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31 Commits

Author SHA1 Message Date
Xuehai Pan
b5c006acac [BE][Easy] enable UFMT for torch/nn/ (#128865) 
Part of #123062

- #123062

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128865
Approved by: https://github.com/ezyang
 2024-07-25 02:48:42 +00:00
NVS Abhilash
eb5487361d docs: fix docstring errors in quantized modules and others (#112695) 
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
 2023-11-07 23:52:16 +00:00
Feisi Fu
d8eae6283d Rename 'torch/ao/nn/quantized._reference' to 'torch/ao/nn/quantized/reference'. (#84974) 
Currently, the path for reference modules contains _ which means it's private (https://github.com/pytorch/pytorch/tree/master/torch/ao/nn/quantized/_reference), but we would like to make it public since the reference module is now enabled by default in the fx graph mode quantization flow and it will be added to eager mode flow as well in the future.

To make '_reference' public, it should satisfy the [public API rules](https://github.com/pytorch/pytorch/wiki/Public-API-definition-and-documentation).
I did in the first commit (prepare '_reference' to be public):
1: add __all__ to public modules and packages;
2. made functions, that are only used in the file that the function is defined, private by adding _ at their names.

Fixes #83090. (we rename the 'torch/ao/nn/quantized/_reference', because of migration #81667.)

This is a dup for the #84786.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84974
Approved by: https://github.com/andrewor14, https://github.com/z-a-f
 2022-09-16 17:49:07 +00:00
zaf
b1455f9424 [quant][ao_migration] torch.nn.quantized._reference → torch.ao.nn.quantized._reference (#78715) 
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] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
    - [X] [Current PR] `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:

- None

Differential Revision: [D36860927](https://our.internmc.facebook.com/intern/diff/D36860927/)

**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860927/)!

Differential Revision: [D36860927](https://our.internmc.facebook.com/intern/diff/D36860927)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78715
Approved by: https://github.com/jerryzh168
 2022-08-25 16:50:36 +00:00
Sergii Dymchenko
591222f5d9 Fix use-dict-literal lint (#83718) 
Fix use-dict-literal pylint suggestions by changing `dict()` to `{}`. This PR should do the change for every Python file except test/jit/test_list_dict.py, where I think the intent is to test the constructor.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83718
Approved by: https://github.com/albanD
 2022-08-24 00:26:46 +00:00
PyTorch MergeBot
355d343fa8 Revert "[quant][ao_migration] torch.nn.quantized._reference → torch.ao.nn.quantized._reference (#78715)" 
This reverts commit a7344e52b9.

Reverted https://github.com/pytorch/pytorch/pull/78715 on behalf of https://github.com/janeyx99 due to sorry, reverting so https://github.com/pytorch/pytorch/pull/78713 could be cleanly reverted
 2022-08-22 07:29:15 +00:00
zaf
a7344e52b9 [quant][ao_migration] torch.nn.quantized._reference → torch.ao.nn.quantized._reference (#78715) 
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] `torch.nn.quantized.dynamic` → `torch.ao.nn.quantized.dynamic`
    - [X] [Current PR] `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:

- None

Differential Revision: [D36860927](https://our.internmc.facebook.com/intern/diff/D36860927/)

**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36860927/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78715
Approved by: https://github.com/jerryzh168
 2022-08-22 05:29:23 +00:00
PyTorch MergeBot
caee732aa1 Revert "[quant][fx] Support keyword arguments for functional linear (#79095)" 
This reverts commit d71fb40d98.

Reverted https://github.com/pytorch/pytorch/pull/79095 on behalf of https://github.com/jerryzh168 due to broken master
 2022-07-09 21:45:01 +00:00
Jerry Zhang
d71fb40d98 [quant][fx] Support keyword arguments for functional linear (#79095) 
Summary:
Fixes: https://github.com/pytorch/pytorch/issues/78117
Fixes: https://github.com/pytorch/pytorch/issues/73463

This PR adds a normalization pass that normalizes all the args to keyword args in positional order and fixes lowering code that previously
only uses node.args to use both args and kwargs instead.

Also tried to add a test for F.conv2d, but since conv2d matches multiple schemas we are doing an extra schema match, and because we are using symbolic values
in `transform`, we don't have a schema match, so F.conv2d still fails with runtime errors. we can resolve this issue later when there is a need.

Another thing I'm considering is to do the normalization with real inputs instead of symbolic inputs and not rely on operator_schemas (which is based on torchscript),
and rely on inspect.signature, I tried this briefly but didn't get too far, it looks like we cannot get the python signature for `torch._C._nn.linear`, it might be possible to fix as well, but will need follow up discussions.

The goal for this PR is just to introduce normalization in our codebase so that we can adapt some downstream code to this, and also fix the F.linear issue.

Test Plan:
python test/test_quantization.py TestQuantizeFx.test_normalize_args

Reviewers:

Subscribers:

Tasks:

Tags:

Differential Revision: [D37163228](https://our.internmc.facebook.com/intern/diff/D37163228)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79095
Approved by: https://github.com/andrewor14
 2022-07-09 20:01:09 +00:00
Vasiliy Kuznetsov
ce0786add2 fx quant: fix warning in util function when cloning tensors (#80883) 
Summary:

Some of the util functions in FX graph mode quantization throw warnings
such as:

```
/Users/vasiliy/pytorch/torch/ao/quantization/fx/utils.py:410: UserWarning: To copy construct from
a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().
requires_grad_(True), rather than torch.tensor(sourceTensor).
```

This PR fixes the warnings by moving the code to the recommended syntax if the
value is a tensor.

Test plan:

```
python test/test_quantization.py -k test_conv_linear_reference
// warning appeared before this PR and disappeared after this PR
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80883
Approved by: https://github.com/jerryzh168
 2022-07-06 12:44:10 +00:00
Terry Chen
8c05f44fe2 [quant] fix int16 quantization scale in conv weight (#74665) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74665

fix int16 quantization scale in conv weight

Test Plan:
python3 test/test_quantization.py TestQuantizeEagerOps.test_int16_reference_module

Imported from OSS

Reviewed By: mrshenli

Differential Revision: D35106497

fbshipit-source-id: 61030786d20d845ef36ea40cdacdd7dcccf12ae9
(cherry picked from commit 187b6bd02c9ccc17416b1620c3b164c84352ef81)
 2022-03-31 06:10:23 +00:00
Jerry Zhang
d64e7634ff [quant] Remove assert for weight since it could be non-Tensor (#74365) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74365

att

Test Plan:
Meta-Internal tests with fx2trt

Imported from OSS

Reviewed By: andrewor14

Differential Revision: D34952754

fbshipit-source-id: 11d392a520c9ab7c9484c96841f2b39fbbbc3f80
(cherry picked from commit e8a2348d5c6f9717b010972819723affba37a0e4)
 2022-03-24 21:27:53 +00:00
Jerry Zhang
7ddf212f33 [quant][fx] Fully align convert with the reference model design and simplify the implementation (#73863) 
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)
 2022-03-11 17:11:30 +00:00
Terry Chen
4e6aefaf72 [Qunat] Refactor reference module mapping (#72755) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72755

Add is_refernece flag in convert function

Test Plan:
python3 test/test_quantization.py TestQuantizeEagerOps.test_conv_transpose_2d

Imported from OSS

Reviewed By: mruberry

Differential Revision: D34188856

fbshipit-source-id: 291014a7b3b4d4b40ca0ca76a80711097dcc4b58
(cherry picked from commit cfba3b8dc0373708712c0d847d590f0d587df002)
 2022-03-08 06:48:04 +00:00
Vasiliy Kuznetsov
727debb18e dbr quant: enable reference module support for torch.qint32 (#73493) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73493

This PR enables basic support for reference modules in DBR quant.
For now, the support is limited to:
1. modules that have reference versions defined only (no functions)
2. torch.qint32 dtype only

Currently, the reference module logic is enabled whenever dtype is
torch.qint32. This is done because this is needed the earliest for
the first use case. A future PR will support more dtypes and also
add the `is_reference` flag to the API.

Test Plan:
```
python test/test_quantization.py TestQuantizeDBR.test_conv_int32_reference_model
```

Reviewed By: jerryzh168

Differential Revision: D34520759

Pulled By: vkuzo

fbshipit-source-id: 363db715315c5c7c20962a1818330ce288948778
(cherry picked from commit 6ccdfe2889c252211f191edc49f4147f66e803a4)
 2022-03-04 17:35:31 +00:00
Jerry Zhang
2ab9702955 [quant][core] Add Embedding and EmbeddingBag reference module (#73436) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73436

This PR adds support reference module support for Embedding and EmbeddingBag, following https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md

* the reference module inherits from the corresponding float module (e.g. nn.Embedding), and the ReferenceQuantizedModule (which defines some utility functions to store qparms for a single weight)
* in forward, we first quantize and then dequantize weight (to generate the pattern) and then feed the weight to the original fp32 op

We'll connect this with fx grpah mode quantization later, in the final PR that deprecates the current convert implementation. Since current convert doesn't
support emitting quantize_per_tensor_dynamic ops, we don't want to implement it and immediately throw away the code, so might be better to just implement this
in the final flow.

Test Plan:
Will be tested later, in the final PR that deprecates the current convert implementation

Imported from OSS

Reviewed By: vkuzo

Differential Revision: D34480325

fbshipit-source-id: bc353f3be035a364e013fa9132d0422f19120ac3
(cherry picked from commit 1722ec2f8d82e9763ef252fed5796fd09d120e34)
 2022-03-02 23:32:54 +00:00
Jerry Zhang
81437e66c1 [quant][fx] Add RNN reference module (#73386) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73386

This PR adds support for RNN reference module, following https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md
This includes: RNNCell, LSTMCell, GRUCell, LSTM

Test Plan:
will be tested in the lowering flow in a separate PR

Imported from OSS

Reviewed By: vkuzo

Differential Revision: D34469445

fbshipit-source-id: 71a13d7d056f7aaccdd98fb477c8a3a38aecc249
(cherry picked from commit 0b10f0d127515556b677eae3150f026ac8cd9acd)
 2022-03-02 10:30:37 +00:00
Jerry Zhang
5723c03bad [quant][core] Refactor implementations for reference module (#73385) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73385

* Refactored some methods to ReferenceQuantizedModule
** _init_weight_qparams, get_quantized_weight, get_wegiht
* Added float16 conversion in _quantize_weight

Test Plan:
python test/test_quantization.py TestQuantizeFx
python test/test_quantization.py TestQuantizeFxOps

Imported from OSS

Reviewed By: vkuzo

Differential Revision: D34469447

fbshipit-source-id: 2082c91631128d31d96b56ef96e3792d45bf3eee
(cherry picked from commit a57318043b27039c1b8a64adb117b51780d6c573)
 2022-03-01 08:33:04 +00:00
Terry Chen
16e2f5d291 [quant] Add ConvTranspose reference module - Reland #73031 (#73094) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73094

Add ConvTranspose reference module

Test Plan:
python3 test/test_quantization.py TestQuantizeEagerOps.test_conv_transpose_2d

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D34352228

fbshipit-source-id: 03062d6b441bc5a3298ec094f421a69c4c3d5c40
(cherry picked from commit 2f2bdd4fcf)
 2022-02-23 02:31:42 +00:00
Jane Xu
477d1bd6cf Revert D34313425: [quant] Add ConvTranspose reference module 
Test Plan: revert-hammer

Differential Revision:
D34313425 (710f12f58e)

Original commit changeset: 3eeec1b24a51

Original Phabricator Diff: D34313425 (710f12f58e)

fbshipit-source-id: aecf9113d2e4cef3ccf4e1a9c4c33b07dc2ad385
(cherry picked from commit 3fcb9cd14d)
 2022-02-18 17:31:20 +00:00
Terry Chen
710f12f58e [quant] Add ConvTranspose reference module (#73031) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73031

Add ConvTranspose reference module

Test Plan:
python3 test/test_quantization.py TestQuantizeEagerOps.test_conv_transpose_2d

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D34313425

fbshipit-source-id: 3eeec1b24a51c7951c4d4b0c7dca43a012468b85
(cherry picked from commit 0ee7c1cc39)
 2022-02-18 06:29:12 +00:00
Nikita Shulga
e6fd28fb05 Revert D34126542: [Qunat] Add ConvTranspose reference module 
Test Plan: revert-hammer

Differential Revision:
D34126542 (7a031ec17f)

Original commit changeset: 7da167695a1f

Original Phabricator Diff: D34126542 (7a031ec17f)

fbshipit-source-id: 14e40884807b9908017ae30af83a8dea23ff1f0f
(cherry picked from commit f99a7f5a69)
 2022-02-16 22:24:15 +00:00
Terry Chen
7a031ec17f [Qunat] Add ConvTranspose reference module (#72473) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72473

Add ConvTranspose reference module

Test Plan:
python3 test/test_quantization.py TestQuantizeEagerOps.test_conv_transpose_op

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D34126542

fbshipit-source-id: 7da167695a1fd9c141059bce14cce4f0608b086c
(cherry picked from commit dee22dcf48)
 2022-02-16 01:56:28 +00:00
Andrew Or
3e43c478a8 [Quant][fx] Lower reference conv[1-3]d module (#69228) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69228

Implement lowering logic for reference conv modules,
similar to https://github.com/pytorch/pytorch/pull/65723.
ghstack-source-id: 145058198

Test Plan:
python test/test_quantization.py TestQuantizeFx.test_conv_lowering

Imported from OSS

Reviewed By: anjali411

Differential Revision: D32890743

fbshipit-source-id: 04f2500628c60b0fbc84d22705164215e190aeba
 2021-12-14 11:23:39 -08:00
Jerry Zhang
06e49ea088 [not4land][quant][fx][graphmode] lower reference linear module example (#65723) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65723

Example lowering reference linear module to fbgemm/qnnpack quantized linear module

Test Plan: Imported from OSS

Reviewed By: vkuzo

Differential Revision: D31567461

fbshipit-source-id: 0b8fffaf8e742ec15cb07bf6a4672cf3e856db2d
 2021-10-18 13:14:39 -07:00
Jerry Zhang
d4a86c1f3b [quant][fx2trt] Add lowering support for reference linear/conv modules (#64368) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/64368

Test Plan:
python torch/fx/experimental/fx2trt/example/quantized_resnet_test.py

Imported from OSS

Reviewed By: 842974287

Differential Revision: D30708738

fbshipit-source-id: 88142b7ce43ed96093597112dab03a2d277de993
 2021-09-10 22:25:27 -07:00
Jerry Zhang
8f88f797db [quant][graphmode][fx] Add reference quantized conv module (#63828) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63828

Added reference quantized conv module for the custom backend flow, the reference quantized module will
have the following code:
```
        w(float) -- quant - dequant \
        x(float) ------------- F.conv2d ---
```
In the full model, we will see
```
        w(float) -- quant - *dequant \
        x -- quant --- *dequant --  *F.conv2d --- *quant - dequant
```
and the backend should be able to fuse the ops with `*` into a quantized linear

Test Plan:
python test/test_quantization.py TestQuantizeFx.test_conv_linear_reference

Imported from OSS

Reviewed By: vkuzo

Differential Revision: D30504749

fbshipit-source-id: e1d8c43a0e0d6d9ea2375b8ca59a9c0f455514fb
 2021-08-30 14:23:17 -07:00
Jerry Zhang
0d0605eaa9 [quant][graphmode][fx] Add reference quantized linear module (#63627) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63627

Added reference quantized linear module for the custom backend flow, the reference quantized module will
have the following code:
```
        w(float) -- quant - dequant \
        x(float) ------------- F.linear ---
```
In the full model, we will see
```
        w(float) -- quant - *dequant \
        x -- quant --- *dequant --  *F.linear --- *quant - dequant
```
and the backend should be able to fuse the ops with `*` into a quantized linear

Test Plan:
python test/test_quantization.py TestQuantizeFx.test_conv_linear_reference

Imported from OSS

Reviewed By: vkuzo

Differential Revision: D30504750

fbshipit-source-id: 5729921745c2b6a0fb344efc3689f3b170e89500
 2021-08-27 22:53:24 -07:00
Sam Estep
4753100a3b Un-ignore F403 in .flake8 (#55838) 
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
 2021-04-13 09:24:07 -07:00
Jerry Zhang
7b54a8fc23 [quant] Reference option for conv module (#52316) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/52316

Test Plan: Imported from OSS

Reviewed By: vkuzo

Differential Revision: D26505642

fbshipit-source-id: e25b1a3cc37c4b744e694946e6ddf1470dd4692b
 2021-02-24 14:54:02 -08:00
Jerry Zhang
636fa8fda8 [quant] Add backend_independent option for quantized linear module (#48192) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48192

This is to allow producing a backend independent quantized module
since some backend don't have packed weight for linear

Test Plan:
test_quantized_module.py

Imported from OSS

Reviewed By: raghuramank100

Differential Revision: D25061645

fbshipit-source-id: a65535e53f35af4f2926af0ee330fdaae6dae996
 2020-11-21 00:32:27 -08:00