vllm.config.attention ¶

@config
class AttentionConfig:
    """Configuration for attention mechanisms in vLLM."""

    backend: AttentionBackendEnum | None = None
    """Attention backend to use. Use "auto" or None for automatic selection."""

    minimax_m3_msa_decode_backend: MiniMaxM3MSADecodeBackend = "triton"
    """Sparse decode kernel used by the MiniMax M3 MSA backend."""

    backend_per_kind: dict[str, AttentionBackendEnum] = field(default_factory=dict)
    """Per-KV-cache-group attention backend overrides, keyed by
    `KVCacheSpecKind` (e.g. `{"mla_attention": "FLASHINFER_MLA",
    "sliding_window_mla": "TRITON_MLA"}`). This lets a model that splits its
    layers across multiple KV-cache groups (e.g. interleaved full and
    sliding-window attention) use a different backend per group.

    An entry overrides `backend` for layers of the matching kind; kinds not
    listed fall back to `backend` (or automatic selection). A selected backend
    that is invalid for that kind raises at startup."""

    flash_attn_version: Literal[2, 3, 4] | None = None
    """Force vllm to use a specific flash-attention version (2, 3, or 4).
    Only valid when using the flash-attention backend."""

    use_prefill_decode_attention: bool = False
    """Use separate prefill and decode kernels for attention instead of
    the unified triton kernel."""

    flash_attn_max_num_splits_for_cuda_graph: int = 32
    """Flash Attention max number splits for cuda graph decode."""

    tq_max_kv_splits_for_cuda_graph: int = 32
    """TurboQuant max NUM_KV_SPLITS for cuda graph decode.
    Fixes the split count so grid dimensions are constant across captures,
    and buffers can be pre-allocated to avoid inflating the memory estimate."""

    use_trtllm_attention: bool | None = None
    """If set to True/False, use or don't use the TRTLLM attention backend
    in flashinfer. If None, auto-detect the attention backend in flashinfer."""

    disable_flashinfer_q_quantization: bool = False
    """If set, when using fp8 kv, do not quantize Q to fp8."""

    mla_prefill_backend: MLAPrefillBackendEnum | None = None
    """MLA prefill backend to use. If None, will be selected automatically.
    Valid options: FLASH_ATTN (FA3/FA4), FLASHINFER, TRTLLM_RAGGED."""

    use_prefill_query_quantization: bool = False
    """If set, quantize query for attention in prefill."""

    use_fp4_indexer_cache: bool | None = None
    """Deprecated alias for `indexer_kv_dtype`; use that instead. True maps to
    `mxfp4`, False is a no-op (it selected the model default already)."""

    indexer_kv_dtype: IndexerKVDType = "auto"
    """Data type for the sparse-attention indexer K cache. "auto" picks the
    model's default (bf16 for MiniMax M3, fp8 for the DeepSeek sparse
    indexer). Quantized formats (fp8, mxfp4, nvfp4) require indexer kernel
    support in the backend."""

    use_non_causal: bool = False
    """Whether to use non-causal (bidirectional) attention."""

    sparse_mla_force_mqa: bool = False
    """Force sparse MLA to use forward_mqa for all requests, including prefill.
    When False (default), pure prefill batches use forward_mha when implemented.
    Set to True to always use the MQA path."""

    flex_attn_block_m: int | None = None
    """Triton kernel BLOCK_M tile size for flex attention.
    Must be a power of 2 >= 16. If None and VLLM_BATCH_INVARIANT=1,
    defaults to 16."""

    flex_attn_block_n: int | None = None
    """Triton kernel BLOCK_N tile size for flex attention.
    Must be a power of 2 >= 16. If None and VLLM_BATCH_INVARIANT=1,
    defaults to 16."""

    flex_attn_q_block_size: int | None = None
    """Logical Q block size for the flex attention block mask.
    Must be a power of 2 and divisible by flex_attn_block_m.
    If None, uses 16 for paged KV attention on PyTorch >= 2.9, and 128
    for encoder-only attention or older PyTorch versions."""

    flex_attn_kv_block_size: int | None = None
    """Logical KV block size for the flex attention block mask.
    Must be a power of 2 and divisible by flex_attn_block_n.
    If None, uses the KV cache block size for paged KV attention on
    PyTorch >= 2.9, and 128 for encoder-only attention or older PyTorch
    versions."""

    def __post_init__(self) -> None:
        msa_aliases: dict[AttentionBackendEnum, MiniMaxM3MSADecodeBackend] = {
            AttentionBackendEnum.CUTLASS_MSA: "cutlass",
            AttentionBackendEnum.TRITON_MSA: "triton",
        }
        if self.backend in msa_aliases:
            self.minimax_m3_msa_decode_backend = msa_aliases[self.backend]
            # The alias selects only MiniMax's sparse decode kernel. Dense
            # layers still use the platform's normal automatic backend.
            self.backend = None

        if self.use_fp4_indexer_cache is not None:
            logger.warning(
                "use_fp4_indexer_cache is deprecated and will be removed in "
                "v0.19. Use indexer_kv_dtype instead (True -> 'mxfp4')."
            )
            if self.use_fp4_indexer_cache:
                if self.indexer_kv_dtype not in ("auto", "mxfp4"):
                    raise ValueError(
                        "use_fp4_indexer_cache=True conflicts with "
                        f"indexer_kv_dtype={self.indexer_kv_dtype!r}. Set only "
                        "indexer_kv_dtype."
                    )
                self.indexer_kv_dtype = "mxfp4"

    def resolve_indexer_kv_dtype(self, default: IndexerKVDType) -> IndexerKVDType:
        """Resolve `indexer_kv_dtype`, substituting `default` for "auto"."""
        if self.indexer_kv_dtype == "auto":
            return default
        return self.indexer_kv_dtype

    def compute_hash(self) -> str:
        """
        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.
        """
        from vllm.config.utils import get_hash_factors, hash_factors

        # Folded into indexer_kv_dtype by __post_init__.
        ignored_factors: set[str] = {"use_fp4_indexer_cache"}
        factors = get_hash_factors(self, ignored_factors)
        return hash_factors(factors)

    @field_validator("backend", mode="before")
    @classmethod
    def validate_backend_before(cls, value: Any) -> Any:
        """Enable parsing of the `backend` enum type from string.

        The special value "auto" is treated as None, which triggers
        automatic backend selection.
        """
        if isinstance(value, str):
            if value.lower() == "auto":
                return None
            return AttentionBackendEnum[value.upper()]
        return value

    @field_validator("mla_prefill_backend", mode="before")
    @classmethod
    def validate_mla_prefill_backend_before(cls, value: Any) -> Any:
        """Enable parsing of the `mla_prefill_backend` enum type from string."""
        if isinstance(value, str):
            return MLAPrefillBackendEnum[value.upper()]
        return value

    @field_validator("backend_per_kind", mode="before")
    @classmethod
    def validate_backend_per_kind_before(cls, value: Any) -> Any:
        """Parse the `backend_per_kind` map from strings.

        Keys must be valid `KVCacheSpecKind` values; values are parsed like
        `backend` (enum name, case-insensitive).
        """
        from vllm.v1.kv_cache_interface import KVCacheSpecKind

        if not isinstance(value, dict):
            return value
        valid_kinds = {kind.value for kind in KVCacheSpecKind}
        parsed: dict[str, AttentionBackendEnum] = {}
        for kind, backend in value.items():
            if kind not in valid_kinds:
                raise ValueError(
                    f"Unknown KV cache group kind '{kind}' in "
                    f"backend_per_kind. Valid kinds are: "
                    f"{', '.join(sorted(valid_kinds))}."
                )
            if isinstance(backend, str):
                backend = AttentionBackendEnum[backend.upper()]
            parsed[kind] = backend
        return parsed