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runtime_inference_context.h 1.8 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. #ifndef INC_GRAPH_RUNTIME_INFERENCE_CONTEXT_H_
  17. #define INC_GRAPH_RUNTIME_INFERENCE_CONTEXT_H_
  18. #include <map>
  19. #include <memory>
  20. #include <mutex>
  21. #include <vector>
  22. #include "external/graph/ge_error_codes.h"
  23. #include "external/graph/tensor.h"
  24. #include "ge_attr_value.h"
  25. namespace ge {
  26. class GE_FUNC_DEV_VISIBILITY GE_FUNC_HOST_VISIBILITY RuntimeInferenceContext {
  27. public:
  28. static graphStatus GetContext(const std::string &context_id, RuntimeInferenceContext **ctx);
  29. static graphStatus CreateContext(const std::string &context_id);
  30. static void DestroyContext(const std::string &context_id);
  31. graphStatus SetTensor(int64_t node_id, int output_id, Tensor &&tensor);
  32. graphStatus GetTensor(int64_t node_id, int output_id, GeTensorPtr &tensor);
  33. graphStatus GetTensor(int64_t node_id, int output_id, Tensor &tensor);
  34. private:
  35. std::map<int64_t, std::vector<Tensor>> tensors_;
  36. std::map<int64_t, std::vector<GeTensorPtr>> ge_tensors_;
  37. std::mutex mu_;
  38. static std::map<std::string, std::unique_ptr<RuntimeInferenceContext>> contexts_;
  39. static std::mutex ctx_mu_;
  40. };
  41. } // namespace ge
  42. #endif // INC_GRAPH_RUNTIME_INFERENCE_CONTEXT_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