/*
// Copyright (c) 2016 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 "convolution_kernel_winograd_2x3_s1_fused.h"

namespace kernel_selector {

    ParamsKey ConvolutionKernel_Winograd_2x3_s1_fused::GetSupportedKey() const
    {
        ParamsKey k;
        k.EnableInputDataType(Datatype::F16);
        k.EnableInputDataType(Datatype::F32);
        k.EnableOutputDataType(Datatype::F16);
        k.EnableOutputDataType(Datatype::F32);
        k.EnableInputWeightsType(WeightsType::F16);
        k.EnableInputWeightsType(WeightsType::F32);
        k.EnableInputLayout(DataLayout::byxf);
        k.EnableOutputLayout(DataLayout::byxf);
        k.EnableTensorOffset();
        k.EnableTensorPitches();
        k.EnableBatching();
        k.EnableBiasPerFeature();
        k.EnableBiasPerOutput();
        k.EnableNonBiasTerm();

        return k;
    }

    JitConstants ConvolutionKernel_Winograd_2x3_s1_fused::GetJitConstants(const convolution_params& params, const DispatchData& runInfo) const
    {
        JitConstants jit = Parent::GetJitConstants(params, runInfo);

        const auto idepth = params.inputs[0].Feature().v;
        const auto input_pad_y = params.inputs[0].Y().pad.before + params.inputs[0].Y().pad.after;
        const auto input_pad_x = params.inputs[0].X().pad.before + params.inputs[0].X().pad.after;
        const auto rows = params.inputs[0].Y().v + input_pad_y;
        const auto cols = params.inputs[0].X().v + input_pad_x;

        auto output_pad_x_before = params.output.GetDims()[0].pad.before;
        auto output_pad_y_before = params.output.GetDims()[1].pad.before;
        auto output_pad_x_after = params.output.GetDims()[0].pad.after;
        auto output_pad_y_after = params.output.GetDims()[1].pad.after;
        auto C4_up16 = ((uint32_t)((idepth + 15) / 16) * 16) / 4;

		//if there's input padding then input offset should be ignored
		const auto inoffset_x = (input_pad_x) ? 0 : params.padding.x;
		const auto inoffset_y = (input_pad_y) ? 0 : params.padding.y;

        jit.AddConstants({
            MakeJitConstant("H", rows),
            MakeJitConstant("W", cols),
            MakeJitConstant("P", rows - 3 + 1 + output_pad_y_before + output_pad_y_after + 2 * inoffset_y),
            MakeJitConstant("Q", cols - 3 + 1 + output_pad_x_before + output_pad_x_after + 2 * inoffset_x),
            MakeJitConstant("R", 3),
            MakeJitConstant("S", 3),
            MakeJitConstant("N", 1),
            MakeJitConstant("px", inoffset_x),
            MakeJitConstant("py", inoffset_y),
            MakeJitConstant("sx", 1),
            MakeJitConstant("sy", 1),

            MakeJitConstant("C4_up16", C4_up16),
            MakeJitConstant("TROWS", rows),
            MakeJitConstant("TCOLS", 4),
            MakeJitConstant("KROWSW", 3),
            MakeJitConstant("KCOLSW", 4),
        });

        return jit;
    }

    ConvolutionKernel_Winograd_2x3_s1_fused::Parent::DispatchData ConvolutionKernel_Winograd_2x3_s1_fused::SetDefault(const convolution_params& arg, int) const
    {
        Parent::DispatchData runInfo = Parent::SetDefault(arg);

        const auto odepth = arg.output.Feature().v;
        const auto input_pad_y = arg.inputs[0].Y().pad.before + arg.inputs[0].Y().pad.after;
        const auto input_pad_x = arg.inputs[0].X().pad.before + arg.inputs[0].X().pad.after;
        const auto rows = arg.inputs[0].Y().v + input_pad_y;
        const auto cols = arg.inputs[0].X().v + input_pad_x;

		//if there's input padding then input offset should be ignored
		const auto inoffset_x = (input_pad_x) ? 0 : arg.padding.x;
		const auto inoffset_y = (input_pad_y) ? 0 : arg.padding.y;

        auto P = rows - 2 + 2 * inoffset_y;
        auto Q = cols - 2 + 2 * inoffset_x;
        auto K = odepth;
        auto N = 1;

        uint32_t global_step[3] = { 14, 4, 16 * 8 };
        uint32_t local_size[3] = { 8, 2, 8 };

		uint32_t zStep = local_size[2];
        runInfo.gws0 = ((uint32_t)((Q + global_step[0] - 1)) / global_step[0]) * local_size[0];
        runInfo.gws1 = ((uint32_t)((P + global_step[1] - 1)) / global_step[1]) * local_size[1];
        runInfo.gws2 = ((uint32_t)((N*K * 8 + global_step[2] - 1)) / global_step[2]) * zStep;

        runInfo.lws0 = local_size[0];
        runInfo.lws1 = local_size[1];
        runInfo.lws2 = local_size[2];

        runInfo.effiency = FORCE_PRIORITY_2;

        return runInfo;
    }

    bool ConvolutionKernel_Winograd_2x3_s1_fused::Validate(const Params& p, const optional_params& o) const
    {
        if (!Parent::Validate(p, o))
        {
            return false;
        }

        const convolution_params& params = static_cast<const convolution_params&>(p);

        if ((params.weights.X().v != 3) || (params.weights.Y().v != 3) ||
            (params.stride.x != 1) ||
            (params.stride.y != 1) ||
            (params.filterSize.x != 3) ||
            (params.filterSize.y != 3) ||
            (params.output.Feature().v % 32) ||
            (params.inputs[0].Feature().v % 32) ||
            (params.output.Feature().pad.before != 0) || (params.output.Feature().pad.after != 0) ||
            (params.output.Batch().pad.before != 0) || (params.output.Batch().pad.after != 0) ||
            //TODO: add support to batch > 1
            (params.inputs[0].Batch().v != 1))
        {
            return{};
        }

        return true;
    }

    KernelsData ConvolutionKernel_Winograd_2x3_s1_fused::GetKernelsData(const Params& params, const optional_params& options) const
    {
        return GetTunedKernelsDataByIndex(params, options);
    }
}