cpu_vec_test.cc 6.8 KB
Newer Older
T
tensor-tang 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

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 <sys/time.h>
#include <cmath>
T
tensor-tang 已提交
17
#include <cstring>
T
tensor-tang 已提交
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"

#include "paddle/fluid/operators/math/cpu_vec.h"

inline double GetCurrentUS() {
  struct timeval time;
  gettimeofday(&time, NULL);
  return 1e+6 * time.tv_sec + time.tv_usec;
}
constexpr int repeat = 1000;

template <typename T>
inline T _sigmoid(T x) {
  const T min = SIGMOID_THRESHOLD_MIN;
  const T max = SIGMOID_THRESHOLD_MAX;
  T tmp = (x < min) ? min : ((x > max) ? max : x);
T
tensor-tang 已提交
37
  return static_cast<T>(1) / (static_cast<T>(1) + std::exp(-tmp));
T
tensor-tang 已提交
38 39 40 41
}

template <typename T>
inline T _tanh(T x) {
T
tensor-tang 已提交
42 43
  return static_cast<T>(2) * _sigmoid<T>(static_cast<T>(2) * x) -
         static_cast<T>(1);
T
tensor-tang 已提交
44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
}

template <typename T>
void ref_sigmoid(const int n, const T* x, T* y) {
  for (int i = 0; i < n; ++i) {
    y[i] = _sigmoid(x[i]);
  }
}

template <typename T>
void ref_tanh(const int n, const T* x, T* y) {
  for (int i = 0; i < n; ++i) {
    y[i] = _tanh(x[i]);
  }
}
template <typename T>
void ref_relu(const int n, const T* x, T* y) {
  for (int i = 0; i < n; ++i) {
    y[i] = x[i] > 0 ? x[i] : 0;
  }
}

template <typename T>
void RandomVec(const int n, T* a) {
  static unsigned int seed = 100;
  std::mt19937 rng(seed++);
  std::uniform_real_distribution<double> uniform_dist(0, 1);
  const T lower = static_cast<T>(-20.f);
T
tensor-tang 已提交
72
  const T upper = static_cast<T>(20.f);
T
tensor-tang 已提交
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
  for (int i = 0; i < n; ++i) {
    a[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
  }
}

template <typename T>
void TestAndBench(const int n, std::function<void(const int, const T*, T*)> tgt,
                  std::function<void(const int, const T*, T*)> ref) {
  std::vector<T> x(n);
  std::vector<T> ytgt(n), yref(n);
  RandomVec<T>(n, x.data());

  const T* x_data = x.data();
  T* ytgt_data = ytgt.data();
  T* yref_data = yref.data();
  auto st = GetCurrentUS();
  for (int i = 0; i < repeat; ++i) {
    tgt(n, x_data, ytgt_data);
  }
  auto mt = GetCurrentUS();
  for (int i = 0; i < repeat; ++i) {
    ref(n, x_data, yref_data);
  }
  auto et = GetCurrentUS();

  VLOG(3) << "Vec size " << n << ": refer takes: " << (et - mt) / repeat
          << " us, tgt takes: " << (mt - st) / repeat;
  for (int i = 0; i < n; ++i) {
    EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
  }
}

TEST(CpuVecTest, sigmoid) {
  namespace jit = paddle::platform::jit;
  using namespace paddle::operators::math;  // NOLINT
108
  for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
T
tensor-tang 已提交
109 110 111 112 113 114 115 116 117 118 119 120
    TestAndBench<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
    TestAndBench<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
    TestAndBench<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
    TestAndBench<float>(sz, vec_sigmoid<float, jit::avx512_common>,
                        ref_sigmoid<float>);
  }
  TestAndBench<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}

TEST(CpuVecTest, tanh) {
  namespace jit = paddle::platform::jit;
  using namespace paddle::operators::math;  // NOLINT
121
  for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
T
tensor-tang 已提交
122 123 124 125 126 127 128 129 130 131 132 133
    TestAndBench<float>(sz, vec_tanh<float>, ref_tanh<float>);
    TestAndBench<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
    TestAndBench<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
    TestAndBench<float>(sz, vec_tanh<float, jit::avx512_common>,
                        ref_tanh<float>);
  }
  TestAndBench<double>(30, vec_tanh<double>, ref_tanh<double>);
}

TEST(CpuVecTest, relu) {
  namespace jit = paddle::platform::jit;
  using namespace paddle::operators::math;  // NOLINT
134
  for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
T
tensor-tang 已提交
135 136 137 138 139 140 141 142
    TestAndBench<float>(sz, vec_relu<float>, ref_relu<float>);
    TestAndBench<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
    TestAndBench<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
    TestAndBench<float>(sz, vec_relu<float, jit::avx512_common>,
                        ref_relu<float>);
  }
  TestAndBench<double>(30, vec_relu<double>, ref_relu<double>);
}
T
tensor-tang 已提交
143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202

template <typename T>
void TestInplace(const int n, std::function<void(const int, const T*, T*)> tgt,
                 std::function<void(const int, const T*, T*)> ref) {
  std::vector<T> x(n);
  std::vector<T> ytgt(n), yref(n);
  RandomVec<T>(n, x.data());

  const T* x_data = x.data();
  T* yref_data = yref.data();
  T* ytgt_data = ytgt.data();
  std::memcpy(yref_data, x_data, sizeof(T) * n);
  std::memcpy(ytgt_data, x_data, sizeof(T) * n);

  ref(n, yref_data, yref_data);
  tgt(n, ytgt_data, ytgt_data);

  for (int i = 0; i < n; ++i) {
    EXPECT_NEAR(ytgt_data[i], yref_data[i], 1e-3);
  }
}

TEST(CpuVecTest, inplace_sigmoid) {
  namespace jit = paddle::platform::jit;
  using namespace paddle::operators::math;  // NOLINT
  for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
    TestInplace<float>(sz, vec_sigmoid<float>, ref_sigmoid<float>);
    TestInplace<float>(sz, vec_sigmoid<float, jit::avx>, ref_sigmoid<float>);
    TestInplace<float>(sz, vec_sigmoid<float, jit::avx2>, ref_sigmoid<float>);
    TestInplace<float>(sz, vec_sigmoid<float, jit::avx512_common>,
                       ref_sigmoid<float>);
  }
  TestInplace<double>(30, vec_sigmoid<double>, ref_sigmoid<double>);
}

TEST(CpuVecTest, inplace_tanh) {
  namespace jit = paddle::platform::jit;
  using namespace paddle::operators::math;  // NOLINT
  for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
    TestInplace<float>(sz, vec_tanh<float>, ref_tanh<float>);
    TestInplace<float>(sz, vec_tanh<float, jit::avx>, ref_tanh<float>);
    TestInplace<float>(sz, vec_tanh<float, jit::avx2>, ref_tanh<float>);
    TestInplace<float>(sz, vec_tanh<float, jit::avx512_common>,
                       ref_tanh<float>);
  }
  TestInplace<double>(30, vec_tanh<double>, ref_tanh<double>);
}

TEST(CpuVecTest, inplace_relu) {
  namespace jit = paddle::platform::jit;
  using namespace paddle::operators::math;  // NOLINT
  for (auto sz : {1, 2, 15, 16, 30, 32, 128, 200, 512}) {
    TestInplace<float>(sz, vec_relu<float>, ref_relu<float>);
    TestInplace<float>(sz, vec_relu<float, jit::avx>, ref_relu<float>);
    TestInplace<float>(sz, vec_relu<float, jit::avx2>, ref_relu<float>);
    TestInplace<float>(sz, vec_relu<float, jit::avx512_common>,
                       ref_relu<float>);
  }
  TestInplace<double>(30, vec_relu<double>, ref_relu<double>);
}