* [cherry-pick] fix fuse_reduce_op quantization bug test=release/1.6

* [cherry-pick] close fuse_all_reduce_ops in PaddleSlim, test=release/1.6

test=release/1.6 test=document_fix

* Follow Wangzhen's comment in PR 18970, test=develop

* Review comments, test=develop

* Leave fake quantization around mul

test=develop

* Replace Fake with Real Quantized Mul

test=develop

* Fix bug in quantize placement pass

Nodes in the graph now have checked type instead of node name when they are to be marked for quantization test=develop

* Make PaddleSlim support PyReader.
* Fix unittest of sensitive pruning.
* Add some assert.

* 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

1. Support customize eval function instead of eval program.
2. Fix loading checkpoint in quantization strategy.
3. Support saving eval model when saving a checkpoint.
4. Fix decoder of loading context in PaddleSlim.
5. Fix restoring from the checkpoint of uniform prune strategy.
6. Support saving eval model and infer model during training.
7. Add ‘unitest’ for saving eval model, saving infer model and uniform pruning restoring from the checkpoint.
8. Fix pruning of depthwise_conv_grad op by updating the groups.

* add fake_quant_dequant_op for average pool2d
* add test

* move tree_conv to fluid.contrib.layers

test=develop

* update API.spec for tree_conv

test=develop

* update tree_conv api to increase unit coverage

test=develop

Optimize amp for multi-gpu to enable FP16 gradients transfer across gpus

* support_dispensable_student_loss, test=develop

* add distillation test, test=develop

* fix distillation test non convergence problem, test=develop

* fix test_distillation fail problem, test=develop

* split teacher checkpoints with student checkpoints, test=develop

* add unittest for graph.merge(), test=develop

Fix float16 optimizer

* delete useless raw variables in Conv2D, test=develop

* adjust the vars number in test_graph_wrapper to pass unittest, test=develop

init new amp, optimize inserting cast op for batchnorm

* test=develop,  communicator merge add => merge average

test=develop

Change backward_guard to optimize_guard to maximize the allreduce overlap

test=develop

* fix bug of getting bool Flags from os.environ, test=develop

* add empty loss_name in CompiledProgram for inplace grad test, test=develop

* python inference api supports numpy and add unit test, fix unit test fail in test_slim_int8_googlenet and test_slim_int8_mobilenet

* update light_nas_strategy: add latency constraint

test=develop

* update light_nas_strategy: update get_model_latency

test=develop

* update light_nas_strategy: add more check

test=develop

* update light_nas test

test=develop

* update light_nas test

    test=develop

* minor update light_nas test

    test=develop

* minor update light_nas test

test=develop

* update light_nas test

test=develop

* update _constrain_func of light_nas_strategy

test=develop

* update _constrain_func of light_nas_strategy

test=develop

* remove unused code

test=develop

* add pool2d quantization support, only for max-pooling.

* add the partial quantization support.

* remove unused inference_transpiler unit-tests

test=develop

* remove InferenceTranspiler usage in quantize_transpiler.py

test=develop

add save cache model api in fleet& add slots shuffle in dataset module & add metric op to calculate ctr related metrics (#18871)

* add ctr related metric layer test=develop

* add save cache and slots shuffle test=develop

* add save cache and slots shuffle test=develop

* fix error

* fix error

* fix style for ci

* fix for comments

* change SlotsShuffle input to std::strinf for generality

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix stylr

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* fix style

* change non-const reference to pointer

* fix style

* fix style

* fix style test=develop

* fix style  test=develop

* add return ins num in ctr metric op

* change dtype to float in metric_op.py

* fix error test=develop

* fix style test=develop

* fix API spec

* fix API spec

* fix API spec test=develop

* add UT test=develop

* update exe.run

test=develop

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