enginex-mthreads-vllm/vllm/v1/attention/backends/mla/flashinfer_mla.py

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

from typing import ClassVar

import torch
from flashinfer.decode import trtllm_batch_decode_with_kv_cache_mla

from vllm.attention.backends.abstract import (
    AttentionLayer,
    AttentionType,
    MultipleOf,
)
from vllm.config.cache import CacheDType
from vllm.logger import init_logger
from vllm.platforms.interface import DeviceCapability
from vllm.v1.attention.backends.mla.common import (
    MLACommonBackend,
    MLACommonImpl,
    MLACommonMetadata,
    MLACommonMetadataBuilder,
    QueryLenSupport,
)
from vllm.v1.attention.backends.utils import AttentionCGSupport, KVCacheLayoutType

logger = init_logger(__name__)

FLASHINFER_MLA_WORKSPACE_BUFFER_SIZE = 128 * 1024 * 1024


class FlashInferMLAMetadataBuilder(MLACommonMetadataBuilder[MLACommonMetadata]):
    _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH
    query_len_support: ClassVar[QueryLenSupport] = QueryLenSupport.UNIFORM


class FlashInferMLABackend(MLACommonBackend):
    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
        "auto",
        "fp8",
        "fp8_e4m3",
    ]

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

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

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

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

    @classmethod
    def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
        return capability.major == 10

    @classmethod
    def get_required_kv_cache_layout(cls) -> "KVCacheLayoutType | None":
        return "HND"


g_fi_workspace = torch.zeros(
    FLASHINFER_MLA_WORKSPACE_BUFFER_SIZE,
    dtype=torch.uint8,
    device="cuda",
)


class FlashInferMLAImpl(MLACommonImpl[MLACommonMetadata]):
    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
        **mla_args,
    ) -> None:
        super().__init__(
            num_heads,
            head_size,
            scale,
            num_kv_heads,
            alibi_slopes,
            sliding_window,
            kv_cache_dtype,
            logits_soft_cap,
            attn_type,
            kv_sharing_target_layer_name,
            **mla_args,
        )

        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
        if any(unsupported_features):
            raise NotImplementedError(
                "FlashInferMLAImpl 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 "
                "FlashInferMLAImpl"
            )

        self._workspace_buffer = g_fi_workspace
        self.bmm1_scale: float | None = None
        self.bmm2_scale: float | None = None

    def _forward_decode(
        self,
        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
        kv_c_and_k_pe_cache: torch.Tensor,
        attn_metadata: MLACommonMetadata,
        layer: AttentionLayer,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        assert kv_c_and_k_pe_cache.numel() > 0
        assert attn_metadata.decode is not None

        if isinstance(q, tuple):
            q_nope, q_pe = q
            q = torch.cat([q_nope, q_pe], dim=-1)

        # trtllm API requires extra dimension q_len_per_request for MTP
        if attn_metadata.num_decode_tokens % attn_metadata.num_decodes != 0:
            logger.warning_once(
                """FlashInferMLAImpl got a query of uneven length.
                This usually indicates an issue in batch reordering
                or incorrect setup in dummy_run."""
            )
            q = q.unsqueeze(1)
        else:
            q = q.view(attn_metadata.num_decodes, -1, q.shape[-2], q.shape[-1])

        if self.bmm1_scale is None:
            self.bmm1_scale = layer._q_scale_float * layer._k_scale_float * self.scale
        if self.bmm2_scale is None:
            self.bmm2_scale = layer._v_scale_float

        o = trtllm_batch_decode_with_kv_cache_mla(
            query=q,
            kv_cache=kv_c_and_k_pe_cache.unsqueeze(1),
            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=attn_metadata.decode.block_table,
            seq_lens=attn_metadata.decode.seq_lens,
            max_seq_len=attn_metadata.max_seq_len,
            bmm1_scale=self.bmm1_scale,
            bmm2_scale=self.bmm2_scale,
        )

        # Flatten the output for consistent shape
        o = o.view(-1, o.shape[-2], o.shape[-1])

        # TODO: Return LSE pending support from Flashinfer API:
        # https://github.com/flashinfer-ai/flashinfer/pull/1566
        return o, None