/*******************************************************************************
* Copyright 2018 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
*     http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/

#include <assert.h>
#include <math.h>

#include "c_types_map.hpp"
#include "type_helpers.hpp"
#include "math_utils.hpp"
#include "mkldnn_thread.hpp"
#include "nstl.hpp"

#include "nhwc_pooling.hpp"

namespace mkldnn {
namespace impl {
namespace cpu {

#define MEM_D(name) name##_d

#define DECLARE_READ_STRIDES(name)                                             \
    const size_t name##_n_stride = MEM_D(name).blocking_desc().strides[0][0];  \
    const size_t name##_d_stride = (!is_3d)                                    \
                                 ? 0                                           \
                                 : MEM_D(name).blocking_desc().strides[0][2];  \
    const size_t name##_h_stride = (!is_3d)                                    \
                                 ? MEM_D(name).blocking_desc().strides[0][2]   \
                                 : MEM_D(name).blocking_desc().strides[0][3];  \
    const size_t name##_w_stride = (!is_3d)                                    \
                                 ? MEM_D(name).blocking_desc().strides[0][3]   \
                                 : MEM_D(name).blocking_desc().strides[0][4];

namespace nhwc_pooling {
    size_t strided_offset(const int _n, const size_t _sn,
                          const int _d, const size_t _sd,
                          const int _h, const size_t _sh,
                          const int _w, const size_t _sw)
    {
        return   _n * _sn
               + _d * _sd
               + _h * _sh
               + _w * _sw;
    }
}

template <impl::data_type_t data_type>
void nhwc_pooling_fwd_t<data_type>::array_div_by_const(const int n,
        const data_t *src, const size_t num, data_t *dst) const
{
    for (int i = 0; i < n; ++i)
    {
        float ftmp = (float)src[i];
        ftmp = ftmp / num;
        dst[i] = math::out_round<data_t>(ftmp);
    }
}

template <impl::data_type_t data_type>
void nhwc_pooling_fwd_t<data_type>::array_add(const int n, const data_t *src,
        data_t *dst) const
{
    for (int i = 0;  i < n; ++i)
    {
        dst[i] += src[i];
    }
}

template <impl::data_type_t data_type>
void nhwc_pooling_fwd_t<data_type>::execute_forward() const {
    using namespace alg_kind;
    using namespace prop_kind;
    using namespace nhwc_pooling;

    auto alg = pd()->desc()->alg_kind;

    auto src = reinterpret_cast<const data_t *>(this->input_memory(0));
    auto dst = reinterpret_cast<data_t *>(this->memory(0));
    unsigned char * ws = reinterpret_cast<unsigned char *>(
                  alg == pooling_max
                      && pd()->desc()->prop_kind == forward_training ?
                  this->memory(1) : nullptr
              );

    const memory_desc_wrapper MEM_D(dst)(pd()->dst_pd());
    const memory_desc_wrapper MEM_D(ws)(pd()->workspace_pd());
    const memory_desc_wrapper MEM_D(src)(pd()->src_pd());

    const int ID = pd()->ID();
    const int IH = pd()->IH();
    const int IW = pd()->IW();
    const int KD = pd()->KD();
    const int KH = pd()->KH();
    const int KW = pd()->KW();
    const int SD = pd()->KSD();
    const int SH = pd()->KSH();
    const int SW = pd()->KSW();
    const int padF = pd()->padFront();
    const int padT = pd()->padT();
    const int padL = pd()->padL();
    const int MB = pd()->MB();
    const int OC = pd()->C();
    const int OD = pd()->OD();
    const int OH = pd()->OH();
    const int OW = pd()->OW();

    const bool is_3d = pd()->desc()->src_desc.ndims == 5;
    const data_type_t ws_dt = ws ? ws_d.data_type() : data_type::undef;

    DECLARE_READ_STRIDES(src);
    DECLARE_READ_STRIDES(dst);

    auto apply_offset = [=](int index, int offset) {
        return (index > offset) ? index - offset : 0;
    };

    parallel_nd(MB, OD, OH, OW,
        [&](int mb, int od, int oh, int ow) {
        size_t dst_offset_init = strided_offset(mb, dst_n_stride,
                                                od, dst_d_stride,
                                                oh, dst_h_stride,
                                                ow, dst_w_stride);
        if (alg == pooling_max) {
            size_t ws_offset_init = 0;
            if (ws)
            {
                DECLARE_READ_STRIDES(ws);
                ws_offset_init = strided_offset(mb, ws_n_stride,
                                                od, ws_d_stride,
                                                oh, ws_h_stride,
                                                ow, ws_w_stride);
            }
            // Note: GCC 4.8.5 won't vectorize below
            // simple loops unless they are singled out
            // into separate helper routines:
            //    array_nhwc_initialize, array_nhwc_max
            if (!ws)
                array_nhwc_initialize<false>(OC, dst + dst_offset_init,
                                    ws, ws_offset_init, ws_dt);
            else
                array_nhwc_initialize<true>(OC, dst + dst_offset_init,
                                    ws, ws_offset_init, ws_dt);


            for (int kd = 0; kd < KD; ++kd)
            for (int kh = 0; kh < KH; ++kh)
            for (int kw = 0; kw < KW; ++kw) {
                const int id = od * SD - padF + kd;
                const int ih = oh * SH - padT + kh;
                const int iw = ow * SW - padL + kw;

                if (id < 0 || id >= ID)
                    continue;
                if (ih < 0 || ih >= IH)
                    continue;
                if (iw < 0 || iw >= IW)
                    continue;

                size_t src_offset_init = strided_offset(mb, src_n_stride,
                                                        id, src_d_stride,
                                                        ih, src_h_stride,
                                                        iw, src_w_stride);

                if (!ws)
                    array_nhwc_max<false>(OC,
                       dst + dst_offset_init,
                       src + src_offset_init,
                       ws, ws_offset_init,
                       ws_dt,
                       kd * KH * KW + kh * KW + kw
                    );
                else
                    array_nhwc_max<true>(OC,
                       dst + dst_offset_init,
                       src + src_offset_init,
                       ws, ws_offset_init,
                       ws_dt,
                       kd * KH * KW + kh * KW + kw
                    );
            }
        } else {
            // pooling_avg
            auto d = dst + dst_offset_init;

            utils::array_set(d, 0, OC);

            auto id_start = apply_offset(od * SD, padF);
            auto ih_start = apply_offset(oh * SH, padT);
            auto iw_start = apply_offset(ow * SW, padL);
            auto id_end = nstl::min(od * SD - padF + KD, ID);
            auto ih_end = nstl::min(oh * SH - padT + KH, IH);
            auto iw_end = nstl::min(ow * SW - padL + KW, IW);

            // it is cheaper to actually count this in a loop
            // as the typical kernel is small
            size_t num_summands = 0;

            for (int id = id_start; id < id_end; ++id)
            for (int ih = ih_start; ih < ih_end; ++ih)
            for (int iw = iw_start; iw < iw_end; ++iw) {
                size_t src_offset_init = strided_offset(mb, src_n_stride,
                                                        id, src_d_stride,
                                                        ih, src_h_stride,
                                                        iw, src_w_stride);
                auto s = src + src_offset_init;

                // need to move the loop to separate function
                // for GCC 4.8.5 to vectorize
                array_add(OC, s, d);

                num_summands++;
            }

            num_summands = (alg == pooling_avg_include_padding) ?
                    KW * KH * KD : num_summands;

            // need to move the loop to separate function
            // for GCC 4.8.5 to vectorize
            array_div_by_const(OC, d, num_summands, d);
        }
    });
}

template <impl::data_type_t data_type>
void nhwc_pooling_bwd_t<data_type>::execute_backward() const {
    using namespace alg_kind;
    using namespace nhwc_pooling;

    auto diff_dst = reinterpret_cast<const data_t *>(this->input_memory(0));
    auto ws = pd()->desc()->alg_kind != alg_kind::pooling_max ? nullptr
        : reinterpret_cast<const unsigned char *>(this->input_memory(1));
    auto diff_src = reinterpret_cast<data_t *>(this->memory(0));

    const memory_desc_wrapper MEM_D(diff_dst)(pd()->diff_dst_pd());
    const memory_desc_wrapper MEM_D(ws)(pd()->workspace_pd());
    const memory_desc_wrapper MEM_D(diff_src)(pd()->diff_src_pd());

    const int ID = pd()->ID();
    const int IH = pd()->IH();
    const int IW = pd()->IW();
    const int KD = pd()->KD();
    const int KH = pd()->KH();
    const int KW = pd()->KW();
    const int SD = pd()->KSD();
    const int SH = pd()->KSH();
    const int SW = pd()->KSW();
    const int OC = pd()->C();
    const int padF = pd()->padFront();
    const int padT = pd()->padT();
    const int padL = pd()->padL();
    const int OD = pd()->OD();
    const int OH = pd()->OH();
    const int OW = pd()->OW();

    const bool is_3d = pd()->desc()->diff_src_desc.ndims == 5;
    auto alg = pd()->desc()->alg_kind;

    DECLARE_READ_STRIDES(diff_src);
    DECLARE_READ_STRIDES(diff_dst);

    auto apply_offset = [=](int index, int offset) {
        return (index > offset) ? index - offset : 0;
    };

    const int MB = pd()->MB();

    parallel_nd(MB, ID, IH, IW,
        [&](int mb, int id, int ih, int iw) {
        size_t src_offset_init = strided_offset(mb, diff_src_n_stride,
                                                id, diff_src_d_stride,
                                                ih, diff_src_h_stride,
                                                iw, diff_src_w_stride);

        // check if kernel windows are disjoint, in this case there's no
        // update needed and we just write there once, no initialization
        // required.
        if (!(KD == SD && KH == SH && KW == SW))
            for (int oc = 0; oc < OC; ++oc)
                diff_src[src_offset_init + oc] = data_type_t(0);

        // Find out which output cells may correspond to current
        // input position. Current input postition divided by
        // stride, with integer divide rounding down, is the
        // right-most output.
        // Left-most output may be computed if we decrement input
        // by (kernel_size - 1) and then do the same division by
        // stride.
        int od_left  = nstl::max((id + padF - KD + 1) / SD,  0);
        int oh_left  = nstl::max((ih + padT - KH + 1) / SH,  0);
        int ow_left  = nstl::max((iw + padL - KW + 1) / SW,  0);
        // Notice +1 here to preserve the C loop "less than"
        // condition for continuing the for loop.
        int od_right = nstl::min((id + padF) / SD + 1     , OD);
        int oh_right = nstl::min((ih + padT) / SH + 1     , OH);
        int ow_right = nstl::min((iw + padL) / SW + 1     , OW);

        for (int od = od_left; od < od_right; ++od)
        for (int oh = oh_left; oh < oh_right; ++oh)
        for (int ow = ow_left; ow < ow_right; ++ow) {
            const int kd = id - od*SD + padF;
            const int kh = ih - oh*SH + padT;
            const int kw = iw - ow*SW + padL;

            if (kd < 0 || kd >= KD)
                continue;
            if (kh < 0 || kh >= KH)
                continue;
            if (kw < 0 || kw >= KW)
                continue;

            size_t dst_offset_init = strided_offset(mb, diff_dst_n_stride,
                                                    od, diff_dst_d_stride,
                                                    oh, diff_dst_h_stride,
                                                    ow, diff_dst_w_stride);

            if (alg == pooling_max) {
                DECLARE_READ_STRIDES(ws);
                size_t ws_offset_init = strided_offset(mb, ws_n_stride,
                                                       od, ws_d_stride,
                                                       oh, ws_h_stride,
                                                       ow, ws_w_stride);
                const int index = kd * KH * KW + kh * KW + kw;

                PRAGMA_OMP_SIMD()
                for (int oc = 0; oc < OC; ++oc) {
                    const int index_from_ws =
                                    (MEM_D(ws).data_type() == data_type::u8)
                                    ? (int)ws[ws_offset_init + oc]
                                    : ((int *)ws)[ws_offset_init + oc];

                    const data_t d = diff_dst[dst_offset_init + oc];

                    // Check if kernel windows are disjoint, in this case
                    // there's no update needed and we just write there once
                    // otherwise we add value to the contents.
                    if (!(KD == SD && KH == SH && KW == SW))
                        diff_src[src_offset_init + oc] +=
                                                   (index_from_ws == index)
                                                   ? d
                                                   : data_type_t(0);
                    else
                        diff_src[src_offset_init + oc] =
                                                   (index_from_ws == index)
                                                   ? d
                                                   : data_type_t(0);
                }
            } else {
                // pooling_avg
                auto id_start = apply_offset(od*SD, padF);
                auto ih_start = apply_offset(oh*SH, padT);
                auto iw_start = apply_offset(ow*SW, padL);
                auto id_end = nstl::min(od*SD - padF + KD, ID);
                auto ih_end = nstl::min(oh*SH - padT + KH, IH);
                auto iw_end = nstl::min(ow*SW - padL + KW, IW);

                auto num_summands = (alg == pooling_avg_include_padding)
                  ? KW*KH*KD
                  : (ih_end - ih_start)*(iw_end - iw_start)*(id_end - id_start);

                PRAGMA_OMP_SIMD()
                for (int oc = 0; oc < OC; ++oc) {
                    const data_t d = diff_dst[dst_offset_init + oc];
                    // Check if kernel windows are disjoint, in this case
                    // there's no update needed and we just write there once
                    // otherwise we add value to the contents.
                    if (!(KD == SD && KH == SH && KW == SW))
                      diff_src[src_offset_init + oc] += d / num_summands;
                    else
                      diff_src[src_offset_init + oc] = d / num_summands;
                }
            }
        }
    });
}

template struct nhwc_pooling_fwd_t<data_type::f32>;
template struct nhwc_pooling_bwd_t<data_type::f32>;

}
}
}

// vim: et ts=4 sw=4 cindent cino^=l0,\:0,N-s
