You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

pass_manager_unittest.cc 3.2 kB

5 years ago
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106
  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 <gtest/gtest.h>
  17. #define protected public
  18. #define private public
  19. #include "inc/pass_manager.h"
  20. #include "common/debug/log.h"
  21. #include "common/debug/memory_dumper.h"
  22. #include "common/op/attr_value_util.h"
  23. #include "common/op/ge_op_utils.h"
  24. #include "common/types.h"
  25. #include "graph/debug/ge_attr_define.h"
  26. #include "graph/types.h"
  27. #include "graph/utils/attr_utils.h"
  28. #include "graph/utils/graph_utils.h"
  29. #include "graph/utils/op_desc_utils.h"
  30. #include "graph/utils/tensor_utils.h"
  31. #undef protected
  32. #undef private
  33. using namespace testing;
  34. using namespace domi;
  35. using namespace ge;
  36. class SuccessGraphPass : public GraphPass {
  37. Status Run(ComputeGraphPtr graph) { return domi::SUCCESS; }
  38. };
  39. class NotChangedGraphPass : public GraphPass {
  40. Status Run(ComputeGraphPtr graph) { return domi::NOT_CHANGED; }
  41. };
  42. class ErrorGraphPass : public GraphPass {
  43. Status Run(ComputeGraphPtr graph) { return domi::FAILED; }
  44. };
  45. class UTEST_graph_passes_pass_manager_pass : public testing::Test {
  46. protected:
  47. void SetUp() {}
  48. void TearDown() {}
  49. };
  50. NodePtr AddNode(ComputeGraphPtr graph) {
  51. GeTensorDesc tensor_desc(GeShape({1}), FORMAT_NHWC, DT_INT32);
  52. OpDescPtr opdesc = make_shared<OpDesc>("test", "Add");
  53. opdesc->AddInputDesc(tensor_desc);
  54. opdesc->AddOutputDesc(tensor_desc);
  55. NodePtr node = graph->AddNode(opdesc);
  56. return node;
  57. }
  58. ComputeGraphPtr CreatePadGraph() {
  59. ComputeGraphPtr graph = std::make_shared<ComputeGraph>("test");
  60. return graph;
  61. }
  62. TEST_F(UTEST_graph_passes_pass_manager_pass, all_pass_success) {
  63. PassManager manager;
  64. manager.AddPass(new SuccessGraphPass);
  65. EXPECT_EQ(manager.GraphPasses().size(), 1);
  66. ComputeGraphPtr graph = CreatePadGraph();
  67. Status status = manager.Run(graph);
  68. EXPECT_EQ(domi::SUCCESS, status);
  69. }
  70. TEST_F(UTEST_graph_passes_pass_manager_pass, graph_pass_success) {
  71. ComputeGraphPtr graph = CreatePadGraph();
  72. SuccessGraphPass pass;
  73. vector<GraphPass *> passes = {&pass};
  74. Status status = PassManager::Run(graph, passes);
  75. EXPECT_EQ(domi::SUCCESS, status);
  76. }
  77. TEST_F(UTEST_graph_passes_pass_manager_pass, graph_pass_not_changed) {
  78. ComputeGraphPtr graph = CreatePadGraph();
  79. NotChangedGraphPass pass;
  80. vector<GraphPass *> passes = {&pass};
  81. Status status = PassManager::Run(graph, passes);
  82. EXPECT_EQ(domi::NOT_CHANGED, status);
  83. }
  84. TEST_F(UTEST_graph_passes_pass_manager_pass, graph_pass_error) {
  85. ComputeGraphPtr graph = CreatePadGraph();
  86. ErrorGraphPass pass;
  87. vector<GraphPass *> passes = {&pass};
  88. Status status = PassManager::Run(graph, passes);
  89. EXPECT_EQ(domi::FAILED, status);
  90. }

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