* MKLDNN v1.0 rebase to Paddle 1.6
test=develop

* Add hacky paddle::string::to_string() implementation

* vectorize<int64-t>() -> vectorize() cleanup
test=develop

* PADDLE_ENFORCE and void_cast fixes
test=develop

* Rebase changes
test=develop

* Cosmetics
test=develop

* Delete MKL from mkldnn.cmake
test=develop

* CMake debug commands
test=develop

* Delete MKLDNN_VERBOSE and rebase fixes
test=develop

* Rebase fixes
test=develop

* Temporarily disable int8 resnet101 vgg16 and vgg19 tests
test=develop

* Add libmkldnn.so.1 to python setup
test=develop

* Add libmkldnn.so.1 to inference_lib cmake after rebase
test=develop

* Post rebase fixes + FC int8 changes
test=develop

* Fix LRN NHWC
test=develop

* Fix NHWC conv3d
test=develop

* Windows build fix + next conv3d fix
test=develop

* Fix conv2d on AVX2 machines
test=develop

* add examples for error msg spec, test=develop

* change ENFORCE to ENFORCE_**, test=develop

* fix error, test=develop

test=develop

test=develop

* using MKLDNNMemoryFormat = mkldnn::memory::format changes
test=develop

* PADDLE_ENFORCE update
test=develop

- Refactor step 1

- Compilation fix

- Yet another compilation fix

- Even more compilation fix

- Lint fixes

test=develop

- Removed deprectaed PADDLE_ENFORCE occurance

test=develop

- Candidate fix to BN forward

- Lint fixes

test=develop

- Refactoring in data_layout_transform

- compilation fix

- Another comppilation fix

- Step further into darkness

- Yet another compilation fix

- Yet another compilation fix

- missing header

- compilation fix

- Added MKLDNN -> Paddle conversion in fetch op

test=develop

- Compilation fix

test=develop

- Lint

test=develop

- Mul fix

- Fix to MKLDNN MUL op and Elementwise MUL UT

test=develop

- Workaround for diffrent weights with groups representation Paddle vs
  MKL-DNN.

test=develop

- Candidate fix for 5D convolution with groups

- Refactor of fix for conv3d and conv2d in fetch op

test=develop

- Compilation fix

- Still same compilation fix

- Compilation fix

- Compilation fix

- Reverted refactoring of fixes

- Adapted test_conv2d_int8_mkldnn so it exects data in NCHW format
  not NHWC

test=develop

- minor fix in UT

test=develop

- Lint fixes

test=develop

test=develop

* Revert "[MKL-DNN] Fix to crash of Transformer when mkldnn is to be used (#16233)"

This reverts commit 13816dd4.
Apart from enabling transformer for MKL-DNN

* Revert "- MKL-DNN pooling updated to set_prim_desc"

This reverts commit c63f6b20.

Conflicts:
	paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc

* Revert "[MKL-DNN] MKL-DNN specific Tensor modification (#15429)"

test=develop

This reverts commit dec9cf53.

* - concat compilation fix

- lint

test=develop

- Lint fixes

test=develop

- Lint fixes

test=develop

- Fix Transpose MKLDNN op

test=develop

* - Implemented draft of primitive desc keeping in Tensor

test=develop

- TransposeMKLDNNHandler::AcquireSrcMemory was reimplemented

- Added nchw and nc formats setting for sake of compatiblity

Fixed unit tests

- Worakaround to problem with 5D data in conv

- Added 3D and 1D MKL-DNN formats for name handles for tensor

test=develop

- Fix to UTs

test=develop

- Conv fp32 op was updated

Cosmetic fixes

test=develop

- tensor mkldnn cosmetics

test=develop

- Moved most of mkl-dnn specific code from Tensor to mkl-dnn utils

* - Lint fixes

test=develop

* - setting prim dec in Tensor , sets also layout to kMKLDNN

test=develop

* - Moved creation of prim desc totally out of Tensor

test=develop

* - Cosmetic fixes adter review

test=develop

test=develop

When data flow from a MKLDNN OP kernel to a non-MKLDNN OP kernel,
data layout transform (via MKLDNN reorder) will occur even when
those two OP kernels share same layout. Add code to remove this
unnecessary reorder.

test=develop

* remove template operator

* "fix compile"

* "fix ci"

* "fix ci"

* elementwise_add with bcast: Brian's implementation by Brian added, with default bcasts

* elementwise_add with bcast: GetExpectedKernelType added to elementwise_op

* elementwise_add with bcast: use_mkldnn attribute added

* elementwise_add with bcast: changes after review and some formatting

* elementwise_add with bcast: changes after style check

* elementwise_add with bcast: changes after style check cont.

* elementwise_add with bcast: MKLDNN unittests added

* elementwise_add with bcast: original unittests with use_mkldnn flag

* elementwise_add with bcast: handling of MKLDNN format corrected

* elementwise_add with bcast: setting MKLDNN format turned into lambda

* elementwise_add with bcast: MKDNN format setting turned into separate function

* elementwise_add with bcast: condition for choosing MKLDNN simplified

* elementwise_add with bcast: fix for MKLDNN format set incorrectly in bcasts

* elementwise_add with bcast: changes in unittests for broadcasts

* elementwise_add with bcast: fixes in unittests regarding dimensions

* elementwise_add with bcast: bring back correct format setting in mklml grad path

* elementwise_add with bcast: fixed compilation error

* Add MKLDNN layout support in Paddle

Add MKLDNN layout in Paddle so that MKLDNN friendly memory layout
can be used in MKLDNN enabled OP kernel. Before this commit, NCHW
is hardcode to be used in all MKLDNN op kernels. As a result,
non-optimized execution path is selected in MKLDNN primitive which
bring worse performance.
Besides framework change, three MKLDNN OP kernels were updated
for using new MKLDNN layout. They are conv/pool2d/batch_norm.
Other MKLDNN OP kernels need be also updated in similar way to
achieve best performance.

* Add MKLDNN layout support in activation OP

* Don't populate layout from input to output when kMKLDNN in

* Refine pool mkldnn op kernel

* MKLDNN layout

* Remove the inferitance from tensor file

* MKLDNN layout: refactoring

* Remove additional #define to register new operator

* Prepare mkldnn tests to work with layout

* "fix decode bug"

* "follow commnet"

* "fix error"

* "fix hook bug"

* fix based comment

* fix copyright

* fix based on comment

* add data layout transform and optimize the implementation of data_transform

* init data_type_transform

* split data_layout_transform

* tmp rm data_transform_test

* change device_data_transform to data_device_transform

* clean code

* clean code