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get_data_ops.h 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. #ifndef GE_OP_GET_DATA_OPS_H_
  17. #define GE_OP_GET_DATA_OPS_H_
  18. #include "graph/operator_reg.h"
  19. namespace ge {
  20. REG_OP(MakeIterator)
  21. .INPUT(x, TensorType::ALL())
  22. .INPUT(x1, TensorType::ALL())
  23. .ATTR(_kernel, String, "dp")
  24. .OP_END_FACTORY_REG(MakeIterator)
  25. REG_OP(IteratorV2)
  26. .OUTPUT(y, TensorType::ALL())
  27. .ATTR(output_types, ListInt, {})
  28. .ATTR(output_shapes,ListListInt, {{}, {}})
  29. .ATTR(container, String, "")
  30. .ATTR(shared_name, String, "")
  31. .OP_END_FACTORY_REG(IteratorV2)
  32. REG_OP(IteratorGetNext)
  33. .INPUT(x, TensorType::ALL())
  34. .DYNAMIC_OUTPUT(y, TensorType::ALL())
  35. .ATTR(output_types, ListInt, {})
  36. .ATTR(output_shapes, ListListInt, {{},{}})
  37. .ATTR(output_num, Int, 1)
  38. .ATTR(_kernel, String, "dp")
  39. .OP_END_FACTORY_REG(IteratorGetNext)
  40. REG_OP(DeviceQueueDataset)
  41. .OUTPUT(y, TensorType::ALL())
  42. .ATTR(output_types, ListInt, {})
  43. .ATTR(output_shapes, ListListInt, {{},{}})
  44. .ATTR(channel_name, String, "")
  45. .ATTR(_iterator_name, String, "IteratorV2")
  46. .OP_END_FACTORY_REG(DeviceQueueDataset)
  47. } // namespace ge
  48. #endif // GE_OP_GET_DATA_OPS_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

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