* refine ptq according comments
* reuse the module to calculate kl threshold

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

* dygraph post training quantization

* refine the ptq config

* refine ptq quantizer

* 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.

* Move hapi form paddle/incubate to paddle

* Remove vision/datasets/utils.py and clean code

* Add sample code for conll05

* Print pull path when saving model

* Fix sample code after paramter_list of SGD is changed to parameters

* Fix bug in wmt16 datase

* add InputSpec

* add unittest for tensorSpec and SimpleNet

* remove ProgramTranslator.save_inference_model

* adapt save_quantized_model

* revert buffer check implemention

* remove useless import function

* Add the imperative quantization aware training.
*  This is the python part of Imperative QAT. test=develop