flash_mla_interface.py

from typing import Optional, Tuple

import torch

import flash_mla.cuda as flash_mla_cuda

def get_mla_metadata(
    cache_seqlens: torch.Tensor,
    num_q_tokens_per_head_k: int,
    num_heads_k: int,
    num_heads_q: Optional[int] = None,
    is_fp8_kvcache: bool = False,
    topk: Optional[int] = None
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Arguments:
        cache_seqlens: (batch_size), dtype torch.int32.
        num_q_tokens_per_head_k: Equals to num_q_tokens_per_q_seq * num_heads_q // num_heads_k.
        num_heads_k: The number of k heads.
        num_heads_q: The number of q heads. This argument is optional when sparse attention is not enabled
        is_fp8_kvcache: Whether the k_cache and v_cache are in fp8 format.
        topk: If not None, sparse attention will be enabled, and only tokens in the `indices` array passed to `flash_mla_with_kvcache_sm90` will be attended to.

    Returns:
        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), dtype torch.int32.
        num_splits: (batch_size + 1), dtype torch.int32.
    """
    return flash_mla_cuda.get_mla_decoding_metadata(cache_seqlens, num_q_tokens_per_head_k, num_heads_k, num_heads_q, is_fp8_kvcache, topk)


def flash_mla_with_kvcache(
    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: Optional[float] = None,
    causal: bool = False,
    is_fp8_kvcache: bool = False,
    indices: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Arguments:
        q: (batch_size, seq_len_q, num_heads_q, head_dim).
        k_cache: (num_blocks, page_block_size, num_heads_k, head_dim).
        block_table: (batch_size, max_num_blocks_per_seq), torch.int32.
        cache_seqlens: (batch_size), torch.int32.
        head_dim_v: Head dimension of v.
        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, returned by get_mla_metadata.
        num_splits: (batch_size + 1), torch.int32, returned by get_mla_metadata.
        softmax_scale: float. The scale of QK^T before applying softmax. Default to 1 / sqrt(head_dim).
        causal: bool. Whether to apply causal attention mask.
        is_fp8_kvcache: bool. Whether the k_cache and v_cache are in fp8 format. For the format of FP8 KV cache, please refer to README.md
        indices: (batch_size, seq_len_q, topk), torch.int32. If not None, sparse attention will be enabled, and only tokens in the `indices` array will be attended to. Invalid indices should be set to -1 or numbers >= total_seq_len_kv. For details about how to set up `indices`, please refer to README.md.

    Returns:
        out: (batch_size, seq_len_q, num_heads_q, head_dim_v).
        softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32.
    """
    if softmax_scale is None:
        softmax_scale = q.shape[-1] ** (-0.5)
    if indices is not None:
        assert causal == False, "causal must be `false` if sparse attention is enabled."
    out, softmax_lse = flash_mla_cuda.fwd_kvcache_mla(
        q,
        k_cache,
        head_dim_v,
        cache_seqlens,
        block_table,
        softmax_scale,
        causal,
        tile_scheduler_metadata,
        num_splits,
        is_fp8_kvcache,
        indices
    )
    return out, softmax_lse


def flash_mla_sparse_fwd(
    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 = flash_mla_cuda.sparse_prefill_fwd(
        q, kv, indices, sm_scale, d_v
    )
    return results


def _flash_attn_varlen_forward(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    cu_seqlens_qo: torch.Tensor,
    cu_seqlens_kv: torch.Tensor,
    max_seqlen_qo: int,
    max_seqlen_kv: int,
    out: Optional[torch.Tensor] = None,
    lse: Optional[torch.Tensor] = None,
    causal: bool = False,
    softmax_scale: Optional[float] = None,
    is_varlen: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor]:
    qo_total_len, num_qo_heads, head_dim_qk = q.shape
    kv_total_len, num_kv_heads, head_dim_vo = v.shape

    mask_mode_code = 1 if causal else 0
    if softmax_scale is None:
        softmax_scale = head_dim_qk ** (-0.5)

    if out is None:
        out = torch.empty(qo_total_len, num_qo_heads, head_dim_vo, device=q.device, dtype=q.dtype)
    if lse is None:
        # Make lse contiguous on seqlen dim
        lse = torch.empty(num_qo_heads, qo_total_len, device=q.device, dtype=torch.float32).T

    workspace_buffer = torch.empty(32 * 1024 * 1024, dtype=torch.uint8, device=q.device)
    flash_mla_cuda.dense_prefill_fwd(
        workspace_buffer,
        q,
        k,
        v,
        cu_seqlens_qo,
        cu_seqlens_kv,
        out,
        lse,
        mask_mode_code,
        softmax_scale,
        max_seqlen_qo,
        max_seqlen_kv,
        is_varlen,
    )

    return out, lse


def _flash_attn_varlen_backward(
    do: torch.Tensor,
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    out: torch.Tensor,
    lse: torch.Tensor,
    cu_seqlens_qo: torch.Tensor,
    cu_seqlens_kv: torch.Tensor,
    max_seqlen_qo: int,
    max_seqlen_kv: int,
    dq: Optional[torch.Tensor] = None,
    dk: Optional[torch.Tensor] = None,
    dv: Optional[torch.Tensor] = None,
    causal: bool = False,
    softmax_scale: Optional[float] = None,
    is_varlen: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    qo_total_len, num_qo_heads, head_dim_qk = q.shape
    kv_total_len, num_kv_heads, head_dim_vo = v.shape

    # TODO: fix bwd GQA
    if num_qo_heads != num_kv_heads:
        raise ValueError(f"SM100 bwd doesn't support GQA now. num_qo_heads: {num_qo_heads}, num_kv_heads: {num_kv_heads}.")

    mask_mode_code = 1 if causal else 0
    if softmax_scale is None:
        softmax_scale = head_dim_qk ** (-0.5)

    if dq is None:
        dq = torch.empty(qo_total_len, num_qo_heads, head_dim_qk, device=q.device, dtype=q.dtype)
    if dk is None:
        dk = torch.empty(kv_total_len, num_kv_heads, head_dim_qk, device=q.device, dtype=q.dtype)
    if dv is None:
        dv = torch.empty(kv_total_len, num_kv_heads, head_dim_vo, device=q.device, dtype=q.dtype)

    max_seqlen_qo_aligned = (max_seqlen_qo + 7) // 8 * 8
    bs = cu_seqlens_qo.shape[0] - 1
    workspace_bytes = 0
    workspace_bytes += 4 * bs * max_seqlen_qo_aligned * num_qo_heads * head_dim_qk  # dQ_acc
    workspace_bytes += 4 * max_seqlen_qo_aligned * bs * num_qo_heads * 2  # sum_OdO and scaled_lse
    if num_qo_heads != num_kv_heads:
        workspace_bytes += 2 * kv_total_len * num_qo_heads * (head_dim_qk + head_dim_vo)  # dKV_acc
    workspace_buffer = torch.empty(workspace_bytes, dtype=torch.uint8, device=q.device)
    flash_mla_cuda.dense_prefill_bwd(
        workspace_buffer,
        do,
        q,
        k,
        v,
        out,
        lse,
        cu_seqlens_qo,
        cu_seqlens_kv,
        dq,
        dk,
        dv,
        mask_mode_code,
        softmax_scale,
        max_seqlen_qo,
        max_seqlen_kv,
        is_varlen,
    )

    return dq, dk, dv


class FlashAttnVarlenFunc(torch.autograd.Function):
    def forward(
        ctx,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        cu_seqlens_qo: torch.Tensor,
        cu_seqlens_kv: torch.Tensor,
        max_seqlen_qo: int,
        max_seqlen_kv: int,
        causal: bool = False,
        softmax_scale: Optional[float] = None,
        is_varlen: bool = True,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        out, lse = _flash_attn_varlen_forward(
            q, k, v,
            cu_seqlens_qo, cu_seqlens_kv, max_seqlen_qo, max_seqlen_kv,
            causal=causal, softmax_scale=softmax_scale,
            is_varlen=is_varlen,
        )
        ctx.save_for_backward(q, k, v, out, lse, cu_seqlens_qo, cu_seqlens_kv)
        ctx.max_seqlen_qo = max_seqlen_qo
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.causal = causal
        ctx.softmax_scale = softmax_scale
        ctx.is_varlen = is_varlen
        return out, lse

    def backward(
        ctx,
        do: torch.Tensor,
        dlse: torch.Tensor,
    ):
        del dlse  # LSE doesn't support backward currently
        q, k, v, out, lse, cu_seqlens_qo, cu_seqlens_kv = ctx.saved_tensors
        dq, dk, dv = _flash_attn_varlen_backward(
            do, q, k, v, out, lse,
            cu_seqlens_qo, cu_seqlens_kv, ctx.max_seqlen_qo, ctx.max_seqlen_kv,
            causal=ctx.causal, softmax_scale=ctx.softmax_scale,
            is_varlen=ctx.is_varlen,
        )
        return dq, dk, dv, None, None, None, None, None, None, None


def flash_attn_varlen_func(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    cu_seqlens_qo: torch.Tensor,
    cu_seqlens_kv: torch.Tensor,
    max_seqlen_qo: int,
    max_seqlen_kv: int,
    dropout_p: float = 0.0,
    softmax_scale: Optional[float] = None,
    causal: bool = False,
    deterministic: bool = False,
    is_varlen: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor]:
    assert dropout_p == 0.0
    assert not deterministic
    return FlashAttnVarlenFunc.apply(
        q, k, v,
        cu_seqlens_qo, cu_seqlens_kv, max_seqlen_qo, max_seqlen_kv,
        causal, softmax_scale, is_varlen,
    )


def flash_attn_varlen_qkvpacked_func(
    qkv: torch.Tensor,
    cu_seqlens: torch.Tensor,
    max_seqlen: int,
    head_dim_qk: int,
    dropout_p: float = 0.0,
    softmax_scale: Optional[float] = None,
    causal: bool = False,
    deterministic: bool = False,
    is_varlen: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor]:
    assert dropout_p == 0.0
    assert not deterministic
    return FlashAttnVarlenFunc.apply(
        qkv[:, :, :head_dim_qk], qkv[:, :, head_dim_qk:head_dim_qk * 2], qkv[:, :, head_dim_qk * 2:],
        cu_seqlens, cu_seqlens, max_seqlen, max_seqlen,
        causal, softmax_scale, is_varlen,
    )


def flash_attn_varlen_kvpacked_func(
    q: torch.Tensor,
    kv: torch.Tensor,
    cu_seqlens_qo: torch.Tensor,
    cu_seqlens_kv: torch.Tensor,
    max_seqlen_qo: int,
    max_seqlen_kv: int,
    head_dim_qk: int,
    dropout_p: float = 0.0,
    softmax_scale: Optional[float] = None,
    causal: bool = False,
    deterministic: bool = False,
    is_varlen: bool = True,
) -> Tuple[torch.Tensor, torch.Tensor]:
    assert dropout_p == 0.0
    assert not deterministic
    return FlashAttnVarlenFunc.apply(
        q, kv[:, :, :head_dim_qk], kv[:, :, head_dim_qk:],
        cu_seqlens_qo, cu_seqlens_kv, max_seqlen_qo, max_seqlen_kv,
        causal, softmax_scale, is_varlen,
    )