- Executor is nt always having FLAGS_use_mkldnn set to true

* Add conv transpose BF16

* Share function GetWeightsTz

* Adjust to review and fix op compatibility

* Add bias to unique handler name

* Remove errors related to paddle enforce

* Add conv2d_transpose to bf16 list and kernel refator

* added external reorder to profiler

* added external and internal reorders to profiler

* added internal and external reorder to profiler

* added formatting to int/ext reorder commit

* removed unnecessary comment

* - condidate fix to issue #25537

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* - UT for transpose NHWC

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* Add mechanism for blocking oneDNN cache clearing

* Review changes and Add thread guards

* Change use_quantizer attribute name and data type

* Fix problem with setting attribute

* Add changes due to review

* Small change in function

* Restore use_quantizer attr for compatibility

* Add oneDNN fusion_gru kernel and fix fc+gru pass
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* Formatting changes
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* Lint fixes
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* Add memory::format_tag::any to GRU weights
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* Fix build with CUDA

* Fix build with CUDA v2

* Added DNNL cache management for DyGraph

* move FLAGS_use_mkldnn to more general CMakeLists, getu use of the flag in ClearGradients

* missing file

* Fixes after review

* Bringing back original idea of place for 'use_mkldnn' flag to be accessible from platform nad imperative.

* Removed duplicate and added docs

* Fixes for CI

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* Initial FP32 DNNL MatMul Implementation

* Implement int8 DNNL MatMul

* Unify in-kernel-naming, clean UTs

* MatmuL: Introduce op caching

* Final adjustments

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* Remove dy_graph disablement

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* Change dnnl header name to new one

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* Contrain multi head check to prevent fails

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* Resolve dnnl header problems on MAC CI

* Variable namings to kernel and skip_grad_ci added

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* Prevent MAC CI from failing

* Prevent windows build from failing

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* Modify UTs to conform to the rules

* Modify MatMul aux functions namings

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* MKLDNN v1.0 rebase to Paddle 1.6
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* Add hacky paddle::string::to_string() implementation

* vectorize<int64-t>() -> vectorize() cleanup
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* PADDLE_ENFORCE and void_cast fixes
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* Rebase changes
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* Cosmetics
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* Delete MKL from mkldnn.cmake
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* CMake debug commands
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* Delete MKLDNN_VERBOSE and rebase fixes
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* Rebase fixes
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* Temporarily disable int8 resnet101 vgg16 and vgg19 tests
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* Add libmkldnn.so.1 to python setup
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* Add libmkldnn.so.1 to inference_lib cmake after rebase
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* Post rebase fixes + FC int8 changes
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* Fix LRN NHWC
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* Fix NHWC conv3d
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* Windows build fix + next conv3d fix
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* Fix conv2d on AVX2 machines
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* Add asymetric padding support for mkldnn pooling
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* Add asymetric padding support for mkldnn conv
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* Add asymetric padding support for mkldnn conv_transpose
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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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- 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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* 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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- Added new header for MKLDNN reuse functionality

- Extended conv2d_transpose GetExpectedKernelType for MKL-DNN supporrt

- Buildable conv transpose mkldnn and conv mkldnn using conv template

- Conv2d transpose roughlt implemented and buildable

- Added modifications conv2d transpose MKLDNN unit tests

- Fix to UT of conv2d transpose mkldnn op

- Wrong type of MKLDNN primitive was chosen for conv2d transpose

- HAcks for conv2d transpose

- UT enalbed

- Replaced copying loop with memcpy

- Draft of passing lambda into AcquireMemory

- Made reorder (IOHW->OIHW) to be called only once

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