* disable ut test_parallel_executor_fetch_isolated_var,test=document_fix

* test for limiting ut exec time as 15S

* fix an error caused by cannot find ut

* fix some error

* can not find test_transformer

* fix error caused by ut not run in windows

* fix error caused by Compiler Options

* fix error caused by setting timeout value as 15 in python/paddle/tests/CMakeLists.txt

* setting timeout value to 120s for old ut

* add the timeout value setting

* fix error caused by ut only run in coverage_ci

* add analyzer_transformer_profile_tester

* fix some error

* fix some error

* fix error with inference option

* fix error with inference option setting as ON_INFER

* add some ut to set timeout

* modified some option

* fix error

* fix some timeout error

* fix error

* fix error

* fix timeout for test_analyzer_bfloat16_resnet50

* fix error

* setting timeout properity for some ut

* first pr for new ut timeout as 15S

* Implement the function of OueScaleForTraining/OutScaleForInference in dygraph

test=develop

* replace config by kwargs

* change save path form dir to prefix

* fix failed unittests

* revert unittest name change

* polish en docs

* add more tests for coverage

* default open dygraph mode

* fix CI-Mac

* fix Mac-CI other unittest file

* fix CI-Py3

* fix test_communicator_geo and test_buffer_shared_memory_reuse_pass

* add enable_static to fix CI-Py3

* add enable_static to fix CI-coverage

* delete try except

* 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.

* fix dimensions error for mobilenetv1_KL_quant

fixes AssertionError: The size of weight scales vector (1000) does not match the number of output channels (1024) in the weights tensor fc7_weights.

add mul test

* remove comment

* add third case unit test

* Add the option to execute unit tests only at night

* set ut nightly label for 3 cases.

* Add mnist test for post training quantization, test=develop

* find timeout unittests

* setting timeout value

* fix some error

* fix some error

* fix some error

* fix no newline of end file error

* 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>

* saving inference model for user defined quantization model

* saving inference model for user defined quantization model

* fixed ci coverage

test=develop

* Add the imperative quantization aware training.
*  This is the python part of Imperative QAT. 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>

* 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

* Update for QAT INT8 MKL-DNN document, added info on VNNI in Windows, benchmark results added and updated

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