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@ -97,7 +97,7 @@ void MKLDNNTester::randomWgtDatas() {
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parameters_[REF][i]->randomize();
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dnnValue->copyFrom(*refValue);
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VLOG(lvl_) << "Random weight data " << parameters_[DNN][i]->getName();
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VLOG(MKLDNN_TESTS) << "Random weight " << parameters_[DNN][i]->getName();
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printVector(dnnValue);
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}
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}
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@ -109,7 +109,7 @@ void MKLDNNTester::randomBotDatas() {
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dataLayers_[REF][i]->getOutputValue()->randomizeUniform();
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dataLayers_[DNN][i]->getOutputValue()->copyFrom(
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*(dataLayers_[REF][i]->getOutputValue()));
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VLOG(lvl_) << "Input " << i << " data:";
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VLOG(MKLDNN_TESTS) << "Random Foward, InputValue " << i;
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printMatrix(dataLayers_[REF][i]->getOutputValue());
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}
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}
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@ -118,12 +118,12 @@ void MKLDNNTester::randomTopDiffs() {
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refLayer_->getOutputGrad()->randomizeUniform();
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dnnLayer_->getOutput(CPU_DEVICE)
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.grad->copyFrom(*(refLayer_->getOutputGrad()));
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VLOG(lvl_) << "Random Backward Input, TopDiff: ";
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VLOG(MKLDNN_TESTS) << "Random Backward, OutputGrad";
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printMatrix(refLayer_->getOutputGrad());
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}
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void MKLDNNTester::checkForward() {
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VLOG(MKLDNN_ALL) << "Check Forward";
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VLOG(MKLDNN_TESTS) << "Check Forward";
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printTopDatas();
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double delta =
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compareMatrix(dnnLayer_->getOutputValue(), refLayer_->getOutputValue());
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@ -131,15 +131,15 @@ void MKLDNNTester::checkForward() {
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}
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void MKLDNNTester::checkBackwardData() {
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VLOG(MKLDNN_ALL) << "Check Backward Data";
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VLOG(MKLDNN_TESTS) << "Check Backward Data";
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// TODO(TJ): uncomment me when batch norm ready
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// const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
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for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
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const MatrixPtr& dnnDiff = dataLayers_[DNN][i]->getOutputGrad();
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const MatrixPtr& refDiff = dataLayers_[REF][i]->getOutputGrad();
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VLOG(lvl_) << "Mkldnn Backward Output BotDiff " << i;
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VLOG(MKLDNN_ALL) << "MKLDNN Backward Result: InputGrad " << i;
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printMatrix(dnnDiff);
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VLOG(lvl_) << "Reference Backward Output BotDiff " << i;
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VLOG(MKLDNN_ALL) << "Reference Backward Result: InputGrad " << i;
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printMatrix(refDiff);
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double delta = compareMatrix(dnnDiff, refDiff);
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@ -153,7 +153,7 @@ void MKLDNNTester::checkBackwardData() {
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}
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void MKLDNNTester::checkBackwardWgts() {
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VLOG(MKLDNN_ALL) << "Check Backward Weight";
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VLOG(MKLDNN_TESTS) << "Check Backward Weight";
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CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
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vector<VectorPtr> dnnWgts; // used to temply save mkldnn weights
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saveWgt(parameters_[DNN], dnnWgts);
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@ -165,9 +165,11 @@ void MKLDNNTester::checkBackwardWgts() {
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for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
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const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
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const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
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VLOG(lvl_) << "Mkldnn Output weight " << parameters_[DNN][i]->getName();
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VLOG(MKLDNN_ALL) << "MKLDNN Result: weight value"
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<< parameters_[DNN][i]->getName();
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printVector(dnn);
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VLOG(lvl_) << "Reference Output weight " << parameters_[REF][i]->getName();
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VLOG(MKLDNN_ALL) << "Reference Result: weight value "
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<< parameters_[REF][i]->getName();
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printVector(ref);
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double delta = compareVector(dnn, ref);
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@ -240,7 +242,8 @@ void MKLDNNTester::printTopDatas() {
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}
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for (int n = 0; n < NUM; ++n) {
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VLOG(lvl_) << testLayers_[n]->getType() << " forward output TopData: ";
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VLOG(MKLDNN_ALL) << testLayers_[n]->getType()
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<< " Forward Result: OutputValue";
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printMatrix(testLayers_[n]->getOutputValue());
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}
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}
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@ -252,7 +255,7 @@ void MKLDNNTester::printMatrix(const MatrixPtr& m) {
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std::ostringstream ostr;
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m->print(ostr);
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VLOG(lvl_) << std::endl << ostr.str();
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VLOG(MKLDNN_ALL) << std::endl << ostr.str();
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}
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void MKLDNNTester::printVector(const VectorPtr& v) {
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@ -262,7 +265,7 @@ void MKLDNNTester::printVector(const VectorPtr& v) {
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std::ostringstream ostr;
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v->print(ostr, v->getSize());
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VLOG(lvl_) << std::endl << ostr.str();
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VLOG(MKLDNN_ALL) << std::endl << ostr.str();
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}
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double MKLDNNTester::getDelta(const real* d1,
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@ -314,7 +317,7 @@ void MKLDNNTester::runOnce() {
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UpdateCallback updateCallback = [](Parameter* para) {
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auto& grad = para->getBuf(PARAMETER_GRADIENT);
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auto& value = para->getBuf(PARAMETER_VALUE);
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real lr = 1e-3;
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real lr = 1e-2;
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value->add(*grad, lr);
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grad->zeroMem();
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};
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@ -340,10 +343,9 @@ void MKLDNNTester::run(const TestConfig& dnn,
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size_t batchSize,
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size_t inputImgH,
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size_t inputImgW,
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bool printDetails,
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size_t iter,
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float epsilon,
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bool log,
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int level) {
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float epsilon) {
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CHECK(dnn.layerConfig.type().compare(0, 7, "mkldnn_") == 0 ||
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dnn.layerConfig.active_type().compare(0, 7, "mkldnn_") == 0)
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<< "should be MKLDNN layer or MKLDNN activation";
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@ -359,10 +361,9 @@ void MKLDNNTester::run(const TestConfig& dnn,
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ih_ = inputImgH;
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iw_ = inputImgW;
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log_ = printDetails;
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iter_ = iter;
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eps_ = epsilon;
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log_ = log;
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lvl_ = level;
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// Firstly test mkldnn init from PARAM_FORMAT_ORIGINAL weight
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reset(dnn, ref, batchSize);
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@ -531,9 +532,11 @@ void MKLDNNTester::getOutResult(const std::string& configPath,
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void MKLDNNTester::compareResult(DataOut& ref, DataOut& dnn, float eps) {
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CHECK_EQ(ref.outValues.size(), dnn.outValues.size());
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CHECK_EQ(ref.paraValues.size(), dnn.paraValues.size());
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VLOG(MKLDNN_TESTS) << "compare value size: " << ref.outValues.size();
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for (size_t i = 0; i < ref.outValues.size(); i++) {
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EXPECT_LE(fabs(compareMatrix(ref.outValues[i], dnn.outValues[i])), eps);
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}
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VLOG(MKLDNN_TESTS) << "compare param size: " << ref.outValues.size();
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for (size_t i = 0; i < ref.paraValues.size(); i++) {
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EXPECT_LE(fabs(compareVector(ref.paraValues[i], dnn.paraValues[i])), eps);
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}
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@ -544,9 +547,10 @@ void MKLDNNTester::runBranchesTest(const std::string& configPath,
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float eps) {
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DataIn in;
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initArgument(in, configPath, iter);
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DataOut outCpu, outDnn;
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VLOG(MKLDNN_TESTS) << "runing cpu network";
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getOutResult(configPath, in, outCpu, false, iter);
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VLOG(MKLDNN_TESTS) << "runing mkldnn network";
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getOutResult(configPath, in, outDnn, true, iter);
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compareResult(outCpu, outDnn, eps);
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Tao Luo
7 years ago
committed by
GitHub