do some odd jobs, test=develop

* remove ctr reader, all functions are satisfied in dataset

test=develop

test=develop

Socket can't be pickled.
test=develop

* sequnce mask support max length tensor input; test=develop

* add rnn_impl.py; test=develop

* add basic gru lstm unittest; test=develop

* fix api spec; test=develop

* fix sequence_mask op bug;
test=develop
test=document_preview

* change +-*x to elmentwise_op; test=develop

* add mkl flag; test=develop

* fix rnn impl bug; test=develop

* update api spec; test=develop

* fix doc bug; test=develop

* fix lstm bugs; test=develop

test=develop

1. fix the bug that out_put_var in SaveSelectedRows would be empty string
2. use merge_sparse_lookup_table to replace sum op for load_persistables_for_inference
3. fix the bug in _clone_var_in_block_ when the var is SELECTED_ROWS.

* Enable MKL-DNN for slim FP32 vs. INT8 tests

test=develop

* added test for MobileNetV1 with MKL-DNN

test=develop

* QAT doc first version

* follow the comments, test=develop

* update the benchmark, test=develop

* Change to channel-wise, test=develop

* add multi process reader
test=develop

* fix spelling errors; test=develop

* Update API.spec

update md5

* Update API.spec

* change the order of api;test=develop

test=develop

* update CPU_NUM config
test=develop

* added unit test for QAT FP32 & INT8 comparison

test=develop

* enabled other models and updated filenames

test=develop

* added accuracy check and multiple batch handling

test=develop

* removed quantization_mkldnn_pass.py

test=develop

* cleanup

test=develop

* updated model paths

test=develop

* renamed tests without MKL-DNN

test=develop

* fix reusing mkldnn pool2d primitive

test=develop

* add performance measuring

test=develop

* fix accuracy statistics

test=develop

* removed non-mkldnn tests

test=develop

* added conv2d_depthwise->conv2d mkldnn transformation

test=develop

* format update

test=develop

* fixed creating key for pool2d grad

test=develop

* added pass

* Fix the accuracy issue while using float precision to get the scale.

test=develop

* Fix the format issue when 'X' is not nchw.

test=develop

* removed output comparing and changed number of images

test=develop

* cmake and comment fix

test=develop

* updated acc threshold for QAT comparison tests

test=develop

* added OMP_NUM_THREADS setting

test=develop

* enable all QAT INT8 tests

test=develop

* restored upstream version of a file

test=develop

* modified directory names

test=develop

* Cherry-pick fix random Python3 CI failure.

In some tests, SWEs used "print('xxx').format('xxx')". The syntax
is only supported in Python2, not python3. However, since those
lines are related to data download, if the CI machines already have
the data, it passes CI tests. That causes random failure.

* Cherry-pick: disable CUDNN case of test_warpctc_op

Also temporary disable a unit test. The test will be fixed under high priority.

* fix logging unable. test=develop

* unset sys.stdout for stream handler. test=develop

* fix newly add basicConfig. test=develop

* fix import error. test=develop

1. Enhence auto pruning. 
2. Fix save/load graph when using py_reader.
test=develop

* Add auto pruning strategy.
1. Fix compressor.
2. Enhence graph executor.
3. Add SAController
4. Add auto pruning strategy.
5. Add unitest for auto pruning strategy.
test=develop

* Init light-nas

* Add light nas.

* Some fix.
test=develop

* Fix sa controller.
test=develop

* Fix unitest of light nas.
test=develop

* Fix setup.py.in and API.spec.
test=develop

* Fix unitest.
1. Fix unitest on windows.
2. Fix package importing in tests directory.

* 1. Remove unused comments.
2. Expose eval_epoch option.
3. Remove unused function in search_agent.
4. Expose max_client_num to yaml file.
5. Move flops constraint to on_epoch_begin function
test=develop

* Fix light nas strategy.
test=develop

* Make controller server stable.
test=develop

* 1. Add try exception to compressor.
2. Remove unitest of light-nas for windows.
test=develop

* Add comments
Enhence controller
test=develop

* Fix comments.
test=develop

* refactor PR 16865

* delete mergetool files

* test=develop

* test=develop

* test=develop

* test=develop

* create dir for int8 model before call SaveOptimModel

* test=develop

* mkldnn int8 only support linux; test=develop

* refine code; test=develop

* remove comment; test=develop

* refine code; test=develop

* fix bug; test=develop

* add exception for mkldnn_post_training_strategy

* reuse int8v2 CAPI dataset; test=develop

* fix accuracy check bug; test=develop

* remove tab

* convert files to unix format

* test=develop

* reduce CI time;test=develop

* reduce CI time and refine code;test=develop

* refine comment; test=develop

* add cmake FLAGS;test=develop

* remove predict_num;test=develop

* Enhance fused_elementwise_activation op.
test=develop

* Move the api fused_elementwise_activation to contrib.
test=develop

* Add including files.
test=develop

* Add the support of sigmoid in fused_elementwise_activetion op.

* Update API.spec.
test=develop

* add data parallel batch

* add quant_dequant_pass, test=develop

* Add quant_dequant before some ops, such as the elementwise_add op. This is required by TensorRT. test=develop

* Add LAMB optimizer

* Expose LAMB Optimizer's APIs

test=develop, test=document_preview

* Cleanup code & doc

test=develop, test=document_preview

* Update lamb optimizer's formula

test=develop

* init auto loss scaling

test=develop

* change API.spec

* change ifelse to switch and use reduce_sum to optimize checking isfinite

test=develop

* Remove redundant code

test=develop

* resolve #17184

Fix the KL algorithm bug when calculated the size of tensor.

* Add the missing test=develop.

test=develop

test_distillation_strategy always failed on a machine with 4 gpus only, disable temporarily and need to figure out the root cause and add it back later

Update the folder name generation mechanism for saving the quantized model and weights.
The folder name would be unique by adding the timestamp postfix.

test=develop

* Init mixed precision training interface

* Add fp16 test script

test=develop

* All initializers support float16

test=develop

* Code cleanup & add more code annotations

test=develop

* Update API spec

test=develop

* Add usage example in doc

test=develop