CPU: Implement column-wise and row-wise broadcast

src/ngraph/runtime/cpu/emitter.cpp

@@ -204,7 +204,7 @@ void Emitter::EMITTER_DECL(EmitDot)
     }
     else
     {
-        throw ngraph_error("Dot product for given tensors unimplemented");
+        throw ngraph_error("Dot product not implemented for given inputs");
     }
 }
 
@@ -811,6 +811,45 @@ void Emitter::EMITTER_DECL(EmitBroadcast)
                        EIGEN_VECTOR_FORMAT(inputs[0].get_layout<DenseTensorViewLayout>()->get_size()) ")(0, 0);\n"
               "    }\n";
     }
+    else if (arg_shape.size() == 1 && result_shape.size() == 2)
+    {
+        if (broadcast->get_broadcast_axes() == AxisSet{1})
+        {
+            auto out_layout = outputs[0].get_layout<DenseTensorViewLayout>();
+
+            TU += "    {\n"
+                  "        auto arg0 = call_frame->get_tensor_view_data<" + element_type_names[TI(result_element_type)] +
+                           ">(" + to_string(inputs[0].get_index()) + ");\n"
+                  "        auto out  = call_frame->get_tensor_view_data<" + element_type_names[TI(result_element_type)] +
+                           ">(" + to_string(outputs[0].get_index()) + ");\n"
+                  "        EigenMatrix<" + element_type_names[TI(result_element_type)] + ">(out, " +
+                           EIGEN_MATRIX_FORMAT(out_layout->get_shape(), out_layout->get_strides()) + ").colwise() =\n"
+                  "        EigenVector<" + element_type_names[TI(result_element_type)] + ">(arg0, "
+                           EIGEN_VECTOR_FORMAT(inputs[0].get_layout<DenseTensorViewLayout>()->get_size()) ");\n"
+                  "    }\n";
+        }
+        else if (broadcast->get_broadcast_axes() == AxisSet{0})
+        {
+            auto out_layout = outputs[0].get_layout<DenseTensorViewLayout>();
+
+            TU += "    {\n"
+                  "        auto arg0 = call_frame->get_tensor_view_data<" + element_type_names[TI(result_element_type)] +
+                           ">(" + to_string(inputs[0].get_index()) + ");\n"
+                  "        auto out  = call_frame->get_tensor_view_data<" + element_type_names[TI(result_element_type)] +
+                           ">(" + to_string(outputs[0].get_index()) + ");\n"
+                  "        EigenMatrix<" + element_type_names[TI(result_element_type)] + ">(out, " +
+                           EIGEN_MATRIX_FORMAT(out_layout->get_shape(), out_layout->get_strides()) + ").rowwise() =\n"
+                  "        EigenVector<" + element_type_names[TI(result_element_type)] + ">(arg0, "
+                           EIGEN_VECTOR_FORMAT(inputs[0].get_layout<DenseTensorViewLayout>()->get_size()) ").transpose();\n"
+                  "    }\n";
+        }
+        else
+        {
+            throw ngraph_error(
+                "Internal error: axis set for vector-matrix broadcast is neither {0} nor "
+                "{1}");
+        }
+    }
     else
     {
         throw ngraph_error("Broadcast not implemented for given inputs");

test/cpu.cpp

@@ -1221,8 +1221,7 @@ TEST(cpu, broadcast_trivial)
     ASSERT_EQ((vector<float>{2, 4, 6, 8, 16, 32, 64, 128}), result->get_vector());
 }
 
-/*
-TEST(execute, broadcast_vector_colwise)
+TEST(cpu, broadcast_vector_colwise)
 {
     auto shape_a = Shape{3};
     auto A = make_shared<op::Parameter>(element::Float32::element_type(), shape_a);
@@ -1231,7 +1230,7 @@ TEST(execute, broadcast_vector_colwise)
     auto f = make_shared<Function>(
         make_shared<op::Broadcast>(A, shape_r, AxisSet{1}), rt, op::Parameters{A});
 
-    auto manager = runtime::Manager::get("NGVM");
+    auto manager = runtime::Manager::get("CPU");
     auto external = manager->compile(f);
     auto backend = manager->allocate_backend();
     auto cf = backend->make_call_frame(external);
@@ -1245,7 +1244,7 @@ TEST(execute, broadcast_vector_colwise)
     ASSERT_EQ((vector<float>{1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3}), result->get_vector());
 }
 
-TEST(execute, broadcast_vector_rowwise)
+TEST(cpu, broadcast_vector_rowwise)
 {
     auto shape_a = Shape{4};
     auto A = make_shared<op::Parameter>(element::Float32::element_type(), shape_a);
@@ -1254,7 +1253,7 @@ TEST(execute, broadcast_vector_rowwise)
     auto f = make_shared<Function>(
         make_shared<op::Broadcast>(A, shape_r, AxisSet{0}), rt, op::Parameters{A});
 
-    auto manager = runtime::Manager::get("NGVM");
+    auto manager = runtime::Manager::get("CPU");
     auto external = manager->compile(f);
     auto backend = manager->allocate_backend();
     auto cf = backend->make_call_frame(external);
@@ -1268,7 +1267,7 @@ TEST(execute, broadcast_vector_rowwise)
     ASSERT_EQ((vector<float>{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4}), result->get_vector());
 }
 
-TEST(execute, broadcast_vector_rowwise_int64)
+TEST(cpu, broadcast_vector_rowwise_int64)
 {
     auto shape_a = Shape{4};
     auto A = make_shared<op::Parameter>(element::Int64::element_type(), shape_a);
@@ -1277,7 +1276,7 @@ TEST(execute, broadcast_vector_rowwise_int64)
     auto f = make_shared<Function>(
         make_shared<op::Broadcast>(A, shape_r, AxisSet{0}), rt, op::Parameters{A});
 
-    auto manager = runtime::Manager::get("NGVM");
+    auto manager = runtime::Manager::get("CPU");
     auto external = manager->compile(f);
     auto backend = manager->allocate_backend();
     auto cf = backend->make_call_frame(external);
@@ -1292,6 +1291,7 @@ TEST(execute, broadcast_vector_rowwise_int64)
               result->get_vector());
 }
 
+/*
 TEST(execute, convert_int32_float32)
 {
     auto shape = Shape{2, 2};