Switch some get_inputs uses to use the newer inputs (#2968)

* Switch some get_inputs uses to use the newer inputs

* Review comments

src/ngraph/builder/quantization/quantized_linear_matmul.cpp

@@ -84,7 +84,7 @@ namespace ngraph
                         output_zero_point->get_element_type(),
                         axes,
                         op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN);
-                    return q_dot;
+                    return move(q_dot);
                 }
             }
 

src/ngraph/op/fused/group_conv.cpp

@@ -99,11 +99,11 @@ Shape op::GroupConvolution::get_weights_dimensions() const
     const size_t OC = 0;
     const size_t OC_IN_OUTPUT = 1;
     const size_t IC = 1;
-    Shape weights_shape_groups{get_inputs().at(1).get_shape()};
+    Shape weights_shape_groups{get_input_shape(1)};
     // adjust output and channel given a number of groups
 
     weights_shape_groups.at(OC) = get_shape().at(OC_IN_OUTPUT) / get_groups();
-    weights_shape_groups.at(IC) = get_inputs().at(0).get_shape().at(IC) / get_groups();
+    weights_shape_groups.at(IC) = get_input_shape(0).at(IC) / get_groups();
     // push_front the number of groups
     weights_shape_groups.insert(weights_shape_groups.begin(), get_groups());
     return weights_shape_groups;

src/ngraph/pattern/matcher.cpp

@@ -87,7 +87,7 @@ namespace ngraph
                 for (auto entry : m_pattern_map)
                 {
                     // leaf label
-                    if (entry.first->get_inputs().empty())
+                    if (entry.first->get_input_size() == 0)
                     {
                         label_exclusions.push_back(entry.second);
                     }

src/ngraph/runtime/cpu/builder/convolution.cpp

@@ -255,7 +255,7 @@ namespace ngraph
                 auto arg2_buffer_index = external_function->get_buffer_index(args[2].get_name());
                 auto arg3_buffer_index = external_function->get_buffer_index(args[3].get_name());
                 auto out_buffer_index = external_function->get_buffer_index(out[0].get_name());
-                size_t arg3_size = node->get_inputs()[3].get_tensor().size();
+                size_t arg3_size = node->input(3).get_tensor().size();
 
                 if (runtime::cpu::mkldnn_utils::use_mkldnn_kernel(node))
                 {
@@ -324,7 +324,7 @@ namespace ngraph
                 auto arg1_buffer_index = external_function->get_buffer_index(args[1].get_name());
                 auto arg2_buffer_index = external_function->get_buffer_index(args[2].get_name());
                 auto out_buffer_index = external_function->get_buffer_index(out[0].get_name());
-                size_t arg2_size = node->get_inputs()[2].get_tensor().size();
+                size_t arg2_size = node->input(2).get_tensor().size();
 
                 if (runtime::cpu::mkldnn_utils::use_mkldnn_kernel(node))
                 {

src/ngraph/runtime/cpu/builder/quantized_conv.cpp

@@ -327,7 +327,7 @@ namespace ngraph
                     auto arg5_buffer_index =
                         external_function->get_buffer_index(args[5].get_name());
                     auto out0_buffer_index = external_function->get_buffer_index(out[0].get_name());
-                    size_t arg3_size = node->get_inputs()[3].get_tensor().size();
+                    size_t arg3_size = node->input(3).get_tensor().size();
 
                     auto scales_size = shape_size(args[4].get_shape());
                     auto sum_scales_size = shape_size(args[5].get_shape());
@@ -447,7 +447,7 @@ namespace ngraph
                     auto arg5_buffer_index =
                         external_function->get_buffer_index(args[5].get_name());
                     auto out0_buffer_index = external_function->get_buffer_index(out[0].get_name());
-                    size_t arg3_size = node->get_inputs()[3].get_tensor().size();
+                    size_t arg3_size = node->input(3).get_tensor().size();
 
                     auto scales_size = shape_size(args[4].get_shape());
                     auto sum_scales_size = shape_size(args[5].get_shape());

src/ngraph/runtime/cpu/cpu_visualize_tree.cpp

@@ -31,7 +31,7 @@ static void visualize_layout_format(const Node& node, ostream& ss)
 {
     try
     {
-        auto input_desc = node.get_inputs().at(0).get_tensor().get_tensor_layout();
+        auto input_desc = node.input(0).get_tensor().get_tensor_layout();
         auto result_desc = node.get_output_tensor_ptr()->get_tensor_layout();
 
         auto in_tvl = static_pointer_cast<runtime::cpu::LayoutDescriptor>(input_desc);

src/ngraph/runtime/cpu/op/batch_mat_mul_transpose.cpp

@@ -94,8 +94,8 @@ void op::BatchMatMulTranspose::generate_adjoints(autodiff::Adjoints& adjoints,
 {
     auto delta = deltas.at(0); // NxIxK
 
-    auto arg0 = get_inputs().at(0).get_output().get_node(); // NxIxJ (maybe transposed)
-    auto arg1 = get_inputs().at(1).get_output().get_node(); // NxJxK (maybe transposed)
+    auto arg0 = input(0).get_source_output().get_node_shared_ptr(); // NxIxJ (maybe transposed)
+    auto arg1 = input(1).get_source_output().get_node_shared_ptr(); // NxJxK (maybe transposed)
 
     // If arg1 is already transposed, it does not need to be transposed again
     auto delta_dot_arg1 =

src/ngraph/runtime/cpu/op/group_conv_bias.cpp

@@ -85,10 +85,10 @@ Shape op::GroupConvolutionBias::get_weights_dimensions()
     const size_t OC_IN_OUTPUT = 1;
     const size_t IC = 1;
 
-    Shape weights_shape_groups{get_inputs().at(1).get_shape()};
+    Shape weights_shape_groups{get_input_shape(1)};
 
     weights_shape_groups.at(OC) = get_shape().at(OC_IN_OUTPUT) / get_groups();
-    weights_shape_groups.at(IC) = get_inputs().at(0).get_shape().at(IC) / get_groups();
+    weights_shape_groups.at(IC) = get_input_shape(0).at(IC) / get_groups();
 
     // push_front the number of groups
     weights_shape_groups.insert(weights_shape_groups.begin(), get_groups());

src/ngraph/runtime/cpu/pass/cpu_fusion.cpp

@@ -534,8 +534,12 @@ void ngraph::runtime::cpu::pass::CPUFusion::construct_batch_norm_relu()
                      << m.get_match_root()->get_name();
 
         auto pattern_map = m.get_pattern_map();
-        auto m_bn = std::static_pointer_cast<ngraph::op::BatchNormTraining>(
-            m.get_match_root()->get_argument(0)->get_inputs().at(0).get_output().get_node());
+        auto m_bn =
+            std::static_pointer_cast<ngraph::op::BatchNormTraining>(m.get_match_root()
+                                                                        ->get_argument(0)
+                                                                        ->input(0)
+                                                                        .get_source_output()
+                                                                        .get_node_shared_ptr());
 
         if (!mkldnn_utils::can_use_mkldnn_batchnorm_fprop(m_bn.get()))
         {
@@ -603,7 +607,7 @@ void ngraph::runtime::cpu::pass::CPUFusion::construct_batch_norm_relu_global_sta
 
         auto pattern_map = m.get_pattern_map();
 
-        auto bn_match = m.get_match_root()->get_inputs().at(0).get_output().get_node();
+        auto bn_match = m.get_match_root()->input(0).get_source_output().get_node_shared_ptr();
         if (bn_match->get_users().size() > 1)
         {
             NGRAPH_DEBUG << "Relu isn't the only user of BatchNorm's output";