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.h 1.5 kB

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455
  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. /** @defgroup FUSION_PASS_GROUP Fusion Pass Interface */
  17. #ifndef INC_REGISTER_GRAPH_OPTIMIZER_PASS_H_
  18. #define INC_REGISTER_GRAPH_OPTIMIZER_PASS_H_
  19. #include "graph/compute_graph.h"
  20. #include "register/graph_optimizer/graph_optimize_register_error_codes.h"
  21. namespace fe {
  22. /** fusion pass
  23. * @ingroup GRAPH_PASS_GROUP
  24. * network level pass
  25. */
  26. template <typename T>
  27. class Pass {
  28. public:
  29. virtual ~Pass() {}
  30. /** execute pass
  31. *
  32. * @param [in] graph, the graph waiting for pass level optimization
  33. * @return SUCCESS, successfully optimized the graph by the pass
  34. * @return NOT_CHANGED, the graph did not change
  35. * @return FAILED, fail to modify graph
  36. */
  37. virtual Status Run(ge::ComputeGraph &graph) = 0;
  38. void SetName(const string &name) { name_ = name; }
  39. string GetName() { return name_; }
  40. private:
  41. string name_;
  42. };
  43. } // namespace fe
  44. #endif // INC_REGISTER_GRAPH_OPTIMIZER_PASS_H_

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