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117 lines
3.9 KiB
117 lines
3.9 KiB
/* 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 <iostream>
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#include <iterator>
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#include <random>
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#include <sstream>
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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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using Tensor = framework::Tensor;
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template <typename DeviceContext, typename T>
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class SamplingIdKernel : public framework::OpKernel<T> {
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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<T> ins_vector;
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framework::TensorToVector(*input, context.device_context(), &ins_vector);
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std::vector<T> ids(batch_size);
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for (size_t i = 0; i < batch_size; ++i) {
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double r = getRandReal();
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int idx = width - 1;
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for (int j = 0; j < width; ++j) {
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if ((r -= ins_vector[i * width + j]) < 0) {
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idx = j;
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break;
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}
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}
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ids[i] = ins_vector[i * width + idx];
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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>("Out");
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output->Resize(framework::make_ddim(out_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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private:
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double getRandReal() const {
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std::random_device
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rd; // Will be used to obtain a seed for the random number engine
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std::mt19937 gen(rd()); // Standard mersenne_twister_engine seeded with
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// rd()
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std::uniform_real_distribution<> dis(1.0, 2.0);
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return dis(gen);
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}
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};
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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 SamplingIdOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SamplingIdOp should not be null.");
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auto input_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE(input_dims.size() == 2,
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"Input(X, Filter) should be 2-D tensor.");
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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", "SamplingId data tensor.");
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AddComment(R"DOC(
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SamplingId Operator.
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A layer for sampling id from multinomial distribution from the
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input layer. Sampling one id for one sample.)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_OPERATOR(sampling_id, ops::SamplingIdOp, ops::SamplingIdOpMaker,
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paddle::framework::EmptyGradOpMaker);
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REGISTER_OP_CPU_KERNEL(
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sampling_id, 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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