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stateless_random_ops.h 2.7 kB

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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 stateless_random_ops.h
  18. * \brief
  19. */
  20. #ifndef OPS_BUILT_IN_OP_PROTO_INC_STATELESS_RANDOM_OPS_H_
  21. #define OPS_BUILT_IN_OP_PROTO_INC_STATELESS_RANDOM_OPS_H_
  22. #include "graph/operator.h"
  23. #include "graph/operator_reg.h"
  24. namespace ge {
  25. /**
  26. *@brief Draws samples from a multinomial distribution . \n
  27. *@par Inputs:
  28. include:
  29. *@li logits:2-D Tensor with shape [batch_size, num_classes]. Each slice [i, :]
  30. *represents the unnormalized log probabilities for all classes.
  31. *@li num_samples:0-D. Number of independent samples to draw for each row slice.
  32. *@li seed:The seed to generate random . \n
  33. *@par Attributes:
  34. *output_dtype:Output data type . \n
  35. *@par Outputs:
  36. *y:Output random number . \n
  37. *@see StatelessMultinomial()
  38. *@par Third-party framework compatibility
  39. *compatible with StatelessMultinomial op of tensorflow
  40. */
  41. REG_OP(StatelessMultinomial)
  42. .INPUT(logits, TensorType({DT_FLOAT16,DT_FLOAT,DT_DOUBLE}))
  43. .INPUT(num_samples, TensorType({DT_INT32}))
  44. .INPUT(seed, TensorType({DT_INT32, DT_INT64}))
  45. .OUTPUT(y, TensorType({DT_INT32, DT_INT64}))
  46. .ATTR(output_dtype, Type, DT_INT64)
  47. .OP_END_FACTORY_REG(StatelessMultinomial)
  48. /**
  49. *@brief Outputs deterministic pseudorandom random integers from a uniform distribution . \n
  50. *@par Inputs:
  51. *@li shape: The shape of the output tensor.
  52. *@li seed: 2 seeds (shape [2]).
  53. *@li minval: Minimum value (inclusive, scalar).
  54. *@li maxval: Maximum value (exclusive, scalar) . \n
  55. *@par Outputs:
  56. *y: Returns Random values with specified shape . \n
  57. *@par Third-party framework compatibility
  58. * Compatible with TensorFlow StatelessRandomUniformInt operator.
  59. */
  60. REG_OP(StatelessRandomUniformInt)
  61. .INPUT(shape, TensorType({DT_INT32, DT_INT64}))
  62. .INPUT(seed, TensorType({DT_INT32, DT_INT64}))
  63. .INPUT(minval, TensorType({DT_INT32, DT_INT64}))
  64. .INPUT(maxval, TensorType({DT_INT32, DT_INT64}))
  65. .OUTPUT(y, TensorType({DT_INT32, DT_INT64}))
  66. .OP_END_FACTORY_REG(StatelessRandomUniformInt)
  67. } // namespace ge
  68. #endif // OPS_BUILT_IN_OP_PROTO_INC_STATELESS_RANDOM_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