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,  // MKLDNN layer
    REF = 1,  // Reference layer
    NUM = 2,  // Number of total
  };

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_ = MKLDNN_ALL;
  }

  ~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 = MKLDNN_ALL);
  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))
   * The return value should smaller than eps when passing.
   */
  double getDelta(const real* d1,
                  const real* d2,
                  size_t len,
                  const float failRate = 1e-3,
                  const float thres = 0.1);
};

}  //  namespace paddle