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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/sampling_id_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class SamplingIdOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of RowConvOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of RowConvOp should not be null.");
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auto input_dims = ctx->GetInputDim("X");
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framework::DDim dims = input_dims;
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ctx->SetOutputDim("Out", dims);
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ctx->ShareLoD("X", "Out");
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}
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};
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class SamplingIdOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"The input tensor of softmax. "
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"2-D with shape [batch_size, input_feature_dimensions].");
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AddOutput("Out", "Sliced data tensor.");
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AddComment(R"DOC(
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SamplingId Operator.
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@brief A layer for sampling id from multinomial distribution from the
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input layer. Sampling one id for one sample. The result is stored in
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output_.ids.
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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slice, ops::SamplingIdKernel<paddle::platform::CUDADeviceContext, float>,
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ops::SamplingIdKernel<paddle::platform::CUDADeviceContext, double>,
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ops::SamplingIdKernel<paddle::platform::CUDADeviceContext, int>,
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ops::SamplingIdKernel<paddle::platform::CUDADeviceContext, int64_t>);
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include <algorithm>
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#include <vector>
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#include "paddle/fluid/operators/sampling_id_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class SamplingIdOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext *ctx) const override {}
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}
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(samplingid, ops::SamplingIdOp, ops::SamplingIdOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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slice, ops::SamplingIdKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SamplingIdKernel<paddle::platform::CPUDeviceContext, int64_t>,
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ops::SamplingIdKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SamplingIdKernel<paddle::platform::CPUDeviceContext, double>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <random>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class SamplingIdKernel : public framework::OpKernel<T> {
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/// Produces random floating-point values, uniformly distributed on [0, 1).
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std::uniform_real_distribution<double> rand1_;
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* input = context.Input<Tensor>("X");
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const int batch_size = static_cast<int>(input->dims()[0]);
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const int width = static_cast<int>(input->dims()[1]);
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std::vector<int> ids(batchSize);
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auto& reng = get();
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for (size_t i = 0; i < batchSize; ++i) {
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double r = rand1_(reng);
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int id = dim - 1;
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for (int j = 0; j < dim; ++j) {
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if ((r -= buf[i * dim + j]) < 0) {
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id = j;
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break;
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}
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}
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ids[i] = id;
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}
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std::vector<int64_t> out_dim;
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out_dim.push_back(static_cast<int64_t>(batch_size));
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Tensor* output = context.Output<Tensor>("Output");
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output->Resize(framework::make_ddim(in_dim));
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output->mutable_data<T>(context.GetPlace());
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framework::TensorFromVector(ids, context.device_context(), output);
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}
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std::default_random_engine& get() {
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auto engine = new std::default_random_engine;
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engine->seed(defaultSeed);
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return *engine;
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}
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private:
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unsigned int defaultSeed = 0;
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}
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} // namespace operators
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} // namespace paddle
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