Merge branch 'master' into jmenon/codegen

src/ngraph/descriptor/layout/tensor_view_layout.hpp

@@ -51,6 +51,11 @@ namespace ngraph
                 /// With non-linear buffers, this will need to be something other than size_t.
                 virtual size_t get_index_offset(const std::vector<size_t>& indices) = 0;
 
+                const element::Type& get_element_type() const
+                {
+                    return m_tensor_view.get_tensor_view_type()->get_element_type();
+                }
+
                 const Shape& get_shape() const
                 {
                     return m_tensor_view.get_tensor_view_type()->get_shape();

src/ngraph/ops/convert.cpp

@@ -17,9 +17,10 @@
 #include "ngraph/ngraph.hpp"
 
 using namespace std;
+using namespace ngraph;
 using namespace ngraph::op;
 
-void Convert::propagate_types()
+const element::Type& Convert::propagate_element_types(const element::Type& arg_element_type) const
 {
-    throw ngraph_error("NIY");
+    return m_element_type;
 }

src/ngraph/ops/convert.hpp

@@ -27,9 +27,9 @@ namespace ngraph
             {
             }
 
+            virtual const element::Type&
+                propagate_element_types(const element::Type& arg_element_type) const override;
             virtual std::string description() const override { return "Convert"; }
-            virtual void propagate_types() override;
-
         protected:
             const ngraph::element::Type& m_element_type;
         };

src/ngraph/runtime/eigen/convert.hpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// ----------------------------------------------------------------------------
+
+#pragma once
+
+#include "ngraph/runtime/call_frame.hpp"
+#include "ngraph/runtime/eigen/utils.hpp"
+#include "ngraph/runtime/instruction.hpp"
+#include "ngraph/runtime/tensor_view.hpp"
+#include "ngraph/runtime/tensor_view_info.hpp"
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace eigen
+        {
+            template <typename ETI, typename ETO>
+            class ConvertInstruction : public Instruction
+            {
+            public:
+                ConvertInstruction(const TensorViewInfo& arg, const TensorViewInfo& out)
+                    : m_arg(arg)
+                    , m_out(out)
+                {
+                }
+
+                virtual void execute(CallFrame& call_frame) const override
+                {
+                    EigenArray1d<ETO>(call_frame, m_out) =
+                        EigenArray1d<ETI>(call_frame, m_arg).template cast<typename ETO::type>();
+                }
+
+            protected:
+                TensorViewInfo m_arg;
+                TensorViewInfo m_out;
+            };
+        }
+    }
+}

test/type_prop.cpp

@@ -237,9 +237,49 @@ TEST(type_prop, concat_deduce_elem_type_mismatch)
     }
 }
 
-//
-// Tests for dot product.
-//
+TEST(type_prop, convert_deduce)
+{
+    // Deduce type
+    auto param = make_shared<op::Parameter>(element::Float32::element_type(), Shape{2, 3, 4});
+    auto c = make_shared<op::Convert>(param, element::Int32::element_type());
+    c->propagate_types();
+    auto c_vt = c->get_value_type();
+    ASSERT_EQ(*c_vt, TensorViewType(element::Int32::element_type(), Shape{2, 3, 4}));
+}
+
+TEST(type_prop, convert_deduce_correct)
+{
+    // Check deduced type against incorrectly specified type
+    auto param = make_shared<op::Parameter>(element::Float32::element_type(), Shape{2, 3, 4});
+    auto c = make_shared<op::Convert>(param, element::Int32::element_type());
+    c->set_value_type(make_shared<TensorViewType>(element::Int32::element_type(), Shape{2, 3, 4}));
+    c->propagate_types();
+    auto c_vt = c->get_value_type();
+    ASSERT_EQ(*c_vt, TensorViewType(element::Int32::element_type(), Shape{2, 3, 4}));
+}
+
+TEST(type_prop, convert_deduce_incorrect)
+{
+    // Check deduced type against incorrectly specified type
+    auto param = make_shared<op::Parameter>(element::Float32::element_type(), Shape{2, 3, 4});
+    auto c = make_shared<op::Convert>(param, element::Int32::element_type());
+    c->set_value_type(make_shared<TensorViewType>(element::Int32::element_type(), Shape{2, 14, 4}));
+    try
+    {
+        c->propagate_types();
+        // Should have thrown, so fail if it didn't
+        FAIL() << "Deduced type should disagree with specified type";
+    }
+    catch (const ngraph_error& error)
+    {
+        EXPECT_EQ(error.what(), std::string("Setting value type to a different ValueType"));
+    }
+    catch (...)
+    {
+        FAIL() << "Deduced type check failed for unexpected reason";
+    }
+}
+
 TEST(type_prop, dot_deduce_scalar_2d)
 {
     // Deduce type for scalar/matrix arguments