text: /*
  // 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 "embed_kernel_ref.h"
  #include "kernel_selector_utils.h"
  #include "common_tools.h"
  
  namespace kernel_selector
  {
  
  	ParamsKey EmbedKernelRef::GetSupportedKey() const
  	{
  		ParamsKey k;
  		k.EnableInputDataType(Datatype::F16);
  		k.EnableInputDataType(Datatype::F32);
  		k.EnableInputDataType(Datatype::INT8);
  		k.EnableOutputDataType(Datatype::F16);
  		k.EnableOutputDataType(Datatype::F32);
  		k.EnableOutputDataType(Datatype::INT8);
  		k.EnableInputWeightsType(WeightsType::F16);
  		k.EnableInputWeightsType(WeightsType::F32);
  		k.EnableInputWeightsType(WeightsType::INT8);
          k.EnableAllInputLayout();
  		k.EnableOutputLayout(DataLayout::bf);
  		k.EnableBiasPerOutput();
  		k.EnableBiasPerFeature();
  		k.EnableTensorOffset();
  		k.EnableTensorPitches();
  		k.EnableBatching();
  		return k;
  	}
  
  	JitConstants EmbedKernelRef::GetJitConstants(const embed_params& params) const
  	{
          JitConstants jit = WeightBiasKernelBase::GetJitConstants(params);
          const auto& input = params.inputs[0];
          const auto x_size = input.LogicalSize() / input.Batch().v;
          const auto w_size = params.weights.OFM().v;
          jit.AddConstant(MakeJitConstant("INPUT0_ELEMENTS_COUNT", x_size));
          jit.AddConstant(MakeJitConstant("NUM_OUTPUT_SIZE", w_size));
  
          return jit;
  	}
  
  	EmbedKernelRef::DispatchData EmbedKernelRef::SetDefault(const embed_params& params) const
  	{
  		DispatchData kd;
  		std::vector<size_t> global = { params.inputs[0].Y().v , params.weights.OFM().v, params.inputs[0].Batch().v };
  		std::vector<size_t> local = GetOptimalLocalWorkGroupSizes(global);
  
  		kd.gws0 = global[0];
  		kd.gws1 = global[1];
  		kd.gws2 = global[2];
  
  		kd.lws0 = local[0];
  		kd.lws1 = local[1];
  		kd.lws2 = 1;
  		return kd;
  
  	}
  
  	KernelsData EmbedKernelRef::GetKernelsData(const Params& params, const optional_params& options) const
  	{
  		assert(params.GetType() == KernelType::EMBED);
  
  		const embed_params& orgParams = static_cast<const embed_params&>(params);
  
  		const std::vector<WeightsLayout> weightsLayouts = {
  			WeightsLayout::oiyx,
  		};
  
  		DispatchData runInfo = SetDefault(orgParams);
  		KernelData kd = KernelData::Default<embed_params>(params);
  		embed_params& newParams = *static_cast<embed_params*>(kd.params.get());
  
  		bool succeed = UpdateWeightsParams(
  			newParams,
  			options,
  			weightsLayouts,
  			kd.weightsReorderParams);
  
  		if (!succeed)
  		{
  			return{};
  		}
  
  		auto cldnn_jit = GetJitConstants(newParams);
  		auto entry_point = GetEntryPoint(kernelName, newParams.layerID, options);
  		auto jit = CreateJit(kernelName, cldnn_jit, entry_point);
  
  		auto& kernel = kd.kernels[0];
  
  		FillCLKernelData(kernel, runInfo, kernelName, jit, entry_point, ROUND_ROBIN, true, !newParams.bias.empty());
  
  		kd.estimatedTime = runInfo.effiency;
  
  		return{ kd };
  	}
  
  	
  }