flash-attention/hopper/mainloop_bwd_sm80.hpp

/******************************************************************************
 * Copyright (c) 2024, Tri Dao.
 ******************************************************************************/

#pragma once

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

#include "cute/tensor.hpp"

#include "seqlen.h"
#include "mask.h"
#include "softmax.h"
#include "utils.h"

namespace flash {

using namespace cute;

template <int Stages, int Stages_dO, class TileShape_MNK_, class Element_, class ElementAccum_, class ArchTag_,
        bool Is_causal_, bool Is_local_, bool Has_softcap_, bool Varlen_, bool Deterministic,
        bool SdP_swapAB_, bool dKV_swapAB_, bool dQ_swapAB_,
        int NumMmaWarpGroups=2, int AtomLayoutMSdP=1, int AtomLayoutNdKV=8, int AtomLayoutMdQ=1,
        bool V_in_regs=false>
struct CollectiveMainloopBwdSm80 {

    static constexpr int kStages = Stages;
    static constexpr int kStages_dO = Stages_dO;
    static_assert(kStages >= kStages_dO);
    using TileShape_MNK = TileShape_MNK_;
    using Element = Element_;
    using ElementAccum = ElementAccum_;
    using ArchTag = ArchTag_;
    static constexpr bool Is_causal = Is_causal_;
    static constexpr bool Is_local = Is_local_;
    static constexpr bool Has_softcap = Has_softcap_;
    static constexpr bool Varlen = Varlen_;
    using SeqlenInfo_t = flash::SeqlenInfoQK<Varlen, CUTE_STATIC_V(get<0>(TileShape_MNK{}))>;
    static constexpr int NumMmaWarps = NumMmaWarpGroups * cutlass::NumWarpsPerWarpGroup;

    static constexpr bool SdP_swapAB = SdP_swapAB_;
    static constexpr bool dKV_swapAB = dKV_swapAB_;
    static constexpr bool dQ_swapAB = dQ_swapAB_;

    static constexpr bool Q_dO_same_stages = kStages == kStages_dO;

    static constexpr int kBlockM = get<0>(TileShape_MNK{});
    static constexpr int kBlockN = get<1>(TileShape_MNK{});
    static constexpr int kHeadDim = get<2>(TileShape_MNK{});

    static_assert(ArchTag::kMinComputeCapability >= 80);

    static constexpr bool Has_cp_async = ArchTag::kMinComputeCapability >= 80;

    static constexpr int NumMmaThreads = NumMmaWarps * cutlass::NumThreadsPerWarp;
    static constexpr int NumProducerThreads = NumMmaThreads;  // For compatibility with TileScheduler

    using MMA_Atom_Arch = std::conditional_t<
        ArchTag::kMinComputeCapability >= 80,
        std::conditional_t<
            std::is_same_v<Element, cutlass::half_t>,
            MMA_Atom<SM80_16x8x16_F32F16F16F32_TN>,
            MMA_Atom<SM80_16x8x16_F32BF16BF16F32_TN>
        >,
        MMA_Atom<SM75_16x8x8_F32F16F16F32_TN>
    >;

    static_assert(NumMmaWarps % AtomLayoutMSdP == 0);
    static_assert(NumMmaWarps % AtomLayoutNdKV == 0);
    static_assert(NumMmaWarps % AtomLayoutMdQ == 0);
    static constexpr bool Mma_dKV_is_RS = AtomLayoutMSdP == 1 && AtomLayoutNdKV == NumMmaWarps && SdP_swapAB && !dKV_swapAB;
    static constexpr bool Mma_dQ_is_RS = AtomLayoutMSdP == NumMmaWarps && AtomLayoutMdQ == NumMmaWarps && !SdP_swapAB && !dQ_swapAB;  // If dQ_swapAB we can't use RS

    using AtomLayoutSdP = std::conditional_t<
        !SdP_swapAB,
        Layout<Shape<Int<AtomLayoutMSdP>, Int<NumMmaWarps / AtomLayoutMSdP>, _1>>,
        Layout<Shape<Int<NumMmaWarps / AtomLayoutMSdP>, Int<AtomLayoutMSdP>, _1>>
    >;
    static constexpr bool MmaSdPEvenN = ((!SdP_swapAB ? kBlockN : kBlockM) / size<1>(AtomLayoutSdP{})) % 16 == 0;
    using TiledMmaSdP = TiledMMA<
        MMA_Atom_Arch,
        AtomLayoutSdP,
        Tile<Int<16 * CUTE_STATIC_V(size<0>(AtomLayoutSdP{}))>, Int<(MmaSdPEvenN ? 16 : 8) * CUTE_STATIC_V(size<1>(AtomLayoutSdP{}))>, _16>>;

    using AtomLayoutdKV = std::conditional_t<
        !dKV_swapAB,
        Layout<Shape<Int<AtomLayoutNdKV>, Int<NumMmaWarps / AtomLayoutNdKV>, _1>>,
        Layout<Shape<Int<NumMmaWarps / AtomLayoutNdKV>, Int<AtomLayoutNdKV>, _1>>
    >;
    static constexpr bool MmadKVEvenN = ((!dKV_swapAB ? kHeadDim : kBlockN) / size<1>(AtomLayoutdKV{})) % 16 == 0;
    using TiledMmadKV = TiledMMA<
        MMA_Atom_Arch,
        AtomLayoutdKV,
        Tile<Int<16 * CUTE_STATIC_V(size<0>(AtomLayoutdKV{}))>, Int<(MmadKVEvenN ? 16 : 8) * CUTE_STATIC_V(size<1>(AtomLayoutdKV{}))>, _16>>;

    using AtomLayoutdQ = std::conditional_t<
        !dQ_swapAB,
        Layout<Shape<Int<AtomLayoutMdQ>, Int<NumMmaWarps / AtomLayoutMdQ>, _1>>,
        Layout<Shape<Int<NumMmaWarps / AtomLayoutMdQ>, Int<AtomLayoutMdQ>, _1>>
    >;
    static constexpr bool MmadQEvenN = ((!dQ_swapAB ? kHeadDim : kBlockM) / size<1>(AtomLayoutdQ{})) % 16 == 0;
    using TiledMmadQ = TiledMMA<
        MMA_Atom_Arch,
        AtomLayoutdQ,
        Tile<Int<16 * CUTE_STATIC_V(size<0>(AtomLayoutdQ{}))>, Int<(MmadQEvenN ? 16 : 8) * CUTE_STATIC_V(size<1>(AtomLayoutdQ{}))>, _16>>;

    static constexpr int kGmemElemsPerLoad = sizeof(cute::uint128_t) / sizeof(Element);
    static_assert(kHeadDim % kGmemElemsPerLoad == 0, "Headdim must be a multiple of kGmemElemsPerLoad");
    // We want each "row" to have 64 elements (128 bytes, i.e. 1 cache line). E.g. if hdim=128, we want each
    // thread to have 4 loads in the M direction and 2 vectorized load in the K direction.
    static constexpr int kBytePerRow = kHeadDim * sizeof(Element);
    static constexpr int kBlockKGmem = (kBytePerRow % 128 == 0 ? 128 : (kBytePerRow % 64 == 0 ? 64 : 32)) / sizeof(Element);

    static constexpr int kSwizzle = kBlockKGmem == 128 ? 4 : (kBlockKGmem == 64 ? 3 : (kBlockKGmem == 32 ? 2 : 1));
    static constexpr int kSwizzleBase = sizeof(Element) == 4 ? 2 : (sizeof(Element) == 2 ? 3 : 4);

    // We need to accommodate both Q and Q^T (and dO and dO^T) in shared memory.
    // Q & dO are used in the SdP Mma and Q^T and dO^T are used in the dKV Mma.
    // Since this is GMMA::Major::K, the M dimension (kBlockM) doesn't matter for the layout, only the K dimension
    // changes the layout.
    using SmemLayoutAtomQdO = decltype(
        composition(Swizzle<kSwizzle, kSwizzleBase, kSwizzleBase>{},
                    Layout<Shape<_8, Int<kBlockKGmem>>,
                           Stride<Int<kBlockKGmem>, _1>>{}));
    using SmemLayoutQ =
        decltype(tile_to_shape(SmemLayoutAtomQdO{},
                 make_shape(shape<0>(TileShape_MNK{}), shape<2>(TileShape_MNK{}), Int<kStages>{})));
    using SmemLayoutdO =
        decltype(tile_to_shape(SmemLayoutAtomQdO{},
                 make_shape(shape<0>(TileShape_MNK{}), shape<2>(TileShape_MNK{}), Int<kStages_dO>{})));

    using SmemLayoutAtomKV = decltype(
        composition(Swizzle<kSwizzle, kSwizzleBase, kSwizzleBase>{},
                    // TODO: FA2 has a slightly different layout, does it matter?
                    Layout<Shape<_8, Int<kBlockKGmem>>,
                           Stride<Int<kBlockKGmem>, _1>>{}));
    using SmemLayoutK = decltype(tile_to_shape(SmemLayoutAtomKV{}, select<1, 2>(TileShape_MNK{})));

    using SmemLayoutV = decltype(tile_to_shape(SmemLayoutAtomKV{}, select<1, 2>(TileShape_MNK{})));

    // TD [2023-03-19]: Idk why kPBlockN = 16 and kSwizzlePdS=3 is the fastest.
    static constexpr int kPBlockN = kBlockN % 64 == 0 ? 64 : (kBlockN % 32 == 0 ? 32 : 16);
    static_assert(kPBlockN == 16 || kPBlockN == 32 || kPBlockN == 64);
    // static constexpr int kSwizzlePdS = kPBlockN == 16 ? 1 : (kPBlockN == 32 ? 2 : 3);
    static constexpr int kSwizzlePdS = 3;
    using SmemLayoutAtomPdS = decltype(
        composition(Swizzle<kSwizzlePdS, kSwizzleBase, kSwizzleBase>{},
                    Layout<Shape<Int<kBlockM>, Int<kPBlockN>>,
                           Stride<Int<kPBlockN>, _1>>{}));
    using SmemLayoutPdS = decltype(tile_to_shape(
        SmemLayoutAtomPdS{},
        make_shape(Int<kBlockM>{}, Int<kBlockN>{})));

    // We set stride to be multiple of 64 so that if ShuffleLSE, even if threads read from sLSE but out of bounds,
    // it's still a valid smem address.
    using SmemLayoutLSE = cute::Layout<cute::Shape<Int<kBlockM>, Int<kStages>>, cute::Stride<_1, Int<cute::round_up(kBlockM, 64)>>>;
    using SmemLayoutLSEMma = std::conditional_t<
        SdP_swapAB,
        cute::Layout<cute::Shape<Int<kBlockN>, Int<kBlockM>, Int<kStages>>, cute::Stride<_0, _1, Int<cute::round_up(kBlockM, 64)>>>,
        cute::Layout<cute::Shape<Int<kBlockM>, Int<kBlockN>, Int<kStages>>, cute::Stride<_1, _0, Int<cute::round_up(kBlockM, 64)>>>
    >;

    // Note this is the transpose in terms of the view, not in terms of memory.
    using SmemLayoutQt =
        decltype(cute::composition(SmemLayoutQ{},
                                   make_layout(make_shape(get<2>(TileShape_MNK{}), get<0>(TileShape_MNK{}), Int<kStages>{}),
                                               make_stride(Int<kBlockM>{}, _1{}, Int<kBlockM * kHeadDim>{}))));
    using SmemLayoutdOt =
        decltype(cute::composition(SmemLayoutdO{},
                                   make_layout(make_shape(get<2>(TileShape_MNK{}), get<0>(TileShape_MNK{}), Int<kStages_dO>{}),
                                               make_stride(Int<kBlockM>{}, _1{}, Int<kBlockM * kHeadDim>{}))));
    using SmemLayoutKt =
        decltype(cute::composition(SmemLayoutK{},
                                   make_layout(make_shape(get<2>(TileShape_MNK{}), get<1>(TileShape_MNK{})),
                                               make_stride(Int<kBlockN>{}, _1{}))));
    using SmemLayoutPdSt =
        decltype(cute::composition(SmemLayoutPdS{},
                                   make_layout(make_shape(Int<kBlockN>{}, Int<kBlockM>{}),
                                               make_stride(Int<kBlockM>{}, _1{}))));

    // Thread layout, 256 or 384 threads per row
    using R2SLayoutAtomdQaccum = Layout<Shape<Int<NumMmaThreads>>>;
    using R2STiledCopydQaccum = decltype(make_tiled_copy(Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, ElementAccum>{}, R2SLayoutAtomdQaccum{},
                                                         Layout<Shape < _1>>{}));  // Val layout, 1 vals per store

    using SmemCopyAtom = Copy_Atom<SM75_U32x4_LDSM_N, Element>;
    using SmemCopyAtomTransposed = Copy_Atom<SM75_U16x8_LDSM_T, Element>;
    // For the case where the N dimension of MmaSdP is divisible by 8 but not by 16
    using SmemCopyAtomHalf = Copy_Atom<SM75_U32x2_LDSM_N, Element>;
    // For the case where the N dimension of MmadQ is divisible by 8 but not by 16
    using SmemCopyAtomTransposedHalf = Copy_Atom<SM75_U16x4_LDSM_T, Element>;
    // If !SdP_swapAB, the accum registers hold P / dS, otherwise they hold Pt / dSt.
    // If PdS_major is MN, then we need to "transpose" the write.
    // TODO: check this write
    using R2SCopyAtomPdS = Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, Element>;

    // We use CACHEGLOBAL instead of CACHEALWAYS for both Q and K/V, since we won't be reading
    // from the same address by the same threadblock. This is slightly faster.
    using GmemCopyStruct = std::conditional_t<
        Has_cp_async,
        SM80_CP_ASYNC_CACHEGLOBAL_ZFILL<cute::uint128_t>,
        AutoVectorizingCopyWithAssumedAlignment<128>
    >;
    using GmemCopyAtom = Copy_Atom<GmemCopyStruct, Element>;

    static constexpr int kGmemThreadsPerRow = kBlockKGmem / kGmemElemsPerLoad;
    static_assert(NumMmaThreads % kGmemThreadsPerRow == 0, "NumMmaThreads must be a multiple of kGmemThreadsPerRow");
    using GmemLayoutAtom = Layout<Shape <Int<NumMmaThreads / kGmemThreadsPerRow>, Int<kGmemThreadsPerRow>>,
                                  Stride<Int<kGmemThreadsPerRow>, _1>>;
    using GmemTiledCopyQKV = decltype(
        make_tiled_copy(GmemCopyAtom{},
                        GmemLayoutAtom{},
                        Layout<Shape<_1, Int<kGmemElemsPerLoad>>>{}));  // Val layout, 8 or 16 vals per read
    using GmemCopyAtomLSE = Copy_Atom<GmemCopyStruct, float>;
    using GmemLayoutAtomLSE = Layout<Shape<Int<NumMmaThreads>>>;
    using GmemTiledCopyLSE = decltype(make_tiled_copy(GmemCopyAtomLSE{}, GmemLayoutAtomLSE{},
                                                      Layout<Shape<_4>>{}));  // Val layout, 4 vals per store
    // So that we don't have to check if we overshot kBlockM when we load Q
    // static_assert(kBlockM % CUTE_STATIC_V(shape<0>(GmemLayoutAtom{})) == 0);

    using ShapeQKV = cute::Shape<int32_t, int32_t, int32_t, int32_t>;  // (seqlen, d, head, batch)
    using StrideQKV = cute::Stride<int64_t, _1, int64_t, int64_t>;
    using ShapeLSE = cute::Shape<int32_t, int32_t, int32_t>;  // (seqlen, head, batch)
    using StrideLSE = cute::Stride<_1, int64_t, int64_t>;  // (seqlen, head, batch)
    using ShapedQaccum = cute::Shape<int32_t, int32_t, int32_t>;  // (seqlen_q * d, head, batch)
    using StridedQaccum = cute::Stride<_1, int64_t, int64_t>;

    // These are tuned for speed. They don't affect correctness.
    // We have separate iterations with causal masking. Not necessary for hdim 128 but for hdim 64
    // this helps quite a bit to not have to do causal masking for most of the iterations.
    // For hdim 192, separating masking iterations results in register spills.
    // static constexpr bool SeparateMaskingIterations = kHeadDim <= 64;
    static constexpr bool SeparateMaskingIterations = false;
    // Do we keep the LSE and dPsum in each thread, or split them across 8 threads that share them and then
    // shuffle to get the value whenever we need? This can reduce register pressure when SdP_swapAB, where each
    // thread needs to keep statistics for (kBlockM / 4) rows. If !SdP_swapAB, each thread only needs to keep
    // statistic for 2 rows.
    // static constexpr bool ShuffleLSE = SdP_swapAB && kHeadDim <= 64;
    // static constexpr bool ShuffledPsum = SdP_swapAB && kHeadDim <= 64;
    static constexpr bool ShuffleLSE = SdP_swapAB && false;
    static constexpr bool ShuffledPsum = SdP_swapAB && false;

    static constexpr bool Share_QV_Smem = V_in_regs;
    using SmemP_t = std::conditional_t<Mma_dKV_is_RS, cute::array<Element, 0>, cute::array_aligned<Element, cute::cosize_v<SmemLayoutPdS>>>;

    struct TensorStorageSharedQV : cute::aligned_struct<128> {
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutK>> smem_k;
        union {
            cute::array_aligned<Element, cute::cosize_v<SmemLayoutV>> smem_v;
            cute::array_aligned<Element, cute::cosize_v<SmemLayoutQ>> smem_q;
        };
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutdO>> smem_do;
        cute::array_aligned<ElementAccum, cute::cosize_v<SmemLayoutLSE>, 128> smem_lse;
        cute::array_aligned<ElementAccum, cute::cosize_v<SmemLayoutLSE>, 128> smem_dpsum;
        SmemP_t smem_p;
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutPdS>> smem_ds;
    };

    struct TensorStorageSeparateQV : cute::aligned_struct<128> {
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutK>> smem_k;
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutV>> smem_v;
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutQ>> smem_q;
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutdO>> smem_do;
        cute::array_aligned<ElementAccum, cute::cosize_v<SmemLayoutLSE>, 128> smem_lse;
        cute::array_aligned<ElementAccum, cute::cosize_v<SmemLayoutLSE>, 128> smem_dpsum;
        SmemP_t smem_p;
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutPdS>> smem_ds;
    };

    using TensorStorage = std::conditional_t<Share_QV_Smem, TensorStorageSharedQV, TensorStorageSeparateQV>;

    // Host side kernel arguments
    struct Arguments {
        Element const* const ptr_Q;
        ShapeQKV const shape_Q;
        StrideQKV const stride_Q;
        Element const* const ptr_K;
        ShapeQKV const shape_K;
        StrideQKV const stride_K;
        Element const* const ptr_V;
        StrideQKV const stride_V;
        Element const* const ptr_dO;
        StrideQKV const stride_dO;
        ElementAccum* const ptr_dQaccum;
        ShapedQaccum const shape_dQaccum;
        StridedQaccum const stride_dQaccum;
        float const* const ptr_LSE_log2;
        ShapeLSE const shape_LSE;
        StrideLSE const stride_LSE_log2;
        float const* const ptr_dPsum;
        StrideLSE const stride_dPsum;
        float const softmax_scale;
        int const window_size_left, window_size_right, sink_token_length;
        float const softcap_val;
        int const num_batch;
        int* const dq_semaphore;
        int const* const cu_seqlens_q = nullptr;
        int const* const cu_seqlens_k = nullptr;
        int const* const seqused_q = nullptr;
        int const* const seqused_k = nullptr;
    };

    // Device side kernel params
    struct Params {
        Element const* const ptr_Q;
        ShapeQKV const shape_Q;
        StrideQKV const stride_Q;
        Element const* const ptr_K;
        ShapeQKV const shape_K;
        StrideQKV const stride_K;
        Element const* const ptr_V;
        StrideQKV const stride_V;
        Element const* const ptr_dO;
        StrideQKV const stride_dO;
        ElementAccum* const ptr_dQaccum;
        ShapedQaccum const shape_dQaccum;
        StridedQaccum stride_dQaccum;
        cutlass::FastDivmod qhead_per_khead_divmod;
        float const* const ptr_LSE_log2;
        ShapeLSE const shape_LSE;
        StrideLSE const stride_LSE_log2;
        float const* const ptr_dPsum;
        StrideLSE const stride_dPsum;
        float const softmax_scale, softmax_scale_log2;
        int const window_size_left, window_size_right, sink_token_length;
        float const softcap_val;
        int const num_batch;
        int *const dq_semaphore;
        int const *const cu_seqlens_q = nullptr;
        int const *const cu_seqlens_k = nullptr;
        int const *const seqused_q = nullptr;
        int const *const seqused_k = nullptr;
    };

    static Params
    to_underlying_arguments(Arguments const& args) {
        if constexpr (Deterministic) { assert(args.dq_semaphore != nullptr); }
        // If there's tanh softcapping, we do tanh(scores * softmax_scale / softcap_val) * softcap_val.
        // Right after this, we multiply by log2(e) before applying exp2.
        // To reduce the number of instructions, we instead pre-multiply softmax_scale / softcap_val
        // (assigning it to params.softcap_val) and pre-multiply softcap_val * log2(e)
        // (assigning it to params.softmax_scale_log2).
        // In the backward, we need to multiply by
        // (1 - tanh^2) * softmax_scale / softcap_val * softcap_val = (1 - tanh^2) * softmax_scale.
        // Instead we multiply by (1 - tanh^2) and multiply dK and dV by params.softmax_scale
        // (the original softmax_scale) at the end.
        return {args.ptr_Q, args.shape_Q, args.stride_Q,
                args.ptr_K, args.shape_K, args.stride_K,
                args.ptr_V, args.stride_V,
                args.ptr_dO, args.stride_dO,
                args.ptr_dQaccum, args.shape_dQaccum, args.stride_dQaccum,
                cutlass::FastDivmod(cute::ceil_div(get<2>(args.shape_Q), get<2>(args.shape_K))),
                args.ptr_LSE_log2, args.shape_LSE, args.stride_LSE_log2, args.ptr_dPsum, args.stride_dPsum,
                args.softmax_scale,
                !Has_softcap ? float(args.softmax_scale * M_LOG2E) : float(args.softcap_val * M_LOG2E),
                args.window_size_left, args.window_size_right, args.sink_token_length,
                !Has_softcap ? 0.f : args.softmax_scale / args.softcap_val,
                args.num_batch, args.dq_semaphore,
                args.cu_seqlens_q, args.cu_seqlens_k, args.seqused_q, args.seqused_k};
    }

    CUTLASS_DEVICE
    cute::tuple<int, int> get_m_block_min_max(Params const& params, SeqlenInfo_t const& seqlen_info,
                                              int n_block, int bidb) {
        static constexpr int kBlockM = get<0>(TileShape_MNK{});
        int const seqlen_q = seqlen_info.seqlen_q;
        int const seqlen_k = seqlen_info.seqlen_k;
        int m_block_max = cute::ceil_div(seqlen_q, kBlockM);
        if constexpr (Is_local) {
            static constexpr int kBlockN = get<1>(TileShape_MNK{});
            if (n_block >= cute::ceil_div(params.sink_token_length, kBlockN)) {
                m_block_max = std::min(m_block_max, cute::ceil_div((n_block + 1) * kBlockN + seqlen_q - seqlen_k + params.window_size_left, kBlockM));
            }
        }
        int m_block_min = 0;
        if constexpr (Is_causal || Is_local) {
            m_block_min = std::max(m_block_min, (n_block * kBlockN + seqlen_q - seqlen_k - params.window_size_right) / kBlockM);
        }
        return {m_block_min, m_block_max};
    }

    template <typename SharedStorage, typename FrgTensordKV>
    CUTLASS_DEVICE bool
    mma(Params const& params,
        FrgTensordKV& tdKrdK,
        FrgTensordKV& tdVrdV,
        int thread_idx,
        cute::tuple<int32_t, int32_t, int32_t> block_coord,
        SharedStorage& shared_storage
        ) {
        static_assert(is_rmem<FrgTensordKV>::value, "dK and dV tensor must be rmem resident.");

        int n_block = get<0>(block_coord);
        int bidh = get<1>(block_coord);
        int bidb = get<2>(block_coord);
        SeqlenInfo_t seqlen_info{
            bidb, get<0>(params.shape_Q), size<0>(params.shape_K),
            params.cu_seqlens_q, params.cu_seqlens_k, params.seqused_q, params.seqused_k
        };
        auto m_block_min_max = get_m_block_min_max(params, seqlen_info, n_block, bidb);
        int const m_block_min = get<0>(m_block_min_max);
        int const m_block_max = get<1>(m_block_min_max);
        // It's possible to have m_block_max <= m_block_min. Exit early
        if constexpr (Is_causal || Is_local || Varlen) {
            if (m_block_max <= m_block_min) { return false; }
        }

        Tensor sQ = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_q.data()), SmemLayoutQ{});
        Tensor sdO = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_do.data()), SmemLayoutdO{});
        Tensor sK = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_k.data()), SmemLayoutK{});
        Tensor sV = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_v.data()), SmemLayoutV{});
        Tensor sQt = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_q.data()), SmemLayoutQt{});
        Tensor sdOt = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_do.data()), SmemLayoutdOt{});
        Tensor sKt = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_k.data()), SmemLayoutKt{});
        Tensor sP = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_p.data()), SmemLayoutPdS{});
        Tensor sPt = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_p.data()), SmemLayoutPdSt{});
        Tensor sdS = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_ds.data()), SmemLayoutPdS{});
        Tensor sdSt = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_ds.data()), SmemLayoutPdSt{});
        Tensor sLSE = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_lse.data()), SmemLayoutLSE{});
        Tensor sdPsum = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_dpsum.data()), SmemLayoutLSE{});
        Tensor sLSEMma = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_lse.data()), SmemLayoutLSEMma{});
        Tensor sdPsumMma = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_dpsum.data()), SmemLayoutLSEMma{});

        bool const is_varlen_q = Varlen && params.cu_seqlens_q;
        bool const is_varlen_k = Varlen && params.cu_seqlens_k;
        int bidh_kv = params.qhead_per_khead_divmod.divide(bidh);
        Tensor mQ = make_tensor(make_gmem_ptr(params.ptr_Q), params.shape_Q, params.stride_Q)(_, _, bidh, !is_varlen_q ? bidb : 0);
        Tensor mdO = make_tensor(make_gmem_ptr(params.ptr_dO), params.shape_Q, params.stride_dO)(_, _, bidh, !is_varlen_q ? bidb : 0);
        Tensor mK = make_tensor(make_gmem_ptr(params.ptr_K), params.shape_K, params.stride_K)(_, _, bidh_kv, !is_varlen_k ? bidb : 0);
        Tensor mV = make_tensor(make_gmem_ptr(params.ptr_V), params.shape_K, params.stride_V)(_, _, bidh_kv, !is_varlen_k ? bidb : 0);
        Tensor mLSE = make_tensor(make_gmem_ptr(params.ptr_LSE_log2), params.shape_LSE, params.stride_LSE_log2)(_, bidh, !is_varlen_q ? bidb : 0);
        Tensor mdPsum = make_tensor(make_gmem_ptr(params.ptr_dPsum), params.shape_LSE, params.stride_dPsum)(_, bidh, !is_varlen_q ? bidb : 0);
        Tensor mdQaccum = make_tensor(make_gmem_ptr(reinterpret_cast<ElementAccum*>(params.ptr_dQaccum)),
                                      params.shape_dQaccum, params.stride_dQaccum)(_, bidh, !is_varlen_q ? bidb : 0);

        Tensor gQ = local_tile(domain_offset(make_coord(seqlen_info.offset_q, _0{}), mQ), select<0, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (M, K, _)
        Tensor gdO = local_tile(domain_offset(make_coord(seqlen_info.offset_q, _0{}), mdO), select<0, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (M, K, _)
        Tensor gK = local_tile(domain_offset(make_coord(seqlen_info.offset_k, _0{}), mK), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{}));  // (N, K)
        Tensor gV = local_tile(domain_offset(make_coord(seqlen_info.offset_k, _0{}), mV), select<1, 2>(TileShape_MNK{}), make_coord(n_block, _0{}));  // (N, K)
        Tensor gLSE = local_tile(domain_offset(make_coord(seqlen_info.offset_q_padded), mLSE), select<0>(TileShape_MNK{}), make_coord(_));  // (M, _)
        Tensor gdPsum = local_tile(domain_offset(make_coord(seqlen_info.offset_q_padded), mdPsum), select<0>(TileShape_MNK{}), make_coord(_));  // (M, _)
        Tensor gdQaccum = local_tile(domain_offset(make_coord(seqlen_info.offset_q_padded * kHeadDim), mdQaccum), Shape<Int<kBlockM * kHeadDim>>{}, make_coord(_));  // (M * K, _)

        GmemTiledCopyQKV gmem_tiled_copy_QKV;
        auto gmem_thr_copy_QKV = gmem_tiled_copy_QKV.get_thread_slice(thread_idx);
        auto gmem_thr0_copy_QKV = gmem_tiled_copy_QKV.get_thread_slice(_0{});  // For index calculation
        GmemTiledCopyLSE gmem_tiled_copy_lse;
        auto gmem_thr_copy_lse = gmem_tiled_copy_lse.get_thread_slice(thread_idx);
        R2STiledCopydQaccum r2s_tiled_copy_dQaccum;
        auto r2s_thr_copy_dQaccum = r2s_tiled_copy_dQaccum.get_thread_slice(thread_idx);

        Tensor tQgQ = gmem_thr_copy_QKV.partition_S(gQ);
        Tensor tQsQ = gmem_thr_copy_QKV.partition_D(sQ);
        Tensor tdOgdO = gmem_thr_copy_QKV.partition_S(gdO);
        Tensor tdOsdO = gmem_thr_copy_QKV.partition_D(sdO);
        Tensor tLSEgLSE = gmem_thr_copy_lse.partition_S(gLSE);
        Tensor tLSEsLSE = gmem_thr_copy_lse.partition_D(sLSE);
        Tensor tLSEgdPsum = gmem_thr_copy_lse.partition_S(gdPsum);
        Tensor tLSEsdPsum = gmem_thr_copy_lse.partition_D(sdPsum);
        // We can reuse r2s_thr_copy_dQaccum for this partitioning
        Tensor tdQgdQaccum = r2s_thr_copy_dQaccum.partition_D(gdQaccum);
        // if (blockIdx.x == 0 && threadIdx.x == 128) { print(mdQaccum); printf("\n"); print(gdQaccum_); printf("\n"); print(gdQaccum); printf("\n"); print(tdQgdQaccum); printf("\n"); }

        TiledMmaSdP tiled_mma_SdP;
        TiledMmadKV tiled_mma_dKV;
        TiledMmadQ tiled_mma_dQ;

        auto thr_mma_SdP = tiled_mma_SdP.get_thread_slice(thread_idx);
        auto thr_mma_dKV = tiled_mma_dKV.get_thread_slice(thread_idx);
        auto thr_mma_dQ = tiled_mma_dQ.get_thread_slice(thread_idx);

        // Allocate "fragments/descriptors"
        // We have to use the templated mma_partition_fragment_AB instead of cute::conditional_return or lambda,
        // because some partition_fragment_A/B don't compile.
        // https://stackoverflow.com/questions/50051473/if-constexpr-in-c17-does-not-work-in-a-non-templated-function
        Tensor tdPrV = mma_partition_fragment_AB</*A=*/SdP_swapAB>(thr_mma_SdP, sV);

        // Copy Atom retiling
        auto smem_copy_atom_SdP_B = cute::conditional_return<MmaSdPEvenN>(SmemCopyAtom{}, SmemCopyAtomHalf{});
        auto smem_tiled_copy_QdO = cute::conditional_return<!SdP_swapAB>(make_tiled_copy_A(SmemCopyAtom{}, tiled_mma_SdP), make_tiled_copy_B(smem_copy_atom_SdP_B, tiled_mma_SdP));
        auto smem_thr_copy_QdO = smem_tiled_copy_QdO.get_thread_slice(thread_idx);
        Tensor tSsQ = smem_thr_copy_QdO.partition_S(sQ);
        Tensor tdPsdO = smem_thr_copy_QdO.partition_S(sdO);

        auto smem_tiled_copy_KV = cute::conditional_return<!SdP_swapAB>(make_tiled_copy_B(smem_copy_atom_SdP_B, tiled_mma_SdP), make_tiled_copy_A(SmemCopyAtom{}, tiled_mma_SdP));
        auto smem_thr_copy_KV = smem_tiled_copy_KV.get_thread_slice(thread_idx);
        Tensor tSsK = smem_thr_copy_KV.partition_S(sK);
        Tensor tdPsV = smem_thr_copy_KV.partition_S(sV);

        auto r2s_tiled_copy_PdS = make_tiled_copy_C(R2SCopyAtomPdS{}, tiled_mma_SdP);
        auto r2s_thr_copy_PdS = r2s_tiled_copy_PdS.get_thread_slice(thread_idx);
        Tensor tPsP = r2s_thr_copy_PdS.partition_D(cute::conditional_return<!SdP_swapAB>(sP, sPt));      // ((Atom,AtomNum),PIPE_M,PIPE_N)
        Tensor tdSsdS = r2s_thr_copy_PdS.partition_D(cute::conditional_return<!SdP_swapAB>(sdS, sdSt));      // ((Atom,AtomNum),PIPE_M,PIPE_N)
        // if (blockIdx.x == 0 && threadIdx.x == 128) { print(r2s_thr_copy_PdS); print(sP); printf("\n"); print(sPt); printf("\n"); print(tPsP); printf("\n"); print(tdSsdS); printf("\n"); }

        auto smem_copy_atom_dKV_B = cute::conditional_return<MmadKVEvenN>(SmemCopyAtomTransposed{}, SmemCopyAtomTransposedHalf{});
        auto smem_tiled_copy_PdSt = cute::conditional_return<!dKV_swapAB>(make_tiled_copy_A(SmemCopyAtomTransposed{}, tiled_mma_dKV), make_tiled_copy_B(smem_copy_atom_dKV_B, tiled_mma_dKV));
        auto smem_thr_copy_PdSt = smem_tiled_copy_PdSt.get_thread_slice(thread_idx);
        Tensor tdVsPt = smem_thr_copy_PdSt.partition_S(sPt);
        Tensor tdKsdSt = smem_thr_copy_PdSt.partition_S(sdSt);

        auto smem_tiled_copy_QdOt = cute::conditional_return<!dKV_swapAB>(make_tiled_copy_B(smem_copy_atom_dKV_B, tiled_mma_dKV), make_tiled_copy_A(SmemCopyAtomTransposed{}, tiled_mma_dKV));
        auto smem_thr_copy_QdOt = smem_tiled_copy_QdOt.get_thread_slice(thread_idx);
        Tensor tdVsdOt = smem_thr_copy_QdOt.partition_S(sdOt);
        Tensor tdKsQt = smem_thr_copy_QdOt.partition_S(sQt);

        auto smem_tiled_copy_dS = cute::conditional_return<!dQ_swapAB>(
            make_tiled_copy_A(SmemCopyAtom{}, tiled_mma_dQ),
            make_tiled_copy_B(cute::conditional_return<MmadQEvenN>(SmemCopyAtom{}, SmemCopyAtomHalf{}), tiled_mma_dQ));
        auto smem_thr_copy_dS = smem_tiled_copy_dS.get_thread_slice(thread_idx);
        Tensor tdQsdS = smem_thr_copy_dS.partition_S(sdS);

        auto smem_tiled_copy_Kt = cute::conditional_return<!dQ_swapAB>(
            make_tiled_copy_B(cute::conditional_return<MmadQEvenN>(SmemCopyAtomTransposed{}, SmemCopyAtomTransposedHalf{}), tiled_mma_dQ),
            make_tiled_copy_A(SmemCopyAtomTransposed{}, tiled_mma_dQ));
        auto smem_thr_copy_Kt = smem_tiled_copy_Kt.get_thread_slice(thread_idx);
        Tensor tdQsKt = smem_thr_copy_Kt.partition_S(sKt);

        // thr_mma_SdP.partition_C(sLSEMma) has shape (MMA=4, MMA_M, MMA_N, PIPE), we only take the col indices
        // or row indices, depending on whether SdP_swapAB.
        Tensor tSsLSEMma = logical_divide(thr_mma_SdP.partition_C(sLSEMma), Shape<_2>{});  // (2, 2, MMA_M, MMA_N, PIPE)
        Tensor tSsLSE = group_modes<0, 2>(cute::conditional_return<!SdP_swapAB>(
            tSsLSEMma(make_coord(_0{}, _), _, _0{}, _),  // (2, MMA_M, PIPE)
            tSsLSEMma(make_coord(_, _0{}), _0{}, _, _)));  // (2, MMA_N, PIPE)
        Tensor tSsdPsumMma = logical_divide(thr_mma_SdP.partition_C(sdPsumMma), Shape<_2>{});
        Tensor tSsdPsum = group_modes<0, 2>(cute::conditional_return<!SdP_swapAB>(
            tSsdPsumMma(make_coord(_0{}, _), _, _0{}, _),  // (2, MMA_M, PIPE)
            tSsdPsumMma(make_coord(_, _0{}), _0{}, _, _)));  // (2, MMA_N, PIPE)
        // if (blockIdx.x == 0 && threadIdx.x == 128) { print(sLSEMma); printf("\n"); print(tLSEsLSE); printf("\n"); }
        // If we want to split the stats among the 8 threads that share the same rows.
        static constexpr int kStatsPerThread = cute::ceil_div(decltype(size(tSsLSE))::value, 8);

        // Predicates
        Tensor cQ = cute::make_identity_tensor(select<0, 2>(TileShape_MNK{}));
        Tensor tQcQ = gmem_thr_copy_QKV.partition_S(cQ);
        Tensor t0QcQ = gmem_thr0_copy_QKV.partition_S(cQ);
        Tensor tQpQ = make_tensor<bool>(make_shape(size<2>(tQsQ)));
        #pragma unroll
        for (int k = 0; k < size(tQpQ); ++k) { tQpQ(k) = get<1>(tQcQ(_0{}, _0{}, k)) < get<1>(params.shape_Q); }
        Tensor cLSE = cute::make_identity_tensor(select<0>(TileShape_MNK{}));
        Tensor tLSEcLSE = gmem_thr_copy_lse.partition_S(cLSE);

        int const seqlen_q = seqlen_info.seqlen_q;
        int const seqlen_k = seqlen_info.seqlen_k;

        flash::Mask<kBlockM, kBlockN, false /*PackGQA*/, TiledMmaSdP, SdP_swapAB> mask(
            thread_idx, seqlen_q, seqlen_k, params.window_size_left, params.window_size_right, params.sink_token_length,
            params.qhead_per_khead_divmod
        );

        {
            Tensor tKgK = gmem_thr_copy_QKV.partition_S(gK);  // (KCPY, KCPY_N, KCPY_K, nblocksN)
            Tensor tKsK = gmem_thr_copy_QKV.partition_D(sK);
            Tensor tVgV = gmem_thr_copy_QKV.partition_S(gV);  // (VCPY, VCPY_N, VCPY_K, nblocksN)
            Tensor tVsV = gmem_thr_copy_QKV.partition_D(sV);
            // Predicates
            Tensor cKV = cute::make_identity_tensor(select<1, 2>(TileShape_MNK{}));
            Tensor tKVcKV = gmem_thr_copy_QKV.partition_S(cKV);
            Tensor t0KVcKV = gmem_thr0_copy_QKV.partition_S(cKV);
            Tensor tKVpKV = make_tensor<bool>(make_shape(size<2>(tKsK)));
            #pragma unroll
            for (int k = 0; k < size(tKVpKV); ++k) { tKVpKV(k) = get<1>(tKVcKV(_0{}, _0{}, k)) < get<1>(params.shape_K); }
            // Do we need bound check to make sure the row doesn't go above kBlockN
            static constexpr bool EvenN = kBlockN % CUTE_STATIC_V(shape<0>(GmemLayoutAtom{})) == 0;
            // static_assert(EvenN);  // It simplifies the loading of K and V
            // Instead of passing in tKVcKV, we pass in t0KVcKV and subtract the offset from the limit
            // (seqlen_k - n_block * kBlockN). This is because the entries of t0KVcKV are known at compile time.
            // int const seqlenk_row_limit = -int(get<0>(tKVcKV(_0{}, _0{}, _0{}))) + (EvenN
            //     ? seqlen_info.seqlen_k - n_block * kBlockN
            //     : std::min(seqlen_info.seqlen_k - n_block * kBlockN, kBlockN));
            // // Need Clear_OOB_MN to be true here since the gemm will sum over the kBlockN dimension
            // flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/true, /*Clear_OOB_K=*/true>(
            //     gmem_tiled_copy_QKV, tVgV, tVsV, t0KVcKV, tKVpKV, seqlenk_row_limit);
            int const seqlenk_row_limit = seqlen_k - n_block * kBlockN - get<0>(tKVcKV(_0{}, _0{}, _0{}));
            #pragma unroll
            for (int m = 0; m < size<1>(tVsV); ++m) {
                // If kBlockN doesn't evenly divide the tiled copy, only the last `m` needs to be checked
                if (EvenN || m < size<1>(tVsV) - 1 || get<0>(tKVcKV(_0{}, m, _0{})) < kBlockN) {
                    bool const predicate_n = get<0>(t0KVcKV(_0{}, m, _0{})) < seqlenk_row_limit;
                    #pragma unroll
                    for (int k = 0; k < size<2>(tVsV); ++k) {
                        cute::copy(gmem_tiled_copy_QKV.with(tKVpKV(k) && predicate_n), tVgV(_, m, k), tVsV(_, m, k));
                    }
                }
            }
            if constexpr (V_in_regs) { flash::cp_async_fence(); }
            // flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/true, /*Clear_OOB_K=*/true>(
            //     gmem_tiled_copy_QKV, tKgK, tKsK, t0KVcKV, tKVpKV, seqlenk_row_limit);
            #pragma unroll
            for (int m = 0; m < size<1>(tKsK); ++m) {
                if (EvenN || m < size<1>(tKsK) - 1 || get<0>(tKVcKV(_0{}, m, _0{})) < kBlockN) {
                    bool const predicate_n = get<0>(t0KVcKV(_0{}, m, _0{})) < seqlenk_row_limit;
                    #pragma unroll
                    for (int k = 0; k < size<2>(tKsK); ++k) {
                        cute::copy(gmem_tiled_copy_QKV.with(tKVpKV(k) && predicate_n), tKgK(_, m, k), tKsK(_, m, k));
                    }
                }
            }
            flash::cp_async_fence();
        }

        if constexpr (V_in_regs) {
            flash::cp_async_wait<1>();
            __syncthreads();
            Tensor tdPrV_copy_view = smem_thr_copy_KV.retile_D(tdPrV);
            Tensor tdPsV_copy_view = smem_thr_copy_KV.partition_S(sV);
            cute::copy(smem_tiled_copy_KV, tdPsV_copy_view, tdPrV_copy_view);
            __syncthreads();  // Sync to avoid loading Q to smem_q, which overlaps with smem_v
        }

        // Do we need bound check to make sure the row doesn't go above kBlockM
        static constexpr int kBlockM = get<0>(TileShape_MNK{});
        static constexpr bool EvenM = kBlockM % CUTE_STATIC_V(shape<0>(GmemLayoutAtom{})) == 0;

        auto load_Q_LSE = [&] (int const m_block, int const smem_pipe_write) {
            // if (cute::thread0()) { printf("Inside load_Q_LSE, m_block = %d, smem_pipe_write = %d\n", m_block, smem_pipe_write); }
            Tensor tQsQ_cur = tQsQ(_, _, _, smem_pipe_write);
            Tensor tQgQ_cur = tQgQ(_, _, _, m_block);
            // Instead of passing in tQcQ, we pass in t0QcQ and subtract the offset from the limit
            // (seqlen_q - m_block * kBlockM). This is because the entries of t0QcQ are known at compile time.
            // int const seqlenq_row_limit = -int(get<0>(tQcQ(_0{}, _0{}, _0{}))) + (EvenM
            //     ? seqlen_info.seqlen_q - m_block * kBlockM
            //     : std::min(seqlen_info.seqlen_q - m_block * kBlockM, kBlockM));
            // Need Clear_OOB_MN to be true here since the gemm will sum over the kBlockM dimension
            // flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/true, /*Clear_OOB_K=*/true>(
            //     gmem_tiled_copy_QKV, tQgQ(_, _, _, m_block), tQsQ_cur, t0QcQ, tQpQ, seqlenq_row_limit);
            int const seqlenq_row_limit = seqlen_info.seqlen_q - m_block * kBlockM - get<0>(tQcQ(_0{}, _0{}, _0{}));
            #pragma unroll
            for (int m = 0; m < size<1>(tQsQ); ++m) {
                // If kBlockM doesn't evenly divide the tiled copy, only the last `m` needs to be checked
                if (EvenM || m < size<1>(tQsQ) - 1 || get<0>(tQcQ(_0{}, m, _0{})) < kBlockM) {
                    bool const predicate_m = get<0>(t0QcQ(_0{}, m, _0{})) < seqlenq_row_limit;
                    #pragma unroll
                    for (int k = 0; k < size<2>(tQsQ); ++k) {
                        cute::copy(gmem_tiled_copy_QKV.with(tQpQ(k) && predicate_m), tQgQ_cur(_, m, k), tQsQ_cur(_, m, k));
                    }
                }
            }
            Tensor tLSEgLSE_cur = tLSEgLSE(_, _, m_block);
            Tensor tLSEsLSE_cur = tLSEsLSE(_, _, smem_pipe_write);
            // We made sure LSE length is padded so we read `kBlockM` elements so that all
            // elements in sLSE are filled. Without this we might have uninitialized sLSE values.
            #pragma unroll
            for (int m = 0; m < size<1>(tLSEsLSE); ++m) {
                if (get<0>(tLSEcLSE(_0{}, m)) < kBlockM) {
                    cute::copy(gmem_tiled_copy_lse, tLSEgLSE_cur(_, m), tLSEsLSE_cur(_, m));
                }
            }
        };

        auto load_dO_dPsum = [&] (int const m_block, int const smem_pipe_write) {
            // if (cute::thread0()) { printf("Inside load_dO_dPsum, m_block = %d, smem_pipe_write = %d\n", m_block, smem_pipe_write); }
            Tensor tdOsdO_cur = tdOsdO(_, _, _, smem_pipe_write);
            Tensor tdOgdO_cur = tdOgdO(_, _, _, m_block);
            // int const seqlenq_row_limit = -int(get<0>(tQcQ(_0{}, _0{}, _0{}))) + (EvenM
            //     ? seqlen_info.seqlen_q - m_block * kBlockM
            //     : std::min(seqlen_info.seqlen_q - m_block * kBlockM, kBlockM));
            // flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/true, /*Clear_OOB_K=*/true>(
            //     gmem_tiled_copy_QKV, tdOgdO(_, _, _, m_block), tdOsdO_cur, t0QcQ, tQpQ, seqlenq_row_limit);
            int const seqlenq_row_limit = seqlen_info.seqlen_q - m_block * kBlockM - get<0>(tQcQ(_0{}, _0{}, _0{}));
            #pragma unroll
            for (int m = 0; m < size<1>(tdOsdO); ++m) {
                // If kBlockM doesn't evenly divide the tiled copy, only the last `m` needs to be checked
                if (EvenM || m < size<1>(tdOsdO) - 1 || get<0>(tQcQ(_0{}, m, _0{})) < kBlockM) {
                    bool const predicate_m = get<0>(t0QcQ(_0{}, m, _0{})) < seqlenq_row_limit;
                    #pragma unroll
                    for (int k = 0; k < size<2>(tdOsdO); ++k) {
                        cute::copy(gmem_tiled_copy_QKV.with(tQpQ(k) && predicate_m), tdOgdO_cur(_, m, k), tdOsdO_cur(_, m, k));
                    }
                }
            }
            Tensor tLSEgdPsum_cur = tLSEgdPsum(_, _, m_block);
            Tensor tLSEsdPsum_cur = tLSEsdPsum(_, _, smem_pipe_write);
            #pragma unroll
            for (int m = 0; m < size<1>(tLSEsdPsum); ++m) {
                if (get<0>(tLSEcLSE(_0{}, m)) < kBlockM) {
                    cute::copy(gmem_tiled_copy_lse, tLSEgdPsum_cur(_, m), tLSEsdPsum_cur(_, m));
                }
            }
        };

        int m_block = m_block_min;

        // Note, using the for_each() function here to ensure `stage` is of type Int<x>.
        for_each(make_int_sequence<kStages>{}, [&] (auto stage) {
            static constexpr bool Is_first_stage = CUTE_STATIC_V(stage) == 0;
            static constexpr bool Is_last_stage = CUTE_STATIC_V(stage) == kStages - 1;
            if constexpr (!Is_last_stage || kStages == 1) {
                if (Is_first_stage || m_block + stage < m_block_max) {
                    load_Q_LSE(m_block + stage, stage);
                }
            }
            // We want the fence outside the if statement to have a fixed number of cp.async commits.
            // so that we can wait with the correct number of outstanding commits.
            cute::cp_async_fence();
            if constexpr (stage < kStages_dO) {
                if (Is_first_stage || m_block + stage < m_block_max) {
                    load_dO_dPsum(m_block + stage, stage);
                }
                cute::cp_async_fence();
            }
        });

        int smem_pipe_read = 0, smem_pipe_read_do = 0, smem_pipe_write = kStages - 1, smem_pipe_write_do = 0;

        auto load_Q_next = [&] {
            // if (cute::thread0()) { printf("m_block = %d, m_block_max = %d, smem_pipe_write = %d\n", m_block, m_block_max, smem_pipe_write); }
            if (m_block + (kStages > 1 ? kStages - 1 : 1) < m_block_max) {
                load_Q_LSE(m_block + (kStages > 1 ? kStages - 1 : 1), kStages > 1 ? smem_pipe_write : 0);
            }
            cute::cp_async_fence();
        };

        auto load_dO_next = [&] {
            // int smem_pipe_write_do_cur = Q_dO_same_stages ? smem_pipe_write : smem_pipe_write_do;
            if (m_block + kStages_dO < m_block_max) {
                // load_dO_dPsum(m_block + kStages_dO, kStages_dO > 1 ? smem_pipe_write_do_cur : 0);
                load_dO_dPsum(m_block + kStages_dO, kStages_dO > 1 ? smem_pipe_write_do : 0);
            }
            cute::cp_async_fence();
        };

        clear(tdKrdK);
        clear(tdVrdV);

        auto bwd_step = [&](int m_block, auto mask_fn) {
            Tensor tSrS = partition_fragment_C(tiled_mma_SdP, select<!SdP_swapAB ? 0 : 1, !SdP_swapAB ? 1 : 0>(TileShape_MNK{}));
            clear(tSrS);
            flash::cp_async_wait<(kStages > 1) ? 1 : 0>();
            __syncthreads();
            Tensor tSrQ = mma_partition_fragment_AB</*A=*/!SdP_swapAB>(thr_mma_SdP, sQ(_, _, _0{}));
            Tensor tSrK = mma_partition_fragment_AB</*A=*/SdP_swapAB>(thr_mma_SdP, sK);
            // if (cute::thread0()) { print(tiled_mma_SdP); print(tSrS); printf("\n"); print(tSrQ); printf("\n"); print(tSrK); printf("\n"); print(tSsQ); printf("\n"); print(tSsK); printf("\n"); }
            flash::gemm_sm80<false /*A_in_regs*/, false /*B_in_regs*/, SdP_swapAB>(
                tSrS, tSrQ, tSrK, tSsQ(_, _, _, kStages > 1 ? smem_pipe_read : 0), tSsK,
                tiled_mma_SdP, smem_tiled_copy_QdO, smem_tiled_copy_KV, smem_thr_copy_QdO, smem_thr_copy_KV, nullptr /*hook*/);
            Tensor tLSErLSE = cute::conditional_return<!ShuffleLSE>(make_fragment_like(tSsLSE(_, _0{})), make_tensor<ElementAccum>(Int<kStatsPerThread>{}));
            if constexpr (!ShuffleLSE) {
                cute::copy(tSsLSE(_, kStages > 1 ? smem_pipe_read : 0), tLSErLSE);
            } else {
                #pragma unroll
                for (int i = 0; i < kStatsPerThread; ++i) {
                    // It's ok to read OOB, since we made sure sLSE is large enough and we won't use the OOB values
                    tLSErLSE(i) = tSsLSE((thread_idx % 32) / 4 + i * 8, kStages > 1 ? smem_pipe_read : 0);
                }
            }
            if constexpr (Has_softcap) { flash::apply_softcap(tSrS, params.softcap_val); }

            // Reshape tSrS from (4, MMA_N, MMA_M) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
            Tensor scores = make_tensor(tSrS.data(), flash::convert_layout_acc_rowcol</*Transposed=*/SdP_swapAB>(tSrS.layout()));
            // dtanh needs to happen before masking, otherwise we get 1 - (-inf)^2 = NaN in the dtanh
            // if (cute::thread0()) { print_tensor(scores); }
            auto dtanh = [&] { if constexpr (Has_softcap) return flash::calculate_dtanh(scores); else return nullptr; }();
            mask_fn(tSrS, m_block);
            #pragma unroll
            for (int mi = 0; mi < size<0>(scores); ++mi) {
                float const lse_scaled = [&] {
                    if constexpr (!ShuffleLSE) return tLSErLSE(mi);
                    else return __shfl_sync(0xffffffff, tLSErLSE(mi / 8), (mi % 8) * 4 + (thread_idx % 4));
                }();
                #pragma unroll
                for (int ni = 0; ni < size<1>(scores); ++ni) {
                    scores(mi, ni) = exp2f(scores(mi, ni) * params.softmax_scale_log2 - lse_scaled);
                }
            }

            Tensor tdPrdP = partition_fragment_C(tiled_mma_SdP, select<!SdP_swapAB ? 0 : 1, !SdP_swapAB ? 1 : 0>(TileShape_MNK{}));
            clear(tdPrdP);
            int smem_pipe_read_do_cur = Q_dO_same_stages ? smem_pipe_read : smem_pipe_read_do;
            flash::cp_async_wait<(kStages_dO > 1) ? 1 : 0>();
            __syncthreads();
            auto hook = cute::conditional_return<(kStages > 1)>(load_Q_next, nullptr);
            Tensor tdPrdO = mma_partition_fragment_AB</*A=*/!SdP_swapAB>(thr_mma_SdP, sdO(_, _, _0{}));
            Tensor tdPrV_cur = cute::conditional_return<V_in_regs>(tdPrV, mma_partition_fragment_AB</*A=*/SdP_swapAB>(thr_mma_SdP, sV));
            flash::gemm_sm80<false /*A_in_regs*/, V_in_regs, SdP_swapAB>(
                tdPrdP, tdPrdO, tdPrV_cur, tdPsdO(_, _, _, kStages_dO > 1 ? smem_pipe_read_do_cur : 0), tdPsV,
                tiled_mma_SdP, smem_tiled_copy_QdO, smem_tiled_copy_KV, smem_thr_copy_QdO, smem_thr_copy_KV, hook);
            Tensor tLSErdPsum = cute::conditional_return<!ShuffledPsum>(make_fragment_like(tSsdPsum(_, _0{})), make_tensor<ElementAccum>(Int<kStatsPerThread>{}));
            if constexpr (!ShuffledPsum) {
                cute::copy(tSsdPsum(_, kStages_dO > 1 ? smem_pipe_read_do_cur : 0), tLSErdPsum);
            } else {
                #pragma unroll
                for (int i = 0; i < kStatsPerThread; ++i) {
                    tLSErdPsum(i) = tSsdPsum((thread_idx % 32) / 4 + i * 8, kStages_dO > 1 ? smem_pipe_read_do_cur : 0);
                }
            }

            // Reshape tdPrdP from (4, MMA_N, MMA_M) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
            Tensor dS = make_tensor(tdPrdP.data(), scores.layout());
            #pragma unroll
            for (int mi = 0; mi < size<0>(dS); ++mi) {
                float const dP_sum_cur = [&] {
                    if constexpr (!ShuffledPsum) return tLSErdPsum(mi);
                    else return __shfl_sync(0xffffffff, tLSErdPsum(mi / 8), (mi % 8) * 4 + (thread_idx % 4));
                }();
                #pragma unroll
                for (int ni = 0; ni < size<1>(dS); ++ni) {
                    dS(mi, ni) = scores(mi, ni) * (dS(mi, ni) - dP_sum_cur);
                    if constexpr (Has_softcap) { dS(mi, ni) *= dtanh(mi, ni); }
                }
            }
            // if (cute::thread0()) { print_tensor(dS); }

            // Convert scores from fp32 to fp16/bf16
            Tensor rP = make_tensor_like<Element>(tSrS);
            flash::convert_type_out(tSrS, rP);
            if constexpr (!Mma_dKV_is_RS) {
                Tensor tPaP = r2s_thr_copy_PdS.retile_S(rP);  // ((Atom,AtomNum), MMA_N, MMA_N)
                cute::copy(r2s_tiled_copy_PdS, tPaP, tPsP);
            }
            Tensor rdS = make_tensor_like<Element>(tdPrdP);
            flash::convert_type_out(tdPrdP, rdS);
            if constexpr (!Mma_dKV_is_RS) { __syncthreads(); }  // Make sure P is written
            // For hdim 64, It's faster to write to smem_dS first before the dV gemm
            Tensor tdSadS = r2s_thr_copy_PdS.retile_S(rdS);   // ((Atom,AtomNum), MMA_N, MMA_N)
            cute::copy(r2s_tiled_copy_PdS, tdSadS, tdSsdS);

            Tensor tdVrdO = mma_partition_fragment_AB</*A=*/dKV_swapAB>(thr_mma_dKV, sdOt(_, _, _0{}));
            Tensor tdVsdO_cur = tdVsdOt(_, _, _, kStages_dO > 1 ? smem_pipe_read_do_cur : 0);
            if constexpr (Mma_dKV_is_RS) {
                Tensor tdVrP = make_tensor(rP.data(), convert_layout_acc_Aregs<TiledMmadKV>(tSrS.layout()));
                flash::gemm_rs_sm80(tdVrdV, tdVrP, tdVrdO, tdVsdO_cur, tiled_mma_dKV, smem_tiled_copy_QdOt, smem_thr_copy_QdOt);
            } else {
                Tensor tdVrP = mma_partition_fragment_AB</*A=*/!dKV_swapAB>(thr_mma_dKV, sPt);
                flash::gemm_sm80<false /*A_in_regs*/, false /*B_in_regs*/, /*SwapAB=*/dKV_swapAB>(
                    tdVrdV, tdVrP, tdVrdO, tdVsPt, tdVsdO_cur,
                    tiled_mma_dKV, smem_tiled_copy_PdSt, smem_tiled_copy_QdOt, smem_thr_copy_PdSt, smem_thr_copy_QdOt, nullptr);
            }
            // if (cute::thread0()) { print_tensor(tdVrdV); }
            __syncthreads();  // make sure sdS is written
            auto do_mma_dQ = [&] (auto hook) {
            Tensor tdQrdQ = partition_fragment_C(tiled_mma_dQ, select<!dQ_swapAB ? 0 : 2, !dQ_swapAB ? 2 : 0>(TileShape_MNK{}));
            clear(tdQrdQ);
            Tensor tdQrdS = mma_partition_fragment_AB</*A=*/!dQ_swapAB>(thr_mma_dQ, sdS);
            Tensor tdQrK = mma_partition_fragment_AB</*A=*/dQ_swapAB>(thr_mma_dQ, sKt);
            flash::gemm_sm80<false /*A_in_regs*/, false /*B_in_regs*/, /*SwapAB=*/dQ_swapAB>(
                tdQrdQ, tdQrdS, tdQrK, tdQsdS, tdQsKt, tiled_mma_dQ,
                // smem_tiled_copy_dS, smem_tiled_copy_Kt, smem_thr_copy_dS, smem_thr_copy_Kt, load_dO_next);
                smem_tiled_copy_dS, smem_tiled_copy_Kt, smem_thr_copy_dS, smem_thr_copy_Kt, hook);
            // if (cute::thread0()) { print_tensor(tdQrdQ); }
            // We can reuse r2s_thr_copy_dQaccum for this partitioning
            Tensor tdQrdQ_atomic = r2s_thr_copy_dQaccum.retile_S(tdQrdQ);
            Tensor tdQgdQaccum_atomic = tdQgdQaccum(_, _, m_block);
            static_assert(CUTE_STATIC_V(size(tdQrdQ_atomic)) == CUTE_STATIC_V(size(tdQgdQaccum_atomic)));
            #pragma unroll
            for (int i = 0; i < size(tdQrdQ_atomic); ++i) { atomicAdd(&tdQgdQaccum_atomic(i), tdQrdQ_atomic(i)); }
            };
            // If kStages == 1, we want to do Mma_dK first so we can start loading Q for the next iteration
            if constexpr (kStages > 1) { do_mma_dQ(load_dO_next); }
            Tensor tdKrQ = mma_partition_fragment_AB</*A=*/dKV_swapAB>(thr_mma_dKV, sQt(_, _, _0{}));
            Tensor tdKsQ_cur = tdKsQt(_, _, _, kStages > 1 ? smem_pipe_read : 0);
            if constexpr (Mma_dKV_is_RS) {
                Tensor tdKrdS = make_tensor(rdS.data(), convert_layout_acc_Aregs<TiledMmadKV>(tdPrdP.layout()));
                flash::gemm_rs_sm80(tdKrdK, tdKrdS, tdKrQ, tdKsQ_cur, tiled_mma_dKV, smem_tiled_copy_QdOt, smem_thr_copy_QdOt);
            } else {
                Tensor tdKrdS = mma_partition_fragment_AB</*A=*/!dKV_swapAB>(thr_mma_dKV, sdSt);
                flash::gemm_sm80<false /*A_in_regs*/, false /*B_in_regs*/, /*SwapAB=*/dKV_swapAB>(
                    tdKrdK, tdKrdS, tdKrQ, tdKsdSt, tdKsQ_cur,
                    tiled_mma_dKV, smem_tiled_copy_PdSt, smem_tiled_copy_QdOt, smem_thr_copy_PdSt, smem_thr_copy_QdOt, cute::conditional_return<(kStages > 1)>(nullptr, load_dO_next));
            }
            if constexpr (kStages == 1) {  
                __syncthreads();
                do_mma_dQ(load_Q_next);
            }
            // if (cute::thread0()) { print_tensor(tdKrdK); }

            smem_pipe_read = smem_pipe_read < kStages - 1 ? smem_pipe_read + 1 : 0;
            smem_pipe_read_do = smem_pipe_read_do < kStages_dO - 1 ? smem_pipe_read_do + 1 : 0;
            smem_pipe_write = smem_pipe_write < kStages - 1 ? smem_pipe_write + 1 : 0;
            smem_pipe_write_do = smem_pipe_write_do < kStages_dO - 1 ? smem_pipe_write_do + 1 : 0;

        };

        // We have separate iterations with causal masking. Not necessary for hdim 128 but for hdim 64
        // this helps quite a bit to not have to do causal masking for most of the iterations.
        if constexpr ((Is_causal || Is_local) && SeparateMaskingIterations) {
            auto mask_fn = [&](auto& tSrS, int m_block) { mask.template apply<true /*Seqlenk_mask*/, Is_causal, Is_local>(tSrS, m_block, n_block); };
            int const m_block_masking_max = ((n_block + 1) * kBlockN - 1 + seqlen_q - seqlen_k - params.window_size_right) / kBlockM + 1;
            CUTLASS_PRAGMA_NO_UNROLL
            for (; m_block < std::min(m_block_max, m_block_masking_max); ++m_block) {
                bwd_step(m_block, mask_fn);
            }
        }

        static constexpr int kBlockN = get<1>(TileShape_MNK{});
        int const m_block_max_before_local_mask = !Is_local || !SeparateMaskingIterations
            ? m_block_max
            : std::min(m_block_max, (n_block * kBlockN + seqlen_q - seqlen_k + params.window_size_left) / kBlockM);

        auto mask_fn = [&](auto& tSrS, int m_block) { mask.template apply<true /*Seqlenk_mask*/, Is_causal && !SeparateMaskingIterations, Is_local && !SeparateMaskingIterations>(tSrS, m_block, n_block); };
        CUTLASS_PRAGMA_NO_UNROLL
        for (; m_block < m_block_max_before_local_mask; ++m_block) {
            bwd_step(m_block, mask_fn);
        }

        if constexpr (Is_local && SeparateMaskingIterations) {
            auto mask_fn = [&](auto& tSrS, int m_block) { mask.template apply<true /*Seqlenk_mask*/, false /*Causal_mask*/, Is_local>(tSrS, m_block, n_block); };
            CUTLASS_PRAGMA_NO_UNROLL
            for (; m_block < m_block_max; ++m_block) {
                bwd_step(m_block, mask_fn);
            }
        }

        // if (blockIdx.x == 0 && threadIdx.x == 128) { print_tensor(tdVrdV); }
        #pragma unroll
        for (int i = 0; i < size(tdKrdK); ++i) { tdKrdK(i) *= params.softmax_scale; }

        return true;
    }

};

} // namespace flash