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ragged_math_ops.h 2.1 kB

3 years ago
3 years ago
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  1. /**
  2. * Copyright 2019 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. /*!
  17. * \file ragged_math_ops.h
  18. * \brief
  19. */
  20. #ifndef OPS_BUILT_IN_OP_PROTO_INC_RAGGED_MATH_OPS_H_
  21. #define OPS_BUILT_IN_OP_PROTO_INC_RAGGED_MATH_OPS_H_
  22. #include "graph/operator.h"
  23. #include "graph/operator_reg.h"
  24. namespace ge {
  25. /**
  26. *@brief Returns a `RaggedTensor` containing the specified sequences of numbers . \n
  27. *@par Inputs:
  28. *@li starts: The starts of each range.
  29. *@li limits: The limits of each range.
  30. *@li deltas: The deltas of each range . \n
  31. *@par Outputs:
  32. *@li rt_dense_values:The `flat_values` for the returned `RaggedTensor`.
  33. *@li rt_nested_splits:The `row_splits` for the returned `RaggedTensor`. \n
  34. *@par Attributes:
  35. *Tsplits:A type of rt_nested_splits.
  36. *@attention Constraints:
  37. *The input tensors `starts`, `limits`, and `deltas` may be scalars or vectors.
  38. *The vector inputs must all have the same size. Scalar inputs are broadcast
  39. *to match the size of the vector inputs . \n
  40. *@par Third-party framework compatibility
  41. * Compatible with tensorflow RaggedRange operator.
  42. */
  43. REG_OP(RaggedRange)
  44. .INPUT(starts, TensorType({DT_FLOAT,DT_DOUBLE,DT_INT32,DT_INT64}))
  45. .INPUT(limits, TensorType({DT_FLOAT,DT_DOUBLE,DT_INT32,DT_INT64}))
  46. .INPUT(deltas, TensorType({DT_FLOAT,DT_DOUBLE,DT_INT32,DT_INT64}))
  47. .OUTPUT(rt_nested_splits, TensorType({DT_INT32, DT_INT64}))
  48. .OUTPUT(rt_dense_values, TensorType({DT_FLOAT,DT_DOUBLE,DT_INT32,DT_INT64}))
  49. .REQUIRED_ATTR(Tsplits, Type)
  50. .OP_END_FACTORY_REG(RaggedRange)
  51. } // namespace ge
  52. #endif // OPS_BUILT_IN_OP_PROTO_INC_RAGGED_MATH_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