/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. 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. */ #pragma once #include #include #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/op_registry.h" namespace paddle { namespace operators { using Tensor = framework::Tensor; template class SamplingIdKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { const Tensor* input = context.Input("X"); const int batch_size = static_cast(input->dims()[0]); const int width = static_cast(input->dims()[1]); std::vector ins_vector; framework::TensorToVector(*input, context.device_context(), &ins_vector); std::vector ids(batch_size); for (size_t i = 0; i < batch_size; ++i) { double r = this->get_rand(); int id = width - 1; for (int j = 0; j < width; ++j) { if ((r -= ins_vector[i * width + j]) < 0) { id = j; break; } } ids[i] = id; } std::vector out_dim; out_dim.push_back(static_cast(batch_size)); Tensor* output = context.Output("Output"); output->Resize(framework::make_ddim(out_dim)); output->mutable_data(context.GetPlace()); framework::TensorFromVector(ids, context.device_context(), output); } double get_rand() const { // Will be used to obtain a seed for the random number engine std::random_device rd; // Standard mersenne_twister_engine seeded with rd() std::mt19937 gen(rd()); std::uniform_real_distribution<> dis(0, 1); return dis(gen); } private: unsigned int defaultSeed = 0; }; } // namespace operators } // namespace paddle