/*******************************************************************************
* Copyright 2016-2018 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 "mkldnn_test_common.hpp"
#include "gtest/gtest.h"

#include "mkldnn.hpp"

namespace mkldnn {

template <typename data_t_src, typename data_t_wei,
          typename data_t_acc, typename data_t_dst>
void compute_ref_conv_relu_fwd(const test_convolution_sizes_t &c,
        const memory &src, const memory &weights, const memory &bias,
        const memory &dst, bool w_bias, float negative_slope)
{
    data_t_src *src_data = (data_t_src *)src.get_data_handle();
    data_t_wei *weights_data = (data_t_wei *)weights.get_data_handle();
    data_t_dst *bias_data
            = (data_t_dst *)(w_bias ? bias.get_data_handle() : nullptr);
    data_t_dst *dst_data = (data_t_dst *)dst.get_data_handle();

    const memory::desc src_d = src.get_primitive_desc().desc();
    const memory::desc weights_d = weights.get_primitive_desc().desc();
    const memory::desc dst_d = dst.get_primitive_desc().desc();

    size_t padded_ic = src_d.data.layout_desc.blocking.padding_dims[1];
    size_t padded_oc = dst_d.data.layout_desc.blocking.padding_dims[1];

#pragma omp parallel for collapse(5) schedule(static)
    for (int n = 0; n < c.mb; n++) {
        for (int g = 0; g < c.ng; g++) {
            for (int oc = 0; oc < c.oc / c.ng; oc++) {
                for (int oh = 0; oh < c.oh; oh++) {
                    for (int ow = 0; ow < c.ow; ow++) {
                        size_t oidx = n * padded_oc * c.oh * c.ow
                                + g * padded_oc / c.ng * c.oh * c.ow
                                + oc * c.oh * c.ow + oh * c.ow + ow;
                        dst_data[map_index(dst_d, oidx)] = bias_data ?
                                bias_data[map_index(
                                        bias.get_primitive_desc().desc(),
                                        g * padded_oc / c.ng + oc)] :
                                data_t_dst{0};
                        for (int ic = 0; ic < c.ic / c.ng; ic++) {
                            for (int kh = 0; kh < c.kh; kh++) {
                                for (int kw = 0; kw < c.kw; kw++) {
                                    int iw = ow * c.strw
                                          - c.padw + kw * (1 + c.dilw);
                                    int ih = oh * c.strh
                                          - c.padh + kh * (1 + c.dilh);
                                    if (iw < 0 || iw >= c.iw) continue;
                                    if (ih < 0 || ih >= c.ih) continue;
                                    size_t iidx = n * padded_ic * c.ih * c.iw
                                            + g * padded_ic / c.ng * c.ih * c.iw
                                            + ic * c.ih * c.iw + ih * c.iw + iw;
                                    size_t widx = g * padded_oc / c.ng * padded_ic
                                                    / c.ng * c.kh * c.kw
                                        + oc * padded_ic / c.ng * c.kh * c.kw
                                        + ic * c.kh * c.kw + kh * c.kw + kw;

                                    dst_data[map_index(dst_d, oidx)]
                                            += src_data[map_index(src_d, iidx)]
                                            * weights_data[map_index(
                                                      weights_d, widx)];
                                }
                            }
                        }

                        if (dst_data[map_index(dst_d, oidx)] < 0) {
                            dst_data[map_index(dst_d, oidx)] =
                                static_cast<data_t_dst>( negative_slope
                                * dst_data[map_index(dst_d, oidx)] );
                        }

                    }
                }
            }
        }
    }
}

template <typename data_t_src, typename data_t_wei,
          typename data_t_acc, typename data_t_dst>
class convolution_relu_test
    : public ::testing::TestWithParam<test_convolution_params_t> {
protected:
    virtual void SetUp() {
        auto p = ::testing::TestWithParam<test_convolution_params_t>::GetParam();
        catch_expected_failures([=](){Test();}, p.expect_to_fail,
                    p.expected_status);
    }

    void Test() {
        auto p = ::testing::TestWithParam<test_convolution_params_t>::GetParam();
        ASSERT_TRUE(p.engine_kind == engine::kind::cpu);
        ASSERT_EQ(p.aalgorithm, convolution_direct);
        auto eng = engine(p.engine_kind, 0);
        float negative_slope = p.relu_negative_slope;

        memory::data_type data_type_src = data_traits<data_t_src>::data_type;
        memory::data_type data_type_dst = data_traits<data_t_dst>::data_type;
        memory::data_type data_type_wei = data_traits<data_t_wei>::data_type;

        test_convolution_sizes_t cd = p.sizes;

        auto c_src_desc = create_md({ cd.mb, cd.ic, cd.ih, cd.iw },
                data_type_src, p.formats.src_format);
        auto c_weights_desc = cd.ng > 1 ?
                create_md({ cd.ng, cd.oc / cd.ng, cd.ic / cd.ng, cd.kh, cd.kw },
                        data_type_wei, p.formats.weights_format) :
                create_md({ cd.oc, cd.ic, cd.kh, cd.kw },
                        data_type_wei, p.formats.weights_format);
        auto c_dst_desc = create_md({ cd.mb, cd.oc, cd.oh, cd.ow },
                data_type_dst, p.formats.dst_format);

        auto c_src = memory({c_src_desc, eng});
        auto c_weights = memory({c_weights_desc, eng});
        auto c_dst = memory({c_dst_desc, eng});

        auto dst_ref = memory({c_dst_desc, eng});

        fill_data<data_t_src>(c_src.get_primitive_desc().get_size()
                / sizeof(data_t_src), (data_t_src *)c_src.get_data_handle());
        // TODO: Temporary workaround for testing of convolution + relu
        if (cd.mb) {
            data_t_src *src_data = (data_t_src *)c_src.get_data_handle();
            const int mb_chunk = static_cast<int>(
                (c_src.get_primitive_desc().get_size() / sizeof(data_t_src))
                / cd.mb );
            for (int i = 0; i < cd.mb * mb_chunk; ++i) {
                if ((i / mb_chunk) % 2) src_data[i] *= (data_t_src)-1.;
            }
        }

        fill_data<data_t_wei>(
                c_weights.get_primitive_desc().get_size()
                / sizeof(data_t_wei),(data_t_wei *)c_weights.get_data_handle());
        fill_data<data_t_dst>(
                c_dst.get_primitive_desc().get_size()
                / sizeof(data_t_dst),(data_t_dst *)c_dst.get_data_handle());

        bool with_bias = p.formats.bias_format != memory::format::format_undef;
        auto c_bias_desc = with_bias ?
                create_md({ cd.oc }, data_type_dst, p.formats.bias_format) :
                create_md({}, data_type_dst, p.formats.bias_format);
        auto c_bias = memory({c_bias_desc, eng});
        if (with_bias) {
            fill_data<data_t_dst>(
                    c_bias.get_primitive_desc().get_size() / sizeof(data_t_dst),
                    (data_t_dst *)c_bias.get_data_handle(), 1., true);
        }
        check_zero_tail<data_t_src>(1, c_src);
        check_zero_tail<data_t_wei>(1, c_weights);
        check_zero_tail<data_t_dst>(1, c_dst);

        std::vector<int> padR = {
            right_padding(cd.ih, cd.oh, cd.kh, cd.padh, cd.strh, cd.dilh),
            right_padding(cd.iw, cd.ow, cd.kw, cd.padw, cd.strw, cd.dilw)
        };

        auto conv_desc = with_bias
            ? convolution_forward::desc(prop_kind::forward_scoring,
                    p.aalgorithm, c_src_desc, c_weights_desc, c_bias_desc,
                    c_dst_desc, { cd.strh, cd.strw }, { cd.dilh, cd.dilw },
                    { cd.padh, cd.padw }, padR, padding_kind::zero)
        : convolution_forward::desc(prop_kind::forward_scoring,
                p.aalgorithm, c_src_desc, c_weights_desc, c_dst_desc,
                { cd.strh, cd.strw }, { cd.dilh, cd.dilw },
                { cd.padh, cd.padw }, padR, padding_kind::zero);

        auto conv_relu_desc =
            convolution_relu_forward::desc(conv_desc, negative_slope);
        auto conv_primitive_desc =
            convolution_relu_forward::primitive_desc(conv_relu_desc, eng);

        auto conv = with_bias
            ? convolution_relu_forward(conv_primitive_desc,
                    c_src, c_weights, c_bias, c_dst)
            : convolution_relu_forward(conv_primitive_desc,
                    c_src, c_weights, c_dst);
        std::vector<primitive> pipeline;
        pipeline.push_back(conv);

        stream(stream::kind::lazy).submit(pipeline).wait();

        compute_ref_conv_relu_fwd<data_t_src, data_t_wei, data_t_wei,
            data_t_dst>(cd, c_src, c_weights, c_bias, dst_ref, with_bias,
                    negative_slope);
        check_zero_tail<data_t_dst>(1, dst_ref);
        compare_data<data_t_dst>(dst_ref, c_dst);
        check_zero_tail<data_t_dst>(0, c_dst);

    }
};

}
