* 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

set default python3,fix incompatible,cache dir for third party,unify error code,for windows (#26178)

* set default python3 for paddle windows,test=win

* set default python3,cache dir for third party,error code,test=win

* fix some incompatible

* fix some error

* set virtual environment,test=win

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

Integrated Trainer of Parameter Server (API add `fluid.contrib.layers.sparse_embedding` only) (#22957)

* Integrated Trainer of Parameter Server

* 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

test=develop

* add bilateral slice op

* 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

Update DNNL QAT document 2.0-alpha

[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

* update local data preprocess doc

* update for 2.0 QAT

test=develop

test=document_fix

* update benchmark data

test=develop

test=document_fix
Co-authored-by: NWojciech Uss <wojciech.uss@intel.com>

* add batch fc op, test=develop

* add batch_fc_op, test=develop

* fix untest, test=develop

* rm check_dygraph, test=develop

* fix comment, test=develop

* fix comment, test=develop

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

* added support for channelwise