* fix a quantization bug
Co-authored-by: NXGZhang <46363693+XGZhang11@users.noreply.github.com>

* [cherry-pick 2.2]support quantization of bert

support quantization for maumul_v2

* Update quantization_pass.py

* quant support matmul_v2

* fix format

Co-authored-by: Njoanna.wozna.intel <joanna.wozna@intel.com>

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

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

* fix undef var

* fix undef var

* 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

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

* 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