/*
// 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.h"

namespace kernel_selector {

    constexpr uint32_t winograd_tile_n = 4;
    constexpr uint32_t winograd_tile_m = 8;
    constexpr uint32_t winograd_input_tile_width = 4;
    constexpr uint32_t winograd_input_tile_height = 1;

    ParamsKey ConvolutionKernel_Winograd_2x3_s1::GetSupportedKey() const
    {
        ParamsKey k;
        k.EnableInputDataType(Datatype::F16);
        k.EnableOutputDataType(Datatype::F16);
        k.EnableInputDataType(Datatype::F32);
        k.EnableOutputDataType(Datatype::F32);
        k.EnableInputWeightsType(WeightsType::F16);
        k.EnableInputWeightsType(WeightsType::F32);
        k.EnableInputLayout(DataLayout::winograd_2x3_s1_data);
        k.EnableOutputLayout(DataLayout::winograd_2x3_s1_data);
        k.EnableTensorOffset();
        k.EnableTensorPitches();
        k.EnableSubGroup();
        k.EnableBiasPerFeature();
        k.EnableBiasPerOutput();
        k.EnableNonBiasTerm();
        k.EnableBatching();
        k.EnableSplitSupport();
        return k;
    }

    JitConstants ConvolutionKernel_Winograd_2x3_s1::GetJitConstants(const convolution_params& params, const DispatchData& runInfo) const
    {
        JitConstants jit = Parent::GetJitConstants(params, runInfo);

        const size_t input_tile_width = winograd_input_tile_width;
        const size_t input_tile_height = winograd_input_tile_height;
        const size_t winograd_filter_height = params.filterSize.y; //for this format, winograd filter is considered to be a set of 1d filters so its height should remain the same as original filter's

        const size_t nr_tiles_x = Align(params.output.X().v, 4) / input_tile_width; //input is already in winograd domain, so simply divide its width by tile's width to get tiles count
        const size_t nr_tiles_y = Align(params.output.Y().v, 8) / input_tile_height;
        const size_t total_tiles_count = nr_tiles_x * nr_tiles_y;

        jit.AddConstants({
            MakeJitConstant("INPUT0_SIZE_WINOGRAD_X",      Align(params.inputs[0].X().v, 4)),
            MakeJitConstant("INPUT0_SIZE_WINOGRAD_Y",      Align(params.inputs[0].Y().v - 2, 8) + 2),
            MakeJitConstant("N",      params.output.Feature().v),
            MakeJitConstant("M",      total_tiles_count),
            MakeJitConstant("K",      params.inputs[0].Feature().v * winograd_filter_height),
        });

        return jit;
    }

    ConvolutionKernel_Winograd_2x3_s1::Parent::DispatchData ConvolutionKernel_Winograd_2x3_s1::SetDefault(const convolution_params& arg, int) const
    {
        Parent::DispatchData runInfo = Parent::SetDefault(arg);

        const size_t tile_n = winograd_tile_n; //goes in-depth
        const size_t tile_m = winograd_tile_m; //goes over flattened x and y

        const size_t input_tile_width = winograd_input_tile_width;
        const size_t input_tile_height = winograd_input_tile_height;

        const size_t nr_tiles_x = Align(arg.output.X().v, 4) / input_tile_width; //input is already in winograd domain, so simply divide its width by tile's width to get tiles count
        const size_t nr_tiles_y = Align(arg.output.Y().v, 8) / input_tile_height;

        runInfo.gws0 = arg.output.Feature().v / tile_n;
        runInfo.gws1 = nr_tiles_x * nr_tiles_y / tile_m;
        runInfo.gws2 = input_tile_width * input_tile_height * arg.inputs[0].Batch().v;

        runInfo.lws0 = 8;
        runInfo.lws1 = 1;
        runInfo.lws2 = 1;

        runInfo.effiency = FORCE_PRIORITY_4;

        return runInfo;
    }

    bool ConvolutionKernel_Winograd_2x3_s1::Validate(const Params& p, const optional_params& o) const
    {
        if (!Parent::Validate(p, o))
        {
            return false;
        }

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

        const bool bStrideOK = (cp.stride.x == 1 && cp.stride.y == 1);
        const bool bDilationOK = (cp.stride.x == 1 && cp.stride.y == 1);
        const bool bFilter3x3 = (cp.filterSize.x == 3 && cp.filterSize.y == 3);
        const bool bNoSplit = cp.split == 1;

        if (!bStrideOK || !bDilationOK || !bFilter3x3 || !bNoSplit)
        {
            return false;
        }

        return true;
    }

    KernelsData ConvolutionKernel_Winograd_2x3_s1::GetKernelsData(const Params& params, const optional_params& options) const
    {
        return GetTunedKernelsDataByIndex(params, options);
    }
}