Merge branch 'master' into examples

066fb338 · Ashok Emani · GitHub · bd9c17c5 · ab1cb2e6 · 066fb338
Unverified Commit 066fb338 authored Feb 15, 2018 by Ashok Emani Committed by GitHub Feb 15, 2018
140 changed files
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -26,20 +26,24 @@ endif()

 project (ngraph)

-SET( GCC_MIN_VERSION 4.8)
-SET( CLANG_MIN_VERSION 3.8)
+SET(GCC_MIN_VERSION 4.8)
+SET(CLANG_MIN_VERSION 3.8)
+SET(APPLE_CLANG_MIN_VERSION 9.0)

 if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
    if (CMAKE_CXX_COMPILER_VERSION VERSION_LESS GCC_MIN_VERSION)
        message(FATAL_ERROR "GCC version must be at least ${GCC_MIN_VERSION}!")
    endif()
 elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
-    # require at least clang 3.8
    if (CMAKE_CXX_COMPILER_VERSION VERSION_LESS CLANG_MIN_VERSION)
        message(FATAL_ERROR "Clang version must be at least ${CLANG_MIN_VERSION}!")
    endif()
+elseif ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "AppleClang")
+    if (CMAKE_CXX_COMPILER_VERSION VERSION_LESS APPLE_CLANG_MIN_VERSION)
+        message(FATAL_ERROR "Apple Clang version must be at least ${APPLE_CLANG_MIN_VERSION}!")
+    endif()
 else()
-    message(WARNING "You are using an unsupported compiler! Compilation has only been tested with Clang ( ${CLANG_MIN_VERSION} and up) and GCC ( ${GCC_MIN_VERSION} and up). ")
+    message(WARNING "You are using an unsupported compiler. Compilation has only been tested with Clang (${CLANG_MIN_VERSION} and up), Apple Clang (${APPLE_CLANG_MIN_VERSION} and up), and GCC (${GCC_MIN_VERSION} and up).")
 endif()

 if($ENV{NGRAPH_USE_PREBUILT_LLVM})
@@ -133,19 +137,29 @@ elseif(NGRAPH_GPU_ENABLE)
    message(FATAL_ERROR "GPU was required but CUDA library was not found")
 endif()

+#-----------------------------------------------------------------------------------------------
+# distributed support
+#-----------------------------------------------------------------------------------------------
+if(NGRAPH_DISTRIBUTED_ENABLE)
+    find_package(MPI REQUIRED)
+    if(MPI_CXX_FOUND)
+        add_definitions(-DNGRAPH_DISTRIBUTED)
+    endif()
+endif()
+
 #-----------------------------------------------------------------------------------------------
 # External projects install directory
 #-----------------------------------------------------------------------------------------------

 # Root for external installs, when using `make`, e.g.
-# ${EXTERNAL_INSTALL_DIR}
-# ├── eigen               <-- ${EIGEN_INSTALL_DIR}
+# ${EXTERNAL_PROJECTS_ROOT}
+# ├── eigen
 # │   ├── include
 # │   │   └── eigen3      <-- ${EIGEN_INCLUDE_DIR}
 # │   │       ├── Eigen
 # │   ...
 # │
-# └── mkldnn              <-- ${MKLDNN_INSTALL_DIR}
+# └── mkldnn
 #     ├── include         <-- ${MKLDNN_INCLUDE_DIR}
 #     │   ├── mkldnn.h
 #     │   ...

--- a/README.md
+++ b/README.md
-# Intel® nGraph™ library  project
+# Intel® nGraph™ library 

-Welcome to the Intel nGraph project, an open source C++ library for developers
-of Deep Learning (DL) systems. Here you will find a suite of components, APIs, 
-and documentation that can be used to compile and run Deep Neural Network (DNN) 
+Welcome to Intel nGraph, an open source C++ library for developers of Deep 
+Learning (DL) systems. Here you will find a suite of components, APIs, and 
+documentation that can be used to compile and run Deep Neural Network (DNN) 
 models defined in a variety of frameworks.  

 The nGraph library translates a framework’s representation of computations into 
@@ -14,7 +14,8 @@ and data layout abstraction.

 See our [install] docs for how to get started. 

-For this early release, we provide framework integration guides to compile 
+For this early release, we provide [framework integration guides] to compile 
 MXNet and TensorFlow-based projects.  

-[install]: http://ngraph.nervanasys.com/docs/cpp/installation.html
\ No newline at end of file
+[install]: http://ngraph.nervanasys.com/docs/cpp/installation.html
+[framework integration guides]:http://ngraph.nervanasys.com/docs/cpp/framework-integration-guides.html
--- a/changes.md
+++ b/changes.md
 # API Changes

+## Changes to ops
+
+* The namespace `ngraph::op` is only for actual ops. Helpers have been moved into
+  `ngraph::op::util`:
+  + `BinaryElementwiseArithmetic`
+  + `BinaryElementwiseComparison`
+  + `BinaryElementwise`
+  + `RequiresTensorViewArgs`
+  + `UnaryElementwiseArithmetic`
+  + `UnaryElementwise`
+  Ops defined outside of nGraph core will need to get the base class from `ngraph::op::util` and 
+  change the include file to `#include "ngraph/ops/util/requires_tensor_view_args.hpp"`, etc.
+
+  See any of the core ops for an example.
+
 ## Changes to convolution and pooling ops

 * Backprop ops have been added for convolution ops.

--- a/cmake/external_eigen.cmake
+++ b/cmake/external_eigen.cmake
@@ -17,8 +17,6 @@
 # Enable ExternalProject CMake module
 include(ExternalProject)

-set(EIGEN_INSTALL_DIR ${EXTERNAL_INSTALL_DIR}/eigen)
-set(EIGEN_PROJECT eigen)
 set(EIGEN_GIT_TAG d608d9f3f577118981acbdd40da9dcf6b514668a)
 set(EIGEN_GIT_URL https://github.com/jmenon/eigen)

@@ -29,25 +27,35 @@ set(EIGEN_GIT_URL https://github.com/jmenon/eigen)
 # The 'BUILD_BYPRODUCTS' argument was introduced in CMake 3.2.
 if (${CMAKE_VERSION} VERSION_LESS 3.2)
    ExternalProject_Add(
-        ${EIGEN_PROJECT}
+        ext_eigen
        GIT_REPOSITORY ${EIGEN_GIT_URL}
        GIT_TAG ${EIGEN_GIT_TAG}
        UPDATE_COMMAND ""
-        CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${EIGEN_INSTALL_DIR} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
+        CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${EXTERNAL_PROJECTS_ROOT}/eigen -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
+        TMP_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/tmp"
+        STAMP_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/stamp"
+        DOWNLOAD_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/download"
+        SOURCE_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/src"
+        BINARY_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/build"
+        INSTALL_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen"
        )
 else()
    ExternalProject_Add(
-        ${EIGEN_PROJECT}
+        ext_eigen
        GIT_REPOSITORY ${EIGEN_GIT_URL}
        GIT_TAG ${EIGEN_GIT_TAG}
        UPDATE_COMMAND ""
-        CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${EIGEN_INSTALL_DIR} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-        BUILD_BYPRODUCTS "${EIGEN_INSTALL_DIR}/include/eigen3"
+        CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${EXTERNAL_PROJECTS_ROOT}/eigen -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
+        TMP_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/tmp"
+        STAMP_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/stamp"
+        DOWNLOAD_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/download"
+        SOURCE_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/src"
+        BINARY_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/build"
+        INSTALL_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen"
+        BUILD_BYPRODUCTS "${EXTERNAL_PROJECTS_ROOT}/eigen/include/eigen3"
        )
 endif()

 #----------------------------------------------------------------------------------------------------------

-ExternalProject_Get_Property(eigen source_dir binary_dir)
-
-set(EIGEN_INCLUDE_DIR "${EIGEN_INSTALL_DIR}/include/eigen3" PARENT_SCOPE)
+set(EIGEN_INCLUDE_DIR "${EXTERNAL_PROJECTS_ROOT}/eigen/include/eigen3" PARENT_SCOPE)
--- a/cmake/external_gtest.cmake
+++ b/cmake/external_gtest.cmake
@@ -27,46 +27,53 @@ SET(GTEST_GIT_LABEL release-1.8.0)
 # The 'BUILD_BYPRODUCTS' argument was introduced in CMake 3.2.
 if (${CMAKE_VERSION} VERSION_LESS 3.2)
    ExternalProject_Add(
-        gtest
+        ext_gtest
        GIT_REPOSITORY ${GTEST_GIT_REPO_URL}
        GIT_TAG ${GTEST_GIT_LABEL}
-        PREFIX ${CMAKE_CURRENT_BINARY_DIR}/gtest
        # Disable install step
        INSTALL_COMMAND ""
        UPDATE_COMMAND ""
        CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
+        TMP_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/tmp"
+        STAMP_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/stamp"
+        DOWNLOAD_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/download"
+        SOURCE_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/src"
+        BINARY_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/build"
+        INSTALL_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest"
        )
 else()
    ExternalProject_Add(
-        gtest
+        ext_gtest
        GIT_REPOSITORY ${GTEST_GIT_REPO_URL}
        GIT_TAG ${GTEST_GIT_LABEL}
-        PREFIX ${CMAKE_CURRENT_BINARY_DIR}/gtest
        # Disable install step
        INSTALL_COMMAND ""
        UPDATE_COMMAND ""
        CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-        BUILD_BYPRODUCTS "${CMAKE_CURRENT_BINARY_DIR}/gtest/src/gtest-build/googlemock/gtest/libgtest.a"
+        TMP_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/tmp"
+        STAMP_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/stamp"
+        DOWNLOAD_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/download"
+        SOURCE_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/src"
+        BINARY_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest/build"
+        INSTALL_DIR "${EXTERNAL_PROJECTS_ROOT}/gtest"
+        BUILD_BYPRODUCTS "${EXTERNAL_PROJECTS_ROOT}/gtest/build/googlemock/gtest/libgtest.a"
        )
 endif()

 #----------------------------------------------------------------------------------------------------------

-# Get GTest source and binary directories from CMake project
-ExternalProject_Get_Property(gtest source_dir binary_dir)
-
 get_filename_component(
    GTEST_INCLUDE_DIR
-    "${CMAKE_CURRENT_BINARY_DIR}/gtest/src/gtest/googletest/include"
+    "${EXTERNAL_PROJECTS_ROOT}/gtest/src/googletest/include"
    ABSOLUTE)
 set(GTEST_INCLUDE_DIR "${GTEST_INCLUDE_DIR}" PARENT_SCOPE)

 # Create a libgtest target to be used as a dependency by test programs
 add_library(libgtest IMPORTED STATIC GLOBAL)
-add_dependencies(libgtest gtest)
+add_dependencies(libgtest ext_gtest)

 # Set libgtest properties
 set_target_properties(libgtest PROPERTIES
-    "IMPORTED_LOCATION" "${binary_dir}/googlemock/gtest/libgtest.a"
+    "IMPORTED_LOCATION" "${EXTERNAL_PROJECTS_ROOT}/gtest/build/googlemock/gtest/libgtest.a"
    "IMPORTED_LINK_INTERFACE_LIBRARIES" "${CMAKE_THREAD_LIBS_INIT}"
 )
--- a/cmake/external_llvm.cmake
+++ b/cmake/external_llvm.cmake
--- a/cmake/external_mkldnn.cmake
+++ b/cmake/external_mkldnn.cmake
@@ -20,11 +20,10 @@ include(ExternalProject)
 # Fetch and install MKL-DNN
 #----------------------------------------------------------------------------------------------------------

-if(NGRAPH_CPU_ENABLE AND NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
+if(NGRAPH_CPU_ENABLE)

    set(MKLDNN_GIT_REPO_URL https://github.com/intel/mkl-dnn)
    set(MKLDNN_GIT_TAG "3e1f8f5")
-    set(MKLDNN_INSTALL_DIR ${EXTERNAL_INSTALL_DIR}/mkldnn)

    # The 'BUILD_BYPRODUCTS' argument was introduced in CMake 3.2.
    if(${CMAKE_VERSION} VERSION_LESS 3.2)
@@ -38,7 +37,13 @@ if(NGRAPH_CPU_ENABLE AND NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
            CMAKE_ARGS
                -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
                -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-                -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
+                -DCMAKE_INSTALL_PREFIX=${EXTERNAL_PROJECTS_ROOT}/mkldnn
+            TMP_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/tmp"
+            STAMP_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/stamp"
+            DOWNLOAD_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/download"
+            SOURCE_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/src"
+            BINARY_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/build"
+            INSTALL_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn"
            )
    else()
        ExternalProject_Add(
@@ -51,8 +56,14 @@ if(NGRAPH_CPU_ENABLE AND NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
            CMAKE_ARGS
                -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
                -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-                -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
-            BUILD_BYPRODUCTS "${MKLDNN_INSTALL_DIR}/include/mkldnn.hpp"
+                -DCMAKE_INSTALL_PREFIX=${EXTERNAL_PROJECTS_ROOT}/mkldnn
+            TMP_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/tmp"
+            STAMP_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/stamp"
+            DOWNLOAD_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/download"
+            SOURCE_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/src"
+            BINARY_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/build"
+            INSTALL_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn"
+            BUILD_BYPRODUCTS "${EXTERNAL_PROJECTS_ROOT}/mkldnn/include/mkldnn.hpp"
            )
    endif()

@@ -67,7 +78,7 @@ if(NGRAPH_CPU_ENABLE AND NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
        )


-    set(MKLDNN_INCLUDE_DIR "${MKLDNN_INSTALL_DIR}/include" PARENT_SCOPE)
-    set(MKLDNN_LIB_DIR "${MKLDNN_INSTALL_DIR}/lib" PARENT_SCOPE)
+    set(MKLDNN_INCLUDE_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/include" PARENT_SCOPE)
+    set(MKLDNN_LIB_DIR "${EXTERNAL_PROJECTS_ROOT}/mkldnn/lib" PARENT_SCOPE)

 endif()
--- a/cmake/external_tbb.cmake
+++ b/cmake/external_tbb.cmake
@@ -22,13 +22,11 @@ if(NGRAPH_TBB_ENABLE)
    set(TBB_GIT_REPO_URL https://github.com/01org/tbb)
    set(TBB_GIT_TAG "tbb_2018")

-    if(NOT ${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
-        configure_file(${CMAKE_SOURCE_DIR}/cmake/tbb_fetch.cmake.in ${CMAKE_CURRENT_BINARY_DIR}/tbb/CMakeLists.txt)
-        execute_process(COMMAND "${CMAKE_COMMAND}" -G "${CMAKE_GENERATOR}" .
-          WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tbb")
-        execute_process(COMMAND "${CMAKE_COMMAND}" --build .
-          WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tbb")
+    configure_file(${CMAKE_SOURCE_DIR}/cmake/tbb_fetch.cmake.in ${CMAKE_CURRENT_BINARY_DIR}/tbb/CMakeLists.txt)
+    execute_process(COMMAND "${CMAKE_COMMAND}" -G "${CMAKE_GENERATOR}" .
+      WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tbb")
+    execute_process(COMMAND "${CMAKE_COMMAND}" --build .
+      WORKING_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/tbb")

-        set(TBB_ROOT ${CMAKE_CURRENT_BINARY_DIR}/tbb/tbb-src PARENT_SCOPE)
-    endif()
+    set(TBB_ROOT ${CMAKE_CURRENT_BINARY_DIR}/tbb/tbb-src PARENT_SCOPE)
 endif()
--- a/contrib/docker/Makefile
+++ b/contrib/docker/Makefile
@@ -79,19 +79,9 @@ build_docker_image: expand_dockerfile_templates
 build_docker: build_docker_image

 # Build docs
-docs: sphinx_doc doxygen_doc
+docs: sphinx_doc

-doxygen_doc:
-	# doxygen html docs build
-	docker run --rm --tty \
-            ${VOLUME} \
-	    ${DOCKER_RUN_ENV} \
-            --env RUN_UID="$(shell id -u)" \
-            --env RUN_CMD="set -e ; set -o pipefail ; cd ${DOCKUSER_HOME}/ngraph-cpp-test/doc/doxygen; env VERBOSE=1 make html 2>&1 | tee make_sphinx_html.log" \
-            "build_ngraph_cpp:${DBUILD_VERSION}" \
-	    sh -c "${DOCKUSER_HOME}/ngraph-cpp-test/contrib/docker/run_as_user.sh"
-
-sphinx_doc:
+sphinx_doc: build_docker_image
 	# sphinx html docs build
 	docker run --rm --tty \
            ${VOLUME} \

--- a/doc/sphinx/Makefile
+++ b/doc/sphinx/Makefile
@@ -19,12 +19,6 @@ help:
 %: Makefile
 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

-
-doxy-code:
-	$(Q)(cat ngraph.doxyfile ; echo "STRIP_FROM_PATH=${NGRAPH_BASE}" ) | doxygen -  2>&1 | tee doc.log
-
-doxy: doxy-code
-
 clean:
 	@rm -rf $(BUILDDIR)/*
 	@rm -rf html
@@ -32,7 +26,10 @@ clean:
 	@rm -rf doxygen
 	@rm -rf latex

-htmldocs: doxy html
+doxy-code:
+	$(Q)(cat ngraph.doxyfile ; echo "STRIP_FROM_PATH=${NGRAPH_BASE}" ) | doxygen -  2>&1 | tee doc.log
+
+html: doxy-code

 pickle:
 	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
@@ -41,8 +38,6 @@ pickle:

 json: prep
 	$(SPHINXBUILD) -t $(DOC_TAG) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
-	@rm -rf samples
-	@rm -rf boards
 	@echo
 	@echo "Build finished; now you can process the JSON files."


--- a/doc/sphinx/source/graphics/ngraph-hub.png
+++ b/doc/sphinx/source/graphics/ngraph-hub.png
--- a/doc/sphinx/source/graphics/ngraph-hub.xcf
+++ b/doc/sphinx/source/graphics/ngraph-hub.xcf
--- a/doc/sphinx/source/index.rst
+++ b/doc/sphinx/source/index.rst
@@ -17,10 +17,11 @@
 Intel nGraph library project
 #############################

-Welcome to the Intel nGraph project, an open source C++ library for developers
-of :abbr:`Deep Learning (DL)` (DL) systems. Here you will find a suite of 
-components, APIs, and documentation that can be used to compile and run  
-:abbr:`Deep Neural Network (DNN)` (DNN) models defined in a variety of frameworks.  
+Welcome to Intel nGraph, an open source C++ library for developers of 
+:abbr:`Deep Learning (DL)` (DL) systems. Here you will find a suite 
+of components, APIs, and documentation that can be used to compile 
+and run :abbr:`Deep Neural Network (DNN)` (DNN) models defined in a 
+variety of frameworks.  

 .. figure:: graphics/ngraph-hub.png  


--- a/doc/sphinx/source/installation.rst
+++ b/doc/sphinx/source/installation.rst
@@ -7,24 +7,23 @@ Install the Intel® nGraph™ library
 Build Environments
 ==================

-The |release| version of |project| supports Linux\* or UNIX-based 
-systems which have recent updates of the following packages and 
-prerequisites: 
+The |release| version of |project| supports Linux\*-based systems which 
+have recent updates of the following packages and prerequisites: 

 .. csv-table::
   :header: "Operating System", "Compiler", "Build System", "Status", "Additional Packages"
   :widths: 25, 15, 25, 20, 25
   :escape: ~

-   CentOS 7.4 64-bit, CLang 3.4, GCC 4.8 + CMake 2.8, supported, ``patch diffutils zlib1g-dev libtinfo-dev`` 
+   CentOS 7.4 64-bit, GCC 4.8, CMake 3.2, supported, ``patch diffutils zlib1g-dev libtinfo-dev`` 
   Ubuntu 16.04 (LTS) 64-bit, CLang 3.9, CMake 3.5.1 + GNU Make, supported, ``build-essential cmake clang-3.9 git libtinfo-dev``
-   Ubuntu 16.04 (LTS) 64-bit, CLang 4.0, CMake 3.5.1 + GNU Make, officially unsupported, ``build-essential cmake clang-4.0 git libtinfo-dev``
   Clear Linux\* OS for Intel Architecture, CLang 5.0.1, CMake 3.10.2, experimental, bundles ``machine-learning-basic dev-utils python3-basic python-basic-dev``

-On Ubuntu 16.04 with ``gcc-5.4.0`` or ``clang-3.9``, the recommended option 
-is to add ``-DNGRAPH_USE_PREBUILT_LLVM=TRUE`` to the :command:`cmake` command. 
-This gets a pre-built tarball of LLVM+Clang from `llvm.org`_, and substantially 
-reduces build times.
+Other configurations may work, but aren't tested; on Ubuntu 16.04 with 
+``gcc-5.4.0`` or ``clang-3.9``, for example, we recommend adding 
+``-DNGRAPH_USE_PREBUILT_LLVM=TRUE`` to the :command:`cmake` command in step 4
+below. This gets a pre-built tarball of LLVM+Clang from `llvm.org`_, and will
+substantially reduce build time.

 If using ``gcc-4.8``, it may be necessary to add symlinksfrom ``gcc`` to
 ``gcc-4.8``, and from ``g++`` to ``g++-4.8``, in your :envvar:`PATH`, even 
@@ -33,7 +32,7 @@ flags when building. (You should NOT supply the `-DNGRAPH_USE_PREBUILT_LLVM`
 flag in this case, because the prebuilt tarball supplied on llvm.org is not 
 compatible with a gcc-4.8 based build.)

-Support for macOS is limited; see the macOS development prerequisites 
+Support for macOS is limited; see the `macOS development prerequisites`_ 
 section at the end of this page for details.


@@ -95,6 +94,7 @@ information about how to change or customize this location.
   the ``doc/sphinx`` directory to build HTML API docs inside the 
   ``/docs/doxygen/`` directory. 

+.. macos_development_prerequisites: 

 macOS Development Prerequisites
 -------------------------------

--- a/doc/sphinx/source/ops/constant.rst
+++ b/doc/sphinx/source/ops/constant.rst
+.. constant.rst:
+
+########
+Constant
+########
+
+Description
+===========
+
+Literal constant tensor.
+
+The output is a tensor initialized from the ``values`` attribute.
+
+Attributes
+----------
+
+-----------------+------------------------------+---------------------------------------+
+| Name            | Type                         | Notes                                 |
+=================+==============================+=======================================+
+| ``type``        | ``ngraph::element::type``    | The element type of the value         |
+|                 |                              | in the computation.                   |
+-----------------+------------------------------+---------------------------------------+
+| ``shape``       | ``ngraph::Shape``            | The shape of the constant.            |
+-----------------+------------------------------+---------------------------------------+
+| ``values``      | ``const std::vector<T>&``    | Constant elements in row-major order. |
+|                 |                              | T must be compatible with the element |
+|                 |                              | type.                                 |
+-----------------+------------------------------+---------------------------------------+
+
+Outputs
+-------
+
+-----------------+-------------------------+--------------------------------+
+| Name            | Element Type            | Shape                          |
+=================+=========================+================================+
+| ``output``      | ``type``                | ``shape``                      |
+-----------------+-------------------------+--------------------------------+
+
+
+C++ Interface
+=============
+
+.. doxygenclass:: ngraph::op::Constant
+   :members:
--- a/doc/sphinx/source/ops/convert.rst
+++ b/doc/sphinx/source/ops/convert.rst
+.. convert.rst:
+
+#######
+Convert
+#######
+
+Description
+===========
+
+Convert a tensor from one element type to another.
+
+
+Inputs
+------
+
+-----------------+-------------------------+--------------------------------+
+| Name            | Element Type            | Shape                          |
+=================+=========================+================================+
+| ``arg``         | Any                     | Any                            |
+-----------------+-------------------------+--------------------------------+
+
+Attributes
+----------
+
+------------------+---------------------------+---------------------------------+
+| Name             | Type                      | Notes                           |
+==================+===========================+=================================+
+| ``element_type`` | ``ngraph::element::type`` | The element type of the result. |
+------------------+---------------------------+---------------------------------+
+
+Outputs
+-------
+
+-----------------+-------------------------+--------------------------------+
+| Name            | Element Type            | Shape                          |
+=================+=========================+================================+
+| ``output``      | ``element_type``        | Same as ``arg``.               |
+-----------------+-------------------------+--------------------------------+
+
+
+Backprop
+========
+
+.. math::
+
+   \overline{\texttt{arg}} \leftarrow \texttt{Convert}(\Delta,\texttt{arg->get_element_type()})
+
+
+C++ Interface
+=============
+
+.. doxygenclass:: ngraph::op::Convert
+   :members:
--- a/doc/sphinx/source/ops/convolution.rst
+++ b/doc/sphinx/source/ops/convolution.rst
@@ -125,3 +125,4 @@ C++ Interface
  .. doxygenclass:: ngraph::op::Convolution
     :members:

+     
\ No newline at end of file
--- a/doc/sphinx/source/ops/cos.rst
+++ b/doc/sphinx/source/ops/cos.rst
+.. cos.rst:
+
+###
+Cos
+###
+
+Description
+===========
+
+Elementwise cosine operation.
+
+Produces a tensor of the same element type and shape as ``arg``,
+where the value at each coordinate of ``output`` is the cosine of the
+value at the corresponding coordinate of ``arg``.
+
+Inputs
+------
+
+-----------------+-------------------------+--------------------------------+
+| Name            | Element Type            | Shape                          |
+=================+=========================+================================+
+| ``arg``         | Any                     | Any                            |
+-----------------+-------------------------+--------------------------------+
+
+Outputs
+-------
+
+-----------------+-------------------------+--------------------------------+
+| Name            | Element Type            | Shape                          |
+=================+=========================+================================+
+| ``output``      | Same as ``arg``         | Same as ``arg``.               |
+-----------------+-------------------------+--------------------------------+
+
+
+Mathematical Definition
+=======================
+
+.. math::
+
+   \texttt{output}_{i_0, \ldots, i_{n-1}} = \cos(\texttt{arg}_{i_0, \ldots, i_{n-1}})
+
+
+Backprop
+========
+
+.. math::
+
+   \overline{\texttt{arg}} \leftarrow -\Delta\ \sin(\texttt{arg})
+
+
+C++ Interface
+=============
+
+.. doxygenclass:: ngraph::op::Cos
+   :members:
--- a/doc/sphinx/source/ops/cosh.rst
+++ b/doc/sphinx/source/ops/cosh.rst
+.. cosh.rst:
+
+####
+Cosh
+####
+
+Description
+===========
+
+Elementwise hyperbolic cosine operation.
+
+Produces a tensor of the same element type and shape as ``arg``, where
+the value at each coordinate of ``output`` is the hyperbolic cosine of
+the value at the corresponding coordinate of ``arg``.
+
+Inputs
+------
+
+-----------------+-------------------------+--------------------------------+
+| Name            | Element Type            | Shape                          |
+=================+=========================+================================+
+| ``arg``         | Any                     | Any                            |
+-----------------+-------------------------+--------------------------------+
+
+Outputs
+-------
+
+-----------------+-------------------------+--------------------------------+
+| Name            | Element Type            | Shape                          |
+=================+=========================+================================+
+| ``output``      | Same as ``arg``         | Same as ``arg``.               |
+-----------------+-------------------------+--------------------------------+
+
+
+Mathematical Definition
+=======================
+
+.. math::
+
+   \texttt{output}_{i_0, \ldots, i_{n-1}} = \cosh(\texttt{arg}_{i_0, \ldots, i_{n-1}})
+
+
+Backprop
+========
+
+.. math::
+
+   \overline{\texttt{arg}} \leftarrow \Delta\ \sinh(\texttt{arg})
+
+
+C++ Interface
+=============
+
+.. doxygenclass:: ngraph::op::Cosh
+   :members:
--- a/doc/sphinx/source/ops/index.rst
+++ b/doc/sphinx/source/ops/index.rst
@@ -58,4 +58,9 @@ Not currently a comprehensive list.
   broadcast.rst
   ceiling.rst
   concatenate.rst
+   constant.rst
+   convert.rst
   convolution.rst
+   cos.rst
+   cosh.rst
+
--- a/doc/sphinx/source/project/about.rst
+++ b/doc/sphinx/source/project/about.rst
@@ -17,6 +17,30 @@ Architecture CPUs, the Intel® Nervana Neural Network Processor™ (NNP),
 and NVIDIA\* GPUs. Currently-supported compiler optimizations include efficient 
 memory management and data layout abstraction. 

+Why is this needed?
+--------------------
+
+When Deep Learning (DL) frameworks first emerged as the vehicle for training 
+and inference models, they were designed around kernels optimized for a 
+particular platform. As a result, many backend details were being exposed in 
+the model definitions, making the adaptability and portability of DL models 
+to other or more advanced backends inherently complex and expensive.
+
+The traditional approach means that an algorithm developer cannot easily adapt 
+his or her model to different backends. Making a model run on a different 
+framework is also problematic because the user must separate the essence of 
+the model from the performance adjustments made for the backend, translate 
+to similar ops in the new framework, and finally make the necessary changes 
+for the preferred backend configuration on the new framework.
+
+We designed the Intel nGraph project to substantially reduce these kinds of 
+engineering complexities. While optimized kernels for deep-learning primitives 
+are provided through the project and via libraries like Intel® Math Kernel 
+Library (Intel® MKL) for Deep Neural Networks (Intel® MKL-DNN), there are 
+several compiler-inspired ways in which performance can be further optimized.
+
+=======
+
 The *nGraph core* uses a strongly-typed and platform-neutral stateless graph 
 representation for computations. Each node, or *op*, in the graph corresponds
 to one step in a computation, where each step produces zero or more tensor
@@ -39,4 +63,7 @@ read more about design decisions and what is tentatively in the pipeline
 for development in our `SysML conference paper`_.

 .. _frontend: http://neon.nervanasys.com/index.html/
-.. _SysML conference paper: https://arxiv.org/pdf/1801.08058.pdf
\ No newline at end of file
+.. _SysML conference paper: https://arxiv.org/pdf/1801.08058.pdf
+.. _MXNet: http://mxnet.incubator.apache.org/
+.. _TensorFlow: https://www.tensorflow.org/
+
--- a/doc/sphinx/source/testing-libngraph.rst
+++ b/doc/sphinx/source/testing-libngraph.rst
@@ -31,14 +31,12 @@ training/inference model with one of the backends that are now enabled.
 For this early |release| release, we're providing :doc:`framework-integration-guides`, 
 for:

-* :doc:`framework-integration-guides` framework,  
-* :doc:`framework-integration-guides` framework, and
+* :doc:`MXNet<framework-integration-guides>` framework,  
+* :doc:`Tensorflow<framework-integration-guides>` framework, and
 * neon™ `frontend framework`_.

 Integration guides for other frameworks are tentatively forthcoming.

 .. _GTest framework: https://github.com/google/googletest.git
-.. _MXNet: http://mxnet.incubator.apache.org/
-.. _TensorFlow: https://www.tensorflow.org/
 .. _frontend framework: http://neon.nervanasys.com/index.html/

--- a/src/ngraph/CMakeLists.txt
+++ b/src/ngraph/CMakeLists.txt
@@ -36,9 +36,7 @@ set (SRC
    ops/abs.cpp
    ops/add.cpp
    ops/avg_pool.cpp
-    ops/binary_elementwise_arithmetic.cpp
-    ops/binary_elementwise_comparison.cpp
-    ops/binary_elementwise.cpp
+    ops/batch_norm.cpp
    ops/broadcast.cpp
    ops/concatenate.cpp
    ops/constant.cpp
@@ -78,8 +76,12 @@ set (SRC
    ops/sum.cpp
    ops/tan.cpp
    ops/tanh.cpp
-    ops/unary_elementwise_arithmetic.cpp
-    ops/unary_elementwise.cpp
+    ops/util/binary_elementwise_arithmetic.cpp 
+    ops/util/binary_elementwise_comparison.cpp 
+    ops/util/binary_elementwise.cpp 
+    ops/util/requires_tensor_view_args.cpp 
+    ops/util/unary_elementwise_arithmetic.cpp 
+    ops/util/unary_elementwise.cpp
    pass/dump_sorted.cpp
    pass/graph_rewrite.cpp
    pass/inliner.cpp
@@ -169,10 +171,15 @@ if (NGRAPH_CPU_ENABLE AND LLVM_INCLUDE_DIR AND
        runtime/cpu/cpu_kernel_utils.cpp
        runtime/cpu/cpu_emitter.cpp
        runtime/cpu/cpu_external_function.cpp
+        runtime/cpu/cpu_tensor_view.cpp
        runtime/cpu/cpu_tensor_view_wrapper.cpp
+        runtime/cpu/cpu_layout_descriptor.cpp
        runtime/cpu/cpu_tracing.cpp
+        runtime/cpu/mkldnn_utils.cpp
+        runtime/cpu/ops/convert_layout.cpp
        runtime/cpu/ops/matmul_bias.cpp
        runtime/cpu/pass/cpu_fusion.cpp
+        runtime/cpu/pass/cpu_layout.cpp
    )
    # LLVM binary builds are typically built without RTTI
    # The built-in headers are in a version-specific directory
@@ -197,6 +204,19 @@ if (NGRAPH_CPU_ENABLE AND LLVM_INCLUDE_DIR AND
    set_source_files_properties(codegen/compiler.cpp PROPERTIES COMPILE_DEFINITIONS "${HEADER_SEARCH_DEFINES}")
    set(NGRAPH_CPU_DEBUGINFO_ENABLE 0 CACHE STRING "Enable debuginfo in the CPU backend")

+if(NGRAPH_DISTRIBUTED_ENABLE AND MPI_CXX_INCLUDE_PATH)
+    include_directories(SYSTEM ${MPI_C_INCLUDE_PATH} ${MPI_CXX_INCLUDE_PATH})
+    link_directories(${MPI_C_LIBRARIES} ${MPI_CXX_LIBRARIES})
+
+    # Add sources for distributed ngraph
+    # and all its dependencies
+    set(SRC ${SRC}
+        ops/allreduce.cpp
+    )
+    set_property(SOURCE codegen/compiler.cpp APPEND PROPERTY COMPILE_DEFINITIONS
+        "MPI_HEADER_PATH=\"${MPI_C_INCLUDE_PATH}\";")
+endif()
+
    # GPU backend current requires CPU because they share compiler.cpp,
    # and compiler.cpp requires MKLDNN
    if(NGRAPH_GPU_ENABLE)
@@ -231,7 +251,7 @@ if (NGRAPH_CPU_ENABLE AND LLVM_INCLUDE_DIR AND MKLDNN_INCLUDE_DIR)
    # Generate the resource file containing all headers used by the codegen compiler
    add_custom_target(header_resource
        resource_generator --output ${CMAKE_BINARY_DIR}/header_resource.hpp --base codegen
-        DEPENDS resource_generator eigen ext_llvm ext_mkldnn
+        DEPENDS resource_generator ext_eigen ext_llvm ext_mkldnn
        BYPRODUCTS
    )
    add_dependencies(ngraph header_resource)
@@ -286,6 +306,10 @@ if(NGRAPH_GPU_ENABLE AND CUDA_LIBRARIES)
    target_link_libraries(ngraph PRIVATE ${CUDA_LIBRARIES} ${CUDA_CUBLAS_LIBRARIES} ${CUDNN_LIBRARIES})
 endif()

+if(MPI_CXX_INCLUDE_PATH)
+    target_link_libraries(ngraph PRIVATE ${MPI_CXX_LIBRARIES})
+endif()
+
 # Argon
 if (NGRAPH_ARGON_ENABLE)
    target_link_libraries(ngraph PRIVATE ${ARGON_TRANSFORMER_LIB_DIR}/libargon.so)
@@ -306,35 +330,33 @@ install(DIRECTORY
    FILES_MATCHING PATTERN "*.hpp"
 )

-if (NOT APPLE)
-    install(DIRECTORY
-        ${MKLDNN_LIB_DIR}/
-        DESTINATION "${NGRAPH_INSTALL_LIB}"
-    )
+install(DIRECTORY
+    ${MKLDNN_LIB_DIR}/
+    DESTINATION "${NGRAPH_INSTALL_LIB}"
+)

-    if (NGRAPH_TBB_ENABLE)
-        install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/tbb_build/tbb_release/
-            DESTINATION ${NGRAPH_INSTALL_LIB}
-            FILES_MATCHING PATTERN "libtbb.so.*"
-        )
-        install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/tbb_build/tbb_debug/
-            DESTINATION ${NGRAPH_INSTALL_LIB}
-            FILES_MATCHING PATTERN "libtbb_debug.so.*"
-        )
-    endif()
+if (NGRAPH_TBB_ENABLE)
+    install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/tbb_build/tbb_release/
+        DESTINATION ${NGRAPH_INSTALL_LIB}
+        FILES_MATCHING PATTERN "libtbb.so.*"
+    )
+    install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/tbb_build/tbb_debug/
+        DESTINATION ${NGRAPH_INSTALL_LIB}
+        FILES_MATCHING PATTERN "libtbb_debug.so.*"
+    )
+endif()

-    if (NGRAPH_ARGON_ENABLE)
-        install(DIRECTORY ${ARGON_TRANSFORMER_LIB_DIR}/
-            DESTINATION ${NGRAPH_INSTALL_LIB}
-            FILES_MATCHING PATTERN "*.so"
-        )
-        install(DIRECTORY ${ARGON_TRANSFORMER_INCLUDE_DIR}/
-            DESTINATION ${NGRAPH_INSTALL_INCLUDE}
-            FILES_MATCHING PATTERN "*.hpp"
-        )
-        install(DIRECTORY ${ARGON_TRANSFORMER_INCLUDE_DIR}/
-            DESTINATION ${NGRAPH_INSTALL_INCLUDE}
-            FILES_MATCHING PATTERN "*.h"
-        )
-    endif()
+if (NGRAPH_ARGON_ENABLE)
+    install(DIRECTORY ${ARGON_TRANSFORMER_LIB_DIR}/
+        DESTINATION ${NGRAPH_INSTALL_LIB}
+        FILES_MATCHING PATTERN "*.so"
+    )
+    install(DIRECTORY ${ARGON_TRANSFORMER_INCLUDE_DIR}/
+        DESTINATION ${NGRAPH_INSTALL_INCLUDE}
+        FILES_MATCHING PATTERN "*.hpp"
+    )
+    install(DIRECTORY ${ARGON_TRANSFORMER_INCLUDE_DIR}/
+        DESTINATION ${NGRAPH_INSTALL_INCLUDE}
+        FILES_MATCHING PATTERN "*.h"
+    )
 endif()
--- a/src/ngraph/codegen/compiler.cpp
+++ b/src/ngraph/codegen/compiler.cpp
@@ -151,6 +151,8 @@ void codegen::StaticCompiler::initialize()
    // Prepare DiagnosticEngine
    IntrusiveRefCntPtr<DiagnosticOptions> diag_options = new DiagnosticOptions();
    diag_options->ErrorLimit = 20;
+    diag_options->ShowCarets = false;
+    diag_options->ShowFixits = false;
    IntrusiveRefCntPtr<DiagnosticIDs> diag_id(new DiagnosticIDs());
    DiagnosticsEngine diag_engine(diag_id, &*diag_options);

@@ -351,6 +353,15 @@ void codegen::StaticCompiler::configure_search_path()
 {
 #ifdef USE_BUILTIN
    load_headers_from_resource();
+#elif defined(__APPLE__)
+    add_header_search_path(EIGEN_HEADERS_PATH);
+    add_header_search_path(MKLDNN_HEADERS_PATH);
+    add_header_search_path(TBB_HEADERS_PATH);
+    add_header_search_path(NGRAPH_HEADERS_PATH);
+    add_header_search_path(INSTALLED_HEADERS_PATH);
+    add_header_search_path(CLANG_BUILTIN_HEADERS_PATH);
+
+    add_header_search_path("/Library/Developer/CommandLineTools/usr/include/c++/v1");
 #else
    // Add base toolchain-supplied header paths
    // Ideally one would use the Linux toolchain definition in clang/lib/Driver/ToolChains.h
@@ -400,6 +411,10 @@ void codegen::StaticCompiler::configure_search_path()
    // Only needed for GPU backend
    add_header_search_path(CUDA_HEADER_PATHS);
 #endif
+
+#ifdef NGRAPH_DISTRIBUTED
+    add_header_search_path(MPI_HEADER_PATH);
+#endif
 }

 void codegen::StaticCompiler::load_headers_from_resource()

--- a/src/ngraph/codegen/execution_engine.cpp
+++ b/src/ngraph/codegen/execution_engine.cpp
@@ -78,6 +78,13 @@ void codegen::ExecutionEngine::finalize()

 void* codegen::ExecutionEngine::get_pointer_to_named_function(const std::string& func_name)
 {
+// For whatever reason, macOS seems to expect that we prefix this with an underscore.
+#ifdef __APPLE__
+    std::string fname = "_" + func_name;
+#else
+    const std::string& fname = func_name;
+#endif
+
    // set AbortOnFailure flag to false so call fails by returning nullptr
-    return m_execution_engine->getPointerToNamedFunction(func_name, false);
+    return m_execution_engine->getPointerToNamedFunction(fname, false);
 }
--- a/src/ngraph/log.cpp
+++ b/src/ngraph/log.cpp
@@ -26,45 +26,45 @@

 using namespace std;

-namespace nervana
+namespace ngraph
 {
    class thread_starter;
 }

-string nervana::logger::log_path;
-deque<string> nervana::logger::queue;
+string ngraph::logger::log_path;
+deque<string> ngraph::logger::queue;
 static mutex queue_mutex;
 static condition_variable queue_condition;
 static unique_ptr<thread> queue_thread;
 static bool active = false;

-std::ostream& nervana::get_nil_stream()
+std::ostream& ngraph::get_nil_stream()
 {
    static std::stringstream nil;
    return nil;
 }

-class nervana::thread_starter
+class ngraph::thread_starter
 {
 public:
-    thread_starter() { nervana::logger::start(); }
-    virtual ~thread_starter() { nervana::logger::stop(); }
+    thread_starter() { ngraph::logger::start(); }
+    virtual ~thread_starter() { ngraph::logger::stop(); }
 };

-static nervana::thread_starter _starter;
+static ngraph::thread_starter _starter;

-void nervana::logger::set_log_path(const string& path)
+void ngraph::logger::set_log_path(const string& path)
 {
    log_path = path;
 }

-void nervana::logger::start()
+void ngraph::logger::start()
 {
    active = true;
    queue_thread = unique_ptr<thread>(new thread(&thread_entry, nullptr));
 }

-void nervana::logger::stop()
+void ngraph::logger::stop()
 {
    {
        unique_lock<std::mutex> lk(queue_mutex);
@@ -74,12 +74,12 @@ void nervana::logger::stop()
    queue_thread->join();
 }

-void nervana::logger::process_event(const string& s)
+void ngraph::logger::process_event(const string& s)
 {
    cout << s << "\n";
 }

-void nervana::logger::thread_entry(void* param)
+void ngraph::logger::thread_entry(void* param)
 {
    unique_lock<std::mutex> lk(queue_mutex);
    while (active)
@@ -93,14 +93,14 @@ void nervana::logger::thread_entry(void* param)
    }
 }

-void nervana::logger::log_item(const string& s)
+void ngraph::logger::log_item(const string& s)
 {
    unique_lock<std::mutex> lk(queue_mutex);
    queue.push_back(s);
    queue_condition.notify_one();
 }

-nervana::log_helper::log_helper(LOG_TYPE type, const char* file, int line, const char* func)
+ngraph::log_helper::log_helper(LOG_TYPE type, const char* file, int line, const char* func)
 {
    switch (type)
    {
@@ -124,7 +124,7 @@ nervana::log_helper::log_helper(LOG_TYPE type, const char* file, int line, const
    _stream << "\t";
 }

-nervana::log_helper::~log_helper()
+ngraph::log_helper::~log_helper()
 {
    cout << _stream.str() << endl;
    // logger::log_item(_stream.str());

--- a/src/ngraph/log.hpp
+++ b/src/ngraph/log.hpp
@@ -20,7 +20,7 @@
 #include <sstream>
 #include <stdexcept>

-namespace nervana
+namespace ngraph
 {
    class conststring
    {
@@ -93,30 +93,30 @@ namespace nervana
    extern std::ostream& get_nil_stream();

 #define NGRAPH_ERR                                                                                 \
-    nervana::log_helper(nervana::LOG_TYPE::_LOG_TYPE_ERROR,                                        \
-                        nervana::get_file_name(__FILE__),                                          \
-                        __LINE__,                                                                  \
-                        __PRETTY_FUNCTION__)                                                       \
+    ngraph::log_helper(ngraph::LOG_TYPE::_LOG_TYPE_ERROR,                                          \
+                       ngraph::get_file_name(__FILE__),                                            \
+                       __LINE__,                                                                   \
+                       __PRETTY_FUNCTION__)                                                        \
        .stream()
 #define NGRAPH_WARN                                                                                \
-    nervana::log_helper(nervana::LOG_TYPE::_LOG_TYPE_WARNING,                                      \
-                        nervana::get_file_name(__FILE__),                                          \
-                        __LINE__,                                                                  \
-                        __PRETTY_FUNCTION__)                                                       \
+    ngraph::log_helper(ngraph::LOG_TYPE::_LOG_TYPE_WARNING,                                        \
+                       ngraph::get_file_name(__FILE__),                                            \
+                       __LINE__,                                                                   \
+                       __PRETTY_FUNCTION__)                                                        \
        .stream()
 #define NGRAPH_INFO                                                                                \
-    nervana::log_helper(nervana::LOG_TYPE::_LOG_TYPE_INFO,                                         \
-                        nervana::get_file_name(__FILE__),                                          \
-                        __LINE__,                                                                  \
-                        __PRETTY_FUNCTION__)                                                       \
+    ngraph::log_helper(ngraph::LOG_TYPE::_LOG_TYPE_INFO,                                           \
+                       ngraph::get_file_name(__FILE__),                                            \
+                       __LINE__,                                                                   \
+                       __PRETTY_FUNCTION__)                                                        \
        .stream()

 // #define NGRAPH_DEBUG                                                                               \
-//     nervana::log_helper(nervana::LOG_TYPE::_LOG_TYPE_DEBUG,                                        \
-//                         nervana::get_file_name(__FILE__),                                          \
-//                         __LINE__,                                                                  \
-//                         __PRETTY_FUNCTION__)                                                       \
+//     ngraph::log_helper(ngraph::LOG_TYPE::_LOG_TYPE_DEBUG,                                          \
+//                        ngraph::get_file_name(__FILE__),                                            \
+//                        __LINE__,                                                                   \
+//                        __PRETTY_FUNCTION__)                                                        \
 //         .stream()

-#define NGRAPH_DEBUG nervana::get_nil_stream()
+#define NGRAPH_DEBUG ngraph::get_nil_stream()
 }
--- a/src/ngraph/ngraph.hpp
+++ b/src/ngraph/ngraph.hpp
@@ -127,3 +127,7 @@
 #include "ngraph/shape.hpp"
 #include "ngraph/types/element_type.hpp"
 #include "ngraph/types/type.hpp"
+
+#ifdef NGRAPH_DISTRIBUTED
+#include "ngraph/ops/allreduce.hpp"
+#endif
--- a/src/ngraph/ops/abs.hpp
+++ b/src/ngraph/ops/abs.hpp
@@ -18,7 +18,7 @@

 #include <memory>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -26,7 +26,7 @@ namespace ngraph
    {
        /// \brief Elementwise absolute value operation.
        ///
-        class Abs : public UnaryElementwiseArithmetic
+        class Abs : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an absolute value operation.
@@ -45,7 +45,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Abs>(new_args.at(0));
            }


--- a/src/ngraph/ops/acos.hpp
+++ b/src/ngraph/ops/acos.hpp
@@ -18,7 +18,7 @@

 #include <memory>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -26,7 +26,7 @@ namespace ngraph
    {
        /// \brief Elementwise inverse cosine (arccos) operation.
        ///
-        class Acos : public UnaryElementwiseArithmetic
+        class Acos : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an arccos operation.
@@ -45,7 +45,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Acos>(new_args.at(0));
            }
        };

--- a/src/ngraph/ops/add.hpp
+++ b/src/ngraph/ops/add.hpp
@@ -18,7 +18,7 @@

 #include <memory>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -26,7 +26,7 @@ namespace ngraph
    {
        /// \brief Elementwise addition operation.
        ///
-        class Add : public BinaryElementwiseArithmetic
+        class Add : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an addition operation.
@@ -47,7 +47,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Add>(new_args.at(0), new_args.at(1));
            }


--- a/src/ngraph/ops/allreduce.cpp
+++ b/src/ngraph/ops/allreduce.cpp
+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// ----------------------------------------------------------------------------
+
+#ifdef NGRAPH_DISTRIBUTED
+
+#include "ngraph/ops/allreduce.hpp"
+
+using namespace std;
+using namespace ngraph;
+
+op::AllReduce::AllReduce(const std::shared_ptr<Node>& arg)
+    : RequiresTensorViewArgs("AllReduce", {arg})
+{
+    auto& input = m_inputs.at(0);
+    set_value_type_checked(
+        make_shared<TensorViewType>(input.get_element_type(), input.get_shape()));
+
+    if ((arg->get_element_type() != element::f32) && (arg->get_element_type() != element::f64))
+    {
+        throw ngraph_error("Unsupported data type for AllReduce");
+    }
+}
+
+#endif
--- a/src/ngraph/ops/allreduce.hpp
+++ b/src/ngraph/ops/allreduce.hpp
+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// ----------------------------------------------------------------------------
+
+#pragma once
+
+#ifdef NGRAPH_DISTRIBUTED
+
+#include <memory>
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        class AllReduce : public util::RequiresTensorViewArgs
+        {
+        public:
+            AllReduce(const std::shared_ptr<Node>& arg);
+
+            virtual std::shared_ptr<Node> copy_with_new_args(
+                const std::vector<std::shared_ptr<Node>>& new_args) const override
+            {
+                if (new_args.size() != 1)
+                {
+                    throw ngraph_error("Incorrect number of new arguments");
+                }
+                return std::make_shared<AllReduce>(new_args.at(0));
+            }
+        };
+    }
+}
+
+#endif
--- a/src/ngraph/ops/asin.hpp
+++ b/src/ngraph/ops/asin.hpp
@@ -18,7 +18,7 @@

 #include <memory>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -26,7 +26,7 @@ namespace ngraph
    {
        /// \brief Elementwise inverse sine (arcsin) operation.
        ///
-        class Asin : public UnaryElementwiseArithmetic
+        class Asin : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an arcsin operation.
@@ -45,7 +45,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Asin>(new_args.at(0));
            }
        };

--- a/src/ngraph/ops/atan.hpp
+++ b/src/ngraph/ops/atan.hpp
@@ -18,7 +18,7 @@

 #include <memory>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -26,7 +26,7 @@ namespace ngraph
    {
        /// \brief Elementwise inverse tangent (arctan) operation.
        ///
-        class Atan : public UnaryElementwiseArithmetic
+        class Atan : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an arctan operation.
@@ -45,7 +45,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Atan>(new_args.at(0));
            }
        };

--- a/src/ngraph/ops/avg_pool.hpp
+++ b/src/ngraph/ops/avg_pool.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -24,7 +24,7 @@ namespace ngraph
    {
        /// \brief Batched average pooling operation, with optional padding and window stride.
        ///
-        class AvgPool : public RequiresTensorViewArgs
+        class AvgPool : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a batched average pooling operation.
@@ -69,7 +69,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }

                return std::make_shared<AvgPool>(new_args.at(0),
                                                 m_window_shape,
@@ -96,7 +98,7 @@ namespace ngraph
            Shape m_padding_above;
        };

-        class AvgPoolBackprop : public RequiresTensorViewArgs
+        class AvgPoolBackprop : public util::RequiresTensorViewArgs
        {
        public:
            AvgPoolBackprop(const Shape& forward_arg_shape,
@@ -110,7 +112,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }

                AvgPoolBackprop* avpn = new AvgPoolBackprop(m_forward_arg_shape,
                                                            new_args.at(0),

--- a/src/ngraph/ops/batch_norm.cpp
+++ b/src/ngraph/ops/batch_norm.cpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include "ngraph/ops/batch_norm.hpp"
+#include "ngraph/ops/constant.hpp"
+
+ngraph::op::BatchNorm::BatchNorm(double eps,
+                                 std::shared_ptr<ngraph::Node> gamma,
+                                 std::shared_ptr<ngraph::Node> beta,
+                                 std::shared_ptr<ngraph::Node> input,
+                                 std::shared_ptr<ngraph::Node> mean,
+                                 std::shared_ptr<ngraph::Node> variance)
+    : RequiresTensorViewArgs("BatchNorm", {gamma, beta, input, mean, variance})
+    , m_bn_input_shape(input->get_shape())
+    , m_bn_variance_shape(variance->get_shape())
+    , m_bn_mean_shape(mean->get_shape())
+    , m_epsilon(eps)
+{
+    add_output(input->get_element_type(), m_bn_input_shape);
+
+    if (m_bn_input_shape.size() < 2)
+    {
+        throw ngraph_error("input tensor to batchnorm much have tensor of atleast rank 2");
+    }
+
+    if (m_bn_input_shape[1] == 0)
+    {
+        throw ngraph_error(
+            "input tensor must have atleast one channel axis for batch normalization");
+    }
+
+    if ((m_bn_mean_shape.size() != 1) && (m_bn_variance_shape.size() != 1) &&
+        (gamma->get_shape().size() != 1) && (beta->get_shape().size() != 1))
+    {
+        throw ngraph_error("gamma, beta, mean, variance shoud have all rank 1");
+    }
+
+    // assuming input shape (N, C, H, W), check if the size of mean and
+    // variance are equal to channel axis
+    if (mean->get_shape()[0] != m_bn_input_shape[1])
+    {
+        throw ngraph_error("mean size is not equal to input channel size");
+    }
+
+    if (variance->get_shape()[0] != m_bn_input_shape[1])
+    {
+        throw ngraph_error("variance size is not equal to input channel size");
+    }
+
+    if (variance->get_shape().size() != mean->get_shape().size())
+    {
+        throw ngraph_error("mean and variance rank does not match");
+    }
+
+    if (gamma->get_shape().size() != beta->get_shape().size())
+    {
+        throw ngraph_error("gamma and beta rank does not match");
+    }
+
+    if (input->get_element_type() != mean->get_element_type())
+    {
+        throw ngraph_error("input tensor and mean element type does not match");
+    }
+
+    if (input->get_element_type() != variance->get_element_type())
+    {
+        throw ngraph_error("input tensor and variance element type does not match");
+    }
+
+    if (gamma->get_element_type() != beta->get_element_type())
+    {
+        throw ngraph_error("gamma and beta element type does not match");
+    }
+}
+
+std::shared_ptr<ngraph::Node> ngraph::op::BatchNorm::copy_with_new_args(
+    const std::vector<std::shared_ptr<ngraph::Node>>& new_args) const
+{
+    if (new_args.size() != 5)
+        throw ngraph_error("Incorrect number of new arguments");
+    return std::make_shared<BatchNorm>(
+        m_epsilon, new_args.at(0), new_args.at(1), new_args.at(2), new_args.at(3), new_args.at(4));
+}
--- a/src/ngraph/ops/batch_norm.hpp
+++ b/src/ngraph/ops/batch_norm.hpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#pragma once
+
+#include <memory>
+#include "ngraph/node.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"
+#include "ngraph/util.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        class BatchNorm : public util::RequiresTensorViewArgs
+        {
+        public:
+            BatchNorm(double eps,
+                      std::shared_ptr<Node> gamma,
+                      std::shared_ptr<Node> beta,
+                      std::shared_ptr<Node> input,
+                      std::shared_ptr<Node> mean,
+                      std::shared_ptr<Node> variance);
+
+            const Shape& get_inputs_shape() const { return m_bn_input_shape; }
+            const Shape& get_variance_shape() const { return m_bn_variance_shape; }
+            const Shape& get_mean_shape() const { return m_bn_mean_shape; }
+            double get_eps_value() const { return m_epsilon; }
+            virtual std::shared_ptr<Node> copy_with_new_args(
+                const std::vector<std::shared_ptr<Node>>& new_args) const override;
+
+        private:
+            Shape m_bn_input_shape;
+            Shape m_bn_variance_shape;
+            Shape m_bn_mean_shape;
+            double m_epsilon;
+        };
+    }
+}
--- a/src/ngraph/ops/broadcast.hpp
+++ b/src/ngraph/ops/broadcast.hpp
@@ -16,14 +16,14 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
    namespace op
    {
        /// \brief Operation which "adds" axes to an input tensor, replicating elements from the input as needed along the new axes.
-        class Broadcast : public RequiresTensorViewArgs
+        class Broadcast : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a conversion operation.
@@ -40,7 +40,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Broadcast>(new_args.at(0), m_shape, m_broadcast_axes);
            }


--- a/src/ngraph/ops/ceiling.hpp
+++ b/src/ngraph/ops/ceiling.hpp
@@ -16,14 +16,14 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
    namespace op
    {
        /// \brief Elementwise ceiling operation.
-        class Ceiling : public UnaryElementwiseArithmetic
+        class Ceiling : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a ceiling operation.
@@ -38,7 +38,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Ceiling>(new_args.at(0));
            }
        };

--- a/src/ngraph/ops/concatenate.hpp
+++ b/src/ngraph/ops/concatenate.hpp
@@ -18,14 +18,14 @@

 #include <memory>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
    namespace op
    {
        /// \brief Concatenation operation.
-        class Concat : public RequiresTensorViewArgs
+        class Concat : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a concatenation operation.

--- a/src/ngraph/ops/constant.hpp
+++ b/src/ngraph/ops/constant.hpp
@@ -29,20 +29,6 @@ namespace ngraph
    namespace op
    {
        /// \brief Class for constants.
-        ///
-        /// ## Parameters
-        ///
-        /// |                 | Description                                                                                                                                                                    |
-        /// | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-        /// | `type`          | The ngraph::element::Type of the tensor constant.                                                                                                                              |
-        /// | `shape`         | The ngraph::Shape of the tensor constant.                                                                                                                                      |
-        /// | `values`        | A list of values to initialize the underlying tensor constant. |
-        ///
-        /// ## Output
-        ///
-        /// | Type                   | Description                                                           |
-        /// | ---------------------- | --------------------------------------------------------------------- |
-        /// | \f$E[d_1,\dots,d_n]\f$ | A constant tensor with the specified element type, shape, and values. |
        class Constant : public Node
        {
        public:

--- a/src/ngraph/ops/convert.hpp
+++ b/src/ngraph/ops/convert.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise.hpp"
 #include "ngraph/types/type.hpp"

 namespace ngraph
@@ -24,28 +24,7 @@ namespace ngraph
    namespace op
    {
        /// \brief Elementwise type conversion operation.
-        ///
-        /// Each scalar in the input tensor is converted to the specified output element type. Note that the conversion may
-        /// result in loss of precision. For example, conversion from `float32` to `int32` is allowed.
-        ///
-        /// ## Parameters
-        ///
-        /// |                | Description                              |
-        /// | -------------- | ---------------------------------------- |
-        /// | `element_type` | The element type \f$E'\f$ to convert to. |
-        ///
-        /// ## Inputs
-        ///
-        /// |       | Type                              | Description                               |
-        /// | ----- | --------------------------------- | ----------------------------------------- |
-        /// | `arg` | \f$E[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape and element type.   |
-        ///
-        /// ## Output
-        ///
-        /// | Type                    | Description                                                                                               |
-        /// | ----------------------- | --------------------------------------------------------------------------------------------------------- |
-        /// | \f$E'[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{convert}_{(E,E')}(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Convert : public UnaryElementwise
+        class Convert : public util::UnaryElementwise
        {
        public:
            /// \brief Constructs a conversion operation.
@@ -58,7 +37,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Convert>(new_args.at(0), m_element_type);
            }


--- a/src/ngraph/ops/convolution.hpp
+++ b/src/ngraph/ops/convolution.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -24,7 +24,7 @@ namespace ngraph
    {
        /// \brief Batched convolution operation, with optional window dilation and stride.
        ///
-        class Convolution : public RequiresTensorViewArgs
+        class Convolution : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a batched convolution operation.
@@ -151,7 +151,7 @@ namespace ngraph
        };

        /// \brief Data batch backprop for batched convolution operation.
-        class ConvolutionBackpropData : public RequiresTensorViewArgs
+        class ConvolutionBackpropData : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a batched-convolution data batch-backprop operation.
@@ -246,7 +246,7 @@ namespace ngraph
        };

        /// \brief Filters backprop for batched convolution operation.
-        class ConvolutionBackpropFilters : public RequiresTensorViewArgs
+        class ConvolutionBackpropFilters : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a batched-convolution filter-backprop operation.

--- a/src/ngraph/ops/cos.hpp
+++ b/src/ngraph/ops/cos.hpp
@@ -16,26 +16,14 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
    namespace op
    {
        /// \brief Elementwise cosine operation.
-        ///
-        /// ## Inputs
-        ///
-        /// |       | Type                              | Description                                     |
-        /// | ----- | --------------------------------- | ----------------------------------------------- |
-        /// | `arg` | \f$N[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape and numeric element type. |
-        ///
-        /// ## Output
-        ///
-        /// | Type                   | Description                                                                          |
-        /// | ---------------------- | ------------------------------------------------------------------------------------ |
-        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \cos(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Cos : public UnaryElementwiseArithmetic
+        class Cos : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a cosine operation.
@@ -50,7 +38,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Cos>(new_args.at(0));
            }


--- a/src/ngraph/ops/cosh.hpp
+++ b/src/ngraph/ops/cosh.hpp
@@ -16,26 +16,14 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
    namespace op
    {
        /// \brief Elementwise hyperbolic cosine (cosh) operation.
-        ///
-        /// ## Inputs
-        ///
-        /// |       | Type                              | Description                                     |
-        /// | ----- | --------------------------------- | ----------------------------------------------- |
-        /// | `arg` | \f$N[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape and numeric element type. |
-        ///
-        /// ## Output
-        ///
-        /// | Type                   | Description                                                                           |
-        /// | ---------------------- | ------------------------------------------------------------------------------------- |
-        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \cosh(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Cosh : public UnaryElementwiseArithmetic
+        class Cosh : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a hyperbolic cosine operation.
@@ -50,7 +38,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Cosh>(new_args.at(0));
            }


--- a/src/ngraph/ops/divide.hpp
+++ b/src/ngraph/ops/divide.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                              |
        /// | ---------------------- | ------------------------------------------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \texttt{arg0}[i_1,\dots,i_n] \mathbin{/} \texttt{arg1}[i_1,\dots,i_n]\f$ |
-        class Divide : public BinaryElementwiseArithmetic
+        class Divide : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a division operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Divide>(new_args.at(0), new_args.at(1));
            }


--- a/src/ngraph/ops/dot.hpp
+++ b/src/ngraph/ops/dot.hpp
@@ -18,7 +18,7 @@

 #include <utility>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -54,7 +54,7 @@ namespace ngraph
        /// | ---------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$E[d_1,\dots,d_n,d''_1,\dots,d''_p]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n,k_1,\dots,k_p] = \Sigma_{0 \le j_1 < d'_1, \dots, 0 \le j_m < d'_m}(\mathtt{arg0}[i_1,\dots,i_n,j_1,\dots,j_m] \cdot \mathtt{arg1}[j_1,\dots,j_m,k_1,\dots,k_p])\f$ or, if \f$m = 0\f$, \f$T[i_1,\dots,i_n,k_1,\dots,k_p] = \mathtt{arg0}[i_1,\dots,i_n] \cdot \mathtt{arg1}[k_1,\dots,k_p]\f$. |
        ///
-        class Dot : public RequiresTensorViewArgs
+        class Dot : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a dot product operation.
@@ -83,7 +83,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Dot>(
                    new_args.at(0), new_args.at(1), m_reduction_axes_count);
            }

--- a/src/ngraph/ops/equal.hpp
+++ b/src/ngraph/ops/equal.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_comparison.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                               | Description                                                                                                                                |
        /// | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
        /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg0}[i_1,\dots,i_n] = \texttt{arg1}[i_1,\dots,i_n]\text{, else } 0\f$ |
-        class Equal : public BinaryElementwiseComparison
+        class Equal : public util::BinaryElementwiseComparison
        {
        public:
            /// \brief Constructs an is-equal operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Equal>(new_args.at(0), new_args.at(1));
            }
        };

--- a/src/ngraph/ops/exp.hpp
+++ b/src/ngraph/ops/exp.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                          |
        /// | ---------------------- | ------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \exp(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Exp : public UnaryElementwiseArithmetic
+        class Exp : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an exponential operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Exp>(new_args.at(0));
            }


--- a/src/ngraph/ops/floor.hpp
+++ b/src/ngraph/ops/floor.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                    |
        /// | ---------------------- | ---------------------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \lfloor \texttt{arg}[i_1,\dots,i_n] \rfloor\f$ |
-        class Floor : public UnaryElementwiseArithmetic
+        class Floor : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a floor operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Floor>(new_args.at(0));
            }
        };

--- a/src/ngraph/ops/get_output_element.hpp
+++ b/src/ngraph/ops/get_output_element.hpp
@@ -54,7 +54,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<GetOutputElement>(new_args.at(0), m_n);
            }


--- a/src/ngraph/ops/greater.hpp
+++ b/src/ngraph/ops/greater.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_comparison.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                               | Description                                                                                                                                  |
        /// | ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg0}[i_1,\dots,i_n] \gt \texttt{arg1}[i_1,\dots,i_n]\text{, else } 0\f$ |
-        class Greater : public BinaryElementwiseComparison
+        class Greater : public util::BinaryElementwiseComparison
        {
        public:
            /// \brief Constructs a greater-than operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Greater>(new_args.at(0), new_args.at(1));
            }
        };

--- a/src/ngraph/ops/greater_eq.hpp
+++ b/src/ngraph/ops/greater_eq.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_comparison.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                               | Description                                                                                                                                   |
        /// | ---------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg0}[i_1,\dots,i_n] \geq \texttt{arg1}[i_1,\dots,i_n]\text{, else } 0\f$ |
-        class GreaterEq : public BinaryElementwiseComparison
+        class GreaterEq : public util::BinaryElementwiseComparison
        {
        public:
            /// \brief Constructs a greater-than-or-equal operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<GreaterEq>(new_args.at(0), new_args.at(1));
            }
        };

--- a/src/ngraph/ops/less.hpp
+++ b/src/ngraph/ops/less.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_comparison.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                               | Description                                                                                                                                  |
        /// | ---------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg0}[i_1,\dots,i_n] \lt \texttt{arg1}[i_1,\dots,i_n]\text{, else } 0\f$ |
-        class Less : public BinaryElementwiseComparison
+        class Less : public util::BinaryElementwiseComparison
        {
        public:
            /// \brief Constructs a less-than operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Less>(new_args.at(0), new_args.at(1));
            }
        };

--- a/src/ngraph/ops/less_eq.hpp
+++ b/src/ngraph/ops/less_eq.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_comparison.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                               | Description                                                                                                                                   |
        /// | ---------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg0}[i_1,\dots,i_n] \leq \texttt{arg1}[i_1,\dots,i_n]\text{, else } 0\f$ |
-        class LessEq : public BinaryElementwiseComparison
+        class LessEq : public util::BinaryElementwiseComparison
        {
        public:
            /// \brief Constructs a less-than-or-equal operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<LessEq>(new_args.at(0), new_args.at(1));
            }
        };

--- a/src/ngraph/ops/log.hpp
+++ b/src/ngraph/ops/log.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                         |
        /// | ---------------------- | ----------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \ln(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Log : public UnaryElementwiseArithmetic
+        class Log : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a natural log operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Log>(new_args.at(0));
            }


--- a/src/ngraph/ops/max_pool.hpp
+++ b/src/ngraph/ops/max_pool.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -41,7 +41,7 @@ namespace ngraph
        ///      T_\textit{out}[a,c,i_1,\dots,i_n] = \max_{j_1 = s_1 i_1, \dots, j_n = s_n i_n}^{j_1 = s_1 i_1 + w_1 - 1, \dots, j_n = s_n i_n + w_n - 1} (T_\textit{in}[a,c,j_1,\dots,j_n])
        /// \f]
        ///
-        class MaxPool : public RequiresTensorViewArgs
+        class MaxPool : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a batched max pooling operation.
@@ -76,7 +76,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<MaxPool>(
                    new_args.at(0), m_window_shape, m_window_movement_strides);
            }
@@ -99,7 +101,7 @@ namespace ngraph
            Shape m_padding_above;
        };

-        class MaxPoolBackprop : public RequiresTensorViewArgs
+        class MaxPoolBackprop : public util::RequiresTensorViewArgs
        {
        public:
            MaxPoolBackprop(const std::shared_ptr<Node>& arg_forward,
@@ -114,7 +116,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }

                MaxPoolBackprop* mpbp = new MaxPoolBackprop(new_args.at(0),
                                                            new_args.at(1),

--- a/src/ngraph/ops/maximum.hpp
+++ b/src/ngraph/ops/maximum.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                        |
        /// | ---------------------- | ------------------------------------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \max(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$ |
-        class Maximum : public BinaryElementwiseArithmetic
+        class Maximum : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a maximum operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Maximum>(new_args.at(0), new_args.at(1));
            }


--- a/src/ngraph/ops/minimum.hpp
+++ b/src/ngraph/ops/minimum.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                        |
        /// | ---------------------- | ------------------------------------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \min(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$ |
-        class Minimum : public BinaryElementwiseArithmetic
+        class Minimum : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a minimum operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Minimum>(new_args.at(0), new_args.at(1));
            }


--- a/src/ngraph/ops/multiply.hpp
+++ b/src/ngraph/ops/multiply.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                        |
        /// | ---------------------- | ------------------------------------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \texttt{arg0}[i_1,\dots,i_n] \cdot \texttt{arg1}[i_1,\dots,i_n]\f$ |
-        class Multiply : public BinaryElementwiseArithmetic
+        class Multiply : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a multiplication operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Multiply>(new_args.at(0), new_args.at(1));
            }


--- a/src/ngraph/ops/negative.hpp
+++ b/src/ngraph/ops/negative.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                       |
        /// | ---------------------- | --------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = -(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Negative : public UnaryElementwiseArithmetic
+        class Negative : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a negation operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Negative>(new_args.at(0));
            }


--- a/src/ngraph/ops/not.cpp
+++ b/src/ngraph/ops/not.cpp
@@ -21,6 +21,6 @@ using namespace ngraph;
 using namespace std;

 op::Not::Not(const shared_ptr<Node>& arg)
-    : op::UnaryElementwise("Not", arg->get_element_type(), arg)
+    : UnaryElementwise("Not", arg->get_element_type(), arg)
 {
 }
--- a/src/ngraph/ops/not.hpp
+++ b/src/ngraph/ops/not.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                               | Description                                                                                                    |
        /// | ---------------------------------- | -------------------------------------------------------------------------------------------------------------- |
        /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg}[i_1,\dots,i_n] = 0\text{, else } 0\f$ |
-        class Not : public UnaryElementwise
+        class Not : public util::UnaryElementwise
        {
        public:
            /// \brief Constructs a logical negation operation.
@@ -47,7 +47,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Not>(new_args.at(0));
            }
        };

--- a/src/ngraph/ops/not_equal.hpp
+++ b/src/ngraph/ops/not_equal.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_comparison.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                               | Description                                                                                                                                   |
        /// | ---------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg0}[i_1,\dots,i_n] \neq \texttt{arg1}[i_1,\dots,i_n]\text{, else } 0\f$ |
-        class NotEqual : public BinaryElementwiseComparison
+        class NotEqual : public util::BinaryElementwiseComparison
        {
        public:
            /// \brief Constructs a not-equal operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<NotEqual>(new_args.at(0), new_args.at(1));
            }
        };

--- a/src/ngraph/ops/one_hot.hpp
+++ b/src/ngraph/ops/one_hot.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -42,7 +42,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                                                                                                                                                                                |
        /// | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$E[d_1,\dots,d_n]\f$ | The tensor \f$T'\f$, where \f$T'[i_1,\dots,i_{m-1},i_m,i_{m+1},\dots,i_n] = 1\f$ if \f$T[i_1,\dots,i_{m-1},i_{m+1},\dots,i_n] = i_m\f$, else \f$0\f$. However, \f$T'\f$ is undefined if any non-integral value or any out-of-bounds value is detected in the input tensor. |
-        class OneHot : public RequiresTensorViewArgs
+        class OneHot : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a one-hot operation.
@@ -56,7 +56,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<OneHot>(new_args.at(0), m_shape, m_one_hot_axis);
            }


--- a/src/ngraph/ops/op.cpp
+++ b/src/ngraph/ops/op.cpp
@@ -18,23 +18,14 @@
 #include <memory>
 #include <sstream>

-#include "ngraph/except.hpp"
+#include "ngraph/common.hpp"
 #include "ngraph/ops/op.hpp"
 #include "ngraph/types/type.hpp"

 using namespace ngraph;
 using namespace std;

-op::RequiresTensorViewArgs::RequiresTensorViewArgs(const std::string& node_type,
-                                                   const std::vector<std::shared_ptr<Node>>& args)
+op::Op::Op(const std::string& node_type, const Nodes& args)
    : Node(node_type, args)
 {
-    for (auto arg : args)
-    {
-        if (arg->get_output_size() != 1)
-        {
-            throw ngraph_error("Arguments for node type \"" + node_type +
-                               "\" must be tensor views");
-        }
-    }
 }
--- a/src/ngraph/ops/op.hpp
+++ b/src/ngraph/ops/op.hpp
--- a/src/ngraph/ops/pad.hpp
+++ b/src/ngraph/ops/pad.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -56,7 +56,7 @@ namespace ngraph
        /// (Note that `below` and `above` here refer respectively to lower- or higher-numbered coordinate indices, and numbering starts at the upper-left corner;
        /// thus inserting a row "below" actually inserts it at the "top" of the matrix.)
        ///
-        class Pad : public RequiresTensorViewArgs
+        class Pad : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a generic padding operation.

--- a/src/ngraph/ops/parameter.cpp
+++ b/src/ngraph/ops/parameter.cpp
@@ -22,7 +22,7 @@ using namespace std;
 using namespace ngraph;

 op::Parameter::Parameter(const ngraph::element::Type& element_type, const Shape& shape)
-    : Node("Parameter", {})
+    : Op("Parameter", {})
 {
    add_output(element_type, shape);
 }
@@ -30,7 +30,9 @@ op::Parameter::Parameter(const ngraph::element::Type& element_type, const Shape&
 shared_ptr<Node> op::Parameter::copy_with_new_args(const vector<shared_ptr<Node>>& new_args) const
 {
    if (new_args.size() != 0)
+    {
        throw ngraph_error("Incorrect number of new arguments");
+    }
    const descriptor::Output& output = get_outputs().at(0);
    return make_shared<Parameter>(output.get_element_type(), output.get_shape());
 }

--- a/src/ngraph/ops/parameter.hpp
+++ b/src/ngraph/ops/parameter.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/node.hpp"
+#include "ngraph/ops/op.hpp"
 #include "ngraph/types/type.hpp"

 namespace ngraph
@@ -41,7 +41,7 @@ namespace ngraph
        /// | Type    | Description                                                                                                                 |
        /// | ------- | --------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$T\f$ | The value of the parameter, supplied by the `FunctionCall` to this function or in the initial `ngraph::runtime::CallFrame`. |
-        class Parameter : public Node
+        class Parameter : public op::Op
        {
        protected:
            virtual void generate_adjoints(autodiff::Adjoints& adjoints,

--- a/src/ngraph/ops/power.hpp
+++ b/src/ngraph/ops/power.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                    |
        /// | ---------------------- | -------------------------------------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \texttt{arg0}[i_1,\dots,i_n]^{\texttt{arg1}[i_1,\dots,i_n]}\f$ |
-        class Power : public BinaryElementwiseArithmetic
+        class Power : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an exponentiation operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Power>(new_args.at(0), new_args.at(1));
            }


--- a/src/ngraph/ops/reduce.hpp
+++ b/src/ngraph/ops/reduce.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -82,7 +82,7 @@ namespace ngraph
        /// | Type                                      | Description                                                                                                      |
        /// | ----------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
        /// | \f$E[\textit{delete}(A,d_1,\dots,d_n)]\f$ | The tensor \f$T\f$, where \f$T\f$ is the input tensor with the `reduction_axes` \f$A\f$ eliminated by reduction. |
-        class Reduce : public RequiresTensorViewArgs
+        class Reduce : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a reduction operation.
@@ -100,7 +100,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Reduce>(
                    new_args.at(0), new_args.at(1), m_reduction_function, m_reduction_axes);
            }

--- a/src/ngraph/ops/reduce_window.hpp
+++ b/src/ngraph/ops/reduce_window.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -50,7 +50,7 @@ namespace ngraph
        /// | Type                     | Description                                                                                                                                                                                                                                                                                                                     |
        /// | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$E[d'_1,\dots,d'_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{reduce}(\mathit{reduction\_function},\mathit{arg\_init},V)\f$ where \f$V\f$ is the set of values in the input tensor within the window defined by the lower bound \f$(s_1i_1,\dots,s_ni_n)\f$ and the noninclusive upper bound \f$(s_1i_1 + w_1,\dots,s_ni_n + w_n)\f$. |
-        class ReduceWindow : public RequiresTensorViewArgs
+        class ReduceWindow : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a reduce-window operation.
@@ -70,7 +70,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<ReduceWindow>(new_args.at(0),
                                                      new_args.at(1),
                                                      m_reduction_function,

--- a/src/ngraph/ops/remainder.hpp
+++ b/src/ngraph/ops/remainder.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -38,7 +38,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                       |
        /// | ---------------------- | ----------------------------------------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \texttt{arg0}[i_1,\dots,i_n] \mod \texttt{arg1}[i_1,\dots,i_n]\f$ |
-        class Remainder : public BinaryElementwiseArithmetic
+        class Remainder : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a remainder operation.
@@ -54,7 +54,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Remainder>(new_args.at(0), new_args.at(1));
            }
        };

--- a/src/ngraph/ops/replace_slice.hpp
+++ b/src/ngraph/ops/replace_slice.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -45,7 +45,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                                                                                                                                 |
        /// | ---------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$E[d_1,\dots,d_n]\f$ | The tensor \f$T\f$ where \f$T[i_1,\dots,i_n] = \texttt{arg1}[j_1,\dots,j_n]\f$ if \f$j_1,\dots,j_n\f$ is in bounds for `arg1` and for all \f$m\f$, \f$i_m = l_m + j_m s_m\f$, otherwise \f$\texttt{arg0}[i_1,\dots,i_n]\f$. |
-        class ReplaceSlice : public RequiresTensorViewArgs
+        class ReplaceSlice : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a tensor slice replacement operation.
@@ -78,7 +78,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<ReplaceSlice>(
                    new_args.at(0), new_args.at(1), m_lower_bounds, m_upper_bounds, m_strides);
            }

--- a/src/ngraph/ops/reshape.hpp
+++ b/src/ngraph/ops/reshape.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -55,7 +55,7 @@ namespace ngraph
        /// | Type                     | Description                                                                                            |
        /// | ------------------------ | ------------------------------------------------------------------------------------------------------ |
        /// | \f$E[d'_1,\dots,d'_m]\f$ | The tensor \f$T\f$, where \f$T\f$ is the input tensor with its elements rearranged as described above. |
-        class Reshape : public RequiresTensorViewArgs
+        class Reshape : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a reshape operation.
@@ -73,7 +73,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Reshape>(new_args.at(0), m_input_order, m_output_shape);
            }


--- a/src/ngraph/ops/reverse.hpp
+++ b/src/ngraph/ops/reverse.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -43,7 +43,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                                                                               |
        /// | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$E[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \texttt{arg}[j_1,\dots,j_n]\f$ and \f$j_k = d_k - i_k - 1\f$ if axis \f$k\f$ is in the reverse set; else \f$j_k = i_k\f$. |
-        class Reverse : public RequiresTensorViewArgs
+        class Reverse : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a reverse operation.
@@ -56,7 +56,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Reverse>(new_args.at(0), m_reversed_axes);
            }


--- a/src/ngraph/ops/select.hpp
+++ b/src/ngraph/ops/select.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -37,7 +37,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                                                                             |
        /// | ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
        /// | \f$E[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \texttt{arg1}[i_1,\dots,i_n]\text{ if }\texttt{arg0}[i_1,\dots,i_n] \neq 0\text{, else }\texttt{arg2}[i_1,\dots,i_n]\f$ |
-        class Select : public RequiresTensorViewArgs
+        class Select : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a selection operation.
@@ -53,7 +53,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 3)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Select>(new_args.at(0), new_args.at(1), new_args.at(2));
            }


--- a/src/ngraph/ops/select_and_scatter.hpp
+++ b/src/ngraph/ops/select_and_scatter.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -68,7 +68,7 @@ namespace ngraph
        /// | Type                   | Description          |
        /// | ---------------------- | -------------------- |
        /// | \f$E[d_1,\dots,d_n]\f$ | See above algorithm. |
-        class SelectAndScatter : public RequiresTensorViewArgs
+        class SelectAndScatter : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a select-and-scatter operation.
@@ -91,7 +91,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 3)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<SelectAndScatter>(new_args.at(0),
                                                          new_args.at(1),
                                                          new_args.at(2),

--- a/src/ngraph/ops/sign.hpp
+++ b/src/ngraph/ops/sign.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -37,7 +37,7 @@ namespace ngraph
        /// | Type                   | Description                                                                          |
        /// | ---------------------- | ------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \text{sgn}(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Sign : public UnaryElementwiseArithmetic
+        class Sign : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an elementwise sign operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Sign>(new_args.at(0));
            }
        };

--- a/src/ngraph/ops/sin.hpp
+++ b/src/ngraph/ops/sin.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                          |
        /// | ---------------------- | ------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \sin(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Sin : public UnaryElementwiseArithmetic
+        class Sin : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a sine operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Sin>(new_args.at(0));
            }


--- a/src/ngraph/ops/sinh.hpp
+++ b/src/ngraph/ops/sinh.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                           |
        /// | ---------------------- | ------------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \sinh(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Sinh : public UnaryElementwiseArithmetic
+        class Sinh : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a hyperbolic sine operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Sinh>(new_args.at(0));
            }


--- a/src/ngraph/ops/slice.hpp
+++ b/src/ngraph/ops/slice.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -48,7 +48,7 @@ namespace ngraph
        /// | Type                                                                           | Description                       |
        /// | ------------------------------------------------------------------------------ | --------------------------------- |
        /// | \f$E[d'_1,\dots,d'_n]\f$ where \f$d'_i = \lceil(u_i - l_i)\, /\, s_i\rceil\f$. | The tensor sliced from the input. |
-        class Slice : public RequiresTensorViewArgs
+        class Slice : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a tensor slice operation.
@@ -76,7 +76,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Slice>(
                    new_args.at(0), m_lower_bounds, m_upper_bounds, m_strides);
            }

--- a/src/ngraph/ops/sqrt.hpp
+++ b/src/ngraph/ops/sqrt.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                           |
        /// | ---------------------- | ------------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \sqrt{\texttt{arg}[i_1,\dots,i_n]}\f$ |
-        class Sqrt : public UnaryElementwiseArithmetic
+        class Sqrt : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a square operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Sqrt>(new_args.at(0));
            }


--- a/src/ngraph/ops/subtract.hpp
+++ b/src/ngraph/ops/subtract.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -36,7 +36,7 @@ namespace ngraph
        /// | Type                   | Description                                                                                                    |
        /// | ---------------------- | -------------------------------------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \texttt{arg0}[i_1,\dots,i_n] - \texttt{arg1}[i_1,\dots,i_n]\f$ |
-        class Subtract : public BinaryElementwiseArithmetic
+        class Subtract : public util::BinaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs an subtraction operation.
@@ -52,7 +52,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 2)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Subtract>(new_args.at(0), new_args.at(1));
            }


--- a/src/ngraph/ops/sum.hpp
+++ b/src/ngraph/ops/sum.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"

 namespace ngraph
 {
@@ -76,7 +76,7 @@ namespace ngraph
        /// | Type                                      | Description                                                                                                      |
        /// | ----------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
        /// | \f$N[\textit{delete}(A,d_1,\dots,d_n)]\f$ | The tensor \f$T\f$, where \f$T\f$ is the input tensor with the `reduction_axes` \f$A\f$ eliminated by summation. |
-        class Sum : public RequiresTensorViewArgs
+        class Sum : public util::RequiresTensorViewArgs
        {
        public:
            /// \brief Constructs a summation operation.
@@ -89,7 +89,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Sum>(new_args.at(0), m_reduction_axes);
            }


--- a/src/ngraph/ops/tan.hpp
+++ b/src/ngraph/ops/tan.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                          |
        /// | ---------------------- | ------------------------------------------------------------------------------------ |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \tan(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Tan : public UnaryElementwiseArithmetic
+        class Tan : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a tangent operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Tan>(new_args.at(0));
            }


--- a/src/ngraph/ops/tanh.hpp
+++ b/src/ngraph/ops/tanh.hpp
@@ -16,7 +16,7 @@

 #pragma once

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 namespace ngraph
 {
@@ -35,7 +35,7 @@ namespace ngraph
        /// | Type                   | Description                                                                           |
        /// | ---------------------- | ------------------------------------------------------------------------------------- |
        /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \tanh(\texttt{arg}[i_1,\dots,i_n])\f$ |
-        class Tanh : public UnaryElementwiseArithmetic
+        class Tanh : public util::UnaryElementwiseArithmetic
        {
        public:
            /// \brief Constructs a hyperbolic tangent operation.
@@ -50,7 +50,9 @@ namespace ngraph
                const std::vector<std::shared_ptr<Node>>& new_args) const override
            {
                if (new_args.size() != 1)
+                {
                    throw ngraph_error("Incorrect number of new arguments");
+                }
                return std::make_shared<Tanh>(new_args.at(0));
            }


--- a/src/ngraph/ops/binary_elementwise.cpp
+++ b/src/ngraph/ops/binary_elementwise.cpp
@@ -17,15 +17,15 @@
 #include <memory>

 #include "ngraph/log.hpp"
-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise.hpp"

 using namespace std;
 using namespace ngraph;

-op::BinaryElementwise::BinaryElementwise(const std::string& node_type,
-                                         const element::Type& result_element_type,
-                                         const std::shared_ptr<Node>& arg0,
-                                         const std::shared_ptr<Node>& arg1)
+op::util::BinaryElementwise::BinaryElementwise(const std::string& node_type,
+                                               const element::Type& result_element_type,
+                                               const std::shared_ptr<Node>& arg0,
+                                               const std::shared_ptr<Node>& arg1)
    : RequiresTensorViewArgs(node_type, Nodes{arg0, arg1})
 {
    auto& input_0 = get_inputs().at(0);

--- a/src/ngraph/ops/util/binary_elementwise.hpp
+++ b/src/ngraph/ops/util/binary_elementwise.hpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#pragma once
+
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        namespace util
+        {
+            /// \brief Abstract base class for elementwise binary operations, i.e., operations where the same
+            ///        scalar binary operation is applied to each corresponding pair of elements in two same-shaped
+            ///        input tensors.
+            ///
+            /// For example, if the underlying operation (determined by the subclass) is \f$\mathit{op}(x,y)\f$, the input tensors
+            /// \f$[[x_0,y_0],[z_0,w_0]]\f$ and \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
+            ///
+            /// ## Inputs
+            ///
+            /// |        | Type                                | Description                                                                                                                                                     |
+            /// | ------ | ----------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+            /// | `arg0` | \f$E_0[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape. Subclasses may impose restrictions on the element type \f$E_0\f$.                |
+            /// | `arg1` | \f$E_1[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of the same shape as `arg0`. Subclasses may impose restrictions on the element type \f$E_1\f$. |
+            ///
+            /// ## Output
+            ///
+            /// | Type                     | Description                                                                                                                                                                                                                                                                                             |
+            /// | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+            /// | \f$E_2[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$. This will always have the same shape as the input tensors, but subclasses must determine the element type \f$E_2\f$. |
+            class BinaryElementwise : public RequiresTensorViewArgs
+            {
+            protected:
+                /// \brief Constructs a biary elementwise operation.
+                ///
+                /// \param arg0 Node that produces the first input tensor.
+                /// \param arg1 Node that produces the second input tensor.
+                BinaryElementwise(const std::string& node_type,
+                                  const element::Type& result_element_type,
+                                  const std::shared_ptr<Node>& arg0,
+                                  const std::shared_ptr<Node>& arg1);
+            };
+        }
+    }
+}
--- a/src/ngraph/ops/binary_elementwise_arithmetic.cpp
+++ b/src/ngraph/ops/binary_elementwise_arithmetic.cpp
@@ -14,14 +14,15 @@
 * limitations under the License.
 *******************************************************************************/

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_arithmetic.hpp"

 using namespace std;
 using namespace ngraph;

-op::BinaryElementwiseArithmetic::BinaryElementwiseArithmetic(const std::string& node_type,
-                                                             const std::shared_ptr<Node>& arg0,
-                                                             const std::shared_ptr<Node>& arg1)
+op::util::BinaryElementwiseArithmetic::BinaryElementwiseArithmetic(
+    const std::string& node_type,
+    const std::shared_ptr<Node>& arg0,
+    const std::shared_ptr<Node>& arg1)
    : BinaryElementwise(node_type, arg0->get_element_type(), arg0, arg1)
 {
    if (arg0->get_element_type() != arg1->get_element_type())

--- a/src/ngraph/ops/util/binary_elementwise_arithmetic.hpp
+++ b/src/ngraph/ops/util/binary_elementwise_arithmetic.hpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#pragma once
+
+#include "ngraph/ops/util/binary_elementwise.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        namespace util
+        {
+            /// \brief Abstract base class for elementwise binary arithmetic operations, i.e., operations where the same
+            ///        scalar binary arithmetic operation is applied to each corresponding pair of elements in two same-shaped
+            ///        input tensors.
+            ///
+            /// For example, if the underlying arithmetic operation (determined by the subclass) is \f$\mathit{op}(x,y)\f$, the input tensors
+            /// \f$[[x_0,y_0],[z_0,w_0]]\f$ and \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
+            ///
+            /// ## Inputs
+            ///
+            /// |        | Type                              | Description                                                              |
+            /// | ------ | --------------------------------- | ------------------------------------------------------------------------ |
+            /// | `arg0` | \f$N[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape. The element type \f$N\f$ may be any numeric type. |
+            /// | `arg1` | \f$N[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of the same shape and element type as `arg0`.                   |
+            ///
+            /// ## Output
+            ///
+            /// | Type                   | Description                                                                                                                                                                                            |
+            /// | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+            /// | \f$N[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$. This will always have the same shape and element type as the input tensors. |
+            class BinaryElementwiseArithmetic : public BinaryElementwise
+            {
+            public:
+                /// \brief Constructs a binary elementwise arithmetic operation.
+                ///
+                /// \param arg0 Node that produces the first input tensor.
+                /// \param arg1 Node that produces the second input tensor.
+                BinaryElementwiseArithmetic(const std::string& node_type,
+                                            const std::shared_ptr<Node>& arg0,
+                                            const std::shared_ptr<Node>& arg1);
+            };
+        }
+    }
+}
--- a/src/ngraph/ops/binary_elementwise_comparison.cpp
+++ b/src/ngraph/ops/binary_elementwise_comparison.cpp
@@ -14,14 +14,14 @@
 * limitations under the License.
 *******************************************************************************/

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/binary_elementwise_comparison.hpp"

 using namespace std;
 using namespace ngraph;

-op::BinaryElementwiseComparison::BinaryElementwiseComparison(const string& node_type,
-                                                             const shared_ptr<Node>& arg0,
-                                                             const shared_ptr<Node>& arg1)
+op::util::BinaryElementwiseComparison::BinaryElementwiseComparison(const string& node_type,
+                                                                   const shared_ptr<Node>& arg0,
+                                                                   const shared_ptr<Node>& arg1)
    : BinaryElementwise(node_type, element::boolean, arg0, arg1)
 {
    if (arg0->get_element_type() != arg1->get_element_type())

--- a/src/ngraph/ops/util/binary_elementwise_comparison.hpp
+++ b/src/ngraph/ops/util/binary_elementwise_comparison.hpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#pragma once
+
+#include "ngraph/ops/util/binary_elementwise.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        namespace util
+        {
+            /// \brief Abstract base class for elementwise binary comparison operations, i.e., operations where the same
+            ///        scalar binary comparison operation is applied to each corresponding pair of elements in two same-shaped
+            ///        input tensors.
+            ///
+            /// For example, if the underlying comparison operation (determined by the subclass) is \f$\mathit{op}(x,y)\f$, the input tensors
+            /// \f$[[x_0,y_0],[z_0,w_0]]\f$ and \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
+            ///
+            /// ## Inputs
+            ///
+            /// |        | Type                              | Description                                            |
+            /// | ------ | --------------------------------- | ------------------------------------------------------ |
+            /// | `arg0` | \f$E[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape and element type.                |
+            /// | `arg1` | \f$E[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of the same shape and element type as `arg0`. |
+            ///
+            /// ## Output
+            ///
+            /// | Type                               | Description                                                                                                                                                                                                        |
+            /// | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+            /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$. This will always have the same shape as the input tensors, and the element type `bool`. |
+            class BinaryElementwiseComparison : public BinaryElementwise
+            {
+            public:
+                /// \brief Constructs a binary elementwise comparison operation.
+                ///
+                /// \param arg0 Node that produces the first input tensor.
+                /// \param arg1 Node that produces the second input tensor.
+                BinaryElementwiseComparison(const std::string& node_type,
+                                            const std::shared_ptr<Node>& arg0,
+                                            const std::shared_ptr<Node>& arg1);
+            };
+        }
+    }
+}
--- a/src/ngraph/ops/util/requires_tensor_view_args.cpp
+++ b/src/ngraph/ops/util/requires_tensor_view_args.cpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#include <algorithm>
+#include <memory>
+#include <sstream>
+
+#include "ngraph/except.hpp"
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"
+#include "ngraph/types/type.hpp"
+
+using namespace ngraph;
+using namespace std;
+
+op::util::RequiresTensorViewArgs::RequiresTensorViewArgs(const std::string& node_type,
+                                                         const Nodes& args)
+    : Op(node_type, args)
+{
+    for (auto arg : args)
+    {
+        if (arg->get_output_size() != 1)
+        {
+            throw ngraph_error("Arguments for node type \"" + node_type +
+                               "\" must be tensor views");
+        }
+    }
+}
--- a/src/ngraph/ops/util/requires_tensor_view_args.hpp
+++ b/src/ngraph/ops/util/requires_tensor_view_args.hpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#pragma once
+
+#include "ngraph/common.hpp"
+#include "ngraph/ops/op.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        namespace util
+        {
+            /// \brief Abstract base class for ops on tensors views.
+            class RequiresTensorViewArgs : public ngraph::op::Op
+            {
+            protected:
+                /// \brief Constructs an operation on tensor view arguments.
+                ///
+                /// \param args The nodes producing this node's input tensors.
+                RequiresTensorViewArgs(const std::string& node_type, const Nodes& args);
+            };
+        }
+    }
+}
--- a/src/ngraph/ops/unary_elementwise.cpp
+++ b/src/ngraph/ops/unary_elementwise.cpp
@@ -16,14 +16,14 @@

 #include <memory>

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise.hpp"

 using namespace std;
 using namespace ngraph;

-op::UnaryElementwise::UnaryElementwise(const std::string& node_type,
-                                       const element::Type& result_element_type,
-                                       const std::shared_ptr<Node>& arg)
+op::util::UnaryElementwise::UnaryElementwise(const std::string& node_type,
+                                             const element::Type& result_element_type,
+                                             const std::shared_ptr<Node>& arg)
    : RequiresTensorViewArgs(node_type, Nodes{arg})
 {
    auto& input = get_inputs().at(0);

--- a/src/ngraph/ops/util/unary_elementwise.hpp
+++ b/src/ngraph/ops/util/unary_elementwise.hpp
+/*******************************************************************************
+* Copyright 2017-2018 Intel Corporation
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*     http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*******************************************************************************/
+
+#pragma once
+
+#include "ngraph/ops/util/requires_tensor_view_args.hpp"
+
+namespace ngraph
+{
+    namespace op
+    {
+        namespace util
+        {
+            /// \brief Abstract base class for elementwise unary operations, i.e., operations where the same
+            ///        scalar operation is applied to each element.
+            ///
+            /// For example, if the underlying operation (determined by the subclass) is \f$\mathit{op}(x)\f$, the input tensor
+            /// \f$[[x,y],[z,w]]\f$ will be mapped to \f$[[\mathit{op}(x),\mathit{op}(y)],[\mathit{op}(z),\mathit{op}(w)]]\f$.
+            ///
+            /// ## Inputs
+            ///
+            /// |       | Type                              | Description                                                                                                                                   |
+            /// | ----- | --------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
+            /// | `arg` | \f$E[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape. Subclasses may impose restrictions on the element type \f$E\f$. |
+            ///
+            /// ## Output
+            ///
+            /// | Type                    | Description                                                                                                                                                                                                                                                            |
+            /// | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+            /// | \f$E'[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg}[i_1,\dots,i_n])\f$. This will always have the same shape as the input tensor, but subclasses must determine the element type \f$E'\f$. |
+            class UnaryElementwise : public RequiresTensorViewArgs
+            {
+            protected:
+                /// \brief Constructs a unary elementwise tensor operation.
+                ///
+                /// \param arg Node that produces the input tensor.
+                UnaryElementwise(const std::string& node_type,
+                                 const element::Type& result_element_type,
+                                 const std::shared_ptr<Node>& arg);
+            };
+        }
+    }
+}
--- a/src/ngraph/ops/unary_elementwise_arithmetic.cpp
+++ b/src/ngraph/ops/unary_elementwise_arithmetic.cpp
@@ -14,12 +14,12 @@
 * limitations under the License.
 *******************************************************************************/

-#include "ngraph/ops/op.hpp"
+#include "ngraph/ops/util/unary_elementwise_arithmetic.hpp"

 using namespace ngraph;

-op::UnaryElementwiseArithmetic::UnaryElementwiseArithmetic(const std::string& node_type,
-                                                           const std::shared_ptr<Node>& arg)
+op::util::UnaryElementwiseArithmetic::UnaryElementwiseArithmetic(const std::string& node_type,
+                                                                 const std::shared_ptr<Node>& arg)
    : UnaryElementwise(node_type, arg->get_element_type(), arg)
 {
    if (arg->get_element_type() == element::boolean)

--- a/src/ngraph/ops/util/unary_elementwise_arithmetic.hpp
+++ b/src/ngraph/ops/util/unary_elementwise_arithmetic.hpp
--- a/src/ngraph/ops/xla_get_tuple_element.hpp
+++ b/src/ngraph/ops/xla_get_tuple_element.hpp
--- a/src/ngraph/pattern/matcher.cpp
+++ b/src/ngraph/pattern/matcher.cpp
--- a/src/ngraph/runtime/cpu/cpu_backend.cpp
+++ b/src/ngraph/runtime/cpu/cpu_backend.cpp
--- a/src/ngraph/runtime/cpu/cpu_call_frame.cpp
+++ b/src/ngraph/runtime/cpu/cpu_call_frame.cpp
--- a/src/ngraph/runtime/cpu/cpu_call_frame.hpp
+++ b/src/ngraph/runtime/cpu/cpu_call_frame.hpp
--- a/src/ngraph/runtime/cpu/cpu_emitter.cpp
+++ b/src/ngraph/runtime/cpu/cpu_emitter.cpp
--- a/src/ngraph/runtime/cpu/cpu_emitter.hpp
+++ b/src/ngraph/runtime/cpu/cpu_emitter.hpp
--- a/src/ngraph/runtime/cpu/cpu_external_function.cpp
+++ b/src/ngraph/runtime/cpu/cpu_external_function.cpp
--- a/src/ngraph/runtime/cpu/cpu_external_function.hpp
+++ b/src/ngraph/runtime/cpu/cpu_external_function.hpp
--- a/src/ngraph/runtime/cpu/cpu_layout_descriptor.cpp
+++ b/src/ngraph/runtime/cpu/cpu_layout_descriptor.cpp
--- a/src/ngraph/runtime/cpu/cpu_layout_descriptor.hpp
+++ b/src/ngraph/runtime/cpu/cpu_layout_descriptor.hpp
--- a/src/ngraph/runtime/cpu/cpu_tensor_view.cpp
+++ b/src/ngraph/runtime/cpu/cpu_tensor_view.cpp
--- a/src/ngraph/runtime/cpu/cpu_tensor_view.hpp
+++ b/src/ngraph/runtime/cpu/cpu_tensor_view.hpp
--- a/src/ngraph/runtime/cpu/cpu_tracing.cpp
+++ b/src/ngraph/runtime/cpu/cpu_tracing.cpp
--- a/src/ngraph/runtime/cpu/cpu_tracing.hpp
+++ b/src/ngraph/runtime/cpu/cpu_tracing.hpp
--- a/src/ngraph/runtime/cpu/mkldnn_utils.cpp
+++ b/src/ngraph/runtime/cpu/mkldnn_utils.cpp
--- a/src/ngraph/runtime/cpu/mkldnn_utils.hpp
+++ b/src/ngraph/runtime/cpu/mkldnn_utils.hpp
--- a/src/ngraph/runtime/cpu/ops/convert_layout.cpp
+++ b/src/ngraph/runtime/cpu/ops/convert_layout.cpp
--- a/src/ngraph/runtime/cpu/ops/convert_layout.hpp
+++ b/src/ngraph/runtime/cpu/ops/convert_layout.hpp
--- a/src/ngraph/runtime/cpu/ops/matmul_bias.cpp
+++ b/src/ngraph/runtime/cpu/ops/matmul_bias.cpp
--- a/src/ngraph/runtime/cpu/ops/matmul_bias.hpp
+++ b/src/ngraph/runtime/cpu/ops/matmul_bias.hpp
--- a/src/ngraph/runtime/cpu/pass/cpu_fusion.cpp
+++ b/src/ngraph/runtime/cpu/pass/cpu_fusion.cpp
--- a/src/ngraph/runtime/cpu/pass/cpu_fusion.hpp
+++ b/src/ngraph/runtime/cpu/pass/cpu_fusion.hpp
--- a/src/ngraph/runtime/cpu/pass/cpu_layout.cpp
+++ b/src/ngraph/runtime/cpu/pass/cpu_layout.cpp
--- a/src/ngraph/runtime/cpu/pass/cpu_layout.hpp
+++ b/src/ngraph/runtime/cpu/pass/cpu_layout.hpp
--- a/src/ngraph/runtime/gpu/gpu_util.hpp
+++ b/src/ngraph/runtime/gpu/gpu_util.hpp
--- a/src/ngraph/runtime/interpreter/int_call_frame.hpp
+++ b/src/ngraph/runtime/interpreter/int_call_frame.hpp
--- a/src/ngraph/runtime/kernel/allreduce.hpp
+++ b/src/ngraph/runtime/kernel/allreduce.hpp
--- a/src/ngraph/runtime/tensor_view.hpp
+++ b/src/ngraph/runtime/tensor_view.hpp
--- a/src/ngraph/serializer.cpp
+++ b/src/ngraph/serializer.cpp
--- a/src/resource/CMakeLists.txt
+++ b/src/resource/CMakeLists.txt
--- a/src/resource/main.cpp
+++ b/src/resource/main.cpp
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
--- a/test/cpu_fusion.cpp
+++ b/test/cpu_fusion.cpp
--- a/test/distributed.cpp
+++ b/test/distributed.cpp
--- a/test/main.cpp
+++ b/test/main.cpp
--- a/test/models/mxnet/bn_fprop_b2c3h2w2.json
+++ b/test/models/mxnet/bn_fprop_b2c3h2w2.json
--- a/test/pattern.cpp
+++ b/test/pattern.cpp
--- a/third-party/CMakeLists.txt
+++ b/third-party/CMakeLists.txt