flash_attn_interface.py

import torch
import torch.nn as nn
from einops import rearrange

# isort: off
# We need to import the CUDA kernels after importing torch
import flash_attn_2_cuda as flash_attn_cuda

# isort: on


def _get_block_size(device, head_dim, is_dropout, is_causal):
    # This should match the block sizes in the CUDA kernel
    assert head_dim <= 256
    major, minor = torch.cuda.get_device_capability(device)
    is_sm8x = major == 8 and minor > 0  # Only include sm86 and sm89, exclude sm80 (A100)
    is_sm80 = major == 8 and minor == 0
    is_sm90 = major == 9 and minor == 0
    if head_dim <= 32:
        return 128, 128
    if head_dim <= 64:
        return (128, 128) if not is_dropout else (128, 64)
    elif head_dim <= 96:
        return (64, 64) if (is_sm8x and is_causal) else (128, 64)
    elif head_dim <= 128:
        if is_sm8x:
            return (64, 64) if (not is_dropout and is_causal) else (128, 32)
        else:
            return 128, (64 if not is_dropout else 32)
    elif head_dim <= 160:
        if is_sm8x:
            return (128, 64) if not is_causal else (64, 64)
        else:
            return 128, 32
    elif head_dim <= 192:
        return (128, 64) if not is_dropout else (64, 64)
    elif head_dim <= 224:
        return (128, 64) if (is_sm80 or is_sm90) else (64, 64)
    elif head_dim <= 256:
        return (128, 64) if is_sm80 else (64, 64)


def _flash_attn_forward(q, k, v, dropout_p, softmax_scale, causal, return_softmax):
    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
    q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
    out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda.fwd(
        q, k, v, None, dropout_p, softmax_scale, causal, return_softmax, None
    )
    return out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state


def _flash_attn_varlen_forward(
    q,
    k,
    v,
    cu_seqlens_q,
    cu_seqlens_k,
    max_seqlen_q,
    max_seqlen_k,
    dropout_p,
    softmax_scale,
    causal,
    return_softmax,
):
    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
    q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
    out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = flash_attn_cuda.varlen_fwd(
        q,
        k,
        v,
        None,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        dropout_p,
        softmax_scale,
        False,
        causal,
        return_softmax,
        None,
    )
    # if out.isnan().any() or softmax_lse.isnan().any():
    #     breakpoint()
    return out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state


def _flash_attn_backward(
    dout, q, k, v, out, softmax_lse, dq, dk, dv, dropout_p, softmax_scale, causal, rng_state=None
):
    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
    # dq, dk, dv are allocated by us so they should already be contiguous
    dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
    dq, dk, dv, softmax_d, = flash_attn_cuda.bwd(
        dout,
        q,
        k,
        v,
        out,
        softmax_lse,
        dq,
        dk,
        dv,
        dropout_p,
        softmax_scale,
        causal,
        None,
        rng_state,
    )
    return dq, dk, dv, softmax_d


def _flash_attn_varlen_backward(
    dout,
    q,
    k,
    v,
    out,
    softmax_lse,
    dq,
    dk,
    dv,
    cu_seqlens_q,
    cu_seqlens_k,
    max_seqlen_q,
    max_seqlen_k,
    dropout_p,
    softmax_scale,
    causal,
    rng_state=None,
):
    maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
    # dq, dk, dv are allocated by us so they should already be contiguous
    dout, q, k, v, out = [maybe_contiguous(x) for x in (dout, q, k, v, out)]
    dq, dk, dv, softmax_d, = flash_attn_cuda.varlen_bwd(
        dout,
        q,
        k,
        v,
        out,
        softmax_lse,
        dq,
        dk,
        dv,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        dropout_p,
        softmax_scale,
        False,
        causal,
        None,
        rng_state,
    )
    # if dk.isnan().any() or dk.isnan().any() or dv.isnan().any() or softmax_d.isnan().any():
    #     breakpoint()
    return dq, dk, dv, softmax_d


class FlashAttnQKVPackedFunc(torch.autograd.Function):
    @staticmethod
    def forward(ctx, qkv, dropout_p, softmax_scale, causal, return_softmax):
        if softmax_scale is None:
            softmax_scale = qkv.shape[-1] ** (-0.5)
        out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_forward(
            qkv[:, :, 0],
            qkv[:, :, 1],
            qkv[:, :, 2],
            dropout_p,
            softmax_scale,
            causal=causal,
            return_softmax=return_softmax and dropout_p > 0,
        )
        ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
        ctx.dropout_p = dropout_p
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        return out if not return_softmax else (out, softmax_lse, S_dmask)

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors
        qkv_shape = q.shape[:-2] + (3, *q.shape[-2:])
        dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
        _flash_attn_backward(
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            dqkv[:, :, 0],
            dqkv[:, :, 1],
            dqkv[:, :, 2],
            ctx.dropout_p,
            ctx.softmax_scale,
            ctx.causal,
            rng_state=rng_state,
        )
        dqkv = dqkv[..., : dout.shape[-1]]  # We could have padded the head dimension
        return dqkv, None, None, None, None


class FlashAttnVarlenQKVPackedFunc(torch.autograd.Function):
    @staticmethod
    def forward(ctx, qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_softmax):
        if softmax_scale is None:
            softmax_scale = qkv.shape[-1] ** (-0.5)
        out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_varlen_forward(
            qkv[:, 0],
            qkv[:, 1],
            qkv[:, 2],
            cu_seqlens,
            cu_seqlens,
            max_seqlen,
            max_seqlen,
            dropout_p,
            softmax_scale,
            causal=causal,
            return_softmax=return_softmax and dropout_p > 0,
        )
        ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens, rng_state)
        ctx.dropout_p = dropout_p
        ctx.max_seqlen = max_seqlen
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        return out if not return_softmax else (out, softmax_lse, S_dmask)

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse, cu_seqlens, rng_state = ctx.saved_tensors
        qkv_shape = q.shape[:-2] + (3, *q.shape[-2:])
        dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
        _flash_attn_varlen_backward(
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            dqkv[:, 0],
            dqkv[:, 1],
            dqkv[:, 2],
            cu_seqlens,
            cu_seqlens,
            ctx.max_seqlen,
            ctx.max_seqlen,
            ctx.dropout_p,
            ctx.softmax_scale,
            ctx.causal,
            rng_state=rng_state,
        )
        dqkv = dqkv[..., : dout.shape[-1]]  # We could have padded the head dimension
        return dqkv, None, None, None, None, None, None


class FlashAttnKVPackedFunc(torch.autograd.Function):
    @staticmethod
    def forward(ctx, q, kv, dropout_p, softmax_scale, causal, return_softmax):
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)
        out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_forward(
            q,
            kv[:, :, 0],
            kv[:, :, 1],
            dropout_p,
            softmax_scale,
            causal=causal,
            return_softmax=return_softmax and dropout_p > 0,
        )
        ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
        ctx.dropout_p = dropout_p
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        return out if not return_softmax else (out, softmax_lse, S_dmask)

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors
        dq = torch.empty_like(q)
        kv_shape = k.shape[:-2] + (2, *k.shape[-2:])
        dkv = torch.empty(kv_shape, dtype=k.dtype, device=k.device)
        _flash_attn_backward(
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            dq,
            dkv[:, :, 0],
            dkv[:, :, 1],
            ctx.dropout_p,
            ctx.softmax_scale,
            ctx.causal,
            rng_state=rng_state,
        )
        dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
        dkv = dkv[..., : dout.shape[-1]]
        return dq, dkv, None, None, None, None


class FlashAttnVarlenKVPackedFunc(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        q,
        kv,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        dropout_p,
        softmax_scale,
        causal,
        return_softmax,
    ):
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)
        out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_varlen_forward(
            q,
            kv[:, 0],
            kv[:, 1],
            cu_seqlens_q,
            cu_seqlens_k,
            max_seqlen_q,
            max_seqlen_k,
            dropout_p,
            softmax_scale,
            causal=causal,
            return_softmax=return_softmax and dropout_p > 0,
        )
        ctx.save_for_backward(
            q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state
        )
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_k = max_seqlen_k
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        return out if not return_softmax else (out, softmax_lse, S_dmask)

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors
        dq = torch.empty_like(q)
        kv_shape = k.shape[:-2] + (2, *k.shape[-2:])
        dkv = torch.empty(kv_shape, dtype=k.dtype, device=k.device)
        _flash_attn_varlen_backward(
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            dq,
            dkv[:, 0],
            dkv[:, 1],
            cu_seqlens_q,
            cu_seqlens_k,
            ctx.max_seqlen_q,
            ctx.max_seqlen_k,
            ctx.dropout_p,
            ctx.softmax_scale,
            ctx.causal,
            rng_state=rng_state,
        )
        dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
        dkv = dkv[..., : dout.shape[-1]]
        return dq, dkv, None, None, None, None, None, None, None, None


class FlashAttnFunc(torch.autograd.Function):
    @staticmethod
    def forward(ctx, q, k, v, dropout_p, softmax_scale, causal, return_softmax):
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)
        out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_forward(
            q,
            k,
            v,
            dropout_p,
            softmax_scale,
            causal=causal,
            return_softmax=return_softmax and dropout_p > 0,
        )
        ctx.save_for_backward(q, k, v, out_padded, softmax_lse, rng_state)
        ctx.dropout_p = dropout_p
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        return out if not return_softmax else (out, softmax_lse, S_dmask)

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse, rng_state = ctx.saved_tensors
        dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
        _flash_attn_backward(
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            dq,
            dk,
            dv,
            ctx.dropout_p,
            ctx.softmax_scale,
            ctx.causal,
            rng_state=rng_state,
        )
        dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
        dk = dk[..., : dout.shape[-1]]
        dv = dv[..., : dout.shape[-1]]
        return dq, dk, dv, None, None, None, None, None, None, None, None


class FlashAttnVarlenFunc(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        dropout_p,
        softmax_scale,
        causal,
        return_softmax,
    ):
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)
        out, q, k, v, out_padded, softmax_lse, S_dmask, rng_state = _flash_attn_varlen_forward(
            q,
            k,
            v,
            cu_seqlens_q,
            cu_seqlens_k,
            max_seqlen_q,
            max_seqlen_k,
            dropout_p,
            softmax_scale,
            causal=causal,
            return_softmax=return_softmax and dropout_p > 0,
        )
        ctx.save_for_backward(
            q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state
        )
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_k = max_seqlen_k
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        return out if not return_softmax else (out, softmax_lse, S_dmask)

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, rng_state = ctx.saved_tensors
        dq, dk, dv = torch.empty_like(q), torch.empty_like(k), torch.empty_like(v)
        _flash_attn_varlen_backward(
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            dq,
            dk,
            dv,
            cu_seqlens_q,
            cu_seqlens_k,
            ctx.max_seqlen_q,
            ctx.max_seqlen_k,
            ctx.dropout_p,
            ctx.softmax_scale,
            ctx.causal,
            rng_state=rng_state,
        )
        dq = dq[..., : dout.shape[-1]]  # We could have padded the head dimension
        dk = dk[..., : dout.shape[-1]]
        dv = dv[..., : dout.shape[-1]]
        return dq, dk, dv, None, None, None, None, None, None, None, None


def flash_attn_qkvpacked_func(
    qkv, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
):
    """dropout_p should be set to 0.0 during evaluation
    If Q, K, V are already stacked into 1 tensor, this function will be faster than
    calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
    of the gradients of Q, K, V.
    For multi-query and grouped-query attention (MQA/GQA), please see
    flash_attn_kvpacked_func and flash_attn_func.

    Arguments:
        qkv: (batch_size, seqlen, 3, nheads, headdim)
        dropout_p: float. Dropout probability.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
           testing only. The returned probabilities are not guaranteed to be correct
           (they might not have the right scaling).
    Return:
        out: (batch_size, seqlen, nheads, headdim).
        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
            normalization factor).
        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
            The output of softmax (possibly with different scaling). It also encodes the dropout
            pattern (negative means that location was dropped, nonnegative means it was kept).
    """
    return FlashAttnQKVPackedFunc.apply(qkv, dropout_p, softmax_scale, causal, return_attn_probs)


def flash_attn_kvpacked_func(
    q, kv, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
):
    """dropout_p should be set to 0.0 during evaluation
    If K, V are already stacked into 1 tensor, this function will be faster than
    calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
    of the gradients of K, V.
    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.

    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
        1 1 1 1 0
        1 1 1 1 1
    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
        0 0
        0 0
        0 0
        1 0
        1 1
    If the row of the mask is all zero, the output will be zero.

    Arguments:
        q: (batch_size, seqlen, nheads, headdim)
        kv: (batch_size, seqlen, 2, nheads_k, headdim)
        dropout_p: float. Dropout probability.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
           testing only. The returned probabilities are not guaranteed to be correct
           (they might not have the right scaling).
    Return:
        out: (batch_size, seqlen, nheads, headdim).
        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
            normalization factor).
        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
            The output of softmax (possibly with different scaling). It also encodes the dropout
            pattern (negative means that location was dropped, nonnegative means it was kept).
    """
    return FlashAttnKVPackedFunc.apply(q, kv, dropout_p, softmax_scale, causal, return_attn_probs)


def flash_attn_func(
    q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False
):
    """dropout_p should be set to 0.0 during evaluation
    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.

    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
        1 1 1 1 0
        1 1 1 1 1
    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
        0 0
        0 0
        0 0
        1 0
        1 1
    If the row of the mask is all zero, the output will be zero.

    Arguments:
        q: (batch_size, seqlen, nheads, headdim)
        k: (batch_size, seqlen, nheads_k, headdim)
        v: (batch_size, seqlen, nheads_k, headdim)
        dropout_p: float. Dropout probability.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
           testing only. The returned probabilities are not guaranteed to be correct
           (they might not have the right scaling).
    Return:
        out: (batch_size, seqlen, nheads, headdim).
        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
            normalization factor).
        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
            The output of softmax (possibly with different scaling). It also encodes the dropout
            pattern (negative means that location was dropped, nonnegative means it was kept).
    """
    return FlashAttnFunc.apply(q, k, v, dropout_p, softmax_scale, causal, return_attn_probs)


def flash_attn_varlen_qkvpacked_func(
    qkv,
    cu_seqlens,
    max_seqlen,
    dropout_p=0.0,
    softmax_scale=None,
    causal=False,
    return_attn_probs=False,
):
    """dropout_p should be set to 0.0 during evaluation
    If Q, K, V are already stacked into 1 tensor, this function will be faster than
    calling flash_attn_varlen_func on Q, K, V since the backward pass avoids explicit concatenation
    of the gradients of Q, K, V.
    For multi-query and grouped-query attention (MQA/GQA), please see
    flash_attn_varlen_kvpacked_func and flash_attn_varlen_func.

    Arguments:
        qkv: (total, 3, nheads, headdim), where total = total number of tokens in the batch.
        cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
           of the sequences in the batch, used to index into qkv.
        max_seqlen: int. Maximum sequence length in the batch.
        dropout_p: float. Dropout probability.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
           testing only. The returned probabilities are not guaranteed to be correct
           (they might not have the right scaling).
    Return:
        out: (total, nheads, headdim).
        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
            normalization factor).
        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
            The output of softmax (possibly with different scaling). It also encodes the dropout
            pattern (negative means that location was dropped, nonnegative means it was kept).
    """
    return FlashAttnVarlenQKVPackedFunc.apply(
        qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, causal, return_attn_probs
    )


def flash_attn_varlen_kvpacked_func(
    q,
    kv,
    cu_seqlens_q,
    cu_seqlens_k,
    max_seqlen_q,
    max_seqlen_k,
    dropout_p=0.0,
    softmax_scale=None,
    causal=False,
    return_attn_probs=False,
):
    """dropout_p should be set to 0.0 during evaluation
    If K, V are already stacked into 1 tensor, this function will be faster than
    calling flash_attn_func on Q, K, V since the backward pass avoids explicit concatenation
    of the gradients of K, V.
    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.

    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
        1 1 1 1 0
        1 1 1 1 1
    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
        0 0
        0 0
        0 0
        1 0
        1 1
    If the row of the mask is all zero, the output will be zero.

    Arguments:
        q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
        kv: (total_k, 2, nheads_k, headdim), where total_k = total number of key tokens in the batch.
        cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
           of the sequences in the batch, used to index into q.
        cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
           of the sequences in the batch, used to index into kv.
        max_seqlen_q: int. Maximum query sequence length in the batch.
        max_seqlen_k: int. Maximum key sequence length in the batch.
        dropout_p: float. Dropout probability.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
           testing only. The returned probabilities are not guaranteed to be correct
           (they might not have the right scaling).
    Return:
        out: (total, nheads, headdim).
        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
            normalization factor).
        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
            The output of softmax (possibly with different scaling). It also encodes the dropout
            pattern (negative means that location was dropped, nonnegative means it was kept).
    """
    return FlashAttnVarlenKVPackedFunc.apply(
        q,
        kv,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        dropout_p,
        softmax_scale,
        causal,
        return_attn_probs,
    )


def flash_attn_varlen_func(
    q,
    k,
    v,
    cu_seqlens_q,
    cu_seqlens_k,
    max_seqlen_q,
    max_seqlen_k,
    dropout_p=0.0,
    softmax_scale=None,
    causal=False,
    return_attn_probs=False,
):
    """dropout_p should be set to 0.0 during evaluation
    Supports multi-query and grouped-query attention (MQA/GQA) by passing in K, V with fewer heads
    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.

    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
        1 1 1 1 0
        1 1 1 1 1
    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
        0 0
        0 0
        0 0
        1 0
        1 1
    If the row of the mask is all zero, the output will be zero.

    Arguments:
        q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
        k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
        v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch.
        cu_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
           of the sequences in the batch, used to index into q.
        cu_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
           of the sequences in the batch, used to index into kv.
        max_seqlen_q: int. Maximum query sequence length in the batch.
        max_seqlen_k: int. Maximum key sequence length in the batch.
        dropout_p: float. Dropout probability.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        return_attn_probs: bool. Whether to return the attention probabilities. This option is for
           testing only. The returned probabilities are not guaranteed to be correct
           (they might not have the right scaling).
    Return:
        out: (total, nheads, headdim).
        softmax_lse [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen). The
            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
            normalization factor).
        S_dmask [optional, if return_attn_probs=True]: (batch_size, nheads, seqlen, seqlen).
            The output of softmax (possibly with different scaling). It also encodes the dropout
            pattern (negative means that location was dropped, nonnegative means it was kept).
    """
    return FlashAttnVarlenFunc.apply(
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_k,
        max_seqlen_q,
        max_seqlen_k,
        dropout_p,
        softmax_scale,
        causal,
        return_attn_probs,
    )


def flash_attn_with_kvcache(
    q,
    k_cache,
    v_cache,
    k=None,
    v=None,
    cache_seqlens=None,
    softmax_scale=None,
    causal=False,
    num_splits=0,
):
    """
    If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
    k and v. This is useful for incremental decoding: you can pass in the cached keys/values from
    the previous step, and update them with the new keys/values from the current step, and do
    attention with the updated cache, all in 1 kernel.

    If you pass in k / v, you must make sure that the cache is large enough to hold the new values.
    For example, the KV cache could be pre-allocated with the max sequence length, and you can use
    cache_seqlens to keep track of the current sequence lengths of each sequence in the batch.

    Does not support backward pass.

    Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
    than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
    For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
    0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.

    If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
    For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
        1 1 1 1 0
        1 1 1 1 1
    If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
        0 0
        0 0
        0 0
        1 0
        1 1
    If the row of the mask is all zero, the output will be zero.

    Arguments:
        q: (batch_size, seqlen, nheads, headdim)
        k_cache: (batch_size, seqlen_cache, nheads_k, headdim)
        v_cache: (batch_size, seqlen_cache, nheads_k, headdim)
        k [optional]: (batch_size, seqlen, nheads_k, headdim). If not None, we concatenate k with
            k_cache, starting at the indices specified by cache_seqlens.
        v [optional]: (batch_size, seqlen, nheads_k, headdim). Similar to k.
        cache_seqlens: (batch_size,), dtype torch.int32. The sequence lengths of the KV cache.
        softmax_scale: float. The scaling of QK^T before applying softmax.
            Default to 1 / sqrt(headdim).
        causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
        num_splits: int. If > 1, split the key/value into this many chunks along the sequence.
           If num_splits == 1, we don't split the key/value. If num_splits == 0, we use a heuristic
           to automatically determine the number of splits.
           Don't change this unless you know what you are doing.

    Return:
        out: (batch_size, seqlen, nheads, headdim).
    """
    assert k_cache.stride(-1) == 1, "k_cache must have contiguous last dimension"
    assert v_cache.stride(-1) == 1, "v_cache must have contiguous last dimension"
    maybe_contiguous = lambda x: x.contiguous() if x is not None and x.stride(-1) != 1 else x
    q, k, v = [maybe_contiguous(x) for x in (q, k, v)]
    if softmax_scale is None:
        softmax_scale = q.shape[-1] ** (-0.5)
    out, softmax_lse = flash_attn_cuda.fwd_kvcache(
        q, k_cache, v_cache, k, v, cache_seqlens, None, softmax_scale, causal, num_splits
    )
    return out