* 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

post quantize support insert fake_quantize_dequantize node before the OPs that will be used in VIS's faceid models (#30659)

test=develop

* Fix the error of save_quantized_model

* Collect weight threshold of lstm, test=develop

* skip quantizing ops in cpu inference, 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

* Quantization supports 2.0 APIs

* Fix the error of save_quantized_model

* Avoid the scale to be infinity in quant2_int8_mkldnn_pass, test=develop
* support quantized model for paddle2.0 dygraph, test=develop

* Support quantizing program_desc, test=develop

* Fix unittest:test_imperative_skip_out

* rm check_import_scipy, rm chunk_eval and mean_iou in paddle.metric.__init__.py

* Revert "rm check_import_scipy, rm chunk_eval and mean_iou in paddle.metric.__init__.py"

This reverts commit 179ba8c2b22bc31fe8d8a126e31820792cbd0f4e.

* delete paddle.metric.chunk_eval and paddle.metric.mean_iou

* delete paddle.nn.clip and paddle.nn.clip_by_norm

* delete paddle.nn.functional.activation.hard_sigmoid and paddle.nn.functional.activation.hard_swish

* delete paddle.nn.Pool2D, paddle.nn.BilinearTensorProduct, paddle.nn.RowConv, paddle.nn.functional.row_conv

* fix extension import error

* fix unittest for row_conv and Pool2D

* Optimiz the unittest test_imperative_out_scale

test=develop

* Impelement 2.0 API version Conv2d and Linear layer quantization in imperative mode.

* use cudnn softmax in static Lenet

* Modified ChannelwiseQAT Unittest for 2.0 API.

* For CI python coverage.

* upgrade comment string to raw string

* fix string in

* fix string with ' '

* revert update on comments

* upgrade only necessary

* fix sample code checker

* fix comments with '''

* support user-defined quant and preprocess

* 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

* Add test attribute in channelwise_quant op, 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.

* Remove the cache in post_traning_quantization, test=develop

* 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

* Move hapi form paddle/incubate to paddle

* Remove vision/datasets/utils.py and clean code

* Add sample code for conll05

* Print pull path when saving model

* Fix sample code after paramter_list of SGD is changed to parameters

* Fix bug in wmt16 datase