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34 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
zaf
c92e5ac95b [quant][ao_migration] torch.nn.quantized.modules → torch.ao.nn.quantized.modules (#78713) 
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] [Current PR] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
    - [ ] `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 @vkuzo
  - docs/source/conf.py
  - docs/source/quantization.rst
- [quantize_fx](torch/ao/quantization/quantize_fx.py) @jerryzh168
- [common test routine](test/quantization/ao_migration/common.py) @HDCharles
- JIT stuff @jamesr66a
  - torch/csrc/jit/passes/hoist_conv_packed_params.cpp
  - torch/csrc/jit/passes/quantization/helper.h
  - torch/csrc/jit/serialization/import_source.cpp

Differential Revision: [D38926012](https://our.internmc.facebook.com/intern/diff/D38926012/)

Differential Revision: [D38926012](https://our.internmc.facebook.com/intern/diff/D38926012)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78713
Approved by: https://github.com/jerryzh168
 2022-08-25 16:50:33 +00:00
PyTorch MergeBot
6a9c02339d Revert "[quant][ao_migration] torch.nn.quantized.modules → torch.ao.nn.quantized.modules (#78713)" 
This reverts commit 432f037498.

Reverted https://github.com/pytorch/pytorch/pull/78713 on behalf of https://github.com/janeyx99 due to Reverting for breaking (trunk-only) ios build
 2022-08-22 07:32:37 +00:00
zaf
432f037498 [quant][ao_migration] torch.nn.quantized.modules → torch.ao.nn.quantized.modules (#78713) 
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] [Current PR] `torch.nn.quantized.modules` → `torch.ao.nn.quantized.modules`
    - [ ] `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 @vkuzo
  - docs/source/conf.py
  - docs/source/quantization.rst
- [quantize_fx](torch/ao/quantization/quantize_fx.py) @jerryzh168
- [common test routine](test/quantization/ao_migration/common.py) @HDCharles
- JIT stuff @jamesr66a
  - torch/csrc/jit/passes/hoist_conv_packed_params.cpp
  - torch/csrc/jit/passes/quantization/helper.h
  - torch/csrc/jit/serialization/import_source.cpp

Differential Revision: [D36860145](https://our.internmc.facebook.com/intern/diff/D36860145/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78713
Approved by: https://github.com/jerryzh168
 2022-08-22 01:38:55 +00:00
joncrall
4618371da5 Integrate xdoctest - Rebased (#82797) 
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
 2022-08-12 02:08:01 +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
anjali411
f68f77610a Add __all__ to torch.nn.quantized, fx.passes, ao.nn and amp submodules (#80376) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/80376
Approved by: https://github.com/albanD
 2022-06-27 21:36:27 +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
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
dzdang
ab1e88e392 [Quant][Eager][improvement] Added 4 bit support for eager mode quantization flow (reland PR 69806) (#72277) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72277

Minor modifications were made to support 4 bit embedding quantized module in eager mode quantization flow and to allow for testing of the changes

Test Plan:
In pytorch main dir, execute
```
python test_quantization.py TestPostTrainingStatic.test_quantized_embedding
```

Reviewed By: jerryzh168

Differential Revision: D33994545

Pulled By: dzdang

fbshipit-source-id: faafad54b7b07fc393904ba55c2b2ac934c276f7
(cherry picked from commit 042ffb2091)
 2022-02-04 14:10:30 +00:00
dzdang
bfdf45cc89 [Quant][improvement] Added 4 bit support for embedding quantized module (reland PR 69769) (#72276) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72276

Added 4 bit support and the correpsonding test in the module api. Restructured the test_quantized_module for both 4 & 8 bit support.

Test Plan:
In pytorch main dir, execute
```
python test/test_quantization.py TestStaticQuantizedModule.test_embedding_api
```

Reviewed By: dagitses

Differential Revision: D33994544

Pulled By: dzdang

fbshipit-source-id: 49f04f267913e9f3f9649305b233055157c82dee
(cherry picked from commit c8c8e6fb44)
 2022-02-04 14:10:30 +00:00
Digant Desai
b613fbdbf2 Back out "[Quant] Added 4 bit support for embedding quantized module" (#70273) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70273

Original commit changeset: 73e63383cf60

Original Phabricator Diff: D33152674 (9f512e129b)

Test Plan: CI

Reviewed By: larryliu0820

Differential Revision: D33268459

fbshipit-source-id: 051bfcbbad3fa083301a3cea508d00946d6db881
 2021-12-21 21:28:04 -08:00
Digant Desai
47ba28f3b5 Back out "[Quant][Eager] Added 4 bit support for eager mode quantization flow" (#70272) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70272

Original commit changeset: 5cdaac5aee9b

Original Phabricator Diff: D33152675 (75718e5059)

Test Plan: CI

Reviewed By: larryliu0820

Differential Revision: D33268415

fbshipit-source-id: 99eb3209d513149ed23a1d9071d1b1c12174d09a
 2021-12-21 21:28:01 -08:00
David Dang
75718e5059 [Quant][Eager] Added 4 bit support for eager mode quantization flow (#69806) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69806

Minor modifications were made to support 4 bit embedding quantized module in eager mode quantization flow and to allow for testing of the changes

Test Plan:
In pytorch main dir, execute
```
python test_quantization.py TestPostTrainingStatic.test_quantized_embedding
```
to run the series of tests, including the newly added test_embedding_4bit
function

Imported from OSS

Reviewed By: jbschlosser

Differential Revision: D33152675

fbshipit-source-id: 5cdaac5aee9b8850e61c99e74033889bcfec5d9f
 2021-12-19 06:14:12 -08:00
David Dang
9f512e129b [Quant] Added 4 bit support for embedding quantized module (#69769) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69769

Added 4 bit support and the correpsonding test in the module api. Restructured the test_quantized_module for both 4 & 8 bit support.

Test Plan:
In pytorch main dir, execute
```
python test/test_quantization.py TestStaticQuantizedModule.test_embedding_api
```

Imported from OSS

Reviewed By: jbschlosser

Differential Revision: D33152674

fbshipit-source-id: 73e63383cf60994ab34cc7b4eedd8f32a806cf7f
 2021-12-18 22:26:24 -08:00
Ben Koopman
f3983f9c47 [quant][embdding qat] Re-land Add FX support for QAT EmbeddingBag (#69334) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69334

Original PR #68121 broke with incompatible qengine for Mac OS, this PR re-introduces changes with fix

Add FX support for QAT EmbeddingBag operator, previously only eager mode support.

Test Plan:
pytest test/quantization/fx/test_quantize_fx.py  -v -k "test_qat_embeddingbag_linear"

Imported from OSS

Reviewed By: jingsh

Differential Revision: D32815153

fbshipit-source-id: 33654ce29de6e81920bf3277a75027fe403a1eb2
 2021-12-08 05:57:20 -08:00
Nikita Shulga
ec4c749024 Revert D32318435: [quant][embdding qat] Add FX support for QAT EmbeddingBag 
Test Plan: revert-hammer

Differential Revision:
D32318435 (4484c04513)

Original commit changeset: 8b5d1a5d5422

fbshipit-source-id: e46d431f92a5c3f86c757695164d1eb5b0041298
 2021-12-02 14:27:17 -08:00
Ben Koopman
4484c04513 [quant][embdding qat] Add FX support for QAT EmbeddingBag (#68121) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68121

Add FX support for QAT EmbeddingBag operator, previously only eager mode support.

Test Plan:
pytest test/quantization/fx/test_quantize_fx.py  -v -k "test_qat_embeddingbag_linear"

Imported from OSS

Reviewed By: supriyar

Differential Revision: D32318435

fbshipit-source-id: 8b5d1a5d5422972c49676f9e470d5fbe29dd503b
 2021-12-02 09:05:07 -08:00
Ben Koopman
6c9cf5e6ea [quant][embedding qat] eager mode QAT for Embeddings (#66429) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66429

Test Plan: Imported from OSS

Reviewed By: HDCharles, supriyar

Differential Revision: D31618284

Pulled By: b-koopman

fbshipit-source-id: 0c0e2e86b98da9f29e9b2fc2a35c59424f94cbba
 2021-11-18 05:57:11 -08:00
Ben Koopman
0036e41143 [quant][embedding qat] Add eager QAT test for EmbeddingBag+Linear model (#66334) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/66334

Test Plan: Imported from OSS

Reviewed By: HDCharles

Differential Revision: D31618283

Pulled By: b-koopman

fbshipit-source-id: bb824a341f1aa9d7e83f8e66d320a9dfd348a1d7
 2021-10-19 07:03:36 -07:00
Zafar Takhirov
b23709df03 [ao_migration] torch/nn/quantized: torch.quantization -> torch.ao.quantization (#65900) 
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
 2021-10-08 16:19:53 -07:00
Vasiliy Kuznetsov
a7cc07f109 quantized embedding: make error message clearer (#66051) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66051

Make the error message clearer when quantized embedding is converted
with an unsupported dtype. This is helpful when debugging quantization
errors on new models.

Test Plan:
```
class M(nn.Module):
    def __init__(self):
        super().__init__()
        self.embedding = nn.Embedding(1, 1)

m = M().eval()
m.qconfig = torch.quantization.QConfig(
    activation=torch.quantization.MinMaxObserver.with_args(dtype=torch.qint8),
    weight=torch.quantization.MinMaxObserver.with_args(dtype=torch.qint8))
m.embedding.qconfig = m.qconfig
mp = torch.quantization.prepare(m)
mq = torch.quantization.convert(m)
// error message now includes the incorrect dtype
```

Imported from OSS

Reviewed By: dagitses

Differential Revision: D31472848

fbshipit-source-id: 86f6d90bc0ad611aa9d1bdae24497bc6f3d2acaa
 2021-10-08 08:32:22 -07:00
Ben Koopman
a58ff186e8 [quant][embedding qat] Add basic EmbeddingBag QAT fakeQuant workflow (#65443) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/65443

Test Plan: Imported from OSS

Reviewed By: dagitses, supriyar

Differential Revision: D31456445

Pulled By: b-koopman

fbshipit-source-id: 0edda6e272d9005fce65f2ba6a5e6abc831836de
 2021-10-07 20:19:29 -07:00
Supriya Rao
8a974a482c [quant] Add support for quantization of Embedding{Bag} in dynamic quant APIs (#65674) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65674

Before this PR user had to use the eager mode static quantization APIs to quantize Embedding/EmbeddingBag modules.
With this PR they can use either the static or dynamic quantization APIs for Embedding quantization

The only qconfig supported for embedding quantization is float_qparams_weight_only_qconfig whcih is currently enforced in the from_float
method of the quantized Embedding/Embedding modules.

To combine embedding quantization with Linear dynamic quantization, user can use the qconfig_dict to specify different qconfig for each module type.

The prepare/convert APIs can still be used to quantize Embeddings, with the caveat that user need to ensure input to Embedding ops are FP32.

Addresses Issue #65185
ghstack-source-id: 139935419

Test Plan:
python test/test_quantization.py

Imported from OSS

Reviewed By: gchanan

Differential Revision: D31211199

fbshipit-source-id: 8c747881caee5ccbf8b93c6704b08d132049dea4
 2021-10-06 23:19:38 -07:00
Supriya Rao
554a1a70c7 [quant] update embedding module to not store qweight (#50418) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50418

previously we were storing the quantized weight as a module attribute, whcih
was resulting in the weight getting stored as part of the model.
We don't need this since we already store the unpacked weights as part of the model.

Test Plan:
Before
```
Archive:  tmp.pt
 Length   Method    Size  Cmpr    Date    Time   CRC-32   Name
--------  ------  ------- ---- ---------- ----- --------  ----
     586  Stored      586   0% 00-00-1980 00:00 5fefdda0  tmp/extra/producer_info.json
 1588700  Stored  1588700   0% 00-00-1980 00:00 04e0da4c  tmp/data/0
   63548  Stored    63548   0% 00-00-1980 00:00 0ceb1f45  tmp/data/1
   63548  Stored    63548   0% 00-00-1980 00:00 517bc3ab  tmp/data/2
 1588700  Stored  1588700   0% 00-00-1980 00:00 dbe88c73  tmp/data/3
   63548  Stored    63548   0% 00-00-1980 00:00 d8dc47c4  tmp/data/4
   63548  Stored    63548   0% 00-00-1980 00:00 b9e0c20f  tmp/data/5
    1071  Stored     1071   0% 00-00-1980 00:00 10dc9350  tmp/data.pkl
     327  Defl:N      203  38% 00-00-1980 00:00 dfddb661  tmp/code/__torch__/___torch_mangle_0.py
     185  Stored      185   0% 00-00-1980 00:00 308f580b  tmp/code/__torch__/___torch_mangle_0.py.debug_pkl
    1730  Defl:N      515  70% 00-00-1980 00:00 aa11f799  tmp/code/__torch__/torch/nn/quantized/modules/embedding_ops.py
    1468  Defl:N      636  57% 00-00-1980 00:00 779609a6  tmp/code/__torch__/torch/nn/quantized/modules/embedding_ops.py.debug_pkl
       0  Stored        0   0% 00-00-1980 00:00 00000000  tmp/code/__torch__/torch/classes/quantized.py
       6  Stored        6   0% 00-00-1980 00:00 816d0907  tmp/code/__torch__/torch/classes/quantized.py.debug_pkl
       4  Stored        4   0% 00-00-1980 00:00 57092f6d  tmp/constants.pkl
       2  Stored        2   0% 00-00-1980 00:00 55679ed1  tmp/version
--------          -------  ---                            -------
 3436971          3434800   0%                            16 files
```
After
```
Archive:  tmp.pt
 Length   Method    Size  Cmpr    Date    Time   CRC-32   Name
--------  ------  ------- ---- ---------- ----- --------  ----
 1588700  Stored  1588700   0% 00-00-1980 00:00 a4da6981  tmp/data/0
   63548  Stored    63548   0% 00-00-1980 00:00 74d9b607  tmp/data/1
   63548  Stored    63548   0% 00-00-1980 00:00 e346a0c2  tmp/data/2
     952  Stored      952   0% 00-00-1980 00:00 eff8706e  tmp/data.pkl
     375  Defl:N      227  40% 00-00-1980 00:00 96c77b68  tmp/code/__torch__/quantization/test_quantize/___torch_mangle_23.py
     228  Defl:N      162  29% 00-00-1980 00:00 6a378113  tmp/code/__torch__/quantization/test_quantize/___torch_mangle_23.py.debug_pkl
    1711  Defl:N      509  70% 00-00-1980 00:00 66d8fd61  tmp/code/__torch__/torch/nn/quantized/modules/embedding_ops.py
    1473  Defl:N      634  57% 00-00-1980 00:00 beb2323b  tmp/code/__torch__/torch/nn/quantized/modules/embedding_ops.py.debug_pkl
       0  Stored        0   0% 00-00-1980 00:00 00000000  tmp/code/__torch__/torch/classes/quantized.py
       6  Stored        6   0% 00-00-1980 00:00 816d0907  tmp/code/__torch__/torch/classes/quantized.py.debug_pkl
       4  Stored        4   0% 00-00-1980 00:00 57092f6d  tmp/constants.pkl
       2  Stored        2   0% 00-00-1980 00:00 55679ed1  tmp/version
--------          -------  ---                            -------
 1720547          1718292   0%                            12 files
```

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D25879879

fbshipit-source-id: e09427a60d4c44dd1a190575e75f3ed9cde6358f
 2021-01-14 10:38:06 -08:00
Richard Barnes
638086950d Clean up type annotations in torch/nn/quantized/modules (#49941) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49941

Test Plan: Sandcastle

Reviewed By: jerryzh168

Differential Revision: D25718715

fbshipit-source-id: bbe450d937cf7ef634e003c09146e308180d1d58
 2021-01-06 11:03:08 -08:00
Alex Henrie
5f2ec6293d Unused variables in neural net classes and functions (#50100) 
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
 2021-01-06 08:16:57 -08:00
Jerry Zhang
576fa09157 [quant][fix] Fix quant type classification for float_qparam qconfig (#48069) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48069

also renamed float_qparam_dynamic_qconfig to float_qparam_weight_only_qconfig
It's not used in user code yet so we only need to update the tests.

Test Plan: Imported from OSS

Reviewed By: supriyar

Differential Revision: D25010175

fbshipit-source-id: caa3eaa5358a8bc5c808bf5f64e6ebff3e0b61e8
 2020-11-18 18:22:08 -08:00
Supriya Rao
31888b2e77 [quant][pyper] Rename the sparse argument for embedding_bag ops (#46003) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46003

sparse is confusing because itt is used in training for sparse gradients

Test Plan: Imported from OSS

Reviewed By: radkris-git, qizzzh

Differential Revision: D24178248

fbshipit-source-id: 0a2b595f3873d33b2ce25839b6eee31d2bfd3b0d
 2020-10-08 16:15:28 -07:00
Supriya Rao
43dc7ef933 [quant] Support for 4-bit quantized EmbeddingBag module (#45865) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45865

Test Plan:
python test/test_quantization.py TestPostTrainingStatic.test_quantized_embedding_bag
python test/test_quantization.py TestStaticQuantizedModule.test_embedding_bag_api

Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D24120995

fbshipit-source-id: c55fc6b2cfd683d14d2a05be7c04f787fdf8cc79
 2020-10-06 21:11:52 -07:00
Xiang Gao
20ac736200 Remove py2 compatible future imports (#44735) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44735

Reviewed By: mruberry

Differential Revision: D23731306

Pulled By: ezyang

fbshipit-source-id: 0ba009a99e475ddbe22981be8ac636f8a1c8b02f
 2020-09-16 12:55:57 -07:00
Supriya Rao
646ffd4886 [quant] Move EmbeddingBag eager quantization to static (#44217) 
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
 2020-09-08 19:05:02 -07:00