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abs_ad.py 2.0 kB

5 years ago
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  1. #!/usr/bin/env python3
  2. # coding: utf-8
  3. # Copyright 2019 Huawei Technologies Co., Ltd
  4. #
  5. # Licensed under the Apache License, Version 2.0 (the "License");
  6. # you may not use this file except in compliance with the License.
  7. # You may obtain a copy of the License at
  8. #
  9. # http://www.apache.org/licenses/LICENSE-2.0
  10. #
  11. # Unless required by applicable law or agreed to in writing, software
  12. # distributed under the License is distributed on an "AS IS" BASIS,
  13. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  14. # See the License for the specific language governing permissions and
  15. # limitations under the License.
  16. """operator dsl function: abs_ad"""
  17. import akg
  18. from akg.ops.math import abs
  19. from akg.utils import validation_check as vc_util
  20. @vc_util.check_input_type(akg.tvm.tensor.Tensor, akg.tvm.tensor.Tensor)
  21. def abs_ad(head, in_data):
  22. """
  23. Compute gradient of abs operator with automatic differentiate.
  24. Args:
  25. head (tvm.tensor.Tensor): Tensor of type float16, float32, int8, uint8, int32.
  26. in_data (tvm.tensor.Tensor): Tensor of type float16, float32, int8, uint8, int32.
  27. Returns:
  28. tvm.tensor.Tensor has the same shape as input.
  29. """
  30. dtype = in_data.dtype
  31. # check head's validation.
  32. vc_util.check_shape(head.shape)
  33. vc_util.ops_dtype_check(head.dtype, vc_util.DtypeForDavinci.ALL_TYPES)
  34. need_cast_dtype = ["int8", "int32", "uint8"]
  35. abs_data = abs.abs_value(in_data)
  36. if head.dtype in need_cast_dtype:
  37. head = akg.tvm.compute(head.shape, lambda *indice: head(*indice).astype("float16"), name='head_cast')
  38. if dtype in need_cast_dtype:
  39. abs_data = akg.tvm.compute(abs_data.shape,
  40. lambda *indice: abs_data(*indice).astype("float16"),
  41. name='abs_cast')
  42. jacs = list(akg.differentiate(abs_data, [in_data], head))
  43. if dtype in need_cast_dtype:
  44. jacs[0] = akg.tvm.compute(jacs[0].shape, lambda *indice: jacs[0](*indice).astype(dtype), name='res')
  45. return jacs[0]

AKG(Auto Kernel Generator)对深度神经网络中的算子进行优化,并提供特定模式下的算子自动融合功能。AKG与MindSpore的图算融合功能协同工作,可提升在不同硬件后端上运行网络的性能。