// Adapted from https://github.com/Dao-AILab/flash-attention/blob/main/hopper/utils.h

#pragma once

#include <assert.h>
#include <stdint.h>
#include <stdlib.h>

#include <cuda_bf16.h>

#include <cute/tensor.hpp>

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

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

namespace flash {

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

template<typename T>
struct MaxOp {
__device__ __forceinline__ T operator()(T const & x, T const & y) { return x > y ? x : y; }
};

template <>
struct MaxOp<float> {
// This is slightly faster
__device__ __forceinline__ float operator()(float const &x, float const &y) { return max(x, y); }
};

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

template<typename T>
struct SumOp {
__device__ __forceinline__ T operator()(T const & x, T const & y) { return x + y; }
};

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

template<int THREADS>
struct Allreduce {
    static_assert(THREADS == 32 || THREADS == 16 || THREADS == 8 || THREADS == 4);
    template<typename T, typename Operator>
    static __device__ __forceinline__ T run(T x, Operator &op) {
        constexpr int OFFSET = THREADS / 2;
        x = op(x, __shfl_xor_sync(uint32_t(-1), x, OFFSET));
        return Allreduce<OFFSET>::run(x, op);
    }
};

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

template<>
struct Allreduce<2> {
template<typename T, typename Operator>
static __device__ __forceinline__ T run(T x, Operator &op) {
    x = op(x, __shfl_xor_sync(uint32_t(-1), x, 1));
    return x;
}
};

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

template <bool zero_init=false, int wg_wait=0, bool arrive=true, bool commit=true, typename Tensor0, typename Tensor1, typename Tensor2, typename TiledMma>
__forceinline__ __device__ void gemm(TiledMma &tiled_mma, Tensor0 const &tCrA, Tensor1 const &tCrB, Tensor2 &tCrC) {
    constexpr bool Is_RS = !cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value;
    // Need to cast away const on tCrA since warpgroup_fence_operand doesn't take const
    if constexpr (Is_RS) { cute::warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA)); }
    warpgroup_fence_operand(tCrC);
    if constexpr (arrive) {
        warpgroup_arrive();
    }
    if constexpr (zero_init) {
        tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
        // Unroll the K mode manually to set scale D to 1
        CUTLASS_PRAGMA_UNROLL
        for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
            cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), tCrC);
            tiled_mma.accumulate_ = GMMA::ScaleOut::One;
        }
    } else {
        // cute::gemm(tiled_mma, tCrA, tCrB, tCrC);
        // Unroll the K mode manually to set scale D to 1
        CUTLASS_PRAGMA_UNROLL
        for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
            cute::gemm(tiled_mma, tCrA(_,_,k_block), tCrB(_,_,k_block), tCrC);
            tiled_mma.accumulate_ = GMMA::ScaleOut::One;
        }
    }
    if constexpr (commit) {
        warpgroup_commit_batch();
    }
    if constexpr (wg_wait >= 0) { warpgroup_wait<wg_wait>(); }
    warpgroup_fence_operand(tCrC);
    if constexpr (Is_RS) { warpgroup_fence_operand(const_cast<Tensor0 &>(tCrA)); }
}

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

// For SM80, convert acc_layout from (MMA=4, MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, MMA_N))
// For SM90, convert acc_layout from ((2, 2, V), MMA_M, MMA_N) to (nrow=(2, MMA_M), ncol=(2, V, MMA_N))
template<bool Transposed=false, typename Layout0>
__forceinline__ __device__ auto convert_layout_acc_rowcol(Layout0 acc_layout) {
    if constexpr (decltype(rank<0>(acc_layout))::value == 3) {  // SM90
        static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
        static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
        static_assert(decltype(rank(acc_layout))::value == 3);
        auto l = acc_layout;
        if constexpr (!Transposed) {
            return make_layout(make_layout(get<0, 1>(l), get<1>(l)), make_layout(get<0, 0>(l), get<0, 2>(l), get<2>(l)));
        } else {
             return make_layout(make_layout(get<0, 0>(l), get<0, 2>(l), get<2>(l)), make_layout(get<0, 1>(l), get<1>(l)));
        }

    } else {  // SM80
        static_assert(decltype(size<0>(acc_layout))::value == 4);
        static_assert(decltype(rank(acc_layout))::value == 3);
        auto l = logical_divide(acc_layout, Shape<_2>{});  // ((2, 2), MMA_M, MMA_N)
        if constexpr (!Transposed) {
            return make_layout(make_layout(get<0, 1>(l), get<1>(l)), make_layout(get<0, 0>(l), get<2>(l)));
        } else {
            return make_layout(make_layout(get<0, 0>(l), get<2>(l)), make_layout(get<0, 1>(l), get<1>(l)));
        }
    }
};

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

// For SM80, convert acc_layout from (MMA=4, MMA_M, MMA_N) to ((4, 2), MMA_M, MMA_N / 2)
// if using m16n8k16, or to (4, MMA_M, MMA_N) if using m16n8k8.
// For SM90, FP16/BF16, convert acc_layout from ((2, 2, N / 8), MMA_M, MMA_N) to ((2, 2, 2), MMA_M, (N / 16, MMA_N))
// For SM90, FP8, convert acc_layout from ((2, 2, N / 8), MMA_M, MMA_N) to ((4, 2, 2), MMA_M, (N / 32, MMA_N))
template<typename MMA_Traits, typename Layout0>
__forceinline__ __device__ auto convert_layout_acc_Aregs(Layout0 acc_layout) {
    using X = Underscore;
    if constexpr (decltype(rank<0>(acc_layout))::value == 3) {  // SM90
        static_assert(decltype(size<0, 0>(acc_layout))::value == 2);
        static_assert(decltype(size<0, 1>(acc_layout))::value == 2);
        static_assert(decltype(rank(acc_layout))::value == 3);
        static_assert(decltype(rank(get<0>(acc_layout)))::value == 3);
        if constexpr (sizeof(typename MMA_Traits::ValTypeA) == 2) {
            auto l = logical_divide(get<0, 2>(acc_layout), Tile<_2>{});  // ((2, N / 16))
            return make_layout(make_layout(get<0, 0>(acc_layout), get<0, 1>(acc_layout), get<0, 0>(l)), get<1>(acc_layout), coalesce(make_layout(get<0, 1>(l), get<2>(acc_layout))));
        } else {
            static_assert(sizeof(typename MMA_Traits::ValTypeA) == 1);
            static_assert(decltype(stride<0, 0>(acc_layout))::value == 1);
            static_assert(decltype(stride<0, 1>(acc_layout))::value == 2);
            auto l = logical_divide(get<0, 2>(acc_layout), Tile<Layout<Shape<_2, _2>>>{});  // (((2, 2), N / 32))
            // This combines the first two modes (<0, 0> and <0, 1>) into one mode.
            // Will require register shuffling later to be correct.
            return make_layout(make_layout(Layout<_4>{}, get<0, 0, 0>(l), get<0, 0, 1>(l)),
                               get<1>(acc_layout),
                               coalesce(make_layout(get<0, 1>(l), get<2>(acc_layout))));  // ((4, 2, 2), MMA_M, N / 32 * MMA_N)
            // This combination is right but doesn't work with register shuffling.
            // return make_layout(make_layout(coalesce(make_layout(get<0, 0>(acc_layout), get<0, 0, 0>(l))), get<0, 1>(acc_layout), get<0, 0, 1>(l)),
            //                    get<1>(acc_layout),
            //                    coalesce(make_layout(get<0, 1>(l), get<2>(acc_layout))));
        }
    } else {  // SM80
        static_assert(decltype(size<0>(acc_layout))::value == 4);
        static_assert(decltype(rank(acc_layout))::value == 3);
        constexpr int mma_shape_K = get<2>(typename MMA_Traits::Shape_MNK{});
        static_assert(mma_shape_K == 8 || mma_shape_K == 16);
        if constexpr (mma_shape_K == 8) {
            return acc_layout;
        } else {
            auto l = logical_divide(acc_layout, Shape<X, X, _2>{});  // (4, MMA_M, (2, MMA_N / 2)))
            return make_layout(make_layout(get<0>(l), get<2, 0>(l)), get<1>(l), get<2, 1>(l));
        }
    }
};

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

template <typename To_type, typename Engine, typename Layout>
__forceinline__ __device__ auto convert_type(Tensor<Engine, Layout> const &tensor) {
    using From_type = typename Engine::value_type;
    constexpr int numel = decltype(size(tensor))::value;
    cutlass::NumericArrayConverter<To_type, From_type, numel> convert_op;
    // HACK: this requires tensor to be "contiguous"
    auto frag = convert_op(*reinterpret_cast<const cutlass::Array<From_type, numel> *>(tensor.data()));
    return make_tensor(make_rmem_ptr<To_type>(&frag), tensor.layout());
}

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

// Blocks until all but N previous cp.async.commit_group operations have committed.
// This differs from cute::cp_async_wait in that when N = 0 we don't call cp.async.wait_all
// (which is equivalent to commit_group then wait_group 0).
// Instead we just call cp.async.wait_group 0, which is slightly faster.
// https://github.com/NVIDIA/cutlass/blob/master/include/cute/arch/copy_sm80.hpp#L113
template <int N>
CUTE_HOST_DEVICE
void cp_async_wait() {
#if defined(CUTE_ARCH_CP_ASYNC_SM80_ENABLED)
    asm volatile("cp.async.wait_group %0;\n" :: "n"(N));
#endif
}

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

template <bool Is_even_MN=true, bool Is_even_K=true, bool Clear_OOB_MN=false, bool Clear_OOB_K=true,
          typename TiledCopy, typename Engine0, typename Layout0, typename Engine1, typename Layout1,
          typename Engine2, typename Layout2, typename Engine3, typename Layout3>
__forceinline__ __device__ void copy(TiledCopy tiled_copy, Tensor<Engine0, Layout0> const &S,
                            Tensor<Engine1, Layout1> &D, Tensor<Engine2, Layout2> const &identity_MN,
                            Tensor<Engine3, Layout3> const &predicate_K, const int max_MN=0) {
    CUTE_STATIC_ASSERT_V(rank(S) == Int<3>{});
    CUTE_STATIC_ASSERT_V(rank(D) == Int<3>{});
    CUTE_STATIC_ASSERT_V(size<0>(S) == size<0>(D));                     // MMA
    CUTE_STATIC_ASSERT_V(size<1>(S) == size<1>(D));                     // MMA_M
    CUTE_STATIC_ASSERT_V(size<2>(S) == size<2>(D));                     // MMA_K
    // There's no case where !Clear_OOB_K && Clear_OOB_MN
    static_assert(!(Clear_OOB_MN && !Clear_OOB_K));
    #pragma unroll
    for (int m = 0; m < size<1>(S); ++m) {
        if (Is_even_MN || get<0>(identity_MN(0, m, 0)) < max_MN) {
            #pragma unroll
            for (int k = 0; k < size<2>(S); ++k) {
                if (Is_even_K || predicate_K(k)) {
                    cute::copy(tiled_copy, S(_, m, k), D(_, m, k));
                } else if (Clear_OOB_K) {
                    cute::clear(D(_, m, k));
                }
            }
        } else if (Clear_OOB_MN) {
            cute::clear(D(_, m, _));
        }
    }
}

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

}  // namespace flash