = get_served_model_name(model,
                                                       served_model_name)
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        self.multimodal_config = self._init_multimodal_config(
            limit_mm_per_prompt)
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        if not self.skip_tokenizer_init:
            self._verify_tokenizer_mode()
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        self.is_attention_free = self._init_attention_free()
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        self.is_hybrid = self._init_is_hybrid()
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        self.has_noops = self._init_has_noops()
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        self.has_inner_state = self._init_has_inner_state()

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        if current_platform.is_neuron():
            self.override_neuron_config = override_neuron_config
        else:
            self.override_neuron_config = None
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        supported_tasks, task = self._resolve_task(task)
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        self.supported_tasks = supported_tasks
        self.task: Final = task
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        if self.task in ("draft", "generate"):
            self.truncation_side = "left"
        else:
            self.truncation_side = "right"
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        self.pooler_config = self._init_pooler_config(override_pooler_config)
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        self.logits_processor_pattern = logits_processor_pattern
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        self.generation_config = generation_config
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        self.override_generation_config = override_generation_config or {}
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        self._verify_quantization()
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        self._verify_cuda_graph()
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        self._verify_bnb_config()
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    @property
    def registry(self):
        return ModelRegistry

    @property
    def architectures(self) -> list[str]:
        return getattr(self.hf_config, "architectures", [])

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    def maybe_pull_model_tokenizer_for_s3(self, model: str,
                                          tokenizer: str) -> None:
        """
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        Pull the model config or tokenizer to a temporary
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        directory in case of S3.

        Args:
            model: The model name or path.
            tokenizer: The tokenizer name or path.

        """
        if is_s3(model) or is_s3(tokenizer):
            if is_s3(model):
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                s3_model = S3Model()
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                s3_model.pull_files(
                    model, allow_pattern=["*.model", "*.py", "*.json"])
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                self.model_weights = self.model
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                self.model = s3_model.dir
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            if is_s3(tokenizer):
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                s3_tokenizer = S3Model()
                s3_tokenizer.pull_files(
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                    model, ignore_pattern=["*.pt", "*.safetensors", "*.bin"])
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                self.tokenizer = s3_tokenizer.dir
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    def _init_multimodal_config(
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        self, limit_mm_per_prompt: Optional[dict[str, int]]
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    ) -> Optional["MultiModalConfig"]:
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        if self.registry.is_multimodal_model(self.architectures):
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            return MultiModalConfig(limit_per_prompt=limit_mm_per_prompt or {})
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        if limit_mm_per_prompt:
            raise ValueError("`limit_mm_per_prompt` is only supported for "
                             "multimodal models.")

        return None
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    def _get_encoder_config(self):
        return get_sentence_transformer_tokenizer_config(
            self.model, self.revision)

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    def _init_pooler_config(
        self,
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        override_pooler_config: Optional["PoolerConfig"],
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    ) -> Optional["PoolerConfig"]:
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        if self.runner_type == "pooling":
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            user_config = override_pooler_config or PoolerConfig()

            base_config = get_pooling_config(self.model, self.revision)
            if base_config is not None:
                # Only set values that are not overridden by the user
                for k, v in base_config.items():
                    if getattr(user_config, k) is None:
                        setattr(user_config, k, v)

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            if self.is_matryoshka:
                if user_config.normalize is None:
                    user_config.normalize = True
                elif not user_config.normalize:
                    raise ValueError(
                        "`normalize` must be enabled (set to True) "
                        "for models that are compatible with "
                        "Matryoshka Representation.")

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            return user_config

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        return None

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    def _init_attention_free(self) -> bool:
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        return self.registry.is_attention_free_model(self.architectures)
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    def _init_is_hybrid(self) -> bool:
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        return self.registry.is_hybrid_model(self.architectures)
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    def _init_has_noops(self) -> bool:
        architectures = getattr(self.hf_config, "architectures", [])
        return self.registry.is_noops_model(architectures)

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    def _init_has_inner_state(self) -> bool:
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        return self.registry.model_has_inner_state(self.architectures)
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    def _verify_tokenizer_mode(self) -> None:
        tokenizer_mode = self.tokenizer_mode.lower()
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        if tokenizer_mode not in ["auto", "slow", "mistral", "custom"]:
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            raise ValueError(
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
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                "either 'auto', 'slow', 'mistral' or 'custom'.")
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        self.tokenizer_mode = tokenizer_mode
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    def _get_preferred_task(
        self,
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        architectures: list[str],
        supported_tasks: set[_ResolvedTask],
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    ) -> Optional[_ResolvedTask]:
        model_id = self.model
        if get_pooling_config(model_id, self.revision):
            return "embed"
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        if self.registry.is_cross_encoder_model(architectures):
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            return "score"
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        if self.registry.is_transcription_model(architectures):
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            return "transcription"
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        suffix_to_preferred_task: list[tuple[str, _ResolvedTask]] = [
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            # Other models follow this pattern
            ("ForCausalLM", "generate"),
            ("ForConditionalGeneration", "generate"),
            ("ForSequenceClassification", "classify"),
            ("ChatModel", "generate"),
            ("LMHeadModel", "generate"),
            ("EmbeddingModel", "embed"),
            ("RewardModel", "reward"),
        ]
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        _, arch = self.registry.inspect_model_cls(architectures)
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        for suffix, pref_task in suffix_to_preferred_task:
            if arch.endswith(suffix) and pref_task in supported_tasks:
                return pref_task

        return None

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    def _resolve_task(
        self,
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        task_option: Union[TaskOption, Literal["draft"]],
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    ) -> tuple[set[_ResolvedTask], _ResolvedTask]:
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        if task_option == "draft":
            return {"draft"}, "draft"

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        registry = self.registry
        architectures = self.architectures
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        runner_support: dict[RunnerType, bool] = {
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            # NOTE: Listed from highest to lowest priority,
            # in case the model supports multiple of them
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            "transcription": registry.is_transcription_model(architectures),
            "generate": registry.is_text_generation_model(architectures),
            "pooling": registry.is_pooling_model(architectures),
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        }
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        supported_runner_types_lst: list[RunnerType] = [
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            runner_type
            for runner_type, is_supported in runner_support.items()
            if is_supported
        ]

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        supported_tasks_lst: list[_ResolvedTask] = [
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            task for runner_type in supported_runner_types_lst
            for task in _RUNNER_TASKS[runner_type]
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        ]
        supported_tasks = set(supported_tasks_lst)

        if task_option == "auto":
            selected_task = next(iter(supported_tasks_lst))
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            if len(supported_tasks_lst) > 1:
                preferred_task = self._get_preferred_task(
                    architectures, supported_tasks)
                if preferred_task is not None:
                    selected_task = preferred_task
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                logger.info(
                    "This model supports multiple tasks: %s. "
                    "Defaulting to '%s'.", supported_tasks, selected_task)
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        else:
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            # Aliases
            if task_option == "embedding":
                preferred_task = self._get_preferred_task(
                    architectures, supported_tasks)
                if preferred_task != "embed":
                    msg = ("The 'embedding' task will be restricted to "
                           "embedding models in a future release. Please "
                           "pass `--task classify`, `--task score`, or "
                           "`--task reward` explicitly for other pooling "
                           "models.")
                    warnings.warn(msg, DeprecationWarning, stacklevel=2)

                task_option = preferred_task or "embed"

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            if task_option not in supported_tasks:
                msg = (
                    f"This model does not support the '{task_option}' task. "
                    f"Supported tasks: {supported_tasks}")
                raise ValueError(msg)

            selected_task = task_option
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        return supported_tasks, selected_task
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    def _parse_quant_hf_config(self):
        quant_cfg = getattr(self.hf_config, "quantization_config", None)
        if quant_cfg is None:
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            # compressed-tensors uses a "compression_config" key
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            quant_cfg = getattr(self.hf_config, "compression_config", None)
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        return quant_cfg

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    def _verify_quantization(self) -> None:
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        supported_quantization = QUANTIZATION_METHODS
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        optimized_quantization_methods = [
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            "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
            "awq_marlin", "fbgemm_fp8", "compressed_tensors",
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            "compressed-tensors", "experts_int8", "quark", "nvfp4"
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        ]
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        if self.quantization is not None:
            self.quantization = self.quantization.lower()

        # Parse quantization method from the HF model config, if available.
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        quant_cfg = self._parse_quant_hf_config()

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        if quant_cfg is not None:
            quant_method = quant_cfg.get("quant_method", "").lower()
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            # Detect which checkpoint is it
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            for name in QUANTIZATION_METHODS:
                method = get_quantization_config(name)
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                quantization_override = method.override_quantization_method(
                    quant_cfg, self.quantization)
                if quantization_override:
                    quant_method = quantization_override
                    self.quantization = quantization_override
                    break
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            # Verify quantization configurations.
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            if self.quantization is None:
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                self.quantization = quant_method
            elif self.quantization != quant_method:
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                raise ValueError(
                    "Quantization method specified in the model config "
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                    f"({quant_method}) does not match the quantization "
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                    f"method specified in the `quantization` argument "
                    f"({self.quantization}).")

        if self.quantization is not None:
            if self.quantization not in supported_quantization:
                raise ValueError(
                    f"Unknown quantization method: {self.quantization}. Must "
                    f"be one of {supported_quantization}.")
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            from vllm.platforms import current_platform
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            current_platform.verify_quantization(self.quantization)
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            if self.quantization not in optimized_quantization_methods:
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                logger.warning(
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                    "%s quantization is not fully "
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                    "optimized yet. The speed can be slower than "
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                    "non-quantized models.", self.quantization)
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    def _verify_cuda_graph(self) -> None:
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        if self.max_seq_len_to_capture is None:
            self.max_seq_len_to_capture = self.max_model_len
        self.max_seq_len_to_capture = min(self.max_seq_len_to_capture,
                                          self.max_model_len)
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        ROCM_UNSUPPORTED_MODELS = ['mllama']
        if (self.hf_config.model_type in ROCM_UNSUPPORTED_MODELS
                and not self.enforce_eager and current_platform.is_rocm()):
            logger.warning(
                "CUDA graph is not supported for %s on ROCm yet, fallback "
                "to the eager mode.", self.hf_config.model_type)
            self.enforce_eager = True
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    def _verify_bnb_config(self) -> None:
        """
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        The current version of bitsandbytes (0.45.3) with 8-bit models does not
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        yet support CUDA graph.
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        # TODO Remove this when bitsandbytes supports.
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        """
        is_bitsandbytes = self.quantization == "bitsandbytes"
        has_quantization_config = (getattr(self.hf_config,
                                           "quantization_config", None)
                                   is not None)
        is_8bit = (self.hf_config.quantization_config.get(
            "load_in_8bit", False) if has_quantization_config else False)
        if all([
                is_bitsandbytes,
                has_quantization_config,
                is_8bit,
                not self.enforce_eager,
        ]):
            logger.warning(
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                "CUDA graph is not supported on BitsAndBytes 8bit yet, "
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                "fallback to the eager mode.")
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            self.enforce_eager = True

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    def _verify_with_expert_parallelism(self) -> None:
        num_expert_names = [
            "moe_num_experts",  # Dbrx
            "num_experts",  # Jamba
            "n_routed_experts",  # DeepSeek
            "num_local_experts",  # Mixtral
        ]
        num_experts = 0
        for name in num_expert_names:
            num_experts = getattr(self.hf_text_config, name, 0)
            if num_experts > 0:
                break
        if num_experts < 1:
            raise ValueError(
                "Number of experts in the model must be greater than 0 "
                "when expert parallelism is enabled.")

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    def verify_async_output_proc(self, parallel_config, speculative_config,
                                 device_config) -> None:
        if not self.use_async_output_proc:
            # Nothing to check
            return

        if parallel_config.pipeline_parallel_size > 1:
            self.use_async_output_proc = False
            return

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        # Reminder: Please update docs/source/features/compatibility_matrix.md
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        # If the feature combo become valid
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        from vllm.platforms import current_platform
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        if not current_platform.is_async_output_supported(self.enforce_eager):
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            self.use_async_output_proc = False
            return

        if envs.VLLM_USE_RAY_SPMD_WORKER:
            self.use_async_output_proc = False
            return

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        # Async postprocessor is not necessary for pooling models
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        # since there is no token generation
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        if self.runner_type == "pooling":
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            self.use_async_output_proc = False

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        # Reminder: Please update docs/source/features/compatibility_matrix.md
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        # If the feature combo become valid
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        if speculative_config:
            self.use_async_output_proc = False

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    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
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        if parallel_config.distributed_executor_backend == "external_launcher":
            assert self.seed is not None, (
                "Seed must be set when using external launcher backend to "
                "make sure sampling results are the same across workers.")

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        total_num_attention_heads = getattr(self.hf_text_config,
                                            "num_attention_heads", 0)
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        tensor_parallel_size = parallel_config.tensor_parallel_size
        if total_num_attention_heads % tensor_parallel_size != 0:
            raise ValueError(
                f"Total number of attention heads ({total_num_attention_heads})"
                " must be divisible by tensor parallel size "
                f"({tensor_parallel_size}).")

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        if parallel_config.enable_expert_parallel:
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            self._verify_with_expert_parallelism()

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        pipeline_parallel_size = parallel_config.pipeline_parallel_size
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        if pipeline_parallel_size > 1:
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            if not self.registry.is_pp_supported_model(self.architectures):
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                raise NotImplementedError(
                    "Pipeline parallelism is not supported for this model. "
                    "Supported models implement the `SupportsPP` interface.")

            if self.use_async_output_proc:
                self.use_async_output_proc = False
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    def get_hf_config_sliding_window(
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            self) -> Union[Optional[int], list[Optional[int]]]:
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        """Get the sliding window size, or None if disabled."""
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        # Some models, like Qwen2 and Qwen1.5, use `use_sliding_window` in
        # addition to sliding window size. We check if that field is present
        # and if it's False, return None.
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        if (hasattr(self.hf_text_config, "use_sliding_window")
                and not self.hf_text_config.use_sliding_window):
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            return None
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        return getattr(self.hf_text_config, "sliding_window", None)
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    def get_sliding_window(self) -> Optional[Union[int, list[Optional[int]]]]:
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        """Get the sliding window size, or None if disabled.
        """
        # If user disables sliding window, return None.
        if self.disable_sliding_window:
            return None
        # Otherwise get the value from the hf config.
        return self.get_hf_config_sliding_window()

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    def get_vocab_size(self) -> int:
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        return self.hf_text_config.vocab_size
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    def get_hidden_size(self) -> int:
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        return self.hf_text_config.hidden_size
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    @property
    def is_deepseek_mla(self) -> bool:
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        if not hasattr(self.hf_text_config, "model_type"):
            return False
        elif self.hf_text_config.model_type in \
            ('deepseek_v2', 'deepseek_v3', 'deepseek_mtp'):
            return self.hf_text_config.kv_lora_rank is not None
        elif self.hf_text_config.model_type == 'eagle':
            # if the model is an EAGLE module, check for the
            # underlying architecture
            return self.hf_text_config.model.model_type in \
                    ('deepseek_v2', 'deepseek_v3') \
                and self.hf_text_config.kv_lora_rank is not None
        return False
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    def get_head_size(self) -> int:
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        # TODO remove hard code
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        if self.is_deepseek_mla:
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            qk_rope_head_dim = getattr(self.hf_text_config, "qk_rope_head_dim",
                                       0)
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            if self.use_mla:
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                return self.hf_text_config.kv_lora_rank + qk_rope_head_dim
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            else:
                qk_nope_head_dim = getattr(self.hf_text_config,
                                           "qk_nope_head_dim", 0)
                if qk_rope_head_dim and qk_nope_head_dim:
                    return qk_rope_head_dim + qk_nope_head_dim
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        if hasattr(self.hf_text_config,
                   "model_type") and (self.hf_text_config.model_type
                                      == "zamba2"):
            return self.hf_text_config.attention_head_dim

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        if self.is_attention_free:
            return 0

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        if hasattr(self.hf_text_config, "head_dim"):
            return self.hf_text_config.head_dim
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        # FIXME(woosuk): This may not be true for all models.
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        return (self.hf_text_config.hidden_size //
                self.hf_text_config.num_attention_heads)
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    def get_total_num_kv_heads(self) -> int:
        """Returns the total number of KV heads."""
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        # For GPTBigCode & Falcon:
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        # NOTE: for falcon, when new_decoder_architecture is True, the
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        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
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        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
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        new_decoder_arch_falcon = (
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            self.hf_config.model_type in falcon_model_types
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            and getattr(self.hf_config, "new_decoder_architecture", False))
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        if not new_decoder_arch_falcon and getattr(self.hf_text_config,
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                                                   "multi_query", False):
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            # Multi-query attention, only one KV head.
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            # Currently, tensor parallelism is not supported in this case.
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            return 1
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        # For DBRX and MPT
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        if self.hf_config.model_type == "mpt":
            if "kv_n_heads" in self.hf_config.attn_config:
                return self.hf_config.attn_config["kv_n_heads"]
            return self.hf_config.num_attention_heads
        if self.hf_config.model_type == "dbrx":
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            return getattr(self.hf_config.attn_config, "kv_n_heads",
                           self.hf_config.num_attention_heads)

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        if self.hf_config.model_type == "nemotron-nas":
            for block in self.hf_config.block_configs:
                if not block.attention.no_op:
                    return self.hf_config.num_attention_heads \
                        // block.attention.n_heads_in_group

            raise RuntimeError("Couldn't determine number of kv heads")

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        if self.is_attention_free:
            return 0

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        attributes = [
            # For Falcon:
            "n_head_kv",
            "num_kv_heads",
            # For LLaMA-2:
            "num_key_value_heads",
            # For ChatGLM:
            "multi_query_group_num",
        ]
        for attr in attributes:
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            num_kv_heads = getattr(self.hf_text_config, attr, None)
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            if num_kv_heads is not None:
                return num_kv_heads

        # For non-grouped-query attention models, the number of KV heads is
        # equal to the number of attention heads.
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        return self.hf_text_config.num_attention_heads
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    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
        """Returns the number of KV heads per GPU."""
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        if self.use_mla:
            # When using MLA during decode it becomes MQA
            return 1

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        total_num_kv_heads = self.get_total_num_kv_heads()
        # If tensor parallelism is used, we divide the number of KV heads by
        # the tensor parallel size. We will replicate the KV heads in the
        # case where the number of KV heads is smaller than the tensor
        # parallel size so each GPU has at least one KV head.
        return max(1,
                   total_num_kv_heads // parallel_config.tensor_parallel_size)
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    def get_num_attention_heads(self,
                                parallel_config: "ParallelConfig") -> int:
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        num_heads = getattr(self.hf_text_config, "num_attention_heads", 0)
        return num_heads // parallel_config.tensor_parallel_size
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    def get_layers_start_end_indices(
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            self, parallel_config: "ParallelConfig") -> tuple[int, int]:
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        from vllm.distributed.utils import get_pp_indices
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        if self.hf_text_config.model_type == "deepseek_mtp":
            total_num_hidden_layers = getattr(self.hf_text_config,
                                              "num_nextn_predict_layers", 0)
        else:
            total_num_hidden_layers = getattr(self.hf_text_config,
                                              "num_hidden_layers", 0)
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        # the layout order is: DP x PP x TP
        pp_rank = (parallel_config.rank // parallel_config.tensor_parallel_size
                   ) % parallel_config.pipeline_parallel_size
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        pp_size = parallel_config.pipeline_parallel_size
        start, end = get_pp_indices(total_num_hidden_layers, pp_rank, pp_size)
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        return start, end
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    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
        start, end = self.get_layers_start_end_indices(parallel_config)
        return end - start
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    def get_num_layers_by_block_type(
        self,
        parallel_config: "ParallelConfig",
        block_type: LayerBlockType = LayerBlockType.attention,
    ) -> int:
        # This function relies on 'layers_block_type' in hf_config,
        # for w/o this attribute, we will need to have workarounds like so
        attn_block_type = block_type == LayerBlockType.attention
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        is_transformer = not self.is_hybrid and \
                            not self.has_noops and \
                            not self.is_attention_free
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        start, end = self.get_layers_start_end_indices(parallel_config)

        if is_transformer:
            # Handle the basic case first
            return end - start if attn_block_type else 0
        elif self.is_attention_free:
            # Attention free
            # Note that this code assumes there
            # is only one type of attention-free block type.
            return 0 if attn_block_type else end - start
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        elif self.has_noops:
            block_configs = self.hf_config.block_configs
            return sum(not bc.attention.no_op
                       for bc in block_configs[start:end])
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        else:
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            # Hybrid model Jamba
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            layers_block_type_value = getattr(self.hf_config,
                                              "layers_block_type", None)
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            if layers_block_type_value is not None:
                if hasattr(self.hf_text_config,
                           "model_type") and (self.hf_text_config.model_type
                                              == "zamba2"):
                    if attn_block_type:
                        return sum(t == "hybrid"
                                   for t in layers_block_type_value[start:end])
                    else:
                        return self.get_num_layers(parallel_config)
                return sum(t == block_type.value
                           for t in layers_block_type_value[start:end])

            # Hybrid model Minimax
            attn_type_list = getattr(self.hf_config, "attn_type_list", None)
            if attn_type_list:
                return sum(t == 1 for t in attn_type_list[start:end])

            if layers_block_type_value is None and attn_type_list is None:
                raise ValueError(
                    "The model is an hybrid without a"
                    "layers_block_type or an attn_type_list in the hf_config,"
                    "cannot determine the num of "
                    f"{block_type.value} layers")

            return sum(t == 1 for t in attn_type_list[start:end])
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    def get_multimodal_config(self) -> "MultiModalConfig":
        """
        Get the multimodal configuration of the model.

        Raises:
            ValueError: If the model is not multimodal.
        """
        if self.multimodal_config is None:
            raise ValueError("The model is not multimodal.")

        return self.multimodal_config

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    def try_get_generation_config(self) -> dict[str, Any]:
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        if self.generation_config in ("auto", "vllm"):
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            config = try_get_generation_config(
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                self.hf_config_path or self.model,
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                trust_remote_code=self.trust_remote_code,
                revision=self.revision,
            )
        else:
            config = try_get_generation_config(
                self.generation_config,
                trust_remote_code=self.trust_remote_code,
            )

        if config is None:
            return {}

        return config.to_diff_dict()

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    def get_diff_sampling_param(self) -> dict[str, Any]:
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        """
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        This method returns a dictionary containing the parameters
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        that differ from the default sampling parameters. If
        `generation_config` is `"vllm"`, an empty dictionary is returned.
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        Returns:
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            dict[str, Any]: A dictionary with the differing sampling
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            parameters, if `generation_config` is `"vllm"` an empty dictionary.
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        """
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        if self.generation_config == "vllm":
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            config = {}
        else:
            config = self.try_get_generation_config()

        # Overriding with given generation config
        config.update(self.override_generation_config)

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        available_params = [
            "repetition_penalty",
            "temperature",
            "top_k",
            "top_p",
            "min_p",
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            "max_new_tokens",
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        ]
        if any(p in config for p in available_params):
            diff_sampling_param = {
                p: config.get(p)
                for p in available_params if config.get(p) is not None
            }
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            # Huggingface definition of max_new_tokens is equivalent
            # to vLLM's max_tokens
            if "max_new_tokens" in diff_sampling_param:
                diff_sampling_param["max_tokens"] = diff_sampling_param.pop(
                    "max_new_tokens")
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        else:
            diff_sampling_param = {}
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        if diff_sampling_param:
            logger.warning_once(
                "Default sampling parameters have been overridden by the "
                "model's Hugging Face generation config recommended from the "
                "model creator. If this is not intended, please relaunch "
                "vLLM instance with `--generation-config vllm`.")
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        return diff_sampling_param

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    @property
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    def is_encoder_decoder(self) -> bool:
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        """Extract the HF encoder/decoder model flag."""
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        return is_encoder_decoder(self.hf_config)

    @property
    def uses_mrope(self) -> bool:
        return uses_mrope(self.hf_config)
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    @property
    def is_multimodal_model(self) -> bool:
        return self.multimodal_config is not None

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    @property
    def is_cross_encoder(self) -> bool:
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        return self.registry.is_cross_encoder_model(self.architectures)
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    @property
    def use_mla(self) -> bool:
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        return self.is_deepseek_mla and not envs.VLLM_MLA_DISABLE
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    @property
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    def supported_runner_types(self) -> set[RunnerType]:
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        return {_TASK_RUNNER[task] for task in self.supported_tasks}

    @property
    def runner_type(self) -> RunnerType:
        return _TASK_RUNNER[self.task]

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    @property
    def is_v1_compatible(self) -> bool:
        architectures = getattr(self.hf_config, "architectures", [])
        return ModelRegistry.is_v1_compatible(architectures)

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    @property
    def is_matryoshka(self) -> bool:
        return (hasattr(self.hf_config, "matryoshka_dimensions")
                or getattr(self.hf_config, "is_matryoshka", False))

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BlockSize = Literal[8, 16, 32, 64, 128]
CacheDType = Literal["auto", "fp8", "fp8_e4m3", "fp8_e5m2"]
PrefixCachingHashAlgo = Literal["builtin", "sha256"]


@config
@dataclass
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class CacheConfig:
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    """Configuration for the KV cache."""
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    block_size: Optional[BlockSize] = None
    """Size of a contiguous cache block in number of tokens. This is ignored on
    neuron devices and set to `--max-model-len`. On CUDA devices, only block
    sizes up to 32 are supported. On HPU devices, block size defaults to 128.
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    """
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    gpu_memory_utilization: float = 0.9
    """The fraction of GPU memory to be used for the model executor, which can
    range from 0 to 1. For example, a value of 0.5 would imply 50% GPU memory
    utilization. If unspecified, will use the default value of 0.9. This is a
    per-instance limit, and only applies to the current vLLM instance. It does
    not matter if you have another vLLM instance running on the same GPU. For
    example, if you have two vLLM instances running on the same GPU, you can
    set the GPU memory utilization to 0.5 for each instance."""
    swap_space: float = 4
    """Size of the CPU swap space per GPU (in GiB)."""
    cache_dtype: CacheDType = "auto"
    """Data type for kv cache storage. If "auto", will use model data type.
    CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ROCm (AMD GPU) supports
    fp8 (=fp8_e4m3)."""
    is_attention_free: bool = False
    """Whether the model is attention-free. This is primarily set in
    `ModelConfig` and that value should be manually duplicated here."""
    num_gpu_blocks_override: Optional[int] = None
    """Number of GPU blocks to use. This overrides the profiled `num_gpu_blocks`
    if specified. Does nothing if `None`. Used for testing preemption."""
    sliding_window: Optional[int] = None
    """Sliding window size for the KV cache. This is primarily set in
    `ModelConfig` and that value should be manually duplicated here."""
    enable_prefix_caching: Optional[bool] = None
    """Whether to enable prefix caching. Disabled by default for V0. Enabled by
    default for V1."""
    prefix_caching_hash_algo: PrefixCachingHashAlgo = "builtin"
    """Set the hash algorithm for prefix caching:\n
    - "builtin" is Python's built-in hash.\n
    - "sha256" is collision resistant but with certain overheads."""
    cpu_offload_gb: float = 0
    """The space in GiB to offload to CPU, per GPU. Default is 0, which means
    no offloading. Intuitively, this argument can be seen as a virtual way to
    increase the GPU memory size. For example, if you have one 24 GB GPU and
    set this to 10, virtually you can think of it as a 34 GB GPU. Then you can
    load a 13B model with BF16 weight, which requires at least 26GB GPU memory.
    Note that this requires fast CPU-GPU interconnect, as part of the model is
    loaded from CPU memory to GPU memory on the fly in each model forward pass.
    """
    calculate_kv_scales: bool = False
    """This enables dynamic calculation of `k_scale` and `v_scale` when
    kv_cache_dtype is fp8. If `False`, the scales will be loaded from the model
    checkpoint if available. Otherwise, the scales will default to 1.0."""

    # Will be set after profiling.
    num_gpu_blocks: Optional[int] = field(default=None, init=False)
    """The number of blocks to allocate for GPU memory."""
    num_cpu_blocks: Optional[int] = field(default=None, init=False)
    """The number of blocks to allocate for CPU memory."""
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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.
        """
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        factors.append(self.cache_dtype)
        # `cpu_offload_gb` does not use `torch.compile` yet.
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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    def __post_init__(self) -> None:
        self.swap_space_bytes = self.swap_space * GiB_bytes

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        # metrics info
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    def _verify_args(self) -> None:
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        if self.cpu_offload_gb < 0:
            raise ValueError("CPU offload space must be non-negative"
                             f", but got {self.cpu_offload_gb}")

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        if self.gpu_memory_utilization > 1.0:
            raise ValueError(
                "GPU memory utilization must be less than 1.0. Got "
                f"{self.gpu_memory_utilization}.")

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    def _verify_cache_dtype(self) -> None:
        if self.cache_dtype == "auto":
            pass
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        elif self.cache_dtype in get_args(CacheDType):
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            logger.info(
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                "Using fp8 data type to store kv cache. It reduces the GPU "
                "memory footprint and boosts the performance. "
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                "Meanwhile, it may cause accuracy drop without a proper "
                "scaling factor")
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        else:
            raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")

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    def _verify_prefix_caching(self) -> None:
        if not self.enable_prefix_caching:
            return

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            raise NotImplementedError(
                "Prefix caching is not supported with sliding window. "
                "Run with --disable-sliding-window to use prefix caching.")

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                not in get_args(PrefixCachingHashAlgo)):
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                "Unknown prefix caching hash algorithm: "
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                f"{self.prefix_caching_hash_algo}. Must be one of "
                f"{get_args(PrefixCachingHashAlgo)}.")
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    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_cpu_memory = get_cpu_memory()
        # FIXME(woosuk): Here, it is assumed that the GPUs in a tensor parallel
        # group are in the same node. However, the GPUs may span multiple nodes.
        num_gpus_per_node = parallel_config.tensor_parallel_size
        cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node

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        msg = (f"{cpu_memory_usage / GiB_bytes:.2f} GiB out of the "
               f"{total_cpu_memory / GiB_bytes:.2f} GiB total CPU memory "
               "is allocated for the swap space.")
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        if cpu_memory_usage > 0.7 * total_cpu_memory:
            raise ValueError("Too large swap space. " + msg)
        elif cpu_memory_usage > 0.4 * total_cpu_memory:
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PoolType = Literal["ray"]


@config
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@dataclass
class TokenizerPoolConfig:
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    """Configuration for the tokenizer pool."""
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    pool_size: int = 0
    """Number of tokenizer workers in the pool to use for asynchronous
    tokenization. If 0, will use synchronous tokenization."""

    pool_type: Union[PoolType, type["BaseTokenizerGroup"]] = "ray"
    """Type of tokenizer pool to use for asynchronous tokenization. Ignored if
    tokenizer_pool_size is 0."""

    extra_config: dict = field(default_factory=dict)
    """Additional config for the pool. The way the config will be used depends
    on the pool type. This should be a JSON string that will be parsed into a
    dictionary. Ignored if tokenizer_pool_size is 0."""
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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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                               usedforsecurity=False).hexdigest()
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                self.pool_type, type):
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            raise ValueError(f"Unknown pool type: {self.pool_type}")
        if not isinstance(self.extra_config, dict):
            raise ValueError("extra_config must be a dictionary.")

    @classmethod
    def create_config(
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    ) -> Optional["TokenizerPoolConfig"]:
        """Create a TokenizerPoolConfig from the given parameters.
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        Args:
            tokenizer_pool_size: Number of tokenizer workers in the pool.
            tokenizer_pool_type: Type of the pool.
            tokenizer_pool_extra_config: Additional config for the pool.
                The way the config will be used depends on the
                pool type. This can be a JSON string (will be parsed).
        """
        if tokenizer_pool_size:
            if isinstance(tokenizer_pool_extra_config, str):
                tokenizer_pool_extra_config_parsed = json.loads(
                    tokenizer_pool_extra_config)
            else:
                tokenizer_pool_extra_config_parsed = (
                    tokenizer_pool_extra_config or {})
            tokenizer_pool_config = cls(tokenizer_pool_size,
                                        tokenizer_pool_type,
                                        tokenizer_pool_extra_config_parsed)
        else:
            tokenizer_pool_config = None
        return tokenizer_pool_config


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class LoadFormat(str, enum.Enum):
    AUTO = "auto"
    PT = "pt"
    SAFETENSORS = "safetensors"
    NPCACHE = "npcache"
    DUMMY = "dummy"
    TENSORIZER = "tensorizer"
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    FASTSAFETENSORS = "fastsafetensors"
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@config
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@dataclass
class LoadConfig:
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    """Configuration for loading the model weights."""

    load_format: Union[str, LoadFormat,
                       "BaseModelLoader"] = LoadFormat.AUTO.value
    """The format of the model weights to load:\n
    - "auto" will try to load the weights in the safetensors format and fall
    back to the pytorch bin format if safetensors format is not available.\n
    - "pt" will load the weights in the pytorch bin format.\n
    - "safetensors" will load the weights in the safetensors format.\n
    - "npcache" will load the weights in pytorch format and store a numpy cache
    to speed up the loading.\n
    - "dummy" will initialize the weights with random values, which is mainly
    for profiling.\n
    - "tensorizer" will use CoreWeave's tensorizer library for fast weight
    loading. See the Tensorize vLLM Model script in the Examples section for
    more information.\n
    - "runai_streamer" will load the Safetensors weights using Run:ai Model
    Streamer.\n
    - "bitsandbytes" will load the weights using bitsandbytes quantization.\n
    - "sharded_state" will load weights from pre-sharded checkpoint files,
    supporting efficient loading of tensor-parallel models.\n
    - "gguf" will load weights from GGUF format files (details specified in
    https://github.com/ggml-org/ggml/blob/master/docs/gguf.md).\n
    - "mistral" will load weights from consolidated safetensors files used by
    Mistral models."""
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    download_dir: Optional[str] = None
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    """Directory to download and load the weights, default to the default
    cache directory of Hugging Face."""
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    model_loader_extra_config: dict = field(default_factory=dict)
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    """Extra config for model loader. This will be passed to the model loader
    corresponding to the chosen load_format. This should be a JSON string that
    will be parsed into a dictionary."""
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    ignore_patterns: Optional[Union[list[str], str]] = None
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    """The list of patterns to ignore when loading the model. Default to
    "original/**/*" to avoid repeated loading of llama's checkpoints."""
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    use_tqdm_on_load: bool = True
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    """Whether to enable tqdm for showing progress bar when loading model
    weights."""
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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    def __post_init__(self):
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        if isinstance(self.load_format, str):
            load_format = self.load_format.lower()
            self.load_format = LoadFormat(load_format)
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        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
            logger.info(
                "Ignoring the following patterns when downloading weights: %s",
                self.ignore_patterns)
        else:
            self.ignore_patterns = ["original/**/*"]

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DistributedExecutorBackend = Literal["ray", "mp", "uni", "external_launcher"]


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@config
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@dataclass
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class ParallelConfig:
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    """Configuration for the distributed execution."""
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    pipeline_parallel_size: int = 1
    """Number of pipeline parallel groups."""
    tensor_parallel_size: int = 1
    """Number of tensor parallel groups."""
    data_parallel_size: int = 1
    """Number of data parallel groups. MoE layers will be sharded according to
    the product of the tensor parallel size and data parallel size."""
    data_parallel_rank: int = 0
    """Rank of the data parallel group."""
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    data_parallel_rank_local: Optional[int] = None
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    """Local rank of the data parallel group, defaults to global rank."""
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    data_parallel_master_ip: str = "127.0.0.1"
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    """IP of the data parallel master."""
    data_parallel_master_port: int = 29500
    """Port of the data parallel master."""
    enable_expert_parallel: bool = False
    """Use expert parallelism instead of tensor parallelism for MoE layers."""
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    max_parallel_loading_workers: Optional[int] = None
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    """Maximum number of parallal loading workers when loading model
    sequentially in multiple batches. To avoid RAM OOM when using tensor
    parallel and large models."""
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    disable_custom_all_reduce: bool = False
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    """Disable the custom all-reduce kernel and fall back to NCCL."""
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    tokenizer_pool_config: Optional[TokenizerPoolConfig] = None
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    """Config for the tokenizer pool. If None, will use synchronous
    tokenization."""
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    ray_workers_use_nsight: bool = False
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    """Whether to profile Ray workers with nsight, see https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler."""
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    placement_group: Optional["PlacementGroup"] = None
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    """ray distributed model workers placement group."""
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    distributed_executor_backend: Optional[Union[DistributedExecutorBackend,
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                                                 type["ExecutorBase"]]] = None
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    """Backend to use for distributed model
    workers, either "ray" or "mp" (multiprocessing). If the product
    of pipeline_parallel_size and tensor_parallel_size is less than
    or equal to the number of GPUs available, "mp" will be used to
    keep processing on a single host. Otherwise, this will default
    to "ray" if Ray is installed and fail otherwise. Note that tpu
    and hpu only support Ray for distributed inference."""
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    worker_cls: str = "auto"
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    """The full name of the worker class to use. If "auto", the worker class
    will be determined based on the platform."""
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    sd_worker_cls: str = "auto"
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    """The full name of the worker class to use for speculative decofing.
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    If "auto", the worker class will be determined based on the platform."""
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    worker_extension_cls: str = ""
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    """The full name of the worker extension class to use. The worker extension
    class is dynamically inherited by the worker class. This is used to inject
    new attributes and methods to the worker class for use in collective_rpc
    calls."""
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    world_size: int = field(init=False)
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    """world_size is TPxPP, it affects the number of workers we create."""
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    world_size_across_dp: int = field(init=False)
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    """world_size_across_dp is TPxPPxDP, it is the size of the world
    including data parallelism."""
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    rank: int = 0
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    """Global rank in distributed setup."""
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    def get_next_dp_init_port(self) -> int:
        """
        We might need to initialize process groups in multiple
        processes that is related to data parallelism,
        e.g. both in the worker and in the engine, which
        can live in different processes. To avoid port conflicts, we
        increment the port number each time we need to initialize a
        new process group related to data parallelism.
        """
        answer = self.data_parallel_master_port
        self.data_parallel_master_port += 1
        return answer

    def stateless_init_dp_group(self) -> "ProcessGroup":
        from vllm.distributed.utils import (
            stateless_init_torch_distributed_process_group)

        # use gloo since the engine process might not have cuda device
        dp_group = stateless_init_torch_distributed_process_group(
            self.data_parallel_master_ip,
            self.get_next_dp_init_port(),
            self.data_parallel_rank,
            self.data_parallel_size,
            backend="gloo")

        return dp_group

    @staticmethod
    def has_unfinished_dp(dp_group: "ProcessGroup",
youkaichao's avatar
youkaichao committed
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                          has_unfinished: bool) -> bool:
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        tensor = torch.tensor([has_unfinished],
                              dtype=torch.int32,
                              device="cpu")
        # dp rank 0: has_unfinished_seqs=True
        # dp rank 1: has_unfinished_seqs=False
        # aggregated: has_unfinished_seqs=True
        # so this is an OR operation, i.e. MAX in integers
        torch.distributed.all_reduce(tensor, op=ReduceOp.MAX, group=dp_group)
        aggregated_has_unfinished = bool(tensor.item())
        return aggregated_has_unfinished

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    def compute_hash(self):
        """
        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.
        """
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        factors: list[Any] = []
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        factors.append(self.pipeline_parallel_size)
        factors.append(self.tensor_parallel_size)
        return hashlib.sha256(str(factors).encode()).hexdigest()

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    def __post_init__(self) -> None:
        self.world_size = self.pipeline_parallel_size * \
            self.tensor_parallel_size
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        if self.data_parallel_size > 1:
            # Data parallel was specified in the engine args.
            self.data_parallel_master_port = get_open_port()
            # TODO multi-node
        else:
            # Otherwise fall back to env vars (e.g. for offline SPMD case).
            self.data_parallel_size = envs.VLLM_DP_SIZE
            self.data_parallel_rank = envs.VLLM_DP_RANK
            self.data_parallel_rank_local = envs.VLLM_DP_RANK_LOCAL
            self.data_parallel_master_ip = envs.VLLM_DP_MASTER_IP
            self.data_parallel_master_port = envs.VLLM_DP_MASTER_PORT

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        self.world_size_across_dp = self.world_size * self.data_parallel_size
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        if self.distributed_executor_backend == "external_launcher":
            import os
            os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
            logger.info("Disabling V1 multiprocessing for external launcher.")

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        ray_only_devices: list[str] = []
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        from vllm.platforms import current_platform
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        if (current_platform.device_type in ray_only_devices
                and self.world_size > 1):
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            if self.distributed_executor_backend is None:
                self.distributed_executor_backend = "ray"
            if self.distributed_executor_backend != "ray":
                raise ValueError(
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                    f"{current_platform.device_type.upper()} backend only "
                    "supports Ray for distributed inference.")
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        if self.distributed_executor_backend is None and self.world_size > 1:
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            # We use multiprocessing by default if world_size fits on the
            # current node and we aren't in a ray placement group.

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            from vllm.executor import ray_utils
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            backend: DistributedExecutorBackend = "mp"
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            ray_found = ray_utils.ray_is_available()
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            if current_platform.is_neuron():
                # neuron uses single process to control multiple devices
                backend = "uni"
            elif (current_platform.is_cuda()
                  and cuda_device_count_stateless() < self.world_size):
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                if not ray_found:
                    raise ValueError("Unable to load Ray which is "
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                                     "required for multi-node inference, "
                                     "please install Ray with `pip install "
                                     "ray`.") from ray_utils.ray_import_err
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                backend = "ray"
            elif ray_found:
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                if self.placement_group:
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                    backend = "ray"
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                else:
                    from ray import is_initialized as ray_is_initialized
                    if ray_is_initialized():
                        from ray.util import get_current_placement_group
                        if get_current_placement_group():
                            backend = "ray"
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            self.distributed_executor_backend = backend
            logger.info("Defaulting to use %s for distributed inference",
                        backend)
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        if self.distributed_executor_backend is None and self.world_size == 1:
            self.distributed_executor_backend = "uni"

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        self._verify_args()

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    @property
    def use_ray(self) -> bool:
        return self.distributed_executor_backend == "ray" or (
            isinstance(self.distributed_executor_backend, type)
            and self.distributed_executor_backend.uses_ray)

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    def _verify_args(self) -> None:
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        # Lazy import to avoid circular import
        from vllm.executor.executor_base import ExecutorBase
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        from vllm.platforms import current_platform
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        if self.distributed_executor_backend not in (
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                "ray", "mp", "uni",
                "external_launcher", None) and not (isinstance(
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                    self.distributed_executor_backend, type) and issubclass(
                        self.distributed_executor_backend, ExecutorBase)):
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            raise ValueError(
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                "Unrecognized distributed executor backend "
                f"{self.distributed_executor_backend}. Supported "
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                "values are 'ray', 'mp' 'uni', 'external_launcher' or"
                " custom ExecutorBase subclass.")
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        if self.use_ray:
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            from vllm.executor import ray_utils
            ray_utils.assert_ray_available()
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        if not current_platform.use_custom_allreduce():
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            self.disable_custom_all_reduce = True
            logger.info(
                "Disabled the custom all-reduce kernel because it is not "
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                "supported on current platform.")
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        if self.ray_workers_use_nsight and not self.use_ray:
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            raise ValueError("Unable to use nsight profiling unless workers "
                             "run with Ray.")
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        assert isinstance(self.worker_extension_cls, str), (
            "worker_extension_cls must be a string (qualified class name).")

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SchedulerPolicy = Literal["fcfs", "priority"]


@config
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@dataclass
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class SchedulerConfig:
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    """Scheduler configuration."""
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    runner_type: RunnerType = "generate"
    """The runner type to launch for the model."""
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    max_num_batched_tokens: int = None  # type: ignore
    """Maximum number of tokens to be processed in a single iteration.
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    This config has no static default. If left unspecified by the user, it will
    be set in `EngineArgs.create_engine_config` based on the usage context."""
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    max_num_seqs: int = None  # type: ignore
    """Maximum number of sequences to be processed in a single iteration.
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    This config has no static default. If left unspecified by the user, it will
    be set in `EngineArgs.create_engine_config` based on the usage context."""
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    max_model_len: int = None  # type: ignore
    """Maximum length of a sequence (including prompt and generated text). This
    is primarily set in `ModelConfig` and that value should be manually
    duplicated here."""
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    max_num_partial_prefills: int = 1
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    """For chunked prefill, the maximum number of sequences that can be
    partially prefilled concurrently."""
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    max_long_partial_prefills: int = 1
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    """For chunked prefill, the maximum number of prompts longer than
    long_prefill_token_threshold that will be prefilled concurrently. Setting
    this less than max_num_partial_prefills will allow shorter prompts to jump
    the queue in front of longer prompts in some cases, improving latency."""
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    long_prefill_token_threshold: int = 0
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    """For chunked prefill, a request is considered long if the prompt is
    longer than this number of tokens."""
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    num_lookahead_slots: int = 0
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    """The number of slots to allocate per sequence per
    step, beyond the known token ids. This is used in speculative
    decoding to store KV activations of tokens which may or may not be
    accepted.

    NOTE: This will be replaced by speculative config in the future; it is
    present to enable correctness tests until then."""
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    delay_factor: float = 0.0
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    """Apply a delay (of delay factor multiplied by previous
    prompt latency) before scheduling next prompt."""
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    enable_chunked_prefill: bool = None  # type: ignore
    """If True, prefill requests can be chunked based
    on the remaining max_num_batched_tokens."""
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    is_multimodal_model: bool = False
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    """True if the model is multimodal."""

    # TODO (ywang96): Make this configurable.
    max_num_encoder_input_tokens: int = field(init=False)
    """Multimodal encoder compute budget, only used in V1.
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    NOTE: This is not currently configurable. It will be overridden by
    max_num_batched_tokens in case max multimodal embedding size is larger."""

    # TODO (ywang96): Make this configurable.
    encoder_cache_size: int = field(init=False)
    """Multimodal encoder cache size, only used in V1.

    NOTE: This is not currently configurable. It will be overridden by
    max_num_batched_tokens in case max multimodal embedding size is larger."""
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    preemption_mode: Optional[str] = None
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    """Whether to perform preemption by swapping or
    recomputation. If not specified, we determine the mode as follows:
    We use recomputation by default since it incurs lower overhead than
    swapping. However, when the sequence group has multiple sequences
    (e.g., beam search), recomputation is not currently supported. In
    such a case, we use swapping instead."""
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    num_scheduler_steps: int = 1
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    """Maximum number of forward steps per scheduler call."""
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    multi_step_stream_outputs: bool = True
    """If False, then multi-step will stream outputs at the end of all steps"""
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    send_delta_data: bool = False
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    """Private API. If used, scheduler sends delta data to
    workers instead of an entire data. It should be enabled only
    when SPMD worker architecture is enabled. I.e.,
    VLLM_USE_RAY_SPMD_WORKER=1"""

    policy: SchedulerPolicy = "fcfs"
    """The scheduling policy to use:\n
    - "fcfs" means first come first served, i.e. requests are handled in order
    of arrival.\n
    - "priority" means requests are handled based on given priority (lower
    value means earlier handling) and time of arrival deciding any ties)."""
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    chunked_prefill_enabled: bool = field(init=False)
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    """True if chunked prefill is enabled."""
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    disable_chunked_mm_input: bool = False
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    """If set to true and chunked prefill is enabled, we do not want to
    partially schedule a multimodal item. Only used in V1
    This ensures that if a request has a mixed prompt
    (like text tokens TTTT followed by image tokens IIIIIIIIII) where only
    some image tokens can be scheduled (like TTTTIIIII, leaving IIIII),
    it will be scheduled as TTTT in one step and IIIIIIIIII in the next."""
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    scheduler_cls: Union[str, type[object]] = "vllm.core.scheduler.Scheduler"
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    """The scheduler class to use. "vllm.core.scheduler.Scheduler" is the
    default scheduler. Can be a class directly or the path to a class of form
    "mod.custom_class"."""
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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    def __post_init__(self) -> None:
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        if self.max_model_len is None:
            self.max_model_len = 8192
            logger.warning(
                "max_model_len was is not set. Defaulting to arbitrary value "
                "of %d.", self.max_model_len)

        if self.max_num_seqs is None:
            self.max_num_seqs = 128
            logger.warning(
                "max_num_seqs was is not set. Defaulting to arbitrary value "
                "of %d.", self.max_num_seqs)

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        if self.max_num_batched_tokens is None:
            if self.enable_chunked_prefill:
                if self.num_scheduler_steps > 1:
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                    # Multi-step Chunked-Prefill doesn't allow prompt-chunking
                    # for now. Have max_num_batched_tokens set to max_model_len
                    # so we don't reject sequences on account of a short
                    # max_num_batched_tokens.
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                    self.max_num_batched_tokens = max(
                        self.max_model_len, _DEFAULT_MAX_NUM_BATCHED_TOKENS)
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                else:
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                    self.max_num_batched_tokens = (
                        _DEFAULT_MAX_NUM_BATCHED_TOKENS)
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            else:
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                # If max_model_len is too short, use
                # _DEFAULT_MAX_NUM_BATCHED_TOKENS as the default value
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                # for higher throughput.
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                self.max_num_batched_tokens = max(
                    self.max_model_len, _DEFAULT_MAX_NUM_BATCHED_TOKENS)
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            if self.runner_type == "pooling":
                # Choose specific value for higher throughput
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                self.max_num_batched_tokens = max(
                    self.max_num_batched_tokens,
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                    _POOLING_MODEL_MAX_NUM_BATCHED_TOKENS,
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                )
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            if self.is_multimodal_model:
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                # The value needs to be at least the number of multimodal tokens
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                self.max_num_batched_tokens = max(
                    self.max_num_batched_tokens,
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                    _MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS,
                )

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        self.max_num_encoder_input_tokens = self.max_num_batched_tokens
        self.encoder_cache_size = self.max_num_batched_tokens

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        if self.enable_chunked_prefill:
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            logger.info(
                "Chunked prefill is enabled with max_num_batched_tokens=%d.",
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                self.max_num_batched_tokens)
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        self.chunked_prefill_enabled = self.enable_chunked_prefill
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        if self.max_num_partial_prefills > 1:
            if self.long_prefill_token_threshold == 0:
                self.long_prefill_token_threshold = int(self.max_model_len *
                                                        0.04)

            logger.info(
                "Concurrent partial prefills enabled with "
                "max_num_partial_prefills=%d, max_long_partial_prefills=%d, "
                "long_prefill_token_threshold=%d",
                self.max_num_partial_prefills, self.max_long_partial_prefills,
                self.long_prefill_token_threshold)

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        self._verify_args()

    def _verify_args(self) -> None:
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        if (self.max_num_batched_tokens < self.max_model_len
                and not self.chunked_prefill_enabled):
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            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
                f"smaller than max_model_len ({self.max_model_len}). "
                "This effectively limits the maximum sequence length to "
                "max_num_batched_tokens and makes vLLM reject longer "
                "sequences. Please increase max_num_batched_tokens or "
                "decrease max_model_len.")
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        if self.max_num_batched_tokens < self.max_num_seqs:
            raise ValueError(
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
                "be greater than or equal to max_num_seqs "
                f"({self.max_num_seqs}).")
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        if self.num_lookahead_slots < 0:
            raise ValueError(
                "num_lookahead_slots "
                f"({self.num_lookahead_slots}) must be greater than or "
                "equal to 0.")

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        if self.num_scheduler_steps < 1:
            raise ValueError(
                "num_scheduler_steps "
                f"({self.num_scheduler_steps}) must be greater than or "
                "equal to 1.")

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        if self.max_num_partial_prefills < 1:
            raise ValueError(
                f"max_num_partial_prefills ({self.max_num_partial_prefills}) "
                "must be greater than or equal to 1.")
        elif self.max_num_partial_prefills > 1:
            if not self.chunked_prefill_enabled:
                raise ValueError("Chunked prefill must be enabled to set "
                                 "max_num_partial_prefills > 1.")

            if self.long_prefill_token_threshold > self.max_model_len:
                raise ValueError(
                    "long_prefill_token_threshold "
                    f"({self.long_prefill_token_threshold}) cannot be greater "
                    f"than the max_model_len ({self.max_model_len}).")

        if (self.max_long_partial_prefills
                < 1) or (self.max_long_partial_prefills
                         > self.max_num_partial_prefills):
            raise ValueError(
                f"max_long_partial_prefills ({self.max_long_partial_prefills}) "
                "must be greater than or equal to 1 and less than or equal to "
                f"max_num_partial_prefills ({self.max_num_partial_prefills}).")

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    @property
    def is_multi_step(self) -> bool:
        return self.num_scheduler_steps > 1

2067

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Device = Literal["auto", "cuda", "neuron", "cpu", "tpu", "xpu", "hpu"]


@config
@dataclass
2073
class DeviceConfig:
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    """Configuration for the device to use for vLLM execution."""

    device: Union[Device, torch.device] = "auto"
    """Device type for vLLM execution."""
    device_type: str = field(init=False)
    """Device type from the current platform. This is set in
    `__post_init__`."""
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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.
        """
        # no factors to consider.
        # the device/platform information will be summarized
        # by torch/vllm automatically.
2097
        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2100
2101
        return hash_str

2102
2103
    def __post_init__(self):
        if self.device == "auto":
2104
            # Automated device type detection
2105
            from vllm.platforms import current_platform
2106
            self.device_type = current_platform.device_type
2107
            if not self.device_type:
2108
2109
2110
2111
                raise RuntimeError(
                    "Failed to infer device type, please set "
                    "the environment variable `VLLM_LOGGING_LEVEL=DEBUG` "
                    "to turn on verbose logging to help debug the issue.")
2112
2113
        else:
            # Device type is assigned explicitly
2114
            self.device_type = self.device
2115
2116

        # Some device types require processing inputs on CPU
2117
        if self.device_type in ["neuron"]:
2118
            self.device = torch.device("cpu")
2119
2120
        elif self.device_type in ["tpu"]:
            self.device = None
2121
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2124
        else:
            # Set device with device type
            self.device = torch.device(self.device_type)

2125

2126
@dataclass
2127
class SpeculativeConfig:
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    """
    Configuration for speculative decoding.
    Configurable parameters include:
    - General Speculative Decoding Control:
        - num_speculative_tokens (int): The number of speculative
            tokens, if provided. It will default to the number in the draft
            model config if present, otherwise, it is required.
        - model (Optional[str]): The name of the draft model, eagle head,
            or additional weights, if provided.
        - method (Optional[str]): The name of the speculative method to use.
            If users provide and set the `model` param, the speculative method
            type will be detected automatically if possible, if `model` param
            is not provided, the method name must be provided.
            - Possible values:
                - ngram
                    Related additional configuration:
                    - prompt_lookup_max (Optional[int]):
                        Maximum size of ngram token window when using Ngram
                        proposer, required when method is set to ngram.
                    - prompt_lookup_min (Optional[int]):
                        Minimum size of ngram token window when using Ngram
                        proposer, if provided. Defaults to 1.
                - eagle
                - medusa
                - mlp_speculator
                - draft_model
        - acceptance_method (str): The method to use for accepting draft
            tokens. This can take two possible values: 'rejection_sampler' and
            'typical_acceptance_sampler' for RejectionSampler and
            TypicalAcceptanceSampler respectively. If not specified, it
            defaults to 'rejection_sampler'.
            - Possible values:
                - rejection_sampler
                - typical_acceptance_sampler
                    Related additional configuration:
                    - posterior_threshold (Optional[float]):
                        A threshold value that sets a lower bound on the
                        posterior probability of a token in the target model
                        for it to be accepted. This threshold is used only
                        when we use the TypicalAcceptanceSampler for token
                        acceptance.
                    - posterior_alpha (Optional[float]):
                        Scaling factor for entropy-based threshold, applied
                        when using TypicalAcceptanceSampler.
        - draft_tensor_parallel_size (Optional[int]): The degree of the tensor
            parallelism for the draft model. Can only be 1 or the same as the
            target model's tensor parallel size.
        - disable_logprobs (bool): If set to True, token log probabilities are
            not returned during speculative decoding. If set to False, token
            log probabilities are returned according to the log probability
            settings in SamplingParams. If not specified, it defaults to True.

    - Draft Model Configuration:
        - quantization (Optional[str]): Quantization method that was used to
            quantize the draft model weights. If None, we assume the
            model weights are not quantized. Note that it only takes effect
            when using the draft model-based speculative method.
        - max_model_len (Optional[int]): The maximum model length of the
            draft model. Used when testing the ability to skip
            speculation for some sequences.
        - revision: The specific model version to use for the draft model. It
            can be a branch name, a tag name, or a commit id. If unspecified,
            will use the default version.
        - code_revision: The specific revision to use for the draft model code
            on Hugging Face Hub. It can be a branch name, a tag name, or a
            commit id. If unspecified, will use the default version.
2194

2195
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2220
2221
2222
    - Advanced Control:
        - disable_mqa_scorer (bool): Disable the MQA scorer and fall back to
            batch expansion for scoring proposals. If not specified, it
            defaults to False.
        - disable_by_batch_size (Optional[int]): Disable speculative decoding
            for new incoming requests when the number of enqueued requests is
            larger than this value, if provided.

    Although the parameters above are structured hierarchically, there is no
    need to nest them during configuration.

    Non-configurable internal parameters include:
    - Model Configuration:
        - target_model_config (ModelConfig): The configuration of the target
            model.
        - draft_model_config (ModelConfig): The configuration of the draft
            model initialized internal.
    - Parallelism Configuration:
        - target_parallel_config (ParallelConfig): The parallel configuration
            for the target model.
        - draft_parallel_config (ParallelConfig): The parallel configuration
            for the draft model initialized internal.
    - Execution Control:
        - enable_chunked_prefill (bool): Whether vLLM is configured to use
            chunked prefill or not. Used for raising an error since it's not
            yet compatible with speculative decode.
        - disable_log_stats (bool): Whether to disable the periodic printing of
            stage times in speculative decoding.
2223
    """
2224
2225
2226
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2229
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2254
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2256
2257
2258
    # speculative configs from cli args
    num_speculative_tokens: int = field(default=None,
                                        init=True)  # type: ignore
    method: Optional[str] = None
    acceptance_method: str = "rejection_sampler"
    draft_tensor_parallel_size: Optional[int] = None
    disable_logprobs: bool = True

    model: Optional[str] = None
    quantization: Optional[str] = None
    max_model_len: Optional[int] = None
    revision: Optional[str] = None
    code_revision: Optional[str] = None

    disable_mqa_scorer: bool = False
    disable_by_batch_size: Optional[int] = None
    prompt_lookup_max: Optional[int] = None
    prompt_lookup_min: Optional[int] = None
    posterior_threshold: Optional[float] = None
    posterior_alpha: Optional[float] = None

    # required configuration params passed from engine
    target_model_config: ModelConfig = field(default=None,
                                             init=True)  # type: ignore
    target_parallel_config: ParallelConfig = field(default=None,
                                                   init=True)  # type: ignore
    enable_chunked_prefill: bool = field(default=None,
                                         init=True)  # type: ignore
    disable_log_stats: bool = field(default=None, init=True)  # type: ignore

    # params generated in the post-init stage
    draft_model_config: ModelConfig = field(default=None,
                                            init=True)  # type: ignore
    draft_parallel_config: ParallelConfig = field(default=None,
                                                  init=True)  # type: ignore
2259

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2265
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2267
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2271
2272
2273
    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.
        """
        # no factors to consider.
        # spec decode does not use `torch.compile` yet.
2274
        factors: list[Any] = []
2275
2276
        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
2277
2278
        return hash_str

2279
2280
2281
2282
2283
    @classmethod
    def from_dict(cls, dict_value: dict) -> "SpeculativeConfig":
        """Parse the CLI value for the speculative config."""
        return cls(**dict_value)

2284
2285
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2287
2288
2289
2290
2291
2292
2293
2294
2295
    @staticmethod
    def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
        if hf_config.model_type == "deepseek_v3":
            hf_config.model_type = "deepseek_mtp"
        if hf_config.model_type == "deepseek_mtp":
            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
            hf_config.update({
                "n_predict": n_predict,
                "architectures": ["DeepSeekMTPModel"]
            })
        return hf_config

2296
    def __post_init__(self):
2297

2298
2299
2300
2301
2302
2303
2304
        # Note: "method" is a new parameter that helps to extend the
        # configuration of non-model-based proposers, and the "model" parameter
        # will be used to set the draft model, eagle head, or additional weight
        # when needed. If users do not specify "method", the speculative method
        # will be detected automatically if possible. If the speculative method
        # can not be detected, it will be considered as the "draft_model" by
        # default.
2305
2306
2307
2308

        if self.model is None and self.num_speculative_tokens is not None:
            # TODO(Shangming): Refactor mtp configuration logic when supporting
            # mtp acceleration for more models besides deepseek_v3
2309
2310
            if self.target_model_config and \
                self.target_model_config.hf_text_config.model_type \
2311
                        == "deepseek_v3":
2312
2313
2314
2315
                # use the draft model from the same model:
                self.model = self.target_model_config.model
            elif self.method in ("ngram", "[ngram]"):
                self.model = "ngram"
2316
            else:
2317
2318
2319
                raise ValueError("num_speculative_tokens was provided without "
                                 "speculative model.")

2320
2321
        # Automatically configure the method for ngram when "model" is used
        # instead of "method"
2322
2323
2324
2325
2326
2327
2328
        if self.method is None and (self.model is not None
                                    and self.model in ("ngram", "[ngram]")):
            self.method = "ngram"

        if self.method in ("ngram", "[ngram]"):
            # Unified to "ngram" internally
            self.method = "ngram"
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
            # Set default values if not provided
            if (self.prompt_lookup_min is None
                    and self.prompt_lookup_max is None):
                # TODO(woosuk): Tune these values. They are arbitrarily chosen.
                self.prompt_lookup_min = 5
                self.prompt_lookup_max = 5
            elif self.prompt_lookup_min is None:
                assert self.prompt_lookup_max is not None
                self.prompt_lookup_min = self.prompt_lookup_max
            elif self.prompt_lookup_max is None:
                assert self.prompt_lookup_min is not None
                self.prompt_lookup_max = self.prompt_lookup_min

            # Validate values
2343
            if self.prompt_lookup_min < 1:
2344
2345
2346
2347
2348
                raise ValueError(
                    f"prompt_lookup_min={self.prompt_lookup_min} must be > 0")
            if self.prompt_lookup_max < 1:
                raise ValueError(
                    f"prompt_lookup_max={self.prompt_lookup_max} must be > 0")
2349
            if self.prompt_lookup_min > self.prompt_lookup_max:
2350
2351
2352
                raise ValueError(
                    f"prompt_lookup_min={self.prompt_lookup_min} must "
                    f"be <= prompt_lookup_max={self.prompt_lookup_max}")
2353

2354
2355
2356
            # TODO: current we still need extract vocab_size from target model
            # config, in future, we may try refactor it out, and set
            # draft related config as None here.
2357
2358
            self.draft_model_config = self.target_model_config
            self.draft_parallel_config = self.target_parallel_config
2359
        else:
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
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2379
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2381
2382
2383
2384
2385
2386
2387
2388
            self.prompt_lookup_max = 0
            self.prompt_lookup_min = 0

            if self.model is not None:
                self.draft_model_config = ModelConfig(
                    model=self.model,
                    task="draft",
                    tokenizer=self.target_model_config.tokenizer,
                    tokenizer_mode=self.target_model_config.tokenizer_mode,
                    trust_remote_code=self.target_model_config.
                    trust_remote_code,
                    allowed_local_media_path=self.target_model_config.
                    allowed_local_media_path,
                    dtype=self.target_model_config.dtype,
                    seed=self.target_model_config.seed,
                    revision=self.revision,
                    code_revision=self.code_revision,
                    tokenizer_revision=self.target_model_config.
                    tokenizer_revision,
                    max_model_len=None,
                    spec_target_max_model_len=self.target_model_config.
                    max_model_len,
                    quantization=self.quantization,
                    enforce_eager=self.target_model_config.enforce_eager,
                    max_seq_len_to_capture=self.target_model_config.
                    max_seq_len_to_capture,
                    max_logprobs=self.target_model_config.max_logprobs,
                    hf_overrides=SpeculativeConfig.hf_config_override,
                )
2389

2390
                # Automatically detect the method
2391
2392
2393
                if self.method == 'eagle':
                    pass
                elif "eagle-" in self.draft_model_config.model.lower():
2394
2395
2396
2397
2398
2399
                    self.method = "eagle"
                elif self.draft_model_config.hf_config.model_type == "medusa":
                    self.method = "medusa"
                elif (self.draft_model_config.hf_config.model_type ==
                      "mlp_speculator"):
                    self.method = "mlp_speculator"
2400
                else:
2401
2402
2403
2404
                    self.method = "draft_model"

                # Replace hf_config for EAGLE draft_model
                if self.method == "eagle":
2405
                    if self.enable_chunked_prefill and not envs.VLLM_USE_V1:
2406
                        raise ValueError(
2407
2408
                            "Chunked prefill and EAGLE are not compatible "
                            "when using V0.")
2409
2410
2411
2412
2413
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2415
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2417
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2420
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2422
2423
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2429
2430
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2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444

                    from vllm.transformers_utils.configs.eagle import (
                        EAGLEConfig)
                    if isinstance(self.draft_model_config.hf_config,
                                  EAGLEConfig):
                        pass
                    else:
                        eagle_config = EAGLEConfig(
                            self.draft_model_config.hf_config)
                        self.draft_model_config.hf_config = eagle_config

                if (self.num_speculative_tokens is not None
                        and hasattr(self.draft_model_config.hf_config,
                                    "num_lookahead_tokens")):
                    self.draft_model_config.hf_config.num_lookahead_tokens = \
                    self.num_speculative_tokens

                n_predict = getattr(self.draft_model_config.hf_config,
                                    "n_predict", None)
                if n_predict is not None:
                    if self.num_speculative_tokens is None:
                        # Default to max value defined in draft model config.
                        self.num_speculative_tokens = n_predict
                    elif self.num_speculative_tokens > n_predict and \
                            self.num_speculative_tokens % n_predict != 0:
                        # Ensure divisibility for MTP module reuse.
                        raise ValueError(
                            f"num_speculative_tokens:{self.num_speculative_tokens}"
                            f" must be divisible by {n_predict=}")

                self.draft_tensor_parallel_size = \
                    SpeculativeConfig._verify_and_get_draft_tp(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size,
                        self.draft_model_config.hf_config
                )
2445

2446
2447
2448
2449
2450
2451
                self.draft_model_config.max_model_len = (
                    SpeculativeConfig._maybe_override_draft_max_model_len(
                        self.max_model_len,
                        self.draft_model_config.max_model_len,
                        self.target_model_config.max_model_len,
                    ))
2452

2453
2454
2455
2456
                self.draft_parallel_config = (
                    SpeculativeConfig.create_draft_parallel_config(
                        self.target_parallel_config,
                        self.draft_tensor_parallel_size))
2457

2458
2459
2460
2461
2462
        if self.acceptance_method == "typical_acceptance_sampler":
            if self.posterior_threshold is None:
                self.posterior_threshold = 0.09
            if self.posterior_alpha is None:
                self.posterior_alpha = 0.3
2463

2464
        self._verify_args()
2465

2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
    @staticmethod
    def _maybe_override_draft_max_model_len(
        speculative_max_model_len: Optional[int],
        draft_max_model_len: int,
        target_max_model_len: int,
    ) -> int:
        """Determine the max sequence len for the draft model. This is usually
        the draft_max_model_len, but may be the target_max_model_len if it is
        less than the draft_max_model_len, or may be speculative_max_model_len
        if it is specified.

        This is necessary so that sequences do not exceed the capacity of the
        draft model or the target model.

        speculative_max_model_len is mainly used for testing that sequences can
        skip speculation.
        """

        if speculative_max_model_len is not None:

            if speculative_max_model_len > draft_max_model_len:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {draft_max_model_len=}")

            if speculative_max_model_len > target_max_model_len:
                raise ValueError(f"{speculative_max_model_len=} cannot be "
                                 f"larger than {target_max_model_len=}")

            return speculative_max_model_len

        return min(
            draft_max_model_len,
            target_max_model_len,
        )

2501
    @staticmethod
2502
    def _verify_and_get_draft_tp(
2503
2504
2505
2506
2507
2508
            target_parallel_config: ParallelConfig,
            speculative_draft_tensor_parallel_size: Optional[int],
            draft_hf_config: PretrainedConfig) -> int:
        """
        Verifies and adjusts the tensor parallel size for a draft model
        specified using speculative_draft_tensor_parallel_size.
2509
        """
2510
2511
        # If speculative_draft_tensor_parallel_size is unset then set it
        # appropriately else verify that it is set correctly.
2512
        if speculative_draft_tensor_parallel_size is None:
2513
2514
2515
2516
            if draft_hf_config.model_type == "mlp_speculator":
                speculative_draft_tensor_parallel_size = 1
                if target_parallel_config.tensor_parallel_size > 1:
                    logger.warning(
2517
2518
2519
                        "%s cannot currently be run with tp>1; "
                        "setting speculative_draft_tensor_parallel_size=1",
                        draft_hf_config.model_type)
2520
2521
2522
            else:
                speculative_draft_tensor_parallel_size = \
                    target_parallel_config.tensor_parallel_size
2523
2524
        elif speculative_draft_tensor_parallel_size not in (
                1, target_parallel_config.tensor_parallel_size):
2525
            raise ValueError(
2526
                f"{speculative_draft_tensor_parallel_size=} cannot be "
2527
                f"other value than 1 or target model tensor_parallel_size")
2528
        return speculative_draft_tensor_parallel_size
2529

2530
2531
2532
2533
2534
2535
2536
2537
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    @staticmethod
    def create_draft_parallel_config(
        target_parallel_config: ParallelConfig,
        speculative_draft_tensor_parallel_size: int,
    ) -> ParallelConfig:
        """Create a parallel config for use by the draft worker.

        This is mostly a copy of the target parallel config, except the tp_size.
        """
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        draft_parallel_config = ParallelConfig(
            pipeline_parallel_size=target_parallel_config.
            pipeline_parallel_size,
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            tensor_parallel_size=speculative_draft_tensor_parallel_size,
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            distributed_executor_backend=target_parallel_config.
            distributed_executor_backend,
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            max_parallel_loading_workers=target_parallel_config.
            max_parallel_loading_workers,
            disable_custom_all_reduce=target_parallel_config.
            disable_custom_all_reduce,
            tokenizer_pool_config=target_parallel_config.tokenizer_pool_config,
            ray_workers_use_nsight=target_parallel_config.
            ray_workers_use_nsight,
            placement_group=target_parallel_config.placement_group,
        )

        return draft_parallel_config

    def _verify_args(self) -> None:
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        if self.num_speculative_tokens is None:
            raise ValueError(
                "num_speculative_tokens must be provided with "
                "speculative model unless the draft model config contains an "
                "n_predict parameter.")

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        if self.num_speculative_tokens <= 0:
            raise ValueError("Expected num_speculative_tokens to be greater "
                             f"than zero ({self.num_speculative_tokens}).")

        if self.draft_model_config:
            self.draft_model_config.verify_with_parallel_config(
                self.draft_parallel_config)
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            # Validate and set draft token acceptance related settings.

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        if self.acceptance_method is None:
            raise ValueError("acceptance_method is not set. "
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                             "Expected values are rejection_sampler or "
                             "typical_acceptance_sampler.")

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        if (self.acceptance_method != 'rejection_sampler'
                and self.acceptance_method != 'typical_acceptance_sampler'):
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            raise ValueError(
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                "Expected acceptance_method to be either "
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                "rejection_sampler or typical_acceptance_sampler. Instead it "
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                f"is {self.acceptance_method}")
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        if self.acceptance_method == "typical_acceptance_sampler" and (
            (self.posterior_threshold is not None
             and self.posterior_threshold < 0) or
            (self.posterior_alpha is not None and self.posterior_alpha < 0)):
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            raise ValueError(
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                "Expected the posterior_threshold and posterior_alpha of "
                "typical_acceptance_sampler to be > 0. "
                "Instead found posterior_threshold = "
                f"{self.posterior_threshold} and posterior_alpha = "
                f"{self.posterior_alpha}")

        if (self.disable_by_batch_size is not None
                and self.disable_by_batch_size < 2):
            raise ValueError("Expect the batch size threshold of disabling "
                             "speculative decoding is > 1, but got "
                             f"{self.disable_by_batch_size=}")
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    @property
    def num_lookahead_slots(self) -> int:
        """The number of additional slots the scheduler should allocate per
        step, in addition to the slots allocated for each known token.

        This is equal to the number of speculative tokens, as each speculative
        token must be scored.
        """
        return self.num_speculative_tokens

    def __repr__(self) -> str:
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        method = self.method
        model = None if method == "ngram" else self.draft_model_config.model
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        num_spec_tokens = self.num_speculative_tokens
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        return f"SpeculativeConfig({method=}, {model=}, {num_spec_tokens=})"
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@dataclass
class LoRAConfig:
    max_lora_rank: int
    max_loras: int
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    fully_sharded_loras: bool = False
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    max_cpu_loras: Optional[int] = None
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    lora_dtype: Optional[Union[torch.dtype, str]] = None
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    lora_extra_vocab_size: int = 256
    # This is a constant.
    lora_vocab_padding_size: ClassVar[int] = 256
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    long_lora_scaling_factors: Optional[tuple[float]] = None
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    bias_enabled: bool = False
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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.
        """
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        factors: list[Any] = []
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        factors.append(self.max_lora_rank)
        factors.append(self.max_loras)
        factors.append(self.fully_sharded_loras)
        factors.append(self.lora_dtype)
        factors.append(self.lora_extra_vocab_size)
        factors.append(self.long_lora_scaling_factors)
        factors.append(self.bias_enabled)
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    def __post_init__(self):
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        # Setting the maximum rank to 512 should be able to satisfy the vast
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        # majority of applications.
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        possible_max_ranks = (8, 16, 32, 64, 128, 256, 320, 512)
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        possible_lora_extra_vocab_size = (256, 512)
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        if self.max_lora_rank not in possible_max_ranks:
            raise ValueError(
                f"max_lora_rank ({self.max_lora_rank}) must be one of "
                f"{possible_max_ranks}.")
        if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size:
            raise ValueError(
                f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) "
                f"must be one of {possible_lora_extra_vocab_size}.")
        if self.max_loras < 1:
            raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.")
        if self.max_cpu_loras is None:
            self.max_cpu_loras = self.max_loras
        elif self.max_cpu_loras < self.max_loras:
            raise ValueError(
                f"max_cpu_loras ({self.max_cpu_loras}) must be >= "
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                f"max_loras ({self.max_loras})")
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    def verify_with_cache_config(self, cache_config: CacheConfig):
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        if cache_config.cpu_offload_gb > 0 and not envs.VLLM_USE_V1:
            raise ValueError(
                "V0 LoRA does not support CPU offload, please use V1.")
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    def verify_with_model_config(self, model_config: ModelConfig):
        if self.lora_dtype in (None, "auto"):
            self.lora_dtype = model_config.dtype
        elif isinstance(self.lora_dtype, str):
            self.lora_dtype = getattr(torch, self.lora_dtype)

    def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig):
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        # Reminder: Please update docs/source/features/compatibility_matrix.md
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        # If the feature combo become valid
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        if scheduler_config.chunked_prefill_enabled:
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            logger.warning("LoRA with chunked prefill is still experimental "
                           "and may be unstable.")
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    def verify_lora_support(self):
        if self.long_lora_scaling_factors is not None and envs.VLLM_USE_V1:
            raise ValueError(
                "V1 LoRA does not support long LoRA, please use V0.")

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@dataclass
class PromptAdapterConfig:
    max_prompt_adapters: int
    max_prompt_adapter_token: int
    max_cpu_prompt_adapters: Optional[int] = None
    prompt_adapter_dtype: Optional[torch.dtype] = None

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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    def __post_init__(self):

        if self.max_prompt_adapters < 1:
            raise ValueError(f"max_prompt_adapters "
                             f"({self.max_prompt_adapters}) must be >= 1.")
        if self.max_prompt_adapter_token == 0:
            raise ValueError("max_prompt_adapter_token must be set.")
        if self.max_cpu_prompt_adapters is None:
            self.max_cpu_prompt_adapters = self.max_prompt_adapters

    def verify_with_model_config(self, model_config: ModelConfig):
        if self.prompt_adapter_dtype in (None, "auto"):
            self.prompt_adapter_dtype = model_config.dtype
        elif isinstance(self.prompt_adapter_dtype, str):
            self.prompt_adapter_dtype = getattr(torch,
                                                self.prompt_adapter_dtype)


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@config
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@dataclass
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class MultiModalConfig:
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    """Controls the behavior of multimodal models."""

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    limit_per_prompt: dict[str, int] = field(default_factory=dict)
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    """
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    The maximum number of input items allowed per prompt for each modality.
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    This should be a JSON string that will be parsed into a dictionary.
    Defaults to 1 (V0) or 999 (V1) for each modality.
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    """

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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    def get_limit_per_prompt(self, modality: str) -> int:
        """
        Get the maximum number of input items allowed per prompt
        for the given modality.
        """
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        return self.limit_per_prompt.get(
            modality,
            999 if envs.VLLM_USE_V1 else 1,
        )
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    # TODO: Add configs to init vision tower or not.
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@config
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@dataclass
class PoolerConfig:
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    """Controls the behavior of output pooling in pooling models."""
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    pooling_type: Optional[str] = None
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    """
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    The pooling method of the pooling model. This should be a key in
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    :class:`vllm.model_executor.layers.pooler.PoolingType`.
    """

    normalize: Optional[bool] = None
    """
    Whether to normalize the pooled outputs. Usually, this should be set to
    ``True`` for embedding outputs.
    """

    softmax: Optional[bool] = None
    """
    Whether to apply softmax to the pooled outputs. Usually, this should be set
    to ``True`` for classification outputs.
    """

    step_tag_id: Optional[int] = None
    """
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    If set, only the score corresponding to the ``step_tag_id`` in the
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    generated sentence should be returned. Otherwise, the scores for all tokens
    are returned.
    """

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    returned_token_ids: Optional[list[int]] = None
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    """
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    A list of indices for the vocabulary dimensions to be extracted,
    such as the token IDs of ``good_token`` and ``bad_token`` in the
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    ``math-shepherd-mistral-7b-prm`` model.
    """

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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    @staticmethod
    def from_json(json_str: str) -> "PoolerConfig":
        return PoolerConfig(**json.loads(json_str))
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_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}

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_ROCM_NOT_SUPPORTED_DTYPE: list[str] = []  #
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def _get_and_verify_dtype(
    config: PretrainedConfig,
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    dtype: Union[str, torch.dtype],
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) -> torch.dtype:
    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
    # because config.torch_dtype can be None.
    config_dtype = getattr(config, "torch_dtype", None)
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    # Fallbacks for multi-modal models if the root config
    # does not define torch_dtype
    if config_dtype is None and hasattr(config, "text_config"):
        config_dtype = getattr(config.text_config, "torch_dtype", None)
    if config_dtype is None and hasattr(config, "vision_config"):
        config_dtype = getattr(config.vision_config, "torch_dtype", None)

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    if config_dtype is None:
        config_dtype = torch.float32

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    if isinstance(dtype, str):
        dtype = dtype.lower()
        if dtype == "auto":
            if config_dtype == torch.float32:
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                # Following common practice, we use float16 for float32 models
                torch_dtype = torch.float16
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            else:
                torch_dtype = config_dtype
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            if config.model_type == "plamo2":
                logger.info(
                    "For PLaMo2, we cast models to bfloat16 instead of using "
                    "float16 by default. This is because float16 does not work."
                )
                torch_dtype = torch.bfloat16

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            from vllm.platforms import current_platform
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            if (current_platform.is_cpu()
                    and current_platform.get_cpu_architecture()
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                    == CpuArchEnum.POWERPC
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                    and (config_dtype == torch.float16
                         or config_dtype == torch.float32)):
                logger.info(
                    "For POWERPC, we cast models to bfloat16 instead of "
                    "using float16 by default. Float16 is not currently "
                    "supported for POWERPC.")
                torch_dtype = torch.bfloat16

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            # TODO: change this condition to check if the platform support bf16
            # instead of checking the OS. For instance M2 shall supports bf16
            # already. But we need to modify `cpu_extension.cmake` to activate
            # the feature in the build.
            if (current_platform.is_cpu() and sys.platform.startswith("darwin")
                    and current_platform.get_cpu_architecture()
                    == CpuArchEnum.ARM and config_dtype == torch.bfloat16):
                logger.info("For macOS with Apple Silicon, currently bfloat16 "
                            "is not supported. Setting dtype to float16.")
                torch_dtype = torch.float16

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            if current_platform.is_hpu() and config_dtype == torch.float16:
                logger.info(
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                    "For HPU, we cast models to bfloat16 instead of "
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                    "using float16 by default. Please specify `dtype` if you "
                    "want to use float16.")
                torch_dtype = torch.bfloat16
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        elif dtype == "float16" and config.model_type == "plamo2":
            logger.warning(
                "For PLaMo2, using float16 is unstable and might cause "
                "unexpected behavior. Please use bfloat16 or float32 instead.")
            torch_dtype = torch.float16
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        else:
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            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
                raise ValueError(f"Unknown dtype: {dtype}")
            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
    elif isinstance(dtype, torch.dtype):
        torch_dtype = dtype
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    else:
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        raise ValueError(f"Unknown dtype: {dtype}")
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    # Verify the dtype.
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
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            logger.info("Upcasting %s to %s.", config_dtype, torch_dtype)
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            pass
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
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            logger.info("Downcasting %s to %s.", config_dtype, torch_dtype)
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            pass
        else:
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            # Casting between float16 and bfloat16 is allowed with a warning.
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            logger.warning("Casting %s to %s.", config_dtype, torch_dtype)
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    return torch_dtype
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def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
    max_model_len: Optional[int],
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    disable_sliding_window: bool,
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    sliding_window_len: Optional[Union[int, list[Optional[int]]]],
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    spec_target_max_model_len: Optional[int] = None,
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    encoder_config: Optional[Any] = None,
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) -> int:
    """Get and verify the model's maximum length."""
    derived_max_model_len = float("inf")
    possible_keys = [
        # OPT
        "max_position_embeddings",
        # GPT-2
        "n_positions",
        # MPT
        "max_seq_len",
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        # ChatGLM2
        "seq_length",
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        # Command-R
        "model_max_length",
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        # Whisper
        "max_target_positions",
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        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
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    # Choose the smallest "max_length" from the possible keys.
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    max_len_key = None
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    for key in possible_keys:
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        max_len = getattr(hf_config, key, None)
        if max_len is not None:
            max_len_key = key if max_len < derived_max_model_len \
                else max_len_key
            derived_max_model_len = min(derived_max_model_len, max_len)
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    # For Command-R / Cohere, Cohere2 / Aya Vision models
    if tmp_max_len := getattr(hf_config, "model_max_length", None):
        max_len_key = "model_max_length"
        derived_max_model_len = tmp_max_len
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    # If sliding window is manually disabled, max_length should be less
    # than the sliding window length in the model config.
    if disable_sliding_window and sliding_window_len is not None:
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        sliding_window_len_min = get_min_sliding_window(sliding_window_len)
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        max_len_key = "sliding_window" \
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            if sliding_window_len_min < derived_max_model_len else max_len_key
        derived_max_model_len = min(derived_max_model_len,
                                    sliding_window_len_min)
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    # If none of the keys were found in the config, use a default and
    # log a warning.
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    if derived_max_model_len == float("inf"):
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        if max_model_len is not None:
            # If max_model_len is specified, we use it.
            return max_model_len

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        if spec_target_max_model_len is not None:
            # If this is a speculative draft model, we use the max model len
            # from the target model.
            return spec_target_max_model_len

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        default_max_len = 2048
        logger.warning(
            "The model's config.json does not contain any of the following "
            "keys to determine the original maximum length of the model: "
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            "%s. Assuming the model's maximum length is %d.", possible_keys,
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            default_max_len)
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        derived_max_model_len = default_max_len
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    rope_scaling = getattr(hf_config, "rope_scaling", None)
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    # NOTE(woosuk): Gemma3's max_model_len (128K) is already scaled by RoPE
    # scaling, so we skip applying the scaling factor again.
    if rope_scaling is not None and "gemma3" not in hf_config.model_type:
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        # No need to consider "type" key because of patch_rope_scaling when
        # loading HF config
        rope_type = rope_scaling["rope_type"]
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        if rope_type not in ("su", "longrope", "llama3"):
            if disable_sliding_window:
                # TODO(robertgshaw): Find a model that supports rope_scaling
                # with sliding window to see if this case should be allowed.
                raise NotImplementedError(
                    "Disabling sliding window is not supported for models "
                    "with rope_scaling. Please raise an issue so we can "
                    "investigate.")

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            # NOTE: rope_type == "default" does not define factor
            # https://github.com/huggingface/transformers/blob/v4.45.2/src/transformers/modeling_rope_utils.py
            scaling_factor = rope_scaling.get("factor", 1.0)

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            if rope_type == "yarn":
                derived_max_model_len = rope_scaling[
                    "original_max_position_embeddings"]
            derived_max_model_len *= scaling_factor
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    if encoder_config and "max_seq_length" in encoder_config:
        derived_max_model_len = encoder_config["max_seq_length"]

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    # If the user specified a max length, make sure it is smaller than the
    # derived length from the HF model config.
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    if max_model_len is None:
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        max_model_len = int(derived_max_model_len)
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    elif max_model_len > derived_max_model_len:
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        # Some models might have a separate key for specifying model_max_length
        # that will be bigger than derived_max_model_len. We compare user input
        # with model_max_length and allow this override when it's smaller.
        model_max_length = getattr(hf_config, "model_max_length", None)
        if model_max_length is not None and max_model_len <= model_max_length:
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            if disable_sliding_window:
                # TODO(robertgshaw): Find a model that has model_max_length
                # with sliding window to see if this case should be allowed.
                raise NotImplementedError(
                    "Disabling sliding window is not supported for models "
                    "model_max_length in the config. Please raise an issue "
                    "so we can investigate.")
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        else:
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            msg = (
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                f"User-specified max_model_len ({max_model_len}) is greater "
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                f"than the derived max_model_len ({max_len_key}="
                f"{derived_max_model_len} or model_max_length="
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                f"{model_max_length} in model's config.json). This may lead "
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                "to incorrect model outputs or CUDA errors.")
            if envs.VLLM_ALLOW_LONG_MAX_MODEL_LEN:
                logger.warning(
                    "%s Make sure the value is correct and within the "
                    "model context size.", msg)
            else:
                raise ValueError(
                    f"{msg} To allow overriding this maximum, set "
                    "the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN=1")
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    return int(max_model_len)
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def get_min_sliding_window(
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        sliding_window: Union[int, list[Optional[int]]]) -> int:
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    if isinstance(sliding_window, list):
        return min(s for s in sliding_window if s is not None)

    return sliding_window


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def get_served_model_name(model: str,
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                          served_model_name: Optional[Union[str, list[str]]]):
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    """
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    If the input is a non-empty list, the first model_name in
    `served_model_name` is taken.
    If the input is a non-empty string, it is used directly.
    For cases where the input is either an empty string or an
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    empty list, the fallback is to use `self.model`.
    """
    if not served_model_name:
        return model
    if isinstance(served_model_name, list):
        return served_model_name[0]
    return served_model_name


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GuidedDecodingBackendV0 = Literal["auto", "outlines", "lm-format-enforcer",
                                  "xgrammar"]
GuidedDecodingBackendV1 = Literal["auto", "xgrammar", "guidance"]


@config
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@dataclass
class DecodingConfig:
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    """Dataclass which contains the decoding strategy of the engine."""
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    guided_decoding_backend: Union[
        GuidedDecodingBackendV0,
        GuidedDecodingBackendV1] = "auto" if envs.VLLM_USE_V1 else "xgrammar"
    """Which engine will be used for guided decoding (JSON schema / regex etc)
    by default. With "auto", we will make opinionated choices based on request
    contents and what the backend libraries currently support, so the behavior
    is subject to change in each release."""
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    reasoning_backend: Optional[str] = None
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    """Select the reasoning parser depending on the model that you're using.
    This is used to parse the reasoning content into OpenAI API format.
    Required for `--enable-reasoning`."""
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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    def __post_init__(self):
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        backend = GuidedDecodingParams(
            backend=self.guided_decoding_backend).backend_name
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        if envs.VLLM_USE_V1:
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            valid_guided_backends = get_args(GuidedDecodingBackendV1)
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        else:
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            valid_guided_backends = get_args(GuidedDecodingBackendV0)
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        if backend not in valid_guided_backends:
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            raise ValueError(f"Invalid guided_decoding_backend '{backend}',"
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                             f" must be one of {valid_guided_backends}")
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@dataclass
class ObservabilityConfig:
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    """Configuration for observability - metrics and tracing."""
    show_hidden_metrics: bool = False

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    otlp_traces_endpoint: Optional[str] = None

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    # Collecting detailed timing information for each request can be expensive.

    # If set, collects the model forward time for the request.
    collect_model_forward_time: bool = False

    # If set, collects the model execute time for the request.
    collect_model_execute_time: bool = False

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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    def __post_init__(self):
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        if not is_otel_available() and self.otlp_traces_endpoint is not None:
            raise ValueError(
                "OpenTelemetry is not available. Unable to configure "
                "'otlp_traces_endpoint'. Ensure OpenTelemetry packages are "
                f"installed. Original error:\n{otel_import_error_traceback}")
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class KVTransferConfig(BaseModel):
    """Configuration for distributed KV cache transfer."""

    # The KV connector for vLLM to transmit KV caches between vLLM instances.
    kv_connector: Optional[str] = None

    # The device used by kv connector to buffer the KV cache.
    # Currently only support 'cuda'.
    kv_buffer_device: Optional[str] = "cuda"

    # The buffer size for TorchDistributedConnector. Measured in number of
    # bytes. Recommended value: 1e9 (about 1GB).
    kv_buffer_size: float = 1e9

    # Whether this vLLM instance produces, consumes KV cache, or both. Choices
    # are 'kv_producer', 'kv_consumer', and 'both'.
    kv_role: Optional[str] = None

    # The rank of this vLLM instance in the KV cache transfer. Typical value:
    # 0 for prefill instance, 1 for decode instance.
    # Currently only 1P1D is supported.
    kv_rank: Optional[int] = None

    # The number of parallel instances for KV cache transfer. For
    # PyNcclConnector, this should be 2.
    kv_parallel_size: int = 1

    # The KV connector ip, used to build distributed connection
    kv_ip: str = "127.0.0.1"

    # The KV connector port, used to build distributed connection
    kv_port: int = 14579

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    # any extra config that the connector may need
    kv_connector_extra_config: dict[str, Any] = {}

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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.
        """
        # no factors to consider.
        # this config will not affect the computation graph.
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        factors: list[Any] = []
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        hash_str = hashlib.md5(str(factors).encode(),
                               usedforsecurity=False).hexdigest()
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        return hash_str

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    @classmethod
    def from_cli(cls, cli_value: str) -> "KVTransferConfig":
youkaichao's avatar
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        """Parse the CLI value for the kv cache transfer config."""
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        return KVTransferConfig.model_validate_json(cli_value)

    def model_post_init(self, __context: Any) -> None:

        if self.kv_role is not None and self.kv_role not in [
                "kv_producer", "kv_consumer", "kv_both"
        ]:
            raise ValueError(
                f"Unsupported kv_role: {self.kv_role}. "
                f"Supported roles are `kv_producer`, `kv_consumer`, "
                f"and `kv_both`")

        if self.kv_connector is not None and self.kv_role is None:
            raise ValueError("Please specify kv_disagg_role when kv_connector "
                             "is set, supported roles are `kv_producer`, "
                             "`kv_consumer`, and `kv_both`")

    @property
    def is_kv_transfer_instance(self) -> bool:
        return self.kv_connector is not None and \
            self.kv_role in ["kv_producer", "kv_consumer", "kv_both"]

    @property
    def is_kv_producer(self) -> bool:
        return self.kv_connector is not None and \
            self.kv_role in ["kv_producer", "kv_both"]

    @property
    def is_kv_consumer(self) -> bool:
        return self.kv_connector is not None and \
            self.kv_role in ["kv_consumer", "kv_both"]

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    def get_from_extra_config(self, key, default) -> Any:
        return self.kv_connector_extra_config.get(key, default)

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class CompilationLevel:
    # constants for the levels of the compilation process
    NO_COMPILATION = 0
    DYNAMO_AS_IS = 1
    DYNAMO_ONCE = 2
    PIECEWISE = 3


class CompilationConfig(BaseModel):
    """
    Configuration for compilation.
    It has three parts:
    - Top-level Compilation control:
        - level: the level of compilation.
            - 0: no compilation.
            - 1: dynamo as is.
            - 2: dynamo once.
            - 3: piecewise compilation.
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        - debug_dump_path: the path to dump the debug information.
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        - cache_dir: the directory to store the compiled graph, to
            accelerate Inductor compilation. By default, it will use
            model-related information to generate a cache directory.
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        - backend: the backend for compilation. It needs to be a string.
            - "" (empty string): use the default backend.
            - "eager"/"openxla"/...: use the specified backend registered in PyTorch.
            - "full.module.name": a qualified name which can be used to import the backend function.
            We use string to avoid serialization issues when using compilation in a distributed setting.
            When the compilation level is 1 or 2, the backend is used for the compilation directly (it sees the whole graph).
            When the compilation level is 3, the backend is used for the piecewise compilation (it sees a part of the graph).
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        - custom_ops: fine-grained control over which custom ops to enable/disable.
            Use 'all' to enable all, 'none' to disable all.
            Also specify a list of custom op names to enable (prefixed with a '+'),
            or disable (prefixed with a '-').
            Examples:
                - 'all,-op1' to enable all except op1
                - 'none,+op1,+op2' to enable only op1 and op2
            By default, all custom ops are enabled when running without Inductor
                and disabled when running with Inductor (compile_level >= Inductor).
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        - splitting_ops: a list of ops to split the full graph into subgraphs, used in piecewise compilation.
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    - CudaGraph capture:
        - use_cudagraph: whether to use cudagraph inside compilation.
            - False: cudagraph inside compilation is not used.
            - True: cudagraph inside compilation is used. It requires
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                that all input buffers have fixed addresses, and all
                splitting ops write their outputs to input buffers.
            Note that this is orthogonal to the cudagraph capture logic
            outside of compilation.
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            TODO: move outside cudagraph logic into compilation.
            torch.compile will handle cudagraph capture logic in the future.
        - cudagraph_capture_sizes: sizes to capture cudagraph.
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            - None (default): capture sizes are inferred from vllm config.
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            - list[int]: capture sizes are specified as given.
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        - cudagraph_num_of_warmups: number of warmup runs for cudagraph.
            It means the first several runs will be treated as warmup runs.
            Only after that, the execution will be recorded, and the recorded
            cudagraph will be used for subsequent runs.
        - cudagraph_copy_inputs: whether to copy input tensors for
            cudagraph. If the caller can guarantee that the same input buffers
            are always used, it can set this to False. Otherwise, it should
            set this to True, and the compiler will copy the input to an
            internally managed buffer. Default is False.
    - Inductor compilation:
        - use_inductor: whether to use inductor compilation.
            - False: inductor compilation is not used. graph runs in eager.
            - True: inductor compilation is used. one graph for symbolic shape
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                is compiled. In addition, compile for compile_sizes,
                using configurations in inductor_compile_config.
        - compile_sizes: sizes to compile for inductor. In addition
            to integers, it also supports "cudagraph_capture_sizes" to
            specify the sizes for cudagraph capture.
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        - inductor_compile_config: additional configurations for inductor.
            - None: use default configurations.
        - inductor_passes: additional passes for inductor. It is a dictionary
            from pass name to pass function qualified name. We use function
            name because the config uses json format. If we pass the config
            from Python, functions can also be passed directly via Python object
            constructor, e.g. `CompilationConfig(inductor_passes={"a": func})`
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        - custom inductor passes: see PassConfig for more details
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    Why we have different sizes for cudagraph and inductor:
    - cudagraph: a cudagraph captured for a specific size can only be used
        for the same size. We need to capture all the sizes we want to use.
    - inductor: a graph compiled by inductor for a general shape can be used
        for different sizes. Inductor can also compile for specific sizes,
        where it can have more information to optimize the graph with fully
        static shapes. However, we find the general shape compilation is
        sufficient for most cases. It might be beneficial to compile for
        certain small batchsizes, where inductor is good at optimizing.
    """ # noqa
    level: int = 0
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    debug_dump_path: str = ""
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    cache_dir: str = ""
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    backend: str = ""
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    custom_ops: list[str] = Field(default_factory=list)
    splitting_ops: list[str] = Field(default=None)  # type: ignore
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    use_inductor: bool = True
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    compile_sizes: Optional[list[Union[int, str]]] = Field(default=None)
    inductor_compile_config: dict = Field(default_factory=dict)
    inductor_passes: dict[str, str] = Field(default_factory=dict)
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    use_cudagraph: bool = False
    cudagraph_num_of_warmups: int = 0
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    cudagraph_capture_sizes: Optional[list[int]] = None
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    cudagraph_copy_inputs: bool = False

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    class PassConfig(BaseModel):
        """
        Configuration for custom Inductor passes.
        This is separate from general CompilationConfig so that inductor passes
        don't all have access to full configuration - that would create a cycle
        as the PassManager is set as a property of config.
        - dump_graph_stages: list of stages for which we want to dump the graph.
            Each pass defines its own stages (before, after, maybe in-between).
        - dump_graph_dir: directory to dump the graphs. Default is .
        - enable_fusion: whether to enable the custom fusion pass.
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        - enable_noop: whether to enable the custom no-op elimination pass.
            TODO(luka) better pass enabling system.
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        """
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        dump_graph_stages: list[str] = Field(default_factory=list)
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        dump_graph_dir: Path = Field(default=Path("."))
        enable_fusion: bool = True
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        enable_noop: bool = True
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        def uuid(self):
            """
            Produces a hash unique to the pass configuration.
            Any new fields that affect compilation should be added to the hash.
            Do not include dump_graph_* in the hash - they don't affect
            compilation.
            """
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            dict_ = self.model_dump(include={"enable_fusion", "enable_noop"})
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            return InductorPass.hash_dict(dict_)
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        def model_post_init(self, __context: Any) -> None:
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            if not self.enable_noop and self.enable_fusion:
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                logger.warning_once(
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                    "Fusion enabled but reshape elimination disabled. "
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                    "RMSNorm + quant (fp8) fusion might not work")

    pass_config: PassConfig = Field(default_factory=PassConfig)
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    # not configurable, computed after init
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    max_capture_size: int = PrivateAttr
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    local_cache_dir: str = PrivateAttr  # local cache dir for each rank
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    # optimization:
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    # Intuitively, bs_to_padded_graph_size should be dict[int, int].
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    # since we know all keys are in a range [0, max_capture_size],
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    # we can optimize it to list[int] for better lookup performance.
    bs_to_padded_graph_size: list[int] = PrivateAttr
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    # keep track of enabled and disabled custom ops
    enabled_custom_ops: Counter[str] = PrivateAttr
    disabled_custom_ops: Counter[str] = PrivateAttr
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    traced_files: set[str] = PrivateAttr
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    compilation_time: float = PrivateAttr
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    # Per-model forward context
    # Map from layer name to the attention cls
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    static_forward_context: dict[str, Any] = PrivateAttr
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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.
        """
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        factors: list[Any] = []
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        factors.append(self.level)
        factors.append(self.backend)
        factors.append(self.custom_ops)
        factors.append(self.splitting_ops)
        factors.append(self.use_inductor)
        factors.append(self.inductor_compile_config)
        factors.append(self.inductor_passes)
        factors.append(self.pass_config.uuid())
        return hashlib.sha256(str(factors).encode()).hexdigest()

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    def __repr__(self) -> str:
        exclude = {
            "static_forward_context",
            "enabled_custom_ops",
            "disabled_custom_ops",
            "compilation_time",
            "bs_to_padded_graph_size",
            "pass_config",
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            "traced_files",
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        }
        return self.model_dump_json(exclude=exclude, exclude_unset=True)

    __str__ = __repr__

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    @classmethod
    def from_cli(cls, cli_value: str) -> "CompilationConfig":
        """Parse the CLI value for the compilation config."""
        if cli_value in ["0", "1", "2", "3"]:
            return cls(level=int(cli_value))
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        # do not use `eval`, it is dangerous and can execute arbitrary code
        dict_value = ast.literal_eval(cli_value)
        return CompilationConfig.model_validate(dict_value)
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    def model_post_init(self, __context: Any) -> None:

        count_none = self.custom_ops.count("none")
        count_all = self.custom_ops.count("all")
        assert count_none + count_all <= 1, "Can only specify 'none' or 'all'"

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        # TODO(zou3519/luka): There are 2 issues with auto-functionalization V2:
        # 1. A bug in PyTorch, fixed in 2.7:
        #    https://github.com/pytorch/pytorch/issues/147924
        # 2. Custom passes (fusion) rely on auto-functionalization V1 and don't
        #    work with V2. Addressing this will take extra engineering effort
        #    and it is not yet a priority. RFC here:
        #    https://github.com/vllm-project/vllm/issues/14703

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        if is_torch_equal_or_newer("2.6"):
Michael Goin's avatar
Michael Goin committed
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            KEY = 'enable_auto_functionalized_v2'
            if KEY not in self.inductor_compile_config:
                self.inductor_compile_config[KEY] = False

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        if self.splitting_ops is None:
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            self.splitting_ops = []
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        for k, v in self.inductor_passes.items():
            if not isinstance(v, str):
                assert callable(v), (
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                    f"pass {k} should be callable or a qualified name")
                self.inductor_compile_config[k] = v if isinstance(
                    v, InductorPass) else CallableInductorPass(v)
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                continue

            # resolve function from qualified name
            names = v.split(".")
            module = ".".join(names[:-1])
            func_name = names[-1]
            func = __import__(module).__dict__[func_name]
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            self.inductor_compile_config[k] = func if isinstance(
                func, InductorPass) else CallableInductorPass(func)
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        self.enabled_custom_ops = Counter()
        self.disabled_custom_ops = Counter()
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        self.traced_files = set()
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        self.static_forward_context = {}
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        self.compilation_time = 0.0
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    def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]:
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        if self.level == CompilationLevel.NO_COMPILATION:
            raise ValueError("No compilation level is set.")

        from torch._dynamo.backends.registry import list_backends
        torch_backends = list_backends(exclude_tags=tuple())
        if self.level in [
                CompilationLevel.DYNAMO_AS_IS, CompilationLevel.DYNAMO_ONCE
        ]:
            if self.backend == "":
                return "eager"
            if self.backend in torch_backends:
                return self.backend
            return resolve_obj_by_qualname(self.backend)

        # TODO: pass user-specified backend to piecewise compilation
        # merge with the config use_inductor
        assert self.level == CompilationLevel.PIECEWISE
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        from vllm.compilation.backends import VllmBackend
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        return VllmBackend(vllm_config)
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    def init_with_cudagraph_sizes(self,
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                                  cudagraph_capture_sizes: list[int]) -> None:
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        """To complete the initialization of config,
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        we need to know the cudagraph sizes."""

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        if self.cudagraph_capture_sizes is None:
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            self.cudagraph_capture_sizes = cudagraph_capture_sizes
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        else:
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            # de-duplicate the sizes provided by the config
            self.cudagraph_capture_sizes = list(
                set(self.cudagraph_capture_sizes))
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            logger.info(("cudagraph sizes specified by model runner"
                         " %s is overridden by config %s"),
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                        cudagraph_capture_sizes, self.cudagraph_capture_sizes)

        computed_compile_sizes = []
        if self.compile_sizes is not None:
            # de-duplicate the sizes provided by the config
            self.compile_sizes = list(set(self.compile_sizes))
            for x in self.compile_sizes:
                if isinstance(x, str):
                    assert x == "cudagraph_capture_sizes", \
                    "Unrecognized size type in compile_sizes, " \
                    f"expect 'cudagraph_capture_sizes', got {x}"
                    computed_compile_sizes.extend(self.cudagraph_capture_sizes)
                else:
                    assert isinstance(x, int)
                    computed_compile_sizes.append(x)
        self.compile_sizes = computed_compile_sizes  # type: ignore
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        # sort to make sure cudagraph capture sizes are in descending order
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        self.cudagraph_capture_sizes.sort(reverse=True)
        self.max_capture_size = self.cudagraph_capture_sizes[
            0] if self.cudagraph_capture_sizes else 0
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        # pre-compute the mapping from batch size to padded graph size
        self.bs_to_padded_graph_size = [
            0 for i in range(self.max_capture_size + 1)
        ]
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        for end, start in zip(self.cudagraph_capture_sizes,
                              self.cudagraph_capture_sizes[1:] + [0]):
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            for bs in range(start, end):
                if bs == start:
                    self.bs_to_padded_graph_size[bs] = start
                else:
                    self.bs_to_padded_graph_size[bs] = end
        self.bs_to_padded_graph_size[
            self.max_capture_size] = self.max_capture_size
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    def set_splitting_ops_for_v1(self):
        # If default, override splitting ops for piecewise cudagraph on V1.
        # NOTE: this function needs to be called
        if not self.splitting_ops:
            self.splitting_ops = [
                "vllm.unified_attention",
                "vllm.unified_attention_with_output",
            ]

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@dataclass
class VllmConfig:
    """Dataclass which contains all vllm-related configuration. This
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    simplifies passing around the distinct configurations in the codebase.
    """

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    model_config: ModelConfig = field(default=None, init=True)  # type: ignore
    cache_config: CacheConfig = field(default=None, init=True)  # type: ignore
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    parallel_config: ParallelConfig = field(default_factory=ParallelConfig,
                                            init=True)
    scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig,
                                              init=True)
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    device_config: DeviceConfig = field(default=None,
                                        init=True)  # type: ignore
    load_config: LoadConfig = field(default=None, init=True)  # type: ignore
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    lora_config: Optional[LoRAConfig] = None
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    speculative_config: SpeculativeConfig = field(default=None,
                                                  init=True)  # type: ignore
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    decoding_config: Optional[DecodingConfig] = None
    observability_config: Optional[ObservabilityConfig] = None
    prompt_adapter_config: Optional[PromptAdapterConfig] = None
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    quant_config: Optional[QuantizationConfig] = None
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    compilation_config: CompilationConfig = field(default=None,
                                                  init=True)  # type: ignore
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    kv_transfer_config: KVTransferConfig = field(default=None,
                                                 init=True)  # type: ignore
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    # some opaque config, only used to provide additional information
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    # for the hash computation, mainly used for testing, debugging or out of
    # tree config registration.
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    additional_config: SupportsHash = field(default=None,
                                            init=True)  # type: ignore
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    instance_id: str = ""
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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.
        """
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        factors: list[Any] = []
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        # summarize vllm config
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        vllm_factors: list[Any] = []
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        from vllm import __version__
        vllm_factors.append(__version__)
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        vllm_factors.append(envs.VLLM_USE_V1)
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        if self.model_config:
            vllm_factors.append(self.model_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.cache_config:
            vllm_factors.append(self.cache_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.parallel_config:
            vllm_factors.append(self.parallel_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.scheduler_config:
            vllm_factors.append(self.scheduler_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.device_config:
            vllm_factors.append(self.device_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.load_config:
            vllm_factors.append(self.load_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.lora_config:
            vllm_factors.append(self.lora_config.compute_hash())
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            # 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))
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        else:
            vllm_factors.append("None")
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        if self.speculative_config:
            vllm_factors.append(self.speculative_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.decoding_config:
            vllm_factors.append(self.decoding_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.observability_config:
            vllm_factors.append(self.observability_config.compute_hash())
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        else:
            vllm_factors.append("None")
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        if self.prompt_adapter_config:
            vllm_factors.append(self.prompt_adapter_config.compute_hash())
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        else:
            vllm_factors.append("None")
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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())
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        else:
            vllm_factors.append("None")
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        if self.kv_transfer_config:
            vllm_factors.append(self.kv_transfer_config.compute_hash())
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        else:
            vllm_factors.append("None")
        if self.additional_config:
            vllm_factors.append(self.additional_config.compute_hash())
        else:
            vllm_factors.append("None")
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        factors.append(vllm_factors)

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

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    def pad_for_cudagraph(self, batch_size: int) -> int:
        # if batch_size > self.compilation_config.max_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_capture_size
        return self.compilation_config.bs_to_padded_graph_size[batch_size]
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    @staticmethod
    def _get_quantization_config(
            model_config: ModelConfig,
            load_config: LoadConfig) -> Optional[QuantizationConfig]:
        """Get the quantization config."""
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        from vllm.platforms import current_platform
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        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}")
            return quant_config
        return None
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    def with_hf_config(
        self,
        hf_config: PretrainedConfig,
        architectures: Optional[list[str]] = None,
    ) -> "VllmConfig":
        if architectures is not None:
            hf_config = copy.deepcopy(hf_config)
            hf_config.architectures = architectures

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

        return replace(self, model_config=model_config)

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    def __post_init__(self):
        """Verify configs are valid & consistent with each other.
        """
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        if self.model_config is not None:
            self.model_config.verify_async_output_proc(self.parallel_config,
                                                       self.speculative_config,
                                                       self.device_config)
            self.model_config.verify_with_parallel_config(self.parallel_config)

        if self.cache_config is not None:
            self.cache_config.verify_with_parallel_config(self.parallel_config)
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        if self.lora_config:
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            self.lora_config.verify_with_cache_config(self.cache_config)
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            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)
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            self.lora_config.verify_lora_support()
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        if self.prompt_adapter_config:
            self.prompt_adapter_config.verify_with_model_config(
                self.model_config)
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        if self.quant_config is None and \
            self.model_config is not None and self.load_config is not None:
            self.quant_config = VllmConfig._get_quantization_config(
                self.model_config, self.load_config)
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        from vllm.platforms import current_platform
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        if self.scheduler_config is not None and \
            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):
3841
            logger.warning_once(
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                "Turing devices tensor cores do not support float32 matmul. "
                "To workaround this limitation, vLLM will set 'ieee' input "
                "precision for chunked prefill triton kernels.")

3846
        if self.compilation_config is None:
3847
            self.compilation_config = CompilationConfig()
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        if envs.VLLM_USE_V1 and self.model_config is not None and \
            not self.model_config.enforce_eager:
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            # NOTE(woosuk): Currently, we use inductor because the piecewise
            # CUDA graphs do not work properly with the custom CUDA kernels.
            # FIXME(woosuk): Disable inductor to reduce the compilation time
            # and avoid any potential issues with the inductor.
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            # FIXME(rob): Add function to set all of these.
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            self.compilation_config.custom_ops = ["none"]
            self.compilation_config.use_cudagraph = True
            self.compilation_config.use_inductor = True
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            self.compilation_config.cudagraph_num_of_warmups = 1
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            self.compilation_config.pass_config.enable_fusion = False
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            self.compilation_config.pass_config.enable_noop = False
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            self.compilation_config.level = CompilationLevel.PIECEWISE
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            self.compilation_config.set_splitting_ops_for_v1()
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3864
        self._set_cudagraph_sizes()
3865

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        if self.cache_config is not None and \
            self.cache_config.cpu_offload_gb > 0 and \
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            self.compilation_config.level != CompilationLevel.NO_COMPILATION \
                and not envs.VLLM_USE_V1:
3870
            logger.warning(
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                "CPU offload is not supported with `torch.compile` in v0 yet."
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                " Disabling `torch.compile`.")
            self.compilation_config.level = CompilationLevel.NO_COMPILATION

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        if ((not envs.VLLM_USE_V1) and self.lora_config is not None
                and self.compilation_config.level
                != CompilationLevel.NO_COMPILATION):
            logger.warning(
                "LoRA for V0 is not supported with `torch.compile` yet. "
                "Disabling `torch.compile`.")
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            self.compilation_config.level = CompilationLevel.NO_COMPILATION

3883

3884
        if self.model_config and self.model_config.use_mla and \
3885
            not (current_platform.is_cuda() or current_platform.is_rocm()):
3886
            logger.info(
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                "MLA is enabled on a non-GPU platform; forcing chunked "
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                "prefill and prefix caching to be disabled.")
            self.scheduler_config.enable_chunked_prefill = False
            self.scheduler_config.chunked_prefill_enabled = False
            self.scheduler_config.max_num_batched_tokens = max(
                self.scheduler_config.max_model_len,
                _DEFAULT_MAX_NUM_BATCHED_TOKENS)

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

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        current_platform.check_and_update_config(self)

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

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    def _set_cudagraph_sizes(self):
        """
        cudagraph batchsize padding logic:

        `[1, 2, 4] + [8 * i for i in range(1, 1025)]` is a list of all possible
        batch sizes that cudagraph will capture.

        Depending on the engine's configuration of `max_num_seqs`, the
        candidate batch sizes to capture cudagraph will shrink to the subset
        which just cover the range of `[1, max_num_seqs]`. In the common case,
        `max_num_seqs` is 256, and the cudagraph batch sizes will be
        `[1, 2, 4, 8, 16, 24, 32, 40, ..., 256]`.

        However, if users specify the cudagraph capture sizes through
        compilation config, we will use the specified sizes instead.

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        In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
        will be the final sizes to capture cudagraph (in descending order).
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        During runtime, if batchsize is larger than
3923
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
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        no cudagraph will be used.
        If the batch size is no larger than
3926
        `vllm_config.compilation_config.cudagraph_capture_sizes`,
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        we can quickly find the padded graph size for a given batch size by
        looking up `vllm_config.compilation_config.bs_to_padded_graph_size`.
        """

        # calculate the default `batch_size_capture_list`
        if not envs.VLLM_USE_V1:
            batch_size_capture_list = []
            max_batchsize_to_capture = 0
            if self.scheduler_config is not None and \
                self.model_config is not None and \
                    not self.model_config.enforce_eager:

                possible_sizes = [1, 2, 4] + [8 * i for i in range(1, 1025)]
                # find the minimum size that is larger than max_num_seqs,
                # which then becomes the max_batchsize_to_capture
                larger_sizes = [
                    x for x in possible_sizes
                    if x >= self.scheduler_config.max_num_seqs
                ]
                if larger_sizes:
                    max_batchsize_to_capture = larger_sizes[0]
                else:
                    max_batchsize_to_capture = possible_sizes[-1]

                # filter out the sizes that are
                # larger than max_batchsize_to_capture
                batch_size_capture_list = [
                    size for size in possible_sizes
                    if size <= max_batchsize_to_capture
                ]
        else:
            batch_size_capture_list = []
            if self.model_config is not None and \
                not self.model_config.enforce_eager:
                batch_size_capture_list = [1, 2, 4
                                           ] + [i for i in range(8, 513, 8)]
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                max_num_tokens = self.scheduler_config.max_num_batched_tokens
                batch_size_capture_list = [
                    size for size in batch_size_capture_list
                    if size <= max_num_tokens
                ]
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        self.compilation_config.init_with_cudagraph_sizes(
            batch_size_capture_list)

3972
    def __str__(self):
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        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"override_neuron_config={self.model_config.override_neuron_config},"
            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},"
            f" pipeline_parallel_size={self.parallel_config.pipeline_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"decoding_config={self.decoding_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"num_scheduler_steps={self.scheduler_config.num_scheduler_steps}, "
            f"multi_step_stream_outputs={self.scheduler_config.multi_step_stream_outputs}, "  # noqa
            f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
            f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, "  # noqa
            f"use_async_output_proc={self.model_config.use_async_output_proc}, "
4003
            f"disable_mm_preprocessor_cache={self.model_config.disable_mm_preprocessor_cache!r}, "  # noqa
4004
            f"mm_processor_kwargs={self.model_config.mm_processor_kwargs}, "
4005
4006
            f"pooler_config={self.model_config.pooler_config!r}, "
            f"compilation_config={self.compilation_config!r}")
4007
4008
4009
4010
4011
4012


_current_vllm_config: Optional[VllmConfig] = None


@contextmanager
4013
def set_current_vllm_config(vllm_config: VllmConfig, check_compile=False):
4014
    """
4015
    Temporarily set the current vLLM config.
4016
    Used during model initialization.
4017
    We save the current vLLM config in a global variable,
4018
    so that all modules can access it, e.g. custom ops
4019
    can access the vLLM config to determine how to dispatch.
4020
4021
4022
4023
4024
4025
4026
4027
    """
    global _current_vllm_config
    old_vllm_config = _current_vllm_config
    from vllm.compilation.counter import compilation_counter
    num_models_seen = compilation_counter.num_models_seen
    try:
        _current_vllm_config = vllm_config
        yield
4028
4029
4030
    except Exception:
        raise
    else:
4031
4032
4033
4034
        logger.debug("enabled custom ops: %s",
                     vllm_config.compilation_config.enabled_custom_ops)
        logger.debug("disabled custom ops: %s",
                     vllm_config.compilation_config.disabled_custom_ops)
4035
4036
        if check_compile and \
            vllm_config.compilation_config.level == CompilationLevel.PIECEWISE \
4037
4038
4039
4040
4041
4042
4043
4044
4045
            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"
4046
                " if you want it to be supported.",
4047
                vllm_config.model_config.model)
4048
    finally:
4049
4050
4051
4052
4053
4054
4055
4056
        _current_vllm_config = old_vllm_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.
4057
        logger.warning("Current vLLM config is not set.")
4058
4059
4060
        from vllm.config import VllmConfig
        return VllmConfig()
    return _current_vllm_config