Yu Yang
8 years ago
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/* 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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#include <paddle/utils/PythonUtil.h>
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#include "paddle/trainer/Trainer.h"
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#include <gtest/gtest.h>
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DECLARE_string(config);
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DECLARE_string(config_args);
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DEFINE_string(merger,
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"./paddle_merge_model",
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"path to paddle_merge_model binary");
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using namespace paddle; // NOLINT
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using namespace std; // NOLINT
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static const string& configFile = "trainer/tests/sample_trainer_config.conf";
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static const string& mergedModelFile = "./test_model_file";
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static const string& modelDir = "./test_model_dir";
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void checkBuffer(real* vec1, real* vec2, size_t len) {
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for (size_t i = 0; i < len; i++) {
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EXPECT_EQ(vec1[i], vec2[i]) << "vec1:" << vec1[i] << " vec2:" << vec2[i];
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}
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}
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void checkParameters(vector<ParameterPtr> A, vector<ParameterPtr> B) {
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CHECK_EQ(B.size(), A.size()) << "parameter size not equal";
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for (size_t i = 0; i < A.size(); i++) {
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auto vec1 = A[i]->getBuf(PARAMETER_VALUE);
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auto vec2 = B[i]->getBuf(PARAMETER_VALUE);
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CHECK_EQ(vec1->useGpu_, vec2->useGpu_) << "use gpu not equal";
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CHECK_EQ(vec1->getSize(), vec2->getSize()) << "size not equal";
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if (vec1->useGpu_ == false) {
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checkBuffer(vec1->getData(), vec2->getData(), vec1->getSize());
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} else {
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VectorPtr cpuVec1 = Vector::create(vec1->getSize(), false);
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VectorPtr cpuVec2 = Vector::create(vec2->getSize(), false);
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cpuVec1->copyFrom(*vec1, HPPL_STREAM_DEFAULT);
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cpuVec2->copyFrom(*vec2, HPPL_STREAM_DEFAULT);
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hl_stream_synchronize(HPPL_STREAM_DEFAULT);
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checkBuffer(cpuVec1->getData(), cpuVec2->getData(), cpuVec1->getSize());
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}
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}
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}
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TEST(GradientMachine, create) {
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#ifdef PADDLE_ONLY_CPU
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FLAGS_use_gpu = false;
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#endif
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mkDir(modelDir.c_str());
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FLAGS_config = configFile;
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FLAGS_config_args = "with_cost=False";
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auto config = TrainerConfigHelper::createFromFlagConfig();
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// save model to directory
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unique_ptr<GradientMachine> gradientMachine1(
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GradientMachine::create(*config));
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gradientMachine1->saveParameters(modelDir);
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Trainer trainer;
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trainer.init(config);
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ParameterUtil* paramUtil = trainer.getParameterUtilPtr();
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if (paramUtil != NULL) {
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paramUtil->saveConfigWithPath(modelDir);
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}
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// create a different GradientMachine
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unique_ptr<GradientMachine> gradientMachine2(
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GradientMachine::create(*config));
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gradientMachine2->randParameters();
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// merge config and model to one file
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string cmd = FLAGS_merger + " --model_dir=" + modelDir +
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" --config_args=with_cost=False" + " --model_file=" +
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mergedModelFile;
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LOG(INFO) << cmd;
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int ret = system(cmd.c_str());
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EXPECT_EQ(0, ret);
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if (ret) {
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return;
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}
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// create GradientMachine from the merged model
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DataConfig dataConfig;
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unique_ptr<GradientMachine> gradientMachine3(
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GradientMachine::create(mergedModelFile, &dataConfig));
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CHECK(gradientMachine3);
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EXPECT_EQ(dataConfig.type(), "simple");
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EXPECT_EQ(dataConfig.feat_dim(), 3);
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// compare the parameters of GradientMachine and GradientMachine3
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std::vector<ParameterPtr> paraMachine1 = gradientMachine1->getParameters();
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std::vector<ParameterPtr> paraMachine3 = gradientMachine3->getParameters();
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checkParameters(paraMachine1, paraMachine3);
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// Test that the GradientMachine created from the merged model
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// is same as the orginnal one.
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vector<Argument> inArgs(1);
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vector<Argument> outArgs;
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int inputDim = 3;
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int numSamples = 2;
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CpuMatrix cpuInput(numSamples, inputDim);
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for (int i = 0; i < numSamples; ++i) {
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for (int j = 0; j < inputDim; ++j) {
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cpuInput.getData()[i * inputDim + j] =
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rand() / (real)RAND_MAX; // NOLINT TODO(yuyang): use rand_r
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}
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}
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MatrixPtr input = Matrix::create(numSamples,
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inputDim,
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/* trans */ false,
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FLAGS_use_gpu);
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input->copyFrom(cpuInput);
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inArgs[0].value = input;
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gradientMachine1->forward(inArgs, &outArgs, PASS_TEST);
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EXPECT_EQ((size_t)1, outArgs.size());
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vector<Argument> outArgs2;
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gradientMachine2->forward(inArgs, &outArgs2, PASS_TEST);
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CpuMatrix out1(outArgs[0].value->getHeight(), outArgs[0].value->getWidth());
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CpuMatrix out2(outArgs2[0].value->getHeight(), outArgs2[0].value->getWidth());
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out1.copyFrom(*outArgs[0].value);
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out2.copyFrom(*outArgs2[0].value);
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for (size_t i = 0; i < out1.getHeight() * out1.getWidth(); i++) {
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EXPECT_NE(out1.getData()[i], out2.getData()[i]);
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}
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gradientMachine3->forward(inArgs, &outArgs2, PASS_TEST);
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out2.copyFrom(*outArgs2[0].value);
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checkBuffer(
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out1.getData(), out2.getData(), out2.getHeight() * out2.getWidth());
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cmd = " rm -rf " + modelDir + "/*";
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LOG(INFO) << "cmd " << cmd;
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ret = system(cmd.c_str());
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EXPECT_EQ(0, ret);
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if (ret) {
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return;
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}
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cmd = " rm -rf " + mergedModelFile;
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LOG(INFO) << "cmd " << cmd;
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ret = system(cmd.c_str());
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EXPECT_EQ(0, ret);
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if (ret) {
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return;
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}
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// clean up
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rmDir(modelDir.c_str());
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remove(mergedModelFile.c_str());
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}
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int main(int argc, char** argv) {
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initMain(argc, argv);
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initPython(argc, argv);
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testing::InitGoogleTest(&argc, argv);
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return RUN_ALL_TESTS();
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}
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