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bitwise_ops.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 GE_OP_BITWISE_OPS_H_
  17. #define GE_OP_BITWISE_OPS_H_
  18. #include "graph/operator_reg.h"
  19. namespace ge {
  20. /**
  21. *@brief Element-wise computes the bitwise right-shift of x and y.
  22. *@par Inputs:
  23. *Input "x" is a k-dimensional tensor. Inputs "num_lower" and "num_upper" \n
  24. are 0D scalars.
  25. * @li x: A Tensor. Must be one of the following types: int8, int16, int32, \n
  26. int64, uint8, uint16, uint32, uint64. \n
  27. * @li y: A Tensor. Has the same type as "x". \n
  28. *@par Outputs:
  29. * z: A Tensor. Has the same type as "x". \n
  30. *@attention Constraints: \n
  31. *Unique runs on the Ascend AI CPU, which delivers poor performance. \n
  32. *@par Third-party framework compatibility
  33. *Compatible with the TensorFlow operator RightShift.
  34. */
  35. REG_OP(RightShift)
  36. .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, \
  37. DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64}))
  38. .INPUT(y, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, \
  39. DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64}))
  40. .OUTPUT(z, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, \
  41. DT_UINT8, DT_UINT16, DT_UINT32, DT_UINT64}))
  42. .OP_END_FACTORY_REG(RightShift)
  43. } // namespace ge
  44. #endif // GE_OP_BITWISE_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