remove legacy testing code

paddle/testing/TestMain.cpp

-/* Copyright (c) 2016 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 "paddle/legacy/utils/Util.h"
-
-int main(int argc, char** argv) {
-  testing::InitGoogleTest(&argc, argv);
-  paddle::initMain(argc, argv);
-  return RUN_ALL_TESTS();
-}

paddle/testing/TestUtil.cpp

-/* Copyright (c) 2016 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 "TestUtil.h"
-#include <gflags/gflags.h>
-#include "paddle/legacy/math/SparseMatrix.h"
-
-DEFINE_int32(fixed_seq_length, 0, "Produce some sequence of fixed length");
-
-namespace paddle {
-
-std::string randStr(const int len) {
-  std::string str =
-      "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
-  std::string s = "";
-  for (int i = 0; i < len; ++i) s += str[(rand() % 62)];  // NOLINT
-  return s;
-}
-
-MatrixPtr makeRandomSparseMatrix(size_t height,
-                                 size_t width,
-                                 bool withValue,
-                                 bool useGpu,
-                                 bool equalNnzPerSample) {
-#ifndef PADDLE_MOBILE_INFERENCE
-  std::vector<int64_t> ids(height);
-  std::vector<int64_t> indices(height + 1);
-  indices[0] = 0;
-
-  std::function<size_t()> randomer = [] { return uniformRandom(10); };
-  if (equalNnzPerSample) {
-    size_t n = 0;
-    do {
-      n = uniformRandom(10);
-    } while (!n);
-    randomer = [=] { return n; };
-  }
-  for (size_t i = 0; i < height; ++i) {
-    indices[i + 1] = indices[i] + std::min(randomer(), width);
-    ids[i] = i;
-  }
-
-  if (!withValue) {
-    std::vector<sparse_non_value_t> data;
-    data.resize(indices[height] - indices[0]);
-    for (size_t i = 0; i < data.size(); ++i) {
-      data[i].col = uniformRandom(width);
-    }
-    auto mat = Matrix::createSparseMatrix(
-        height, width, data.size(), NO_VALUE, SPARSE_CSR, false, useGpu);
-    if (useGpu) {
-      std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
-          ids.data(), indices.data(), data.data(), HPPL_STREAM_DEFAULT);
-    } else {
-      std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
-          ids.data(), indices.data(), data.data());
-    }
-    return mat;
-  } else {
-    std::vector<sparse_float_value_t> data;
-    data.resize(indices[height] - indices[0]);
-    for (size_t i = 0; i < data.size(); ++i) {
-      data[i].col = uniformRandom(width);
-      data[i].value = rand() / static_cast<float>(RAND_MAX);  // NOLINT
-    }
-    auto mat = Matrix::createSparseMatrix(
-        height, width, data.size(), FLOAT_VALUE, SPARSE_CSR, false, useGpu);
-    if (useGpu) {
-      std::dynamic_pointer_cast<GpuSparseMatrix>(mat)->copyFrom(
-          ids.data(), indices.data(), data.data(), HPPL_STREAM_DEFAULT);
-    } else {
-      std::dynamic_pointer_cast<CpuSparseMatrix>(mat)->copyFrom(
-          ids.data(), indices.data(), data.data());
-    }
-    return mat;
-  }
-#endif
-  return nullptr;
-}
-
-void generateSequenceStartPositions(size_t batchSize,
-                                    IVectorPtr& sequenceStartPositions) {
-  ICpuGpuVectorPtr gpuCpuVec;
-  generateSequenceStartPositions(batchSize, gpuCpuVec);
-  sequenceStartPositions = gpuCpuVec->getMutableVector(false);
-}
-
-void generateSequenceStartPositions(size_t batchSize,
-                                    ICpuGpuVectorPtr& sequenceStartPositions) {
-  int numSeqs;
-  if (FLAGS_fixed_seq_length != 0) {
-    numSeqs = std::ceil((float)batchSize / (float)FLAGS_fixed_seq_length);
-  } else {
-    numSeqs = batchSize / 10 + 1;
-  }
-  sequenceStartPositions =
-      ICpuGpuVector::create(numSeqs + 1, /* useGpu= */ false);
-  int* buf = sequenceStartPositions->getMutableData(false);
-  int64_t pos = 0;
-  int len = FLAGS_fixed_seq_length;
-  int maxLen = 2 * batchSize / numSeqs;
-  for (int i = 0; i < numSeqs; ++i) {
-    if (FLAGS_fixed_seq_length == 0) {
-      len = uniformRandom(
-                std::min<int64_t>(maxLen, batchSize - pos - numSeqs + i)) +
-            1;
-    }
-    buf[i] = pos;
-    pos += len;
-    VLOG(1) << " len=" << len;
-  }
-  buf[numSeqs] = batchSize;
-}
-
-void generateSubSequenceStartPositions(
-    const ICpuGpuVectorPtr& sequenceStartPositions,
-    ICpuGpuVectorPtr& subSequenceStartPositions) {
-  int numSeqs = sequenceStartPositions->getSize() - 1;
-  const int* buf = sequenceStartPositions->getData(false);
-  int numOnes = 0;
-  for (int i = 0; i < numSeqs; ++i) {
-    if (buf[i + 1] - buf[i] == 1) {
-      ++numOnes;
-    }
-  }
-  // each seq has two sub-seq except length 1
-  int numSubSeqs = numSeqs * 2 - numOnes;
-  subSequenceStartPositions =
-      ICpuGpuVector::create(numSubSeqs + 1, /* useGpu= */ false);
-  int* subBuf = subSequenceStartPositions->getMutableData(false);
-  int j = 0;
-  for (int i = 0; i < numSeqs; ++i) {
-    if (buf[i + 1] - buf[i] == 1) {
-      subBuf[j++] = buf[i];
-    } else {
-      int len = uniformRandom(buf[i + 1] - buf[i] - 1) + 1;
-      subBuf[j++] = buf[i];
-      subBuf[j++] = buf[i] + len;
-    }
-  }
-  subBuf[j] = buf[numSeqs];
-}
-
-void generateMDimSequenceData(const IVectorPtr& sequenceStartPositions,
-                              IVectorPtr& cpuSequenceDims) {
-  /* generate sequences with 2 dims */
-  int numSeqs = sequenceStartPositions->getSize() - 1;
-  int numDims = 2;
-
-  cpuSequenceDims = IVector::create(numSeqs * numDims, /* useGpu= */ false);
-  int* bufStarts = sequenceStartPositions->getData();
-  int* bufDims = cpuSequenceDims->getData();
-
-  for (int i = 0; i < numSeqs; i++) {
-    int len = bufStarts[i + 1] - bufStarts[i];
-    /* get width and height randomly */
-    std::vector<int> dimVec;
-    for (int j = 0; j < len; j++) {
-      if (len % (j + 1) == 0) {
-        dimVec.push_back(1);
-      }
-    }
-    int idx = rand() % dimVec.size();  // NOLINT use rand_r
-    bufDims[i * numDims] = dimVec[idx];
-    bufDims[i * numDims + 1] = len / dimVec[idx];
-  }
-}
-
-void generateMDimSequenceData(const ICpuGpuVectorPtr& sequenceStartPositions,
-                              IVectorPtr& cpuSequenceDims) {
-  /* generate sequences with 2 dims */
-  int numSeqs = sequenceStartPositions->getSize() - 1;
-  int numDims = 2;
-
-  cpuSequenceDims = IVector::create(numSeqs * numDims, /* useGpu= */ false);
-  const int* bufStarts = sequenceStartPositions->getData(false);
-  int* bufDims = cpuSequenceDims->getData();
-
-  for (int i = 0; i < numSeqs; i++) {
-    int len = bufStarts[i + 1] - bufStarts[i];
-    /* get width and height randomly */
-    std::vector<int> dimVec;
-    for (int j = 0; j < len; j++) {
-      if (len % (j + 1) == 0) {
-        dimVec.push_back(1);
-      }
-    }
-    int idx = rand() % dimVec.size();  // NOLINT use rand_r
-    bufDims[i * numDims] = dimVec[idx];
-    bufDims[i * numDims + 1] = len / dimVec[idx];
-  }
-}
-
-void checkMatrixEqual(const MatrixPtr& a, const MatrixPtr& b) {
-  EXPECT_EQ(a->getWidth(), b->getWidth());
-  EXPECT_EQ(a->getHeight(), b->getHeight());
-  EXPECT_EQ(a->isTransposed(), b->isTransposed());
-  for (size_t r = 0; r < a->getHeight(); ++r) {
-    for (size_t c = 0; c < a->getWidth(); ++c) {
-      EXPECT_FLOAT_EQ(a->getElement(r, c), b->getElement(r, c));
-    }
-  }
-}
-
-void checkVectorEqual(const IVectorPtr& a, const IVectorPtr& b) {
-  EXPECT_EQ(a->getSize(), b->getSize());
-  for (size_t r = 0; r < a->getSize(); ++r) {
-    EXPECT_FLOAT_EQ(a->get(r), b->get(r));
-  }
-}
-}  // namespace paddle

paddle/testing/TestUtil.h

-/* Copyright (c) 2016 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 <gtest/gtest.h>
-#include "paddle/legacy/math/Matrix.h"
-
-namespace paddle {
-
-std::string randStr(const int len);
-
-inline int uniformRandom(int n) { return n == 0 ? 0 : rand() % n; }
-
-inline bool approximatelyEqual(float a, float b, float epsilon) {
-  return fabs(a - b) <= ((fabs(a) < fabs(b) ? fabs(b) : fabs(a)) * epsilon);
-}
-
-MatrixPtr makeRandomSparseMatrix(size_t height,
-                                 size_t width,
-                                 bool withValue,
-                                 bool useGpu,
-                                 bool equalNnzPerSample = false);
-
-/**
- * @brief generate sequenceStartPositions for INPUT_SEQUENCE_DATA,
- *        INPUT_HASSUB_SEQUENCE_DATA and INPUT_SEQUENCE_LABEL
- *
- * @param batchSize                      batchSize
- *        sequenceStartPositions[out] generation output
- */
-void generateSequenceStartPositions(size_t batchSize,
-                                    IVectorPtr& sequenceStartPositions);
-
-void generateSequenceStartPositions(size_t batchSize,
-                                    ICpuGpuVectorPtr& sequenceStartPositions);
-
-/**
- * @brief generate subSequenceStartPositions for INPUT_HASSUB_SEQUENCE_DATA
- *        according to sequenceStartPositions
- *
- * @param sequenceStartPositions[in]     input
- *        subSequenceStartPositions[out] generation output
- */
-void generateSubSequenceStartPositions(const IVectorPtr& sequenceStartPositions,
-                                       IVectorPtr& subSequenceStartPositions);
-
-void generateSubSequenceStartPositions(
-    const ICpuGpuVectorPtr& sequenceStartPositions,
-    ICpuGpuVectorPtr& subSequenceStartPositions);
-
-/**
- * @brief generate cpuSequenceDims for INPUT_SEQUENCE_MDIM_DATA according to
- *        sequenceStartPositions
- *
- * @param sequenceStartPositions[in]     input
- *        cpuSequenceDims[out]              generation output
- */
-void generateMDimSequenceData(const IVectorPtr& sequenceStartPositions,
-                              IVectorPtr& cpuSequenceDims);
-void generateMDimSequenceData(const ICpuGpuVectorPtr& sequenceStartPositions,
-                              IVectorPtr& cpuSequenceDims);
-
-void checkMatrixEqual(const MatrixPtr& a, const MatrixPtr& b);
-
-void checkVectorEqual(const IVectorPtr& a, const IVectorPtr& b);
-}  // namespace paddle