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linalg_ops.h 3.4 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_LINALG_OPS_H_
  17. #define GE_OP_LINALG_OPS_H_
  18. #include "graph/operator_reg.h"
  19. #include "../graph/operator.h"
  20. namespace ge {
  21. REG_OP(CholeskyGrad)
  22. .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
  23. .INPUT(grad, TensorType({DT_FLOAT, DT_DOUBLE}))
  24. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  25. .OP_END_FACTORY_REG(CholeskyGrad)
  26. REG_OP(Cholesky)
  27. .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
  28. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  29. .OP_END_FACTORY_REG(Cholesky)
  30. REG_OP(LogMatrixDeterminant)
  31. .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
  32. .OUTPUT(sign, TensorType({DT_FLOAT, DT_DOUBLE}))
  33. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  34. .OP_END_FACTORY_REG(LogMatrixDeterminant)
  35. REG_OP(MatrixDeterminant)
  36. .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
  37. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  38. .OP_END_FACTORY_REG(MatrixDeterminant)
  39. REG_OP(MatrixInverse)
  40. .INPUT(x, TensorType({DT_FLOAT, DT_DOUBLE}))
  41. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  42. .ATTR(adjoint, Bool, false)
  43. .OP_END_FACTORY_REG(MatrixInverse)
  44. REG_OP(MatrixSolve)
  45. .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE}))
  46. .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE}))
  47. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  48. .ATTR(adjoint, Bool, false)
  49. .OP_END_FACTORY_REG(MatrixSolve)
  50. REG_OP(MatrixSolveLs)
  51. .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE}))
  52. .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE}))
  53. .INPUT(l2, TensorType({DT_DOUBLE}))
  54. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  55. .ATTR(fast, Bool, true)
  56. .OP_END_FACTORY_REG(MatrixSolveLs)
  57. REG_OP(MatrixTriangularSolve)
  58. .INPUT(matrix, TensorType({DT_FLOAT, DT_DOUBLE}))
  59. .INPUT(rhs, TensorType({DT_FLOAT, DT_DOUBLE}))
  60. .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE}))
  61. .ATTR(lower, Bool, true)
  62. .ATTR(adjoint, Bool, false)
  63. .OP_END_FACTORY_REG(MatrixTriangularSolve)
  64. REG_OP(Qr)
  65. .INPUT(x, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
  66. .OUTPUT(q, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
  67. .OUTPUT(r, TensorType({ DT_FLOAT16, DT_FLOAT, DT_DOUBLE }))
  68. .ATTR(full_matrices, Bool, false)
  69. .OP_END_FACTORY_REG(Qr)
  70. REG_OP(SelfAdjointEig)
  71. .INPUT(x, TensorType({ DT_DOUBLE, DT_FLOAT }))
  72. .OUTPUT(eigen_value, TensorType({ DT_DOUBLE, DT_FLOAT }))
  73. .OUTPUT(eigen_vector, TensorType({ DT_DOUBLE, DT_FLOAT }))
  74. .ATTR(compute_v, Bool, true)
  75. .OP_END_FACTORY_REG(SelfAdjointEig)
  76. REG_OP(Svd)
  77. .INPUT(x, TensorType({ DT_DOUBLE, DT_FLOAT }))
  78. .OUTPUT(sigma, TensorType({ DT_DOUBLE, DT_FLOAT }))
  79. .OUTPUT(u, TensorType({ DT_DOUBLE, DT_FLOAT }))
  80. .OUTPUT(v, TensorType({ DT_DOUBLE, DT_FLOAT }))
  81. .ATTR(compute_uv, Bool, true)
  82. .ATTR(full_matrices, Bool, false)
  83. .OP_END_FACTORY_REG(Svd)
  84. } // namespace ge
  85. #endif // GE_OP_LINALG_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