vllm.config.multimodal ¶

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

    limit_per_prompt: dict[str, int] = field(default_factory=dict)
    """The maximum number of input items allowed per prompt for each modality.
    Defaults to 1 (V0) or 999 (V1) for each modality.

    For example, to allow up to 16 images and 2 videos per prompt:
    `{"image": 16, "video": 2}`"""
    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
    """Additional args passed to process media inputs, keyed by modalities.
    For example, to set num_frames for video, set
    `--media-io-kwargs '{"video": {"num_frames": 40} }'`"""
    mm_processor_kwargs: Optional[dict[str, object]] = None
    """Arguments to be forwarded to the model's processor for multi-modal data,
    e.g., image processor. Overrides for the multi-modal processor obtained
    from `transformers.AutoProcessor.from_pretrained`.

    The available overrides depend on the model that is being run.

    For example, for Phi-3-Vision:
    `{"num_crops": 4}`."""
    mm_processor_cache_gb: float = 4
    """The size (in GiB) of the multi-modal processor cache, which is used to
    avoid re-processing past multi-modal inputs.

    This cache is duplicated for each API process and engine core process,
    resulting in a total memory usage of
    `mm_processor_cache_gb * (api_server_count + data_parallel_size)`.

    Set to `0` to disable this cache completely (not recommended)."""
    mm_processor_cache_type: MMCacheType = "lru"
    """Type of cache to use for the multi-modal preprocessor/mapper. If `shm`,
    use shared memory FIFO cache. If `lru`, use mirrored LRU cache."""
    mm_shm_cache_max_object_size_mb: int = 128
    """Size limit (in MiB) for each object stored in the multi-modal processor
    shared memory cache. Only effective when `mm_processor_cache_type` is
    `"shm"`."""
    mm_encoder_tp_mode: MMEncoderTPMode = "weights"
    """Indicates how to optimize multi-modal encoder inference using tensor
    parallelism (TP).

    - `"weights"`: Within the same vLLM engine, split the weights of
        each layer across TP ranks. (default TP behavior)\n
    - `"data"`: Within the same vLLM engine, split the batched input data
        across TP ranks to process the data in parallel, while hosting
        the full weights on each TP rank.
        This batch-level DP is not to be confused with API request-level
        DP (which is controlled by `--data-parallel-size`).
        This is only supported on a per-model basis and falls back to
        `"weights"` if the encoder does not support DP."""
    interleave_mm_strings: bool = False
    """Enable fully interleaved support for multimodal prompts, while using
    --chat-template-content-format=string."""
    skip_mm_profiling: bool = False
    """When enabled, skips multimodal memory profiling and only profiles with
    language backbone model during engine initialization.

    This reduces engine startup time but shifts the responsibility to users for
    estimating the peak memory usage of the activation of multimodal encoder and
    embedding cache."""
    video_pruning_rate: Optional[float] = None
    """Sets pruning rate for video pruning via Efficient Video Sampling.
    Value sits in range [0;1) and determines fraction of media tokens
    from each video to be pruned.
    """

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

    def get_limit_per_prompt(self, modality: str) -> int:
        """
        Get the maximum number of input items allowed per prompt
        for the given modality.
        """
        return self.limit_per_prompt.get(
            modality,
            999 if envs.VLLM_USE_V1 else 1,
        )

    def merge_mm_processor_kwargs(
        self,
        inference_kwargs: Mapping[str, object],
    ) -> dict[str, object]:
        """
        Get the keyword arguments to pass to the multi-modal processor
        according to the extra arguments passed during inference.
        """
        kwargs = self.mm_processor_kwargs or {}
        return kwargs | dict(inference_kwargs)

    def is_multimodal_pruning_enabled(self):
        return (self.video_pruning_rate is not None
                and self.video_pruning_rate > 0)