# Copyright (c) 2023, Tri Dao.

from typing import Optional, Union

import torch
import torch.nn as nn

# isort: off
# We need to import the CUDA kernels after importing torch
import flash_attn_3_cuda

# isort: on


def maybe_contiguous(x):
    return x.contiguous() if x is not None and x.stride(-1) != 1 else x


def _flash_attn_forward(
        q,
        k,
        v,
        k_new,
        v_new,
        qv,
        out,
        cu_seqlens_q,
        cu_seqlens_k,
        cu_seqlens_k_new,
        seqused_q,
        seqused_k,
        max_seqlen_q,
        max_seqlen_k,
        page_table,
        kv_batch_idx,
        leftpad_k,
        rotary_cos,
        rotary_sin,
        q_descale,
        k_descale,
        v_descale,
        softmax_scale,
        causal,
        window_size=(-1, -1),
        softcap=0.0,
        rotary_interleaved=True,
        num_splits=1,
        pack_gqa=None,
        sm_margin=0):
    q, k, k_new, v_new = [maybe_contiguous(x) for x in (q, k, k_new, v_new)]
    v = v.contiguous() if v.stride(-1) != 1 and v.stride(-3) != 1 else v
    cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new = [
        maybe_contiguous(x) for x in (cu_seqlens_q, cu_seqlens_k, cu_seqlens_k_new)
    ]
    seqused_q, seqused_k = [maybe_contiguous(x) for x in (seqused_q, seqused_k)]
    page_table, kv_batch_idx, leftpad_k = [
        maybe_contiguous(x) for x in (page_table, kv_batch_idx, leftpad_k)
    ]
    rotary_cos, rotary_sin = [maybe_contiguous(x) for x in (rotary_cos, rotary_sin)]
    out, softmax_lse, *rest = flash_attn_3_cuda.fwd(
        q,
        k,
        v,
        k_new,
        v_new,
        qv,
        out,
        cu_seqlens_q,
        cu_seqlens_k,
        cu_seqlens_k_new,
        seqused_q,
        seqused_k,
        max_seqlen_q,
        max_seqlen_k,
        page_table,
        kv_batch_idx,
        leftpad_k,
        rotary_cos,
        rotary_sin,
        q_descale,
        k_descale,
        v_descale,
        softmax_scale,
        causal,
        window_size[0],
        window_size[1],
        softcap,
        rotary_interleaved,
        num_splits,
        pack_gqa,
        sm_margin,
    )
    return (out, softmax_lse, *rest)


def _flash_attn_backward(
        dout,
        q,
        k,
        v,
        out,
        softmax_lse,
        cu_seqlens_q,
        cu_seqlens_k,
        sequed_q,
        sequed_k,
        max_seqlen_q,
        max_seqlen_k,
        dq,
        dk,
        dv,
        softmax_scale,
        causal,
        window_size=(-1, -1),
        softcap=0.0,
        deterministic=False,
        sm_margin=0,
):
    # 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, *rest = flash_attn_3_cuda.bwd(
        dout,
        q,
        k,
        v,
        out,
        softmax_lse,
        dq,
        dk,
        dv,
        cu_seqlens_q,
        cu_seqlens_k,
        sequed_q,
        sequed_k,
        max_seqlen_q,
        max_seqlen_k,
        softmax_scale,
        causal,
        window_size[0],
        window_size[1],
        softcap,
        deterministic,
        sm_margin,
    )
    return dq, dk, dv, softmax_d


class FlashAttnQKVPackedFunc(torch.autograd.Function):
    @staticmethod
    def forward(
        ctx,
        qkv,
        softmax_scale,
        causal,
        q_descale=None, k_descale=None, v_descale=None,
        window_size=(-1, -1),
        softcap=0.0,
        deterministic=False,
        num_heads_q=None,
    ):
        if softmax_scale is None:
            softmax_scale = qkv.shape[-1] ** (-0.5)
        if qkv.dim() == 5:
            assert qkv.shape[-3] == 3
            q, k, v = qkv.unbind(dim=-3)
        else:
            assert qkv.dim() == 4
            assert num_heads_q is not None
            num_heads_k = (qkv.shape[2] - num_heads_q) // 2
            assert num_heads_k * 2 + num_heads_q == qkv.shape[2]
            q, k, v = qkv.split([num_heads_q, num_heads_k, num_heads_k], dim=-2)
        out, q, k, v, out_padded, softmax_lse = _flash_attn_forward(
            q,
            k,
            v,
            softmax_scale,
            causal=causal,
            q_descale=q_descale, k_descale=k_descale, v_descale=v_descale,
            window_size=window_size,
            softcap=softcap,
        )
        ctx.save_for_backward(q, k, v, out_padded, softmax_lse)
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        ctx.window_size = window_size
        ctx.softcap = softcap
        ctx.deterministic = deterministic
        ctx.ndim = qkv.dim()
        # return out, softmax_lse
        return out

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse = ctx.saved_tensors
        if ctx.ndim == 5:
            qkv_shape = q.shape[:-2] + (3, *q.shape[-2:])
            dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
            dq, dk, dv = dqkv.unbind(dim=-3)
        else:
            num_heads_q = q.shape[2]
            num_heads_k = k.shape[2]
            qkv_shape = q.shape[:-2] + (num_heads_q + num_heads_k * 2, *q.shape[-1:])
            dqkv = torch.empty(qkv_shape, dtype=q.dtype, device=q.device)
            dq, dk, dv = dqkv.split([num_heads_q, num_heads_k, num_heads_k], dim=-2)
        _flash_attn_backward(
            dout,
            q,
            k,
            v,
            out,
            softmax_lse,
            dq,
            dk,
            dv,
            ctx.softmax_scale,
            ctx.causal,
            ctx.window_size,
            ctx.softcap,
            ctx.deterministic,
        )
        dqkv = dqkv[..., : dout.shape[-1]]  # We could have padded the head dimension
        return dqkv, None, None, None, None, None, None, None, None, None, None


class FlashAttnFunc(torch.autograd.Function):

    @staticmethod
    def forward(
        ctx,
        q,
        k,
        v,
        softmax_scale,
        causal,
        qv=None,
        q_descale=None, k_descale=None, v_descale=None,
        window_size=(-1, -1),
        softcap=0.0,
        num_splits=1,
        pack_gqa=None,
        deterministic=False,
        sm_margin=0,
    ):
        if softmax_scale is None:
            softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
        # out, q, k, v, out_padded, softmax_lse = _flash_attn_forward(
        out, softmax_lse, *rest = _flash_attn_forward(
            q,
            k,
            v,
            None, None,  # k_new, v_new
            qv,  # qv
            None,  # out
            None, None, None,   # cu_seqlens_q/k/k_new
            None, None,   # seqused_q/k
            None, None,   # max_seqlen_q/k
            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
            None, None,  # rotary_cos/sin
            q_descale, k_descale, v_descale,
            softmax_scale,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            num_splits=num_splits,
            pack_gqa=pack_gqa,
            sm_margin=sm_margin,
        )
        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse)
        ctx.save_for_backward(q, k, v, out, softmax_lse)
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        ctx.window_size = window_size
        ctx.softcap = softcap
        ctx.deterministic = deterministic
        ctx.sm_margin = sm_margin
        return out, softmax_lse

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse = 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,
            None, None, # cu_seqlens_q, cu_seqlens_k,
            None, None, # sequed_q, sequed_k,
            None, None, # max_seqlen_q, max_seqlen_k,
            dq,
            dk,
            dv,
            ctx.softmax_scale,
            ctx.causal,
            ctx.window_size,
            ctx.softcap,
            ctx.deterministic,
            ctx.sm_margin,
        )
        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, None, None, None, None, None


class FlashAttnVarlenFunc(torch.autograd.Function):

    @staticmethod
    def forward(
        ctx,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_k,
        seqused_q,
        seqused_k,
        max_seqlen_q,
        max_seqlen_k,
        softmax_scale,
        causal,
        qv=None,
        q_descale=None, k_descale=None, v_descale=None,
        window_size=(-1, -1),
        softcap=0.0,
        num_splits=1,
        pack_gqa=None,
        deterministic=False,
        sm_margin=0,
    ):
        if softmax_scale is None:
            softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
        # out, q, k, v, out_padded, softmax_lse = _flash_attn_varlen_forward(
        out, softmax_lse, *rest = _flash_attn_forward(
            q,
            k,
            v,
            None, None,  # k_new, v_new
            qv,  # qv
            None,  # out
            cu_seqlens_q,
            cu_seqlens_k,
            None,   # cu_seqlens_k_new
            seqused_q,
            seqused_k,
            max_seqlen_q,
            max_seqlen_k,
            None, None, None,   # page_table, kv_batch_idx, leftpad_k,
            None, None,  # rotary_cos/sin
            q_descale, k_descale, v_descale,
            softmax_scale,
            causal=causal,
            window_size=window_size,
            softcap=softcap,
            num_splits=num_splits,
            pack_gqa=pack_gqa,
            sm_margin=sm_margin,
        )
        # ctx.save_for_backward(q, k, v, out_padded, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k)
        ctx.save_for_backward(q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k)
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_k = max_seqlen_k
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
        ctx.window_size = window_size
        ctx.softcap = softcap
        ctx.deterministic = deterministic
        ctx.sm_margin = sm_margin
        return out, softmax_lse

    @staticmethod
    def backward(ctx, dout, *args):
        q, k, v, out, softmax_lse, cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k = 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,
            cu_seqlens_q,
            cu_seqlens_k,
            seqused_q,
            seqused_k,
            ctx.max_seqlen_q,
            ctx.max_seqlen_k,
            dq,
            dk,
            dv,
            ctx.softmax_scale,
            ctx.causal,
            ctx.window_size,
            ctx.softcap,
            ctx.deterministic,
            ctx.sm_margin,
        )
        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, None, None, None, None, None, None, None, None, None, None, None


def flash_attn_qkvpacked_func(
    qkv,
    softmax_scale=None,
    causal=False,
    q_descale=None, k_descale=None, v_descale=None,
    window_size=(-1, -1),
    softcap=0.0,
    deterministic=False,
    num_heads_q=None,
):
    """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.

    If window_size != (-1, -1), implements sliding window local attention. Query at position i
    will only attend to keys between [i - window_size[0], i + window_size[1]] inclusive.

    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).
        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
        softcap: float. Anything > 0 activates softcapping attention.
        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of (-alibi_slope * |i - j|) is added to
            the attention score of query i and key j.
        deterministic: bool. Whether to use the deterministic implementation of the backward pass,
            which is slightly slower and uses more memory. The forward pass is always deterministic.
        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,
        softmax_scale,
        causal,
        q_descale, k_descale, v_descale,
        window_size,
        softcap,
        deterministic,
        num_heads_q,
    )


def flash_attn_func(
    q,
    k,
    v,
    softmax_scale=None,
    causal=False,
    qv=None,
    q_descale=None, k_descale=None, v_descale=None,
    window_size=(-1, -1),
    softcap=0.0,
    num_splits=1,
    pack_gqa=None,
    deterministic=False,
    sm_margin=0,
):
    """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.

    If window_size != (-1, -1), implements sliding window local attention. Query at position i
    will only attend to keys between
    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.

    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).
        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
        alibi_slopes: (nheads,) or (batch_size, nheads), fp32. A bias of
            (-alibi_slope * |i + seqlen_k - seqlen_q - j|)
            is added to the attention score of query i and key j.
        deterministic: bool. Whether to use the deterministic implementation of the backward pass,
            which is slightly slower and uses more memory. The forward pass is always deterministic.
        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).
    """
    return FlashAttnFunc.apply(
        q,
        k,
        v,
        softmax_scale,
        causal,
        qv,
        q_descale, k_descale, v_descale,
        window_size,
        softcap,
        num_splits,
        pack_gqa,
        deterministic,
        sm_margin,
    )


def flash_attn_varlen_func(
    q,
    k,
    v,
    cu_seqlens_q,
    cu_seqlens_k,
    max_seqlen_q,
    max_seqlen_k,
    seqused_q=None,
    seqused_k=None,
    softmax_scale=None,
    causal=False,
    qv=None,
    q_descale=None, k_descale=None, v_descale=None,
    window_size=(-1, -1),
    softcap=0.0,
    num_splits=1,
    pack_gqa=None,
    deterministic=False,
    sm_margin=0,
):
    return FlashAttnVarlenFunc.apply(
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_k,
        seqused_q,
        seqused_k,
        max_seqlen_q,
        max_seqlen_k,
        softmax_scale,
        causal,
        qv,
        q_descale, k_descale, v_descale,
        window_size,
        softcap,
        num_splits,
        pack_gqa,
        deterministic,
        sm_margin,
    )


def flash_attn_combine(out_partial, lse_partial, out=None, out_dtype=None):
    return flash_attn_3_cuda.fwd_combine(out_partial, lse_partial, out, out_dtype)


def flash_attn_with_kvcache(
    q,
    k_cache,
    v_cache,
    k=None,
    v=None,
    qv=None,
    rotary_cos=None,
    rotary_sin=None,
    cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None,
    cache_batch_idx: Optional[torch.Tensor] = None,
    cache_leftpad: Optional[torch.Tensor] = None,
    page_table: Optional[torch.Tensor] = None,
    cu_seqlens_q: Optional[torch.Tensor] = None,
    cu_seqlens_k_new: Optional[torch.Tensor] = None,
    max_seqlen_q: Optional[int] = None,
    q_descale: Optional[torch.Tensor] = None,
    k_descale: Optional[torch.Tensor] = None,
    v_descale: Optional[torch.Tensor] = None,
    softmax_scale=None,
    causal=False,
    window_size=(-1, -1),  # -1 means infinite context window
    softcap=0.0, # 0.0 means deactivated
    rotary_interleaved=True,
    num_splits=0,    # Can be tuned for speed
    pack_gqa=None,   # Can be tuned for speed
    sm_margin=0,     # Can be tuned if some SMs are used for communication
    return_softmax_lse=False,
):
    """
    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.

    Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be
    rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
    If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos
    and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
    If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at
    indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).

    See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function.

    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.

    If window_size != (-1, -1), implements sliding window local attention. Query at position i
    will only attend to keys between
    [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.

    Note: Does not support backward pass.

    Arguments:
        q: (batch_size, seqlen, nheads, headdim)
        k_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no page_table,
            or (num_blocks, page_block_size, nheads_k, headdim) if there's a page_table (i.e. paged KV cache)
            page_block_size must be a multiple of 256.
        v_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim_v) if there's no page_table,
            or (num_blocks, page_block_size, nheads_k, headdim_v) if there's a page_table (i.e. paged KV cache)
        k [optional]: (batch_size, seqlen_new, 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_new, nheads_k, headdim_v). Similar to k.
        qv [optional]: (batch_size, seqlen, nheads, headdim_v)
        rotary_cos [optional]: (seqlen_ro, rotary_dim / 2). If not None, we apply rotary embedding
            to k and q. Only applicable if k and v are passed in. rotary_dim must be divisible by 16.
        rotary_sin [optional]: (seqlen_ro, rotary_dim / 2). Similar to rotary_cos.
        cache_seqlens: int, or (batch_size,), dtype torch.int32. The sequence lengths of the
            KV cache.
        cache_batch_idx: (batch_size,), dtype torch.int32. The indices used to index into the KV cache.
            If None, we assume that the batch indices are [0, 1, 2, ..., batch_size - 1].
            If the indices are not distinct, and k and v are provided, the values updated in the cache
                 might come from any of the duplicate indices.
        cache_leftpad: (batch_size,), dtype torch.int32. The index that the KV cache starts. If None, assume 0.
        page_table [optional]: (batch_size, max_num_blocks_per_seq), dtype torch.int32.
        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).
        window_size: (left, right). If not (-1, -1), implements sliding window local attention.
        softcap: float. Anything > 0 activates softcapping attention.
        rotary_interleaved: bool. Only applicable if rotary_cos and rotary_sin are passed in.
            If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False,
            rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1
            (i.e. GPT-NeoX style).
        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_softmax_lse: bool. Whether to return the logsumexp of the attention scores.

    Return:
        out: (batch_size, seqlen, nheads, headdim).
        softmax_lse [optional, if return_softmax_lse=True]: (batch_size, nheads, seqlen). The
            logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
            normalization factor).
    """
    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"
    if softmax_scale is None:
        softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (-0.5)
    if cache_seqlens is not None and isinstance(cache_seqlens, int):
        cache_seqlens = torch.full(
            (k_cache.shape[0],), cache_seqlens, dtype=torch.int32, device=k_cache.device
        )
        cache_seqlens = maybe_contiguous(cache_seqlens)
    out, softmax_lse, *rest = _flash_attn_forward(
        q,
        k_cache,
        v_cache,
        k,
        v,
        qv,
        None,  # out
        cu_seqlens_q,
        None,  # cu_seqlens_k
        cu_seqlens_k_new,
        None,  # seqused_q
        cache_seqlens,
        max_seqlen_q,
        None,  # max_seqlen_k
        page_table,
        cache_batch_idx,
        cache_leftpad,
        rotary_cos,
        rotary_sin,
        q_descale, k_descale, v_descale,
        softmax_scale,
        causal=causal,
        window_size=window_size,
        softcap=softcap,
        rotary_interleaved=rotary_interleaved,
        num_splits=num_splits,
        pack_gqa=pack_gqa,
        sm_margin=sm_margin,
    )
    # return (out, softmax_lse) if return_softmax_lse else out
    return (out, softmax_lse, *rest) if return_softmax_lse else out
