* fresh commit for the changes

* Working topk on ndims for nvGPU

* fix

* clang

* Added unit test, improved kernel hash and Bob's comment

* int64 test+clang

* Moved argReduce and topk tests to a separate file

* TopK unsupported for IntelGPU

* addressed Fenglei and Chris's comments

* addressed Fenglei and Chris's comments

* IntelGPU backend: Profile data improved

* PR1932. Comments addressed

* Move to TBB2019 and bug fix to capture functor

* Change to use TBB release tag

* remove lightweight from codegen

* Enable TBB flow graph tracing

* add split concat

* fix bug

* fix bug

* fix bug

* add test

* fix test bug

* add comments

* format

* return intead of check processed

* remove .back() since it's not vector anymore.

* format

* change to paramter tests based on Geoff's comments

* types-> type

* change split size to 256

* Reenabled Chris's nvcc building. Improved support for build paths and variations of cuda 8/9 + clang/gcc

* Improved build messages based on feedback

* Add builder for {de}quantize

* Add declaration in header

* Add mkldnn support for {de}quantize

* Add support for {de}quantize with mkldnn

* Add Dex support

* Generalizing some api's and adding a test case for DQ in backend_test.in.cpp

* Unify scale between ngraph and mkldnn

* Check for nullptrs

* PR feedback

* fix unit test failure

* Adding tests for builder and deleting the backend tests

* curly braces

* test rename

* gdb-like interface + tests

* fix not being able to run call twice without call

* fix continue bug

* fix enables; rename kontinue to resume

* switch from lists of functors,enables to vector

* address scott's feedback

* adding a debugger object

* address jayarams feedback

Add in place concat optimization.

Merge branch 'ayzhuang/in-place-concat' of https://github.com/NervanaSystems/ngraph into ayzhuang/in-place-concat

* m_direct_execution is used but not defined when NGRAPH_DEX_ONLY=TRUE

* keep the ifdef and move m_direct_execution out of the ifdef

Merge branch 'ayzhuang/in-place-concat' of https://github.com/NervanaSystems/ngraph into ayzhuang/in-place-concat

* Update ArgReduce to handle i64 indices.

* Formatting.

* Add throw for output types other than int32/64.

* Add output type to hash.

* Add type to throw.

* Interpreter doesn't currently support 64bit output indices for argmin/max and so disabling this test [JIRA:NGRAPH-3183].

* Implement partial shape/type propagation for Convolution; fail for want of unit tests

* Implement unit tests for partial shapes/types for Convolution

* fix Klockwork warnings CPU part 1

* fix spelling error

* fix a typo

* Fix gemm `input_c` broadcasting.

* Comments.

* Add comment

* [ONNX] Non-linear ops

* Style check

* Temp bn update commit.

* Add CUDNNBatchNorm which adds two additional outputs to batchnorm, the batch mean and batch inv variance.
The batch mean is the same as the output mean if the cummulative average factor is 1.0. Add BatchNormCache pass which replaces all BatchNorm ops that are inputs to BatchNormBackprop
with CUDNNBatchNorm which outputs the saved batch statistics directly to the backprop step.

* Updated bn cache pass, removed extra tests, added test checking that provided stats are used in bprop instead of batch stats.
This test was disabled for interpreter as the reference kernel needs to be updated to use provided statistics.

* Formatting.

* Update to new batch norm API.

* CUDNNBatchNorm -> BatchNormTrainingWithStats

* new line

* Preprocess input variance into BN denominator for cudnn (#1885)

* Add explicit cuda kernel to calculate what cuDNN describes as the inverse
variance. In reality, the backward cudnn kernel for BN requires 1.0f / sqrt(variance + eps),
which is the batchnorm denominator for each channel (a numerically stable inverse stddev).

This introduces op annotations for batch norm backprop and updates the cudnn_emitter to support the insertion of this cuda kernel when required.

* Disable second test on INTERPRETER.