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- fix to BWD

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- Cosmetic fixes

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* MKLDNN handler cleanup

* MKLDNN handler cleanup
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* using MKLDNNMemoryFormat = mkldnn::memory::format changes
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* PADDLE_ENFORCE update
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* - First set of modifications

- Compilation fixes

- compilation fix

- Another compilation fix

- Moved AcquireSoftmaxPrimitiveDescriptor call into handler

- MKL-DNN Softmax PD refactor

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- Compilation fix

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- another compilation fix

- cosmetcis

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- Compilation fix

- Fix to crash when softmax backward is created

* - Fixes after review of softmax refactoring

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- Refactor step 1

- Compilation fix

- Yet another compilation fix

- Even more compilation fix

- Lint fixes

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- Removed deprectaed PADDLE_ENFORCE occurance

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- Candidate fix to BN forward

- Lint fixes

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- 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

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- Compilation fix

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- Lint

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- Mul fix

- Fix to MKLDNN MUL op and Elementwise MUL UT

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

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- Candidate fix for 5D convolution with groups

- Refactor of fix for conv3d and conv2d in fetch op

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- 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

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- minor fix in UT

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- Lint fixes

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* Add generalized Conv+Activation MKLDNN fuse pass creation Part2
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* Undefined behaviour of GetAttrIfExists<> FIX
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- Extracted key generation from FWD and GRAD into separate function

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- Compilation fix

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- another compilation

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- optimization of TID to string

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* change INT8 to template so that checking dst_dt with if-else could be removed. CI will be enabled after fixing reviews

* reverse user_residual_memory_p and user_bias_memory_p declaration scope
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- compileation fix

- Yet another compilation fix

- Even yet another compilation fix

- Surprise! Again compilation fix

- lint fixes

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- Fix to workspace acquire of LRN

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- Fix to hash of BWD LRN

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- fix to lrn BWD PD acquire

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- Fixing LRN PD creation

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- cosmetic fix in comment

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- Fixes after review

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* - Added partial draft of pooling acquire

- Workspace support

- compilation fix

- Added draft of pooling backward reimplementation

- Segfault fix

- reverted 'any' for diff_dst crewation in pooling

- Lint fixes

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- lint fixes

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- Further lint fixes

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* - Fixes after review

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* - Lint fixes

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* - Even more lint fixes

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* test=develop, fix docker with paddle nccl problem

* test=develop, fix/gcc_4.8_ubt_link_error

* test=develop, fix code format

* rename mkldnn set/get_cur_thread_id() to set/get_cur_mkldnn_session_id()

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* update session id definition and adjust logic for default behavior

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* reset logic in mkldnn reuse as most of cases work in default.

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* Fix bug in quantize kernel which cause crash in vgg16/19 model

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* refine the code to reduce verbose code; test=develop

* remove useless code; test=develop

1. some key generation method is not aligned with PR#17965
2. enlarge ptr lifetime to avoid memory release if SetBlob fails
   otherwise it will get core dump.

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* refractor the function ConvFwdPrimitiveDesc
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* change according to review
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* use pointer way without boost::optional
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* pass vector to function by reference instead of raw vector
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* change pointer to shared_ptr
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* added unit test for QAT FP32 & INT8 comparison

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* enabled other models and updated filenames

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* added accuracy check and multiple batch handling

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* removed quantization_mkldnn_pass.py

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* cleanup

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* updated model paths

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* renamed tests without MKL-DNN

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* fix reusing mkldnn pool2d primitive

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* add performance measuring

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* fix accuracy statistics

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* removed non-mkldnn tests

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* added conv2d_depthwise->conv2d mkldnn transformation

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* format update

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* fixed creating key for pool2d grad

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* added pass

* Fix the accuracy issue while using float precision to get the scale.

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* Fix the format issue when 'X' is not nchw.

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* removed output comparing and changed number of images

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* cmake and comment fix

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* updated acc threshold for QAT comparison tests

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* added OMP_NUM_THREADS setting

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* enable all QAT INT8 tests

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* restored upstream version of a file

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* modified directory names

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* - removed is_reusing_

* - Added TID to keys for reusing apart from softmax PD

* - compilation fix

* - Yet another compilation fix

* - Batch Norm and Conv adapted

* - Fix to softmax MT

* - Fixes to MT code of MKL-DNN

* - Lint fixes

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* remove attribute in Allocator::Allocate, test=develop

* fix travis ci error, test=develop

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* fix the bug of mobilenet-ssd INT8 inference without overloading GetHash
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* remove the out_grad->format() in TransposeMKLDNNGradOpKernel
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* add INT8 conv+relu6 fuse and enbale mobilentv2 INT8 test
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* change fasle and 0.0 to fuse_brelu and brelu_threshold
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change the "fuse_relu||fuse_brelu" to "unsigned_output"
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* Use relu instead of brelu as INT8 post-op because INT8 brelu is not enabled in mkldnn v0.18
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* continuous-integration fix
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* fuse mul and elementwise add to fc

* Reimplement the FC forward operator

* Fix FC MKLDNN integration by transposing weights

* Add FC MKLDNN Pass

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* 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

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* Adjust FC to support MKLDNN Op placement

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* Adjust Placement Op to set use_mkldnn attribute for graph

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

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* 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

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

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* FC MKL-DNN: fix style changes

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

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* FC MKLDNN: fix CMake paths

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* FC MKLDNN: Refine placement pass graph mkldnn attribute

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* Fix Transpiler error for fuse_conv_eltwise

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* Fix missing STL includes in files

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* FC MKL-DNN: Enable new output size computation

Also, refine pass to comply with newest interface.
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* 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

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* Enable MKL DEBUG for resnet50 analyzer

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* FC MKL-DNN: Improve Hashing function

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* 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

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

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* FC MKL-DNN: Disable transpose in unit test

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* FC MKL-DNN: Remove fc_mkldnn_pass from default list

* Correct Flag for fake data analyzer tests

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

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* FC MKL-DNN: Disable fc in int8 tests

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* fix quantize_squash_pass segfault when there is no tensor linked do Bias input

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* add googlenet test

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* fix concat CreateKey not using input format

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* 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

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* Fix the format issue

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* Add the missing nolint comments.

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* Fix the typos.

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* Register the conv_brelu_mkldnn_fuse_pass for the MKLDNN engine.

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* Adjust the indentation.

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* Add the test_conv_brelu_mkldnn_fuse_pass case.

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

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* - Reuse of conv PD

- conv transpose pd reused

- Added PD reusing of softmax and Batch Norm

- Refactoring and removal of not needed routines of mkl-dnn ops

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- Fix to reusing conv

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- Lint fixes

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- Further lint fixes

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- Lint  fixes

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- lint fixes

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- Lint workaround

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* - Fix after review on including boost as third party header

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* - Fix after review. Name change to something more descriptive

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