* update config

* re-blacken python code

* temporarily disable date and diff_py_file

* skip a format

* trim trailing whitespace

* fix `.cmake-format.py`

* revert npu ut changes, avoid npu ci error

* add quant pass

* use yapf to format all python file

* yapf exclude two unittests file for they rely on writing and reading file, and format will break them

* disable diff_py_file because too many diff files cause command following failed

* clip op extra information when export model,test=ocr

* rename clip_extra parameter to kwargs in save_inference_model, test=ocr

* PTQ save quantized model

* Wrap simulated layer

* post process the inference model

* Save all scales to target ops
* Move quant layers to paddle.nn.quant

* Add wrap for functional api
* Refine the wraped api
* Add unit test for quant functional layers
* Update all unit tests for dygraph qat

* Use layer to calculate output scale
* add backward for moving_average_abs_max_scale and save output scales to op's attr

* Refine saving output scale to infer program

* Fix skip_quant in QAT

* Refine calculating output scale of dygraph qat, test=develop

* Support loading parameters from checkpoint to save quantized model

* Fix the unittest test_moving_average_abs_max_scale_op

* Add unittest of save_quantized_model from checkpoint

* Add comments to explain the function

* Fix the error of save_quantized_model

* Implemented AddQuantDequantPass in imperative quantization.

* Supported LeakyReLU Quantization

* For meeting coverage rate.

* Changed the file name of test of AddQuantDequant

* Implemented more Quantized NoWeightLayers.

* Fix the loss cannot align problem between static and dynamic model quantization, add swish as supported quantized layer in imperative quantization.

* remove noweight_list

* support 2.0 API such as Pool2D and ReLu

* Quantization supports 2.0 APIs

* Fix the error of save_quantized_model

* Optimiz the unittest test_imperative_out_scale

test=develop

* Impelement 2.0 API version Conv2d and Linear layer quantization in imperative mode.

* use cudnn softmax in static Lenet

* Modified ChannelwiseQAT Unittest for 2.0 API.

* For CI python coverage.

* upgrade comment string to raw string

* fix string in

* fix string with ' '

* revert update on comments

* upgrade only necessary

* fix sample code checker

* fix comments with '''

* support user-defined quant and preprocess

* Implement the function of OueScaleForTraining/OutScaleForInference in dygraph

test=develop

* Finished ChannelWiseQuantDequantAbsMaxOp and Passed unittests.

* Finished channel-wise quantize strategy in imperative quantization.

* Added Cuda code of ChannelWiseQuantDequantMaxAbsOP
Add Cuda code of ChannelWiseQuantDequantMaxAbsOp

* Add quant_axis for channel_wise quant.

* fixed a bug in unnitests, which will not trigger axis = 1 case and cannot meet the coverage rate requirement.

* Added some assert infomation and fixed some coding style mistakes.