vllm.v1.attention.backends.mla.flashinfer_mla_sparse ¶

class FlashInferMLASparseBackend(AttentionBackend):
    """FlashInfer MLA backend with sparse attention support.

    This backend uses the FlashInfer TRT-LLM MLA kernel with sparse_mla_top_k
    for models like DeepSeek-V3.2 that use index-based sparse attention.
    """

    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
        "auto",
        "float16",
        "bfloat16",
        "fp8",
        "fp8_e4m3",
    ]

    @staticmethod
    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
        return [32, 64]

    @staticmethod
    def get_name() -> str:
        return "FLASHINFER_MLA_SPARSE"

    @staticmethod
    def get_impl_cls() -> type["FlashInferMLASparseImpl"]:
        return FlashInferMLASparseImpl

    @staticmethod
    def get_builder_cls() -> type["FlashInferMLASparseMetadataBuilder"]:
        return FlashInferMLASparseMetadataBuilder

    @classmethod
    def get_supported_head_sizes(cls) -> list[int]:
        return [576]

    @classmethod
    def is_mla(cls) -> bool:
        return True

    @classmethod
    def is_sparse(cls) -> bool:
        return True

    @classmethod
    def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
        # FlashInfer sparse MLA targets Blackwell (SM 10.x)
        return capability.major == 10

    @classmethod
    def supports_combination(
        cls,
        head_size: int,
        dtype: torch.dtype,
        kv_cache_dtype: CacheDType | None,
        block_size: int | None,
        use_mla: bool,
        has_sink: bool,
        use_sparse: bool,
        device_capability: DeviceCapability,
    ) -> str | None:
        # FlashInfer MLA sparse kernel requires qk_nope_head_dim in [128, 192]
        from vllm.config import get_current_vllm_config

        vllm_config = get_current_vllm_config()
        if vllm_config.model_config is not None:
            hf_text_config = vllm_config.model_config.hf_text_config
            qk_nope_head_dim = getattr(hf_text_config, "qk_nope_head_dim", 1)
            if qk_nope_head_dim not in [128, 192]:
                return (
                    "FlashInfer MLA Sparse kernel requires qk_nope_head_dim "
                    f"in [128, 192], but got {qk_nope_head_dim}"
                )
            # Check for index_topk which indicates sparse model
            if not hasattr(hf_text_config, "index_topk"):
                return "FlashInfer MLA Sparse requires model with index_topk config"
        return None

class FlashInferMLASparseImpl(SparseMLAAttentionImpl[FlashInferMLASparseMetadata]):
    """FlashInfer MLA Sparse implementation.

    Uses the TRT-LLM MLA kernel with sparse_mla_top_k parameter for
    sparse attention computation.
    """

    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: int,
        alibi_slopes: list[float] | None,
        sliding_window: int | None,
        kv_cache_dtype: str,
        logits_soft_cap: float | None,
        attn_type: str,
        kv_sharing_target_layer_name: str | None,
        # MLA Specific Arguments
        topk_indice_buffer: torch.Tensor | None = None,
        indexer: "Indexer | None" = None,
        **mla_args,
    ) -> None:
        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
        if any(unsupported_features):
            raise NotImplementedError(
                "FlashInferMLASparseImpl does not support one of the following: "
                "alibi_slopes, sliding_window, logits_soft_cap"
            )

        if attn_type != AttentionType.DECODER:
            raise NotImplementedError(
                "Encoder self-attention and "
                "encoder/decoder cross-attention "
                "are not implemented for "
                "FlashInferMLASparseImpl"
            )

        self.num_heads = num_heads
        self.head_size = head_size
        self.scale = float(scale)
        self.num_kv_heads = num_kv_heads
        self.kv_cache_dtype = kv_cache_dtype

        # MLA-specific dimensions
        self.kv_lora_rank: int = mla_args["kv_lora_rank"]
        self.qk_nope_head_dim: int = mla_args["qk_nope_head_dim"]
        self.qk_rope_head_dim: int = mla_args["qk_rope_head_dim"]

        assert indexer is not None, "Indexer required for sparse MLA"
        self.topk_indices_buffer: torch.Tensor | None = indexer.topk_indices_buffer

        self._workspace_buffer: torch.Tensor | None = None
        self.bmm1_scale: float | None = None
        self.bmm2_scale: float | None = None

        # fp8 query quantization is required when using fp8 kv_cache,
        # as the TRTLLM-GEN sparse MLA kernel requires matching dtypes
        # for query and kv_cache (mixed bf16+fp8 is not supported).
        self.supports_quant_query_input = True

    def forward_mqa(
        self,
        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: FlashInferMLASparseMetadata,
        layer: AttentionLayer,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        if isinstance(q, tuple):
            q = torch.cat(q, dim=-1)

        num_actual_toks = q.shape[0]

        assert self.topk_indices_buffer is not None
        topk_indices = self.topk_indices_buffer[:num_actual_toks]

        topk_indices_physical, seq_lens = triton_convert_req_index_to_global_index(
            attn_metadata.req_id_per_token[:num_actual_toks],
            attn_metadata.block_table,
            topk_indices,
            BLOCK_SIZE=attn_metadata.block_size,
            NUM_TOPK_TOKENS=topk_indices.shape[1],
            return_valid_counts=True,
        )

        if self._workspace_buffer is None:
            self._workspace_buffer = _get_workspace_buffer(q.device)

        if self.bmm1_scale is None:
            self.bmm1_scale = self.scale
            if is_quantized_kv_cache(self.kv_cache_dtype):
                self.bmm1_scale *= layer._q_scale_float * layer._k_scale_float
        if self.bmm2_scale is None:
            self.bmm2_scale = 1.0
            if is_quantized_kv_cache(self.kv_cache_dtype):
                self.bmm2_scale *= layer._k_scale_float

        o = trtllm_batch_decode_with_kv_cache_mla(
            query=q.unsqueeze(1),
            kv_cache=kv_c_and_k_pe_cache,
            workspace_buffer=self._workspace_buffer,
            qk_nope_head_dim=self.qk_nope_head_dim,
            kv_lora_rank=self.kv_lora_rank,
            qk_rope_head_dim=self.qk_rope_head_dim,
            block_tables=topk_indices_physical.unsqueeze(1),
            seq_lens=seq_lens,
            max_seq_len=attn_metadata.topk_tokens,
            bmm1_scale=self.bmm1_scale,
            bmm2_scale=self.bmm2_scale,
            sparse_mla_top_k=attn_metadata.topk_tokens,
        )
        return o.view(-1, o.shape[-2], o.shape[-1]), None

@dataclass
class FlashInferMLASparseMetadata(AttentionMetadata):
    """Attention metadata for FlashInfer MLA Sparse backend."""

    num_reqs: int
    max_query_len: int
    max_seq_len: int
    num_actual_tokens: int

    # Query start locations
    query_start_loc: torch.Tensor
    slot_mapping: torch.Tensor
    block_table: torch.Tensor
    req_id_per_token: torch.Tensor

    # Sequence lengths for all requests (context + query)
    seq_lens: torch.Tensor

    # Sparse-specific
    block_size: int = 64
    topk_tokens: int = 2048

class FlashInferMLASparseMetadataBuilder(
    AttentionMetadataBuilder[FlashInferMLASparseMetadata]
):
    """Builder for FlashInfer MLA Sparse attention metadata."""

    _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH

    def __init__(
        self,
        kv_cache_spec: AttentionSpec,
        layer_names: list[str],
        vllm_config: VllmConfig,
        device: torch.device,
    ) -> None:
        self.vllm_config = vllm_config
        self.layer_names = layer_names
        self.kv_cache_spec = kv_cache_spec
        self.model_config = vllm_config.model_config
        self.device = device

        self.mla_dims = get_mla_dims(self.model_config)
        self.topk_tokens = vllm_config.model_config.hf_config.index_topk

        self.req_id_per_token_buffer = torch.empty(
            (vllm_config.scheduler_config.max_num_batched_tokens,),
            dtype=torch.int32,
            device=device,
        )

    def build(
        self,
        common_prefix_len: int,
        common_attn_metadata: CommonAttentionMetadata,
        fast_build: bool = False,
    ) -> FlashInferMLASparseMetadata:
        cm = common_attn_metadata
        num_tokens = cm.num_actual_tokens

        # Build req_id_per_token mapping
        starts = np.asarray(cm.query_start_loc_cpu, dtype=np.int32)
        seg_lengths = np.diff(starts)
        req_id_per_token = np.repeat(
            np.arange(seg_lengths.shape[0], dtype=np.int32), seg_lengths
        )

        # Zero-fill for cudagraphs
        self.req_id_per_token_buffer.fill_(0)
        self.req_id_per_token_buffer[: req_id_per_token.shape[0]].copy_(
            torch.from_numpy(req_id_per_token), non_blocking=True
        )
        req_id_per_token_tensor = self.req_id_per_token_buffer[:num_tokens]

        return FlashInferMLASparseMetadata(
            num_reqs=cm.num_reqs,
            max_query_len=cm.max_query_len,
            max_seq_len=cm.max_seq_len,
            num_actual_tokens=cm.num_actual_tokens,
            query_start_loc=cm.query_start_loc,
            slot_mapping=cm.slot_mapping,
            block_table=cm.block_table_tensor,
            req_id_per_token=req_id_per_token_tensor,
            seq_lens=cm.seq_lens,
            block_size=self.kv_cache_spec.block_size,
            topk_tokens=self.topk_tokens,
        )