Merge branch 'master' into bob/fused_tolerance

cmake/external_onnx.cmake

@@ -21,7 +21,7 @@ include(ExternalProject)
 # ONNX.proto definition version
 #------------------------------------------------------------------------------
 
-set(ONNX_VERSION 1.3.0)
+set(ONNX_VERSION 1.5.0)
 
 #------------------------------------------------------------------------------
 # Download and install libonnx ...
@@ -30,6 +30,9 @@ set(ONNX_VERSION 1.3.0)
 set(ONNX_GIT_REPO_URL https://github.com/onnx/onnx.git)
 set(ONNX_GIT_BRANCH rel-${ONNX_VERSION})
 
+add_definitions(-DONNX_BUILD_SHARED_LIBS=ON)
+add_definitions(-DONNX_ML=ON)
+
 ExternalProject_Add(
     ext_onnx
     PREFIX onnx
@@ -58,8 +61,8 @@ ExternalProject_Add(
 
 ExternalProject_Get_Property(ext_onnx SOURCE_DIR BINARY_DIR)
 
-set(ONNX_INCLUDE_DIR ${SOURCE_DIR}/onnx)
-set(ONNX_PROTO_INCLUDE_DIR ${BINARY_DIR}/onnx)
+set(ONNX_INCLUDE_DIR ${SOURCE_DIR})
+set(ONNX_PROTO_INCLUDE_DIR ${BINARY_DIR})
 if (WIN32)
     set(ONNX_LIBRARY ${BINARY_DIR}/${CMAKE_BUILD_TYPE}/onnx.lib)
     set(ONNX_PROTO_LIBRARY ${BINARY_DIR}/${CMAKE_BUILD_TYPE}/onnx_proto.lib)

doc/sphinx/source/python_api/_autosummary/ngraph.ops.rst

@@ -37,6 +37,7 @@ ngraph.ops
       equal
       exp
       floor
+      gelu
       get_output_element
       greater
       greater_eq

python/ngraph/__init__.py

@@ -50,6 +50,7 @@ from ngraph.ops import elu
 from ngraph.ops import equal
 from ngraph.ops import exp
 from ngraph.ops import floor
+from ngraph.ops import gelu
 from ngraph.ops import get_output_element
 from ngraph.ops import greater
 from ngraph.ops import greater_eq

python/ngraph/impl/op/__init__.py

@@ -74,6 +74,7 @@ from _pyngraph.op import Elu
 from _pyngraph.op import Equal
 from _pyngraph.op import Exp
 from _pyngraph.op import Floor
+from _pyngraph.op import Gelu
 from _pyngraph.op import GetOutputElement
 from _pyngraph.op import Greater
 from _pyngraph.op import GreaterEq

python/ngraph/ops.py

@@ -23,7 +23,7 @@ from ngraph.impl import AxisSet, AxisVector, Coordinate, CoordinateDiff, Functio
 from ngraph.impl.op import Abs, Acos, Add, And, Asin, ArgMax, ArgMin, Atan, AvgPool, \
     BatchNormTraining, BatchNormInference, Broadcast, Ceiling, Clamp, Concat, Constant, Convert, \
     Convolution, ConvolutionBackpropData, Cos, Cosh, Divide, Dot, Elu, Equal, Exp, Floor, \
-    GetOutputElement, Greater, GreaterEq, Less, LessEq, Log, LRN, Max, Maximum, MaxPool, \
+    Gelu, GetOutputElement, Greater, GreaterEq, Less, LessEq, Log, LRN, Max, Maximum, MaxPool, \
     Min, Minimum, Multiply, Negative, Not, NotEqual, OneHot, Or, Pad, Parameter, Product, \
     Power, Relu, ReplaceSlice, Reshape, Reverse, Select, Sign, Sin, Sinh, Slice, Softmax, \
     Sqrt, Subtract, Sum, Tan, Tanh, TopK
@@ -527,6 +527,24 @@ def convert(node, new_type, name=None):  # type: (Node, NumericType, str) -> Nod
     return Convert(node, new_element_type)
 
 
+@nameable_op
+def gelu(node, name=None):  # type: (NodeInput, str) -> Node
+    r"""Perform Gaussian Error Linear Unit operation element-wise on data from input node.
+
+    Computes GELU function:
+
+    .. math:: f(x) = 0.5\cdot x\cdot(1 + erf( \dfrac{x}{\sqrt{2}})
+
+    For more information refer to:
+    `Gaussian Error Linear Unit (GELU) <https://arxiv.org/pdf/1606.08415.pdf>`_
+
+    :param node: Input tensor. One of: input node, array or scalar.
+    :param name: Optional output node name.
+    :return: The new node performing a GELU operation on its input data element-wise.
+    """
+    return Gelu(as_node(node))
+
+
 @nameable_op
 def select(selection_node, input_node1, input_node2, name=None):
     # type: (Node, Node, Node, str) -> Node

python/pyngraph/ops/fused/gelu.cpp

+//*****************************************************************************
+// Copyright 2017-2019 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 <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include "ngraph/op/fused/gelu.hpp"
+#include "pyngraph/ops/fused/gelu.hpp"
+
+namespace py = pybind11;
+
+void regclass_pyngraph_op_Gelu(py::module m)
+{
+    py::class_<ngraph::op::Gelu, std::shared_ptr<ngraph::op::Gelu>, ngraph::op::Op> gelu(m, "Gelu");
+    gelu.doc() = "ngraph.impl.op.Gelu wraps ngraph::op::Gelu";
+    gelu.def(py::init<const std::shared_ptr<ngraph::Node>&>());
+}

python/pyngraph/ops/fused/gelu.hpp

+//*****************************************************************************
+// Copyright 2017-2019 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 <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+void regclass_pyngraph_op_Gelu(py::module m);

python/pyngraph/ops/regmodule_pyngraph_op.cpp

@@ -54,6 +54,7 @@ void regmodule_pyngraph_op(py::module m_op)
     regclass_pyngraph_op_Equal(m_op);
     regclass_pyngraph_op_Exp(m_op);
     regclass_pyngraph_op_Floor(m_op);
+    regclass_pyngraph_op_Gelu(m_op);
     regclass_pyngraph_op_GetOutputElement(m_op);
     regclass_pyngraph_op_Greater(m_op);
     regclass_pyngraph_op_GreaterEq(m_op);

python/pyngraph/ops/regmodule_pyngraph_op.hpp

@@ -44,6 +44,7 @@
 #include "pyngraph/ops/floor.hpp"
 #include "pyngraph/ops/fused/clamp.hpp"
 #include "pyngraph/ops/fused/elu.hpp"
+#include "pyngraph/ops/fused/gelu.hpp"
 #include "pyngraph/ops/get_output_element.hpp"
 #include "pyngraph/ops/greater.hpp"
 #include "pyngraph/ops/greater_eq.hpp"

python/setup.py

@@ -184,6 +184,7 @@ sources = [
     'pyngraph/ops/equal.cpp',
     'pyngraph/ops/exp.cpp',
     'pyngraph/ops/floor.cpp',
+    'pyngraph/ops/fused/gelu.cpp',
     'pyngraph/ops/greater.cpp',
     'pyngraph/ops/greater_eq.cpp',
     'pyngraph/ops/less.cpp',

python/test/ngraph/test_ops_fused.py

@@ -19,7 +19,7 @@ import ngraph as ng
 from test.ngraph.util import get_runtime
 
 
-def test_elu_operator():
+def test_elu_operator_with_parameters():
     runtime = get_runtime()
 
     data_shape = [2, 2]
@@ -69,6 +69,38 @@ def test_elu_operator_with_scalar():
     assert np.allclose(result, expected)
 
 
+def test_gelu_operator_with_parameters():
+    runtime = get_runtime()
+
+    data_value = np.array([[-5, 1], [-2, 3]], dtype=np.float32)
+
+    data_shape = [2, 2]
+    parameter_data = ng.parameter(data_shape, name='Data', dtype=np.float32)
+
+    model = ng.gelu(parameter_data)
+    computation = runtime.computation(model, parameter_data)
+
+    result = computation(data_value)
+    expected = np.array([[-1.4901161e-06, 8.4134471e-01], [-4.5500278e-02, 2.9959502]],
+                        dtype=np.float32)
+    assert np.allclose(result, expected)
+
+
+def test_gelu_operator_with_array():
+    runtime = get_runtime()
+
+    data_value = np.array([[-5, 1], [-2, 3]], dtype=np.float32)
+
+    model = ng.gelu(data_value)
+    computation = runtime.computation(model)
+
+    result = computation()
+    expected = np.array([[-1.4901161e-06, 8.4134471e-01], [-4.5500278e-02, 2.9959502]],
+                        dtype=np.float32)
+
+    assert np.allclose(result, expected)
+
+
 def test_clamp_operator():
     runtime = get_runtime()
 
@@ -99,4 +131,5 @@ def test_clamp_operator_with_array():
 
     result = computation()
     expected = np.clip(data_value, min_value, max_value)
+
     assert np.allclose(result, expected)

src/contrib/mlir/lowerer.cpp

@@ -43,6 +43,8 @@ namespace
     using namespace mlir::edsc::op;
     using namespace ngraph::runtime;
     using namespace ngraph::runtime::ngmlir;
+    // Index notation to generate standard (i.e., non-affine) loads and stores.
+    using StdIndexedValue = TemplatedIndexedValue<intrinsics::std_load, intrinsics::std_store>;
 
     class DialectLoweringPass;
 
@@ -682,7 +684,8 @@ namespace
         // Create view to write into result.
         MemRefView vRes(result), vParams(params), vIndices(indices);
         // Indexed Values
-        IndexedValue iRes(result), iParams(params), iIndices(indices);
+        IndexedValue iRes(result), iIndices(indices);
+        StdIndexedValue iParams(params);
 
         // Construct outer loop for params dims. Exclude the axis dim.
         SmallVector<ValueHandle, 4> paramsLbs, paramsUbs;
@@ -894,7 +897,8 @@ namespace
         // Views
         MemRefView vRes(result), vArg(arg);
         // Index Values
-        IndexedValue iRes(result), iArg(arg);
+        StdIndexedValue iRes(result), stdArg(arg);
+        IndexedValue affineArg(arg);
         // Bounds Index Handles
         auto resLbs = vRes.getLbs();
         auto resUbs = vRes.getUbs();
@@ -944,9 +948,9 @@ namespace
                 ValueHandle newRedIdx =
                     std::is_same<RedOp, NGArgMinRedOp>()
                         ? edsc::intrinsics::select(
-                              iArg(allIVs) < iArg(tempIVs), allIVs[axis], currRedIdx)
+                              affineArg(allIVs) < stdArg(tempIVs), allIVs[axis], currRedIdx)
                         : edsc::intrinsics::select(
-                              iArg(tempIVs) < iArg(allIVs), allIVs[axis], currRedIdx);
+                              stdArg(tempIVs) < affineArg(allIVs), allIVs[axis], currRedIdx);
 
                 iRes(nonRedIVs) = ValueHandle::create<IndexCastOp>(newRedIdx, resTy);
             });

src/ngraph/frontend/onnx_import/core/attribute.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "ngraph/except.hpp"
 #include "tensor.hpp"

src/ngraph/frontend/onnx_import/core/graph.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 #include <string>
 #include <vector>
 

src/ngraph/frontend/onnx_import/core/model.cpp

@@ -14,7 +14,7 @@
 // limitations under the License.
 //*****************************************************************************
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "model.hpp"
 #include "ngraph/log.hpp"

src/ngraph/frontend/onnx_import/core/model.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 #include <ostream>
 #include <string>
 #include <unordered_map>

src/ngraph/frontend/onnx_import/core/node.cpp

@@ -14,7 +14,7 @@
 // limitations under the License.
 //*****************************************************************************
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "attribute.hpp"
 #include "graph.hpp"

src/ngraph/frontend/onnx_import/core/tensor.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 #include <utility>
 #include <vector>
 

src/ngraph/frontend/onnx_import/core/value_info.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnx-ml.pb.h>
+#include <onnx/onnx_pb.h>
 
 #include "ngraph/op/constant.hpp"
 #include "ngraph/op/parameter.hpp"

src/ngraph/frontend/onnx_import/utils/common.cpp

@@ -13,7 +13,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 //*****************************************************************************
-#include <onnx-ml.pb.h> // onnx types
+#include <onnx/onnx_pb.h> // onnx types
 
 #include "common.hpp"
 

src/ngraph/frontend/onnxifi/backend_manager.cpp

@@ -15,7 +15,7 @@
 //*****************************************************************************
 
 #include <cstdlib> // std::size_t, std::uintptr_t
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 #include <stdexcept> // std::invalid_agrument, std::out_of_rage
 
 #include "backend.hpp"

src/ngraph/frontend/onnxifi/backend_manager.hpp

@@ -19,7 +19,7 @@
 #include <cstddef> // std::size_t, std::uintptr_t
 #include <map>     // std::map
 #include <mutex>   // std::mutex
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 
 #include "backend.hpp"
 #include "ngraph/runtime/backend.hpp"

src/ngraph/frontend/onnxifi/exceptions.hpp

@@ -16,7 +16,7 @@
 
 #pragma once
 
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 
 namespace ngraph
 {

src/ngraph/frontend/onnxifi/onnxifi.cpp

@@ -16,7 +16,7 @@
 
 #include <cstddef>
 #include <cstdint>
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 #include <stdexcept>
 
 #include "backend_manager.hpp"

src/ngraph/frontend/onnxifi/tensor.hpp

@@ -17,7 +17,7 @@
 #pragma once
 
 #include <memory>
-#include <onnxifi.h>
+#include <onnx/onnxifi.h>
 
 #include "ngraph/runtime/backend.hpp"
 #include "ngraph/runtime/tensor.hpp"

src/ngraph/op/avg_pool.hpp

@@ -173,6 +173,7 @@ namespace ngraph
         class AvgPoolBackprop : public Op
         {
         public:
+            NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             AvgPoolBackprop() = default;

src/ngraph/op/batch_norm.hpp

@@ -92,6 +92,7 @@ namespace ngraph
         class BatchNormInference : public Op
         {
         public:
+            NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             BatchNormInference() = default;

src/ngraph/op/experimental/batch_mat_mul.cpp

@@ -24,8 +24,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::BatchMatMul::type_name{"BatchMatMul"};
+
 op::BatchMatMul::BatchMatMul(const shared_ptr<Node>& arg0, const shared_ptr<Node>& arg1)
-    : Op("BatchMatMul", check_single_output_args({arg0, arg1}))
+    : Op(check_single_output_args({arg0, arg1}))
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/batch_mat_mul.hpp

@@ -32,6 +32,9 @@ namespace ngraph
         class BatchMatMul : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a batch of matmul product operation.
             ///
             /// \param arg0 The node producing the first argument.

src/ngraph/op/experimental/compiled_kernel.cpp

@@ -23,6 +23,8 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::CompiledKernel::type_name{"CompiledKernel"};
+
 shared_ptr<Node> ngraph::op::CompiledKernel::copy_with_new_args(const NodeVector& new_args) const
 {
     auto args = inputs();
@@ -64,7 +66,7 @@ shared_ptr<Node> ngraph::op::CompiledKernel::copy_with_new_args(const NodeVector
 ngraph::op::CompiledKernel::CompiledKernel(const NodeVector& node_list,
                                            const NodeVector& outputs,
                                            const NodeVector& args)
-    : Op("CompiledKernel", check_single_output_args({args}))
+    : Op(check_single_output_args({args}))
     , m_node_list(node_list)
     , m_output_nodes(outputs)
 {

src/ngraph/op/experimental/compiled_kernel.hpp

@@ -32,6 +32,9 @@ namespace ngraph
         class CompiledKernel : public ngraph::op::Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             CompiledKernel(const NodeVector& node_list,
                            const NodeVector& outputs,
                            const NodeVector& args);

src/ngraph/op/experimental/dyn_broadcast.cpp

@@ -20,10 +20,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::DynBroadcast::type_name{"DynBroadcast"};
+
 op::DynBroadcast::DynBroadcast(const shared_ptr<Node>& arg,
                                const shared_ptr<Node>& shape,
                                const shared_ptr<Node>& broadcast_axes)
-    : Op("DynBroadcast", check_single_output_args({arg, shape, broadcast_axes}))
+    : Op(check_single_output_args({arg, shape, broadcast_axes}))
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/dyn_broadcast.hpp

@@ -28,6 +28,9 @@ namespace ngraph
         class DynBroadcast : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a dynamic broadcast operation.
             ///
             /// \param arg            Node that produces the input tensor to be broadcast.

src/ngraph/op/experimental/dyn_pad.cpp

@@ -19,12 +19,14 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::DynPad::type_name{"DynPad"};
+
 op::DynPad::DynPad(const std::shared_ptr<Node>& arg,
                    const std::shared_ptr<Node>& padding_below,
                    const std::shared_ptr<Node>& padding_above,
                    const std::shared_ptr<Node>& padding_value,
                    op::PadMode pad_mode)
-    : Op("DynPad", check_single_output_args({arg, padding_below, padding_above, padding_value}))
+    : Op(check_single_output_args({arg, padding_below, padding_above, padding_value}))
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/dyn_pad.hpp

@@ -27,6 +27,9 @@ namespace ngraph
         class DynPad : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Perform dynamic padding of a tensor
             ///
             /// \param arg The node producing input tensor to be padded.

src/ngraph/op/experimental/dyn_replace_slice.cpp

@@ -24,6 +24,8 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::DynReplaceSlice::type_name{"DynReplaceSlice"};
+
 op::DynReplaceSlice::DynReplaceSlice(const shared_ptr<Node>& arg,
                                      const shared_ptr<Node>& replacement,
                                      const shared_ptr<Node>& lower_bounds,
@@ -34,8 +36,7 @@ op::DynReplaceSlice::DynReplaceSlice(const shared_ptr<Node>& arg,
                                      const AxisSet& new_axis,
                                      const AxisSet& shrink_axis,
                                      const AxisSet& ellipsis_mask)
-    : Op("DynReplaceSlice",
-         check_single_output_args({arg, replacement, lower_bounds, upper_bounds, strides}))
+    : Op(check_single_output_args({arg, replacement, lower_bounds, upper_bounds, strides}))
     , m_lower_bounds_mask(lower_bounds_mask)
     , m_upper_bounds_mask(upper_bounds_mask)
     , m_new_axis(new_axis)

src/ngraph/op/experimental/dyn_replace_slice.hpp

@@ -27,6 +27,9 @@ namespace ngraph
         class DynReplaceSlice : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a dynamic tensor replace-slice operation.
             ///
             /// \param arg The tensor in which to replace the slice.

src/ngraph/op/experimental/dyn_reshape.cpp

@@ -24,10 +24,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::DynReshape::type_name{"DynReshape"};
+
 op::DynReshape::DynReshape(const shared_ptr<Node>& arg,
                            const shared_ptr<Node>& pattern,
                            bool zero_flag)
-    : Op("DynReshape", check_single_output_args({arg, pattern}))
+    : Op(check_single_output_args({arg, pattern}))
     , m_zero_flag(zero_flag)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/dyn_reshape.hpp

@@ -31,6 +31,9 @@ namespace ngraph
         class DynReshape : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a dynamic reshape operation. This operation does not perform transpose.
             ///
             /// \param arg The tensor to be reshaped.

src/ngraph/op/experimental/dyn_slice.cpp

@@ -24,6 +24,8 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::DynSlice::type_name{"DynSlice"};
+
 op::DynSlice::DynSlice(const shared_ptr<Node>& arg,
                        const shared_ptr<Node>& lower_bounds,
                        const shared_ptr<Node>& upper_bounds,
@@ -33,7 +35,7 @@ op::DynSlice::DynSlice(const shared_ptr<Node>& arg,
                        const AxisSet& new_axis,
                        const AxisSet& shrink_axis,
                        const AxisSet& ellipsis_mask)
-    : Op("DynSlice", check_single_output_args({arg, lower_bounds, upper_bounds, strides}))
+    : Op(check_single_output_args({arg, lower_bounds, upper_bounds, strides}))
     , m_lower_bounds_mask(lower_bounds_mask)
     , m_upper_bounds_mask(upper_bounds_mask)
     , m_new_axis(new_axis)

src/ngraph/op/experimental/dyn_slice.hpp

@@ -27,6 +27,9 @@ namespace ngraph
         class DynSlice : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a dynamic tensor slice operation.
             ///
             /// \param arg The tensor to be sliced.

src/ngraph/op/experimental/layers/ctc_greedy_decoder.cpp

@@ -19,10 +19,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::CTCGreedyDecoder::type_name{"CTCGreedyDecoder"};
+
 op::CTCGreedyDecoder::CTCGreedyDecoder(const shared_ptr<Node>& input,
                                        const std::shared_ptr<Node>& seq_len,
                                        const bool ctc_merge_repeated)
-    : Op("CTCGreedyDecoder", check_single_output_args({input, seq_len}))
+    : Op(check_single_output_args({input, seq_len}))
     , m_ctc_merge_repeated(ctc_merge_repeated)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/layers/ctc_greedy_decoder.hpp

@@ -25,6 +25,9 @@ namespace ngraph
         class CTCGreedyDecoder : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a CTCGreedyDecoder operation
             ///
             /// \param input              Logits on which greedy decoding is performed

src/ngraph/op/experimental/layers/detection_output.cpp

@@ -21,15 +21,16 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::DetectionOutput::type_name{"DetectionOutput"};
+
 op::DetectionOutput::DetectionOutput(const std::shared_ptr<Node>& box_logits,
                                      const std::shared_ptr<Node>& class_preds,
                                      const std::shared_ptr<Node>& proposals,
                                      const std::shared_ptr<Node>& aux_class_preds,
                                      const std::shared_ptr<Node>& aux_box_preds,
                                      const DetectionOutputAttrs& attrs)
-    : Op("DetectionOutput",
-         check_single_output_args(
-             {box_logits, class_preds, proposals, aux_class_preds, aux_box_preds}))
+    : Op(check_single_output_args(
+          {box_logits, class_preds, proposals, aux_class_preds, aux_box_preds}))
     , m_attrs(attrs)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/layers/detection_output.hpp

@@ -47,6 +47,9 @@ namespace ngraph
         class DetectionOutput : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a DetectionOutput operation
             ///
             /// \param box_logits			Box logits

src/ngraph/op/experimental/layers/interpolate.cpp

@@ -21,10 +21,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Interpolate::type_name{"Interpolate"};
+
 op::Interpolate::Interpolate(const std::shared_ptr<Node>& image,
                              const std::shared_ptr<Node>& output_shape,
                              const InterpolateAttrs& attrs)
-    : Op("Interpolate", check_single_output_args({image, output_shape}))
+    : Op(check_single_output_args({image, output_shape}))
     , m_attrs(attrs)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/layers/interpolate.hpp

@@ -36,6 +36,9 @@ namespace ngraph
         class Interpolate : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a Interpolate operation
             ///
             /// \param image	    Input image

src/ngraph/op/experimental/layers/prior_box.cpp

@@ -21,10 +21,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::PriorBox::type_name{"PriorBox"};
+
 op::PriorBox::PriorBox(const shared_ptr<Node>& layer_shape,
                        const shared_ptr<Node>& image_shape,
                        const PriorBoxAttrs& attrs)
-    : Op("PriorBox", check_single_output_args({layer_shape, image_shape}))
+    : Op(check_single_output_args({layer_shape, image_shape}))
     , m_attrs(attrs)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/layers/prior_box.hpp

@@ -49,6 +49,9 @@ namespace ngraph
         class PriorBox : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a PriorBox operation
             ///
             /// \param layer_shape    Shape of layer for which prior boxes are computed

src/ngraph/op/experimental/layers/prior_box_clustered.cpp

@@ -21,10 +21,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::PriorBoxClustered::type_name{"PriorBoxClustered"};
+
 op::PriorBoxClustered::PriorBoxClustered(const shared_ptr<Node>& layer_shape,
                                          const shared_ptr<Node>& image_shape,
                                          const PriorBoxClusteredAttrs& attrs)
-    : Op("PriorBoxClustered", check_single_output_args({layer_shape, image_shape}))
+    : Op(check_single_output_args({layer_shape, image_shape}))
     , m_attrs(attrs)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/layers/prior_box_clustered.hpp

@@ -47,6 +47,9 @@ namespace ngraph
         class PriorBoxClustered : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a PriorBoxClustered operation
             ///
             /// \param layer_shape    Shape of layer for which prior boxes are computed

src/ngraph/op/experimental/layers/proposal.cpp

@@ -21,11 +21,13 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Proposal::type_name{"Proposal"};
+
 op::Proposal::Proposal(const std::shared_ptr<Node>& class_probs,
                        const std::shared_ptr<Node>& class_logits,
                        const std::shared_ptr<Node>& image_shape,
                        const ProposalAttrs& attrs)
-    : Op("Proposal", check_single_output_args({class_probs, class_logits, image_shape}))
+    : Op(check_single_output_args({class_probs, class_logits, image_shape}))
     , m_attrs(attrs)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/layers/proposal.hpp

@@ -57,6 +57,9 @@ namespace ngraph
         class Proposal : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a Proposal operation
             ///
             /// \param class_probs     Class probability scores

src/ngraph/op/experimental/layers/psroi_pooling.cpp

@@ -19,6 +19,8 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::PSROIPooling::type_name{"PSROIPooling"};
+
 op::PSROIPooling::PSROIPooling(const shared_ptr<Node>& input,
                                const std::shared_ptr<Node>& coords,
                                const size_t output_dim,
@@ -26,7 +28,7 @@ op::PSROIPooling::PSROIPooling(const shared_ptr<Node>& input,
                                const float spatial_scale,
                                const Shape& num_bins,
                                const std::string& kind)
-    : Op("PSROIPooling", check_single_output_args({input, coords}))
+    : Op(check_single_output_args({input, coords}))
     , m_output_dim(output_dim)
     , m_group_size(group_size)
     , m_spatial_scale(spatial_scale)

src/ngraph/op/experimental/layers/psroi_pooling.hpp

@@ -25,6 +25,9 @@ namespace ngraph
         class PSROIPooling : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a PSROIPooling operation
             ///
             /// \param input          Input feature map {N, C, ...}

src/ngraph/op/experimental/layers/region_yolo.cpp

@@ -19,6 +19,8 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::RegionYolo::type_name{"RegionYolo"};
+
 op::RegionYolo::RegionYolo(const shared_ptr<Node>& input,
                            const size_t num_coords,
                            const size_t num_classes,
@@ -27,7 +29,7 @@ op::RegionYolo::RegionYolo(const shared_ptr<Node>& input,
                            const vector<int64_t>& mask,
                            const int axis,
                            const int end_axis)
-    : Op("RegionYolo", check_single_output_args({input}))
+    : Op(check_single_output_args({input}))
     , m_num_coords(num_coords)
     , m_num_classes(num_classes)
     , m_num_regions(num_regions)

src/ngraph/op/experimental/layers/region_yolo.hpp

@@ -25,6 +25,9 @@ namespace ngraph
         class RegionYolo : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a RegionYolo operation
             ///
             /// \param input          Input

src/ngraph/op/experimental/layers/reorg_yolo.cpp

@@ -21,8 +21,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::ReorgYolo::type_name{"ReorgYolo"};
+
 op::ReorgYolo::ReorgYolo(const shared_ptr<Node>& input, const Strides& strides)
-    : Op("ReorgYolo", check_single_output_args({input}))
+    : Op(check_single_output_args({input}))
     , m_strides(strides)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/layers/reorg_yolo.hpp

@@ -25,6 +25,9 @@ namespace ngraph
         class ReorgYolo : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a ReorgYolo operation
             ///
             /// \param input          Input

src/ngraph/op/experimental/layers/roi_pooling.cpp

@@ -19,12 +19,14 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::ROIPooling::type_name{"ROIPooling"};
+
 op::ROIPooling::ROIPooling(const shared_ptr<Node>& input,
                            const std::shared_ptr<Node>& coords,
                            const Shape& output_size,
                            const float spatial_scale,
                            const std::string& kind)
-    : Op("ROIPooling", check_single_output_args({input, coords}))
+    : Op(check_single_output_args({input, coords}))
     , m_output_size(output_size)
     , m_spatial_scale(spatial_scale)
     , m_kind(kind)

src/ngraph/op/experimental/layers/roi_pooling.hpp

@@ -25,6 +25,9 @@ namespace ngraph
         class ROIPooling : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a ROIPooling operation
             ///
             /// \param input          Input feature map {N, C, ...}

src/ngraph/op/experimental/quantized_concat.cpp

@@ -23,8 +23,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::QuantizedConcat::type_name{"QuantizedConcat"};
+
 op::QuantizedConcat::QuantizedConcat(const NodeVector& args, size_t concatenation_axis)
-    : Op("QuantizedConcat", check_single_output_args(args))
+    : Op(check_single_output_args(args))
     , m_concatenation_axis(concatenation_axis)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/quantized_concat.hpp

@@ -28,6 +28,9 @@ namespace ngraph
         class QuantizedConcat : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a concatenation operation.
             ///
             /// \param args               The nodes producing the input tensors.

src/ngraph/op/experimental/shape_of.cpp

@@ -20,8 +20,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::ShapeOf::type_name{"ShapeOf"};
+
 op::ShapeOf::ShapeOf(const shared_ptr<Node>& arg)
-    : Op("ShapeOf", check_single_output_args({arg}))
+    : Op(check_single_output_args({arg}))
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/shape_of.hpp

@@ -26,6 +26,9 @@ namespace ngraph
         class ShapeOf : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a shape-of operation.
             ShapeOf(const std::shared_ptr<Node>& arg);
 

src/ngraph/op/experimental/tile.cpp

@@ -21,8 +21,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Tile::type_name{"Tile"};
+
 op::Tile::Tile(const std::shared_ptr<Node>& arg, const std::shared_ptr<Node>& repeats)
-    : Op("Tile", check_single_output_args({arg, repeats}))
+    : Op(check_single_output_args({arg, repeats}))
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/tile.hpp

@@ -27,6 +27,9 @@ namespace ngraph
         class Tile : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Perform dynamic padding of a tensor
             ///
             /// \param arg The node producing input tensor to be padded.

src/ngraph/op/experimental/transpose.cpp

@@ -22,8 +22,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Transpose::type_name{"Transpose"};
+
 op::Transpose::Transpose(const shared_ptr<Node>& arg, const shared_ptr<Node>& input_order)
-    : Op("Transpose", check_single_output_args({arg, input_order}))
+    : Op(check_single_output_args({arg, input_order}))
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/transpose.hpp

@@ -28,6 +28,9 @@ namespace ngraph
         class Transpose : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a transpose operation.
             ///
             /// \param arg Node producing the tensor to be transposed.

src/ngraph/op/pad.cpp

@@ -21,12 +21,14 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Pad::type_name{"Pad"};
+
 op::Pad::Pad(const shared_ptr<Node>& arg,
              const shared_ptr<Node>& arg_pad_value,
              const CoordinateDiff& padding_below,
              const CoordinateDiff& padding_above,
              PadMode pad_mode)
-    : Op("Pad", check_single_output_args({arg, arg_pad_value}))
+    : Op(check_single_output_args({arg, arg_pad_value}))
     , m_padding_below(padding_below)
     , m_padding_above(padding_above)
     , m_padding_interior_fake(padding_below.size())

src/ngraph/op/pad.hpp

@@ -28,6 +28,9 @@ namespace ngraph
         class Pad : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a generic padding operation.
             ///
             /// \param arg The node producing input tensor to be padded.

src/ngraph/op/parameter.cpp

@@ -21,10 +21,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Parameter::type_name{"Parameter"};
+
 op::Parameter::Parameter(const element::Type& element_type,
                          const PartialShape& pshape,
                          const bool cacheable)
-    : Op("Parameter", {})
+    : Op(NodeVector{})
     , m_cacheable(cacheable)
     , m_partial_shape(pshape)
     , m_element_type(element_type)

src/ngraph/op/parameter.hpp

@@ -35,6 +35,9 @@ namespace ngraph
                                            const NodeVector& deltas) override;
 
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructions a tensor-typed parameter node.
             ///
             /// \param element_type The element type of the parameter.

src/ngraph/op/passthrough.cpp

@@ -18,12 +18,17 @@
 
 #include "ngraph/op/passthrough.hpp"
 
+using namespace std;
+using namespace ngraph;
+
+const string op::Passthrough::type_name{"Passthrough"};
+
 ngraph::op::Passthrough::Passthrough(const std::string& logical_type,
                                      const std::string& language,
                                      const std::string& function,
                                      const NodeVector& args,
                                      std::vector<std::tuple<element::Type, PartialShape>> outputs)
-    : Op{"Passthrough", args}
+    : Op{args}
     , m_logical_type{logical_type}
     , m_language{language}
     , m_function{function}
@@ -65,5 +70,5 @@ std::shared_ptr<ngraph::Node>
             "Passthrough node input counts cannot be changed for a given Passthrough function"};
     }
     return std::make_shared<Passthrough>(
-        description(), m_language, m_function, new_args, m_output_shapes);
+        m_logical_type, m_language, m_function, new_args, m_output_shapes);
 }

src/ngraph/op/passthrough.hpp

@@ -38,6 +38,9 @@ namespace ngraph
 class ngraph::op::Passthrough final : public Op
 {
 public:
+    NGRAPH_API
+    static const std::string type_name;
+    const std::string& description() const override { return type_name; }
     Passthrough(const std::string& logical_type, // aka "What this operation is doing"
                 const std::string& language,     // The language the implementation is written in
                 const std::string& function,     // The operation implementation

src/ngraph/op/power.cpp

@@ -22,10 +22,12 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Power::type_name{"Power"};
+
 op::Power::Power(const shared_ptr<Node>& arg0,
                  const shared_ptr<Node>& arg1,
                  const AutoBroadcastSpec& autob)
-    : BinaryElementwiseArithmetic("Power", arg0, arg1, autob)
+    : BinaryElementwiseArithmetic(arg0, arg1, autob)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/power.hpp

@@ -39,6 +39,9 @@ namespace ngraph
         class Power : public util::BinaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs an exponentiation operation.
             ///
             /// \param arg0 Node that produces the first input tensor.

src/ngraph/op/relu.cpp

@@ -20,8 +20,11 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Relu::type_name{"Relu"};
+const string op::ReluBackprop::type_name{"ReluBackprop"};
+
 op::Relu::Relu(shared_ptr<Node> arg)
-    : UnaryElementwiseArithmetic("Relu", {arg})
+    : UnaryElementwiseArithmetic(arg)
 {
     constructor_validate_and_infer_types();
 }
@@ -33,7 +36,7 @@ shared_ptr<Node> op::Relu::copy_with_new_args(const NodeVector& new_args) const
 }
 
 op::ReluBackprop::ReluBackprop(shared_ptr<Node> arg, shared_ptr<Node> delta)
-    : BinaryElementwiseArithmetic("ReluBackprop", arg, delta)
+    : BinaryElementwiseArithmetic(arg, delta)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/relu.hpp

@@ -33,6 +33,9 @@ namespace ngraph
         class Relu : public ngraph::op::util::UnaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a Relu operation.
             ///
             /// \param arg Node that produces the input tensor.
@@ -50,6 +53,9 @@ namespace ngraph
         class ReluBackprop : public ngraph::op::util::BinaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a ReluBackprop operation.
             ///
             /// \param arg Node that produces the relu forward input tensor.

src/ngraph/op/reverse.cpp

@@ -23,8 +23,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Reverse::type_name{"Reverse"};
+
 op::Reverse::Reverse(const shared_ptr<Node>& arg, const AxisSet& reversed_axes)
-    : Op("Reverse", check_single_output_args({arg}))
+    : Op(check_single_output_args({arg}))
     , m_reversed_axes(reversed_axes)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/reverse.hpp

@@ -46,6 +46,9 @@ namespace ngraph
         class Reverse : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a reverse operation.
             ///
             /// \param arg The input tensor, some of whose axes are to be reversed.

src/ngraph/op/reverse_sequence.cpp

@@ -25,11 +25,13 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::ReverseSequence::type_name{"ReverseSequence"};
+
 op::ReverseSequence::ReverseSequence(const std::shared_ptr<Node> arg,
                                      const std::shared_ptr<Node> seq_indices,
                                      size_t batch_axis,
                                      size_t seq_axis)
-    : Op("ReverseSequence", check_single_output_args({arg, seq_indices}))
+    : Op(check_single_output_args({arg, seq_indices}))
     , m_batch_axis(batch_axis)
     , m_seq_axis(seq_axis)
 {

src/ngraph/op/reverse_sequence.hpp

@@ -25,6 +25,9 @@ namespace ngraph
         class ReverseSequence : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs an arcsin operation.
             ///
             /// \param arg Node that produces the input tensor.

src/ngraph/op/scatter_add.cpp

@@ -24,6 +24,8 @@ static int INPUTS = 0;
 static int INDICES = 1;
 static int UPDATES = 2;
 
+const string op::ScatterAdd::type_name{"ScatterAdd"};
+
 shared_ptr<Node> op::ScatterAdd::copy_with_new_args(const NodeVector& new_args) const
 {
     check_new_args_count(this, new_args);

src/ngraph/op/scatter_add.hpp

@@ -26,13 +26,16 @@ namespace ngraph
         class ScatterAdd : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \param inputs Tensor
             /// \param indices Index tensor: Data type must be `element::i32` or `element::i64`
             /// \param updates Tensor: Must have same type as inputs
             ScatterAdd(const std::shared_ptr<Node>& inputs,
                        const std::shared_ptr<Node>& indices,
                        const std::shared_ptr<Node>& updates)
-                : Op("ScatterAdd", check_single_output_args({inputs, indices, updates}))
+                : Op(check_single_output_args({inputs, indices, updates}))
             {
                 constructor_validate_and_infer_types();
             }

src/ngraph/op/scatter_nd_add.cpp

@@ -24,6 +24,8 @@ static int INPUTS = 0;
 static int INDICES = 1;
 static int UPDATES = 2;
 
+const string op::ScatterNDAdd::type_name{"ScatterNDAdd"};
+
 shared_ptr<Node> op::ScatterNDAdd::copy_with_new_args(const NodeVector& new_args) const
 {
     check_new_args_count(this, new_args);

src/ngraph/op/scatter_nd_add.hpp

@@ -26,13 +26,16 @@ namespace ngraph
         class ScatterNDAdd : public Op
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \param inputs Tensor
             /// \param indices Index tensor: Data type must be `element::i32` or `element::i64`
             /// \param updates Tensor: Must have same type as inputs
             ScatterNDAdd(const std::shared_ptr<Node>& inputs,
                          const std::shared_ptr<Node>& indices,
                          const std::shared_ptr<Node>& updates)
-                : Op("ScatterNDAdd", check_single_output_args({inputs, indices, updates}))
+                : Op(check_single_output_args({inputs, indices, updates}))
             {
                 constructor_validate_and_infer_types();
             }

src/ngraph/op/sigmoid.cpp

@@ -21,6 +21,9 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Sigmoid::type_name{"Sigmoid"};
+const string op::SigmoidBackprop::type_name{"SigmoidBackprop"};
+
 shared_ptr<Node> op::Sigmoid::copy_with_new_args(const NodeVector& new_args) const
 {
     check_new_args_count(this, new_args);
@@ -28,13 +31,13 @@ shared_ptr<Node> op::Sigmoid::copy_with_new_args(const NodeVector& new_args) con
 }
 
 op::Sigmoid::Sigmoid(shared_ptr<Node> arg)
-    : UnaryElementwiseArithmetic("Sigmoid", arg)
+    : UnaryElementwiseArithmetic(arg)
 {
     constructor_validate_and_infer_types();
 }
 
 op::SigmoidBackprop::SigmoidBackprop(shared_ptr<Node> arg, shared_ptr<Node> delta)
-    : BinaryElementwiseArithmetic("SigmoidBackprop", arg, delta)
+    : BinaryElementwiseArithmetic(arg, delta)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/sigmoid.hpp

@@ -28,6 +28,9 @@ namespace ngraph
         class Sigmoid : public util::UnaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             Sigmoid(std::shared_ptr<Node> arg);
             virtual std::shared_ptr<Node>
                 copy_with_new_args(const NodeVector& new_args) const override;
@@ -40,6 +43,9 @@ namespace ngraph
         class SigmoidBackprop : public util::BinaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a SigmoidBackprop operation.
             ///
             /// \param arg Node that produces the Sigmoid forward input tensor.

src/ngraph/op/sign.cpp

@@ -19,8 +19,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Sign::type_name{"Sign"};
+
 op::Sign::Sign(const shared_ptr<Node>& arg)
-    : UnaryElementwiseArithmetic("Sign", arg)
+    : UnaryElementwiseArithmetic(arg)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/sign.hpp

@@ -27,6 +27,9 @@ namespace ngraph
         class Sign : public util::UnaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs an elementwise sign operation.
             ///
             /// \param arg Node that produces the input tensor.

src/ngraph/op/sin.cpp

@@ -21,8 +21,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Sin::type_name{"Sin"};
+
 op::Sin::Sin(const shared_ptr<Node>& arg)
-    : UnaryElementwiseArithmetic("Sin", arg)
+    : UnaryElementwiseArithmetic(arg)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/sin.hpp

@@ -38,6 +38,9 @@ namespace ngraph
         class Sin : public util::UnaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a sine operation.
             ///
             /// \param arg Node that produces the input tensor.

src/ngraph/op/sinh.cpp

@@ -21,8 +21,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Sinh::type_name{"Sinh"};
+
 op::Sinh::Sinh(const shared_ptr<Node>& arg)
-    : UnaryElementwiseArithmetic("Sinh", arg)
+    : UnaryElementwiseArithmetic(arg)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/sinh.hpp

@@ -26,6 +26,9 @@ namespace ngraph
         class Sinh : public util::UnaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a hyperbolic sine operation.
             ///
             /// \param arg Node that produces the input tensor.

src/ngraph/op/softmax.cpp

@@ -29,8 +29,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Softmax::type_name{"Softmax"};
+
 op::Softmax::Softmax(const shared_ptr<Node>& arg, const AxisSet& axes)
-    : UnaryElementwiseArithmetic("Softmax", arg)
+    : UnaryElementwiseArithmetic(arg)
     , m_axes(axes)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/softmax.hpp

@@ -27,6 +27,9 @@ namespace ngraph
         class Softmax : public util::UnaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a softmax operation.
             ///
             /// \param arg Node that produces the first input tensor.<br>

src/ngraph/op/sqrt.cpp

@@ -21,8 +21,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::Sqrt::type_name{"Sqrt"};
+
 op::Sqrt::Sqrt(const shared_ptr<Node>& arg)
-    : UnaryElementwiseArithmetic("Sqrt", arg)
+    : UnaryElementwiseArithmetic(arg)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/sqrt.hpp

@@ -38,6 +38,9 @@ namespace ngraph
         class Sqrt : public util::UnaryElementwiseArithmetic
         {
         public:
+            NGRAPH_API
+            static const std::string type_name;
+            const std::string& description() const override { return type_name; }
             /// \brief Constructs a square operation.
             ///
             /// \param arg Node that produces the input tensor.

src/ngraph/op/stop_gradient.cpp

@@ -21,8 +21,10 @@
 using namespace std;
 using namespace ngraph;
 
+const string op::StopGradient::type_name{"StopGradient"};
+
 op::StopGradient::StopGradient(const shared_ptr<Node>& arg)
-    : UnaryElementwiseArithmetic("StopGradient", arg)
+    : UnaryElementwiseArithmetic(arg)
 {
     constructor_validate_and_infer_types();
 }