* 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

Update amp_check_finite_and_scale_op and add an updating_loss_scaling op for static graph amp training. (#26240)

* update amp_check_finite_and_scale_op for static_amp.

* use amp_check_finite_and_scale in static graph amp.

* update grads to zero when grads own infinite values(as for amp_checkout_finite_and_scale op).

* add update_loss_scaling op in cpp.

* add update_loss_scaling_op unit test.

* update the doc of the check_finite_and_unscale op

* Update the process of gradients updating skipping if the gradients have infinite values.

* update the way to zero grads.

* update test_update_loss_scaling_op.py

* add log info when find infinite grads.

* add the unit test for UpdateLossScaling Layer.

* add correlation api to contrib

* 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

* refine manual seed

* fix ci problem

* fix unittests

* fix unittest

* set is_init_py=false in manual_seed

* fix unittest

* fix bernoulli_op

* fix(unittest): change random_seed to manual_seed

* 🐞fix(unittest): fix manual_seed

* trigger ci

* fix test_sentiment

* fix test_imperative_save_load

* fix test_uniform_random_op

* fix test_uniform_random_op

* fix test_jit_save_load

* merge develop

* fix manual_seed

* fix manual_seed

* use global engine

* use shared_ptr

* fix double free

* fix bug

* fix bug

* fix bug

* fix test bug

* fix test bug

* fix test bug

* fix ci

* Add the option to execute unit tests only at night

* set ut nightly label for 3 cases.

* add InputSpec

* add unittest for tensorSpec and SimpleNet

* 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

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