You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

logging_ops.h 3.2 kB

5 years ago
5 years ago
5 years ago
5 years ago
5 years ago
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115
  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. /*!
  17. * \file logging_ops.h
  18. * \brief
  19. */
  20. #ifndef GE_OP_LOGGING_OPS_H
  21. #define GE_OP_LOGGING_OPS_H
  22. #include "graph/operator.h"
  23. #include "graph/operator_reg.h"
  24. namespace ge {
  25. /**
  26. *@brief Provides the time since epoch in seconds.
  27. *@par Outputs:
  28. *y: A Tensor of type float64. The timestamp as a double for seconds since \n
  29. the Unix epoch.
  30. *@attention Constraints: \n
  31. *The timestamp is computed when the op is executed, not when it is added to \n
  32. the graph.
  33. *@par Third-party framework compatibility
  34. *Compatible with tensorflow Timestamp operator.
  35. *@par Restrictions:
  36. *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
  37. */
  38. REG_OP(Timestamp)
  39. .OUTPUT(y, TensorType({DT_DOUBLE}))
  40. .OP_END_FACTORY_REG(Timestamp)
  41. /**
  42. *@brief Asserts that the given condition is true.
  43. *@par Inputs:
  44. *If input_condition evaluates to false, print the list of tensors in data. \n
  45. Inputs include: \n
  46. *@li input_condition: The condition to evaluate.
  47. *@li input_data: The tensors to print out when condition is false.
  48. *@par Attributes:
  49. *summarize: Print this many entries of each tensor.
  50. *@par Third-party framework compatibility
  51. *Compatible with tensorflow Assert operator.
  52. *@par Restrictions:
  53. *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
  54. */
  55. REG_OP(Assert)
  56. .INPUT(input_condition, TensorType{DT_BOOL})
  57. .DYNAMIC_INPUT(input_data, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8,
  58. DT_INT16, DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_UINT32,
  59. DT_UINT64, DT_BOOL, DT_DOUBLE, DT_STRING}))
  60. .ATTR(summarize, Int, 3)
  61. .OP_END_FACTORY_REG(Assert)
  62. /**
  63. *@brief Prints a tensor.
  64. *@par Inputs:
  65. *x: The tensor to print, it is a dynamic_input.
  66. *Compatible with aicpu Print operator.
  67. *@par Restrictions:
  68. *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
  69. */
  70. REG_OP(Print)
  71. .DYNAMIC_INPUT(x, TensorType({DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16, DT_UINT16, DT_UINT8, DT_INT32,
  72. DT_INT64, DT_UINT32, DT_UINT64, DT_DOUBLE, DT_STRING}))
  73. .OP_END_FACTORY_REG(Print)
  74. /**
  75. *@brief Prints a string scalar.
  76. *@par Inputs:
  77. *The dtype of input x must be string. Inputs include: \n
  78. *x: The string scalar to print.
  79. *@par Attributes:
  80. *output_stream: A string specifying the output stream or logging level \n
  81. to print to.
  82. *@par Third-party framework compatibility
  83. *Compatible with tensorflow PrintV2 operator.
  84. *@par Restrictions:
  85. *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
  86. */
  87. REG_OP(PrintV2)
  88. .INPUT(x, TensorType({DT_STRING}))
  89. .ATTR(output_stream, String, "stderr")
  90. .OP_END_FACTORY_REG(PrintV2)
  91. } // namespace ge
  92. #endif // GE_OP_LOGGING_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