/*******************************************************************************
* Copyright 2016-2019 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 "c_types_map.hpp"
#include "dnnl_thread.hpp"
#include "dnnl_traits.hpp"
#include "math_utils.hpp"
#include "type_helpers.hpp"

#include "ref_inner_product.hpp"

namespace dnnl {
namespace impl {
namespace cpu {

using math::get_bias;
using math::saturate;

template <data_type_t src_type, data_type_t wei_type, data_type_t dst_type,
        data_type_t acc_type>
void ref_inner_product_fwd_t<src_type, wei_type, dst_type,
        acc_type>::execute_forward(const exec_ctx_t &ctx) const {
    auto src = CTX_IN_MEM(const src_data_t *, DNNL_ARG_SRC);
    auto weights = CTX_IN_MEM(const wei_data_t *, DNNL_ARG_WEIGHTS);
    auto bias = CTX_IN_MEM(const char *, DNNL_ARG_BIAS);
    auto dst = CTX_OUT_MEM(dst_data_t *, DNNL_ARG_DST);

    const memory_desc_wrapper src_d(pd()->src_md());
    const memory_desc_wrapper dst_d(pd()->dst_md());
    const memory_desc_wrapper weights_d(pd()->weights_md(0));
    const memory_desc_wrapper bias_d(pd()->weights_md(1));

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

    const bool src_has_spatial = utils::one_of(src_d.ndims(), 3, 4, 5);
    const int ndims = src_d.ndims() - 2;

    const auto &post_ops = pd()->attr()->post_ops_;
    const bool do_relu = post_ops.len_ == 1;
    const float nslope = do_relu ? post_ops.entry_[0].eltwise.alpha : 0.f;

    auto ker_has_spatial = [=](int mb, int oc) {
        acc_data_t d = 0;
        const int KD = pd()->KD();
        const int KH = pd()->KH();
        const int KW = pd()->KW();
        for (int ic = 0; ic < IC; ++ic) {
            for (int kd = 0; kd < KD; ++kd) {
                for (int kh = 0; kh < KH; ++kh) {
                    for (int kw = 0; kw < KW; ++kw) {
                        switch (ndims) {
                            case 3:
                                d += (acc_data_t)src[src_d.off(
                                             mb, ic, kd, kh, kw)]
                                        * weights[weights_d.off(
                                                oc, ic, kd, kh, kw)];
                                break;
                            case 2:
                                d += (acc_data_t)src[src_d.off(mb, ic, kh, kw)]
                                        * weights[weights_d.off(
                                                oc, ic, kh, kw)];
                                break;
                            case 1:
                                d += (acc_data_t)src[src_d.off(mb, ic, kw)]
                                        * weights[weights_d.off(oc, ic, kw)];
                                break;
                            default: assert(!"unsupported ndims size");
                        }
                    }
                }
            }
        }
        return d;
    };

    auto ker_no_spatial = [=](int mb, int oc) {
        acc_data_t d = 0;
        for (int ic = 0; ic < IC; ++ic) {
            d += (acc_data_t)src[src_d.off(mb, ic)]
                    * weights[weights_d.off(oc, ic)];
        }
        return d;
    };

    parallel_nd(MB, OC, [&](int mb, int oc) {
        float a = bias ? get_bias(bias, bias_d.off(oc),
                          pd()->desc()->bias_desc.data_type)
                       : 0;
        if (src_has_spatial)
            a += ker_has_spatial(mb, oc);
        else
            a += ker_no_spatial(mb, oc);
        if (do_relu && a < (acc_data_t)0) a *= nslope;
        dst[dst_d.off(mb, oc)] = saturate<dst_data_t>(a);
    });
}

using namespace data_type;
template struct ref_inner_product_fwd_t<f32>;
template struct ref_inner_product_fwd_t<u8, s8, f32, s32>;
template struct ref_inner_product_fwd_t<u8, s8, s32, s32>;
template struct ref_inner_product_fwd_t<u8, s8, s8, s32>;
template struct ref_inner_product_fwd_t<u8, s8, u8, s32>;

template <data_type_t diff_src_type, data_type_t wei_type,
        data_type_t diff_dst_type, data_type_t acc_type>
void ref_inner_product_bwd_data_t<diff_src_type, wei_type, diff_dst_type,
        acc_type>::execute_backward_data(const exec_ctx_t &ctx) const {
    auto diff_dst = CTX_IN_MEM(const diff_dst_data_t *, DNNL_ARG_DIFF_DST);
    auto weights = CTX_IN_MEM(const wei_data_t *, DNNL_ARG_WEIGHTS);
    auto diff_src = CTX_OUT_MEM(diff_src_data_t *, DNNL_ARG_DIFF_SRC);

    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
    const memory_desc_wrapper weights_d(pd()->weights_md(0));
    const memory_desc_wrapper diff_src_d(pd()->diff_src_md());

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

    const bool diff_src_has_spatial
            = utils::one_of(diff_src_d.ndims(), 3, 4, 5);
    const int ndims = diff_src_d.ndims() - 2;

    parallel_nd(MB, IC, [&](int mb, int ic) {
        if (diff_src_has_spatial) {
            const int KD = pd()->KD();
            const int KH = pd()->KH();
            const int KW = pd()->KW();
            for_(int kd = 0; kd < KD; ++kd)
            for_(int kh = 0; kh < KH; ++kh)
            for (int kw = 0; kw < KW; ++kw) {
                acc_data_t ds = acc_data_t(0);
                for (int oc = 0; oc < OC; ++oc) {
                    switch (ndims) {
                        case 3:
                            ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)]
                                    * weights[weights_d.off(
                                            oc, ic, kd, kh, kw)]);
                            break;
                        case 2:
                            ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)]
                                    * weights[weights_d.off(oc, ic, kh, kw)]);
                            break;
                        case 1:
                            ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)]
                                    * weights[weights_d.off(oc, ic, kw)]);
                            break;
                        default: assert(!"unsupported ndims size");
                    }
                }
                switch (ndims) {
                    case 3:
                        diff_src[diff_src_d.off(mb, ic, kd, kh, kw)]
                                = (diff_src_data_t)ds;
                        break;
                    case 2:
                        diff_src[diff_src_d.off(mb, ic, kh, kw)]
                                = (diff_src_data_t)ds;
                        break;
                    case 1:
                        diff_src[diff_src_d.off(mb, ic, kw)]
                                = (diff_src_data_t)ds;
                        break;
                    default: assert(!"unsupported ndims size");
                }
            }
        } else {
            acc_data_t ds = acc_data_t(0);
            for (int oc = 0; oc < OC; ++oc) {
                ds += (acc_data_t)(diff_dst[diff_dst_d.off(mb, oc)]
                        * weights[weights_d.off(oc, ic)]);
            }
            diff_src[diff_src_d.off(mb, ic)] = (diff_src_data_t)ds;
        }
    });
}

template struct ref_inner_product_bwd_data_t<f32, f32, f32, f32>;

template <impl::data_type_t data_type>
void ref_inner_product_bwd_weights_t<data_type>::execute_backward_weights(
        const exec_ctx_t &ctx) const {
    auto diff_dst = CTX_IN_MEM(const data_t *, DNNL_ARG_DIFF_DST);
    auto src = CTX_IN_MEM(const data_t *, DNNL_ARG_SRC);
    auto diff_weights = CTX_OUT_MEM(data_t *, DNNL_ARG_DIFF_WEIGHTS);
    auto diff_bias = CTX_OUT_MEM(data_t *, DNNL_ARG_DIFF_BIAS);

    const memory_desc_wrapper src_d(pd()->src_md());
    const memory_desc_wrapper diff_dst_d(pd()->diff_dst_md());
    const memory_desc_wrapper diff_weights_d(pd()->diff_weights_md(0));
    const memory_desc_wrapper diff_bias_d(pd()->diff_weights_md(1));

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

    const bool src_has_spatial = utils::one_of(src_d.ndims(), 3, 4, 5);
    const int ndims = src_d.ndims() - 2;

    parallel_nd(OC, IC, [&](int oc, int ic) {
        if (src_has_spatial) {
            const int KD = pd()->KD();
            const int KH = pd()->KH();
            const int KW = pd()->KW();
            for (int kd = 0; kd < KD; ++kd) {
                for (int kh = 0; kh < KH; ++kh) {
                    for (int kw = 0; kw < KW; ++kw) {
                        data_t *dw(nullptr);
                        switch (ndims) {
                            case 3:
                                dw = &diff_weights[diff_weights_d.off(
                                        oc, ic, kd, kh, kw)];
                                break;
                            case 2:
                                dw = &diff_weights[diff_weights_d.off(
                                        oc, ic, kh, kw)];
                                break;
                            case 1:
                                dw = &diff_weights[diff_weights_d.off(
                                        oc, ic, kw)];
                                break;
                            default: assert(!"unsupported ndims size");
                        }
                        *dw = data_t(0);
                        for (int mb = 0; mb < MB; ++mb) {
                            switch (ndims) {
                                case 3:
                                    *dw += diff_dst[diff_dst_d.off(mb, oc)]
                                            * src[src_d.off(
                                                    mb, ic, kd, kh, kw)];
                                    break;
                                case 2:
                                    *dw += diff_dst[diff_dst_d.off(mb, oc)]
                                            * src[src_d.off(mb, ic, kh, kw)];
                                    break;
                                case 1:
                                    *dw += diff_dst[diff_dst_d.off(mb, oc)]
                                            * src[src_d.off(mb, ic, kw)];
                                    break;
                                default: assert(!"unsupported ndims size");
                            }
                        }
                    }
                }
            }
        } else {
            data_t *dw = &diff_weights[diff_weights_d.off(oc, ic)];
            *dw = data_t(0);
            for (int mb = 0; mb < MB; ++mb) {
                *dw += diff_dst[diff_dst_d.off(mb, oc)]
                        * src[src_d.off(mb, ic)];
            }
        }
    });

    if (diff_bias) {
        diff_bias += diff_bias_d.offset0();

        parallel_nd(OC, [&](int oc) {
            data_t *db = &diff_bias[oc];
            *db = data_t(0);
            for (int mb = 0; mb < MB; ++mb)
                *db += diff_dst[diff_dst_d.off(mb, oc)];
        });
    }
}

template struct ref_inner_product_bwd_weights_t<data_type::f32>;

} // namespace cpu
} // namespace impl
} // namespace dnnl

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