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ge_profiling_manager_unittest.cc 4.8 kB

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  1. /**
  2. * Copyright 2019-2020 Huawei Technologies Co., Ltd
  3. *
  4. * Licensed under the Apache License, Version 2.0 (the "License");
  5. * you may not use this file except in compliance with the License.
  6. * You may obtain a copy of the License at
  7. *
  8. * http://www.apache.org/licenses/LICENSE-2.0
  9. *
  10. * Unless required by applicable law or agreed to in writing, software
  11. * distributed under the License is distributed on an "AS IS" BASIS,
  12. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13. * See the License for the specific language governing permissions and
  14. * limitations under the License.
  15. */
  16. #include <bits/stdc++.h>
  17. #include <dirent.h>
  18. #include <gtest/gtest.h>
  19. #include <fstream>
  20. #include <map>
  21. #include <string>
  22. #define protected public
  23. #define private public
  24. #include "common/profiling/profiling_manager.h"
  25. #include "graph/ge_local_context.h"
  26. #include "inc/framework/common/profiling/ge_profiling.h"
  27. #undef protected
  28. #undef private
  29. using namespace ge;
  30. using namespace std;
  31. class UtestGeProfilinganager : public testing::Test {
  32. protected:
  33. void SetUp() override {}
  34. void TearDown() override {}
  35. };
  36. int32_t ReporterCallback(uint32_t moduleId, uint32_t type, void *data, uint32_t len) {
  37. return -1;
  38. }
  39. TEST_F(UtestGeProfilinganager, init_success) {
  40. setenv("PROFILING_MODE", "true", true);
  41. Options options;
  42. options.device_id = 0;
  43. options.job_id = "0";
  44. options.profiling_mode = "1";
  45. options.profiling_options = R"({"result_path":"/data/profiling","training_trace":"on","task_trace":"on","aicpu_trace":"on","fp_point":"Data_0","bp_point":"addn","ai_core_metrics":"ResourceConflictRatio"})";
  46. struct MsprofGeOptions prof_conf = {{ 0 }};
  47. Status ret = ProfilingManager::Instance().InitFromOptions(options, prof_conf);
  48. EXPECT_EQ(ret, ge::SUCCESS);
  49. }
  50. TEST_F(UtestGeProfilinganager, ParseOptions) {
  51. setenv("PROFILING_MODE", "true", true);
  52. Options options;
  53. options.device_id = 0;
  54. options.job_id = "0";
  55. options.profiling_mode = "1";
  56. options.profiling_options = R"({"result_path":"/data/profiling","training_trace":"on","task_trace":"on","aicpu_trace":"on","fp_point":"Data_0","bp_point":"addn","ai_core_metrics":"ResourceConflictRatio"})";
  57. struct MsprofGeOptions prof_conf = {{ 0 }};
  58. Status ret = ProfilingManager::Instance().ParseOptions(options.profiling_options);
  59. EXPECT_EQ(ret, ge::SUCCESS);
  60. EXPECT_EQ(ProfilingManager::Instance().is_training_trace_, true);
  61. EXPECT_EQ(ProfilingManager::Instance().fp_point_, "Data_0");
  62. EXPECT_EQ(ProfilingManager::Instance().bp_point_, "addn");
  63. }
  64. TEST_F(UtestGeProfilinganager, plungin_init_) {
  65. ProfilingManager::Instance().prof_cb_.msprofReporterCallback = ReporterCallback;
  66. Status ret = ProfilingManager::Instance().PluginInit();
  67. EXPECT_EQ(ret, INTERNAL_ERROR);
  68. ProfilingManager::Instance().prof_cb_.msprofReporterCallback = nullptr;
  69. }
  70. TEST_F(UtestGeProfilinganager, report_data_) {
  71. std::string data = "ge is better than tensorflow.";
  72. std::string tag_name = "fmk";
  73. ProfilingManager::Instance().ReportData(0, data, tag_name);
  74. }
  75. TEST_F(UtestGeProfilinganager, get_fp_bp_point_) {
  76. map<std::string, string> options_map = {
  77. {OPTION_EXEC_PROFILING_OPTIONS,
  78. R"({"result_path":"/data/profiling","training_trace":"on","task_trace":"on","aicpu_trace":"on","fp_point":"Data_0","bp_point":"addn","ai_core_metrics":"ResourceConflictRatio"})"}};
  79. GEThreadLocalContext &context = GetThreadLocalContext();
  80. context.SetGraphOption(options_map);
  81. std::string fp_point;
  82. std::string bp_point;
  83. ProfilingManager::Instance().GetFpBpPoint(fp_point, bp_point);
  84. EXPECT_EQ(fp_point, "Data_0");
  85. EXPECT_EQ(bp_point, "addn");
  86. }
  87. TEST_F(UtestGeProfilinganager, get_fp_bp_point_empty) {
  88. // fp bp empty
  89. map<std::string, string> options_map = {
  90. { OPTION_EXEC_PROFILING_OPTIONS,
  91. R"({"result_path":"/data/profiling","training_trace":"on","task_trace":"on","aicpu_trace":"on","ai_core_metrics":"ResourceConflictRatio"})"}};
  92. GEThreadLocalContext &context = GetThreadLocalContext();
  93. context.SetGraphOption(options_map);
  94. std::string fp_point = "fp";
  95. std::string bp_point = "bp";
  96. ProfilingManager::Instance().bp_point_ = "";
  97. ProfilingManager::Instance().fp_point_ = "";
  98. ProfilingManager::Instance().GetFpBpPoint(fp_point, bp_point);
  99. EXPECT_EQ(fp_point, "");
  100. EXPECT_EQ(bp_point, "");
  101. }
  102. TEST_F(UtestGeProfilinganager, set_step_info_success) {
  103. uint64_t index_id = 0;
  104. auto stream = (rtStream_t)0x1;
  105. Status ret = ProfSetStepInfo(index_id, 0, stream);
  106. EXPECT_EQ(ret, ge::SUCCESS);
  107. ret = ProfSetStepInfo(index_id, 1, stream);
  108. EXPECT_EQ(ret, ge::SUCCESS);
  109. }
  110. TEST_F(UtestGeProfilinganager, set_step_info_failed) {
  111. uint64_t index_id = 0;
  112. auto stream = (rtStream_t)0x1;
  113. Status ret = ProfSetStepInfo(index_id, 1, stream);
  114. EXPECT_EQ(ret, ge::FAILED);
  115. }

图引擎模块(GE)是MindSpore的一个子模块,其代码由C++实现,位于前端模块ME和底层硬件之间,起到承接作用。图引擎模块以ME下发的图作为输入,然后进行一系列的深度图优化操作,最后输出一张可以在底层硬件上高效运行的图。GE针对昇腾AI处理器的硬件结构特点,做了特定的优化工作,以此来充分发挥出昇腾AI处理器的强大算力。在进行模型训练/推理时,GE会被自动调用而用户并不感知。GE主要由GE API和GE Core两部分组成,详细的架构图如下所示