bi_150-vllm/vllm/v1/attention/ops/flashmla.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from: https://github.com/deepseek-ai/FlashMLA/blob/main/flash_mla/flash_mla_interface.py

import torch

from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm import _custom_ops as ops

logger = init_logger(__name__)

if current_platform.is_cuda():
    try:
        import vllm._flashmla_C  # noqa: F401

        _flashmla_C_AVAILABLE = True
    except ImportError:
        _flashmla_C_AVAILABLE = False
else:
    _flashmla_C_AVAILABLE = False

if current_platform.is_cuda():
    try:
        import vllm._flashmla_extension_C  # noqa: F401

        _flashmla_extension_C_AVAILABLE = True
    except ImportError:
        _flashmla_extension_C_AVAILABLE = False
else:
    _flashmla_extension_C_AVAILABLE = False


def _is_flashmla_available() -> tuple[bool, str | None]:
    if not _flashmla_C_AVAILABLE:
        return (
            False,
            "vllm._flashmla_C is not available, likely was not "
            "compiled due to insufficient nvcc version or a supported arch "
            "was not in the list of target arches to compile for.",
        )
    if not _flashmla_extension_C_AVAILABLE:
        return (
            False,
            "vllm._flashmla_extension_C is not available, likely "
            "was not compiled due to a build error.",
        )

    return True, None


def is_flashmla_dense_supported() -> tuple[bool, str | None]:
    """
    Return: is_supported_flag, unsupported_reason (optional).
    """
    is_available, maybe_reason = _is_flashmla_available()
    if not is_available:
        return False, maybe_reason
    if not current_platform.is_device_capability_family(90):
        return False, "FlashMLA Dense is only supported on Hopper devices."
    return True, None


def is_flashmla_sparse_supported() -> tuple[bool, str | None]:
    """
    Return: is_supported_flag, unsupported_reason (optional).
    """
    is_available, maybe_reason = _is_flashmla_available()
    if not is_available:
        return False, maybe_reason
    if not (
        current_platform.is_device_capability_family(90)
        or current_platform.is_device_capability_family(100)
    ):
        return (
            False,
            "FlashMLA Sparse is only supported on Hopper and Blackwell devices.",
        )
    return True, None


def _raise_flashmla_unavailable(*_args, **_kwargs):
    _, reason = _is_flashmla_available()
    raise RuntimeError(reason or "FlashMLA is not available")


if _is_flashmla_available()[0]:
    from vllm.third_party.flashmla.flash_mla_interface import (  # noqa: F401
        FlashMLASchedMeta,
        flash_attn_varlen_func,
        flash_attn_varlen_kvpacked_func,
        flash_attn_varlen_qkvpacked_func,
        flash_mla_sparse_fwd,
        flash_mla_with_kvcache,
        get_mla_metadata,
    )
else:

    class FlashMLASchedMeta:  # type: ignore[no-redef]
        pass

    flash_attn_varlen_func = _raise_flashmla_unavailable  # type: ignore[assignment]
    flash_attn_varlen_kvpacked_func = _raise_flashmla_unavailable  # type: ignore[assignment]
    flash_attn_varlen_qkvpacked_func = _raise_flashmla_unavailable  # type: ignore[assignment]
    flash_mla_sparse_fwd = _raise_flashmla_unavailable  # type: ignore[assignment]
    flash_mla_with_kvcache = _raise_flashmla_unavailable  # type: ignore[assignment]
    get_mla_metadata = _raise_flashmla_unavailable  # type: ignore[assignment]


def get_mla_metadata_dense_fp8(
    cache_seqlens: torch.Tensor,
    num_q_tokens_per_head_k: int,
    num_heads_k: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    if not _is_flashmla_available()[0]:
        _raise_flashmla_unavailable()
    return torch.ops._flashmla_extension_C.get_mla_decoding_metadata_dense_fp8(
        cache_seqlens,
        num_q_tokens_per_head_k,
        num_heads_k,
    )


def flash_mla_with_kvcache_fp8(
    q: torch.Tensor,
    k_cache: torch.Tensor,
    block_table: torch.Tensor,
    cache_seqlens: torch.Tensor,
    head_dim_v: int,
    tile_scheduler_metadata: torch.Tensor,
    num_splits: torch.Tensor,
    softmax_scale: float | None = None,
    causal: bool = False,
    descale_q: torch.Tensor | None = None,
    descale_k: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
    if not _is_flashmla_available()[0]:
        _raise_flashmla_unavailable()
    if softmax_scale is None:
        softmax_scale = q.shape[-1] ** (-0.5)
    out, softmax_lse = torch.ops._flashmla_extension_C.fwd_kvcache_mla_fp8(
        q,
        k_cache,
        head_dim_v,
        cache_seqlens,
        block_table,
        softmax_scale,
        causal,
        tile_scheduler_metadata,
        num_splits,
        descale_q,
        descale_k,
    )
    return out, softmax_lse
def flash_mla_sparse_prefill(
    q: torch.Tensor,
    kv: torch.Tensor,
    indices: torch.Tensor,
    sm_scale: float,
    d_v: int = 512,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Sparse attention prefill kernel

    Args:
    - q: [s_q, h_q, d_qk], bfloat16
    - kv: [s_kv, h_kv, d_qk], bfloat16
    - indices: [s_q, h_kv, topk], int32.
        Invalid indices should be set to -1 or numbers >= s_kv
    - sm_scale: float
    - d_v: The dimension of value vectors. Can only be 512

    Returns:
    - (output, max_logits, lse)
        About the definition of output,
        max_logits and lse, please refer to README.md
    - output: [s_q, h_q, d_v], bfloat16
    - max_logits:  [s_q, h_q], float
    - lse: [s_q, h_q], float, 2-based log-sum-exp
    """
    results = ops.sparse_prefill_fwd(q, kv, indices,sm_scale, d_v)
    return results

#
# TODO: Add fake functions
#
# @register_fake("_flashmla_C::get_mla_metadata")
# def _get_mla_metadata_fake(....) -> Tuple[torch.Tensor, torch.Tensor]:
#     return ....
#
# @register_fake("_flashmla_C::fwd_kvcache_mla")
# def _fwd_kvcache_mla_fake(....) -> Tuple[torch.Tensor, torch.Tensor]:
#     return ....
#