Merge branch 'master' into cyphers/25tomaster

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

@@ -37,6 +37,7 @@ ngraph.ops
       elu
       equal
       exp
+      fake_quantize
       floor
       gelu
       gemm

python/ngraph/__init__.py

@@ -50,6 +50,7 @@ from ngraph.ops import dot
 from ngraph.ops import elu
 from ngraph.ops import equal
 from ngraph.ops import exp
+from ngraph.ops import fake_quantize
 from ngraph.ops import floor
 from ngraph.ops import gelu
 from ngraph.ops import gemm

python/ngraph/impl/op/__init__.py

@@ -74,6 +74,7 @@ from _pyngraph.op import Dot
 from _pyngraph.op import Elu
 from _pyngraph.op import Equal
 from _pyngraph.op import Exp
+from _pyngraph.op import FakeQuantize
 from _pyngraph.op import Floor
 from _pyngraph.op import Gelu
 from _pyngraph.op import Gemm

python/ngraph/ops.py

@@ -22,9 +22,9 @@ 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, DepthToSpace, Divide, Dot, Elu, Equal, Exp, \
-    Floor, Gelu, Gemm, GetOutputElement, Greater, GreaterEq, GRN, Less, LessEq, Log, LRN, Max, \
-    Maximum, MaxPool, Min, Minimum, Multiply, Negative, Not, NotEqual, OneHot, Or, Pad, \
+    Convolution, ConvolutionBackpropData, Cos, Cosh, DepthToSpace, Divide, Dot, Elu, FakeQuantize, \
+    Equal, Exp, Floor, Gelu, Gemm, GetOutputElement, Greater, GreaterEq, GRN, 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
 
@@ -537,6 +537,38 @@ def broadcast_to(node, new_shape, axis=None, name=None):
 
 
 @nameable_op
+def fake_quantize(data, input_low, input_high, output_low, output_high, levels, name=None):
+    # type: (Node, Node, Node, Node, Node, int, str) -> Node
+    r"""Perform an element-wise linear quantization on input data.
+
+    Input floating point values are quantized into a discrete set of floating point values.
+
+    .. code-block:: python
+
+        if x <= input_low:
+            output = output_low
+        if x > input_high:
+            output = output_high
+        else:
+            output = fake_quantize(output)
+
+    Fake quantize uses the following logic:
+
+    .. math:: output =
+            \dfrac{round( \dfrac{data - input\_low}{(input\_high - input\_low)\cdot (levels-1)})}
+            {(levels-1)\cdot (output\_high - output\_low)} + output\_low
+
+    :param data:         The node with data tensor.
+    :param input_low:    The node with the minimum for input values.
+    :param input_high:   The node with the maximum for input values.
+    :param output_low:   The node with the minimum quantized value.
+    :param output_high:  The node with the maximum quantized value.
+    :param levels:       The number of quantization levels. Integer value.
+    :return: New node with quantized value.
+    """
+    return FakeQuantize(data, input_low, input_high, output_low, output_high, levels)
+
+
 def gemm(A,                      # type: Node
          B,                      # type: Node
          C,                      # type: Node

python/pyngraph/ops/fused/fake_quantize.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/fake_quantize.hpp"
+#include "pyngraph/ops/fused/fake_quantize.hpp"
+
+namespace py = pybind11;
+
+void regclass_pyngraph_op_FakeQuantize(py::module m)
+{
+    py::class_<ngraph::op::FakeQuantize, std::shared_ptr<ngraph::op::FakeQuantize>, ngraph::op::Op>
+        fakequantize(m, "FakeQuantize");
+    fakequantize.doc() = "ngraph.impl.op.FakeQuantize wraps ngraph::op::FakeQuantize";
+    fakequantize.def(py::init<const std::shared_ptr<ngraph::Node>&,
+                              const std::shared_ptr<ngraph::Node>&,
+                              const std::shared_ptr<ngraph::Node>&,
+                              const std::shared_ptr<ngraph::Node>&,
+                              const std::shared_ptr<ngraph::Node>&,
+                              int&>());
+}

python/pyngraph/ops/fused/fake_quantize.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_FakeQuantize(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_Elu(m_op);
     regclass_pyngraph_op_Equal(m_op);
     regclass_pyngraph_op_Exp(m_op);
+    regclass_pyngraph_op_FakeQuantize(m_op);
     regclass_pyngraph_op_Floor(m_op);
     regclass_pyngraph_op_Gelu(m_op);
     regclass_pyngraph_op_Gemm(m_op);

python/pyngraph/ops/regmodule_pyngraph_op.hpp

@@ -45,6 +45,7 @@
 #include "pyngraph/ops/fused/clamp.hpp"
 #include "pyngraph/ops/fused/depth_to_space.hpp"
 #include "pyngraph/ops/fused/elu.hpp"
+#include "pyngraph/ops/fused/fake_quantize.hpp"
 #include "pyngraph/ops/fused/gelu.hpp"
 #include "pyngraph/ops/fused/gemm.hpp"
 #include "pyngraph/ops/fused/grn.hpp"

python/setup.py

@@ -184,6 +184,7 @@ sources = [
     'pyngraph/ops/fused/elu.cpp',
     'pyngraph/ops/equal.cpp',
     'pyngraph/ops/exp.cpp',
+    'pyngraph/ops/fused/fake_quantize.cpp',
     'pyngraph/ops/floor.cpp',
     'pyngraph/ops/fused/gelu.cpp',
     'pyngraph/ops/fused/gemm.cpp',

python/test/ngraph/test_ops_fused.py

@@ -69,6 +69,52 @@ def test_elu_operator_with_scalar():
     assert np.allclose(result, expected)
 
 
+def test_fake_quantize():
+    runtime = get_runtime()
+
+    data_value = np.arange(24.0, dtype=np.float32).reshape(1, 2, 3, 4)
+    input_low_value = np.float32(0)
+    input_high_value = np.float32(23)
+    output_low_value = np.float32(2)
+    output_high_value = np.float32(16)
+    levels = np.float32(4)
+
+    data_shape = [1, 2, 3, 4]
+    bound_shape = []
+    parameter_data = ng.parameter(data_shape, name='data', dtype=np.float32)
+    parameter_input_low = ng.parameter(bound_shape, name='input_low', dtype=np.float32)
+    parameter_input_high = ng.parameter(bound_shape, name='input_high', dtype=np.float32)
+    parameter_output_low = ng.parameter(bound_shape, name='output_low', dtype=np.float32)
+    parameter_output_high = ng.parameter(bound_shape, name='output_high', dtype=np.float32)
+
+    model = ng.fake_quantize(parameter_data,
+                             parameter_input_low,
+                             parameter_input_high,
+                             parameter_output_low,
+                             parameter_output_high,
+                             levels)
+    computation = runtime.computation(model,
+                                      parameter_data,
+                                      parameter_input_low,
+                                      parameter_input_high,
+                                      parameter_output_low,
+                                      parameter_output_high)
+
+    result = computation(data_value,
+                         input_low_value,
+                         input_high_value,
+                         output_low_value,
+                         output_high_value)
+
+    expected = np.array([[[[[2., 2., 2., 2.],
+                            [6.6666669, 6.6666669, 6.6666669, 6.6666669],
+                            [6.6666669, 6.6666669, 6.6666669, 6.6666669]],
+                        [[11.33333301, 11.33333301, 11.33333301, 11.33333301],
+                            [11.33333301, 11.33333301, 11.33333301, 11.33333301],
+                            [16., 16., 16., 16.]]]]], dtype=np.float32)
+    assert np.allclose(result, expected)
+
+
 def test_depth_to_space():
     runtime = get_runtime()
 
@@ -219,4 +265,5 @@ def test_grn_operator():
                           [[0.9970545, 0.98994946, 0.9805807, 0.97014254],
                            [0.9593655, 0.9486833, 0.9383431, 0.9284767],
                            [0.91914505, 0.9103665, 0.9021342, 0.8944272]]]], dtype=np.float32)
+
     assert np.allclose(result, expected)

src/ngraph/op/experimental/batch_mat_mul.cpp

@@ -26,8 +26,8 @@ using namespace ngraph;
 
 const string op::BatchMatMul::type_name{"BatchMatMul"};
 
-op::BatchMatMul::BatchMatMul(const shared_ptr<Node>& arg0, const shared_ptr<Node>& arg1)
-    : Op(check_single_output_args({arg0, arg1}))
+op::BatchMatMul::BatchMatMul(const Output<Node>& arg0, const Output<Node>& arg1)
+    : Op({arg0, arg1})
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/batch_mat_mul.hpp

@@ -35,11 +35,12 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            BatchMatMul() = default;
             /// \brief Constructs a batch of matmul product operation.
             ///
             /// \param arg0 The node producing the first argument.
             /// \param arg1 The node producing the second argument.
-            BatchMatMul(const std::shared_ptr<Node>& arg0, const std::shared_ptr<Node>& arg1);
+            BatchMatMul(const Output<Node>& arg0, const Output<Node>& arg1);
 
             virtual void validate_and_infer_types() override;
 

src/ngraph/op/experimental/compiled_kernel.cpp

@@ -63,6 +63,13 @@ shared_ptr<Node> ngraph::op::CompiledKernel::copy_with_new_args(const NodeVector
     return std::make_shared<CompiledKernel>(new_node_list, new_outputs, new_args);
 }
 
+ngraph::op::CompiledKernel::CompiledKernel(const OutputVector& node_list,
+                                           const OutputVector& outputs,
+                                           const OutputVector& args)
+    : CompiledKernel(as_node_vector(node_list), as_node_vector(outputs), as_node_vector(args))
+{
+}
+
 ngraph::op::CompiledKernel::CompiledKernel(const NodeVector& node_list,
                                            const NodeVector& outputs,
                                            const NodeVector& args)

src/ngraph/op/experimental/compiled_kernel.hpp

@@ -35,9 +35,13 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            CompiledKernel() = default;
             CompiledKernel(const NodeVector& node_list,
                            const NodeVector& outputs,
                            const NodeVector& args);
+            CompiledKernel(const OutputVector& node_list,
+                           const OutputVector& outputs,
+                           const OutputVector& args);
             virtual std::shared_ptr<Node>
                 copy_with_new_args(const NodeVector& new_args) const override;
 

src/ngraph/op/experimental/dyn_broadcast.cpp

@@ -23,10 +23,10 @@ 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(check_single_output_args({arg, shape, broadcast_axes}))
+op::DynBroadcast::DynBroadcast(const Output<Node>& arg,
+                               const Output<Node>& shape,
+                               const Output<Node>& broadcast_axes)
+    : Op({arg, shape, broadcast_axes})
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/dyn_broadcast.hpp

@@ -31,15 +31,16 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            DynBroadcast() = default;
             /// \brief Constructs a dynamic broadcast operation.
             ///
             /// \param arg            Node that produces the input tensor to be broadcast.
             /// \param shape          Node that produces shape of the output tensor.
             /// \param broadcast_axes Node that produces the axis positions (0-based) in the result that are being broadcast. The
             ///                        remaining axes in shape must be the same as the shape of arg.
-            DynBroadcast(const std::shared_ptr<Node>& arg,
-                         const std::shared_ptr<Node>& shape,
-                         const std::shared_ptr<Node>& broadcast_axes);
+            DynBroadcast(const Output<Node>& arg,
+                         const Output<Node>& shape,
+                         const Output<Node>& broadcast_axes);
 
             void validate_and_infer_types() override;
 

src/ngraph/op/experimental/dyn_pad.cpp

@@ -21,12 +21,13 @@ 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::DynPad::DynPad(const Output<Node>& arg,
+                   const Output<Node>& padding_below,
+                   const Output<Node>& padding_above,
+                   const Output<Node>& padding_value,
                    op::PadMode pad_mode)
-    : Op(check_single_output_args({arg, padding_below, padding_above, padding_value}))
+    : Op({arg, padding_below, padding_above, padding_value})
+    , m_pad_mode(pad_mode)
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/dyn_pad.hpp

@@ -30,6 +30,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            DynPad() = default;
             /// \brief Perform dynamic padding of a tensor
             ///
             /// \param arg The node producing input tensor to be padded.
@@ -37,10 +38,10 @@ namespace ngraph
             /// \param padding_above The node producing the padding-above widths.
             /// \param padding_value The value to be used for padding. Must be scalar.
             /// \param pad_mode The padding mode: CONSTANT(default), EDGE or REFLECT.
-            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,
+            DynPad(const Output<Node>& arg,
+                   const Output<Node>& padding_below,
+                   const Output<Node>& padding_above,
+                   const Output<Node>& padding_value,
                    PadMode pad_mode = PadMode::CONSTANT);
 
             PadMode get_pad_mode() const { return m_pad_mode; }

src/ngraph/op/experimental/dyn_replace_slice.cpp

@@ -26,17 +26,17 @@ 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,
-                                     const shared_ptr<Node>& upper_bounds,
-                                     const shared_ptr<Node>& strides,
+op::DynReplaceSlice::DynReplaceSlice(const Output<Node>& arg,
+                                     const Output<Node>& replacement,
+                                     const Output<Node>& lower_bounds,
+                                     const Output<Node>& upper_bounds,
+                                     const Output<Node>& strides,
                                      const AxisSet& lower_bounds_mask,
                                      const AxisSet& upper_bounds_mask,
                                      const AxisSet& new_axis,
                                      const AxisSet& shrink_axis,
                                      const AxisSet& ellipsis_mask)
-    : Op(check_single_output_args({arg, replacement, lower_bounds, upper_bounds, strides}))
+    : Op({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

@@ -30,6 +30,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            DynReplaceSlice() = default;
             /// \brief Constructs a dynamic tensor replace-slice operation.
             ///
             /// \param arg The tensor in which to replace the slice.
@@ -43,11 +44,11 @@ namespace ngraph
             /// \param new_axis          Add dimension one axis at the set positions
             /// \param shrink_axis       Delete dimensions at the set positions
             /// \param ellipsis_mask     Inserts missing dimensions on the set position
-            DynReplaceSlice(const std::shared_ptr<Node>& arg,
-                            const std::shared_ptr<Node>& replacement,
-                            const std::shared_ptr<Node>& lower_bounds,
-                            const std::shared_ptr<Node>& upper_bounds,
-                            const std::shared_ptr<Node>& strides,
+            DynReplaceSlice(const Output<Node>& arg,
+                            const Output<Node>& replacement,
+                            const Output<Node>& lower_bounds,
+                            const Output<Node>& upper_bounds,
+                            const Output<Node>& strides,
                             const AxisSet& lower_bounds_mask = AxisSet{},
                             const AxisSet& upper_bounds_mask = AxisSet{},
                             const AxisSet& new_axis = AxisSet{},

src/ngraph/op/experimental/dyn_reshape.cpp

@@ -26,10 +26,8 @@ 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(check_single_output_args({arg, pattern}))
+op::DynReshape::DynReshape(const Output<Node>& arg, const Output<Node>& pattern, bool zero_flag)
+    : Op({arg, pattern})
     , m_zero_flag(zero_flag)
 {
     constructor_validate_and_infer_types();

src/ngraph/op/experimental/dyn_reshape.hpp

@@ -34,6 +34,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            DynReshape() = default;
             /// \brief Constructs a dynamic reshape operation. This operation does not perform transpose.
             ///
             /// \param arg The tensor to be reshaped.
@@ -44,8 +45,8 @@ namespace ngraph
             ///        size is inferred based on element count of input tensor.
             /// \param zero_flag Treats zeros in `pattern` as wildcard flags indicating a copy from input
             ///                  shape at the same index.
-            DynReshape(const std::shared_ptr<Node>& arg,
-                       const std::shared_ptr<Node>& pattern,
+            DynReshape(const Output<Node>& arg,
+                       const Output<Node>& pattern,
                        bool zero_flag = false);
 
             void validate_and_infer_types() override;

src/ngraph/op/experimental/dyn_slice.cpp

@@ -26,16 +26,16 @@ 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,
-                       const shared_ptr<Node>& strides,
+op::DynSlice::DynSlice(const Output<Node>& arg,
+                       const Output<Node>& lower_bounds,
+                       const Output<Node>& upper_bounds,
+                       const Output<Node>& strides,
                        const AxisSet& lower_bounds_mask,
                        const AxisSet& upper_bounds_mask,
                        const AxisSet& new_axis,
                        const AxisSet& shrink_axis,
                        const AxisSet& ellipsis_mask)
-    : Op(check_single_output_args({arg, lower_bounds, upper_bounds, strides}))
+    : Op({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

@@ -30,6 +30,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            DynSlice() = default;
             /// \brief Constructs a dynamic tensor slice operation.
             ///
             /// \param arg The tensor to be sliced.
@@ -42,10 +43,10 @@ namespace ngraph
             /// \param new_axis          Add dimension one axis at the set positions
             /// \param shrink_axis       Delete dimensions at the set positions
             /// \param ellipsis_mask     Inserts missing dimensions on the set position
-            DynSlice(const std::shared_ptr<Node>& arg,
-                     const std::shared_ptr<Node>& lower_bounds,
-                     const std::shared_ptr<Node>& upper_bounds,
-                     const std::shared_ptr<Node>& strides,
+            DynSlice(const Output<Node>& arg,
+                     const Output<Node>& lower_bounds,
+                     const Output<Node>& upper_bounds,
+                     const Output<Node>& strides,
                      const AxisSet& lower_bounds_mask = AxisSet{},
                      const AxisSet& upper_bounds_mask = AxisSet{},
                      const AxisSet& new_axis = AxisSet{},

src/ngraph/op/experimental/generate_mask.cpp

@@ -21,11 +21,6 @@ using namespace ngraph;
 
 const string op::GenerateMask::type_name{"GenerateMask"};
 
-op::GenerateMask::GenerateMask()
-    : Op()
-{
-}
-
 #if 0
 // Not supported until all transformers use nodes instead of attributes
 op::GenerateMask::GenerateMask(const Output<Node>& training,

src/ngraph/op/experimental/generate_mask.hpp

@@ -34,7 +34,7 @@ namespace ngraph
             const std::string& description() const override { return type_name; }
             /// \brief Constructs a GenerateMask node with a given shape, seed,
             /// probability and training/inference mode
-            GenerateMask();
+            GenerateMask() = default;
 
 #if 0
             /// Switch to dynamic arguments when all transformers have switched to using the node values

src/ngraph/op/experimental/quantized_concat.cpp

@@ -25,6 +25,13 @@ using namespace ngraph;
 
 const string op::QuantizedConcat::type_name{"QuantizedConcat"};
 
+op::QuantizedConcat::QuantizedConcat(const OutputVector& args, size_t concatenation_axis)
+    : Op(args)
+    , m_concatenation_axis(concatenation_axis)
+{
+    constructor_validate_and_infer_types();
+}
+
 op::QuantizedConcat::QuantizedConcat(const NodeVector& args, size_t concatenation_axis)
     : Op(check_single_output_args(args))
     , m_concatenation_axis(concatenation_axis)

src/ngraph/op/experimental/quantized_concat.hpp

@@ -31,12 +31,19 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            QuantizedConcat() = default;
             /// \brief Constructs a concatenation operation.
             ///
             /// \param args               The nodes producing the input tensors.
             /// \param concatenation_axis The axis along which to concatenate the input tensors.
             QuantizedConcat(const NodeVector& args, size_t concatenation_axis);
 
+            /// \brief Constructs a concatenation operation.
+            ///
+            /// \param args               The nodes producing the input tensors.
+            /// \param concatenation_axis The axis along which to concatenate the input tensors.
+            QuantizedConcat(const OutputVector& args, size_t concatenation_axis);
+
             void validate_and_infer_types() override;
 
             virtual std::shared_ptr<Node>

src/ngraph/op/experimental/quantized_conv_bias.hpp

@@ -30,6 +30,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            QuantizedConvolutionBias() = default;
             QuantizedConvolutionBias(const Output<Node>& data_batch,
                                      const Output<Node>& filters,
                                      const Output<Node>& bias,

src/ngraph/op/experimental/quantized_conv_relu.hpp

@@ -30,6 +30,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            QuantizedConvolutionRelu() = default;
             QuantizedConvolutionRelu(const Output<Node>& data_batch,
                                      const Output<Node>& filters,
                                      const Strides& window_movement_strides,

src/ngraph/op/experimental/quantized_dot.hpp

@@ -30,6 +30,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            QuantizedDot() = default;
             QuantizedDot(const Output<Node>& data,
                          const Output<Node>& weights,
                          const Output<Node>& scale,

src/ngraph/op/experimental/quantized_dot_bias.hpp

@@ -30,6 +30,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            QuantizedDotBias() = default;
             QuantizedDotBias(const Output<Node>& data,
                              const Output<Node>& weights,
                              const Output<Node>& bias,

src/ngraph/op/experimental/quantized_max_pool.hpp

@@ -29,6 +29,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            QuantizedMaxPool() = default;
             /// \brief Constructs a batched max pooling operation.
             ///
             /// \param arg The node producing the input data batch tensor.

src/ngraph/op/experimental/range.cpp

@@ -24,10 +24,6 @@ using namespace ngraph;
 
 const string op::Range::type_name = "Range";
 
-op::Range::Range()
-{
-}
-
 op::Range::Range(const Output<Node>& start, const Output<Node>& stop, const Output<Node>& step)
     : Op({start, stop, step})
 {

src/ngraph/op/experimental/range.hpp

@@ -31,7 +31,7 @@ namespace ngraph
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
             /// \brief Constructs an unitialized range operation.
-            Range();
+            Range() = default;
 
             /// \brief Constructs a range operation.
             ///

src/ngraph/op/experimental/shape_of.cpp

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

src/ngraph/op/experimental/shape_of.hpp

@@ -29,8 +29,9 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            ShapeOf() = default;
             /// \brief Constructs a shape-of operation.
-            ShapeOf(const std::shared_ptr<Node>& arg);
+            ShapeOf(const Output<Node>& arg);
 
             virtual std::shared_ptr<Node>
                 copy_with_new_args(const NodeVector& new_args) const override;

src/ngraph/op/experimental/tile.cpp

@@ -23,8 +23,8 @@ 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(check_single_output_args({arg, repeats}))
+op::Tile::Tile(const Output<Node>& arg, const Output<Node>& repeats)
+    : Op({arg, repeats})
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/tile.hpp

@@ -30,11 +30,12 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            Tile() = default;
             /// \brief Perform dynamic padding of a tensor
             ///
             /// \param arg The node producing input tensor to be padded.
             /// \param repeats The node producing the per-dimension replication factor
-            Tile(const std::shared_ptr<Node>& arg, const std::shared_ptr<Node>& repeats);
+            Tile(const Output<Node>& arg, const Output<Node>& repeats);
 
             void validate_and_infer_types() override;
 

src/ngraph/op/experimental/transpose.cpp

@@ -24,8 +24,8 @@ 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(check_single_output_args({arg, input_order}))
+op::Transpose::Transpose(const Output<Node>& arg, const Output<Node>& input_order)
+    : Op({arg, input_order})
 {
     constructor_validate_and_infer_types();
 }

src/ngraph/op/experimental/transpose.hpp

@@ -31,6 +31,7 @@ namespace ngraph
             NGRAPH_API
             static const std::string type_name;
             const std::string& description() const override { return type_name; }
+            Transpose() = default;
             /// \brief Constructs a transpose operation.
             ///
             /// \param arg Node producing the tensor to be transposed.
@@ -38,7 +39,7 @@ namespace ngraph
             ///                    input shape. Must be a vector of element type element::i64,
             ///                    with shape [n], where n is the rank of arg. The tensor's
             ///                    value must contain every integer in the range [0,n-1].
-            Transpose(const std::shared_ptr<Node>& arg, const std::shared_ptr<Node>& input_order);
+            Transpose(const Output<Node>& arg, const Output<Node>& input_order);
 
             void validate_and_infer_types() override;