* Add support for new QAT models

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Co-Authored-By: NMichał Gallus <michal.gallus@intel.com>
Co-Authored-By: NWojciech Uss <wojciech.uss@intel.com>

* fixed fps results

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* 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

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* renamed pass

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* 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

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* Enable graph drawing with debug flag

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* Add tests for QATv2

* Fix paths for QATv2 models

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* Add option to save transformed int8 qat model

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* Remove redundant lines from qat mkldnn pass

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* Delegate disablement of avg pooling to qat

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* fix CI bug, test=develop

* Follow Wangzhen's Review, test=develop

* Update API.spec

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* Name False in (is_unsigned, TensorScale) tuple

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* Removing last dims constraints of seq_pad and seq_unpad test=develop

* fix test_layer api code test=develop

* fix sequence_pad_op.cc conflict test=develop

* remove test_analyzer_mm_dnn test=develop

* fix vectorize bug test=develop

* fix vectorize<int> test=develop

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* Add float16 support to `sync_batch_norm_op`

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* Add test for sync_bn with FP16 input

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* Remove constraint that last dimension is forced to be 1 by add
lookup_table_v2 test=develop

* modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop

* Revert "modify into PADDLE_ENFORCE_CUDA_SUCCESS test=develop"

This reverts commit 8a960bfc61e51aa27c3c529df8fb90b93ebd19f9.

* move api into fluid.embedding test=develop

* fix example code test=develop

* move one_hot into fluid.one_hot

* modify api.spec test=develop

* fix loss shape 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.

* support change shuffle thread num
* support change train thread num
* fix receive shuffle data of each channel
* data norm stop gradient
* add check thread_tensor type and root_tensor type when merge metric
* remove sleep in shuffle, add config
* add config of pslib client to client communication
* fix xbox str
* add data norm op testcase
* add flush in trainer finalize

* give warnings when save a model without any parameters test=develop

* delete one line comment test=develop

* add py_reader combination unittest,test=develop

* follow huihuang's comments, test=develop

* make OpTest check grad inplace even if forward has no inplace, test=develop

* do not run PE when enable_inplace is False, test=develop

* add conv3d cuda kernel for float16 type, test=develop

* refactor OpTest for inplace, test=develop

* add comments, test=develop

* add fake_quant_dequant_op for average pool2d
* add test

* add recompute based checkpoints methods for large batch training
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* add append_backward_with_forward_recomputation
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* refine optimizer
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* update backward and optimizer
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* make Variable usable
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* add recompute code

* refine optimizer
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* refine addup _append_backward_ops_with_checkpoints_
1) for recompute part, just cache the grad_op_desc without appending to block
2) before appending grad_op_desc to backward part, addup_repetitive_vars, remove unused branch
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* make method private

* add recompute strategy into DistributedStrategy
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* checkpoint version3
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* remove some print information
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* remove unused sumop
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* try to fix recompute with graph building modules

* add input names to vars should be held

* add memory debug tool

* backup backward

* Fix bugs

* add backward desc for op not in any segments

* add exception info for sub_block

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* modify code style

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* modify code style

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* remove print functions

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* add API spec

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* make Recompute a child class of Optimizer

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* add API spec

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* modify API spec

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* add document for Recompute

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* change API doc of Rcompute

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* code cleaning

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* modify API spec

* fix bugs when segments hold no element

* add testcase for Recompute Optimizer

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* add test for apply_gradient, and code cleaning

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* add test case for load function

* enable CI

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* add test case

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* add sample code for 4 function of recompute optimizer

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* Add RecordHistoryLocalExecScopes
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* optimize the error information when the input for while op has a wrong shape test=develop

* add mse_loss op

* move tree_conv to fluid.contrib.layers

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* update API.spec for tree_conv

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* update tree_conv api to increase unit coverage

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* unify DataLoader APIs, test=develop

* integrate iterable CPU Dataset, test=develop
add GPU dataset supporting, test=develop

* add unittests for dataset, test=develop

* add more docs to dataloader apis, test=develop, test=document_preview

* refine doc, test=develop

* refine doc again, test=develop

* increase coverage, test=develop

* optimize cloud rolemaker, test=develop

* add instance norm op

* Initial, functional commit

* Clean commit related files
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* refactor dygraph,test=develop

* fix failed unittest,test=develop

* polish code,test=develop

* check windows ci error,test=develop
try to fix windows ci error by np.allclose,test=develop

* polish vlog and profiler, test=develop

* try to fix preceding ops order,test=develop

* test transformer in windows ci, test=develop

* use python c-api to speed up tracer.trace,test=develop

* test=develop, fix docker with paddle nccl problem

* test=develop, add ut for debug string and gradient_accumulator

* test=develop, add tests for layer/gradient_accumulator/prepared_op

* test=develop, fix complie error for test_prepared_op

* test=develop, add more ut for dygraph

* test=develop, create API.spec for dygraph api change

* test=develop, refoctor name to make it easier to understand

* test=develop, refoctor name to make it easier to understand

* test=develop, fix multi-gpu failed problem , add Tracer tests, change PADDLEENFORCE to PADDLEENFORCE_EQ

* test=develop, fix ut failed on parallel se-resnext

* test=develop, change one more PADDLE_ENFORCE

* support auto prune in dygraph mode

* test=develop, support auto prune

* test=develop, merge develop conflict

* test=develop, fix test_layer and test_tracer ut

* test=develop, fix bug which may cause stop_gradient disabled with a list of backward inputs

* support 2-level lod of input in sequence_pool test=develop

* fix lod level bug in .cu test=develop

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1. group_norm support data_layout=NHWC
2. modified doc of group_norm

modified interpolate_op to support tensor attribute

1. the parameter out_shape of image_resize、resize_nearest/bilinear/trilinear can be a list or a 1-D tensor variable. If a list, each element can be an integer or a tensor variable with shape: [1].

2. the parameter scale of above Ops can be a 1-D tensor variable.
modified document of image_resize, resize_nearest, resize_bilinear, resize_trilinear and add some code example.

add crop_tensor op. The main difference with crop is :

1. If the argument shape is a list, each element is an integer or a tensor variable with shape: [1]. This way is suitable for the case that the shape may be changed each iteration.

2. If the argument shape is a variable. Its rank must be 1. In crop op, the rank of shape must be the same as x

offsets can be a list, in which each element is an integer or a tensor variavle with shape: [1].

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* Remove constraint that last dimension is forced to be 1 in cross_entropy
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* modify labels last dims test=develop

change _origin_program test=develop

* add precise roi pooling op test=develop

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* detail the description test=develop

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* elaborate the doc for return type test=develop

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* Add fc_elementwise_layernorm_fuse pass and unittest.

* Add fused_fc_elementwise_layernorm op and its GPU kernel.
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* Apply fc_elementwise_layernorm_fuse_pass to GPU inference.

* Add the setting of attrs in the definition of binary_op.
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* Add comment.

* Implement the unittest.
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* Change the unittest name of layer_norm.
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distribute.launch use poll to query subprocess