Fix numeric deriv, add broadcast, reshape, dot, max, min backprop (#211)

src/ngraph/CMakeLists.txt

@@ -41,6 +41,8 @@ set (SRC
     ops/function_call.cpp
     ops/get_tuple_element.cpp
     ops/log.cpp
+    ops/maximum.cpp
+    ops/minimum.cpp
     ops/multiply.cpp
     ops/negative.cpp
     ops/op.cpp

src/ngraph/autodiff/numeric_derivative.cpp

@@ -72,8 +72,7 @@ std::vector<std::shared_ptr<ngraph::runtime::ParameterizedTensorView<ET>>>
     for (size_t i = 0; i < args.size(); ++i)
     {
         auto arg = args[i];
-        auto df_darg = results[i];
-        auto df_darg_it = df_darg->get_vector().begin();
+        auto& res = results[i]->get_vector();
         auto& vec = arg->get_vector();
         for (size_t j = 0; j < vec.size(); j++)
         {
@@ -81,13 +80,14 @@ std::vector<std::shared_ptr<ngraph::runtime::ParameterizedTensorView<ET>>>
             vec[j] += delta;
             cf->tensor_call(args_tv, {inc_y});
             vec[j] = old_val;
-            df_darg_it = std::transform(inc_vec.begin(),
-                                        inc_vec.end(),
-                                        ref_vec.begin(),
-                                        df_darg_it,
-                                        [inv_delta](typename ET::type y1, typename ET::type y0) {
-                                            return inv_delta * (y1 - y0);
-                                        });
+            size_t res_k = j;
+            for (size_t k = 0; k < inc_vec.size(); k++)
+            {
+                auto y1 = inc_vec[k];
+                auto y0 = ref_vec[k];
+                res[res_k] = inv_delta * (y1 - y0);
+                res_k += vec.size();
+            }
         }
     }
     return results;

src/ngraph/node.hpp

@@ -117,6 +117,8 @@ namespace ngraph
         std::shared_ptr<Node> backprop_node(const std::shared_ptr<Node>& x,
                                             const std::shared_ptr<Node>& c);
 
+        /// Returns the shape if this node has tensor type, othetwise error.
+        const Shape& get_shape() const { return m_value_type->get_shape(); }
     protected:
         Nodes m_arguments;
         std::shared_ptr<const ValueType> m_value_type;

src/ngraph/ops/broadcast.cpp

@@ -13,6 +13,7 @@
 // ----------------------------------------------------------------------------
 
 #include "ngraph/ops/broadcast.hpp"
+#include "ngraph/ops/sum.hpp"
 
 using namespace std;
 using namespace ngraph::op;
@@ -46,3 +47,11 @@ void Broadcast::propagate_types()
     set_value_type_checked(
         make_shared<TensorViewType>(arg_tensor_view_type->get_element_type(), m_shape));
 }
+
+void ngraph::op::Broadcast::generate_adjoints(autodiff::Adjoints& adjoints,
+                                              const std::shared_ptr<Node>& delta)
+{
+    auto x = m_arguments[0];
+
+    adjoints.add_delta(x, make_shared<op::Sum>(delta, m_broadcast_axes));
+}

src/ngraph/ops/broadcast.hpp

@@ -80,6 +80,10 @@ namespace ngraph
             /// \return An set containing the indices of the broadcast axes (0-based).
             const AxisSet& get_broadcast_axes() const { return m_broadcast_axes; }
         protected:
+        protected:
+            virtual void generate_adjoints(autodiff::Adjoints& adjoints,
+                                           const std::shared_ptr<Node>& delta) override;
+
             Shape m_shape;
             AxisSet m_broadcast_axes;
         };

src/ngraph/ops/dot.cpp

@@ -12,9 +12,15 @@
 // See the License for the specific language governing permissions and
 // ----------------------------------------------------------------------------
 
+#include <functional>
 #include <memory>
 
+#include "ngraph/ops/broadcast.hpp"
 #include "ngraph/ops/dot.hpp"
+#include "ngraph/ops/multiply.hpp"
+#include "ngraph/ops/reshape.hpp"
+#include "ngraph/ops/sum.hpp"
+#include "ngraph/shape.hpp"
 
 using namespace std;
 using namespace ngraph::op;
@@ -76,3 +82,166 @@ void Dot::propagate_types()
         make_shared<TensorViewType>(arg0_tensor_type->get_element_type(), result_shape);
     set_value_type_checked(result_type);
 }
+
+template <typename T>
+T range(size_t n);
+
+template <>
+ngraph::AxisSet range<ngraph::AxisSet>(size_t n)
+{
+    ngraph::AxisSet result;
+    for (size_t i = 0; i < n; i++)
+    {
+        result.insert(i);
+    }
+    return result;
+}
+
+template <>
+ngraph::AxisVector range<ngraph::AxisVector>(size_t n)
+{
+    ngraph::AxisVector result;
+    for (size_t i = 0; i < n; i++)
+    {
+        result.push_back(i);
+    }
+    return result;
+}
+
+void ngraph::op::Dot::generate_adjoints(autodiff::Adjoints& adjoints,
+                                        const std::shared_ptr<Node>& delta)
+{
+    auto x = m_arguments[0];
+    auto y = m_arguments[1];
+
+    auto x_shape = x->get_shape();
+    auto y_shape = y->get_shape();
+    auto delta_shape = delta->get_shape();
+
+    if (is_scalar(x_shape))
+    {
+        adjoints.add_delta(y, make_shared<Dot>(delta, x));
+        if (is_scalar(y_shape))
+        {
+            // Just multiplication
+            adjoints.add_delta(x, delta * y);
+            return;
+        }
+        // scale dot tensor
+        adjoints.add_delta(x, make_shared<Sum>(delta * y, range<AxisSet>(y_shape.size())));
+        return;
+    }
+    if (is_scalar(y_shape))
+    {
+        // tensor dot scalar
+        adjoints.add_delta(x, make_shared<Dot>(delta, y));
+        adjoints.add_delta(y, make_shared<Sum>(delta * x, range<AxisSet>(x_shape.size())));
+        return;
+    }
+    if (is_vector(y_shape))
+    {
+        if (is_vector(x_shape))
+        {
+            adjoints.add_delta(x, make_shared<Dot>(delta, y));
+        }
+        else
+        {
+            // X has shape IJ, Y has shape J, delta has shape I
+            // delta -> (I, 1)
+            // Y -> (1, J)
+            // delta . Y is (I, J)
+            Shape shape_delta_1 = delta->get_shape();
+            shape_delta_1.push_back(1);
+            auto delta_1 =
+                make_shared<Broadcast>(delta, shape_delta_1, AxisSet{delta->get_shape().size()});
+            Shape shape_1_y{1};
+            shape_1_y.insert(shape_1_y.end(), y_shape.begin(), y_shape.end());
+            auto y_1 = make_shared<Broadcast>(y, shape_1_y, AxisSet{0});
+            adjoints.add_delta(x, make_shared<Dot>(delta_1, y_1));
+        }
+        // X has shape IJ
+        // Y has shape J
+        // delta has shape I
+        // Need to move J to front of X and multiply by Y
+        Shape shape_xt(x_shape.size());
+        AxisVector x_axes(x_shape.size());
+        shape_xt[0] = x_shape.at(x_shape.size() - 1);
+        x_axes[0] = x_shape.size() - 1;
+        for (size_t i = 1; i < x_shape.size(); ++i)
+        {
+            shape_xt[i] = x_shape[i - 1];
+            x_axes[i] = i - 1;
+        }
+        auto x_reshape = make_shared<Reshape>(x, x_axes, shape_xt);
+        adjoints.add_delta(y, make_shared<Dot>(x_reshape, delta));
+        return;
+    }
+    // Tensor tensor case
+    // X is Ij
+    // Y = Kjl
+    // X.Y, delta is IKl
+    //
+    // delta -> I(Kl)
+    // Y -> (Kl)j
+    // delta.Y -> Ij
+    Shape s_I;
+    s_I.insert(s_I.begin(), x_shape.begin(), x_shape.end() - 1);
+    size_t s_j = x_shape[x_shape.size() - 1];
+    Shape s_K;
+    s_K.insert(s_K.begin(), y_shape.begin(), y_shape.end() - 2);
+    size_t s_l = y_shape[y_shape.size() - 1];
+    size_t s_Kl = shape_size(s_K) * s_l;
+
+    Shape shape_delta_I_Kl;
+    shape_delta_I_Kl.insert(shape_delta_I_Kl.end(), s_I.begin(), s_I.end());
+    shape_delta_I_Kl.push_back(s_Kl);
+    AxisVector idx_delta_I_Kl = range<AxisVector>(delta_shape.size());
+    auto delta_I_Kl = make_shared<Reshape>(delta, idx_delta_I_Kl, shape_delta_I_Kl);
+
+    Shape shape_y_Kl_j{s_Kl, s_j};
+    AxisVector idx_y_Kl_j = range<AxisVector>(y_shape.size() - 2);
+    idx_y_Kl_j.push_back(y_shape.size() - 1);
+    idx_y_Kl_j.push_back(y_shape.size() - 2);
+    auto y_Kl_j = make_shared<Reshape>(y, idx_y_Kl_j, shape_y_Kl_j);
+    adjoints.add_delta(x, make_shared<Dot>(delta_I_Kl, y_Kl_j));
+
+    // delta -> K(I)l
+    // X -> j(I)
+    // X.delta -> jKl -> Kjl
+    Shape shape_delta_K_I_l;
+    shape_delta_K_I_l.insert(shape_delta_K_I_l.begin(), s_K.begin(), s_K.end());
+    shape_delta_K_I_l.push_back(shape_size(s_I));
+    shape_delta_K_I_l.push_back(s_l);
+    AxisVector idx_delta = range<AxisVector>(delta_shape.size());
+    AxisVector idx_delta_K_I_l;
+    idx_delta_K_I_l.insert(idx_delta_K_I_l.end(),
+                           idx_delta.begin() + s_I.size(),
+                           idx_delta.begin() + s_I.size() + s_K.size());
+    idx_delta_K_I_l.insert(
+        idx_delta_K_I_l.end(), idx_delta.begin(), idx_delta.begin() + s_I.size());
+    idx_delta_K_I_l.push_back(delta_shape.size() - 1);
+    auto delta_K_I_l = make_shared<Reshape>(delta, idx_delta_K_I_l, shape_delta_K_I_l);
+
+    Shape shape_x_j_I;
+    shape_x_j_I.push_back(s_j);
+    shape_x_j_I.push_back(shape_size(s_I));
+    AxisVector idx_x = range<AxisVector>(x_shape.size());
+    AxisVector idx_x_j_I;
+    idx_x_j_I.push_back(idx_x[idx_x.size() - 1]);
+    idx_x_j_I.insert(idx_x_j_I.end(), idx_x.begin(), idx_x.begin() + idx_x.size() - 1);
+    auto x_j_I = make_shared<Reshape>(x, idx_x_j_I, shape_x_j_I);
+    auto jKl = make_shared<Dot>(x_j_I, delta_K_I_l);
+    Shape shape_Kjl;
+    shape_Kjl.insert(shape_Kjl.end(), s_K.begin(), s_K.end());
+    shape_Kjl.push_back(s_j);
+    shape_Kjl.push_back(s_l);
+    AxisVector idx_Kjl;
+    for (size_t i = 1; i < s_K.size() + 1; ++i)
+    {
+        idx_Kjl.push_back(i);
+    }
+    idx_Kjl.push_back(0);
+    idx_Kjl.push_back(y_shape.size() - 1);
+    auto Klj = make_shared<Reshape>(jKl, idx_Kjl, shape_Kjl);
+    adjoints.add_delta(y, Klj);
+}

src/ngraph/ops/dot.hpp

@@ -116,6 +116,10 @@ namespace ngraph
 
             virtual std::string description() const override { return "Dot"; }
             virtual void propagate_types() override;
+
+        protected:
+            virtual void generate_adjoints(autodiff::Adjoints& adjoints,
+                                           const std::shared_ptr<Node>& delta) override;
         };
     }
 }

src/ngraph/ops/maximum.cpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// ----------------------------------------------------------------------------
+
+#include <memory>
+
+#include "ngraph/ops/convert.hpp"
+#include "ngraph/ops/greater.hpp"
+#include "ngraph/ops/maximum.hpp"
+#include "ngraph/ops/multiply.hpp"
+#include "ngraph/types/element_type.hpp"
+
+using namespace std;
+using namespace ngraph;
+
+void ngraph::op::Maximum::generate_adjoints(autodiff::Adjoints& adjoints,
+                                            const std::shared_ptr<Node>& delta)
+{
+    auto x = m_arguments[0];
+    auto y = m_arguments[1];
+
+    adjoints.add_delta(x,
+                       delta * make_shared<op::Convert>(make_shared<op::Greater>(x, y),
+                                                        element::Float32::element_type()));
+    adjoints.add_delta(y,
+                       delta * make_shared<op::Convert>(make_shared<op::Greater>(y, x),
+                                                        element::Float32::element_type()));
+}

src/ngraph/ops/maximum.hpp

@@ -52,6 +52,9 @@ namespace ngraph
             {
             }
             virtual std::string description() const override { return "Maximum"; }
+        protected:
+            virtual void generate_adjoints(autodiff::Adjoints& adjoints,
+                                           const std::shared_ptr<Node>& delta) override;
         };
     }
 }

src/ngraph/ops/minimum.cpp

+// ----------------------------------------------------------------------------
+// Copyright 2017 Nervana Systems Inc.
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// ----------------------------------------------------------------------------
+
+#include <memory>
+
+#include "ngraph/ops/convert.hpp"
+#include "ngraph/ops/less.hpp"
+#include "ngraph/ops/minimum.hpp"
+#include "ngraph/ops/multiply.hpp"
+#include "ngraph/types/element_type.hpp"
+
+using namespace std;
+using namespace ngraph;
+
+void ngraph::op::Minimum::generate_adjoints(autodiff::Adjoints& adjoints,
+                                            const std::shared_ptr<Node>& delta)
+{
+    auto x = m_arguments[0];
+    auto y = m_arguments[1];
+
+    adjoints.add_delta(x,
+                       delta * make_shared<op::Convert>(make_shared<op::Less>(x, y),
+                                                        element::Float32::element_type()));
+    adjoints.add_delta(y,
+                       delta * make_shared<op::Convert>(make_shared<op::Less>(y, x),
+                                                        element::Float32::element_type()));
+}

src/ngraph/ops/minimum.hpp

@@ -52,6 +52,9 @@ namespace ngraph
             {
             }
             virtual std::string description() const override { return "Minimum"; }
+        protected:
+            virtual void generate_adjoints(autodiff::Adjoints& adjoints,
+                                           const std::shared_ptr<Node>& delta) override;
         };
     }
 }

src/ngraph/ops/reshape.cpp

@@ -78,3 +78,42 @@ void Reshape::propagate_types()
     set_value_type_checked(
         make_shared<TensorViewType>(arg_tensor_view_type->get_element_type(), m_output_shape));
 }
+
+void ngraph::op::Reshape::generate_adjoints(autodiff::Adjoints& adjoints,
+                                            const std::shared_ptr<Node>& delta)
+{
+    auto x = m_arguments[0];
+    auto x_type = x->get_value_type();
+    auto x_tensor_view_type = dynamic_pointer_cast<const TensorViewType>(x_type);
+    if (nullptr == x_type)
+    {
+        throw ngraph_error("Argument to reshape is not a tensor view");
+    }
+    auto x_shape = x_tensor_view_type->get_shape();
+    auto x_rank = x_shape.size();
+    Shape permuted_x_shape(x_rank);
+    AxisVector x_input_order(x_rank);
+    bool is_permuted = false;
+    for (size_t i = 0; i < x_rank; ++i)
+    {
+        size_t permuted_i = m_input_order[i];
+        if (i != permuted_i)
+        {
+            is_permuted = true;
+        }
+        permuted_x_shape[i] = x_shape[permuted_i];
+        x_input_order[permuted_i] = i;
+    }
+    AxisVector input_order(m_output_shape.size());
+    for (size_t i = 0; i < m_output_shape.size(); i++)
+    {
+        input_order[i] = i;
+    }
+    auto reshape = make_shared<op::Reshape>(delta, input_order, permuted_x_shape);
+    if (is_permuted)
+    {
+        reshape = make_shared<op::Reshape>(reshape, x_input_order, x_shape);
+    }
+
+    adjoints.add_delta(x, reshape);
+}

src/ngraph/ops/reshape.hpp

@@ -86,6 +86,9 @@ namespace ngraph
             /// \return The shape of the output tensor.
             const Shape& get_output_shape() const { return m_output_shape; }
         protected:
+            virtual void generate_adjoints(autodiff::Adjoints& adjoints,
+                                           const std::shared_ptr<Node>& delta) override;
+
             const AxisVector m_input_order;
             const Shape m_output_shape;
         };

src/ngraph/runtime/ngvm/eigen/minimum.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/ngvm/call_frame.hpp"
+#include "ngraph/runtime/ngvm/eigen/utils.hpp"
+#include "ngraph/runtime/ngvm/instruction.hpp"
+#include "ngraph/runtime/tensor_view.hpp"
+
+namespace ngraph
+{
+    namespace runtime
+    {
+        namespace ngvm
+        {
+            namespace eigen
+            {
+                template <typename ET>
+                class MinimumInstruction : public Instruction
+                {
+                public:
+                    MinimumInstruction(TensorViewInfo arg0, TensorViewInfo arg1, TensorViewInfo out)
+                        : m_arg0(arg0)
+                        , m_arg1(arg1)
+                        , m_out(out)
+                    {
+                    }
+
+                    virtual void execute(CallFrame& call_frame) const override
+                    {
+                        EigenArray1d<ET>(call_frame, m_out) =
+                            EigenArray1d<ET>(call_frame, m_arg0)
+                                .min(EigenArray1d<ET>(call_frame, m_arg1));
+                    }
+
+                protected:
+                    TensorViewInfo m_arg0;
+                    TensorViewInfo m_arg1;
+                    TensorViewInfo m_out;
+                };
+            }
+        }
+    }
+}

src/ngraph/runtime/ngvm/external_function.cpp

@@ -46,6 +46,7 @@
 #include "ngraph/ops/less_eq.hpp"
 #include "ngraph/ops/log.hpp"
 #include "ngraph/ops/maximum.hpp"
+#include "ngraph/ops/minimum.hpp"
 #include "ngraph/ops/multiply.hpp"
 #include "ngraph/ops/negative.hpp"
 #include "ngraph/ops/not_equal.hpp"
@@ -95,6 +96,7 @@
 #include "ngraph/runtime/ngvm/eigen/matrix_transpose.hpp"
 #include "ngraph/runtime/ngvm/eigen/matrix_vector_product.hpp"
 #include "ngraph/runtime/ngvm/eigen/maximum.hpp"
+#include "ngraph/runtime/ngvm/eigen/minimum.hpp"
 #include "ngraph/runtime/ngvm/eigen/multiply.hpp"
 #include "ngraph/runtime/ngvm/eigen/negate.hpp"
 #include "ngraph/runtime/ngvm/eigen/not_equal.hpp"
@@ -370,6 +372,7 @@ ExternalFunction::OpMap& ExternalFunction::get_op_map()
         REGISTER_NUMERIC_BINOP(op::Less, eigen::LessThanInstruction);
         REGISTER_NUMERIC_BINOP(op::LessEq, eigen::LessEqInstruction);
         REGISTER_NUMERIC_BINOP(op::Maximum, eigen::MaximumInstruction);
+        REGISTER_NUMERIC_BINOP(op::Minimum, eigen::MinimumInstruction);
         REGISTER_NUMERIC_BINOP(op::Multiply, eigen::MultiplyInstruction);
         REGISTER_NUMERIC_BINOP(op::Subtract, eigen::SubtractInstruction);
 

src/ngraph/shape.hpp

@@ -27,4 +27,7 @@ namespace ngraph
 
     /// Row-major strides for a shape
     Strides row_major_strides(const Shape& shape);
+
+    inline bool is_scalar(const Shape& shape) { return 0 == shape.size(); }
+    inline bool is_vector(const Shape& shape) { return 1 == shape.size(); }
 }

src/ngraph/types/type.cpp

@@ -14,8 +14,9 @@
 
 #include <memory>
 
+#include "ngraph/except.hpp"
 #include "ngraph/log.hpp"
-#include "ngraph/ngraph.hpp"
+#include "ngraph/types/type.hpp"
 #include "ngraph/util.hpp"
 
 using namespace std;
@@ -64,6 +65,11 @@ void TupleType::collect_tensor_views(
     }
 }
 
+const Shape& TupleType::get_shape() const
+{
+    throw ngraph_error("get_shape() called on Tuple");
+}
+
 std::ostream& ngraph::operator<<(std::ostream& out, const ValueType& obj)
 {
     out << "ValueType()";

src/ngraph/types/type.hpp

@@ -42,6 +42,7 @@ namespace ngraph
         /// Add tensor views in depth-first order.
         virtual void collect_tensor_views(
             std::vector<std::shared_ptr<const TensorViewType>>& views) const = 0;
+        virtual const Shape& get_shape() const = 0;
         friend std::ostream& operator<<(std::ostream&, const ValueType&);
     };
 
@@ -59,7 +60,7 @@ namespace ngraph
         }
 
         const element::Type& get_element_type() const { return m_element_type; }
-        const Shape& get_shape() const { return m_shape; }
+        virtual const Shape& get_shape() const override { return m_shape; }
         virtual bool operator==(const ValueType& that) const override;
         virtual void collect_tensor_views(
             std::vector<std::shared_ptr<const TensorViewType>>& views) const override;
@@ -95,6 +96,7 @@ namespace ngraph
         virtual bool operator==(const ValueType& that) const override;
         virtual void collect_tensor_views(
             std::vector<std::shared_ptr<const TensorViewType>>& views) const override;
+        virtual const Shape& get_shape() const override;
         friend std::ostream& operator<<(std::ostream&, const TupleType&);
 
     protected:

test/execute.cpp

@@ -898,6 +898,30 @@ TEST(execute, maximum)
     ASSERT_EQ((vector<float>{1, 8, 4, 17, 0, 0.5, 2, 1.5}), result->get_vector());
 }
 
+TEST(execute, minimum)
+{
+    auto shape = Shape{2, 2, 2};
+    auto A = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto B = make_shared<op::Parameter>(element::Float32::element_type(), shape);
+    auto rt = make_shared<TensorViewType>(element::Float32::element_type(), shape);
+    auto f = make_shared<Function>(make_shared<op::Minimum>(A, B), rt, op::Parameters{A, B});
+
+    auto manager = runtime::Manager::get("NGVM");
+    auto external = manager->compile(f);
+    auto backend = manager->allocate_backend();
+    auto cf = backend->make_call_frame(external);
+
+    // Create some tensors for input/output
+    auto a = backend->make_parameterized_tensor_view<element::Float32>(shape);
+    *a = vector<float>{1, 8, -8, 17, -0.5, 0.5, 2, 1};
+    auto b = backend->make_parameterized_tensor_view<element::Float32>(shape);
+    *b = vector<float>{1, 2, 4, 8, 0, 0, 1, 1.5};
+    auto result = backend->make_parameterized_tensor_view<element::Float32>(shape);
+
+    (*cf)({a, b}, {result});
+    ASSERT_EQ((vector<float>{1, 2, -8, 8, -.5, 0, 1, 1}), result->get_vector());
+}
+
 TEST(execute, negative)
 {
     auto shape = Shape{2, 3};