* Collect weight threshold of lstm, 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

* Support quantizing program_desc, test=develop

* 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

* 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

* 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

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

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

* 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>

* support weight quantization in post_training_quanzitaion, test=develop
* add test for weight quantization, test=develop

test=develop

* add ut for comparing FP32 and QAT INT8

* add save qat transformed model python script
test=develop

* updated

* added missing file

* add "with_label"
test=develop

* performance benchmark as unit test
test=develop

* change names of unnecessary thing

* Change CMakeList.txt for model downloading and UT
test=develop

* change names of functions and params for more readable code
test=develop

* Change PADDLE_ENFORCE messages
test=develop

* fix indent problems
test=develop

* indent problems
test=develop

* add post training quantization, test=develop
* specify the quantizable op type, test=develop

* Add support for new QAT models

test=develop
Co-Authored-By: NMichał Gallus <michal.gallus@intel.com>
Co-Authored-By: NWojciech Uss <wojciech.uss@intel.com>

* fixed fps results

test=develop

* fix top5 accuracy drop problem

* updated for new QAT models

* skip quantizing average pooling - dirty but working

* add missing pass

* added missing conv+brelu fuse pass

* removed a call to non-existent pass

test=develop

* renamed pass

test=develop

* Adjust finding pooling scale to newest QAT models

* Remove unnecessary code from quantization_mkldnn_pass

* Copy Pooling input scale to output scale in QAT

* Refactor & remove unused code in QAT

* Incorporate fp32 FC into QAT

test=develop

* Enable graph drawing with debug flag

test=develop

* Add tests for QATv2

* Fix paths for QATv2 models

test=develop

* Add option to save transformed int8 qat model

test=develop

* Remove redundant lines from qat mkldnn pass

test=develop

* Delegate disablement of avg pooling to qat

test=develop

* fix CI bug, test=develop

* Follow Wangzhen's Review, test=develop

* Update API.spec

test=develop

* Name False in (is_unsigned, TensorScale) tuple

test=develop