flash-attention/hopper/mainloop_fwd_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 "pack_gqa.h"
#include "paged_kv.h"
#include "rotary.h"
#include "utils.h"

namespace flash {

using namespace cute;

template <int kNWarps, int Stages, bool Q_in_regs, class TileShape_MNK_, class Element_, class ElementAccum_, class ArchTag_,
        bool Is_causal_, bool Is_local_, bool Has_softcap_, bool Varlen_, bool PagedKV_, bool AppendKV_,
        bool PackGQA_, bool Split_>
struct CollectiveMainloopFwdSm80 {

    static constexpr int kStages = Stages;
    static_assert(kStages > 0, "kStages must be greater than 0");
    using TileShape_MNK = TileShape_MNK_;
    using Element = Element_;
    using ElementAccum = ElementAccum_;
    using ArchTag = ArchTag_;
    static constexpr bool Is_FP8 = cute::is_same_v<Element, cutlass::float_e4m3_t> || cute::is_same_v<Element, cutlass::float_e5m2_t>;;
    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_;
    static constexpr bool PagedKV = PagedKV_;
    static constexpr bool AppendKV = AppendKV_;
    static constexpr bool PackGQA = PackGQA_;
    static constexpr bool Split = Split_;
    static constexpr bool Transpose_V = Is_FP8;
    using SeqlenInfo_t = flash::SeqlenInfoQKNewK<Varlen, AppendKV>;

    static_assert(ArchTag::kMinComputeCapability >= 80);

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

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

    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>
    >;
    using TiledMma = TiledMMA<
        MMA_Atom_Arch,
        Layout<Shape<Int<kNWarps>,_1,_1>>,  // 4x1x1 or 8x1x1 thread group
        Tile<Int<16 * kNWarps>, _16, _16>>;

    static constexpr int NumMmaThreads = size(TiledMma{});
    static constexpr int NumProducerThreads = NumMmaThreads;  // For compatibility with TileScheduler

    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);
    using SmemLayoutAtomQKV = decltype(
        composition(Swizzle<kSwizzle, kSwizzleBase, kSwizzleBase>{},
                    Layout<Shape<_8, Int<kBlockKGmem>>,
                           Stride<Int<kBlockKGmem>, _1>>{}));
    using SmemLayoutQ = decltype(tile_to_shape(SmemLayoutAtomQKV{}, select<0, 2>(TileShape_MNK{})));

    using SmemLayoutK = decltype(tile_to_shape(
        SmemLayoutAtomQKV{},
        make_shape(shape<1>(TileShape_MNK{}), shape<2>(TileShape_MNK{}), Int<kStages>{})));

    using SmemLayoutV = decltype(tile_to_shape(
        SmemLayoutAtomQKV{},
        make_shape(shape<1>(TileShape_MNK{}), shape<2>(TileShape_MNK{}), Int<kStages>{})));
    using SmemLayoutVt = decltype(
        composition(SmemLayoutV{},
                    make_ordered_layout(make_shape(shape<2>(TileShape_MNK{}), shape<1>(TileShape_MNK{}), Int<kStages>{}),
                                        Step<_2, _1, _3>{})));

    using SmemCopyAtom = Copy_Atom<SM75_U32x4_LDSM_N, Element>;
    using SmemCopyAtomTransposed = Copy_Atom<SM75_U16x8_LDSM_T, 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 GmemCopyAtom = Copy_Atom<std::conditional_t<
        Has_cp_async,
        SM80_CP_ASYNC_CACHEGLOBAL_ZFILL<cute::uint128_t>,
        AutoVectorizingCopyWithAssumedAlignment<128>
    >, 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
    // 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);

    // For AppendKV, We want each thread to have at least 2 loads in the K direction since in the case of
    // non-interleaved rotary (combining elements at indices 0 and rotary_dim/2, 1 and rotary_dim/2+1, etc),
    // each thread will load twice from the same row.
    static constexpr int kBytePerHalfRow = kHeadDim / 2 * sizeof(Element);
    static constexpr int kBlockKGmemAppend = (kBytePerHalfRow % 128 == 0 ? 128 : (kBytePerHalfRow % 64 == 0 ? 64 : 32)) / sizeof(Element);
    static constexpr int kGmemThreadsPerRowAppend = kBlockKGmemAppend / kGmemElemsPerLoad;
    static_assert(NumMmaThreads % kGmemThreadsPerRowAppend == 0, "NumMmaThreads must be a multiple of kGmemThreadsPerRowAppend");
    // We assume threads loading the same row are in the same warp. This is for an optimization in PagedKV where
    // these threads share the same page table entry and share the work of computing pointers to paged K and paged V.
    static_assert(cutlass::NumThreadsPerWarp % kGmemThreadsPerRowAppend == 0, "kGmemThreadsPerRowAppend must divide NumThreadsPerWarp");
    using GmemLayoutAtomAppend = Layout<Shape <Int<NumMmaThreads / kGmemThreadsPerRowAppend>, Int<kGmemThreadsPerRowAppend>>,
                                        Stride<Int<kGmemThreadsPerRowAppend>, _1>>;
    // If AppendKV, we'll be loading Q for rotary, and we assume divisibility to avoid predication
    static_assert(!AppendKV || kBlockM % CUTE_STATIC_V(shape<0>(GmemLayoutAtomAppend{})) == 0, "kBlockM must be a multiple of NumMmaThreads / kGmemThreadsPerRowAppend");
    using GmemTiledCopyAppendKV = decltype(
        make_tiled_copy(Copy_Atom<AutoVectorizingCopyWithAssumedAlignment<128>, Element>{},
                        GmemLayoutAtomAppend{},
                        Layout<Shape<_1, Int<kGmemElemsPerLoad>>>{}));  // Val layout, 8 or 16 vals per store

    using ShapeQKV = cute::Shape<int32_t, int32_t, int32_t, int32_t>;  // (seqlen, d, head, batch)
    using StrideQK = cute::Stride<int64_t, _1, int64_t, int64_t>;
    using StrideV = StrideQK;
    // ((qhead_per_khead, seqlen_q), d, nheads_kv, batch, num_splits)
    using ShapeQPacked = std::conditional_t<!PackGQA, ShapeQKV, cute::Shape<cute::Shape<int32_t, int32_t>, int32_t, int32_t, int32_t>>;
    using StrideQPacked = std::conditional_t<!PackGQA, StrideQK, cute::Stride<cute::Stride<int64_t, int64_t>, _1, int64_t, int64_t>>;
    using ShapePageTable = cute::Shape<int32_t, int32_t>;  // (batch, max_num_pages_per_seq)
    using StridePageTable = cute::Stride<int64_t, _1>;
    using ShapeRotary = cute::Shape<int32_t, int32_t>;  // (seqlen_ro, rotary_dim // 2)
    using StrideRotary = cute::Stride<int64_t, _1>;
    using StrideDescale = cute::Stride<int64_t, int64_t>;

    static constexpr bool Share_QV_Smem = Q_in_regs;

    struct TensorStorageSharedQV : cute::aligned_struct<128> {
        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<SmemLayoutK>> smem_k;
    };

    struct TensorStorageSeparateQV : cute::aligned_struct<128> {
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutV>> smem_v;
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutK>> smem_k;
        cute::array_aligned<Element, cute::cosize_v<SmemLayoutQ>> smem_q;
    };

    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;
        StrideQK const stride_Q;
        Element* const ptr_K;  // Not Element const* since we might append to KV cache in-place
        ShapeQKV const shape_K;
        StrideQK const stride_K;
        Element* const ptr_V;
        StrideV const stride_V;
        Element const* const ptr_K_new;
        ShapeQKV const shape_K_new;
        StrideQK const stride_K_new;
        Element const* const ptr_V_new;
        StrideV const stride_V_new;
        Element const* const ptr_rotary_cos;
        ShapeRotary const shape_rotary;
        StrideRotary const stride_rotary_cos;
        Element const* const ptr_rotary_sin;
        StrideRotary const stride_rotary_sin;
        bool const is_rotary_interleaved;
        int const* const ptr_pagetable;
        ShapePageTable const shape_pagetable;
        StridePageTable const stride_pagetable;
        float const softmax_scale;
        float const* ptr_q_descale, *ptr_k_descale, *ptr_v_descale;
        StrideDescale const stride_q_descale, stride_k_descale, stride_v_descale;
        int const window_size_left = -1, window_size_right = -1, sink_token_length = 0;
        float const softcap_val;
        int const num_splits;
        int const* const kv_batch_idx = nullptr;
        int const* const cu_seqlens_q = nullptr;
        int const* const cu_seqlens_k = nullptr;
        int const* const cu_seqlens_k_new = nullptr;
        int const* const seqused_q = nullptr;
        int const* const seqused_k = nullptr;
        int const* const leftpad_k = nullptr;
    };

    // Device side kernel params
    struct Params {
        Element const* const ptr_Q;
        ShapeQKV const shape_Q;
        StrideQK const stride_Q;
        ShapeQPacked const shape_Q_packed;
        StrideQPacked const stride_Q_packed;
        Element* const ptr_K;
        ShapeQKV const shape_K;
        StrideQK const stride_K;
        Element* const ptr_V;
        StrideV const stride_V;
        Element const* const ptr_K_new;
        ShapeQKV const shape_K_new;
        StrideQK const stride_K_new;
        Element const* const ptr_V_new;
        StrideV const stride_V_new;
        Element const* const ptr_rotary_cos;
        ShapeRotary const shape_rotary;
        StrideRotary const stride_rotary_cos;
        Element const* const ptr_rotary_sin;
        StrideRotary const stride_rotary_sin;
        bool const is_rotary_interleaved;
        int const* const ptr_pagetable;
        ShapePageTable const shape_pagetable;
        StridePageTable const stride_pagetable;
        cutlass::FastDivmod page_size_divmod;
        cutlass::FastDivmod qhead_per_khead_divmod;
        float const softmax_scale_log2;
        float const* ptr_q_descale, *ptr_k_descale, *ptr_v_descale;
        StrideDescale const stride_q_descale, stride_k_descale, stride_v_descale;
        float const softcap_val;
        int const window_size_left, window_size_right, sink_token_length;
        int const num_splits;
        int const* const kv_batch_idx = nullptr;
        int const* const cu_seqlens_q = nullptr;
        int const* const cu_seqlens_k = nullptr;
        int const* const cu_seqlens_k_new = nullptr;
        int const* const seqused_q = nullptr;
        int const* const seqused_k = nullptr;
        int const* const leftpad_k = nullptr;
    };

    static Params
    to_underlying_arguments(Arguments const& args) {
        // If PackGQA, reshape Q to be ((qhead_per_khead, seqlen_q), head_size, nhead_k, batch_size)
        int const qhead_per_khead = !PackGQA ? 1 : cute::ceil_div(get<2>(args.shape_Q), get<2>(args.shape_K));
        auto const shape_Q_packed = cute::conditional_return<!PackGQA>(
            args.shape_Q,
            make_shape(make_shape(qhead_per_khead, get<0>(args.shape_Q)), get<1>(args.shape_Q), get<2>(args.shape_K), get<3>(args.shape_Q))
        );
        auto const stride_Q_packed = cute::conditional_return<!PackGQA>(
            args.stride_Q,
            make_stride(make_stride(get<2>(args.stride_Q), get<0>(args.stride_Q)), get<1>(args.stride_Q), get<2>(args.stride_Q) * qhead_per_khead, get<3>(args.stride_Q))
        );
        if (get<1>(args.shape_rotary) > 0) {
            assert(args.ptr_rotary_cos != nullptr && args.ptr_rotary_sin != nullptr);
        }
        assert(args.num_splits >= 1);
        // 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).
        return {args.ptr_Q, args.shape_Q, args.stride_Q, shape_Q_packed, stride_Q_packed,
                args.ptr_K, args.shape_K, args.stride_K, args.ptr_V, args.stride_V,
                args.ptr_K_new, args.shape_K_new, args.stride_K_new, args.ptr_V_new, args.stride_V_new,
                args.ptr_rotary_cos, args.shape_rotary, args.stride_rotary_cos,
                args.ptr_rotary_sin, args.stride_rotary_sin, args.is_rotary_interleaved,
                args.ptr_pagetable, args.shape_pagetable, args.stride_pagetable,
                cutlass::FastDivmod(int(get<0>(args.shape_K))),
                cutlass::FastDivmod(cute::ceil_div(get<2>(args.shape_Q), get<2>(args.shape_K))),
                !Has_softcap ? float(args.softmax_scale * M_LOG2E) : float(args.softcap_val * M_LOG2E),
                args.ptr_q_descale, args.ptr_k_descale, args.ptr_v_descale,
                args.stride_q_descale, args.stride_k_descale, args.stride_v_descale,
                !Has_softcap ? 0.f : args.softmax_scale / args.softcap_val,
                args.window_size_left, args.window_size_right, args.sink_token_length,
                !Split ? 1 : args.num_splits,
                args.kv_batch_idx,
                args.cu_seqlens_q, args.cu_seqlens_k, args.cu_seqlens_k_new,
                args.seqused_q, args.seqused_k, args.leftpad_k};
    }

    CUTLASS_DEVICE
    cute::tuple<int, int> get_n_block_min_max(Params const& params, SeqlenInfo_t const& seqlen_info,
                                              int m_block, int bidb, int split_idx=0, int num_splits=1) {
        static constexpr int kBlockM = get<0>(TileShape_MNK{});
        static constexpr int kBlockN = get<1>(TileShape_MNK{});
        int const seqlen_k = seqlen_info.seqlen_k;
        int const seqlen_q = seqlen_info.seqlen_q;
        int n_block_max = cute::ceil_div(seqlen_k, kBlockN);
        if constexpr (Is_causal || Is_local) {
            int m_idx_max = (m_block + 1) * kBlockM;
            if (PackGQA) { m_idx_max = params.qhead_per_khead_divmod.divide(m_idx_max - 1) + 1 ; }
            n_block_max = std::min(n_block_max,
                                   cute::ceil_div(m_idx_max + seqlen_k - seqlen_q + params.window_size_right, kBlockN));
        }
        int n_block_min = 0;
        if constexpr (Is_local) {
            int m_idx_min = m_block * kBlockM;
            if (PackGQA) { m_idx_min = params.qhead_per_khead_divmod.divide(m_idx_min); }
            n_block_min = std::max(int(0), (m_idx_min + seqlen_k - seqlen_q - params.window_size_left) / kBlockN);
        }
        // if (threadIdx.x == 128) { printf("Inside, bid.x = %d, bid.y = %d, bid.z = %d, split_idx = %d, n_block_min: %d, n_block_max: %d\n", blockIdx.x, blockIdx.y, blockIdx.z, split_idx, n_block_min, n_block_max); }
        if constexpr (Split) {
            int num_n_blocks_per_split = n_block_max <= n_block_min ? 0 : cute::ceil_div(n_block_max - n_block_min, num_splits);
            n_block_min = n_block_min + split_idx * num_n_blocks_per_split;
            n_block_max = std::min(n_block_min + num_n_blocks_per_split, n_block_max);
        }
        // if (threadIdx.x == 128) { printf("After split, inside, bid.y = %d, bid.z = %d, split_idx = %d, n_block_min: %d, n_block_max: %d\n", blockIdx.y, blockIdx.z, split_idx, n_block_min, n_block_max); }
        return {n_block_min, n_block_max};
    }

    template <typename SharedStorage, typename FrgTensorO, typename Softmax>
    CUTLASS_DEVICE bool
    mma(Params const& params,
        FrgTensorO& tOrO,
        Softmax& softmax,
        int const thread_idx,
        SeqlenInfo_t const& seqlen_info,
        cute::tuple<int32_t, int32_t, int32_t, int32_t> block_coord,
        SharedStorage& shared_storage
        ) {
        static_assert(is_rmem<FrgTensorO>::value, "O tensor must be rmem resident.");
        static constexpr int kBlockM = get<0>(TileShape_MNK{});
        static constexpr int kBlockN = get<1>(TileShape_MNK{});

        // can't use auto [m_block, ...] = block_coord since structured binding cannot be captured in lambda
        int const m_block = get<0>(block_coord);
        int const bidh = get<1>(block_coord);
        int const bidb = get<2>(block_coord);
        int const split_idx = get<3>(block_coord);
        int const bidh_kv = !PackGQA ? params.qhead_per_khead_divmod.divide(bidh) : bidh;
        auto n_block_min_max = get_n_block_min_max(params, seqlen_info, m_block, bidb, split_idx, params.num_splits);
        int const n_block_min = get<0>(n_block_min_max);
        int const n_block_max = get<1>(n_block_min_max);
        // It's possible to have n_block_max <= n_block_min. We don't want to load Q or change any barrier
        if constexpr (Is_causal || Is_local || Varlen || Split) {
            if (n_block_max <= n_block_min) { return false; }
        }

        Tensor sQ = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_q.data()), SmemLayoutQ{});
        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 sVt = make_tensor(make_smem_ptr(shared_storage.tensors.mainloop.smem_v.data()), SmemLayoutVt{});

        bool const is_varlen_q = Varlen && params.cu_seqlens_q;
        bool const is_varlen_k = Varlen && params.cu_seqlens_k;

        int const bidb_kv = params.kv_batch_idx == nullptr ? bidb : params.kv_batch_idx[bidb];
        Tensor mQ = make_tensor(make_gmem_ptr(params.ptr_Q + seqlen_info.offset_q * get<0>(params.stride_Q)), params.shape_Q_packed, params.stride_Q_packed)(_, _, bidh, !is_varlen_q ? bidb : 0);
        Tensor gQ = local_tile(mQ, select<0, 2>(TileShape_MNK{}), make_coord(m_block, _0{}));  // (M, K)
        Tensor mK = make_tensor(make_gmem_ptr(params.ptr_K + seqlen_info.offset_k * get<0>(params.stride_K)), params.shape_K, params.stride_K)(_, _, bidh_kv, !is_varlen_k ? bidb_kv : 0);
        Tensor gK = local_tile(mK, select<1, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (N, K, _)
        Tensor mV = make_tensor(make_gmem_ptr(params.ptr_V + seqlen_info.offset_k * get<0>(params.stride_V)), params.shape_K, params.stride_V)(_, _, bidh_kv, !is_varlen_k ? bidb_kv : 0);
        Tensor gV = local_tile(mV, select<1, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (N, 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

        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);

        TiledMma tiled_mma;
        auto thr_mma = tiled_mma.get_slice(thread_idx);

        // Allocate "fragments/descriptors"
        Tensor tSrQ = thr_mma.partition_fragment_A(sQ);

        // Copy Atom retiling
        auto smem_tiled_copy_Q = make_tiled_copy_A(SmemCopyAtom{}, tiled_mma);
        auto smem_thr_copy_Q = smem_tiled_copy_Q.get_thread_slice(thread_idx);
        auto smem_tiled_copy_K = make_tiled_copy_B(SmemCopyAtom{}, tiled_mma);
        auto smem_thr_copy_K = smem_tiled_copy_K.get_thread_slice(thread_idx);
        auto smem_tiled_copy_V = make_tiled_copy_B(SmemCopyAtomTransposed{}, tiled_mma);
        auto smem_thr_copy_V = smem_tiled_copy_V.get_thread_slice(thread_idx);
        Tensor tSsQ = smem_thr_copy_Q.partition_S(sQ);
        Tensor tSsK = smem_thr_copy_K.partition_S(sK);
        Tensor tOsVt = smem_thr_copy_V.partition_S(sVt);

        // 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); }

        int const seqlen_q = seqlen_info.seqlen_q;
        int const seqlen_k = seqlen_info.seqlen_k;
        int n_block = n_block_max - 1;

        // Prologue: load Q, K, V
        // If persistent, we don't need to wait for the previous work_idx to finish
        // since we assume that all MMA threads sync in the epilogue before writing to smem_o.
        // So any thread gets there, all threads must have finished the previous MMA and at least started
        // writing to smem_o.
        // If persistent, need to sync to make sure all threads have finished with smem_o before writing to smem_v
        if constexpr (Share_QV_Smem) { __syncthreads(); }
        if constexpr (!PackGQA) {
            Tensor tQgQ = gmem_thr_copy_QKV.partition_S(gQ);
            Tensor tQsQ = gmem_thr_copy_QKV.partition_D(sQ);
            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); }
            // 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.
            // We don't need to clear the sQ smem tiles since we'll only write out the valid outputs
            flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/true>(
                gmem_tiled_copy_QKV, tQgQ, tQsQ, t0QcQ, tQpQ, seqlen_info.seqlen_q - m_block * kBlockM - get<0>(tQcQ(_0{}, _0{}, _0{}))
            );
        } else {
            using PackGQAt = flash::PackGQAManager<get<0>(TileShape_MNK{}), get<2>(TileShape_MNK{}), NumMmaThreads, Element>;
            PackGQAt::load_Q(mQ, sQ, params.qhead_per_khead_divmod, thread_idx, seqlen_q, m_block);
        }
        cute::cp_async_fence();

        using PagedKVManager_t = PagedKVManager<get<1>(TileShape_MNK{}), get<2>(TileShape_MNK{}), NumMmaThreads, Element, true /*KV_Same_Iter*/>;
        PagedKVManager_t paged_kv_manager(
            params.ptr_pagetable, params.shape_pagetable, params.stride_pagetable,
            params.ptr_K, params.shape_K, params.stride_K,
            params.ptr_V, params.stride_V,
            params.page_size_divmod, bidb_kv, bidh_kv, thread_idx, seqlen_info.seqlen_k, seqlen_info.leftpad_k
        );

        auto load_K = [&] (int const n_block, int const smem_pipe_write, auto need_seqlenk_masking_type) {
            static constexpr bool Seqlenk_mask = decltype(need_seqlenk_masking_type)::value;
            if constexpr (!PagedKV) {
                // 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;
                Tensor tKsK_cur = tKsK(_, _, _, smem_pipe_write);
                // 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
                    : (!Seqlenk_mask ? kBlockN : std::min(seqlen_info.seqlen_k - n_block * kBlockN, kBlockN)));
                // We don't need to clear the sK smem tiles since we'll mask out the scores anyway.
                flash::copy</*Is_even_MN=*/!Seqlenk_mask && EvenN, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/true>(
                    gmem_tiled_copy_QKV, tKgK(_, _, _, n_block), tKsK_cur, t0KVcKV, tKVpKV, seqlenk_row_limit);
            } else {
                paged_kv_manager.template load_page_table<Seqlenk_mask>(n_block);
                paged_kv_manager.template load_K<Seqlenk_mask>(n_block, sK(_, _, smem_pipe_write));
            }
        };

        auto load_V = [&] (int const n_block, int const smem_pipe_write, auto need_seqlenk_masking_type) {
            static constexpr bool Seqlenk_mask = decltype(need_seqlenk_masking_type)::value;
            if constexpr (!PagedKV) {
                // 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;
                Tensor tVsV_cur = tVsV(_, _, _, smem_pipe_write);
                // We don't call flash::copy since it doesn't support bound checking
                // to not overshot kBlockN when writing to smem.
                Tensor tVgV_cur = tVgV(_, _, _, n_block);
                int const seqlenk_row_limit = seqlen_info.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 = !Seqlenk_mask || 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_cur(_, m, k), tVsV_cur(_, m, k));
                        }
                    }
                }
            } else {
                paged_kv_manager.template load_V<Seqlenk_mask>(n_block, sV(_, _, smem_pipe_write));
            }
        };

        auto preprocess_Q = [&] {
            if constexpr (!AppendKV) {
                flash::cp_async_wait<Share_QV_Smem ? 1 : kStages * 2 - 1>();
            } else {
                if (get<1>(params.shape_rotary) > 0) {  // Apply rotary to Q
                    int const offset_rotary = seqlen_info.seqlen_k_og + seqlen_info.leftpad_k;
                    using Rotary_t = Rotary<kBlockM, kHeadDim, NumMmaThreads, Element, !(Is_causal || Is_local) /*FixedPosition*/>;
                    Rotary_t rotary(params.ptr_rotary_cos, params.shape_rotary, params.stride_rotary_cos,
                                    params.ptr_rotary_sin, params.stride_rotary_sin,
                                    params.is_rotary_interleaved, thread_idx, seqlen_q, offset_rotary);
                    int const qhead_per_khead = !PackGQA ? 1 : params.qhead_per_khead_divmod.divisor;
                    if (params.is_rotary_interleaved) {
                        auto [tRrCos, tRrSin] = cute::conditional_return<!PackGQA>(
                            rotary.template load_cos_sin<true /*kInterleaved*/>(m_block),
                            rotary.template load_cos_sin_packgqa<true /*kInterleaved*/>(m_block, params.qhead_per_khead_divmod)
                        );
                        flash::cp_async_wait<Share_QV_Smem ? 1 : kStages * 2 - 1>();
                        __syncthreads();
                        rotary.apply_Q_interleaved(sQ, tRrCos, tRrSin, m_block, qhead_per_khead);
                    } else {
                        auto [tRrCosCont, tRrSinCont] = cute::conditional_return<!PackGQA>(
                            rotary.template load_cos_sin<false /*kInterleaved*/>(m_block),
                            rotary.template load_cos_sin_packgqa<false /*kInterleaved*/>(m_block, params.qhead_per_khead_divmod)
                        );
                        flash::cp_async_wait<Share_QV_Smem ? 1 : kStages * 2 - 1>();
                        __syncthreads();
                        rotary.apply_Q_contiguous(sQ, tRrCosCont, tRrSinCont, m_block, qhead_per_khead);
                    }
                } else {
                    flash::cp_async_wait<Share_QV_Smem ? 1 : kStages * 2 - 1>();
                }
            }

            if constexpr (Q_in_regs) {
                __syncthreads();
                Tensor tSrQ_copy_view = smem_thr_copy_Q.retile_D(tSrQ);
                Tensor tSsQ_copy_view = smem_thr_copy_Q.partition_S(sQ);
                cute::copy(smem_tiled_copy_Q, tSsQ_copy_view, tSrQ_copy_view);
            }
        };

        // If Share_QV_Smem, we load Q, then load 1 stage of K, then (optionally) rotate Q and
        // read from smem_q to registers, then load V.
        // If !Share_QV, Smem, we load Q, load all stages of K & V, then (optionally) rotate Q.

        if constexpr (Share_QV_Smem) {
            load_K(n_block, 0, cute::true_type{} /*Seqlenk_mask*/);
            cute::cp_async_fence();
            preprocess_Q();
            __syncthreads();  // Make sure all threads have read smem_q before loading V
        }

        // For persistent, make sure all threads have finished reading smem_o
        if constexpr (!Share_QV_Smem) { __syncthreads(); }
        // 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 (!Share_QV_Smem || !Is_first_stage) {
                if (Is_first_stage || n_block - stage >= n_block_min) {
                    load_K(n_block - stage, stage, cute::bool_constant<Is_first_stage>{} /*Seqlenk_mask*/);
                }
                // 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 (!Is_last_stage) {
                if (Is_first_stage || n_block - stage >= n_block_min) {
                    load_V(n_block - stage, stage, cute::bool_constant<Is_first_stage>{} /*Seqlenk_mask*/);
                }
                cute::cp_async_fence();
            }
        });

        if constexpr (!Share_QV_Smem) { preprocess_Q(); }

        flash::Mask<kBlockM, kBlockN, PackGQA, TiledMma> mask(
            thread_idx, seqlen_q, seqlen_k, params.window_size_left, params.window_size_right, params.sink_token_length,
            params.qhead_per_khead_divmod
        );

        float softcap_val = params.softcap_val;
        if constexpr (Has_softcap && Is_FP8) {
            float const q_descale = params.ptr_q_descale == nullptr ? 1.0f : params.ptr_q_descale[bidb * get<0>(params.stride_q_descale) + bidh_kv * get<1>(params.stride_q_descale)];
            float const k_descale = params.ptr_k_descale == nullptr ? 1.0f : params.ptr_k_descale[bidb * get<0>(params.stride_k_descale) + bidh_kv * get<1>(params.stride_k_descale)];
            softcap_val *= q_descale * k_descale;
        }
        // Softcapping needs to happen before masking since if we apply after masking, softcapping can turn
        // -inf to e.g. -50.0, which can affect the attention softmax.
        auto scoremod_premask_fn = [&](auto& tSrS) {
            if constexpr (Has_softcap) { flash::apply_softcap(tSrS, softcap_val); }
        };

        int smem_pipe_read = 0, smem_pipe_write = kStages - 1;

        auto load_K_next = [&] {
            if (n_block - kStages >= n_block_min) {
                load_K(n_block - kStages, kStages > 1 ? smem_pipe_write : 0, cute::false_type{} /*Seqlenk_mask*/);
            }
            cute::cp_async_fence();
        };

        auto sync = [&] {
            flash::cp_async_wait<kStages * 2 - 2>();
            __syncthreads();
        };

        clear(tOrO);

        auto fwd_step = [&](int const n_block, auto mask_fn, auto is_first_iter_type, auto check_inf_type) {
            static constexpr bool Is_first_iter = decltype(is_first_iter_type)::value;
            static constexpr bool Check_inf = decltype(check_inf_type)::value;
            Tensor tSrS = partition_fragment_C(tiled_mma, select<0, 1>(TileShape_MNK{}));
            clear(tSrS);
            sync();
            auto load_V_next = [&] {
                if (n_block - kStages + 1 >= n_block_min) {
                    load_V(n_block - kStages + 1, kStages > 1 ? smem_pipe_write : 0, cute::bool_constant<Is_first_iter && kStages == 1>{} /*Seqlenk_mask*/);
                }
                cute::cp_async_fence();
            };
            Tensor tSrQ_cur = cute::conditional_return<Q_in_regs>(tSrQ, thr_mma.partition_fragment_A(sQ));
            Tensor tSrK = thr_mma.partition_fragment_B(sK(_, _, _0{}));
            flash::gemm_sm80<Q_in_regs>(
                tSrS, tSrQ_cur, tSrK, tSsQ, tSsK(_, _, _, kStages > 1 ? smem_pipe_read : 0),
                tiled_mma, smem_tiled_copy_Q, smem_tiled_copy_K, smem_thr_copy_Q, smem_thr_copy_K, load_V_next
            );
            smem_pipe_write = smem_pipe_write < kStages - 1 ? smem_pipe_write + 1 : 0;
            scoremod_premask_fn(tSrS);
            // Faster to load_K before gemm if we only have 1 stage
            if constexpr (kStages == 1) { sync(); load_K_next(); }
            mask_fn(tSrS, n_block);
            Tensor scores_scale = softmax.template max_get_scale</*Is_first=*/Is_first_iter, Check_inf>(tSrS);
            softmax.template online_softmax</*Is_first=*/Is_first_iter, Check_inf>(tSrS);
            if constexpr (Is_FP8) { flash::permute_Cregs_fp8(tSrS); }
            Tensor tOrP_acc = make_tensor(tSrS.data(), flash::convert_layout_acc_Aregs<TiledMma>(tSrS.layout()));
            Tensor tOrP = make_tensor_like<Element>(tOrP_acc);
            convert_type_out(tOrP_acc, tOrP);
            if constexpr (!Is_first_iter) { softmax.rescale_o(tOrO, scores_scale); }
            if constexpr (kStages > 1) { sync(); }
            Tensor tOrV = thr_mma.partition_fragment_B(sVt(_, _, _0{}));
            flash::gemm_rs_sm80(tOrO, tOrP, tOrV, tOsVt(_, _, _, kStages > 1 ? smem_pipe_read : 0), tiled_mma, smem_tiled_copy_V, smem_thr_copy_V);
            if constexpr (kStages > 1) { load_K_next(); }
            smem_pipe_read = smem_pipe_read < kStages - 1 ? smem_pipe_read + 1 : 0;
        };

        auto first_iter_mask_fn = [&](auto& tSrS, int n_block) { mask.template apply<true /*Seqlenk_mask*/, Is_causal, Is_local>(tSrS, m_block, n_block); };
        fwd_step(n_block, first_iter_mask_fn, cute::true_type{} /*is_first_iter*/, cute::true_type{} /*check_inf*/);
        --n_block;
        if constexpr (Is_causal || Is_local) { // Separate iterations with causal or local masking
            auto mask_fn = [&](auto& tSrS, int n_block) { mask.template apply<false /*Seqlenk_mask*/, Is_causal, Is_local>(tSrS, m_block, n_block); };
            int const m_idx_min = !PackGQA ? m_block * kBlockM : params.qhead_per_khead_divmod.divide(m_block * kBlockM);
            int const n_block_min_causal_local_mask =
                std::max(n_block_min, (m_idx_min + seqlen_k - seqlen_q + params.window_size_right) / kBlockN);
            #pragma unroll 1
            for (; n_block >= n_block_min_causal_local_mask; --n_block) {
                fwd_step(n_block, mask_fn, cute::false_type{} /*is_first_iter*/, cute::true_type{} /*check_inf*/);
            }
        }
        int const m_idx_max = !PackGQA ? (m_block + 1) * kBlockM : params.qhead_per_khead_divmod.divide((m_block + 1) * kBlockM - 1) + 1;
        int const n_block_min_before_local_mask = !Is_local
            ? n_block_min
            : std::max(n_block_min,
                        cute::ceil_div(m_idx_max + seqlen_k - seqlen_q - params.window_size_left, kBlockN));
        auto no_mask_fn = [](auto& tSrS, int n_block) { };
        #pragma unroll 1
        for (; n_block >= n_block_min_before_local_mask; --n_block) {
            fwd_step(n_block, no_mask_fn, cute::false_type{} /*is_first_iter*/, cute::false_type{} /*check_inf*/);
        }
        // Separate masking iterations on the left for local attention
        if constexpr (Is_local) {
            auto local_mask_fn = [&](auto& tSrS, int n_block) { mask.template apply<false /*Seqlenk_mask*/, false /*Causal_mask*/, Is_local>(tSrS, m_block, n_block); };
            #pragma unroll 1
            for (; n_block >= n_block_min; --n_block) {
                fwd_step(n_block, local_mask_fn, cute::false_type{} /*is_first_iter*/, cute::bool_constant<Is_local>{} /*check_inf*/);
            }
            // Disable sink token code for now
            // int n_block_sink_max = cute::ceil_div(params.sink_token_length, kBlockN);
            // #pragma unroll 1
            // for (n_block = std::min(n_block, n_block_sink_max - 1); n_block >= 0; --n_block) {
            //     fwd_step(n_block, local_mask_fn, cute::false_type{} /*is_first_iter*/, cute::bool_constant<Is_local>{} /*check_inf*/);
            // }
        }
        float const v_descale = !Is_FP8 || params.ptr_v_descale == nullptr ? 1.0f : params.ptr_v_descale[bidb * get<0>(params.stride_v_descale) + bidh_kv * get<1>(params.stride_v_descale)];
        Tensor scores_scale = softmax.finalize(v_descale);
        softmax.rescale_o(tOrO, scores_scale);
        if constexpr (Is_FP8) { flash::permute_output_fp8(tOrO); }
        return true;
    }

    CUTLASS_DEVICE
    cute::tuple<int, int> get_n_block_k_new_min_max(Params const& params, SeqlenInfo_t const& seqlen_info,
                                                    int m_block, int bidb, int split_idx=0, int num_splits=1) {
        static constexpr int kBlockN = get<1>(TileShape_MNK{});
        auto [n_block_min, n_block_max] = get_n_block_min_max(params, seqlen_info, m_block, bidb, split_idx, num_splits);
        int const idx_k_new_min = std::max(n_block_min * kBlockN - seqlen_info.seqlen_k_og, 0);
        int const idx_k_new_max = std::min(n_block_max * kBlockN - seqlen_info.seqlen_k_og, seqlen_info.seqlen_k_new);
        int const n_block_new_min = idx_k_new_min / kBlockN;
        int const n_block_new_max = idx_k_new_max > idx_k_new_min ? cute::ceil_div(idx_k_new_max, kBlockN) : n_block_new_min;
        // if (threadIdx.x == 128 && m_block == 0) { printf("bidb = %d, seqlen_k_new = %d, seqlen_k_og = %d, n_block_min = %d, n_block_max = %d, idx_k_new_min = %d, idx_k_new_max = %d, n_block_new_min = %d, n_block_new_max = %d\n", bidb, seqlen_k_new, seqlen_k_og, n_block_min, n_block_max, idx_k_new_min, idx_k_new_max, n_block_new_min, n_block_new_max);}
        return {n_block_new_min, n_block_new_max};
    }

    template <typename SharedStorage>
    CUTLASS_DEVICE bool
    store_kv_new(Params const& params,
                 int const thread_idx,
                 SharedStorage &shared_storage,
                 SeqlenInfo_t const& seqlen_info,
                 cute::tuple<int32_t, int32_t, int32_t, int32_t> block_coord
    ) {
        auto [m_block, bidh, bidb, split_idx] = block_coord;
        auto n_block_new_min_max = get_n_block_k_new_min_max(params, seqlen_info, m_block, bidb, split_idx, params.num_splits);
        int const n_block_new_min = get<0>(n_block_new_min_max);
        int const n_block_new_max = get<1>(n_block_new_min_max);
        if (n_block_new_max <= n_block_new_min) { return false; }

        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{});

        int const bidh_kv = !PackGQA ? params.qhead_per_khead_divmod.divide(bidh) : bidh;
        int const bidb_kv = params.kv_batch_idx == nullptr ? bidb : params.kv_batch_idx[bidb];

        bool const is_varlen_k_new = Varlen && params.cu_seqlens_k_new;
        Tensor mKnew = make_tensor(make_gmem_ptr(params.ptr_K_new), params.shape_K_new, params.stride_K_new)(_, _, bidh_kv, !is_varlen_k_new ? bidb : 0);
        Tensor mVnew = make_tensor(make_gmem_ptr(params.ptr_V_new), params.shape_K_new, params.stride_V_new)(_, _, bidh_kv, !is_varlen_k_new ? bidb : 0);

        bool const is_varlen_k = Varlen && params.cu_seqlens_k;
        Tensor mK = make_tensor(make_gmem_ptr(params.ptr_K), params.shape_K, params.stride_K)(_, _, bidh_kv, !is_varlen_k ? bidb_kv : 0);
        Tensor mV = make_tensor(make_gmem_ptr(params.ptr_V), params.shape_K, params.stride_V)(_, _, bidh_kv, !is_varlen_k ? bidb_kv : 0);

        Tensor gKnew = local_tile(domain_offset(make_coord(seqlen_info.offset_k_new, _0{}), mKnew), select<1, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (N, K, _)
        Tensor gVnew = local_tile(domain_offset(make_coord(seqlen_info.offset_k_new, _0{}), mVnew), select<1, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (N, K, _)
        int const offset_k = seqlen_info.offset_k + seqlen_info.seqlen_k_og;
        Tensor gK = local_tile(domain_offset(make_coord(offset_k, _0{}), mK), select<1, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (N, K, _)
        Tensor gV = local_tile(domain_offset(make_coord(offset_k, _0{}), mV), select<1, 2>(TileShape_MNK{}), make_coord(_, _0{}));  // (N, K, _)

        static constexpr int kBlockN = get<1>(TileShape_MNK{});
        static constexpr int kHeadDim = get<2>(TileShape_MNK{});
        int const offset_rotary = seqlen_info.seqlen_k_og + seqlen_info.leftpad_k;
        int const seqlen_k_new = seqlen_info.seqlen_k_new;
        using Rotary_t = Rotary<kBlockN, kHeadDim, NumMmaThreads, Element>;
        Rotary_t rotary(params.ptr_rotary_cos, params.shape_rotary, params.stride_rotary_cos,
                        params.ptr_rotary_sin, params.stride_rotary_sin,
                        params.is_rotary_interleaved, thread_idx, seqlen_k_new, offset_rotary);

        using PagedKVManager_t = PagedKVManager<get<1>(TileShape_MNK{}), get<2>(TileShape_MNK{}), NumMmaThreads, Element, true /*KV_Same_Iter*/, 2 /*LoadsPerRow_LB*/>;
        PagedKVManager_t paged_kv_manager(
            params.ptr_pagetable, params.shape_pagetable, params.stride_pagetable,
            params.ptr_K, params.shape_K, params.stride_K,
            params.ptr_V, params.stride_V,
            params.page_size_divmod, bidb_kv, bidh_kv, thread_idx, seqlen_k_new, offset_k
            // passing offset_k instead of leftpad_k will move the PageTable pointer to the right position
        );

        static_assert(std::is_same_v<GmemLayoutAtomAppend, typename Rotary_t::LayoutAtom>);
        static_assert(!PagedKV || std::is_same_v<GmemLayoutAtomAppend, typename PagedKVManager_t::GmemLayoutAtomKVCpAsync>);
        GmemTiledCopyQKV gmem_tiled_copy_kv_g2s;
        auto gmem_thr_copy_kv_g2s = gmem_tiled_copy_kv_g2s.get_thread_slice(thread_idx);
        auto gmem_thr0_copy_kv_g2s = gmem_tiled_copy_kv_g2s.get_thread_slice(_0{});  // Only for index calculation
        GmemTiledCopyAppendKV gmem_tiled_copy_kv_s2g;
        auto gmem_thr_copy_kv_s2g = gmem_tiled_copy_kv_s2g.get_thread_slice(thread_idx);
        auto gmem_thr0_copy_kv_s2g = gmem_tiled_copy_kv_s2g.get_thread_slice(_0{});  // Only for index calculation
        Tensor tKgKnew = gmem_thr_copy_kv_g2s.partition_S(gKnew);
        Tensor tKsKg2s = gmem_thr_copy_kv_g2s.partition_S(sK);
        Tensor tKsKs2g = gmem_thr_copy_kv_s2g.partition_S(sK);        // ((Atom,AtomNum),ATOM_M,ATOM_N)
        Tensor tKgK = gmem_thr_copy_kv_s2g.partition_D(gK);
        Tensor tVgVnew = gmem_thr_copy_kv_g2s.partition_S(gVnew);        // ((Atom,AtomNum),ATOM_M,ATOM_N)
        Tensor tVsVg2s = gmem_thr_copy_kv_g2s.partition_S(sV);        // ((Atom,AtomNum),ATOM_M,ATOM_N)
        Tensor tVsVs2g = gmem_thr_copy_kv_s2g.partition_S(sV);        // ((Atom,AtomNum),ATOM_M,ATOM_N)
        Tensor tVgV = gmem_thr_copy_kv_s2g.partition_D(gV);
        Tensor cK = cute::make_identity_tensor(select<1, 2>(TileShape_MNK{}));  // (BLK_N,BLK_K) -> (blk_n,blk_k)
        Tensor tKcKg2s = gmem_thr_copy_kv_g2s.partition_D(cK);
        Tensor t0KcKg2s = gmem_thr0_copy_kv_g2s.partition_D(cK);
        Tensor tKpKg2s = make_tensor<bool>(make_shape(size<2>(tKsKg2s)));
        Tensor tKcKs2g = gmem_thr_copy_kv_s2g.partition_D(cK);
        Tensor t0KcKs2g = gmem_thr0_copy_kv_s2g.partition_D(cK);
        Tensor tKpKs2g = make_tensor<bool>(make_shape(size<2>(tKsKs2g)));
        #pragma unroll
        for (int k = 0; k < size(tKpKg2s); ++k) { tKpKg2s(k) = get<1>(tKcKg2s(_0{}, _0{}, k)) < get<1>(params.shape_K); }
        #pragma unroll
        for (int k = 0; k < size(tKpKs2g); ++k) { tKpKs2g(k) = get<1>(tKcKs2g(_0{}, _0{}, k)) < get<1>(params.shape_K); }

        auto load_K_new = [&] (int const n_block, int const smem_pipe_write, auto need_seqlenk_masking_type) {
            static constexpr bool Seqlenk_mask = decltype(need_seqlenk_masking_type)::value;
            static constexpr bool EvenN = kBlockN % CUTE_STATIC_V(shape<0>(GmemLayoutAtom{})) == 0;
            Tensor tKsK_cur = tKsKg2s(_, _, _, smem_pipe_write);
            int const seqlenk_row_limit = -int(get<0>(tKcKg2s(_0{}, _0{}, _0{}))) + (EvenN
                ? seqlen_k_new - n_block * kBlockN
                : (!Seqlenk_mask ? kBlockN : std::min(seqlen_k_new - n_block * kBlockN, kBlockN)));
            // We don't need to clear the sK smem tiles since we won't write them out
            flash::copy</*Is_even_MN=*/!Seqlenk_mask && EvenN, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/true>(
                gmem_tiled_copy_kv_g2s, tKgKnew(_, _, _, n_block), tKsK_cur, t0KcKg2s, tKpKg2s, seqlenk_row_limit);
        };

        auto load_V_new = [&] (int const n_block, int const smem_pipe_write, auto need_seqlenk_masking_type) {
            static constexpr bool Seqlenk_mask = decltype(need_seqlenk_masking_type)::value;
            static constexpr bool EvenN = kBlockN % CUTE_STATIC_V(shape<0>(GmemLayoutAtom{})) == 0;
            Tensor tVsV_cur = tVsVg2s(_, _, _, smem_pipe_write);
            int const seqlenk_row_limit = -int(get<0>(tKcKg2s(_0{}, _0{}, _0{}))) + (EvenN
                ? seqlen_k_new - n_block * kBlockN
                : (!Seqlenk_mask ? kBlockN : std::min(seqlen_k_new - n_block * kBlockN, kBlockN)));
            // We don't need to clear the sV smem tiles since we won't write them out
            flash::copy</*Is_even_MN=*/!Seqlenk_mask && EvenN, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/true>(
                gmem_tiled_copy_kv_g2s, tVgVnew(_, _, _, n_block), tVsV_cur, t0KcKg2s, tKpKg2s, seqlenk_row_limit);
        };

        auto store_K = [&] (int const n_block, int const smem_pipe_read) {
            int const n_limit = std::min(seqlen_k_new - n_block * kBlockN, kBlockN);
            if (get<1>(params.shape_rotary) <= 0) {
                Tensor tKsK_cur = tKsKs2g(_, _, _, smem_pipe_read);
                if constexpr (!PagedKV) {
                    Tensor tKgK_cur = tKgK(_, _, _, n_block);
                    // Clear_OOB_K must be false since we don't want to write zeros to gmem
                    flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
                        gmem_tiled_copy_kv_s2g, tKsK_cur, tKgK_cur, tKcKs2g, tKpKs2g, std::min(seqlen_k_new - n_block * kBlockN, kBlockN)
                    );
                } else {
                    paged_kv_manager.store_K(n_block, tKsK_cur);
                }
            } else {
                Tensor gK_cur = gK(_, _, n_block);
                auto tPrKPtr = cute::conditional_return<PagedKV>(paged_kv_manager.compute_K_ptr(), nullptr);
                if (params.is_rotary_interleaved) {
                    auto [tRrCos, tRrSin] = rotary.template load_cos_sin<true /*kInterleaved*/>(n_block);
                    rotary.template apply_K_interleaved<PagedKV>(sK(_, _, smem_pipe_read), gK_cur, tKpKs2g, tRrCos, tRrSin, tPrKPtr, n_block);
                } else {
                    auto [tRrCosCont, tRrSinCont] = rotary.template load_cos_sin<false /*kInterleaved*/>(n_block);
                    rotary.template apply_K_contiguous<PagedKV>(sK(_, _, smem_pipe_read), gK_cur, tKpKs2g, tRrCosCont, tRrSinCont, tPrKPtr, n_block, get<1>(params.shape_K));
                }
            }
        };

        auto store_V = [&] (int const n_block, int const smem_pipe_read) {
            int const n_limit = std::min(seqlen_k_new - n_block * kBlockN, kBlockN);
            Tensor tVsV_cur = tVsVs2g(_, _, _, smem_pipe_read);
            if constexpr (!PagedKV) {
                Tensor tVgV_cur = tVgV(_, _, _, n_block);
                // Clear_OOB_K must be false since we don't want to write zeros to gmem
                flash::copy</*Is_even_MN=*/false, /*Is_even_K=*/false, /*Clear_OOB_MN=*/false, /*Clear_OOB_K=*/false>(
                    gmem_tiled_copy_kv_s2g, tVsV_cur, tVgV_cur, tKcKs2g, tKpKs2g, n_limit);
            } else {
                paged_kv_manager.store_V(n_block, tVsV_cur);
            }
        };

        int n_block = n_block_new_max - 1;
        // 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 (Is_first_stage || n_block - stage >= n_block_new_min) {
                load_K_new(n_block - stage, stage, cute::bool_constant<Is_first_stage>{} /*Seqlenk_mask*/);
            }
            cute::cp_async_fence();
            // If persistent, need to sync to make sure all threads have finished with smem_o before writing to smem_v
            if constexpr (Is_first_stage) { __syncthreads(); }
            if constexpr (!Is_last_stage) {
                if (Is_first_stage || n_block - stage >= n_block_new_min) {
                    load_V_new(n_block - stage, stage, cute::bool_constant<Is_first_stage>{} /*Seqlenk_mask*/);
                }
                cute::cp_async_fence();
            }
        });

        int smem_pipe_read = 0, smem_pipe_write = kStages - 1;
        #pragma unroll 1
        for (; n_block >= n_block_new_min; --n_block) {
            if constexpr (PagedKV) { paged_kv_manager.template load_page_table<true /*Seqlenk_mask*/>(n_block); }
            flash::cp_async_wait<kStages * 2 - 2>();
            __syncthreads();
            store_K(n_block, kStages > 1 ? smem_pipe_read : 0);
            if (n_block - kStages + 1 >= n_block_new_min) {
                load_V_new(n_block - kStages + 1, kStages > 1 ? smem_pipe_write : 0, cute::bool_constant<kStages == 1>{} /*Seqlenk_mask*/);
            }
            cute::cp_async_fence();
            smem_pipe_write = smem_pipe_write < kStages - 1 ? smem_pipe_write + 1 : 0;
            flash::cp_async_wait<kStages * 2 - 2>();
            __syncthreads();
            store_V(n_block, kStages > 1 ? smem_pipe_read : 0);
            smem_pipe_read = smem_pipe_read < kStages - 1 ? smem_pipe_read + 1 : 0;
            if (n_block - kStages >= n_block_new_min) {
                load_K_new(n_block - kStages, kStages > 1 ? smem_pipe_write : 0, cute::false_type{} /*Seqlenk_mask*/);
            }
            cute::cp_async_fence();
        }

        return true;

    }

};

} // namespace flash