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Author SHA1 Message Date
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
78c8a0d752 [quant][ao_migration] torch.nn.quantized.functional → torch.ao.nn.quantized.functional (#78712) 
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] [Current PR] `torch.nn.quantized.functional` → `torch.ao.nn.quantized.functional`
  - [ ] `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](docs/source/quantization-support.rst) @vkuzo
- [Public API test list](test/allowlist_for_publicAPI.json) @peterbell10

Differential Revision: [D36792967](https://our.internmc.facebook.com/intern/diff/D36792967/)

**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D36792967/)!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78712
Approved by: https://github.com/jerryzh168
 2022-08-18 17:51:54 +00:00
ProGamerGov
357b7d589c Fix docstring inconsistencies: string -> str, boolean -> bool (#82410) 
### Description

Throughout the PyTorch docs and codebase, the `string` type in docstrings is referred to by two separate names. This leads to inconsistent docs, like you can see here: https://pytorch.org/docs/stable/generated/torch.nn.Conv3d.html#torch.nn.Conv3d

This PR fixes this issue by ensuring that all mentions of the string type in docstrings, are using the same format that Sphinx generates hyperlinks for.

### Testing
No testing should be required for this change

Pull Request resolved: https://github.com/pytorch/pytorch/pull/82410
Approved by: https://github.com/jbschlosser
 2022-07-28 21:29:57 +00:00
Kushashwa Ravi Shrimali
452c26bbeb Fix functional.max_poolNd warning spam in the CI 
Fixes https://github.com/pytorch/pytorch/issues/71257.

Warnings have been removed, please see [this](https://github.com/pytorch/pytorch/pull/71258#issuecomment-1058503649) comment.

cc: @Lezcano @jbschlosser @zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71258
Approved by: https://github.com/Lezcano, https://github.com/jbschlosser
 2022-03-04 18:42:23 +00:00
Vasiliy Kuznetsov
92a85ecbab add a quantized hardsigmoid inplace variant (#65740) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65740

fp32 hardsigmoid supports inplace. This PR adds the inplace support to the quantized
hardsigmoid function, to make the signatures match.

Test Plan:
```
python test/test_quantization.py TestQuantizedOps.test_qhardsigmoid
```

Reviewed By: supriyar

Differential Revision: D31992282

Pulled By: vkuzo

fbshipit-source-id: f6be65d72954ab8926b36bb74a5e79d422fbac90
 2021-11-03 07:35:31 -07:00
Vasiliy Kuznetsov
8b1258698e Improve quantization API docs (#66379) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66379

Description:

Creates a quantization API reference and fixes all the docblock errors.

This is #66122 to #66210 squashed together

Test Plan:
```
cd docs
make html
python -m http.server
// open webpage, inspect it, looks good
```

Reviewed By: ejguan

Differential Revision: D31543172

Pulled By: vkuzo

fbshipit-source-id: 9131363d6528337e9f100759654d3f34f02142a9
 2021-10-11 18:46:11 -07:00
Mike Ruberry
09c3e6002b Revert D31447615: Quantization docs: rewrite API reference to be more automated 
Test Plan: revert-hammer

Differential Revision:
D31447615 (7d2526ab20)

Original commit changeset: 09874ad9629f

fbshipit-source-id: 0963c9f5118e243cd299f8cded2bf7b0848a7105
 2021-10-10 01:51:05 -07:00
Vasiliy Kuznetsov
7d2526ab20 Quantization docs: rewrite API reference to be more automated (#66201) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66201

Description:

This PR switches the quantization API reference to use `autosummary`
for each section.  We define the sections and manually write a list
of modules/functions/methods to include, and sphinx does the rest.
A result is a single page where we have every quantization function
and module with a quick autogenerated blurb, and user can click
through to each of them for a full documentation page.

This mimics how the `torch.nn` and `torch.nn.functional` doc
pages are set up.

In detail, for each section before this PR:
* creates a new section using `autosummary`
* adds all modules/functions/methods which were previously in the manual section
* adds any additional modules/functions/methods which are public facing but not previously documented
* deletes the old manual summary and all links to it

Test Plan:
```
cd docs
make html
python -m http.server
// renders well, links work
```

Reviewed By: jerryzh168

Differential Revision: D31447615

Pulled By: vkuzo

fbshipit-source-id: 09874ad9629f9c00eeab79c406579c6abd974901
 2021-10-09 06:46:02 -07:00
Jerry Zhang
8aaca4b46a [reland][quant] Remove nn.quantized.ReLU module and nn.quantized.functional.relu (#47415) (#48038) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48038

nn.ReLU works for both float and quantized input, we don't want to define an nn.quantized.ReLU
that does the same thing as nn.ReLU, similarly for nn.quantized.functional.relu

this also removes the numerical inconsistency for models quantizes nn.ReLU independently in qat mode

Test Plan:
Imported from OSS

Imported from OSS

Reviewed By: vkuzo

Differential Revision: D25000462

fbshipit-source-id: e3609a3ae4a3476a42f61276619033054194a0d2
 2020-11-17 09:52:21 -08:00
Vasiliy Kuznetsov
4779553921 Revert "[quant] Remove nn.quantized.ReLU module and nn.quantized.functional.relu (#47415)" (#47949) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47949

This reverts commit 1478e5ec2a.

Test Plan: Imported from OSS

Reviewed By: supriyar

Differential Revision: D24966363

Pulled By: vkuzo

fbshipit-source-id: ca1126f699eef84027a15df35962728296c8a790
 2020-11-14 08:40:30 -08:00
Yang Wang
8ff0b6fef8 [OpBenchMobile] Enable operator_benchmark to run the benchmark on mobile through AiBench (#47767) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47767

This diff implements the functionality of running benchmark on mobile on top of operator_benchmark framework. It does so through a few steps:

1. create a scripted module from existing benchmark case.
2. run mobile specific optimization pass on the scripted module
3. run the scripted module on AiBench by calling its Python API

A small change in the way of writing a benchmark case is introduced so that both local and mobile run can share the same interface. The change is about having inputs as arguments of the `forward` function, so that mobile optimization pass can be run successfully (otherwise everything will be optimized away by constant propagation).

Test Plan:
## local op_bench run

buck run caffe2/benchmarks/operator_benchmark:benchmark_all_test --  --iterations 1 --warmup_iterations 1

buck run caffe2/benchmarks/operator_benchmark:benchmark_all_test --  --iterations 1 --warmup_iterations 1 --use_jit

Exceptions: `py_module` op in `FakeQuantizePerTensorBaseOpBenchmark` and `FakeQuantizePerChannelBaseOpBenchmark` under JIT mode. These tests also failed in the base version

```
RuntimeError:
Module 'FakeQuantizePerChannelOpBenchmark' has no attribute 'op_func' (This function exists as an attribute on the Python module, but we failed to compile it to a TorchScript function.
The error stack is reproduced here:

Python builtin <built-in method apply of FunctionMeta object at 0x619000c652a0> is currently not supported in Torchscript:
  File "/data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/pt/quantization_test#link-tree/quantization_test.py", line 260
    quant_min: int, quant_max: int
):
    return _LearnableFakeQuantizePerChannelOp.apply(input, scale, zero_point, axis, quant_min, quant_max, 1.0)
           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
:
  File "/data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/pt/quantization_test#link-tree/quantization_test.py", line 313
        axis: int, quant_min: int, quant_max: int
    ):
        return self.op_func(input, scale, zero_point, axis, quant_min, quant_max)
               ~~~~~~~~~~~~ <--- HERE
```

`_consume_op` typing mismatch: chunk, split, qobserver, sort in qunary. These will be fixed in D24774105

## OSS test

python3 -m benchmark_all_test --iterations 1 --warmup_iterations 1 --use_jit
python3 -m benchmark_all_test --iterations 1 --warmup_iterations 1

## saved module graph
```
module __torch__.mobile_benchmark_utils.OpBenchmarkMobile {
  parameters {
  }
  attributes {
    training = True
    num_iters = 1
    benchmark = <__torch__.pt.add_test.___torch_mangle_4.AddBenchmark object at 0x6070001b8b50>
  }
  methods {
    method forward {
      graph(%self : __torch__.mobile_benchmark_utils.OpBenchmarkMobile):
        %12 : None = prim::Constant() # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/mobile_benchmark_utils.py:9:4
        %4 : bool = prim::Constant[value=1]() # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/mobile_benchmark_utils.py:10:8
        %1 : int = prim::GetAttr[name="num_iters"](%self)
         = prim::Loop(%1, %4) # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/mobile_benchmark_utils.py:10:8
          block0(%i : int):
            %6 : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark = prim::GetAttr[name="benchmark"](%self)
            %7 : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark = prim::GetAttr[name="benchmark"](%self)
            %self.inputs_tuple : (Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu), Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu)) = prim::Constant[value=({0.48884}, {0.809042})]()
            %9 : Tensor, %10 : Tensor = prim::TupleUnpack(%self.inputs_tuple)
            %23 : int = prim::Constant[value=1]()
            %24 : Tensor = aten::add(%9, %10, %23) # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/pt/add_test.py:39:15
            -> (%4)
        return (%12)

    }
  }
  submodules {
    module __torch__.pt.add_test.___torch_mangle_4.AddBenchmark {
      parameters {
      }
      attributes {
        mobile_optimized = True
      }
      methods {
        method forward {
          graph(%self : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark,
                %input_one.1 : Tensor,
                %input_two.1 : Tensor):
            %3 : int = prim::Constant[value=1]()
            %4 : Tensor = aten::add(%input_one.1, %input_two.1, %3) # /data/users/wangyang19/fbsource/fbcode/buck-out/dev/gen/caffe2/benchmarks/operator_benchmark/fb/pt/mobile/benchmark_all_test_fbcode#link-tree/pt/add_test.py:39:15
            return (%4)

        }
        method get_inputs {
          graph(%self : __torch__.pt.add_test.___torch_mangle_4.AddBenchmark):
            %self.inputs_tuple : (Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu), Float(1, 1, 1, strides=[1, 1, 1], requires_grad=0, device=cpu)) = prim::Constant[value=({0.48884}, {0.809042})]()
            return (%self.inputs_tuple)

        }
      }
      submodules {
      }
    }
  }
}

```

Reviewed By: kimishpatel

Differential Revision: D24322214

fbshipit-source-id: 335317eca4f40c4083883eb41dc47caf25cbdfd1
 2020-11-12 17:15:05 -08:00
Jerry Zhang
1478e5ec2a [quant] Remove nn.quantized.ReLU module and nn.quantized.functional.relu (#47415) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47415

nn.ReLU works for both float and quantized input, we don't want to define an nn.quantized.ReLU
that does the same thing as nn.ReLU, similarly for nn.quantized.functional.relu

this also removes the numerical inconsistency for models quantizes nn.ReLU independently in qat mode

Test Plan: Imported from OSS

Reviewed By: z-a-f

Differential Revision: D24747035

fbshipit-source-id: b8fdf13e513a0d5f0c4c6c9835635bdf9fdc2769
 2020-11-12 10:56:30 -08:00
Guilherme Leobas
c1e6592964 Enable type-checking of torch.nn.quantized.* modules (#43110) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/43029

I am not changing the following files in this PR:
* `torch/nn/quantized/dynamic/modules/rnn.py` due to https://github.com/pytorch/pytorch/issues/43072
* `torch/nn/quantized/modules/conv.py`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43110

Reviewed By: gchanan

Differential Revision: D23963258

Pulled By: ezyang

fbshipit-source-id: 0fb0fd13af283f6f7b3434e7bbf62165357d1f98
 2020-09-29 18:14:29 -07:00
Zafar
bb478810e0 [quant] torch.max_pool1d (#45152) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/45152

Test Plan: Imported from OSS

Reviewed By: jerryzh168

Differential Revision: D23846473

Pulled By: z-a-f

fbshipit-source-id: 38fd611e568e4f8b39b7a00adeb42c7b99576360
 2020-09-29 01:45:22 -07:00
Gao, Xiang
37658b144b Remove useless py2 compatibility import __future__, part 1 (#43808) 
Summary:
To avoid conflicts, this PR does not remove all imports. More are coming in further PRs.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43808

Reviewed By: wanchaol

Differential Revision: D23436675

Pulled By: ailzhang

fbshipit-source-id: ccc21a1955c244f0804277e9e47e54bfd23455cd
 2020-09-02 19:15:11 -07:00
wudenggang
9600ed9af3 typo fixes (#41632) 
Summary:
typo fixes

Pull Request resolved: https://github.com/pytorch/pytorch/pull/41632

Reviewed By: ezyang

Differential Revision: D22617827

Pulled By: mrshenli

fbshipit-source-id: c2bfcb7cc36913a8dd32f13fc9adc3aa0a9b682f
 2020-07-20 07:23:00 -07:00
Presley Graham
445e7eb01b Add quantized CELU operator by adding additional parameters to quantized ELU (#39199) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39199

Test Plan: Imported from OSS

Differential Revision: D21771202

Pulled By: durumu

fbshipit-source-id: 910de6202fa3d5780497c5bf85208568a09297dd
 2020-07-17 17:56:33 -07:00
Edward Leardi
733b8c23c4 Fix several quantization documentation typos (#40567) 
Summary:
This PR fixes several typos I noticed in the docs here: https://pytorch.org/docs/master/quantization.html. In one case there was a misspelled module [torch.nn.instrinsic.qat](https://pytorch.org/docs/master/quantization.html#torch-nn-instrinsic-qat) which I corrected and am including screenshots of below just in case.

<img width="1094" alt="before" src="https://user-images.githubusercontent.com/54918401/85766765-5cdd6280-b6e5-11ea-93e6-4944cf820b71.png">

<img width="1093" alt="after" src="https://user-images.githubusercontent.com/54918401/85766769-5d75f900-b6e5-11ea-8850-0d1f5ed67b16.png">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40567

Differential Revision: D22311291

Pulled By: ezyang

fbshipit-source-id: 65d1f3dd043357e38a584d9e30f31634a5b0995c
 2020-07-07 09:45:23 -07:00
Zafar
c314e0deb5 [quant] Quantized adaptive_avg_pool3d (#40271) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40271

Closes #40244

Test Plan: Imported from OSS

Reviewed By: vkuzo

Differential Revision: D22134318

Pulled By: z-a-f

fbshipit-source-id: 0489b6c083a3cbc21a1d81d8bfcc499372308088
 2020-06-23 18:13:48 -07:00
Vasiliy Kuznetsov
9bf255573f quant docs: add and clean up ELU (#40377) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40377

Cleans up the docstring for quantized ELU and adds it to the quantization docs.

Test Plan: * build on Mac OS and inspect

Differential Revision: D22162834

Pulled By: vkuzo

fbshipit-source-id: e548fd4dc8d67db27ed19cac4dbdf2a942586759
 2020-06-23 09:02:43 -07:00
Vasiliy Kuznetsov
d27f8eaf92 quant docs: add and clean up hardtanh (#40341) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40341

Cleans up the hardtanh docstring and adds it to quantization docs.

Test Plan: * build and inspect on Mac OS

Differential Revision: D22152636

Pulled By: vkuzo

fbshipit-source-id: c98e635199c8be332aa6958664ff23faad834908
 2020-06-23 09:02:32 -07:00
Vasiliy Kuznetsov
8e74fb6a0c quant docs: add and clean up hardsigmoid (#40340) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40340

Adds and simplifies quantization docs for hardsigmoid

Test Plan:
* build docs on Mac OS
* inspect

Differential Revision: D22152634

Pulled By: vkuzo

fbshipit-source-id: 18da273023fb00e5f0bc1e881b00536492c606d3
 2020-06-23 09:02:29 -07:00
Vasiliy Kuznetsov
c4594a97ae quant docs: clean up hardswish (#40323) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40323

Cleans up the naming and the function param docs for quantized hardswish.
Remove redundant docstrings and link to floating point modules instead.

Test Plan:
* build the docs on Mac OS
* verify that every link works as expected

Differential Revision: D22152638

Pulled By: vkuzo

fbshipit-source-id: fef04874ae460b449c677424a6a1c6dd47054795
 2020-06-23 08:59:34 -07:00
Vasiliy Kuznetsov
13d54c6471 quantized elu: require observation (#40100) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40100

ELU has a range of [-1, inf]. In the original PR which added
the quantized operator we decided to pass the quantization params
from the input.  However, it makes more sense to require observation
for this op.

This PR changes the API to require observation. Next PRs in this stack
will add the eager and graph mode handling.

Test Plan:
```
python test/test_quantization.py TestQuantizedOps.test_qelu
```

Imported from OSS

Differential Revision: D22075083

fbshipit-source-id: 0ea0fd05a00cc7a5f122a2b1de09144bbd586f32
 2020-06-21 09:38:28 -07:00
Paul Shao
6a75f650dd Implement Quantized Version of Threshold Function (#39352) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39352

In this task, the quantized backend of the kernel is implemented for the threshold function, which clamps the entries in a tensor less than or equal to  a given threshold to be a specified value.

The corresponding Python implementation and unit test are also added.

Test Plan:
1. On a devserver, build PyTorch from source by running the command `buck build mode/dev //caffe2:torch`
2. Run the unit test throught the command
`buck test mode/dev //caffe2/test:quantization -- test_qthreshold`

Reviewed By: z-a-f

Differential Revision: D21822446

fbshipit-source-id: e8c869664e6d4c664f0e7fa3957762992118c082
 2020-06-05 23:07:48 -07:00
Supriya Rao
de7025fbdb [quant] Support for functional quantized::conv1d (#38449) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38449

Also update docs to reflect conv1d op support

Test Plan:
python test/test_quantization.py TestQuantizedFunctional.test_conv1d_api

Imported from OSS

Differential Revision: D21575921

fbshipit-source-id: 21c9f6b49ad456cd9d93e97f17cf5b8d87f0da6b
 2020-05-14 16:09:51 -07:00
Jerry Zhang
70f375becf [quant] ConvPackedParams with TorchBind (#35923) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35923

(Note: this ignores all push blocking failures!)

Test Plan:
tbd

Imported from OSS

Differential Revision: D20957089

fbshipit-source-id: 74d8bd628ccba64e902ea6ebabc2b883924050b0
 2020-05-05 20:18:36 -07:00
Vasiliy Kuznetsov
7ac98c9396 graph mode: refactor quantized hardswish API for easier graph handling (#37523) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37523

Makes the quantized hardswish function API more suited to graph mode
handling, which will come in the next PR.

Test Plan:
CI

Imported from OSS

Differential Revision: D21310364

fbshipit-source-id: 0d438dce5b87481d558c07bcccd9fe717200b4dc
 2020-04-29 19:43:48 -07:00
Vasiliy Kuznetsov
7f50162d1e quantized activations: clean up more unneeded quantizations (#36981) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36981

Replaces unneeded quantize calls for remaining quantized
activations with empty tensor creation.
Should be a perf win for anyone who uses these.

Test Plan:
python test/quantization/test_quantized.py TestQuantizedOps

Imported from OSS

Differential Revision: D21185969

fbshipit-source-id: 473b2b8aa40046ea3f0665bd45b03f09e8a7d572
 2020-04-22 16:17:08 -07:00
Vasiliy Kuznetsov
2773ed3082 hardswish: remove unnecessary quantize call (#36980) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36980

Missed this on the original diff, fixing.  Create the output tensor directly instead of quantizing it.

Test Plan:
tests still pass
microbenchmarks show a 2x performance improvment for int8:
https://gist.github.com/vkuzo/3b321b428e4c38e805000961c263286b (this
will depend on input size)

Imported from OSS

Differential Revision: D21185970

fbshipit-source-id: 5b9e93d9f9ac05a8120532bd03ad347541a132c2
 2020-04-22 16:15:54 -07:00
Ralf Gommers
78d5707041 Fix type annotations and make MyPy run on torch/ (#36584) 
Summary:
This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages.

As a result, running `mypy` in the root dir of the repo now runs on:
- `torch/`
- `aten/src/ATen/function_wrapper.py` (the only file already covered in CI)

In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`.

Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through):

<details>

```
torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx'
torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined
torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch"
torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2'
torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2'
torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx'
torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util'
torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework'
torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer'
torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...")
torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items"
torch/nn/_reduction.py:27: error: Name 'Optional' is not defined
torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/_reduction.py:47: error: Name 'Optional' is not defined
torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs
torch/nn/modules/utils.py:27: error: Name 'List' is not defined
torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment"
torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction'
caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs
torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed)
caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs
caffe2/python/__init__.py:7: error: Module has no attribute "CPU"
caffe2/python/__init__.py:8: error: Module has no attribute "CUDA"
caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN"
caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL"
caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL"
caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP"
caffe2/python/__init__.py:13: error: Module has no attribute "HIP"
caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"?
caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"?
caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:35: error: Module has no attribute "cuda"
caffe2/python/__init__.py:37: error: Module has no attribute "cuda"
caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory"
torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs
torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs
torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import)
torch/_tensor_str.py:164: error: Not all arguments converted during string formatting
torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_linalg_utils.py:26: error: Name 'Optional' is not defined
torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Optional' is not defined
torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Optional' is not defined
torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Optional' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched'
torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm")
torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Optional' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/nn/init.py:109: error: Name 'Tensor' is not defined
torch/nn/init.py:126: error: Name 'Tensor' is not defined
torch/nn/init.py:142: error: Name 'Tensor' is not defined
torch/nn/init.py:165: error: Name 'Tensor' is not defined
torch/nn/init.py:180: error: Name 'Tensor' is not defined
torch/nn/init.py:194: error: Name 'Tensor' is not defined
torch/nn/init.py:287: error: Name 'Tensor' is not defined
torch/nn/init.py:315: error: Name 'Tensor' is not defined
torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction'
torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer'
torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_"
torch/jit/_builtins.py:80: error: Module has no attribute "stft"
torch/jit/_builtins.py:81: error: Module has no attribute "cdist"
torch/jit/_builtins.py:82: error: Module has no attribute "norm"
torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm"
torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm"
torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C'
torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...")
torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import)
torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass"
torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache"
torch/_lowrank.py:11: error: Name 'Tensor' is not defined
torch/_lowrank.py:13: error: Name 'Optional' is not defined
torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Optional' is not defined
torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Optional' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:82: error: Name 'Tuple' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:130: error: Name 'Tensor' is not defined
torch/_lowrank.py:130: error: Name 'Optional' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:130: error: Name 'Tuple' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:167: error: Name 'Tensor' is not defined
torch/_lowrank.py:167: error: Name 'Optional' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:167: error: Name 'Tuple' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type
torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases
torch/quantization/observer.py:45: error: Invalid base class "ABC"
torch/quantization/observer.py:127: error: Name 'Tensor' is not defined
torch/quantization/observer.py:127: error: Name 'Tuple' is not defined
torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:192: error: Name 'Tensor' is not defined
torch/quantization/observer.py:192: error: Name 'Tuple' is not defined
torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:534: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tuple' is not defined
torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:894: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:894: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:899: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:902: error: Name 'Tensor' is not defined
torch/quantization/observer.py:925: error: Name 'Tensor' is not defined
torch/quantization/observer.py:928: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:929: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric"
torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:289: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:290: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops"
torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops"
torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined
torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig'
torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__"
torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views'
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16
torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15
torch/functional.py:114: error: Name 'Tuple' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:114: error: Name 'Optional' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]"
torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:204: note: Possible overload variants:
torch/functional.py:204: note:     def __setitem__(self, int, int) -> None
torch/functional.py:204: note:     def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:204: note:     def __getitem__(self, int) -> int
torch/functional.py:204: note:     def __getitem__(self, slice) -> List[int]
torch/functional.py:207: error: "Tensor" has no attribute "copy_"
torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:212: note: Possible overload variants:
torch/functional.py:212: note:     def __setitem__(self, int, int) -> None
torch/functional.py:212: note:     def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:212: note:     def __getitem__(self, int) -> int
torch/functional.py:212: note:     def __getitem__(self, slice) -> List[int]
torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:334: error: Name 'Optional' is not defined
torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]"
torch/functional.py:431: error: Module has no attribute "stft"
torch/functional.py:766: error: Module has no attribute "cdist"
torch/functional.py:768: error: Module has no attribute "cdist"
torch/functional.py:770: error: Module has no attribute "cdist"
torch/functional.py:775: error: Name 'Optional' is not defined
torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'Optional' is not defined
torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'number' is not defined
torch/functional.py:780: error: Name 'norm' already defined on line 775
torch/functional.py:785: error: Name 'Optional' is not defined
torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:785: error: Name 'number' is not defined
torch/functional.py:785: error: Name 'norm' already defined on line 775
torch/functional.py:790: error: Name 'Optional' is not defined
torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:790: error: Name 'norm' already defined on line 775
torch/functional.py:795: error: Name 'norm' already defined on line 775
torch/functional.py:960: error: Name 'Any' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any")
torch/functional.py:960: error: Name 'Tuple' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized"
torch/functional.py:1041: error: Name 'Optional' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1041: error: Name 'Tuple' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1056: error: Name 'Optional' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1056: error: Name 'Tuple' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]")
torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]")
torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]")
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase"
torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]")
torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None")
torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric"
torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module'
torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68
torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass"
torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524
torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer'
torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching'
torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined
torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]")
torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]")
torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...")
torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/__init__.py:158: error: Name 'transforms' is not defined
torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse'
torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore'
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore'
torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584

Reviewed By: seemethere, ailzhang

Differential Revision: D21155985

Pulled By: ezyang

fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1
 2020-04-22 14:17:08 -07:00
Vasiliy Kuznetsov
65df8b3886 hardswish: make it work in static quantization (#36545) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36545

* adds a quantized nn.module for Hardswish so we can observe activation values
* modifies the hardswish op to allow specifying scale + zero_point
* makes hardswish model be properly swapped in static quantization

Test Plan:
added tests and they pass for:
* the new _out flavor of hardswish
* QNNPACK changes
* static quant e2e

Imported from OSS

Differential Revision: D21045320

fbshipit-source-id: ab7e52f0f54a7d5923ab6f58197022cc28c12354
 2020-04-15 18:02:35 -07:00
Vasiliy Kuznetsov
4ef383d5db add type hints on recently added ops to make them scriptable (#35885) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35885

For the ops I added recently, ensure all the typehints are
present, so that JIT can script them.

We might want to look into a test for this in the future.

Test Plan:
scripting works for all of them now:
https://gist.github.com/vkuzo/1d92fdea548ad596310fffcbe95e4438

Imported from OSS

Differential Revision: D20818431

fbshipit-source-id: 0de61eaf70c08d625128c6fffd05788e6e5bb920
 2020-04-06 12:17:16 -07:00
Vasiliy Kuznetsov
b4c4342747 hswish and hardsigmoid: improve docs (#35431) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35431

Resolving z-a-f's comments on earlier PRs on making
the docblocks easier to read.

Test Plan:
render the new docblocks in http://rst.aaroniles.net/

CI

Imported from OSS

Differential Revision: D20658668

fbshipit-source-id: 5ea4a21d6b8dc9d744e2f4ede2f9d5d799fb902f
 2020-03-31 10:01:07 -07:00
Vasiliy Kuznetsov
f1efe51028 add quantized version of hardswish operator (#34820) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34820

Adds quantized version of hardswish, for common quantized operator coverage.

Note:
* we carry over scale and zero_point from the input to the output, because the
  range of the output is unbounded if x > 0
* we also skip the .out function to not allow the user to specify a custom
  scale+zp (flexible on this).

Test Plan:
```
python test/test_quantized.py

https://gist.github.com/vkuzo/f9b579315ed7f5fdb24839e3218d8465
```

Imported from OSS

Differential Revision: D20472905

fbshipit-source-id: 0f2a83e9f5f7b43485fa46caf30e756dc5d492a9
 2020-03-24 15:16:58 -07:00
Vasiliy Kuznetsov
37b234a880 quantized hardsigmoid, take 2 (#34959) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34959

Adds quantized implementation of hardsigmoid.

Original PR was https://github.com/pytorch/pytorch/pull/34607 and had to
be reverted for a test breakage, trying again.

Test Plan:
tests
benchmarks

Imported from OSS

Differential Revision: D20514212

fbshipit-source-id: cc7ae3b67757e2dde5c313c05ce60a0f2625d961
 2020-03-19 13:27:22 -07:00
Shen Li
95f1cb34b9 Revert D20480546: adds quantized implementation of hard sigmoid 
Test Plan: revert-hammer

Differential Revision:
D20480546

Original commit changeset: 9febcb44afd9

fbshipit-source-id: 4461b455e63448cf45237e23c988b492c3e0f1b0
 2020-03-17 19:58:08 -07:00
Vasiliy Kuznetsov
58c5b6d306 adds quantized implementation of hard sigmoid (#34607) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34607

Adds quantized version of hardsigmoid activation.

Note: not implementing the _ and .out versions is
currently intended, because the implementation changes the scale and
zp and it's nice to not allow the user to specify scale
and zp.  Lmk if we should handle this differently.

Test Plan:
tests
benchmarks

Imported from OSS

Differential Revision: D20480546

fbshipit-source-id: 9febcb44afd920125ed2ca4900492f0b712078ea
 2020-03-17 16:01:39 -07:00
Lingyi Liu
5d65b5cd01 Add the 3d upsample quantized op for video model (#34594) 
Summary:
as title, we are currently missing this 3d op, which is required for video related model.

Performance benchmark:
```
import torch, time

for dtype in [torch.qint8, torch.quint8, torch.qint32]:
    print('****', str(dtype), '*****')
    x = torch.rand(1, 56, 64, 56, 256)

    q_x = torch.quantize_per_tensor(x, 0.5, 1, dtype)
    q_x = q_x.permute([0, 4, 1, 2, 3])

    x = x.permute([0, 4, 1, 2, 3])

    NITER = 100

    s = time.time()
    for i in range(NITER):
        float_out = torch.nn.functional.interpolate(x, size=30, scale_factor=None, mode="nearest", align_corners=None)
    time_per_iter_float = (time.time() - s) / NITER

    s = time.time()
    for i in range(NITER):
        quant_out = torch.nn.functional.interpolate(q_x, size=30, scale_factor=None, mode="nearest", align_corners=None)
    time_per_iter_quant = (time.time() - s) / NITER

    ref_quantized = torch.quantize_per_tensor(float_out, 0.5, 1, dtype)
    torch.testing.assert_allclose(ref_quantized.dequantize(), quant_out.dequantize())

    print('time/iter ms (float)', 'time/iter ms (quant)', 'quant/float', sep='\t')
    print(time_per_iter_float * 1000, time_per_iter_quant * 1000, time_per_iter_quant / time_per_iter_float, sep='\t')

    bytes_float = (x.numel() + float_out.numel()) * x.element_size()
    bytes_quant = (q_x.numel() + quant_out.numel()) * q_x.element_size()

    float_bw_gbps = bytes_float / time_per_iter_float / 1e9
    quant_bw_gbps = bytes_quant / time_per_iter_quant / 1e9

    print('GB/s float', 'GB/s quant', sep='\t')
    print(float_bw_gbps, quant_bw_gbps, sep='\t')
```

```
**** torch.qint8 *****
time/iter ms (float)  time/iter ms (quant)  quant/float
1136.8209528923035  1.294245719909668 0.0011384780660638283
GB/s float  GB/s quant
0.20510608588517917 45.03953391792442
**** torch.quint8 *****
time/iter ms (float)  time/iter ms (quant)  quant/float
827.9890131950378 1.11464262008667  0.0013462046021426
GB/s float  GB/s quant
0.28160868355034036 52.29678369508914
**** torch.qint32 *****
time/iter ms (float)  time/iter ms (quant)  quant/float
834.6958303451538 7.481417655944824 0.008963046638020456
GB/s float  GB/s quant
0.2793459455806586  31.16640544920269
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34594

Differential Revision: D20389106

Pulled By: lly-zero-one

fbshipit-source-id: d3a8c2cac58087d8b29e9cae64822f5b2d4c03ba
 2020-03-12 21:06:38 -07:00
Vasiliy Kuznetsov
43c9cc7a9c add quantized ELU activation (#34267) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34267

Adds quantized ELU.

Test Plan:
```
python test/test_quantized.py TestQuantizedOps.test_qelu
```

still need to benchmark, saving that for after the review comments

Imported from OSS

Differential Revision: D20370953

fbshipit-source-id: fe941bf966f72dd9eee2c4b2ef45fe7afb50c866
 2020-03-12 09:31:00 -07:00
Vasiliy Kuznetsov
2e88a78d2e add quantized_hardtanh (#34097) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34097

Adds quantized hardtanh.  Calls the clamp kernel behind the
scenes.

Test Plan:
```
python test/test_quantized.py
```

Imported from OSS

Differential Revision: D20208860

fbshipit-source-id: 165a6a1c22f1dcc479679e5ea0c990d0e9c3b6c5
 2020-03-10 22:27:15 -07:00
Lingyi Liu
b0479506a8 Add the 3d avg pool for video related model (#33339) 
Summary:
```
import torch, time

for dtype in [torch.qint8, torch.quint8, torch.qint32]:
    print('****', str(dtype), '*****')
    x = torch.rand(1, 5, 56, 56, 256)

    q_x = torch.quantize_per_tensor(x, 0.5, 1, dtype)
    q_x = q_x.permute([0, 4, 1, 2, 3])

    x = x.permute([0, 4, 1, 2, 3])

    NITER = 10

    s = time.time()
    for i in range(NITER):
        float_out = torch.nn.functional.avg_pool3d(x, kernel_size=3, stride=None, padding=0)
    time_per_iter_float = (time.time() - s) / NITER

    s = time.time()
    for i in range(NITER):
        quant_out = torch.nn.quantized.functional.avg_pool3d(q_x, kernel_size=3, stride=None, padding=0)
    time_per_iter_quant = (time.time() - s) / NITER
    print('time/iter ms (float)', 'time/iter ms (quant)', 'quant/float', sep='\t')
    print(time_per_iter_float * 1000, time_per_iter_quant * 1000, time_per_iter_quant / time_per_iter_float, sep='\t')
```

```
**** torch.qint8 *****
time/iter ms (float)  time/iter ms (quant)  quant/float
16.286182403564453  0.7308721542358398  0.04487682479080417
**** torch.quint8 *****
time/iter ms (float)  time/iter ms (quant)  quant/float
15.364313125610352  0.6497383117675781  0.042288796541418254
**** torch.qint32 *****
time/iter ms (float)  time/iter ms (quant)  quant/float
15.649032592773438  13.879132270812988  0.8869003363966556
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33339

Differential Revision: D19900904

Pulled By: lly-zero-one

fbshipit-source-id: 4522cc6b4a0751aeda6c7edc258e0cb3f55a8fe3
 2020-03-06 17:44:34 -08:00
JeongUkJae
b10761d890 fix type stub errors (#33762) 
Summary:
I've been using pytorch with type hintings, and I found errors that can be easily fixed. So I'm creating this PR to fix type bugs.

I expected below code should be type-checked without any errors.

```python
import torch
from torch.nn import Linear
from torch.autograd import Variable
from torch.optim import AdamW
from torch.utils import hooks

# nn.Module should have training attribute
module = Linear(10, 20)
module.training

# torch should have dtype bfloat16
tensor2 = torch.tensor([1,2,3], dtype=torch.bfloat16)

# torch.Tensor.cuda should accept int or str value
torch.randn(5).cuda(1)
torch.tensor(5).cuda('cuda:0')

# optimizer should have default attribute
module = Linear(10, 20)
print(AdamW(module.weight).default)

# torch.Tensor should have these boolean attributes
torch.tensor([1]).is_sparse
torch.tensor([1]).is_quantized
torch.tensor([1]).is_mkldnn

# Size class should tuple of int
a, b = torch.tensor([[1,2,3]]).size()

# check modules can be accessed
torch.nn.parallel
torch.autograd.profiler
torch.multiprocessing
torch.sparse
torch.onnx
torch.jit
torch.hub
torch.random
torch.distributions
torch.quantization
torch.__config__
torch.__future__

torch.ops
torch.classes

# Variable class's constructor should return Tensor
def fn_to_test_variable(t: torch.Tensor):
    return None

v = Variable(torch.tensor(1))
fn_to_test_variable(v)

# check RemovableHandle attributes can be accessed
handle = hooks.RemovableHandle({})
handle.id
handle.next_id

# check torch function hints
torch.is_grad_enabled()
```

But current master branch raises errors. (I checked with pyright)

```
$ pyright test.py
Searching for source files
Found 1 source file
test.py
  12:45 - error: 'bfloat16' is not a known member of module
  15:21 - error: Argument of type 'Literal[1]' cannot be assigned to parameter 'device' of type 'Optional[device]'
  'int' is incompatible with 'device'
  Cannot assign to 'None'
  16:22 - error: Argument of type 'Literal['cuda:0']' cannot be assigned to parameter 'device' of type 'Optional[device]'
  'str' is incompatible with 'device'
  Cannot assign to 'None'
  23:19 - error: Cannot access member 'is_sparse' for type 'Tensor'
  Member 'is_sparse' is unknown
  24:19 - error: Cannot access member 'is_quantized' for type 'Tensor'
  Member 'is_quantized' is unknown
  25:19 - error: Cannot access member 'is_mkldnn' for type 'Tensor'
  Member 'is_mkldnn' is unknown
  32:7 - error: 'autograd' is not a known member of module
  33:7 - error: 'multiprocessing' is not a known member of module
  34:7 - error: 'sparse' is not a known member of module
  35:7 - error: 'onnx' is not a known member of module
  36:7 - error: 'jit' is not a known member of module
  37:7 - error: 'hub' is not a known member of module
  38:7 - error: 'random' is not a known member of module
  39:7 - error: 'distributions' is not a known member of module
  40:7 - error: 'quantization' is not a known member of module
  41:7 - error: '__config__' is not a known member of module
  42:7 - error: '__future__' is not a known member of module
  44:7 - error: 'ops' is not a known member of module
  45:7 - error: 'classes' is not a known member of module
  60:7 - error: 'is_grad_enabled' is not a known member of module
20 errors, 0 warnings
Completed in 1.436sec
```

and below list is not checked as errors, but I think these are errors too.

* `nn.Module.training` is not boolean
* return type of `torch.Tensor.size()` is `Tuple[Unknown]`.

 ---

related issues.

https://github.com/pytorch/pytorch/issues/23731, https://github.com/pytorch/pytorch/issues/32824, https://github.com/pytorch/pytorch/issues/31753
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33762

Differential Revision: D20118884

Pulled By: albanD

fbshipit-source-id: 41557d66674a11b8e7503a48476d4cdd0f278eab
 2020-02-27 06:58:53 -08:00
Zafar Takhirov
a23009f98f Quantized leaky relu 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33004

Test Plan: Imported from OSS

Differential Revision: D19740193

Pulled By: z-a-f

fbshipit-source-id: 32542d5465db44190366a2f8b737305a03b5fa76
 2020-02-11 17:56:02 -08:00
Daya Khudia
a2463cbc38 Adding quantized clamp kernel (#30541) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30541

ghstack-source-id: 95450749

Adding quantized clamp kernel

Test Plan:
Added test.

buck test mode/dev //caffe2/test:quantized -- 'test_qclamp \(test_quantized\.TestQuantizedOps\)' --print-passing-details

Differential Revision: D18739628

fbshipit-source-id: 38a029ab96c5b0689bb15c67dc4f274883e74975
 2019-12-12 15:54:40 -08:00
Daya Khudia
2d6b2f39e9 Fix docs so that the example works (#30120) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30120

The example given for functional conv2d didn't work. This diff fixes the example in docs so that it works.

Fixes https://github.com/pytorch/pytorch/issues/29649
ghstack-source-id: 94601559

Test Plan: Tried the example locally

Differential Revision: D18604606

fbshipit-source-id: ff1a4f903e2843efe30d962d4ff00e5065cd1d7e
 2019-11-26 17:38:40 -08:00
Xiaomeng Yang
bf80664515 Add quantized conv3d function (#29686) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29686

Add quantized conv3d function

Test Plan: buck test mode/dev-nosan //caffe2/test:quauntized -- "conv"

Reviewed By: hl475

Differential Revision: D18463090

fbshipit-source-id: f9c3d2920c3fc015bbb2b6a583a582c9f8397b08
 2019-11-14 03:04:51 -08:00
Jianyu Huang
bbff06ee96 Convert conv_prepack to conv2d_prepack and conv_unpack to conv2d_unpack (#29529) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29529

Pull Request resolved: https://github.com/pytorch/glow/pull/3771

We would like to replace `conv_prepack` with `conv2d_prepack` and  `conv_unpack` with `conv2d_unpack`.

This makes the naming consistent between 2D and 3D conv:
```
torch.ops.quantized.conv2d_prepack
torch.ops.quantized.conv2d_unpack
torch.ops.quantized.conv2d
torch.ops.quantized.conv3d_prepack
torch.ops.quantized.conv3d_unpack
torch.ops.quantized.conv3d
```

We should do this earlier rather than later when we have more users for the quantized conv2d ops, for better engineering.

The replacement bash command is as the follows:
```
find ./ -type f -exec sed -i -e 's/quantized::conv_prepack/quantized::conv2d_prepack/g' {} \;
find ./ -type f -exec sed -i -e 's/quantized::conv_unpack/quantized::conv2d_unpack/g' {} \;
find ./ -type f -exec sed -i -e 's/torch.ops.quantized.conv_prepack/torch.ops.quantized.conv2d_prepack/g' {} \;
find ./ -type f -exec sed -i -e 's/torch.ops.quantized.conv_unpack/torch.ops.quantized.conv2d_unpack/g' {} \;
```
ghstack-source-id: 93661879

Test Plan: CI

Reviewed By: jackm321

Differential Revision: D18421079

fbshipit-source-id: 17ae8b1ee79223bd2c5d4bbccd57af6580c4ab12
 2019-11-11 21:54:10 -08:00
Zafar Takhirov
7b3881f68c Adding docstrings for nnq.functional 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/27363

Test Plan: Imported from OSS

Differential Revision: D17758907

Pulled By: zafartahirov

fbshipit-source-id: f560f2726cf51ceebdbf22ebef2d067422340cf2
 2019-10-04 17:19:47 -07:00
Lingyi Liu
03007b3dda Quantized Interpolate Kernel(upsample_bilinear2d) (#26631) 
Summary:
We implement the quantized upsample_bilinear2d case for interpolate kernel in this PR.

For nhwc performance improvement:
import torch, time

for dtype in [torch.qint8, torch.quint8, torch.qint32]:
    print('****', str(dtype), '*****')
    x = torch.rand(1, 56, 56, 256)

    q_x = torch.quantize_per_tensor(x, 0.5, 1, dtype)
    q_x = q_x.permute([0, 3, 1, 2])

    x = x.permute([0, 3, 1, 2])

    NITER = 100

    s = time.time()
    for i in range(NITER):
        float_out = torch.nn.functional.interpolate(x, size=5, scale_factor=None, mode="bilinear", align_corners=True)
    time_per_iter_float = (time.time() - s) / NITER

    s = time.time()
    for i in range(NITER):
        quant_out = torch.nn.quantized.functional.interpolate(q_x, size=5, scale_factor=None, mode="bilinear", align_corners=True)
    time_per_iter_quant = (time.time() - s) / NITER

    ref_quantized = torch.quantize_per_tensor(float_out, 0.5, 1, dtype)
    #  torch.testing.assert_allclose(ref_quantized.dequantize(), quant_out.dequantize())

    print('time/iter ms (float)', 'time/iter ms (quant)', 'quant/float', sep='\t')
    print(time_per_iter_float * 1000, time_per_iter_quant * 1000, time_per_iter_quant / time_per_iter_float, sep='\t')

    bytes_float = (x.numel() + float_out.numel()) * x.element_size()
    bytes_quant = (q_x.numel() + quant_out.numel()) * q_x.element_size()

    float_bw_gbps = bytes_float / time_per_iter_float / 1e9
    quant_bw_gbps = bytes_quant / time_per_iter_quant / 1e9

    print('GB/s float', 'GB/s quant', sep='\t')
    print(float_bw_gbps, quant_bw_gbps, sep='\t')

===========without nhwc handling===========
**** torch.qint8 *****
time/iter ms (float)    time/iter ms (quant)    quant/float
1.999044418334961       2.5860953330993652      1.2936657681940702
GB/s float      GB/s quant
1.6192056416115257      0.3129103516188541
**** torch.quint8 *****
time/iter ms (float)    time/iter ms (quant)    quant/float
2.02730655670166        2.6061582565307617      1.2855274639721328
GB/s float      GB/s quant
1.596632728927902       0.3105014816242217
**** torch.qint32 *****
time/iter ms (float)    time/iter ms (quant)    quant/float
2.0180463790893555      2.4047350883483887      1.1916153728010588
GB/s float      GB/s quant
1.603959172365819       1.3460376636426636

===========with nhwc handling===========

**** torch.qint8 *****
time/iter ms (float)    time/iter ms (quant)    quant/float
2.0913314819335938      0.09696483612060547     0.04636512047863123
GB/s float      GB/s quant
1.5477527249803915      8.345458337015
**** torch.quint8 *****
time/iter ms (float)    time/iter ms (quant)    quant/float
2.1065664291381836      0.09959936141967773     0.04728042754408879
GB/s float      GB/s quant
1.5365591871338384      8.124710725706763
**** torch.qint32 *****
time/iter ms (float)    time/iter ms (quant)    quant/float
2.044203281402588       0.6003522872924805      0.29368521846837126
GB/s float      GB/s quant
1.5834354779917448      5.391607675216635
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26631

Differential Revision: D17521498

Pulled By: llyfacebook

fbshipit-source-id: 385ae0f77777cd8bee385cafb80e492127b7d103
 2019-09-25 13:43:43 -07:00