/*
// Copyright (c) 2016-2020 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 "fused_conv_eltwise_kernel_base.h"
#include "kernel_selector_utils.h"
#include "common_tools.h"
#include <string>
#include <vector>
#include <algorithm>

namespace kernel_selector {
std::string fused_conv_eltwise_params::to_string() const {
    std::stringstream s;

    s << base_params::to_string() << "_";
    if (bias.empty()) {
        s << "no_bias"
          << "_";
    } else {
        s << "bias_" << bias[0].PhysicalSize() << "_";
    }

    s << conv.filterSize.x << "_" << conv.filterSize.y << "_" << conv.filterSize.z << "_";
    s << conv.stride.x << "_" << conv.stride.y << "_" << conv.stride.z << "_";
    s << conv.dilation.x << "_" << conv.dilation.y << "_" << conv.dilation.z << "_";
    s << conv.padding.x << "_" << conv.padding.y << "_" << conv.padding.z << "_";
    s << conv.split;

    return s.str();
}

std::string fused_conv_eltwise_params::to_cache_string_v2() const {
    std::stringstream s;

    s << weight_bias_params::to_cache_string_v2() << ";";
    s << conv.filterSize.x << "_" << conv.filterSize.y << "_" << conv.filterSize.z << ";";
    s << conv.stride.x << "_" << conv.stride.y << "_" << conv.stride.z << ";";
    s << conv.dilation.x << "_" << conv.dilation.y << "_" << conv.dilation.z << ";";
    s << conv.padding.x << "_" << conv.padding.y << "_" << conv.padding.z << ";";
    s << conv.split;

    return s.str();
}

ParamsKey fused_conv_eltwise_params::GetParamsKey() const {
    ParamsKey k = weight_bias_params::GetParamsKey();

    if (conv.split > 1) {
        k.EnableFusedConvEltwSplitSupport();
    }

    if (conv.dilation.x != 1 || conv.dilation.y != 1 || conv.dilation.z != 1) {
        k.EnableFusedConvEltwDilation();
    }

    if (conv.depthwise_separable_opt) {
        k.EnableFusedConvEltwDepthwiseSeparableOpt();
    }

    if (conv.transposed) {
        k.EnableFusedConvEltwTranspose();
    }

    if (conv.local_convolution) {
        k.EnableFusedConvEltwLocalConvolution();
    }

    if (second_input_in_output) {
        k.EnableFusedConvEltwiseRWOutOpt();
    }

    if (depth_to_space_already_fused) {
        k.EnableFusedConvEltwDepthToSpaceFusing();
    }

    return k;
}

bool fused_conv_eltwise_kernel_base::Validate(const Params& p, const optional_params& o) const {
    if (p.GetType() != KernelType::FUSED_CONV_ELTWISE || o.GetType() != KernelType::FUSED_CONV_ELTWISE) {
        return false;
    }

    const fused_conv_eltwise_params& params = static_cast<const fused_conv_eltwise_params&>(p);
    const fused_conv_eltwise_optional_params& optParams = static_cast<const fused_conv_eltwise_optional_params&>(o);

    bool bSupportedWeightsLayout = params.weights.GetLayout() == GetPreferreddWeightsLayout(params);

    const bool bWeightsOK = bSupportedWeightsLayout || optParams.allowStaticInputReordering;

    if (!bWeightsOK) {
        return false;
    }

    return true;
}

JitConstants fused_conv_eltwise_kernel_base::GetJitConstants(const fused_conv_eltwise_params& params,
                                                             const DispatchData& kd) const {
    JitConstants mem_consts = WeightBiasKernelBase::GetJitConstants(params);
    const auto& padding = params.conv.padding;
    const auto& input = params.inputs[0];

    int64_t input_offset_with_padding =
        (int64_t)input.GetFirstElementOffset() - padding.x * input.X().pitch - input.Y().pitch * padding.y;
    input_offset_with_padding = std::max(input_offset_with_padding, (int64_t)0);

    mem_consts.AddConstants({
        MakeJitConstant("STRIDE", params.conv.stride),
        MakeJitConstant("PADDING", params.conv.padding),
        MakeJitConstant("DILATION", params.conv.dilation),
        MakeJitConstant("FILTER_ARRAY_NUM", params.conv.split),
        MakeJitConstant("INPUT0_OFFSET_WITH_PADDING", input_offset_with_padding),
        MakeJitConstant("DEPTHWISE_SEPARABLE_OPT", params.conv.depthwise_separable_opt),
    });

    if (params.conv.local_convolution) {
        mem_consts.AddConstants({MakeJitConstant("LOCAL_CONVOLUTION", params.conv.local_convolution)});
    }

    JitConstants eltw_activations = MakeActivationJitConstants(params.activations, GetUnitType(params), "_ELTW");
    mem_consts.Merge(eltw_activations);
    JitConstants conv_activations = MakeActivationJitConstants(params.conv.activations, GetUnitType(params), "_CONV");
    mem_consts.Merge(conv_activations);

    if (!params.eltw.stride.empty()) {
        mem_consts.AddConstant(MakeJitConstant("ELTW_STRIDE_X", params.eltw.stride[0].x));
        mem_consts.AddConstant(MakeJitConstant("ELTW_STRIDE_Y", params.eltw.stride[0].y));
        mem_consts.AddConstant(MakeJitConstant("ELTW_STRIDE_Z", params.eltw.stride[0].z));
    } else {
        mem_consts.AddConstant(MakeJitConstant("ELTW_STRIDE_X", 1));
        mem_consts.AddConstant(MakeJitConstant("ELTW_STRIDE_Y", 1));
        mem_consts.AddConstant(MakeJitConstant("ELTW_STRIDE_Z", 1));
    }

    mem_consts.AddConstant(MakeJitConstant("IN_OUT_OPT", params.second_input_in_output ? 1 : 0));

    std::vector<uint32_t> unrollLoopParams{params.conv.filterSize.x,
                                           params.conv.filterSize.y,
                                           params.conv.filterSize.z,
                                           (uint32_t)kd.gemmStyle.globalWorkSizeDX,
                                           (uint32_t)kd.gemmStyle.globalWorkSizeDY,
                                           (uint32_t)kd.gemmStyle.globalWorkSizeDZ,
                                           (uint32_t)kd.gemmStyle.subBlockDimM,
                                           (uint32_t)kd.gemmStyle.subBlockDimK,
                                           (uint32_t)kd.gemmStyle.subBlockDimN};

    auto loopCount = *std::max_element(unrollLoopParams.begin(), unrollLoopParams.end());

    JitConstants mem_consts_loop = MakeLoopUnrollParamsJitConstants(loopCount);
    mem_consts.Merge(mem_consts_loop);

    return mem_consts;
}

bool fused_conv_eltwise_kernel_base::CheckWorkGroups(const fused_conv_eltwise_kernel_base::DispatchData& kd) {
    if (kd.gws0 == 0 || kd.gws1 == 0 || kd.gws2 == 0 || kd.lws0 == 0 || kd.lws1 == 0 || kd.lws2 == 0) {
        return false;
    }

    if ((kd.gws0 % kd.lws0) != 0 || (kd.gws1 % kd.lws1) != 0 || (kd.gws2 % kd.lws2) != 0) {
        return false;
    }

    return true;
}

namespace {
bool CheckTensorForSplit(const DataTensor& t, uint32_t split) {
    if (t.PitchesDifferFromLogicalDims()) {
        auto feature = t.Feature();
        auto featureIndex = DataTensor::Channelndex(t.GetLayout(), Tensor::DataChannelName::FEATURE);
        if (featureIndex >= 0 && featureIndex + 1 < static_cast<int>(DataTensor::ChannelsCount(t.GetLayout()))) {
            if (feature.v * split <= t.GetDims()[featureIndex + 1].pitch) {
                Tensor::NDims newDims = t.GetDims();
                newDims[featureIndex].v = feature.v * split;

                DataTensor newTensor{newDims,
                                     t.GetDType(),
                                     t.GetLayout(),
                                     t.GetViewOffset(),
                                     t.PhysicalSize(),
                                     t.GetPaddedVal()};

                if (newTensor.PitchesDifferFromLogicalDims() == false) {
                    return true;
                }
            }
        }

        return false;
    }

    return true;
}
}  // namespace

bool fused_conv_eltwise_kernel_base::CheckPitchForSplitOnly(const fused_conv_eltwise_params& params) {
    // TODO: it's better to add pitch+offset support than handle this case
    return CheckTensorForSplit(params.inputs[0], params.conv.split);
}

fused_conv_eltwise_kernel_base::DispatchData fused_conv_eltwise_kernel_base::SetDefault(
    const fused_conv_eltwise_params& params,
    int) const {
    DispatchData kd;

    const auto& out = params.output;
    kd.fp16UnitUsed = out.GetDType() == Datatype::F16;
    std::vector<size_t> global;
    if (params.output.GetLayout() == DataLayout::bfyx || params.output.GetLayout() == DataLayout::byxf ||
        params.output.GetLayout() == DataLayout::bfzyx || params.output.GetLayout() == DataLayout::b_fs_zyx_fsv16 ||
        params.output.GetLayout() == DataLayout::bs_fs_zyx_bsv16_fsv16) {
        global = {out.X().v, out.Y().v * out.Z().v, out.Feature().v * out.Batch().v};
    } else {
        global = {out.Feature().v * out.Batch().v, out.X().v, out.Y().v * out.Z().v };
    }

    auto local = GetOptimalLocalWorkGroupSizes(global, params.engineInfo);

    kd.gws0 = global[0];
    kd.gws1 = global[1];
    kd.gws2 = global[2];

    kd.lws0 = local[0];
    kd.lws1 = local[1];
    kd.lws2 = local[2];

    kd.cldnnStyle.blockWidth = 1;
    kd.cldnnStyle.blockHeight = 1;
    kd.cldnnStyle.prefetch = 0;
    kd.cldnnStyle.inputBlockArraySize = 0;
    kd.cldnnStyle.inputBlockWidth = 0;

    kd.gemmStyle.globalWorkSizeDX = 1;
    kd.gemmStyle.globalWorkSizeDY = 1;
    kd.gemmStyle.globalWorkSizeDZ = 1;
    kd.gemmStyle.subBlockDimK = 1;
    kd.gemmStyle.subBlockDimM = 0;
    kd.gemmStyle.subBlockDimN = 0;
    kd.efficiency = DONT_USE_IF_HAVE_SOMETHING_ELSE;
    return kd;
}

KernelsData fused_conv_eltwise_kernel_base::GetCommonKernelsData(const Params& params,
                                                                 const optional_params& options,
                                                                 const std::string exeMode,
                                                                 int autoTuneIndex) const {
    if (!Validate(params, options)) {
        return {};
    }

    KernelData kd = KernelData::Default<fused_conv_eltwise_params>(params);
    fused_conv_eltwise_params& newParams = *static_cast<fused_conv_eltwise_params*>(kd.params.get());

    if (NeedPaddedInput()) {
        kd.reorderInput = CovolutionUpdateInputParams(newParams);
    }
    DispatchData runInfo = SetDefault(newParams, autoTuneIndex);

    if (!CheckWorkGroups(runInfo)) {
        // Internal Error - wrong calculation of global/local work group sizes
        return {};
    }

    bool succeed = UpdateWeightsParams(newParams,
                                       options,
                                       GetPreferreddWeightsLayout(newParams),
                                       kd.weightsReorderParams,
                                       GetSupportedKey());

    if (!succeed) {
        return {};
    }

    auto finalKernelName = GetKernelName(newParams);
    auto cldnnJit = GetJitConstants(newParams, runInfo);
    auto entryPoint = GetEntryPoint(finalKernelName, newParams.layerID, options);
    auto jit = CreateJit(finalKernelName, cldnnJit, entryPoint);

    auto& kernel = kd.kernels[0];
    FillCLKernelData(kernel,
                     runInfo,
                     params.engineInfo,
                     finalKernelName,
                     jit,
                     entryPoint,
                     exeMode,
                     true,
                     !newParams.bias.empty(),
                     1);
    kernel.arguments.push_back({ArgumentDescriptor::Types::SPLIT, 0});
    // eltwise's second input
    if (newParams.second_input_in_output) {
        kernel.arguments.push_back({ArgumentDescriptor::Types::OUTPUT, 0});
    } else {
        kernel.arguments.push_back({ArgumentDescriptor::Types::INPUT, 1});
    }

    kd.estimatedTime = runInfo.efficiency;
    kd.autoTuneIndex = autoTuneIndex;

    return {kd};
}

std::string fused_conv_eltwise_kernel_base::GetAutoTuneOptions(int autoTuneIndex) const {
    if ((autoTuneIndex >= 0) && (autoTuneIndex < static_cast<int>(autoTuneOptions.size()))) {
        return autoTuneOptions[autoTuneIndex];
    }

    return DEFAULT;
}

KernelsData fused_conv_eltwise_kernel_base::GetTunedKernelsDataByIndex(const Params& params,
                                                                       const optional_params& options,
                                                                       const int autoTuneIndex) const {
    return GetCommonKernelsData(params, options, GetAutoTuneOptions(autoTuneIndex), autoTuneIndex);
}

KernelsData fused_conv_eltwise_kernel_base::GetKernelsDataForAutoTune(const Params& params,
                                                                      const optional_params& options) const {
    if (!Validate(params, options)) {
        return {};
    }

    KernelsData res = {};

    for (size_t i = 0; i < autoTuneOptions.size(); i++) {
        KernelsData kd = GetTunedKernelsDataByIndex(params, options, static_cast<int>(i));
        if (!kd.empty()) {
            res.emplace_back(kd[0]);
        }
    }

    return res;
}

static DataTensor GetConvolutionBFYXPaddedTensor(const fused_conv_eltwise_params& cp) {
    DataTensor t;
    if (cp.inputs.size() > 1 && (cp.inputs[0].X().v <= cp.inputs[1].X().v))
        t = cp.inputs[1];
    else
        t = cp.inputs[0];
    std::vector<Tensor::Pad> pad{{0, 0}, {0, 0}, {0, 0}, {0, 0}, { 0, 0 } };

    auto& conv = cp.conv;

    pad[0].before = conv.padding.x;
    pad[1].before = conv.padding.y;
    pad[2].before = conv.padding.z;

    const auto inputLimitX = (cp.output.X().v - 1) * conv.stride.x + (conv.filterSize.x - 1) * conv.dilation.x + 1;
    const auto inputLimitY = (cp.output.Y().v - 1) * conv.stride.y + (conv.filterSize.y - 1) * conv.dilation.y + 1;
    const auto inputLimitZ = (cp.output.Z().v - 1) * conv.stride.z + (conv.filterSize.z - 1) * conv.dilation.z + 1;

    pad[0].after = (size_t)std::max(static_cast<int>(inputLimitX) - static_cast<int>(t.X().v) - static_cast<int>(pad[0].before), static_cast<int>(0));
    pad[1].after = (size_t)std::max(static_cast<int>(inputLimitY) - static_cast<int>(t.Y().v) - static_cast<int>(pad[1].before), static_cast<int>(0));
    pad[2].after = (size_t)std::max(static_cast<int>(inputLimitZ) - static_cast<int>(t.Z().v) - static_cast<int>(pad[2].before), static_cast<int>(0));

    Tensor::NDims dims(5);
    const Tensor::NDims& orgDims = cp.inputs[0].GetDims();
    size_t pitch = 1;
    size_t i;
    for (i = 0; i < orgDims.size(); i++) {
        dims[i].pad = pad[i];
        dims[i].v = orgDims[i].v;
        dims[i].pitch = pitch;
        pitch *= dims[i].LogicalDimPadded();
    }
    for (size_t j = i; j < dims.size(); j++) {
        dims[i].pad = { 0, 0 };
        dims[i].v = 1;
        dims[i].pitch = pitch;
    }

    return {dims, t.GetDType(), t.GetLayout()};
}

bool CheckConvolutionPaddedInputDesc(const fused_conv_eltwise_params& params, const DataTensor& reqDesc) {
    bool properPadding = reqDesc.X().pad.before <= params.inputs[0].X().pad.before &&
                         reqDesc.Y().pad.before <= params.inputs[0].Y().pad.before &&
                         reqDesc.Z().pad.before <= params.inputs[0].Z().pad.before &&
                         reqDesc.Feature().pad.before <= params.inputs[0].Feature().pad.before &&
                         reqDesc.Batch().pad.before <= params.inputs[0].Batch().pad.before;

    properPadding &= reqDesc.X().pad.after <= params.inputs[0].X().pad.after &&
                     reqDesc.Y().pad.after <= params.inputs[0].Y().pad.after &&
                     reqDesc.Z().pad.after <= params.inputs[0].Z().pad.after &&
                     reqDesc.Feature().pad.after <= params.inputs[0].Feature().pad.after &&
                     reqDesc.Batch().pad.after <= params.inputs[0].Batch().pad.after;

    properPadding &=
        ((params.conv.padding.x == 0 && params.conv.padding.y == 0 && params.conv.padding.z == 0) ||
            params.inputs[0].GetPaddedVal() == 0.f);

    return properPadding;
}

bool CovolutionUpdateInputParams(fused_conv_eltwise_params& params) {
    const auto req_input = GetConvolutionBFYXPaddedTensor(params);
    const bool bProperInputDesc = CheckConvolutionPaddedInputDesc(params, req_input);

    if (!bProperInputDesc) {
        params.inputs[0] = req_input;
        return true;
    }

    return false;
}

bool FusedConvolutionEltwiseCheckInput(const Params& p, const optional_params& o) {
    const fused_conv_eltwise_params& params = static_cast<const fused_conv_eltwise_params&>(p);
    const fused_conv_eltwise_optional_params& optParams = static_cast<const fused_conv_eltwise_optional_params&>(o);

    const auto req_input = GetConvolutionBFYXPaddedTensor(params);
    const bool bProperInputDesc = CheckConvolutionPaddedInputDesc(params, req_input);
    const bool bInputPadded = optParams.allowInputReordering || bProperInputDesc;

    if (!bInputPadded) {
        return false;
    }

    return true;
}

}  // namespace kernel_selector
