pytorch

9013c92a - [ao] making QConfigMapping print in a user friendly way (#89932)

[ao] making QConfigMapping print in a user friendly way (#89932) Summary: added __repr__ to QConfigMapping and QConfigMultiMapping loosely based on __repr__ for BaseSparsifier example output: ``` >>> import torch >>> print(torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping()) QConfigMapping ( global_qconfig QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) object_type_qconfigs reshape: QConfig(activation=<class 'torch.ao.quantization.observer.ReuseInputObserver'>, weight=<class 'torch.ao.quantization.observer.NoopObserver'>) <class 'torch.nn.modules.conv.Conv1d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <class 'torch.nn.modules.conv.Conv2d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <class 'torch.nn.modules.conv.Conv3d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <class 'torch.nn.modules.conv.ConvTranspose1d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <class 'torch.nn.modules.conv.ConvTranspose2d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <class 'torch.nn.modules.conv.ConvTranspose3d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <class 'torch.nn.modules.linear.Linear'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <built-in method conv1d of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <built-in method conv2d of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <built-in method conv3d of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <built-in method conv_transpose1d of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <built-in method conv_transpose2d of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <built-in method conv_transpose3d of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <built-in function linear>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <class 'torch.nn.modules.activation.ReLU'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <function relu at 0x7f08ad57bc10>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <built-in method relu of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <class 'torch.nn.modules.batchnorm.BatchNorm1d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <class 'torch.nn.modules.batchnorm.BatchNorm2d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <class 'torch.nn.modules.batchnorm.BatchNorm3d'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=functools.partial(<class 'torch.ao.quantization.observer.PerChannelMinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_channel_symmetric){}) <function layer_norm at 0x7f08ad57fca0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=<class 'torch.ao.quantization.observer.PlaceholderObserver'>) <class 'torch.nn.modules.normalization.LayerNorm'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.HistogramObserver'>, reduce_range=True){}, weight=<class 'torch.ao.quantization.observer.PlaceholderObserver'>) <class 'torch.nn.modules.activation.Hardsigmoid'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <function hardsigmoid at 0x7f08ad57f670>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) hardsigmoid: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) hardsigmoid_: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <class 'torch.nn.modules.activation.Sigmoid'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <built-in method sigmoid of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) sigmoid: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) sigmoid_: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <class 'torch.nn.modules.activation.Softmax'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.00390625, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <class 'torch.nn.modules.activation.Tanh'>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.0078125, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) <built-in method tanh of type object at 0x7f08b99497e0>: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.0078125, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) tanh: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.0078125, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) tanh_: QConfig(activation=functools.partial(<class 'torch.ao.quantization.observer.FixedQParamsObserver'>, scale=0.0078125, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255){}, weight=functools.partial(<class 'torch.ao.quantization.observer.MinMaxObserver'>, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric){}) module_name_regex_qconfigs OrderedDict() module_name_qconfigs OrderedDict() module_name_object_type_order_qconfigs OrderedDict() ) ``` Test Plan: python test/test_quantization.py TestFXNumericSuiteNShadows.test_qconfig_multi_mapping_repr python test/test_quantization.py TestQuantizeFx.test_qconfig_mapping_repr Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/89932 Approved by: https://github.com/vkuzo