/* Copyright (c) 2022, Tri Dao.
 */

#include "fmha.h"
#include "fmha_dgrad_kernel_1xN_loop.h"

template<typename Kernel_traits, bool Is_dropout, bool Is_causal, int loop_steps=-1>
__global__ void fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel(Fused_multihead_attention_fprop_params params) {
    fmha::compute_dq_dk_dv_1xN<Kernel_traits, Is_dropout, Is_causal, loop_steps>(params);
}

template<typename Kernel_traits>
void run_fmha_dgrad_fp16_sm80_loop_(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream) {
    constexpr int smem_size_softmax = Kernel_traits::Cta_tile_p::M * Kernel_traits::Cta_tile_p::WARPS_N * sizeof(float);
    constexpr int smem_size_q = Kernel_traits::Smem_tile_q::BYTES_PER_TILE;
    constexpr int smem_size_v = Kernel_traits::Smem_tile_v::BYTES_PER_TILE;
    constexpr int smem_size_dq = Kernel_traits::Smem_tile_o::BYTES_PER_TILE;

    using Smem_tile_s = fmha::Smem_tile_mma_transposed<typename Kernel_traits::Cta_tile_p>;
    constexpr int smem_size_s = Smem_tile_s::BYTES_PER_TILE;
    static_assert(smem_size_s == 16 * Kernel_traits::Cta_tile_p::N * 2);
    static_assert(smem_size_dq == 16 * Kernel_traits::Cta_tile_p::K * 4 * Kernel_traits::Cta_tile_p::WARPS_N);

    constexpr int smem_size_dq_dk_dv = smem_size_q * 2 + smem_size_v * (Kernel_traits::V_IN_REGS ? 1 : 2) + smem_size_dq + smem_size_s * 2;

    bool is_dropout = params.p_dropout < 1.f;  // params.p_dropout is the probability of "keeping"
    bool is_causal = params.is_causal;
    auto kernel = is_dropout
        ? (is_causal ? &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, true, true> : &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, true, false>)
        : (is_causal ? &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, false, true> : &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, false, false>);
    constexpr int N = Kernel_traits::Cta_tile_p::N;
    if (params.s == N) {
        kernel = is_dropout
            ? (is_causal ? &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, true, true, /*loop_steps=*/1> : &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, true, false, /*loop_steps=*/1>)
            : (is_causal ? &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, false, true, /*loop_steps=*/1> : &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, false, false, /*loop_steps=*/1>);
    } else if (params.s == N * 2) {
        kernel = is_dropout
            ? (is_causal ? &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, true, true, /*loop_steps=*/2> : &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, true, false, /*loop_steps=*/2>)
            : (is_causal ? &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, false, true, /*loop_steps=*/2> : &fmha_dgrad_fp16_sm80_dq_dk_dv_loop_kernel<Kernel_traits, false, false, /*loop_steps=*/2>);
    }

    // printf("N = %d, WARPS_N = %d, Smem size = %d\n", N, Kernel_traits::Cta_tile_p::WARPS_N, smem_size_dq_dk_dv);
    if( smem_size_dq_dk_dv >= 48 * 1024 ) {
        FMHA_CHECK_CUDA(cudaFuncSetAttribute(
            kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size_dq_dk_dv));
    }
    dim3 grid(params.b, params.h);
    kernel<<<grid, Kernel_traits::THREADS, smem_size_dq_dk_dv, stream>>>(params);
    FMHA_CHECK_CUDA(cudaPeekAtLastError());
}

void run_fmha_dgrad_fp16_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream) {
    if (params.d == 16) {
        if( params.s == 128 ) {
            using Kernel_traits = FMHA_kernel_traits<128, 16, 16, 1, 8, 0x08u>;
            run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
        } else if( params.s == 256 ) {
            using Kernel_traits = FMHA_kernel_traits<256, 16, 16, 1, 8, 0x08u>;
            run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
        } else {
            // TD [2022-05-15] 512 gives wrong results rn
            // using Kernel_traits = FMHA_kernel_traits<512, 16, 16, 1, 8, 0x08u>;
            using Kernel_traits = FMHA_kernel_traits<256, 16, 16, 1, 8, 0x08u>;
            run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
        }
    } else if (params.d == 32) {
        if( params.s == 128 ) {
            using Kernel_traits = FMHA_kernel_traits<128, 32, 16, 1, 8, 0x08u>;
            run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
        } else if( params.s >= 256 ) {
            using Kernel_traits = FMHA_kernel_traits<256, 32, 16, 1, 8, 0x08u>;
            run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
        }
    } else if (params.d == 64) {
        if( params.s == 128 ) {
            using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 8, 0x08u>;
            run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
        } else if( params.s >= 256 ) {
            auto dprops = at::cuda::getCurrentDeviceProperties();
            if (dprops->major == 8 && dprops->minor == 0) {
                // Don't share smem for K & V, and don't keep V in registers
                // This speeds things up by 2-3% by avoiding register spills, but it
                // uses more shared memory, which is fine on A100 but not other GPUs.
                // For other GPUs, we keep V in registers.
                using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 8, 0x100u>;
                run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
            } else if (dprops->major == 8 && dprops->minor > 0) {
                using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 8, 0x08u>;
                run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
            } else if (dprops->major == 7 && dprops->minor == 5) {
                using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 8, 0x08u>;
                run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
            }
        }
    } else if (params.d == 128) {
        using Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 8, 0x100u>;
        run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
    }
    // if (params.d == 64) {
    //     auto dprops = at::cuda::getCurrentDeviceProperties();
    //     if (dprops->major == 7 && dprops->minor == 5) {
    //         using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 8, 0x08u>;
    //         run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
    //     } else {
    //         if( params.s == 128 ) {
    //             using Kernel_traits = FMHA_kernel_traits<128, 64, 16, 1, 8, 0x08u>;
    //             run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
    //         } else if( params.s >= 256 ) {
    //             if (dprops->major == 8 && dprops->minor == 0) {
    //                 // Don't share smem for K & V, and don't keep V in registers
    //                 // This speeds things up by 2-3% by avoiding register spills, but it
    //                 // uses more shared memory, which is fine on A100 but not other GPUs.
    //                 // For other GPUs, we keep V in registers.
    //                 using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 8, 0x100u>;
    //                 run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
    //             } else if (dprops->major == 8 && dprops->minor > 0) {
    //                 using Kernel_traits = FMHA_kernel_traits<256, 64, 16, 1, 8, 0x08u>;
    //                 run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
    //             }
    //         }
    //     }
    // }
    // if (params.d == 128) {
    //     using Kernel_traits = FMHA_kernel_traits<128, 128, 16, 1, 8, 0x100u>;
    //     run_fmha_dgrad_fp16_sm80_loop_<Kernel_traits>(params, stream);
    // }
}