/*
// Copyright (c) 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 "fully_connected_grad_weights_kernel_base.h"
#include "kernel_selector_utils.h"

namespace kernel_selector 
{
    JitConstants FullyConnectedGradWeightsKernelBase::GetJitConstants(const fully_connected_grad_weights_params& params) const
    {
        JitConstants jit = training_kernel_base::GetJitConstants(params);

        return jit;
    }

    FullyConnectedGradWeightsKernelBase::DispatchData FullyConnectedGradWeightsKernelBase::SetDefault(const fully_connected_grad_weights_params& params) const
    {
        DispatchData kd;

        kd.fp16UnitUsed = params.inputs[0].GetDType() == Datatype::F16;
        size_t gws0 = params.weights.OFM().v * params.weights.IFM().v;
        size_t lws0 = std::min(gws0, static_cast<size_t>(32));
        while (gws0 % lws0)
        {
            lws0--;
        }
        kd.gws0 = gws0;
        kd.gws1 = params.weights.X().v;
        kd.gws2 = params.weights.Y().v;
        kd.lws0 = lws0;
        kd.lws1 = 1;
        kd.lws2 = 1;
        kd.effiency = DONT_USE_IF_HAVE_SOMETHING_ELSE;
        return kd;
    }

    KernelsData FullyConnectedGradWeightsKernelBase::GetKernelsData(const Params& params, const optional_params& options) const
    {
        assert(params.GetType() == KernelType::FULLY_CONNECTED_GRAD_WEIGHTS);

        const fully_connected_grad_weights_params& orgParams = static_cast<const fully_connected_grad_weights_params&>(params);

        const std::vector<WeightsLayout> weightsLayouts = {
            WeightsLayout::oi,
            WeightsLayout::io,
            WeightsLayout::oiyx,
            WeightsLayout::iyxo,
            WeightsLayout::yxio,
            WeightsLayout::oyxi
        };

        DispatchData runInfo = SetDefault(orgParams);
        KernelData kd = KernelData::Default<fully_connected_grad_weights_params>(params);
        fully_connected_grad_weights_params& newParams = *static_cast<fully_connected_grad_weights_params*>(kd.params.get());

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

        if (!succeed)
        {
            return{};
        }

        auto cldnn_jit = GetJitConstants(orgParams);
        auto entry_point = GetEntryPoint(kernelName, orgParams.layerID, options);
        auto jit = CreateJit(kernelName, cldnn_jit, entry_point);

        auto& kernel = kd.kernels[0];
        FillCLKernelData(kernel, runInfo, params.engineInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !orgParams.bias.empty());
        if (orgParams.use_momentum)
        {
            kernel.arguments.push_back({ ArgumentDescriptor::Types::PREV_WEIGHTS_GRADIENT, 0 });
            if (!orgParams.bias.empty())
                kernel.arguments.push_back({ ArgumentDescriptor::Types::PREV_BIAS_GRADIENT, 0 });
        }
        kernel.arguments.push_back({ ArgumentDescriptor::Types::INPUT, 1 });
        kernel.arguments.push_back({ ArgumentDescriptor::Types::LEARNING_RATE, 0 });

        kd.estimatedTime = runInfo.effiency;

        return{ kd };
    }
}