* MaxPoolWithIndices CPU Fusion

* fix test to pass checks in cpu_fusion

* pass test

* clean up

* add a new pass, add layouts

* remove the opt from cpu_fusion

* refactor cpu_layout logic for maxpool, clean up comments

* add comment w.r.t. indices tensor

* rename to cpu_workspace_insertion

* add CPUWorkspaceInsertion pass for TF

* sequence reverse

* fix test

* more tests for reverse_sequence

* remove debug prints, change perms

* fix formatting; remove dead code

* make seq_lengths a parameter

* autodiff + tests

* cse init

* init tests

* clean up; more tests

* remove visualizations

* add code to gpu reverse

* add reverse emitter and kernel builder

* working versrion

* remove in tensor.hpp and cpp

* remove in constructors

* more clean up at tv_wrapper and set_is_output()

* fix liveness

* fix liveness.cpp

* finally fixed liveness

* fix PrimaryTensorView constructor in node.cpp

* fix PrimaryTensorView constructor in cpu_tensor_view

* clang-format

* update tensor print

* clean comments

* rename

* Adding support for mkldnn convolution+bias+relu op to use in batch norm folding

* Style fix

* Style fix

* rename getters to adhere to ngraph coding guidelines

* fix renaminb

* fix build errors

* if a requested backend is not registered then try dlopen to force it to register

* call 'extern C create_backend()' in the opened shared object to register the backend

* use a single name to test for backend

* migrate files

* apply version number to libngraph.so

* fix cmake running with empty build directory

* remove clone --depth option

* migrate files

* apply version number to libngraph.so

* fix cmake running with empty build directory

* Remove single-line comments ending with backslash

These comments cause warnings on gcc.

* Add -DNGRAPH_DEBUG_ENABLE cmake option

* Forgot to update log.hpp

* Better comment in CMakeLists

* Doc through relu, use mathtt instead of texttt in equations.

* Review comments.

* interpreter implementation and tests

* style

* correct

* tolerance

* skip

* type

* cast

* double

* types

* format

* add bn to the inference engine backend

Also the ONNX how to import file was tested and it was discovered
that the wheel was actually output to a different directory than
the one specified.

add templated get_data_ptr() methods to HostTensorView and Constant to make using them a little cleaner. (#924)

* add select op, pass data type for each operand

* fix bugs and apply clang format

* fix index bug

* add enable gpu convolution tests flag in py

* working version

* revert convolution_test.in.app

* use a skip list to skip backend for test

* add comment to remind implement of neeeded ops

Temporarily remove Python API docs. We're working on a better system of generating them.

- Disable autogeneration of summary files.

* Some more op documentation

* Review comments

* wip

* simplified interpreter backend

* pick broadcast if exists

* remove logic for sum

* get at broadcast using the label-on-skip approach

* tests for broadcast fix

*  add comments

* First version of nGraph Python API documentation pages.

* Remove use of (`) apostrophes since they generate Sphinx warnings.

* Fix submodule rendering in Sphinx PythonAPI docs

* Add updated index and conf py files

* Updated version of Python API doc.

* Better description for the autosummary

cleanup some enable flags in cmake. add flags to optionally disable building unit tests and tool (#917)

* simplifier for sum

* add comment, remove visualization passes

* Fix ngraph ONNX install directions

* Remove make j from list of install commands

* Added cudnn batch norm operation to GPU transformer.
Brought batchnorm tests out of cpu_tests and into
backend_tests. Need to add JIRA ticket for interpreter
SKIPS.

* CUDNN batchnorm is implemented. In the ForwardTraining branch
CUDNN seems to calculate the batch mean correctly but the batch variance incorrectly.
Currently the batchnorm output and mean are calculated correctly for tests:
* GPU.batchnorm_fprop_b2c2h3w3_mean_var
* GPU.batchnorm_fprop_b1c2h2w2
* GPU.batchnorm_fprop_b2c2h2w1
but the variance calculated for the batches in these tests is incorrectly calculated by CUDNN.

Also added an additional test and cleaned up some of the old tests.

* MKLDNN internally utilizes the biased estimate of the population variance
and the tests have been crafted to suit MKLDNN. According to the original
batchnorm publication (https://arxiv.org/pdf/1502.03167v3.pdf), population
(unbiased) statistics should be used for inference, and mini-batch (biased)
statistics should be used training (forward/backward). For the variance this
means utlitizing the following equations, respectively:

  (biased)   Var[X] = 1/m * Sum_i(x_i-mu)^2      :: used in training
  (unbiased) Var[X] = 1/(m-1) * Sum_i(x_i-mu)^2  :: used in inference

  s.t. x_i are elements of X and m = N*D*H*W.

For large batch sizes in inference this may not impact convergence as m >> 1,
but for small batch sizes it will. CUDNN internally utilizes the unbiased
variance.

Changes:
* Added Multiply op to Forward pass of batchnorm to convert
  the unbiased variance to a biased one. The op utilizes the
  blending scaling factors to apply the bias factor.
* Adds emission for the BatchNormBackprop kernel and cleans up
  the emitter implementation.

* Added hashing to cudnn::batchnorm op.

* Formatting.

* Changed hashing of epsilon in cudnn batchnorm.

* Remove implicit conversion and default case in switch for bn.

* Added skips for IE transformer on batchnorm.

* add cudnn include path to compiler.cpp

* seperate two path

* PR #892 and #825 which were recently merged both forgot skips for the GPU backend.
Adding them in as they are unimplemented ops.

* The allocation and deletion of primitives was occuring in seperate
translation units with raw c pointers. Because of this, it was not
clear that these were being freed appropriate, nor did it indicate
ownership of the pointers.

In this commit these raw pointers have been converted over to
std::unique_ptrs such that the construction/destruction is managed
automatically. Furthermore, GPUPrimitiveEmitter::insert now only
takes an r-value reference, requiring move-semantics to indicate
that when inserting a primitive, the GPUPrimitiveEmitter takes
ownership of the pointer.

All instances of primitive creation have been modified.

* CUDNN_SAFE_CALL

* Removed redundant comment and made variable names more verbose.

* Change from conditionals to case-switch in pooling to conform to
batchnorm per @fengleitian's suggestion.