// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#include "convolution_kernel_bfyx_to_b_fs_yx_fsv16.h"
#include "kernel_selector_utils.h"
#include <vector>
#include <algorithm>

namespace kernel_selector {

static const size_t sub_group_size = 16;
static const size_t feature_block_size = 16;

ConvolutionKernel_bfyx_to_bfyx_f16::ConvolutionKernel_bfyx_to_bfyx_f16(std::string kernel_name)
    : ConvolutionKernelBase(kernel_name) {
    std::vector<size_t> outputBlockWidths = {2, 4, 8};
    std::vector<std::string> executionModes = ConvolutionKernelBase::autoTuneOptions;

    for (auto w : outputBlockWidths) {
        for (auto exeMode : executionModes) {
            autoTuneOptions.emplace_back(AutoTuneOption{w, exeMode});
        }
    }
}

ConvolutionKernel_bfyx_to_bfyx_f16::AutoTuneOption ConvolutionKernel_bfyx_to_bfyx_f16::GetAutoTuneOptions(
    const Params& /* arg*/,
    int autoTuneIndex) const {
    if (autoTuneIndex >= 0 && autoTuneIndex < static_cast<int>(autoTuneOptions.size()))
        return autoTuneOptions[autoTuneIndex];

    return {8, AGE_BASED};
}

ParamsKey ConvolutionKernel_bfyx_to_bfyx_f16::GetSupportedKey() const {
    ParamsKey k;
    k.EnableInputDataType(Datatype::F16);
    k.EnableInputDataType(Datatype::F32);
    k.EnableOutputDataType(Datatype::F16);
    k.EnableOutputDataType(Datatype::F32);
    k.EnableOutputDataType(Datatype::UINT8);
    k.EnableOutputDataType(Datatype::INT8);
    k.EnableInputWeightsType(WeightsType::F16);
    k.EnableInputWeightsType(WeightsType::F32);
    k.EnableInputLayout(DataLayout::bfyx);
    k.EnableOutputLayout(DataLayout::b_fs_yx_fsv16);
    k.EnableTensorOffset();
    k.EnableTensorPitches();
    // TODO Add dilation support to kernel
    // k.EnableDilation();
    k.EnableBiasPerFeature();
    // TODO Add bias per output support to kernel
    // k.EnableBiasPerOutput();
    k.EnableNonBiasTerm();
    k.EnableSplitSupport();
    k.EnableDepthwiseSeparableOpt();
    k.EnableBatching();
    k.EnableSubGroup();
    k.EnableSubGroupShort();
    k.EnableDifferentTypes();
    return k;
}

ConvolutionKernelBase::DispatchData ConvolutionKernel_bfyx_to_bfyx_f16::SetDefault(const convolution_params& params,
                                                                                   int autoTuneIndex) const {
    DispatchData dispatchData = ConvolutionKernelBase::SetDefault(params);

    const auto& out = params.output;

    auto autoTune = GetAutoTuneOptions(params, autoTuneIndex);
    dispatchData.cldnnStyle.blockWidth = autoTune.blockWidth;

    auto x = out.X().v;
    auto y = out.Y().v;
    auto f = out.Feature().v;
    auto b = out.Batch().v;

    dispatchData.gws[0] = CeilDiv(x, autoTune.blockWidth) * y;
    dispatchData.gws[1] = Align(f, sub_group_size);
    dispatchData.gws[2] = b;

    dispatchData.lws[0] = 1;
    dispatchData.lws[1] = sub_group_size;
    dispatchData.lws[2] = 1;

    return dispatchData;
}

KernelsPriority ConvolutionKernel_bfyx_to_bfyx_f16::GetKernelsPriority(const Params& params, const optional_params& /*options*/) const {
    const auto& p = static_cast<const convolution_params&>(params);

    return p.inputs[0].Batch().v == 1 ? FORCE_PRIORITY_2 : FORCE_PRIORITY_7;
}

bool ConvolutionKernel_bfyx_to_bfyx_f16::Validate(const Params& p, const optional_params& o) const {
    if (!ConvolutionKernelBase::Validate(p, o)) {
        return false;
    }

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

    const auto& input = params.inputs[0];
    const auto& output = params.output;

    // Up to 4 input features allowed
    if (input.Feature().v > 4) {
        return false;
    }

    // Check that padding before features doesn't miss-align the blocks
    if (input.Feature().pad.before % feature_block_size != 0 || output.Feature().pad.before % feature_block_size != 0) {
        return false;
    }

    return true;
}

JitConstants ConvolutionKernel_bfyx_to_bfyx_f16::GetJitConstants(const convolution_params& params,
                                                                 const DispatchData& dispatchData) const {
    auto input = params.inputs[0];
    auto output = params.output;
    auto jit = Parent::GetJitConstants(params, dispatchData);

    auto blockWidth = dispatchData.cldnnStyle.blockWidth;

    if (!params.fused_ops.empty()) {
        auto input_dt = GetActivationType(params);
        FusedOpsConfiguration conf_vec = { "_VEC",
                                           {"b", "(f_block*16)", "y", "x"},
                                           "dst",
                                           input_dt,
                                           blockWidth,
                                           LoadType::LT_ALIGNED_READ,
                                           BoundaryCheck::ENABLED,
                                           IndexType::TENSOR_COORD,
                                           Tensor::DataChannelName::X };
        FusedOpsConfiguration conf_scalar = { "_SCALAR",
                                              {"b", "(f_block*16)", "y", "(x+i)"},
                                              "dst[i]",
                                              input_dt,
                                              1,
                                              LoadType::LT_ALIGNED_READ,
                                              BoundaryCheck::ENABLED,
                                              IndexType::TENSOR_COORD,
                                              Tensor::DataChannelName::X };
        jit.Merge(MakeFusedOpsJitConstants(params, {conf_vec, conf_scalar}));
    }

    size_t input_line_size = std::min(params.stride.x * (blockWidth - 1) + (params.weights.X().v - 1)*params.dilation.x + 1,
                                      input.X().v + input.X().pad.Total());
    size_t input_block_size = CeilDiv(input_line_size * params.filterSize.y, sub_group_size);

    jit.AddConstant(MakeJitConstant("OUTPUT_X_BLOCK_SIZE", blockWidth));

    jit.AddConstant(MakeJitConstant("INPUT_LINE_SIZE", input_line_size));
    jit.AddConstant(MakeJitConstant("INPUT_BLOCK_SIZE", input_block_size));

    jit.AddConstant(MakeJitConstant("SUB_GROUP_SIZE", sub_group_size));
    jit.AddConstant(MakeJitConstant("X_BLOCKS", CeilDiv(output.X().v, blockWidth)));

    if (params.output.Feature().v % feature_block_size != 0) {
        jit.AddConstant(MakeJitConstant("OUTPUT_LEFTOVERS", 1));
    }

    return jit;
}

KernelsData ConvolutionKernel_bfyx_to_bfyx_f16::GetTunedKernelsDataByIndex(const Params& params,
                                                                           const optional_params& options,
                                                                           const int autoTuneIndex) const {
    auto tuneOptions = GetAutoTuneOptions(params, autoTuneIndex);
    return GetCommonKernelsData(params, options, tuneOptions.exeMode, autoTuneIndex);
}

KernelsData ConvolutionKernel_bfyx_to_bfyx_f16::GetKernelsData(const Params& params,
                                                               const optional_params& options) const {
    return GetTunedKernelsDataByIndex(params, options);
}

KernelsData ConvolutionKernel_bfyx_to_bfyx_f16::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;
}

}  // namespace kernel_selector
