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infershape_pass_unittest.cc 1.7 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/infershape_pass.h"
  20. #include "graph/compute_graph.h"
  21. #include "graph/node.h"
  22. #include "graph/operator.h"
  23. #include "graph/operator_factory.h"
  24. #include "graph/operator_reg.h"
  25. #include "graph_builder_utils.h"
  26. #undef protected
  27. #undef private
  28. using namespace std;
  29. using namespace testing;
  30. using namespace ge;
  31. namespace ge {
  32. class UtestGraphInfershapePass : public testing::Test {
  33. protected:
  34. void SetUp() {}
  35. void TearDown() {}
  36. };
  37. TEST_F(UtestGraphInfershapePass, infershape_pass_failed) {
  38. GeTensorDesc ge_tensor_desc(GeShape({-2, 2, 3, 4}), ge::FORMAT_NCHW, DT_FLOAT16);
  39. string type = "AddN";
  40. auto addn_op_desc = std::make_shared<OpDesc>("AddN", type);
  41. addn_op_desc->AddInputDesc(ge_tensor_desc);
  42. addn_op_desc->AddOutputDesc(ge_tensor_desc);
  43. auto graph = std::make_shared<ComputeGraph>("test");
  44. auto addn_node = std::make_shared<Node>(addn_op_desc, graph);
  45. addn_node->Init();
  46. InferShapePass infershape_pass;
  47. EXPECT_EQ(infershape_pass.Run(addn_node), GE_GRAPH_INFERSHAPE_FAILED);
  48. }
  49. } // namespace ge

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