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prevent_gradient_pass_unittest.cc 2.5 kB

5 years ago
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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 <gtest/gtest.h>
  17. #define protected public
  18. #define private public
  19. #include "graph/passes/prevent_gradient_pass.h"
  20. #include "common/op/ge_op_utils.h"
  21. #include "common/types.h"
  22. #include "graph/anchor.h"
  23. #include "graph/attr_value.h"
  24. #include "graph/compute_graph.h"
  25. #include "graph/op_desc.h"
  26. #include "graph/utils/attr_utils.h"
  27. #include "graph/utils/graph_utils.h"
  28. #include "graph/utils/op_desc_utils.h"
  29. #include "graph/utils/tensor_utils.h"
  30. #include "inc/pass_manager.h"
  31. #undef protected
  32. #undef private
  33. using namespace std;
  34. using namespace testing;
  35. using namespace ge;
  36. using namespace domi;
  37. class UTEST_prevent_gradient_pass : public Test {
  38. protected:
  39. NodePtr AddNode(ComputeGraphPtr graph, const string &name, const string &type, int32_t in_anchors_num = 1,
  40. int32_t out_anchors_num = 1) {
  41. GeTensorDesc tensor_desc;
  42. OpDescPtr opdesc = make_shared<OpDesc>(name, type);
  43. for (int32_t i = 0; i < in_anchors_num; i++) {
  44. opdesc->AddInputDesc(tensor_desc);
  45. }
  46. for (int32_t i = 0; i < out_anchors_num; i++) {
  47. opdesc->AddOutputDesc(tensor_desc);
  48. }
  49. NodePtr node = graph->AddNode(opdesc);
  50. return node;
  51. }
  52. };
  53. TEST_F(UTEST_prevent_gradient_pass, succ) {
  54. ComputeGraphPtr graph = std::make_shared<ComputeGraph>("test");
  55. NodePtr node = AddNode(graph, "PreventGradient", PREVENTGRADIENT);
  56. NodePtr reduce_min_node = AddNode(graph, "reduceMin", REDUCEMIN);
  57. GraphUtils::AddEdge(node->GetOutDataAnchor(0), reduce_min_node->GetInDataAnchor(0));
  58. PreventGradientPass pass;
  59. Status status = pass.Run(node);
  60. EXPECT_EQ(status, domi::SUCCESS);
  61. NodePtr found_node = graph->FindNode("PreventGradient");
  62. EXPECT_EQ(found_node, nullptr);
  63. status = pass.Run(reduce_min_node);
  64. EXPECT_EQ(status, domi::SUCCESS);
  65. string type2 = "FrameworkOp";
  66. node->GetOpDesc()->SetType(type2);
  67. status = pass.Run(node);
  68. // EXPECT_EQ(ge::SUCCESS, status);
  69. }

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