* test=develop, add ocr in dygraph test

* test=develop, add cudnn determinist

* test=develop, remove useless code

* test=develop, fix cmake error

test=develop

* add depthwise_conv2d test=develop

* use cpu for ngraph test=develop

* add gradient clip in minimize; test=develop

* fix bug; test=develop

* fix format; test=develop

* move new grad clip to dygraph/grad_clip.py; test=develop

* fix lr decay and grad clip test; test=develop

* seperate dygraph grad clip; test=develop

* fix grad clip test; develop

* fix api spec bug; test=develop

* add blank line, test=develop,test=document_preview

to fix format problem

This reverts commit f34830e2.

* Enable sqrt operator for the nGraph Bridge.

test=develop

* Update activation_op.h

test=develop

* gather_op support int64_t index by adding a template typename

* add UT and rename typename

test=develop

* fuse mul and elementwise add to fc

* Reimplement the FC forward operator

* Fix FC MKLDNN integration by transposing weights

* Add FC MKLDNN Pass

test=develop

* FC MKLDNN Pass: change memcpy to std::copy

* Fix MKLDNN FC handling of mismatch input and weights dims

* Lower tolerance for MKL-DNN in resnet50 test

test=develop

* Adjust FC to support MKLDNN Op placement

test=develop

* Adjust Placement Op to set use_mkldnn attribute for graph

test=develop

* MKLDNN FC: fix weights format so that gemm version is called

test=develop

* FC MKLDNN: Remove tolerance decrease from tester_helper

* FC MKL-DNN: Refactor the code, change input reorder to weight reorder

* MKL-DNN FC: Introduce operator caching

test=develop

* FC MKL-DNN: Fix the tensor type in ExpectedKernelType

test=develop

* FC MKL-DNN: fix style changes

test=develop

* FC MKL-DNN: fallback to native on non-supported dim sizes

test=develop

* FC MKLDNN: fix CMake paths

test=develop

* FC MKLDNN: Refine placement pass graph mkldnn attribute

test=develop

* Fix Transpiler error for fuse_conv_eltwise

test=develop

* Fix missing STL includes in files

test=develop

* FC MKL-DNN: Enable new output size computation

Also, refine pass to comply with newest interface.
test=develop

* FC MKL-DNN: enable only when fc_mkldnn_pass is enabled

* FC MKL-DNN: Allow Weights to use oi or io format

* FC MKL-DNN: Adjust UT to work with correct dims

test=develop

* Enable MKL DEBUG for resnet50 analyzer

test=develop

* FC MKL-DNN: Improve Hashing function

test=develop

* FC MKL-DNN: Fix shape for fc weights in transpiler

* FC MKL-DNN: Update input pointer in re-used fc primitive

* Add log for not handling fc fuse for unsupported dims

test=develop

* FC MKL-DNN: Move transpose from pass to Op Kernel

test=develop

* FC MKL-DNN: Disable transpose in unit test

test=develop

* FC MKL-DNN: Remove fc_mkldnn_pass from default list

* Correct Flag for fake data analyzer tests

test=develop

* FC MKL-DNN: Add comment about fc mkldnn pass disablement

test=develop

* FC MKL-DNN: Disable fc in int8 tests

test=develop

* add __str__ method for tensor and lodtensor to support print test=develop

test=develop

*  Enable assign operator for a ngraph, test=develop

* Cross_entropy operators needs to be updated

* test=develop

* test=develop

Async exe support communicator

test=develop

* add increment op to ngraph engine

test=develop

* fix style errors

test=develop

* Relu6 is the bottleneck op for Mobilenet-v2. As the mkldnn supports the conv/relu6 fusion, we implement it fusion via cpass way. Due to the int8 enabling for this fusion will be supported in MKLDNN v0.20, so this PR is focused on the fp32 optimization.

Below table shows the benchmark(FPS) which measured on skx-8180(28 cores)
Batch size | with fusion | without fusion
-- | -- | --
1 | 214.7 | 53.4
50 | 1219.727 | 137.280

test=develop

* Fix the format issue

test=develop

* Add the missing nolint comments.

test=develop

* Fix the typos.

test=develop

* Register the conv_brelu_mkldnn_fuse_pass for the MKLDNN engine.

test=develop

* Adjust the indentation.

test=develop

* Add the test_conv_brelu_mkldnn_fuse_pass case.

test=develop

* Slightly update the code per Baidu comments.
Let the parameter definition embedded into the code.
That's will make the code easy to understand.

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

test=develop

* add quant_dequant_moving_avg_max_abs op
test=develop

* add more note for quantdequant op
test=develop

* double backward, elementwise_div

* fix dx empty. test=develop

* bug fix (#17392)

fix secure bug

* Eanble stack operator for a Ngraph, test=develop (#17406)

* fix sqrt_grad_grad unittest. test=develop (#17410)

* fix sqrt_grad_grad unittest. test=develop

* disable sqrt_grad_grad unittest. test=develop

* test=develop, fix unittest

* test=develop, fix unittest

* test=develop, fix unittest

* test=develop, fix bug

* fix unittest. test=develop

* fix unittest dx. test=develop

* tmp fix! for test... test=develop

* reduce tmp, test=develop

* test=develop, reduce tmp

* fix broadcast unittest. test=develop

* fix format. test=develop

* refine code. test=develop

* refine code. test=develop

* refine GetDoubleGradSafeTensor. test=develop

* fix format. test=develop

add elementwise_sub_grad_grad op for backward of backward calculation

* add var grad hook test=develop

Refine the Executor when the num_thread=1

* fix sqrt_grad_grad unittest. test=develop

* disable sqrt_grad_grad unittest. test=develop