* add InputSpec

* add unittest for tensorSpec and SimpleNet

* Conv2d_transpose and mul support channnelwise quantization, test=develop
* Skip collecting out threshold for output tensor of which the type is not fp32 or fp64, test=develop
* Fix error in test_user_defined_quantization, test=develop
* Add depthwise_conv_bn_fuse, test=develop
* Add conv_transpose_bn_fuse_pass for post_training_quant, test=develop

* Remove slim from paddle framework
test=develop
Co-authored-by: Nwanghaoshuang <wanghaoshuang@baidu.com>

* Remove the output for moving_average_abs_max_scale op, test=develop

* remove ProgramTranslator.save_inference_model

* adapt save_quantized_model

* revert buffer check implemention

* remove useless import function

* saving inference model for user defined quantization model

* saving inference model for user defined quantization model

* fixed ci coverage

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

* Save output threshold by argname_index, test=develop

test=develop

test=develop

* add user defined func test=develop

* update test=develop

* update test=develop

* fix name conflicts test=develop

* add unittest test=develop

* change 2018 to 2020 test=develop

* add comment test=develop

* add comment for function test=develop

* fix details test=develop

* fix details test=develop

* Post_training_quantization supports optimize model by fusing, test=develop

[Fix bug] Init scale node in OutScaleForTrainingPass and enable test_quantization_scale_pass UT (#24393)

* Init scale node in OutScaleForTrainingPass, test=develop
* Enable test_quantization_scale, test=develop

* Enabled quantize all and skip missing in QAT

* QAT: support range-based quantization and scales from attribute

* added support for channelwise

* Weight quantization support channel_wise_abs_max method to achieve higher accuracy

* Collect output scale for quantized op and fused op
* Post_training_quantizaion sets batch_generator to support lod tensor

* Post_training_quantizaion supports min_max methon

* a test for Ernie QAT INT8 accuracy check

test=develop

* Remove NLP comparison test to split PRs

test=develop

* Fix typo and tabs, delete commented lines

test=develop

* re-combine the 2 PRs, test=develop
Co-authored-by: NMichał Gallus <sand3r@interia.eu>
Co-authored-by: Nbingyanghuang <33643817+bingyanghuang@users.noreply.github.com>

* post_training_quantization support set bits, test=develop

* up, test=develop