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rt_context_util.h 1.7 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 GE_GRAPH_MANAGER_UTIL_RT_CONTEXT_UTIL_H_
  17. #define GE_GRAPH_MANAGER_UTIL_RT_CONTEXT_UTIL_H_
  18. #include <vector>
  19. #include <map>
  20. #include <mutex>
  21. #include "runtime/context.h"
  22. namespace ge {
  23. class RtContextUtil {
  24. public:
  25. static RtContextUtil &GetInstance() {
  26. static RtContextUtil instance;
  27. return instance;
  28. }
  29. void AddRtContext(uint64_t session_id, rtContext_t context);
  30. void AddRtContext(uint64_t session_id, uint32_t graph_id, rtContext_t context);
  31. void DestroyRtContexts(uint64_t session_id);
  32. void DestroyRtContexts(uint64_t session_id, uint32_t graph_id);
  33. void DestroyAllRtContexts();
  34. RtContextUtil &operator=(const RtContextUtil &) = delete;
  35. RtContextUtil(const RtContextUtil &RtContextUtil) = delete;
  36. private:
  37. RtContextUtil() = default;
  38. ~RtContextUtil() {}
  39. void DestroyRtContexts(uint64_t session_id, int64_t graph_id, std::vector<rtContext_t> &contexts);
  40. std::map<uint64_t, std::map<int64_t, std::vector<rtContext_t>>> rt_contexts_;
  41. std::mutex ctx_mutex_;
  42. };
  43. } // namespace ge
  44. #endif // GE_GRAPH_MANAGER_UTIL_RT_CONTEXT_UTIL_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