/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <cmath>
#include <cute/algorithm/copy.hpp>
#include <cute/algorithm/gemm.hpp>

#include <cutlass/cutlass.h>
#include <cutlass/array.h>
#include <cutlass/numeric_types.h>
#include <cutlass/numeric_conversion.h>

#include "block_info.h"
#include "kernel_traits.h"
#include "utils.h"
#include "softmax.h"

namespace flash {

using namespace cute;

////////////////////////////////////////////////////////////////////////////////////////////////////

template<bool Is_first, bool Check_inf=false, typename Tensor0, typename Tensor1, typename Tensor2>
inline __device__ void softmax_rescale_o(Tensor0 &scores, Tensor1 &scores_max, Tensor1 &scores_sum,
                                         Tensor2 &acc_o, float softmax_scale_log2) {
    if (Is_first) {
        flash::template reduce_max</*zero_init=*/true>(scores, scores_max);
        flash::scale_apply_exp2(scores, scores_max, softmax_scale_log2);
        flash::reduce_sum(scores, scores_sum);
    } else {
        Tensor scores_max_prev = make_fragment_like(scores_max);
        cute::copy(scores_max, scores_max_prev);
        flash::template reduce_max</*zero_init=*/false>(scores, scores_max);
        // Reshape acc_o from (MMA=4, MMA_M, MMA_K) to (nrow=(2, MMA_M), ncol=(2, MMA_K))
        Tensor acc_o_rowcol = make_tensor(acc_o.data(), flash::convert_layout_acc_rowcol(acc_o.layout()));
        #pragma unroll
        for (int mi = 0; mi < size(scores_max); ++mi) {
            float scores_max_cur = !Check_inf
                ? scores_max(mi)
                : (scores_max(mi) == -INFINITY ? 0.0f : scores_max(mi));
            float scores_scale = exp2f((scores_max_prev(mi) - scores_max_cur) * softmax_scale_log2);
            scores_sum(mi) *= scores_scale;
            #pragma unroll
            for (int ni = 0; ni < size<1>(acc_o_rowcol); ++ni) { acc_o_rowcol(mi, ni) *= scores_scale; }
        }
        flash::scale_apply_exp2(scores, scores_max, softmax_scale_log2);
        Tensor scores_sum_cur = make_fragment_like(scores_sum);
        flash::reduce_sum(scores, scores_sum_cur);
        #pragma unroll
        for (int mi = 0; mi < size(scores_sum); ++mi) { scores_sum(mi) += scores_sum_cur(mi); }
    }
};

////////////////////////////////////////////////////////////////////////////////////////////////////

template<typename Kernel_traits, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, typename Params>
inline __device__ void compute_attn_1rowblock(const Params &params, const int bidb, const int bidh, const int m_block) {

    using Element = typename Kernel_traits::Element;
    using ElementAccum = typename Kernel_traits::ElementAccum;
    using index_t = typename Kernel_traits::index_t;

    // Shared memory.
    extern __shared__ char smem_[];

    // The thread index.
    const int tidx = threadIdx.x;

    constexpr int kBlockM = Kernel_traits::kBlockM;
    constexpr int kBlockN = Kernel_traits::kBlockN;
    constexpr int kHeadDim = Kernel_traits::kHeadDim;
    constexpr int kNWarps = Kernel_traits::kNWarps;
    constexpr int MMA_M = kBlockM / decltype(size<0>(typename Kernel_traits::TiledMma::TiledShape_MNK{}))::value;

    const BlockInfo</*Varlen=*/!Is_even_MN> binfo(params, bidb);
    if (m_block * kBlockM >= binfo.actual_seqlen_q || binfo.actual_seqlen_k == 0) return;

    const int n_block_min = !Is_local ? 0 : std::max(0, (m_block * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q - params.window_size_left) / kBlockN);
    int n_block_max = cute::ceil_div(binfo.actual_seqlen_k, kBlockN);
    if (Is_causal || Is_local) {
        n_block_max = std::min(n_block_max,
                               cute::ceil_div((m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q + params.window_size_right, kBlockN));
        // if (threadIdx.x == 0 && blockIdx.y == 0 && blockIdx.z == 0) {
        //     printf("m_block = %d, n_block_max = %d\n", m_block, n_block_max);
        // }
        // We exit early and write 0 to gO and gLSE.
        // Otherwise we might read OOB elements from gK and gV.
        if (n_block_max <= n_block_min) {
            const index_t row_offset_o = binfo.q_offset(params.o_batch_stride, params.o_row_stride, bidb)
                + m_block * kBlockM * params.o_row_stride + bidh * params.o_head_stride;
            Tensor gO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.o_ptr) + row_offset_o),
                                    Shape<Int<kBlockM>, Int<kHeadDim>>{},
                                    make_stride(params.o_row_stride, _1{}));
            typename Kernel_traits::GmemTiledCopyO gmem_tiled_copy_O;
            auto gmem_thr_copy_O = gmem_tiled_copy_O.get_thread_slice(tidx);
            Tensor tOgO = gmem_thr_copy_O.partition_D(gO);
            Tensor tOrO = make_tensor<Element>(shape(tOgO));
            clear(tOrO);
            // Construct identity layout for sO
            Tensor cO = make_identity_tensor(make_shape(size<0>(gO), size<1>(gO)));    // (BLK_M,BLK_K) -> (blk_m,blk_k)
            // Repeat the partitioning with identity layouts
            Tensor tOcO = gmem_thr_copy_O.partition_D(cO);
            Tensor tOpO = make_tensor<bool>(make_shape(size<2>(tOgO)));
            if (!Is_even_K) {
                #pragma unroll
                for (int k = 0; k < size(tOpO); ++k) { tOpO(k) = get<1>(tOcO(0, 0, k)) < params.d; }
            }
            // Clear_OOB_K must be false since we don't want to write zeros to gmem
            flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
                gmem_tiled_copy_O, tOrO, tOgO, tOcO, tOpO, binfo.actual_seqlen_q - m_block * kBlockM
            );
            return;
        }
    }
    // if (tidx == 0) { printf("m_block = %d, n_block_min = %d, n_block_max = %d\n", m_block, n_block_min, n_block_max); }

    // We iterate over the blocks in reverse order. This is because the last block is the only one
    // that needs masking when we read K and V from global memory. Moreover, iterating in reverse
    // might save us 1 register (we just need n_block instead of both n_block and n_block_max).

    const index_t row_offset_q = binfo.q_offset(params.q_batch_stride, params.q_row_stride, bidb)
        + m_block * kBlockM * params.q_row_stride + bidh * params.q_head_stride;
    // We move K and V to the last block.
    const index_t row_offset_k = binfo.k_offset(params.k_batch_stride, params.k_row_stride, bidb)
        + (n_block_max - 1) * kBlockN * params.k_row_stride + (bidh / params.h_h_k_ratio) * params.k_head_stride;
    const index_t row_offset_v = binfo.k_offset(params.v_batch_stride, params.v_row_stride, bidb)
        + (n_block_max - 1) * kBlockN * params.v_row_stride + (bidh / params.h_h_k_ratio) * params.v_head_stride;

    Tensor gQ = make_tensor(make_gmem_ptr(reinterpret_cast<const Element *>(params.q_ptr) + row_offset_q),
                            Shape<Int<kBlockM>, Int<kHeadDim>>{},
                            make_stride(params.q_row_stride, _1{}));
    Tensor gK = make_tensor(make_gmem_ptr(reinterpret_cast<const Element *>(params.k_ptr) + row_offset_k),
                            Shape<Int<kBlockN>, Int<kHeadDim>>{},
                            make_stride(params.k_row_stride, _1{}));
    Tensor gV = make_tensor(make_gmem_ptr(reinterpret_cast<const Element *>(params.v_ptr) + row_offset_v),
                            Shape<Int<kBlockN>, Int<kHeadDim>>{},
                            make_stride(params.v_row_stride, _1{}));

    Tensor sQ = make_tensor(make_smem_ptr(reinterpret_cast<Element *>(smem_)),
                            typename Kernel_traits::SmemLayoutQ{});
    // Careful we're using the same smem for sQ and sK | sV if Share_Q_K_smem;
    Tensor sK = make_tensor(sQ.data() + (Kernel_traits::Share_Q_K_smem ? 0 : size(sQ)),
                            typename Kernel_traits::SmemLayoutKV{});
    Tensor sV = make_tensor(sK.data() + size(sK), typename Kernel_traits::SmemLayoutKV{});
    Tensor sVt = make_tensor(sV.data(), typename Kernel_traits::SmemLayoutVtransposed{});
    Tensor sVtNoSwizzle = make_tensor(sV.data(), typename Kernel_traits::SmemLayoutVtransposedNoSwizzle{});

    typename Kernel_traits::GmemTiledCopyQKV gmem_tiled_copy_QKV;
    auto gmem_thr_copy_QKV = gmem_tiled_copy_QKV.get_thread_slice(tidx);
    typename Kernel_traits::GmemTiledCopyP gmem_tiled_copy_P;
    auto gmem_thr_copy_P = gmem_tiled_copy_P.get_thread_slice(tidx);

    Tensor tQgQ = gmem_thr_copy_QKV.partition_S(gQ);
    Tensor tQsQ = gmem_thr_copy_QKV.partition_D(sQ);
    Tensor tKgK = gmem_thr_copy_QKV.partition_S(gK);  // (KCPY, KCPY_N, KCPY_K)
    Tensor tKsK = gmem_thr_copy_QKV.partition_D(sK);
    Tensor tVgV = gmem_thr_copy_QKV.partition_S(gV);  // (VCPY, VCPY_N, VCPY_K)
    Tensor tVsV = gmem_thr_copy_QKV.partition_D(sV);

    typename Kernel_traits::TiledMma tiled_mma;
    auto thr_mma = tiled_mma.get_thread_slice(tidx);
    Tensor tSrQ  = thr_mma.partition_fragment_A(sQ);                           // (MMA,MMA_M,MMA_K)
    Tensor tSrK  = thr_mma.partition_fragment_B(sK);                           // (MMA,MMA_N,MMA_K)
    Tensor tOrVt  = thr_mma.partition_fragment_B(sVtNoSwizzle);                // (MMA, MMA_K,MMA_N)

    Tensor acc_o = partition_fragment_C(tiled_mma, Shape<Int<kBlockM>, Int<kHeadDim>>{});  // MMA, MMA_M, MMA_K

    //
    // Copy Atom retiling
    //

    auto smem_tiled_copy_Q = make_tiled_copy_A(typename Kernel_traits::SmemCopyAtom{}, tiled_mma);
    auto smem_thr_copy_Q = smem_tiled_copy_Q.get_thread_slice(tidx);
    // if (cute::thread0()) {smem_thr_copy_Q.print_all();}
    Tensor tSsQ = smem_thr_copy_Q.partition_S(sQ);
    // if (cute::thread0()) {print(tSsQ.layout()); printf("\n");}

    auto smem_tiled_copy_K = make_tiled_copy_B(typename Kernel_traits::SmemCopyAtom{}, tiled_mma);
    auto smem_thr_copy_K = smem_tiled_copy_K.get_thread_slice(tidx);
    Tensor tSsK = smem_thr_copy_K.partition_S(sK);

    auto smem_tiled_copy_V = make_tiled_copy_B(typename Kernel_traits::SmemCopyAtomTransposed{}, tiled_mma);
    auto smem_thr_copy_V = smem_tiled_copy_V.get_thread_slice(tidx);
    Tensor tOsVt = smem_thr_copy_V.partition_S(sVt);

    // TODO: this might need to change if we change the mma instruction in SM70
    Tensor scores_max = make_tensor<ElementAccum>(Shape<Int<2 * size<1>(acc_o)>>{});
    Tensor scores_sum = make_fragment_like(scores_max);

    //
    // PREDICATES
    //

    // // Allocate predicate tensors for m and n
    // Tensor tQpQ = make_tensor<bool>(make_shape(size<1>(tQsQ), size<2>(tQsQ)), Stride<_1,_0>{});
    // Tensor tKVpKV = make_tensor<bool>(make_shape(size<1>(tKsK), size<2>(tKsK)), Stride<_1,_0>{});

    // Construct identity layout for sQ and sK
    Tensor cQ = make_identity_tensor(make_shape(size<0>(sQ), size<1>(sQ)));    // (BLK_M,BLK_K) -> (blk_m,blk_k)
    Tensor cKV = make_identity_tensor(make_shape(size<0>(sK), size<1>(sK)));    // (BLK_N,BLK_K) -> (blk_n,blk_k)
    // Tensor tScQ = thr_mma.partition_A(cQ);                           // (MMA,MMA_M,MMA_K)
    // if (cute::thread0()) {
    //     print(tScQ.layout()); printf("\n");
    //     for (int i = 0; i < size(tScQ); ++i) {
    //         printf("%d ", get<0>(tScQ(i)));
    //     }
    //     printf("\n");
    //     for (int i = 0; i < size(tScQ); ++i) {
    //         printf("%d ", get<1>(tScQ(i)));
    //     }
    //     printf("\n");
    // }

    // Repeat the partitioning with identity layouts
    Tensor tQcQ = gmem_thr_copy_QKV.partition_S(cQ);       // (ACPY,ACPY_M,ACPY_K) -> (blk_m,blk_k)
    Tensor tKVcKV = gmem_thr_copy_QKV.partition_S(cKV);   // (BCPY,BCPY_N,BCPY_K) -> (blk_n,blk_k)

    // Allocate predicate tensors for k
    Tensor tQpQ = make_tensor<bool>(make_shape(size<2>(tQsQ)));
    Tensor tKVpKV = make_tensor<bool>(make_shape(size<2>(tKsK)));

    // Set predicates for k bounds
    if (!Is_even_K) {
        #pragma unroll
        for (int k = 0; k < size(tQpQ); ++k) { tQpQ(k) = get<1>(tQcQ(0, 0, k)) < params.d; }
        #pragma unroll
        for (int k = 0; k < size(tKVpKV); ++k) { tKVpKV(k) = get<1>(tKVcKV(0, 0, k)) < params.d; }
    }

    // Prologue

    Tensor tQrQ = make_fragment_like(tQgQ);
    // We don't need to clear the sQ smem tiles since we'll only write out the valid outputs
    flash::copy<Is_even_MN, Is_even_K>(gmem_tiled_copy_QKV, tQgQ, tQsQ, tQcQ, tQpQ,
                                       binfo.actual_seqlen_q - m_block * kBlockM);
    if (Kernel_traits::Is_Q_in_regs) { cute::cp_async_fence(); }

    // // Copy rmem to smem
    // // copy(tQrQ, tQsQ);
    // flash::cp_async_wait<0>();
    // __syncthreads();
    // // if (cute::thread(1, 0)) { print(tQsQ); }
    // // Tensor sQNoSwizzle = make_tensor(make_smem_ptr(reinterpret_cast<Element *>(smem_)), typename Kernel_traits::SmemLayoutQNoSwizzle{});
    // // if (cute::thread0()) { print(sQNoSwizzle); }

    if (Kernel_traits::Share_Q_K_smem) {
        flash::cp_async_wait<0>();
        __syncthreads();
        Tensor tSrQ_copy_view = smem_thr_copy_Q.retile_D(tSrQ);
        CUTE_STATIC_ASSERT_V(size<1>(tSsQ) == size<1>(tSrQ_copy_view));            // M
        cute::copy(smem_tiled_copy_Q, tSsQ, tSrQ_copy_view);
        __syncthreads();
    }

    int n_block = n_block_max - 1;
    // We don't need to clear the sK smem tiles since we'll mask out the scores anyway.
    flash::copy<Is_even_MN, Is_even_K>(gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV,
                                       binfo.actual_seqlen_k - n_block * kBlockN);
    cute::cp_async_fence();
    // if (threadIdx.x == 0 && blockIdx.y == 0 && blockIdx.z < 2) { print(tKgK); }
    // __syncthreads();

    if (Kernel_traits::Is_Q_in_regs && !Kernel_traits::Share_Q_K_smem) {
        flash::cp_async_wait<1>();
        __syncthreads();
        Tensor tSrQ_copy_view = smem_thr_copy_Q.retile_D(tSrQ);
        CUTE_STATIC_ASSERT_V(size<1>(tSsQ) == size<1>(tSrQ_copy_view));            // M
        cute::copy(smem_tiled_copy_Q, tSsQ, tSrQ_copy_view);
    }

    clear(acc_o);

    // For performance reason, we separate out two kinds of iterations:
    // those that need masking on S, and those that don't.
    // We need masking on S for the very last block when K and V has length not multiple of kBlockN.
    // We also need masking on S if it's causal, for the last ceil_div(kBlockM, kBlockN) blocks.
    // We will have at least 1 "masking" iteration.

    // If not even_N, then seqlen_k might end in the middle of a block. In that case we need to
    // mask 2 blocks (e.g. when kBlockM == kBlockN), not just 1.
    constexpr int n_masking_steps = (!Is_causal && !Is_local)
        ? 1
        : ((Is_even_MN && Is_causal) ? cute::ceil_div(kBlockM, kBlockN) : cute::ceil_div(kBlockM, kBlockN) + 1);
    #pragma unroll
    for (int masking_step = 0; masking_step < n_masking_steps; ++masking_step, --n_block) {
        Tensor acc_s = partition_fragment_C(tiled_mma, Shape<Int<kBlockM>, Int<kBlockN>>{});  // (MMA=4, MMA_M, MMA_N)
        clear(acc_s);
        flash::cp_async_wait<0>();
        __syncthreads();

        // Advance gV
        if (masking_step > 0) {
            tVgV.data() = tVgV.data() + (-int(kBlockN * params.v_row_stride));
            flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tVgV, tVsV, tKVcKV, tKVpKV);
        } else {
            // Clear the smem tiles to account for predicated off loads
            flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/true>(
                gmem_tiled_copy_QKV, tVgV, tVsV, tKVcKV, tKVpKV, binfo.actual_seqlen_k - n_block * kBlockN
            );
        }
        cute::cp_async_fence();

        flash::gemm</*A_in_regs=*/Kernel_traits::Is_Q_in_regs>(
            acc_s, tSrQ, tSrK, tSsQ, tSsK, tiled_mma, smem_tiled_copy_Q, smem_tiled_copy_K,
            smem_thr_copy_Q, smem_thr_copy_K
        );
        // if (cute::thread0()) { print(acc_s); }

        // Reshape acc_s from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
        Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
        // if (cute::thread0()) { print_tensor(scores); }
        // We don't put the masking before the matmul S = Q K^T because we don't clear sK
        // for rows outside actual_seqlen_k. So those rows could have Inf / NaN, and the matmul
        // can produce Inf / NaN.
        if (!Is_causal && !Is_local) {
            if (!Is_even_MN) { flash::apply_mask(scores, binfo.actual_seqlen_k - n_block * kBlockN); }
        } else {
            // Tensor caccS = make_identity_tensor(Shape<Int<kBlockM>, Int<kBlockN>>{});    // (BLK_M,BLK_N) -> (blk_m,blk_n)
            // Tensor taccScS = thr_mma.partition_C(caccS);                           // (MMA,MMA_M,MMA_N)
            // static_assert(decltype(size<0>(taccScS))::value == 4);
            // // Convert to ((2, 2), MMA_M, MMA_N) then take only the row indices.
            // Tensor idx_row = logical_divide(taccScS, Shape<_2>{})(make_coord(0, _), _, 0);
            // Tensor idx_rowcol = make_tensor(taccScS.data(), flash::convert_layout_acc_rowcol(taccScS.layout()));
            // flash::apply_mask_causal_w_idx(scores, idx_rowcol, n_block * kBlockN, binfo.actual_seqlen_k,
            //                               m_block * kBlockM);
            // Idk why it's get<1> and not get<0> of the stride.
            // if (cute::thread0()) { print(idx_row.layout()); print(stride<1>(idx_row)); printf("stride = %d \n", get<1>(stride<1>(idx_row))); }
            // I can't get the stride from idx_row
            flash::apply_mask_local</*HasWSLeft=*/Is_local>(
                scores, n_block * kBlockN, binfo.actual_seqlen_k,
                // m_block * kBlockM + get<0>(idx_row(0)),
                m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
                binfo.actual_seqlen_q, kNWarps * 16,
                params.window_size_left, params.window_size_right
                // m_block * kBlockM + (tidx / 32) * 16, kNWarps * 16
                // m_block * kBlockM + (tidx / 32) * (kBlockM / kNWarps), 16
            );
            // if (cute::thread0()) { print_tensor(scores); }
        }

        flash::cp_async_wait<0>();
        __syncthreads();
        if (n_block > n_block_min) {
            // Advance gK
            tKgK.data() = tKgK.data() + (-int(kBlockN * params.k_row_stride));
            flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV);
            // This cp_async_fence needs to be in the if block, otherwise the synchronization
            // isn't right and we get race conditions.
            cute::cp_async_fence();
        }

        // TODO: when we have key_padding_mask we'll need to Check_inf
        masking_step == 0
            ? softmax_rescale_o</*Is_first=*/true,  /*Check_inf=*/Is_causal || Is_local>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2)
            : softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_causal || Is_local>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);

        // Convert scores from fp32 to fp16/bf16
        Tensor rP = flash::convert_type<Element>(scores);
        // Reshape rP from (nrow=(2, MMA_M), ncol=(2, MMA_N)) to ((2, 2, 2), MMA_M, MMA_N / 2)
        // if using m16n8k16 or ((2, 2, 1), MMA_M, MMA_N) if using m16n8k8.
        Tensor tOrP = make_tensor(rP.data(), flash::convert_layout_rowcol_Aregs<Kernel_traits::TiledMma>(rP.layout()));
        // if (cute::thread0()) { print(tOrP); }

        flash::gemm_A_in_regs(acc_o, tOrP, tOrVt, tOsVt, tiled_mma, smem_tiled_copy_V, smem_thr_copy_V);
        // if (cute::thread0()) { print(scores); }

        // This check is at the end of the loop since we always have at least 1 iteration
        if (n_masking_steps > 1 && n_block <= n_block_min) {
            --n_block;
            break;
        }
    }

    // These are the iterations where we don't need masking on S
    for (; n_block >= n_block_min; --n_block) {
        Tensor acc_s = partition_fragment_C(tiled_mma, Shape<Int<kBlockM>, Int<kBlockN>>{});  // (MMA=4, MMA_M, MMA_N)
        clear(acc_s);
        flash::cp_async_wait<0>();
        __syncthreads();
        // Advance gV
        tVgV.data() = tVgV.data() + (-int(kBlockN * params.v_row_stride));
        flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tVgV, tVsV, tKVcKV, tKVpKV);
        cute::cp_async_fence();

        flash::gemm</*A_in_regs=*/Kernel_traits::Is_Q_in_regs>(
            acc_s, tSrQ, tSrK, tSsQ, tSsK, tiled_mma, smem_tiled_copy_Q, smem_tiled_copy_K,
            smem_thr_copy_Q, smem_thr_copy_K
        );

        flash::cp_async_wait<0>();
        __syncthreads();
        if (n_block > n_block_min) {
            // Advance gK
            tKgK.data() = tKgK.data() + (-int(kBlockN * params.k_row_stride));
            flash::copy</*Is_even_MN=*/true, Is_even_K>(gmem_tiled_copy_QKV, tKgK, tKsK, tKVcKV, tKVpKV);
            // This cp_async_fence needs to be in the if block, otherwise the synchronization
            // isn't right and we get race conditions.
            cute::cp_async_fence();
        }

        // Reshape acc_s from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
        Tensor scores = make_tensor(acc_s.data(), flash::convert_layout_acc_rowcol(acc_s.layout()));
        if (Is_local && n_block * kBlockN < (m_block + 1) * kBlockM + binfo.actual_seqlen_k - binfo.actual_seqlen_q + params.window_size_right) {
            flash::apply_mask_local(
                scores, n_block * kBlockN, binfo.actual_seqlen_k,
                m_block * kBlockM + (tidx / 32) * 16 + (tidx % 32) / 4,
                binfo.actual_seqlen_q, kNWarps * 16,
                params.window_size_left, params.window_size_right
            );
        }
        softmax_rescale_o</*Is_first=*/false, /*Check_inf=*/Is_local>(scores, scores_max, scores_sum, acc_o, params.scale_softmax_log2);

        Tensor rP = flash::convert_type<Element>(scores);
        // Reshape rP from (nrow=(2, MMA_M), ncol=(2, MMA_N)) to ((2, 2, 2), MMA_M, MMA_N / 2)
        // if using m16n8k16 or ((2, 2, 1), MMA_M, MMA_N) if using m16n8k8.
        Tensor tOrP = make_tensor(rP.data(), flash::convert_layout_rowcol_Aregs<Kernel_traits::TiledMma>(rP.layout()));

        flash::gemm_A_in_regs(acc_o, tOrP, tOrVt, tOsVt, tiled_mma, smem_tiled_copy_V, smem_thr_copy_V);
    }

    // Epilogue

    // Reshape acc_o from (MMA=4, MMA_M, MMA_K) to (nrow=(2, MMA_M), ncol=(2, MMA_K))
    Tensor acc_o_rowcol = make_tensor(acc_o.data(), flash::convert_layout_acc_rowcol(acc_o.layout()));
    Tensor lse = make_fragment_like(scores_sum);
    #pragma unroll
    for (int mi = 0; mi < size<0>(acc_o_rowcol); ++mi) {
        float sum = scores_sum(mi);
        float inv_sum = (sum == 0.f || sum != sum) ? 1.f : 1.f / sum;
        lse(mi) = (sum == 0.f || sum != sum) ? INFINITY : scores_max(mi) * params.scale_softmax + __logf(sum);
        float scale = inv_sum;
        #pragma unroll
        for (int ni = 0; ni < size<1>(acc_o_rowcol); ++ni) { acc_o_rowcol(mi, ni) *= scale; }
    }

    // if (cute::thread0()) { print(acc_o_rowcol); }

    // Convert acc_o from fp32 to fp16/bf16
    Tensor rO = flash::convert_type<Element>(acc_o);
    Tensor sO = make_tensor(sQ.data(), typename Kernel_traits::SmemLayoutO{});    // (SMEM_M,SMEM_N)
    // Partition sO to match the accumulator partitioning
    auto smem_tiled_copy_O = make_tiled_copy_C(typename Kernel_traits::SmemCopyAtomO{}, tiled_mma);
    auto smem_thr_copy_O = smem_tiled_copy_O.get_thread_slice(tidx);
    Tensor taccOrO = smem_thr_copy_O.retile_S(rO);        // ((Atom,AtomNum), MMA_M, MMA_N)
    Tensor taccOsO = smem_thr_copy_O.partition_D(sO);     // ((Atom,AtomNum),PIPE_M,PIPE_N)

    // sO has the same size as sQ, so we don't need to sync here.
    if (Kernel_traits::Share_Q_K_smem) { __syncthreads(); }

    cute::copy(smem_tiled_copy_O, taccOrO, taccOsO);

    const index_t row_offset_o = binfo.q_offset(params.o_batch_stride, params.o_row_stride, bidb)
        + m_block * kBlockM * params.o_row_stride + bidh * params.o_head_stride;
    Tensor gO = make_tensor(make_gmem_ptr(reinterpret_cast<Element *>(params.o_ptr) + row_offset_o),
                            Shape<Int<kBlockM>, Int<kHeadDim>>{},
                            make_stride(params.o_row_stride, _1{}));

    typename Kernel_traits::GmemTiledCopyO gmem_tiled_copy_O;
    auto gmem_thr_copy_O = gmem_tiled_copy_O.get_thread_slice(tidx);
    Tensor tOsO = gmem_thr_copy_O.partition_S(sO);        // ((Atom,AtomNum),ATOM_M,ATOM_N)
    Tensor tOgO = gmem_thr_copy_O.partition_D(gO);

    __syncthreads();

    Tensor tOrO = make_tensor<Element>(shape(tOgO));
    cute::copy(gmem_tiled_copy_O, tOsO, tOrO);

    // Construct identity layout for sO
    Tensor cO = make_identity_tensor(make_shape(size<0>(sO), size<1>(sO)));    // (BLK_M,BLK_K) -> (blk_m,blk_k)
    // Repeat the partitioning with identity layouts
    Tensor tOcO = gmem_thr_copy_O.partition_D(cO);                           // (ACPY,ACPY_M,ACPY_K) -> (blk_m,blk_k)
    Tensor tOpO = make_tensor<bool>(make_shape(size<2>(tOgO)));
    if (!Is_even_K) {
        #pragma unroll
        for (int k = 0; k < size(tOpO); ++k) { tOpO(k) = get<1>(tOcO(0, 0, k)) < params.d; }
    }
    // Clear_OOB_K must be false since we don't want to write zeros to gmem
    flash::copy<Is_even_MN, Is_even_K, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
        gmem_tiled_copy_O, tOrO, tOgO, tOcO, tOpO, binfo.actual_seqlen_q - m_block * kBlockM
    );
}

////////////////////////////////////////////////////////////////////////////////////////////////////

template<typename Kernel_traits, bool Is_causal, bool Is_local, bool Is_even_MN, bool Is_even_K, typename Params>
inline __device__ void compute_attn(const Params &params) {
    const int m_block = blockIdx.x;
    // The block index for the batch.
    const int bidb = blockIdx.y;
    // The block index for the head.
    const int bidh = blockIdx.z;

    // We want the fwd and bwd to generate the same dropout pattern (RNG), without restricting
    // them to have the same number of threads or have to traverse the attention matrix
    // in the same order.
    // In the Philox RNG, we use the offset to store the batch, head, and the lane id
    // (within a warp). We use the subsequence to store the location of the 16 x 32 blocks within
    // the attention matrix. This way, as long as we have the batch, head, and the location of
    // the 16 x 32 block within the attention matrix, we can generate the exact same dropout pattern.

    flash::compute_attn_1rowblock<Kernel_traits, Is_causal, Is_local, Is_even_MN, Is_even_K>(params, bidb, bidh, m_block);
}

} // namespace flash
