test=develop

* Fix bug for inserting add_quant_dequant_op to same variable repetitively in add_quant_dequant_pass, test=develop

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

* add custom black varname test=develop

* fix dtype test=develop

* fix num test=develop

* fix ut test=develop

* fix coverage test=develop

* fix blackvar names test=develop

test=develop

* move pool2d to add_quant_dequant_pass, test=develop

* Fix docs of gru_unit and dynamic_gru.

Fix basic_gru in rnn_impl.py.

Add error messages for param_attr setting in layer_norm api.

Add int64 dtype for expand.

test=develop

* Reopen unit-tests of basic_gru/basic_lstm in rnn_impl.py.
test=develop

* Add unit test for layer_norm api.
test=develop

* Remove the deprecated gru doc fix. test=develop

* Fix basic_gru test coverage. test=develop

* Update API.spec. test=develop

* Update API.spec. test=develop

* Fix test_basic_gru coverage test. test=develop

* Update test_basic_gru in test_layers to use fluid.data
test=develop

* Update test_basic_gru for coverage. test=develop

* fix fuse_reduce_op quantization bug test=develop

* close fuse_all_reduce_ops in PaddleSlim, test=develop

* Leave fake quantization around mul

* Replace Fake with Real Quantized Mul

* Gather all scales from fake_quantize_ops

* Enable uint8 in conv_relu tensors

* Disable int8 mul and restore fake mul

* Fix buf for running QAT on VGG16 and 19

* fix next_tokens of controller

* 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