/*************************************************************************
 * Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 *
 * See LICENSE for license information.
 ************************************************************************/

#ifndef TRANSFORMER_ENGINE_COMMON_RMSNORM_RMSNORM_BWD_KERNELS_CUH_
#define TRANSFORMER_ENGINE_COMMON_RMSNORM_RMSNORM_BWD_KERNELS_CUH_

#include "../utils.cuh"

namespace transformer_engine {
namespace rmsnorm {
using namespace transformer_engine;

template <typename Ktraits>
__global__ __launch_bounds__(Ktraits::THREADS_PER_CTA) void rmsnorm_bwd_tuned_kernel(
    BwdParams params) {
    enum { ROWS_PER_CTA = Ktraits::ROWS_PER_CTA };
    enum { WARPS_M = Ktraits::WARPS_M };
    enum { WARPS_N = Ktraits::WARPS_N };
    enum { THREADS_PER_ROW = Ktraits::THREADS_PER_ROW };
    enum { COLS = Ktraits::COLS };
    enum { BYTES_PER_ROW = Ktraits::BYTES_PER_ROW };
    enum { LDGS = Ktraits::LDGS };
    enum { NUM_ELTS = Ktraits::ELTS_PER_LDG };
    enum { THREADS_PER_WARP = Ktraits::THREADS_PER_WARP };
    enum { CTAS_PER_ROW = Ktraits::CTAS_PER_ROW };

    using compute_t = typename Ktraits::compute_t;
    using index_t = typename Ktraits::index_t;
    using Ivec = typename Ktraits::Ivec;
    using Ovec = typename Ktraits::Ovec;
    using Wvec = typename Ktraits::Wvec;
    using Cvec = typename Ktraits::Cvec;
    using Reducer = typename Ktraits::Reducer;
    using reduce_t = typename Reducer::Type;

    extern __shared__ char smem_[];

    const index_t tidx = threadIdx.x;
    const index_t bidn = blockIdx.x % CTAS_PER_ROW;
    const index_t bidm = blockIdx.x / CTAS_PER_ROW;
    const index_t lane = tidx % THREADS_PER_WARP;
    const index_t warp = tidx / THREADS_PER_WARP;
    const index_t warp_m = warp / Ktraits::WARPS_N;
    const index_t warp_n = warp % Ktraits::WARPS_N;
    const index_t tid_r = warp_n * THREADS_PER_WARP + lane;

    const index_t r = bidm * Ktraits::ROWS_PER_CTA + warp_m;
    const index_t c = bidn * THREADS_PER_ROW + warp_n * THREADS_PER_WARP + lane;

    static_assert(COLS == THREADS_PER_ROW * LDGS * NUM_ELTS * CTAS_PER_ROW);

    Cvec dzy_sum[LDGS];

    memset(dzy_sum, 0, sizeof(dzy_sum));

    compute_t *smem_wgrad = reinterpret_cast<compute_t *>(smem_);
    char *smem_dgrad = smem_ + Ktraits::SMEM_BYTES_WGRAD;

    Reducer reducer(params, bidm, bidn, warp_m, warp_n, lane, smem_dgrad);

    Sum<reduce_t> sum;

    constexpr float rn = 1.f / static_cast<float>(COLS);
    Wvec gamma[LDGS];
    index_t idx = c;
#pragma unroll
    for (int it = 0; it < LDGS; it++) {
        gamma[it].load_from(params.gamma, idx);
        idx += Ktraits::VEC_COLS_PER_LDG;
    }
// TODO if ROWS_PER_CTA does not divide rows, we might get divergence in the
// last blocks with syncthreads!
// grid stride over rows
#pragma unroll 1
    for (int row = r; row < params.rows; row += params.ctas_per_col * ROWS_PER_CTA) {
        const compute_t rs_r = static_cast<const compute_t *>(params.rs)[row];
        Ivec x[LDGS];
        Ovec dz[LDGS];
        index_t idx = row * Ktraits::VEC_COLS + c;
#pragma unroll
        for (int it = 0; it < LDGS; it++) {
            dz[it].load_from(params.dz, idx);
            x[it].load_from(params.x, idx);
            idx += Ktraits::VEC_COLS_PER_LDG;
        }

        compute_t dy[LDGS * NUM_ELTS];
        compute_t y[LDGS * NUM_ELTS];

        compute_t mdyy_local = 0.f;
#pragma unroll
        for (int it = 0; it < LDGS; it++) {
#pragma unroll
            for (int jt = 0; jt < NUM_ELTS; jt++) {
                compute_t x_tmp = x[it].data.elt[jt];
                compute_t y_tmp = rs_r * (x_tmp);
                compute_t dy_tmp = compute_t(gamma[it].data.elt[jt]);
                dy_tmp *= compute_t(dz[it].data.elt[jt]);
                compute_t dz_tmp = dz[it].data.elt[jt];

                mdyy_local += dy_tmp * y_tmp;

                dy[it * NUM_ELTS + jt] = dy_tmp;
                y[it * NUM_ELTS + jt] = y_tmp;

                dzy_sum[it].data.elt[jt] += dz_tmp * y_tmp;
            }
        }

        reduce_t result = reducer.allreduce({0, mdyy_local}, sum);
        mdyy_local = Get<1>::of<reduce_t, compute_t>(result) * rn;

        Ivec dx[LDGS];
        idx = row * Ktraits::VEC_COLS + c;
#pragma unroll
        for (int it = 0; it < LDGS; it++) {
#pragma unroll
            for (int jt = 0; jt < NUM_ELTS; jt++) {
                compute_t dy_tmp = dy[it * NUM_ELTS + jt];
                compute_t y_tmp = y[it * NUM_ELTS + jt];
                compute_t dx_tmp = rs_r * (dy_tmp - (mdyy_local * y_tmp));
                dx[it].data.elt[jt] = dx_tmp;
            }
            dx[it].store_to(params.dx, idx);
            idx += Ktraits::VEC_COLS_PER_LDG;
        }
    }  // end: grid stride loop

    if (WARPS_M == 1) {
        idx = r * Ktraits::VEC_COLS + c;
#pragma unroll
        for (int it = 0; it < LDGS; it++) {
            dzy_sum[it].store_to(params.dgamma_part, idx);
            idx += Ktraits::VEC_COLS_PER_LDG;
        }
    } else {
        static_assert(WARPS_M == 1 || Ktraits::CTAS_PER_ROW == 1,
                      "Multiple rows per CTA not supported for Multi-CTA.");
        // Finalize reduction of part dgamma and dbeta for this CTA
        // by reducing over the rows held across the WARPS_M warps

        // Assumption: blockSize divides hidden size.
        enum { NUM_RES = COLS / Ktraits::THREADS_PER_CTA };
        static_assert(NUM_RES * Ktraits::THREADS_PER_CTA == COLS, "");

        idx = warp_m * Ktraits::VEC_COLS + tid_r;
#pragma unroll
        for (int it = 0; it < LDGS; it++) {
            dzy_sum[it].store_to(smem_wgrad, idx);
            idx += THREADS_PER_ROW;
        }
        __syncthreads();
        compute_t cta_dzy_sum[NUM_RES];
        memset(cta_dzy_sum, 0, sizeof(compute_t) * NUM_RES);
        for (int it = 0; it < ROWS_PER_CTA; it++) {
            for (int jt = 0; jt < NUM_RES; jt++) {
                cta_dzy_sum[jt] += smem_wgrad[it * COLS + tidx + jt * Ktraits::THREADS_PER_CTA];
            }
        }

        compute_t *dgamma_part = static_cast<compute_t *>(params.dgamma_part) + bidm * COLS + tidx;
        for (int jt = 0; jt < NUM_RES; jt++) {
            *dgamma_part = cta_dzy_sum[jt];
            dgamma_part += Ktraits::THREADS_PER_CTA;
        }
    }
}

template <typename Kernel_traits>
__global__ __launch_bounds__(Kernel_traits::THREADS_PER_CTA) void rmsnorm_bwd_finalize_tuned_kernel(
    BwdParams params) {
    using compute_t = typename Kernel_traits::compute_t;
    using weight_t = typename Kernel_traits::weight_t;
    using index_t = typename Kernel_traits::index_t;
    using Reducer = typename Kernel_traits::Reducer;
    using reduce_t = typename Reducer::Type;

    Sum<reduce_t> sum;
    enum { NUM_ELT = Kernel_traits::ELTS_PER_LDG };
    enum { THREADS_PER_WARP = Kernel_traits::THREADS_PER_WARP };

    __shared__ char smem_[Kernel_traits::SMEM_BYTES_PER_CTA];

    constexpr uint32_t bidm = 0;

    const uint32_t bidn = blockIdx.x;
    const uint32_t tidx = threadIdx.x;
    const uint32_t warp = tidx / THREADS_PER_WARP;
    const uint32_t lane = tidx % THREADS_PER_WARP;

    Reducer reducer(params, bidm, bidn, 0, 0, lane, smem_);

    const uint32_t c = bidn * THREADS_PER_WARP + lane;
    const uint32_t c_out = bidn * THREADS_PER_WARP / 2 + lane;
    constexpr uint32_t COL_STRIDE = Kernel_traits::CTAS * THREADS_PER_WARP;
    for (uint32_t col = c, col_out = c_out; col < Kernel_traits::COLS;
         col += COL_STRIDE, col_out += COL_STRIDE / 2) {
        // Each thread sums over NUM_ELT columns.
        Vec<compute_t, NUM_ELT> dbeta_local, dgamma_local;
        memset(&dgamma_local, 0, sizeof(dgamma_local));
        for (uint32_t row = warp; row < params.ctas_per_col; row += Kernel_traits::ROWS_PER_CTA) {
            index_t idx = row * Kernel_traits::COLS + col;

            Vec<compute_t, NUM_ELT> dgamma_part;
            dgamma_part.load_from(params.dgamma_part, idx);
#pragma unroll
            for (int it = 0; it < NUM_ELT; it++) {
                dgamma_local.data.elt[it] += dgamma_part.data.elt[it];
            }
        }

        void *smem_gamma = smem_;

        const int write_row = warp;
        const int write_col = lane ^ write_row;
        const int write_idx = write_row * THREADS_PER_WARP + write_col;

        dgamma_local.store_to(smem_gamma, write_idx);

        __syncthreads();

        // It would be probably safe to reuse the first row of smem_gamma
        void *smem_gamma_out = &smem_[2 * Kernel_traits::SMEM_BYTES_TRANSPOSE];

        // More than one iter iff ROWS_PER_CTA < 32.
        for (int w = warp; w < THREADS_PER_WARP; w += Kernel_traits::ROWS_PER_CTA) {
            const int read_row = lane;
            const int read_col = w ^ read_row;
            const int read_idx = read_row * THREADS_PER_WARP + read_col;

            memset(&dgamma_local, 0, sizeof(dgamma_local));

            // Load gamma transposed
            if (read_row < Kernel_traits::ROWS_PER_CTA) {
                dgamma_local.load_from(smem_gamma, read_idx);
            }

// Call reducer on the loaded value(s) and convert.
#pragma unroll
            for (int it = 0; it < NUM_ELT; it++) {
                compute_t g_i = dgamma_local.data.elt[it];
                g_i = reducer.allreduce(g_i, sum);

                dgamma_local.data.elt[it] = g_i;
            }

            // Leader stores the result at the current column.
            if (lane == 0) {
                dgamma_local.store_to(smem_gamma_out, w);
            }
        }

        // All writes done.
        __syncthreads();

        // Pack and store: 2-wide stores with half the threads.
        if (warp == Kernel_traits::ROWS_PER_CTA - 1 && lane < THREADS_PER_WARP / 2) {
            using src_t = typename TypeToVec2<compute_t>::Type;
            using dst_t = typename TypeToVec2<weight_t>::Type;
            Vec<src_t, NUM_ELT> dgamma_vec2;
            Vec<dst_t, NUM_ELT> dgamma_out2;

            dgamma_vec2.load_from(smem_gamma_out, lane);
#pragma unroll
            for (int it = 0; it < NUM_ELT; it++) {
                dgamma_out2.data.elt[it] =
                    Converter<src_t, dst_t>::convert(dgamma_vec2.data.elt[it]);
            }
            dgamma_out2.store_to(params.dgamma, col_out);
        }
    }
}

template <typename Ktraits>
__global__ __launch_bounds__(Ktraits::THREADS_PER_CTA) void rmsnorm_bwd_general_kernel(
    BwdParams params) {
    enum { LDGS = Ktraits::LDGS };
    enum { NUM_ELTS = Ktraits::ELTS_PER_LDG };
    enum { WARPS_M = Ktraits::WARPS_M };
    enum { WARPS_N = Ktraits::WARPS_N };

    using input_t = typename Ktraits::input_t;
    using weight_t = typename Ktraits::weight_t;
    using compute_t = typename Ktraits::compute_t;
    using output_t = typename Ktraits::output_t;
    using index_t = typename Ktraits::index_t;
    using Ivec = typename Ktraits::Ivec;
    using Ovec = typename Ktraits::Ovec;
    using Wvec = typename Ktraits::Wvec;
    using Cvec = typename Ktraits::Cvec;

    const index_t tidx = threadIdx.x;
    const index_t lane = tidx % THREADS_PER_WARP;
    const index_t warp = tidx / THREADS_PER_WARP;
    const index_t warp_m = warp / WARPS_N;
    const index_t warp_n = warp % WARPS_N;

    const index_t bdimm = WARPS_M;
    const index_t bdimn = WARPS_N * THREADS_PER_WARP;
    const index_t bidm = blockIdx.x / params.ctas_per_row;
    const index_t bidn = blockIdx.x % params.ctas_per_row;

    const index_t gdimm = bdimm * params.ctas_per_col;
    const index_t gdimn = bdimn * params.ctas_per_row;
    const index_t gidm = bidm * bdimm + warp_m;
    const index_t gidn =
        (bidn * THREADS_PER_WARP + warp_n * params.ctas_per_row * THREADS_PER_WARP +
         lane);  // Order threads by warp x cta x lane

    // Objects for weight grads
    Cvec dzy_sum[LDGS];
    memset(dzy_sum, 0, sizeof(dzy_sum));

    // Objects for stats reductions
    using reduce_t = typename Ktraits::Reducer::Type;
    using Reducer = DynamicReducer<reduce_t, WARPS_M, WARPS_N>;
    constexpr int SMEM_BYTES = Reducer::SMEM_BYTES > 0 ? Reducer::SMEM_BYTES : 1;
    __shared__ char smem_[SMEM_BYTES];
    Reducer reducer(params, bidm, bidn, warp_m, warp_n, lane, smem_);
    Sum<reduce_t> sum;
    const compute_t rn = 1.f / static_cast<compute_t>(params.cols);

    // Load weights
    Cvec gamma[LDGS];
#pragma unroll
    for (int it = 0, col = gidn * NUM_ELTS; it < LDGS && col < params.cols;
         it++, col += gdimn * NUM_ELTS) {
        Wvec gamma_in;
        gamma_in.load_from_elts(params.gamma, col, params.cols - col);
        gamma_in.to(gamma[it]);
    }

    for (int cta_row = bidm * bdimm; cta_row < params.rows; cta_row += gdimm) {
        const int row = cta_row + warp_m;
        compute_t rs = 0.f;
        if (row < params.rows) {
            rs = static_cast<const compute_t *>(params.rs)[row];
        }

        Cvec dy[LDGS];
        Cvec y[LDGS];
        compute_t mdy = 0.f;
        compute_t mdyy = 0.f;

#pragma unroll
        for (int it = 0, col = gidn * NUM_ELTS; it < LDGS && row < params.rows && col < params.cols;
             it++, col += gdimn * NUM_ELTS) {
            Ivec x;
            Ovec dz;
            x.load_from_elts(params.x, row * params.cols + col, params.cols - col);
            dz.load_from_elts(params.dz, row * params.cols + col, params.cols - col);
#pragma unroll
            for (int jt = 0; jt < NUM_ELTS; jt++) {
                compute_t x_ij = x.data.elt[jt];
                compute_t y_ij = rs * (x_ij);
                compute_t g_ij = gamma[it].data.elt[jt];
                compute_t dz_ij = dz.data.elt[jt];
                compute_t dy_ij = g_ij * dz_ij;

                y[it].data.elt[jt] = y_ij;
                dy[it].data.elt[jt] = dy_ij;

                mdy += dy_ij;
                mdyy += dy_ij * y_ij;

                dzy_sum[it].data.elt[jt] += dz_ij * y_ij;
            }
        }

        // Reduce over row
        reduce_t result = reducer.allreduce({mdy, mdyy}, sum);
        mdy = Get<0>::of<reduce_t, compute_t>(result) * rn;
        mdyy = Get<1>::of<reduce_t, compute_t>(result) * rn;

// Compute dx
#pragma unroll
        for (int it = 0, col = gidn * NUM_ELTS; it < LDGS && row < params.rows && col < params.cols;
             it++, col += gdimn * NUM_ELTS) {
            Ivec dx;
#pragma unroll
            for (int jt = 0; jt < NUM_ELTS; jt++) {
                compute_t dy_ij = dy[it].data.elt[jt];
                compute_t y_ij = y[it].data.elt[jt];
                dx.data.elt[jt] = rs * (dy_ij - (mdyy * y_ij));
            }
            dx.store_to_elts(params.dx, row * params.cols + col, params.cols - col);
        }
    }

    if constexpr (WARPS_M == 1) {
// Write out local weight grad contributions
#pragma unroll
        for (int it = 0, col = gidn * NUM_ELTS; it < LDGS && col < params.cols;
             it++, col += gdimn * NUM_ELTS) {
            dzy_sum[it].store_to_elts(params.dgamma_part, bidm * params.cols + col,
                                      params.cols - col);
        }
    } else {
        // Reduce weight grad contributions within CTA before writing
        __shared__ Cvec vecs_shared[LDGS][WARPS_M][WARPS_N][THREADS_PER_WARP + 1];

        // Reduce dzy
        __syncthreads();
#pragma unroll
        for (int it = 0, col = gidn * NUM_ELTS; it < LDGS && col < params.cols;
             it++, col += gdimn * NUM_ELTS) {
            if (it != warp_m) {
                dzy_sum[it].store_to(&vecs_shared[it][warp_m][warp_n][lane]);
            }
        }
        __syncthreads();
#pragma unroll
        for (int it = warp_m, col = (gidn + it * gdimn) * NUM_ELTS; it < LDGS && col < params.cols;
             it += WARPS_M, col += WARPS_M * gdimn * NUM_ELTS) {
#pragma unroll
            for (int kt = 0; kt < WARPS_M; kt++) {
                if (kt != warp_m) {
#pragma unroll
                    for (int jt = 0; jt < NUM_ELTS; jt++) {
                        dzy_sum[it].data.elt[jt] += vecs_shared[it][kt][warp_n][lane].data.elt[jt];
                    }
                }
            }
            dzy_sum[it].store_to_elts(params.dgamma_part, bidm * params.cols + col,
                                      params.cols - col);
        }
    }
}

template <typename weight_t, typename compute_t, uint32_t WARPS_M, uint32_t WARPS_N,
          uint32_t BYTES_PER_LDG, uint32_t THREADS_PER_WARP>
__global__ __launch_bounds__(
    WARPS_M *WARPS_N *THREADS_PER_WARP) void rmsnorm_bwd_finalize_general_kernel(BwdParams params) {
    enum { NUM_ELTS = BYTES_PER_LDG / sizeof(compute_t) };
    using Wvec = Vec<weight_t, NUM_ELTS>;
    using Cvec = Vec<compute_t, NUM_ELTS>;

    const int lane = threadIdx.x % THREADS_PER_WARP;
    const int warp_m = threadIdx.y;
    const int warp_n = threadIdx.x / THREADS_PER_WARP;
    const int col = blockIdx.x * blockDim.x + threadIdx.x;

    // Load grad contributions and accumulate locally
    Cvec dgamma;
    dgamma.clear();
    for (int row = warp_m; row < params.ctas_per_col && col < params.cols; row += WARPS_M) {
        Cvec dgamma_part;
        dgamma_part.load_from_elts(params.dgamma_part, row * params.cols + col, params.cols - col);
#pragma unroll
        for (int jt = 0; jt < NUM_ELTS; jt++) {
            dgamma.data.elt[jt] += dgamma_part.data.elt[jt];
        }
    }

    // Reduce dgamma within CTA
    __shared__ Cvec vecs_shared[WARPS_M][WARPS_N][THREADS_PER_WARP + 1];
    dgamma.store_to(&vecs_shared[warp_m][warp_n][lane]);
#pragma unroll
    for (int nrows = WARPS_M / 2; nrows > 0; nrows /= 2) {
        __syncthreads();
        if (warp_m < nrows) {
#pragma unroll
            for (int jt = 0; jt < NUM_ELTS; jt++) {
                vecs_shared[warp_m][warp_n][lane].data.elt[jt] +=
                    vecs_shared[warp_m + nrows][warp_n][lane].data.elt[jt];
            }
        }
    }
    if (warp_m == 0 && col < params.cols) {
        Wvec dgamma_out;
        vecs_shared[warp_m][warp_n][lane].to(dgamma_out);
        dgamma_out.store_to_elts(params.dgamma, col, params.cols - col);
    }
}

}  // namespace rmsnorm
}  // namespace transformer_engine

#endif  // TRANSFORMER_ENGINE_COMMON_RMSNORM_RMSNORM_BWD_KERNELS_CUH_
