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288 lines
9.6 KiB
288 lines
9.6 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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#undef PADDLE_DISABLE_TIMER
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#include <gtest/gtest.h>
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#include <paddle/utils/PythonUtil.h>
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#include <algorithm>
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#include <cstdlib>
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#include "paddle/testing/TestUtil.h"
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#include "paddle/trainer/Trainer.h"
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#include "paddle/utils/Stat.h"
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using namespace paddle; // NOLINT
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using namespace std; // NOLINT
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DECLARE_int32(gpu_id);
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DECLARE_double(checkgrad_eps);
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DEFINE_bool(use_label, true, "input label or sequence label");
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DEFINE_bool(static_para, false, "static parameter");
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struct DataIn {
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std::vector<Argument> inArgs;
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std::vector<MatrixPtr> outGrads;
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std::vector<VectorPtr> paraValues;
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};
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struct DataOut {
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std::vector<MatrixPtr> outValues;
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std::vector<VectorPtr> paraGrads;
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};
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void initArgument(DataIn& data,
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const std::string& configPath,
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bool useGpu = FLAGS_use_gpu) {
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TrainerConfigHelper config(configPath);
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size_t batchSize = config.getOptConfig().batch_size();
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for (const auto& layer_name : config.getModelConfig().input_layer_names()) {
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auto layer_config = std::find_if(config.getModelConfig().layers().begin(),
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config.getModelConfig().layers().end(),
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[=](const LayerConfig& layer_config) {
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return layer_config.name() == layer_name;
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});
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CHECK(layer_config != config.getModelConfig().layers().end());
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size_t layerSize = layer_config->size();
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Argument arg;
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arg.value = Matrix::create(batchSize, layerSize, false, useGpu);
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arg.grad = Matrix::create(batchSize, layerSize, false, useGpu);
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arg.value->randomizeUniform();
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arg.value->add(-0.5);
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arg.value->sigmoid(*arg.value);
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arg.grad->zeroMem();
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if (FLAGS_use_label) {
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arg.ids = VectorT<int>::create(batchSize, useGpu);
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arg.ids->rand(layerSize);
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}
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generateSequenceStartPositions(batchSize, arg.sequenceStartPositions);
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data.inArgs.push_back(arg);
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}
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for (const auto& layer_name : config.getModelConfig().output_layer_names()) {
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auto layer_config = std::find_if(config.getModelConfig().layers().begin(),
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config.getModelConfig().layers().end(),
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[=](const LayerConfig& layer_config) {
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return layer_config.name() == layer_name;
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});
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CHECK(layer_config != config.getModelConfig().layers().end());
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size_t layerSize = layer_config->size();
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MatrixPtr grad = Matrix::create(batchSize, layerSize, false, useGpu);
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grad->randomizeUniform();
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data.outGrads.push_back(grad);
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}
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for (const auto& para_config : config.getModelConfig().parameters()) {
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VectorPtr value = Vector::create(para_config.size(), useGpu);
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value->randnorm(0, 2);
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data.paraValues.push_back(value);
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}
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}
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void calcGradient(DataIn& in, DataOut& out, const std::string& configPath) {
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*ThreadLocalRand::getSeed() = 0;
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srand(0);
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Trainer trainer;
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auto config = std::make_shared<TrainerConfigHelper>(configPath);
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trainer.init(config, false);
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std::vector<ParameterPtr> parameters;
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vector<Argument> outArgs;
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auto gradientMachine = trainer.getGradientMachine();
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parameters = gradientMachine->getParameters();
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if (FLAGS_static_para) {
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for (size_t i = 0; i < parameters.size(); i++) {
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parameters[i]->getBuf(PARAMETER_VALUE)->one();
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}
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} else {
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for (size_t i = 0; i < in.paraValues.size(); i++) {
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parameters[i]->getBuf(PARAMETER_VALUE)->copyFrom(*in.paraValues[i]);
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}
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}
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gradientMachine->start();
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gradientMachine->forward(in.inArgs, &outArgs, PASS_TRAIN);
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for (size_t i = 0; i < in.outGrads.size(); i++) {
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// If the all the layers in the config have no parameters, also
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// not set NeedGradient(), the outArgs[i] will be nullptr.
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outArgs[i].grad->copyFrom(*in.outGrads[i]);
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}
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gradientMachine->backward();
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for (size_t i = 0; i < in.outGrads.size(); i++) {
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MatrixPtr value = Matrix::create(outArgs[i].value->getHeight(),
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outArgs[i].value->getWidth(),
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false,
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false);
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value->copyFrom(*outArgs[i].value);
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out.outValues.push_back(value);
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}
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for (size_t i = 0; i < in.paraValues.size(); i++) {
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VectorPtr grad = Vector::create(
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parameters[i]->getBuf(PARAMETER_GRADIENT)->getSize(), false);
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grad->copyFrom(*parameters[i]->getBuf(PARAMETER_GRADIENT));
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out.paraGrads.push_back(grad);
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}
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for (int i = 0; i < 20; i++) {
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REGISTER_TIMER("forward");
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gradientMachine->forward(in.inArgs, &outArgs, PASS_TRAIN);
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}
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for (int i = 0; i < 20; i++) {
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REGISTER_TIMER("backward");
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gradientMachine->backward();
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}
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gradientMachine->finish();
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}
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void checkBuffer(real* A,
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const char* desA,
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real* B,
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const char* desB,
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size_t len,
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size_t width = 1) {
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int nNum = 0;
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for (size_t i = 0; i < len; ++i) {
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real diff = fabs(A[i] - B[i]);
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if (diff > 0.0f &&
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diff / std::max(fabs(A[i]), fabs(B[i])) > FLAGS_checkgrad_eps) {
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nNum++;
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LOG(INFO) << "Row: " << i / width << ", " << desA << " : " << A[i]
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<< " " << desB << " : " << B[i];
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}
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}
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EXPECT_EQ(0, nNum);
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}
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void compareGradient(DataOut& outA, DataOut& outB) {
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LOG(INFO) << "------------------------------"
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<< " Check Network Output "
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<< "------------------------------";
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for (size_t i = 0; i < outA.outValues.size(); ++i) {
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LOG(INFO) << "OUTPUT VALUE: " << i;
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checkBuffer(outA.outValues[i]->getData(),
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"network A output",
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outB.outValues[i]->getData(),
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"network B output",
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outA.outValues[i]->getElementCnt(),
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outA.outValues[i]->getWidth());
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}
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if (!FLAGS_static_para) {
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LOG(INFO) << "------------------------------"
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<< " Check Parameters "
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<< "------------------------------";
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for (size_t i = 0; i < outA.paraGrads.size(); ++i) {
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LOG(INFO) << "PARAMETER GRADIENT: " << i;
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checkBuffer(outA.paraGrads[i]->getData(),
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"Network A",
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outB.paraGrads[i]->getData(),
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"Network B",
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outA.paraGrads[i]->getSize());
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}
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}
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}
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void compareNetwork(const std::string& config_file_a,
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const std::string& config_file_b) {
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DataIn in;
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initArgument(in, config_file_a);
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DataOut dataA;
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calcGradient(in, dataA, config_file_a);
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LOG(INFO) << "forwardBackward of Network A is finished";
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globalStat.printSegTimerStatus();
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globalStat.reset();
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LOG(INFO) << "\n\n";
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DataOut dataB;
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calcGradient(in, dataB, config_file_b);
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LOG(INFO) << "forwardBackward of the Network B is finished";
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globalStat.printSegTimerStatus();
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globalStat.reset();
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LOG(INFO) << "\n\n";
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compareGradient(dataA, dataB);
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}
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TEST(Compare, concat_dotmul) {
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std::string config_file_a = "./gserver/tests/concat_dotmul_a.conf";
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std::string config_file_b = "./gserver/tests/concat_dotmul_b.conf";
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compareNetwork(config_file_a, config_file_b);
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}
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TEST(Compare, concat_fullmatrix) {
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std::string config_file_a = "./gserver/tests/concat_fullmatrix_a.conf";
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std::string config_file_b = "./gserver/tests/concat_fullmatrix_b.conf";
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compareNetwork(config_file_a, config_file_b);
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}
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TEST(Compare, concat_table) {
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std::string config_file_a = "./gserver/tests/concat_table_a.conf";
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std::string config_file_b = "./gserver/tests/concat_table_b.conf";
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compareNetwork(config_file_a, config_file_b);
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}
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#ifndef PADDLE_ONLY_CPU
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TEST(Compare, img_pool) {
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std::string config_file_a = "./gserver/tests/img_pool_a.conf";
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std::string config_file_b = "./gserver/tests/img_pool_b.conf";
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bool useGpu = FLAGS_use_gpu;
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FLAGS_use_gpu = true;
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compareNetwork(config_file_a, config_file_b);
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FLAGS_use_gpu = useGpu;
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}
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TEST(Compare, img_conv) {
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std::string config_file_a = "./gserver/tests/img_conv_a.conf";
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std::string config_file_b = "./gserver/tests/img_conv_b.conf";
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bool useGpu = FLAGS_use_gpu;
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FLAGS_use_gpu = true;
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compareNetwork(config_file_a, config_file_b);
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FLAGS_use_gpu = useGpu;
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}
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// Test cudnn_conv and exconv give the same result
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TEST(Compare, img_conv2) {
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std::string config_file_a = "./gserver/tests/img_conv_cudnn.py";
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std::string config_file_b = "./gserver/tests/img_conv_exconv.py";
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bool useGpu = FLAGS_use_gpu;
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double eps = FLAGS_checkgrad_eps;
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FLAGS_use_gpu = true;
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FLAGS_checkgrad_eps = 1e-2;
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compareNetwork(config_file_a, config_file_b);
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FLAGS_use_gpu = useGpu;
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FLAGS_checkgrad_eps = eps;
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}
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#endif
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DEFINE_string(config_file_a, "", "config of one network to compare");
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DEFINE_string(config_file_b, "", "config of another network to compare");
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TEST(Compare, network) {
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if (FLAGS_config_file_a != "" && FLAGS_config_file_b != "") {
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compareNetwork(FLAGS_config_file_a, FLAGS_config_file_b);
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}
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}
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int main(int argc, char** argv) {
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testing::InitGoogleTest(&argc, argv);
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paddle::initMain(argc, argv);
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initPython(argc, argv);
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int ret = RUN_ALL_TESTS();
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return ret;
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}
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