Fixes#112632
Before: 171
```
torch/backends/_nnapi/prepare.py:24 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/_nnapi/prepare.py:46 in public method `init`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:60 in public method `forward`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:94 in public function `convert_model_to_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:153 in public function `process_for_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:177 in private nested class `ShapeComputeModule`:
D400: First line should end with a period (not 'n')
torch/backends/_nnapi/serializer.py:19 in public class `NNAPI_OperandCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:35 in public class `NNAPI_OperationCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:133 in public class `NNAPI_FuseCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:140 in public class `OperandValueSourceType`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:150 in public class `TorchScalarTypes`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:154 in public function `approx_equal`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:158 in public function `tensor_size`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:172 in public function `change_element`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:194 in public class `DimOrder`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:225 in public method `use_nchw`:
D102: Missing docstring in public method
torch/backends/_nnapi/serializer.py:233 in public function `broadcast_shapes`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:260 in public function `get_conv_pool_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:284 in public function `fix_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:301 in public function `reverse_map_dim`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:312 in public function `flex_name`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:1337 in private method `_do_add_binary`:
D400: First line should end with a period (not 's')
torch/backends/_nnapi/serializer.py:1337 in private method `_do_add_binary`:
D401: First line should be in imperative mood; try rephrasing (found 'Helper')
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
D202: No blank lines allowed after function docstring (found 1)
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
D400: First line should end with a period (not ':')
torch/backends/cuda/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D209: Multi-line docstring closing quotes should be on a separate line
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D400: First line should end with a period (not 's')
torch/backends/cuda/__init__.py:30 in public function `is_built`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cuda/__init__.py:37 in public class `cuFFTPlanCacheAttrContextProp`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:40 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:44 in public method `__get__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:47 in public method `__set__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:54 in public class `cuFFTPlanCache`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:54 in public class `cuFFTPlanCache`:
D400: First line should end with a period (not 'e')
torch/backends/cuda/__init__.py:60 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:73 in public method `clear`:
D102: Missing docstring in public method
torch/backends/cuda/__init__.py:78 in public class `cuFFTPlanCacheManager`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:78 in public class `cuFFTPlanCacheManager`:
D400: First line should end with a period (not ',')
torch/backends/cuda/__init__.py:89 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:93 in public method `__getitem__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:106 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:109 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:116 in public class `cuBLASModule`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:117 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:126 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:147 in public function `preferred_linalg_library`:
D202: No blank lines allowed after function docstring (found 1)
torch/backends/cuda/__init__.py:204 in public class `SDPBackend`:
D204: 1 blank line required after class docstring (found 0)
torch/backends/cudnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cudnn/__init__.py:81 in public function `version`:
D400: First line should end with a period (not 'N')
torch/backends/cudnn/__init__.py:81 in public function `version`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cudnn/__init__.py:95 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cudnn/__init__.py:99 in public function `is_acceptable`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:122 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:150 in public function `flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:174 in public class `CudnnModule`:
D101: Missing docstring in public class
torch/backends/cudnn/__init__.py:175 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkl/__init__.py:5 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/mkl/__init__.py:14 in public class `verbose`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/mkl/__init__.py:14 in public class `verbose`:
D400: First line should end with a period (not 'y')
torch/backends/mkl/__init__.py:41 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:44 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkl/__init__.py:53 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkldnn/__init__.py:9 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/mkldnn/__init__.py:19 in public class `verbose`:
D205: 1 blank line required between summary line and description (found 0)
torch/backends/mkldnn/__init__.py:19 in public class `verbose`:
D400: First line should end with a period (not 'y')
torch/backends/mkldnn/__init__.py:47 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkldnn/__init__.py:50 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:59 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:64 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:71 in public function `flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:81 in public class `MkldnnModule`:
D101: Missing docstring in public class
torch/backends/mkldnn/__init__.py:82 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/openmp/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/openmp/__init__.py:5 in public function `is_available`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/intrinsic/qat/modules/conv_fused.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/intrinsic/qat/modules/linear_fused.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/intrinsic/qat/modules/linear_relu.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/dynamic/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/dynamic/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/embedding_ops.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/qat/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantizable/modules/activation.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantizable/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/sparse.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/utils.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/__init__.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/functional.py:1 at module level:
D400: First line should end with a period (not 'l')
torch/nn/quantized/modules/__init__.py:1 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/activation.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/batchnorm.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/conv.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/dropout.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/embedding_ops.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/functional_modules.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/linear.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/normalization.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/rnn.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/quantized/modules/utils.py:2 at module level:
D400: First line should end with a period (not 's')
torch/nn/utils/_expanded_weights/conv_utils.py:13 in public function `conv_picker`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:23 in public function `conv_args_and_kwargs`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:31 in public function `conv_normalizer`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:35 in public function `conv_input_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:43 in public function `int_padding_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:59 in public function `conv_padding_for_same`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:66 in public function `conv_backward`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:131 in public function `conv_unfold_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:166 in public function `conv_group_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
D401: First line should be in imperative mood (perhaps 'Extract', not 'Extracts')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:6 in public function `is_batch_first`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
D300: Use """triple double quotes""" (found '''-quotes)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
D400: First line should end with a period (not 'e')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
D300: Use """triple double quotes""" (found '''-quotes)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
D400: First line should end with a period (not ')')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:84 in public function `maybe_scale_by_batch_size`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:90 in public function `set_grad_sample_if_exists`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:108 in public function `unpack_expanded_weight_or_tensor`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
D400: First line should end with a period (not 't')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
D401: First line should be in imperative mood (perhaps 'Calculate', not 'Calculates')
torch/nn/utils/convert_parameters.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D400: First line should end with a period (not 'd')
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
D401: First line should be in imperative mood; try rephrasing (found 'This')
torch/nn/utils/rnn.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/rnn.py:28 in public class `PackedSequence`:
D204: 1 blank line required after class docstring (found 0)
torch/nn/utils/rnn.py:63 in public method `__new__`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:73 in public method `pin_memory`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:80 in public method `cuda`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:87 in public method `cpu`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:94 in public method `double`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:97 in public method `float`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:100 in public method `half`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:103 in public method `long`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:106 in public method `int`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:109 in public method `short`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:112 in public method `char`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:115 in public method `byte`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:119 in public method `to`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:119 in public method `to`:
D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
torch/nn/utils/rnn.py:146 in public method `is_cuda`:
D400: First line should end with a period (not 'u')
torch/nn/utils/rnn.py:150 in public method `is_pinned`:
D400: First line should end with a period (not 'y')
torch/nn/utils/rnn.py:150 in public method `is_pinned`:
D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/utils/rnn.py:198 in public function `invert_permutation`:
D103: Missing docstring in public function
torch/nn/utils/rnn.py:274 in public function `pad_packed_sequence`:
D401: First line should be in imperative mood (perhaps 'Pad', not 'Pads')
torch/nn/utils/rnn.py:347 in public function `pad_sequence`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:347 in public function `pad_sequence`:
D400: First line should end with a period (not '`')
torch/nn/utils/rnn.py:408 in public function `unpad_sequence`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:408 in public function `unpad_sequence`:
D400: First line should end with a period (not 's')
torch/nn/utils/rnn.py:454 in public function `pack_sequence`:
D400: First line should end with a period (not 's')
torch/nn/utils/rnn.py:490 in public function `unpack_sequence`:
D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:490 in public function `unpack_sequence`:
D400: First line should end with a period (not 's')
171
```
After: 81
```
torch/backends/_nnapi/prepare.py:24 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/_nnapi/prepare.py:46 in public method `init`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:60 in public method `forward`:
D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:94 in public function `convert_model_to_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:153 in public function `process_for_nnapi`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:19 in public class `NNAPI_OperandCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:35 in public class `NNAPI_OperationCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:133 in public class `NNAPI_FuseCode`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:140 in public class `OperandValueSourceType`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:150 in public class `TorchScalarTypes`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:154 in public function `approx_equal`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:158 in public function `tensor_size`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:172 in public function `change_element`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:194 in public class `DimOrder`:
D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:225 in public method `use_nchw`:
D102: Missing docstring in public method
torch/backends/_nnapi/serializer.py:233 in public function `broadcast_shapes`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:260 in public function `get_conv_pool_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:284 in public function `fix_shape`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:301 in public function `reverse_map_dim`:
D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:312 in public function `flex_name`:
D103: Missing docstring in public function
torch/backends/cuda/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cuda/__init__.py:39 in public class `cuFFTPlanCacheAttrContextProp`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:42 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:46 in public method `__get__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:49 in public method `__set__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:63 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:76 in public method `clear`:
D102: Missing docstring in public method
torch/backends/cuda/__init__.py:91 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:95 in public method `__getitem__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:108 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:111 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:118 in public class `cuBLASModule`:
D101: Missing docstring in public class
torch/backends/cuda/__init__.py:119 in public method `__getattr__`:
D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:128 in public method `__setattr__`:
D105: Missing docstring in magic method
torch/backends/cudnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/cudnn/__init__.py:99 in public function `is_acceptable`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:122 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:150 in public function `flags`:
D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:174 in public class `CudnnModule`:
D101: Missing docstring in public class
torch/backends/cudnn/__init__.py:175 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkl/__init__.py:42 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:45 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkl/__init__.py:54 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/backends/mkldnn/__init__.py:48 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/mkldnn/__init__.py:51 in public method `__enter__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:60 in public method `__exit__`:
D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:65 in public function `set_flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:72 in public function `flags`:
D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:82 in public class `MkldnnModule`:
D101: Missing docstring in public class
torch/backends/mkldnn/__init__.py:83 in public method `__init__`:
D107: Missing docstring in __init__
torch/backends/openmp/__init__.py:1 at module level:
D104: Missing docstring in public package
torch/nn/utils/_expanded_weights/conv_utils.py:13 in public function `conv_picker`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:23 in public function `conv_args_and_kwargs`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:31 in public function `conv_normalizer`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:35 in public function `conv_input_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:43 in public function `int_padding_for_string_padding`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:59 in public function `conv_padding_for_same`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:66 in public function `conv_backward`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:131 in public function `conv_unfold_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:166 in public function `conv_group_weight_grad_sample`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:6 in public function `is_batch_first`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:87 in public function `maybe_scale_by_batch_size`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:93 in public function `set_grad_sample_if_exists`:
D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:111 in public function `unpack_expanded_weight_or_tensor`:
D103: Missing docstring in public function
torch/nn/utils/convert_parameters.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/rnn.py:1 at module level:
D100: Missing docstring in public module
torch/nn/utils/rnn.py:64 in public method `__new__`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:74 in public method `pin_memory`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:81 in public method `cuda`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:88 in public method `cpu`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:95 in public method `double`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:98 in public method `float`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:101 in public method `half`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:104 in public method `long`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:107 in public method `int`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:110 in public method `short`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:113 in public method `char`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:116 in public method `byte`:
D102: Missing docstring in public method
torch/nn/utils/rnn.py:198 in public function `invert_permutation`:
D103: Missing docstring in public function
81
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112695
Approved by: https://github.com/mikaylagawarecki
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] [Current PR] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
- [BC test](test/quantization/bc/test_backward_compatibility.py) @vkuzo
- [IR emitter](torch/csrc/jit/frontend/ir_emitter.cpp) @jamesr66a
- [JIT serialization](torch/csrc/jit/serialization/import_source.cpp) @IvanKobzarev @jamesr66a
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860660/)!
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78714
Approved by: https://github.com/jerryzh168
Context: In order to avoid the cluttering of the `torch.nn` namespace
the quantized modules namespace is moved to `torch.ao.nn`.
The list of the `nn.quantized` files that are being migrated:
- [ ] `torch.nn.quantized` → `torch.ao.nn.quantized`
- [X] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
- [X] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
- [X] [Current PR] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
- [ ] `torch.nn.quantized._reference` → `torch.ao.nn.quantized._reference`
- [ ] `torch.nn.quantizable` → `torch.ao.nn.quantizable`
- [ ] `torch.nn.qat` → `torch.ao.nn.qat`
- [ ] `torch.nn.qat.modules` → `torch.ao.nn.qat.modules`
- [ ] `torch.nn.qat.dynamic` → `torch.ao.nn.qat.dynamic`
- [ ] `torch.nn.intrinsic` → `torch.ao.nn.intrinsic`
- [ ] `torch.nn.intrinsic.modules` → `torch.ao.nn.intrinsic.modules`
- [ ] `torch.nn.intrinsic.qat` → `torch.ao.nn.intrinsic.qat`
- [ ] `torch.nn.intrinsic.quantized` → `torch.ao.nn.intrinsic.quantized`
- [ ] `torch.nn.intrinsic.quantized.modules` → `torch.ao.nn.intrinsic.quantized.modules`
- [ ] `torch.nn.intrinsic.quantized.dynamic` → `torch.ao.nn.intrinsic.quantized.dynamic`
Majority of the files are just moved to the new location.
However, specific files need to be double checked:
- [Documentation](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10
- [BC test](test/quantization/bc/test_backward_compatibility.py) @vkuzo
- [IR emitter](torch/csrc/jit/frontend/ir_emitter.cpp) @jamesr66a
- [JIT serialization](torch/csrc/jit/serialization/import_source.cpp) @IvanKobzarev @jamesr66a
Differential Revision: [D36860660](https://our.internmc.facebook.com/intern/diff/D36860660/)
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860660/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78714
Approved by: https://github.com/jerryzh168
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73863
This PR fully aligns the convert function with the design: https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md
and simplifies the implementation of convert function by always produce a reference quantized model (with reference patterns) first,
and then lower the model to a quantized model that is runnable with PyTorch native backend (fbgemm/qnnpack).
This PR makes the convert.py much easier to understand than the previous implementation, and we are able to remove majority of code
in quantization_patterns.py as well (in followup PRs).
Test Plan:
```
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps
python test/test_quantization.py TestFXNumericSuiteCoreAPIs
python test/test_quantization.py TestFXNumericSuiteCoreAPIsModels
```
and other internal/oss regression tests
Imported from OSS
Reviewed By: andrewor14
Differential Revision: D34778506
fbshipit-source-id: 0678b66addf736039a8749b352f6f569caca962b
(cherry picked from commit 33ec9caf23f3ab373d827117efbd9db0668b2437)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72662
This commit was produced by running
```
python -m libcst.tool codemod --no-format --jobs=1 convert_type_comments.ConvertTypeComments caffe2/torch/nn/ --no-quote-annotations
```
and then manually fixing unreadable lines by breaking up very
long function defintiion (unfortuantely
it's very difficult to fully automate tranforms of code that
isn't autoformatted).
Test Plan:
Wait for CI. This should be safe, the types all appear to be valid - but it's
always good to let the jit tests run, in some cases we find typing errors that
crash tests.
Reviewed By: jbschlosser, albanD
Differential Revision: D34147388
fbshipit-source-id: 40701228837a927b54239ab87699b4b3169546b7
(cherry picked from commit 05a900c43f)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71110
as mentioned in https://github.com/pytorch/pytorch/issues/70480 the dynamic conv ops are currently missing a key feature to bring their performance in line with other dynamic ops, this diff disables conv/convT from being automatically quantized with convert dynamic
Test Plan: buck test //caffe2/test:quantization --test-selectors test_quantized_module#TestDynamicQuantizedModule
Reviewed By: vkuzo
Differential Revision: D33511152
fbshipit-source-id: 50618fbe734c898664c390f896e70c68f1df3208
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68176
it should be noted that for the modules, reduce_range is set to
true by default in a similar fashion to linear_dynamic.
Test Plan:
python test/test_quantization.py TestDynamicQuantizedModule
python test/test_quantization.py TestDynamicQuantizedConv
python test/test_quantization.py TestQuantizedConv
Imported from OSS
Reviewed By: kimishpatel
Differential Revision: D32374003
fbshipit-source-id: 011562bd0f4d817387d53bb113df2600aa60a7a3
Summary:
**Summary:** This commit adds the `torch.nn.qat.dynamic.modules.Linear`
module, the dynamic counterpart to `torch.nn.qat.modules.Linear`.
Functionally these are very similar, except the dynamic version
expects a memoryless observer and is converted into a dynamically
quantized module before inference.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67325
Test Plan:
`python3 test/test_quantization.py TestQuantizationAwareTraining.test_dynamic_qat_linear`
**Reviewers:** Charles David Hernandez, Jerry Zhang
**Subscribers:** Charles David Hernandez, Supriya Rao, Yining Lu
**Tasks:** 99696812
**Tags:** pytorch
Reviewed By: malfet, jerryzh168
Differential Revision: D32178739
Pulled By: andrewor14
fbshipit-source-id: 5051bdd7e06071a011e4e7d9cc7769db8d38fd73
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66379
Description:
Creates a quantization API reference and fixes all the docblock errors.
This is #66122 to #66210 squashed together
Test Plan:
```
cd docs
make html
python -m http.server
// open webpage, inspect it, looks good
```
Reviewed By: ejguan
Differential Revision: D31543172
Pulled By: vkuzo
fbshipit-source-id: 9131363d6528337e9f100759654d3f34f02142a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66201
Description:
This PR switches the quantization API reference to use `autosummary`
for each section. We define the sections and manually write a list
of modules/functions/methods to include, and sphinx does the rest.
A result is a single page where we have every quantization function
and module with a quick autogenerated blurb, and user can click
through to each of them for a full documentation page.
This mimics how the `torch.nn` and `torch.nn.functional` doc
pages are set up.
In detail, for each section before this PR:
* creates a new section using `autosummary`
* adds all modules/functions/methods which were previously in the manual section
* adds any additional modules/functions/methods which are public facing but not previously documented
* deletes the old manual summary and all links to it
Test Plan:
```
cd docs
make html
python -m http.server
// renders well, links work
```
Reviewed By: jerryzh168
Differential Revision: D31447615
Pulled By: vkuzo
fbshipit-source-id: 09874ad9629f9c00eeab79c406579c6abd974901
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65900
This changes the imports in the `caffe2/torch/nn/quantized` to include the new import locations.
```
codemod -d torch/nn/quantized --extensions py 'torch.quantization' 'torch.ao.quantization'
```
Test Plan: `python test/run_test.py`
Reviewed By: jerryzh168
Differential Revision: D31301193
fbshipit-source-id: 58efb1ad51a8b441e2a3bd5b91af11eab6b9331f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63799
Add a new module that can be used for module swap with the nni.LinearReLU module in convert function.
Supports INT8 currently (since FP16 op doesn't have relu fusion yet).
Fixes#55393
Test Plan:
python test/test_quantization.py test_dynamic_fusion
Imported from OSS
Reviewed By: heitorschueroff
Differential Revision: D30502812
fbshipit-source-id: 3668e4f001a0626d469e17ac323acf582ee28a51
Summary:
As this diff shows, currently there are a couple hundred instances of raw `noqa` in the codebase, which just ignore all errors on a given line. That isn't great, so this PR changes all existing instances of that antipattern to qualify the `noqa` with respect to a specific error code, and adds a lint to prevent more of this from happening in the future.
Interestingly, some of the examples the `noqa` lint catches are genuine attempts to qualify the `noqa` with a specific error code, such as these two:
```
test/jit/test_misc.py:27: print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
test/jit/test_misc.py:28: print(f"format blank") # noqa F541
```
However, those are still wrong because they are [missing a colon](https://flake8.pycqa.org/en/3.9.1/user/violations.html#in-line-ignoring-errors), which actually causes the error code to be completely ignored:
- If you change them to anything else, the warnings will still be suppressed.
- If you add the necessary colons then it is revealed that `E261` was also being suppressed, unintentionally:
```
test/jit/test_misc.py:27:57: E261 at least two spaces before inline comment
test/jit/test_misc.py:28:35: E261 at least two spaces before inline comment
```
I did try using [flake8-noqa](https://pypi.org/project/flake8-noqa/) instead of a custom `git grep` lint, but it didn't seem to work. This PR is definitely missing some of the functionality that flake8-noqa is supposed to provide, though, so if someone can figure out how to use it, we should do that instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56272
Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI run (before this PR was finished) failed:
- https://github.com/pytorch/pytorch/runs/2365189927
Reviewed By: janeyx99
Differential Revision: D27830127
Pulled By: samestep
fbshipit-source-id: d6dcf4f945ebd18cd76c46a07f3b408296864fcb
Summary:
Generally wildcard imports are bad for the reasons described here: https://www.flake8rules.com/rules/F403.html
This PR replaces wildcard imports with an explicit list of imported items where possible, and adds a `# noqa: F403` comment in the other cases (mostly re-exports in `__init__.py` files).
This is a prerequisite for https://github.com/pytorch/pytorch/issues/55816, because currently [`tools/codegen/dest/register_dispatch_key.py` simply fails if you sort its imports](https://github.com/pytorch/pytorch/actions/runs/742505908).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55838
Test Plan: CI. You can also run `flake8` locally.
Reviewed By: jbschlosser
Differential Revision: D27724232
Pulled By: samestep
fbshipit-source-id: 269fb09cb4168f8a51fd65bfaacc6cda7fb87c34
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49964
`torch.nn.modules.linear._LinearWithBias` is only used in the transformers, and is completely identical to the `torch.nn.Linear`.
This PR creates a mapping so that this module would be treated the same as the Linear.
Test Plan:
```
python test/test_quantization.py TestDynamicQuantizedModule TestStaticQuantizedModule
```
Differential Revision: D25731589
Reviewed By: jerryzh168
Pulled By: z-a-f
fbshipit-source-id: 1b2697014e250e97d3010cdb542f9d130b71fbc3
Summary:
These unused variables were identified by [pyflakes](https://pypi.org/project/pyflakes/). They can be safely removed to simplify the code and possibly improve performance.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50100
Reviewed By: ezyang
Differential Revision: D25797764
Pulled By: smessmer
fbshipit-source-id: ced341aee692f429d2dcc3a4ef5c46c8ee99cabb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47068
Filter the dtype config before performing the quantization in linear
Test Plan: Imported from OSS
Reviewed By: supriyar
Differential Revision: D24627907
fbshipit-source-id: 162fa47b3fcf6648049f8bc0438e41ee97ac19e9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44217
Move the tests to static ones as well
Test Plan:
python test/test_quantization.py TestStaticQuantizedModule.test_embedding_bag_api
Imported from OSS
Reviewed By: raghuramank100
Differential Revision: D23547386
fbshipit-source-id: 41f81c31e1613098ecf6a7eff601c7dcd4b09c76
Summary:
To avoid conflicts, this PR does not remove all imports. More are coming in further PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43808
Reviewed By: wanchaol
Differential Revision: D23436675
Pulled By: ailzhang
fbshipit-source-id: ccc21a1955c244f0804277e9e47e54bfd23455cd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43090
To match the floating point module
Test Plan: Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23167518
fbshipit-source-id: 29db596e10731be4cfed7efd18f33a0b3dbd0ca7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43088
Create quantized module that the user can use to perform embedding bag quantization
The module uses the EmbeddingPackedParams to store the weights which can be serialized /deserialized
using TorchBind custom classes (C++ get/setstate code)
Following PR will add support for `from_float` to convert from float to quantized module
Test Plan:
python test/test_quantization.py TestDynamicQuantizedModule.test_embedding_bag_api
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23167519
fbshipit-source-id: 029d7bb44debf78c4ef08bfebf267580ed94d033
Summary:
Raise and assert used to have a hard-coded error message "Exception". User provided error message was ignored. This PR adds support to represent user's error message in TorchScript.
This breaks backward compatibility because now we actually need to script the user's error message, which can potentially contain unscriptable expressions. Such programs can break when scripting, but saved models can still continue to work.
Increased an op count in test_mobile_optimizer.py because now we need aten::format to form the actual exception message.
This is built upon an WIP PR: https://github.com/pytorch/pytorch/pull/34112 by driazati
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41907
Reviewed By: ngimel
Differential Revision: D22778301
Pulled By: gmagogsfm
fbshipit-source-id: 2b94f0db4ae9fe70c4cd03f4048e519ea96323ad
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40931
Fix docstrings for dynamic quantized Linear/LSTM and associated classes
ghstack-source-id: 107064446
Test Plan: Docs show up in correctly
Differential Revision: D22360787
fbshipit-source-id: 8e357e081dc59ee42fd7f12ea5079ce5d0cc9df2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40101
Create three tests for LSTMs:
1. test_qlstm: Test to check numerics of quantized LSTM operator.
2. test_lstm_api: To check the LSTM module and compare
it with the quantized LSTM op
3. test_quantized_rnn: Check the dynamic quantization workflow, scriptability and serialization of quantized
LSTM
ghstack-source-id: 105997268
(Note: this ignores all push blocking failures!)
Test Plan:
buck test caffe2/test:quantization -- 'test_lstm_api \(quantization\.test_quantized_module\.TestDynamicQuantizedModule\)' --print-passing-details
buck test caffe2/test:quantization -- 'test_quantized_rnn \(quantization\.test_quantize\.TestPostTrainingDynamic\)'
buck test caffe2/test:quantization -- 'test_qlstm \(quantization\.test_quantized_op\.TestDynamicQuantizedRNNOp\)' --print-passing-details
Differential Revision: D22070826
fbshipit-source-id: 46c333e19b9eab8fa5cab6f132e89b80a635791a
Summary:
Create three tests for LSTMs:
1. test_qlstm: Test to check numerics of quantized LSTM operator.
2. test_lstm_api: To check the LSTM module and compare
it with the quantized LSTM op
3. test_quantized_rnn: Check the dynamic quantization workflow, scriptability and serialization of quantized
LSTM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38851
ghstack-source-id: 105945574
(Note: this ignores all push blocking failures!)
Test Plan:
buck test caffe2/test:quantization -- 'test_lstm_api \(quantization\.test_quantized_module\.TestDynamicQuantizedModule\)' --print-passing-details
buck test caffe2/test:quantization -- 'test_quantized_rnn \(quantization\.test_quantize\.TestPostTrainingDynamic\)'
buck test caffe2/test:quantization -- 'test_qlstm \(quantization\.test_quantized_op\.TestDynamicQuantizedRNNOp\)' --print-passing-details
Differential Revision: D21628596
fbshipit-source-id: 4aeda899f2e5f14bfbe3d82096cb4ce89c725fa1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39604
This change preserves BC for older models that are saved with reduce_range set to false.
Newer models will use the version information in RNN module to toggle reduce_range parameter
Internally this is implemented using a new CellParams type that calls the linear functions with reduce_range option set to true.
New models serialized will use the CellParams struct for the `__getstate__` and `__setstate__` calls. Older models using QuantizedCellParamsDynamic will continue to use their original serialization/de-serialization methods
tested using LSTM BC test and test_quantized_rnn
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D21977600
fbshipit-source-id: 0cb0e098b87207b537574d3beeab1f341c41c0d2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39125
switch to setting reduce_range to true for version > 3.
Models serialized with older state_dict will have version <=3 so will be run with reduce_range=false
Verified with backward compatibility tests (works with no changes to these tests)
Test Plan:
python test/test_quantization.py
Imported from OSS
Differential Revision: D21769689
fbshipit-source-id: 131f2ae736e31705222e82bdc77480f2f1826fe8
Summary:
Previously dynamic LSTM modules weren't able to save/load from state_dict since PackedParameter used in RNNs isn't serializable from python
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39105
Test Plan: python test/test_quantization.py TestSerialization
Reviewed By: jerryzh168
Differential Revision: D21752256
Pulled By: supriyar
fbshipit-source-id: ef82cf21ce21a3a1304d147ed0da538c639f952d
