Try universal node creator

src/ngraph/common.hpp

@@ -23,12 +23,25 @@ namespace ngraph
 {
     class Node;
     class Parameter;
+    class ValueType;
 
+    template<typename T, typename ...A>
+    std::shared_ptr<T> node(A&&... args)
+    {
+        return std::make_shared<T>(args...);
+    }
+
+    /// Zero or more value types
+    using ValueTypes = std::vector<std::shared_ptr<ValueType>>;
+    
     /// Zero or more nodes
     using Nodes = std::vector<std::shared_ptr<Node>>;
+
+    /// A sequence of axes
+    using AxisList = std::vector<size_t>;
     
-    /// A set of indices, for example, reduction axes
-    using IndexSet = std::set<size_t>;
+    /// A set of axes, for example, reduction axes
+    using AxisSet = std::set<size_t>;
 
     /// A list of parameters
     using Parameters = std::vector<std::shared_ptr<Parameter>>;

src/ngraph/ops/broadcast.hpp

@@ -19,15 +19,13 @@ namespace ngraph
     class BroadcastOp : public BuiltinOp
     {
     public:
-        using Axes = std::vector<size_t>;
-
         /**
          ** /param arg The tensor view to be broadcast.
          ** /param shape The shape of the result
          ** /param broadcast_axes 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.
          **/
-        BroadcastOp(const Node::ptr& arg, const Shape& shape, const Axes& broadcast_axes)
+        BroadcastOp(const Node::ptr& arg, const Shape& shape, AxisSet& broadcast_axes)
             : BuiltinOp({arg})
             , m_shape(shape)
             , m_broadcast_axes(broadcast_axes)
@@ -39,13 +37,13 @@ namespace ngraph
 
     protected:
         Shape               m_shape;
-        Axes m_broadcast_axes;
+        AxisSet m_broadcast_axes;
     };
 
     namespace op
     {
         Node::ptr broadcast(const Node::ptr&           tensor,
                             const Shape&               shape,
-                            const BroadcastOp::Axes&& broadcast_axes);
+                            AxisSet&& broadcast_axes);
     }
 }

src/ngraph/ops/constant.hpp

@@ -59,14 +59,6 @@ namespace ngraph
                 
         typename T::type value() const { return m_value; }
 
-        // Make a constant from any value that can be converted to the C++ type we use
-        // to represent the values.
-        template <typename U>
-        static std::shared_ptr<ScalarConstant<T>> make(U value)
-        {
-            return std::make_shared<ScalarConstant<T>>(value);
-        }
-
     protected:
         typename T::type m_value;
     };

src/ngraph/ops/parameter.hpp

@@ -37,6 +37,7 @@ namespace ngraph
 
     public:
         Parameter(const ValueType::ptr& value_type);
+        Parameter(const ngraph::element::Type element_type, const Shape& shape);
 
         std::string         description() const override { return "Parameter"; }
         virtual void        propagate_types() override;

src/ops/broadcast.cpp

@@ -25,7 +25,7 @@ using namespace ngraph;
  **/
 Node::ptr ngraph::op::broadcast(const Node::ptr&      tensor,
                                 const Shape&          shape,
-                                const BroadcastOp::Axes&& broadcast_axes)
+                                AxisSet&& broadcast_axes)
 {
     return make_shared<BroadcastOp>(tensor, shape, broadcast_axes);
 }

src/ops/parameter.cpp

@@ -26,6 +26,11 @@ Parameter::Parameter(const ValueType::ptr& value_type)
 {
 }
 
+Parameter::Parameter(const ngraph::element::Type element_type, const Shape& shape)
+    : Parameter(make_shared<TensorViewType>(element_type, shape))
+{
+}
+
 void Parameter::assign_function(Function* function, size_t index)
 {
     if (nullptr != m_function)

test/build_graph.cpp

@@ -20,29 +20,16 @@
 using namespace std;
 using namespace ngraph;
 
-
-template<typename T, typename ...A>
-std::shared_ptr<T> myfun(A&&... args)
-{
-    return std::make_shared<T>(args...);
-}
-
-template<>
-std::shared_ptr<Parameter> myfun<Parameter> (ngraph::element::Type&& element_type, Shape&& shape)
-{
-    return make_shared<Parameter>(make_shared<TensorViewType>(element_type, shape));
-}
-
 TEST(build_graph, build_simple)
 {
     // Function with 4 parameters
-    auto arg0        = op::parameter(element::Float::element_type(), Shape{7, 3});
-    auto arg1        = op::parameter(element::Float::element_type(), Shape{3});
-    auto arg2        = op::parameter(element::Float::element_type(), Shape{32, 7});
-    auto arg3        = op::parameter(element::Float::element_type(), Shape{32, 7});
-    auto broadcast_1 = op::broadcast(arg3, Shape{10, 32, 7}, BroadcastOp::Axes{0});
-    auto b1 = myfun<BroadcastOp>(arg3, Shape{10, 32, 7}, BroadcastOp::Axes{0});
-    auto dot         = op::dot(arg2, arg0);
+    auto arg0        = node<Parameter>(element::Float::element_type(), Shape{7, 3});
+    auto arg1        = node<Parameter>(element::Float::element_type(), Shape{3});
+    auto arg2        = node<Parameter>(element::Float::element_type(), Shape{32, 7});
+    auto arg3        = node<Parameter>(element::Float::element_type(), Shape{32, 7});
+    auto broadcast_1 = node<BroadcastOp>(arg3, Shape{10, 32, 7}, AxisSet{0});
+    auto b1 = node<BroadcastOp>(arg3, Shape{10, 32, 7}, AxisSet{0});
+    auto dot         = node<DotOp>(arg2, arg0);
     ASSERT_EQ(dot->arguments()[0], arg2);
     ASSERT_EQ(dot->arguments()[1], arg0);
 
@@ -55,14 +42,14 @@ TEST(build_graph, build_simple)
 TEST(build_graph, as_type)
 {
     // Check upcasting a ValueType::ptr that is a TensorViewType to a TensorViewType and Tuple.
-    ValueType::ptr tv_vt = make_shared<TensorViewType>(element::Float::element_type(), Shape{2, 3, 5});
-    auto           tv_tv = dynamic_pointer_cast<TensorViewType>(tv_vt);
+    auto tv_vt = make_shared<TensorViewType>(element::Float::element_type(), Shape{2, 3, 5});
+    auto tv_tv = dynamic_pointer_cast<TensorViewType>(tv_vt);
     ASSERT_EQ(tv_vt, tv_tv);
     auto tv_tp = dynamic_pointer_cast<TupleType>(tv_vt);
     ASSERT_EQ(nullptr, tv_tp);
 
     // Check upcasting a ValueType::ptr that is a TupleType to a TensorViewType and Tuple.
-    ValueType::ptr tp_vt = make_shared<TupleType>(vector<ValueType::ptr>{tv_vt, tv_vt});
+    auto tp_vt = make_shared<TupleType>(ValueTypes{tv_vt, tv_vt});
     auto           tp_tv = dynamic_pointer_cast<TensorViewType>(tp_vt);
     ASSERT_EQ(nullptr, tp_tv);
     auto tp_tp = dynamic_pointer_cast<TupleType>(tp_vt);
@@ -72,15 +59,15 @@ TEST(build_graph, as_type)
 // Check node comparisons
 TEST(build_graph, node_comparison)
 {
-    auto arg0 = op::parameter(element::Float::element_type(), {32, 3});
-    auto arg1 = op::parameter(element::Float::element_type(), {3});
-    auto arg2 = op::parameter(element::Float::element_type(), {32});
+    auto arg0 = node<Parameter>(element::Float::element_type(), Shape{32, 3});
+    auto arg1 = node<Parameter>(element::Float::element_type(), Shape{3});
+    auto arg2 = node<Parameter>(element::Float::element_type(), Shape{32});
 
     auto dot = op::dot(arg0, arg1);
     auto add = op::add(dot, arg2);
 
-    auto parg        = op::parameter(element::Float::element_type(), {});
-    auto pattern_dot = op::dot(parg, parg);
+    auto parg        = node<Parameter>(element::Float::element_type(), Shape{});
+    auto pattern_dot = node<DotOp>(parg, parg);
     ASSERT_TRUE(pattern_dot->is_same_op_type(dot));
     // TODO This passes because typeid is not behaving as documented.
     // Need to figure out what's wrong.
@@ -90,20 +77,21 @@ TEST(build_graph, node_comparison)
 TEST(build_graph, literal)
 {
     // float scalar from a float
-    auto float0 = FloatScalarConstant::make(3.0);
+    //auto float0 = FloatScalarConstant::make(3.0);
+    auto float0 = node<FloatScalarConstant>(3.0);
     auto float_scalar_type =  make_shared<TensorViewType>(element::Float::element_type(), Shape{});
     ASSERT_EQ(float0->value(), 3.0);
     ASSERT_EQ(*float0->value_type(), float_scalar_type);
-    auto d = op::dot(float0, float0);
+    auto d = node<DotOp>(float0, float0);
     ASSERT_EQ(d->arguments().at(0), float0);
     ASSERT_EQ(d->arguments().at(1), float0);
 
     // float scalar from an int
-    auto float1 = FloatScalarConstant::make(3);
+    auto float1 = node<FloatScalarConstant>(3);
     ASSERT_EQ(float1->value(), 3);
     ASSERT_EQ(*float1->value_type(), float_scalar_type);
     
-    auto int32_0 = Int32ScalarConstant::make(3.0);
+    auto int32_0 = node<Int32ScalarConstant>(3.0);
     auto int32_scalar_type =  make_shared<TensorViewType>(element::Int32::element_type(), Shape{});
     ASSERT_EQ(int32_0->value(), 3);
     ASSERT_EQ(*int32_0->value_type(), int32_scalar_type);