add mkldnn tester

paddle/gserver/layers/MkldnnFcLayer.cpp

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "MkldnnFcLayer.h"
+#include "paddle/utils/Logging.h"
 #include "paddle/utils/Stat.h"
 
 namespace paddle {
@@ -41,6 +42,7 @@ bool MkldnnFcLayer::init(const LayerMap& layerMap,
   // create weight
   weight_ =
       std::unique_ptr<Weight>(new Weight(oc_, iLayerSize_, parameters_[0], 0));
+  initWgt();
 
   // create biases
   if (biasParameter_.get() != NULL) {
@@ -49,6 +51,22 @@ bool MkldnnFcLayer::init(const LayerMap& layerMap,
   return true;
 }
 
+void MkldnnFcLayer::initWgt() {
+  // The weight_ is transposed from initial paddle weight
+  MatrixPtr paddleWgt = Matrix::create(
+      weight_->getW()->getData(), iLayerSize_, oc_, false, false);
+
+  std::ostringstream ostr;
+  paddleWgt->print(ostr);
+  VLOG(DNN_BASE) << ostr.str();
+
+  // Firstly in mkldnn, the matrix is transposed from initial paddle weight
+  MatrixPtr paddleWgtT;
+  paddleWgt->transpose(paddleWgtT, true);
+
+  weight_->getW()->copyFrom(*paddleWgtT);
+}
+
 void MkldnnFcLayer::reshape() {
   const Argument& input = getInput(0);
   int batchSize = input.getBatchSize();

paddle/gserver/layers/MkldnnFcLayer.h

@@ -41,6 +41,8 @@ public:
   bool init(const LayerMap& layerMap,
             const ParameterMap& parameterMap) override;
 
+  void initWgt();
+
   void reshape();
 
   void forward(PassType passType) override;

paddle/gserver/layers/MkldnnLayer.cpp

@@ -26,7 +26,8 @@ namespace paddle {
 bool MkldnnLayer::init(const LayerMap& layerMap,
                        const ParameterMap& parameterMap) {
   CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
-                          << "Please set WITH_MKLDNN=ON";
+                          << "Please set WITH_MKLDNN=ON "
+                          << "and set use_mkldnn=True";
   // TODO(TJ): deivecId
   return Layer::init(layerMap, parameterMap);
 }

paddle/gserver/tests/CMakeLists.txt

@@ -18,6 +18,15 @@ add_unittest_without_exec(test_LayerGrad
 add_test(NAME test_LayerGrad
     COMMAND test_LayerGrad)
 
+########## test_Mkldnn layers and activations ##########
+if(WITH_MKLDNN)
+    add_unittest_without_exec(test_Mkldnn
+        test_Mkldnn.cpp
+        MkldnnTester.cpp
+        LayerGradUtil.cpp)
+    add_test(NAME test_Mkldnn COMMAND test_Mkldnn)
+endif()
+
 ################ test_CRFLayerGrad ####################
 add_unittest_without_exec(test_CRFLayerGrad
     test_CRFLayerGrad.cpp

paddle/gserver/tests/MkldnnTester.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "MkldnnTester.h"
+#include "paddle/gserver/layers/MkldnnBase.h"
+
+namespace paddle {
+
+// init data layer and test layer of both dnn and reference
+void MkldnnTester::reset(const TestConfig& dnn,
+                         const TestConfig& ref,
+                         size_t batchSize) {
+  const bool trans = false;
+  const bool useGpu = false;
+
+  // clear
+  configs_.clear();
+  layerNames_.clear();
+  dataLayers_.clear();
+  datas_.clear();
+  layerMaps_.clear();
+  parameters_.clear();
+  testLayers_.clear();
+
+  // resize
+  configs_.resize(NUM);
+  layerNames_.resize(NUM);
+  dataLayers_.resize(NUM);
+  datas_.resize(NUM);
+  layerMaps_.resize(NUM);
+  parameters_.resize(NUM);
+  testLayers_.resize(NUM);
+
+  // reset configs and layer names
+  configs_[DNN] = dnn;
+  configs_[REF] = ref;
+  layerNames_[DNN] = "mkldnn";     // the first is mkldnn layer
+  layerNames_[REF] = "reference";  // second is reference layer
+
+  // reset others
+  for (size_t i = 0; i < NUM; ++i) {
+    configs_[i].layerConfig.set_name(layerNames_[i]);
+    initDataLayer(configs_[i],
+                  &(dataLayers_[i]),
+                  &(datas_[i]),
+                  &(layerMaps_[i]),
+                  layerNames_[i],
+                  batchSize,
+                  trans,
+                  useGpu);
+    initTestLayer(
+        configs_[i], &(layerMaps_[i]), &(parameters_[i]), &(testLayers_[i]));
+  }
+  dnnLayer_ = testLayers_[DNN];
+  refLayer_ = testLayers_[REF];
+  EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
+  EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
+
+  setInputImgSize();
+}
+
+void MkldnnTester::setInputImgSize() {
+  for (size_t n = 0; n < dataLayers_.size(); ++n) {
+    for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+      // TODO(TJ): fix me when concat and elewise ready
+      dataLayers_[n][i]->getOutput().setFrameHeight(ih_);
+      dataLayers_[n][i]->getOutput().setFrameWidth(iw_);
+    }
+  }
+}
+
+// init randome parameters of ref, and copy to mkldnn
+void MkldnnTester::randomWgtDatas() {
+  EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
+  for (size_t i = 0; i < parameters_[REF].size(); ++i) {
+    const VectorPtr& dnnValue = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
+    const VectorPtr& refValue = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
+    parameters_[REF][i]->randomize();
+    dnnValue->copyFrom(*refValue);
+
+    VLOG(lvl_) << "Random weight data " << parameters_[DNN][i]->getName();
+    printVector(dnnValue);
+  }
+}
+
+// random botdata of ref layer and copy same to mkldnn
+void MkldnnTester::randomBotDatas() {
+  CHECK_EQ(dataLayers_.size(), NUM);
+  for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
+    dataLayers_[REF][i]->getOutputValue()->randomizeUniform();
+    dataLayers_[DNN][i]->getOutputValue()->copyFrom(
+        *(dataLayers_[REF][i]->getOutputValue()));
+    VLOG(lvl_) << "Input " << i << " data:";
+    printMatrix(dataLayers_[REF][i]->getOutputValue());
+  }
+}
+
+void MkldnnTester::randomTopDiffs() {
+  refLayer_->getOutputGrad()->randomizeUniform();
+  dnnLayer_->getOutputGrad()->copyFrom(*(refLayer_->getOutputGrad()));
+  VLOG(lvl_) << "Random dom Backward Input, TopDiff: ";
+  printMatrix(refLayer_->getOutputGrad());
+}
+
+void MkldnnTester::checkForward() {
+  printTopDatas();
+  double delta = compareMatrix(testLayers_[DNN]->getOutputValue(),
+                               testLayers_[REF]->getOutputValue());
+  VLOG(DNN_TESTS_DETAILS) << "Check Forward";
+  EXPECT_LE(fabs(delta), eps_);
+}
+
+void MkldnnTester::checkBackwardData() {
+  const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
+  for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
+    const MatrixPtr& dnnDiff = dataLayers_[DNN][i]->getOutputGrad();
+    const MatrixPtr& refDiff = dataLayers_[REF][i]->getOutputGrad();
+    VLOG(lvl_) << "Mkldnn Backward Output BotDiff " << i;
+    printMatrix(dnnDiff);
+    VLOG(lvl_) << "Reference Backward Output BotDiff " << i;
+    printMatrix(refDiff);
+
+    double delta = compareMatrix(dnnDiff, refDiff);
+    EXPECT_LE(fabs(delta), eps_);
+    if (isBN) {
+      // the other two inputs in batch norm are for moving mean and var
+      break;
+    }
+  }
+}
+
+void MkldnnTester::checkBackwardWgts() {
+  CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
+  vector<VectorPtr> dnnWgts;  // used to temply save mkldnn weights
+  saveWgt(parameters_[DNN], dnnWgts);
+
+  // TODO(TJ): cvtWgtToPaddle
+  for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
+    const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
+    const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
+    VLOG(lvl_) << "Mkldnn Output weight " << parameters_[DNN][i]->getName();
+    printVector(dnn);
+    VLOG(lvl_) << "Reference Output weight " << parameters_[REF][i]->getName();
+    printVector(ref);
+
+    double delta = compareVector(dnn, ref);
+    EXPECT_LE(fabs(delta), eps_);
+  }
+
+  VLOG(DNN_TESTS_DETAILS) << "Restore dnn weights before comapre";
+  restoreWgt(dnnWgts, parameters_[DNN]);
+}
+
+void MkldnnTester::saveWgt(const vector<ParameterPtr>& from,
+                           vector<VectorPtr>& to) {
+  const bool useGpu = false;
+  to.resize(from.size());
+  for (size_t i = 0; i < to.size(); ++i) {
+    const VectorPtr& wgt = from[i]->getBuf(PARAMETER_VALUE);
+    to[i] = Vector::create(wgt->getSize(), useGpu);
+    to[i]->copyFrom(*wgt);
+  }
+}
+
+void MkldnnTester::restoreWgt(const vector<VectorPtr>& from,
+                              vector<ParameterPtr>& to) {
+  CHECK_EQ(from.size(), to.size());
+  for (size_t i = 0; i < from.size(); ++i) {
+    const VectorPtr& wgt = to[i]->getBuf(PARAMETER_VALUE);
+    wgt->copyFrom(*from[i]);
+  }
+}
+
+// clear parameters grad
+void MkldnnTester::clearWgtDiffs() {
+  for (size_t n = 0; n < parameters_.size(); ++n) {
+    for (size_t i = 0; i < parameters_[n].size(); ++i) {
+      const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
+      if (grad) {
+        grad->zeroMem();
+      }
+    }
+  }
+}
+
+void MkldnnTester::clearBotDiffs() {
+  // dnn and ref
+  for (size_t n = 0; n < dataLayers_.size(); ++n) {
+    // all inputs layers
+    for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+      dataLayers_[n][i]->getOutputGrad()->zeroMem();
+    }
+  }
+}
+
+void MkldnnTester::clearBotDiffs(int n) {
+  CHECK_LT(n, NUM);
+  // all inputs layers
+  for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
+    dataLayers_[n][i]->getOutputGrad()->zeroMem();
+  }
+}
+
+void MkldnnTester::clearTopDatas() {
+  for (size_t i = 0; i < testLayers_.size(); ++i) {
+    testLayers_[i]->getOutputValue()->zeroMem();
+  }
+}
+
+void MkldnnTester::printTopDatas() {
+  if (!log_) {
+    return;
+  }
+
+  for (int n = 0; n < NUM; ++n) {
+    VLOG(lvl_) << testLayers_[n]->getType() << " forward output TopData: ";
+    printMatrix(testLayers_[n]->getOutputValue());
+  }
+}
+
+void MkldnnTester::printMatrix(const MatrixPtr& m) {
+  if (!log_) {
+    return;
+  }
+#ifdef _DEBUG
+  std::ostream str;
+  m->print(str);
+  VLOG(lvl_) << str;
+#endif
+}
+
+void MkldnnTester::printVector(const VectorPtr& v) {
+  if (!log_) {
+    return;
+  }
+
+  CHECK(v);
+  CHECK(v->getData());
+  const real* pd = v->getData();
+  const size_t sz = v->getSize();
+  std::stringstream row;
+  for (size_t i = 0; i < sz; ++i) {
+    row << pd[i] << ", ";
+  }
+  VLOG(lvl_) << row.str();
+}
+
+double MkldnnTester::getDelta(const real* d1,
+                              const real* d2,
+                              size_t len,
+                              const float failRate,
+                              const float thres) {
+  double delta = 0, sum = 0;
+  int failCnt = 0;
+  const double eps = 1e-5;
+  double maxOut = 0;
+  for (size_t i = 0; i < len; ++i) {
+    double ref = fabs(d2[i]);
+    double diff = fabs(d1[i] - d2[i]);
+    delta += diff;
+    sum += ref;
+    if (ref > eps && fabs(d1[i]) > eps && diff / ref > thres) {
+      maxOut = std::max(maxOut, diff / ref);
+      failCnt++;
+    }
+  }
+  EXPECT_TRUE(std::isnormal(sum));
+  EXPECT_FALSE(std::isinf(sum));
+  EXPECT_FALSE(std::isnan(delta));
+  VLOG(DNN_TESTS_MORE) << "reference avg data: " << sum / len
+                       << ", delta: " << delta / sum << ", failCnt:" << failCnt;
+  return (failCnt / (float)len) > failRate ? maxOut : delta / sum;
+}
+
+double MkldnnTester::compareMatrix(const MatrixPtr& m1, const MatrixPtr& m2) {
+  CHECK_EQ(m1->getElementCnt(), m2->getElementCnt());
+  return getDelta(m1->getData(), m2->getData(), m1->getElementCnt());
+}
+
+double MkldnnTester::compareVector(const VectorPtr& v1, const VectorPtr& v2) {
+  CHECK_EQ(v1->getSize(), v2->getSize());
+  return getDelta(v1->getData(), v2->getData(), v1->getSize());
+}
+
+void MkldnnTester::runOnce() {
+  // test forward
+  randomBotDatas();
+  dnnLayer_->forward(PASS_TRAIN);
+  refLayer_->forward(PASS_TRAIN);
+  checkForward();
+
+  // test backward
+  randomTopDiffs();
+  dnnLayer_->backward(nullptr);
+  refLayer_->backward(nullptr);
+  checkBackwardData();
+  checkBackwardWgts();
+
+  // clear buffers
+  // ref code will addto the diff, dnn code will writeto it
+  clearBotDiffs(REF);
+  // below two should be coverd by test layers
+  // clearTopDatas();
+  // clearWgtDiffs();
+}
+
+void MkldnnTester::run(const TestConfig& dnn,
+                       const TestConfig& ref,
+                       size_t batchSize,
+                       size_t inputImgH,
+                       size_t inputImgW,
+                       size_t iter,
+                       float epsilon,
+                       bool log,
+                       int level) {
+  VLOG(DNN_TESTS) << "Test MKLDNN functionality: " << dnn.layerConfig.type()
+                  << " vs " << ref.layerConfig.type();
+  ih_ = inputImgH;
+  iw_ = inputImgW;
+  iter_ = iter;
+  eps_ = epsilon;
+  log_ = log;
+  lvl_ = level;
+
+  // Firstly always set flag false to initial from paddle weight
+  TestConfig first = dnn;
+  //  first.layerConfig.set_init_wgt_from_mkldnn(false);
+
+  // reset and run once
+  reset(first, ref, batchSize);
+  randomWgtDatas();
+  clearWgtDiffs();
+  clearBotDiffs();
+
+  VLOG(DNN_TESTS) << "Check Iteration 0";
+  runOnce();
+
+  // firstly get the flag
+  bool initWgtFromMkldnn = false;
+  // dnn.layerConfig.has_init_wgt_from_mkldnn() &&
+  // dnn.layerConfig.init_wgt_from_mkldnn();
+
+  if (initWgtFromMkldnn) {
+    // after run once the mkldnn weight has been stored in dnnlayer
+    // then save the weigths and restart again
+    vector<VectorPtr> dnnWgts, refWgts;
+    CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
+    saveWgt(parameters_[DNN], dnnWgts);
+    saveWgt(parameters_[REF], refWgts);
+
+    // restart again with flag true
+    reset(dnn, ref, batchSize);
+
+    // restore wgt
+    restoreWgt(dnnWgts, parameters_[DNN]);
+    restoreWgt(refWgts, parameters_[REF]);
+    clearWgtDiffs();
+    clearBotDiffs();
+
+    // at least run once
+    runOnce();
+  }
+
+  for (size_t i = 1; i < iter_; ++i) {
+    VLOG(DNN_TESTS) << "Check Iteration " << i;
+    runOnce();
+  }
+}
+
+}  //  namespace paddle

paddle/gserver/tests/MkldnnTester.h

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <string>
+#include <vector>
+#include "LayerGradUtil.h"
+#include "paddle/gserver/layers/MkldnnBase.h"
+
+namespace paddle {
+
+/**
+ * @brief test the functionality of Mkldnnlayers
+ * refer to paddle original function
+ */
+class MkldnnTester {
+  enum {
+    DNN = 0,
+    REF = 1,
+    NUM = 2,
+  };
+
+protected:
+  std::vector<TestConfig> configs_;
+  vector<string> layerNames_;
+  vector<vector<DataLayerPtr>> dataLayers_;
+  vector<vector<Argument>> datas_;
+  vector<LayerMap> layerMaps_;
+  vector<vector<ParameterPtr>> parameters_;
+  vector<LayerPtr> testLayers_;
+  LayerPtr dnnLayer_, refLayer_;
+
+  /// run some iterations, all the result should pass
+  size_t iter_;
+  /// whether to print out the details
+  bool log_;
+  /// vlog level to print the matrix details datas
+  int lvl_;
+  /// epsilon
+  float eps_;
+  /// input image size, default 1
+  size_t ih_, iw_;
+
+public:
+  explicit MkldnnTester(size_t iter = 3, float epsilon = 1e-4) {
+    iter_ = iter;
+    eps_ = epsilon;
+    log_ = false;
+    lvl_ = DNN_TESTS_MORE;
+  }
+
+  ~MkldnnTester() {}
+
+public:
+  void run(const TestConfig& dnn,
+           const TestConfig& ref,
+           size_t batchSize,
+           size_t inputImgH = 1,
+           size_t inputImgW = 1,
+           size_t iter = 3,
+           float epsilon = 1e-4,
+           bool log = false,
+           int level = DNN_TESTS_MORE);
+  void setLogLevel(int lvl) { lvl_ = lvl; }
+
+private:
+  void reset(const TestConfig& dnn, const TestConfig& ref, size_t batchSize);
+  void setInputImgSize();
+  void runOnce();
+
+  void randomWgtDatas();
+  void randomBotDatas();
+  void randomTopDiffs();
+
+  void checkForward();
+  void checkBackwardData();
+  void checkBackwardWgts();
+
+  void clearWgtDiffs();
+  void clearBotDiffs();
+  void clearBotDiffs(int n);  // clear specific layer
+  void clearTopDatas();
+
+  void printTopDatas();
+  void printMatrix(const MatrixPtr& m);
+  void printVector(const VectorPtr& v);
+
+  void saveWgt(const vector<ParameterPtr>& from, vector<VectorPtr>& to);
+  void restoreWgt(const vector<VectorPtr>& from, vector<ParameterPtr>& to);
+
+  double compareMatrix(const MatrixPtr& m1, const MatrixPtr& m2);
+  double compareVector(const VectorPtr& v1, const VectorPtr& v2);
+
+  /**
+   * Get delta percent
+   * if many(>failRate) wrong(abs(dnn-ref)/abs(ref)>thres) points return the
+   * max(diff/ref)
+   * else return sum(abs(a-b)) / sum(abs(b)) should smaller than eps
+   */
+  double getDelta(const real* d1,
+                  const real* d2,
+                  size_t len,
+                  const float failRate = 1e-3,
+                  const float thres = 0.1);
+};
+
+}  //  namespace paddle

paddle/gserver/tests/test_Mkldnn.cpp

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <gtest/gtest.h>
+#include <string>
+#include <vector>
+#include "MkldnnTester.h"
+#include "ModelConfig.pb.h"
+
+using namespace paddle;  // NOLINT
+
+DECLARE_bool(thread_local_rand_use_global_seed);
+DECLARE_bool(use_gpu);
+DECLARE_bool(use_mkldnn);
+
+struct testFCDesc {
+  int bs;
+  int ic;
+  int oc;
+  int ih, iw;  // oh == ow == 1
+};
+
+void testFcLayer(const testFCDesc& pm) {
+  const std::string compareTypes[] = {"mkldnn_fc", "fc"};
+  TestConfig cfg;
+  cfg.layerConfig.set_type(compareTypes[0]);
+  cfg.layerConfig.set_size(pm.oc);
+  cfg.inputDefs.push_back(
+      {INPUT_DATA,
+       "layer_0",
+       /* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
+       /* size of weight= */ size_t(pm.oc * pm.ic * pm.ih * pm.iw)});
+  cfg.layerConfig.add_inputs();
+
+  MkldnnTester tester;
+  for (auto biasSize : {pm.oc, 0}) {
+    cfg.biasSize = biasSize;
+    TestConfig ref = cfg;
+    ref.layerConfig.set_type(compareTypes[1]);
+    for (auto bs : {pm.bs, 1}) {
+      tester.run(cfg, ref, bs, pm.ih, pm.iw);
+    }
+  }
+}
+
+TEST(MkldnnLayer, fcLayer) {
+  testFcLayer({2, 2, 3, 1, 1}); /*
+   testFcLayer({16, 32, 64, 1, 1});
+   testFcLayer({8, 16, 32, 13, 13});
+   testFcLayer({4, 12, 18, 13, 11});
+   testFcLayer({2, 64, 32, 16, 16});
+   testFcLayer({15, 3, 6, 16, 16});*/
+}
+
+// TODO(TJ): add branch test
+
+int main(int argc, char** argv) {
+  testing::InitGoogleTest(&argc, argv);
+  FLAGS_use_gpu = false;
+  FLAGS_use_mkldnn = true;
+  initMain(argc, argv);
+  FLAGS_thread_local_rand_use_global_seed = true;
+  srand(1);
+  return RUN_ALL_TESTS();
+}