* PTQ save quantized model

* Wrap simulated layer

* post process the inference model

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

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

* refine the old code

* support moving_average_abs_max and per_channel_abs_max

* Add moving_average_abs_max_scale op

* Convert the test program

* dygraph post training quantization

* refine the ptq config

* refine ptq quantizer

* Combine amp and qat
* add unit test

* del py2 code2

* fix test timeout

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

* Update paths to Quant models

* Update description

* support ernie trt-int8 for inference

* fix reshape

* Support converting the model from fp32 to fp16

* 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

post quantize support insert fake_quantize_dequantize node before the OPs that will be used in VIS's faceid models (#30659)

test=develop

* Fix the error of save_quantized_model

* Collect weight threshold of lstm, test=develop

* skip quantizing ops in cpu inference, test=develop

* 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

* Avoid the scale to be infinity in quant2_int8_mkldnn_pass, test=develop
* support quantized model for paddle2.0 dygraph, test=develop

* Support quantizing program_desc, test=develop