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build_task_utils.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_SINGLE_OP_TASK_BUILD_TASK_UTILS_H_
  17. #define GE_SINGLE_OP_TASK_BUILD_TASK_UTILS_H_
  18. #include <vector>
  19. #include <sstream>
  20. #include "graph/op_desc.h"
  21. #include "single_op/single_op.h"
  22. #include "single_op/single_op_model.h"
  23. namespace ge {
  24. class BuildTaskUtils {
  25. public:
  26. static constexpr int kAddressIndexWorkspace = 2;
  27. static std::vector<std::vector<void *>> GetAddresses(const OpDescPtr &op_desc, const SingleOpModelParam &param);
  28. static std::vector<void *> JoinAddresses(const std::vector<std::vector<void *>> &addresses);
  29. static std::vector<void *> GetKernelArgs(const OpDescPtr &op_desc, const SingleOpModelParam &param);
  30. static std::string GetTaskInfo(const OpDescPtr &op_desc);
  31. template<typename T>
  32. static std::string VectorToString(const std::vector<T> &values)
  33. {
  34. std::stringstream ss;
  35. ss << '[';
  36. auto size = values.size();
  37. for (size_t i = 0; i < size; ++i) {
  38. ss << values[i];
  39. if (i != size - 1) {
  40. ss << ", ";
  41. }
  42. }
  43. ss << ']';
  44. return ss.str();
  45. }
  46. };
  47. } // namespace ge
  48. #endif // GE_SINGLE_OP_TASK_BUILD_TASK_UTILS_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