[ONNX] Update Min, Max, Mean and Sum ops to use v1 version of ops (set_1 namespace) (#4157)

* Changes version from v0 to default for Min and Max ops.

* Added broadcast option in variadic function.

* Added tests.

* Added test for opset8.

* Undo changes for Mean op.
Co-authored-by: Tomasz Dołbniak <tomasz.dolbniak@intel.com>
Co-authored-by: Scott Cyphers <diyessi@users.noreply.github.com>

src/ngraph/frontend/onnx_import/op/max.hpp

@@ -19,7 +19,6 @@
 #include "core/node.hpp"
 #include "default_opset.hpp"
 #include "ngraph/node.hpp"
-#include "ngraph/opsets/opset0.hpp"
 #include "utils/variadic.hpp"
 
 namespace ngraph
@@ -32,7 +31,8 @@ namespace ngraph
             {
                 inline NodeVector max(const Node& node)
                 {
-                    return variadic::make_ng_variadic_op<ngraph::opset0::Maximum>(node);
+                    return variadic::make_ng_variadic_op<default_opset::Maximum>(
+                        node, ngraph::op::AutoBroadcastSpec::NONE);
                 }
 
             } // namespace set_1

src/ngraph/frontend/onnx_import/op/min.hpp

@@ -19,7 +19,7 @@
 #include "core/node.hpp"
 #include "default_opset.hpp"
 #include "ngraph/node.hpp"
-#include "ngraph/opsets/opset0.hpp"
+#include "ngraph/op/util/broadcasting.hpp"
 #include "utils/variadic.hpp"
 
 namespace ngraph
@@ -32,7 +32,8 @@ namespace ngraph
             {
                 inline NodeVector min(const Node& node)
                 {
-                    return variadic::make_ng_variadic_op<ngraph::opset0::Minimum>(node);
+                    return variadic::make_ng_variadic_op<default_opset::Minimum>(
+                        node, ngraph::op::AutoBroadcastSpec::NONE);
                 }
 
             } // namespace set_1

src/ngraph/frontend/onnx_import/op/sum.hpp

@@ -19,6 +19,7 @@
 #include "core/node.hpp"
 #include "default_opset.hpp"
 #include "ngraph/node.hpp"
+#include "ngraph/op/util/broadcasting.hpp"
 #include "ngraph/opsets/opset0.hpp"
 #include "utils/variadic.hpp"
 
@@ -32,7 +33,8 @@ namespace ngraph
             {
                 inline NodeVector sum(const Node& node)
                 {
-                    return variadic::make_ng_variadic_op<ngraph::opset0::Add>(node);
+                    return variadic::make_ng_variadic_op<default_opset::Add>(
+                        node, ngraph::op::AutoBroadcastSpec::NONE);
                 }
 
             } // namespace set_1

src/ngraph/frontend/onnx_import/utils/variadic.hpp

@@ -40,18 +40,22 @@ namespace ngraph
             ///
             /// \return nGraph node equivalent of the ONNX operation
             template <class T>
-            inline NodeVector make_ng_variadic_op(const Node& node)
+            inline NodeVector
+                make_ng_variadic_op(const Node& node,
+                                    const ngraph::op::AutoBroadcastSpec& auto_broadcast =
+                                        ngraph::op::AutoBroadcastSpec::NUMPY)
             {
-                NodeVector ng_inputs{node.get_ng_inputs()};
+                const NodeVector ng_inputs{node.get_ng_inputs()};
 
                 // Templated binary operation - Creates Add, Minimum, Maximum, etc.
-                auto binary_operation = [](const std::shared_ptr<ngraph::Node>& arg0,
+                const auto binary_operation =
+                    [&auto_broadcast](const std::shared_ptr<ngraph::Node>& arg0,
                                       const std::shared_ptr<ngraph::Node>& arg1) {
-                    return std::make_shared<T>(arg0, arg1);
+                        return std::make_shared<T>(arg0, arg1, auto_broadcast);
                     };
 
                 // Create a result node as a series of binary operations
-                auto result = std::accumulate(
+                const auto result = std::accumulate(
                     std::next(std::begin(ng_inputs)), // First operand value - the second input
                     std::end(ng_inputs),              // Last value - final input
                     ng_inputs.front(),                // Initial value - first input

test/models/onnx/max.prototxt

@@ -29,7 +29,7 @@ graph {
         elem_type: 1
         shape {
           dim {
-            dim_value: 3
+            dim_value: 1
           }
         }
       }
@@ -63,5 +63,5 @@ graph {
   }
 }
 opset_import {
-  version: 7
+  version: 8
 }

test/models/onnx/max_opset1.prototxt

+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "data_0"
+    input: "data_1"
+    input: "data_2"
+    output: "result"
+    op_type: "Max"
+  }
+  name: "test_max_example"
+  input {
+    name: "data_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "data_1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "data_2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}

test/models/onnx/mean.prototxt

@@ -16,7 +16,7 @@ graph {
         elem_type: 1
         shape {
           dim {
-            dim_value: 3
+            dim_value: 1
           }
         }
       }
@@ -63,5 +63,5 @@ graph {
   }
 }
 opset_import {
-  version: 7
+  version: 8
 }

test/models/onnx/mean_opset1.prototxt

+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "data_0"
+    input: "data_1"
+    input: "data_2"
+    output: "result"
+    op_type: "Mean"
+  }
+  name: "test_mean_example"
+  input {
+    name: "data_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "data_1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "data_2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}

test/models/onnx/min_two_inputs.prototxt

@@ -15,7 +15,7 @@ graph {
         elem_type: 1
         shape {
           dim {
-            dim_value: 3
+            dim_value: 1
           }
         }
       }
@@ -49,5 +49,5 @@ graph {
   }
 }
 opset_import {
-  version: 7
+  version: 8
 }

test/models/onnx/min_two_inputs_opset1.prototxt

+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "data_0"
+    input: "data_1"
+    output: "result"
+    op_type: "Min"
+  }
+  name: "test_min_two_inputs"
+  input {
+    name: "data_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "data_1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}

test/models/onnx/sum.prototxt

@@ -16,7 +16,7 @@ graph {
         elem_type: 1
         shape {
           dim {
-            dim_value: 3
+            dim_value: 1
           }
         }
       }
@@ -63,5 +63,5 @@ graph {
   }
 }
 opset_import {
-  version: 7
+  version: 8
 }

test/models/onnx/sum_opset1.prototxt

+ir_version: 3
+producer_name: "nGraph ONNX Importer"
+graph {
+  node {
+    input: "data_0"
+    input: "data_1"
+    input: "data_2"
+    output: "result"
+    op_type: "Sum"
+  }
+  name: "test_sum_example"
+  input {
+    name: "data_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "data_1"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "data_2"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}

test/onnx/onnx_import.in.cpp

@@ -411,17 +411,31 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_relu)
     EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
 }
 
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sum_opset1)
+{
+    // Simple Sum test for opset1.
+    auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/sum_opset1.prototxt"));
+
+    auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
+    test_case.add_input<float>({3.f, 0.f, 2.f});
+    test_case.add_input<float>({1.f, 3.f, 4.f});
+    test_case.add_input<float>({2.f, 6.f, 6.f});
+    test_case.add_expected_output<float>(Shape{3}, {6.f, 9.f, 12.f});
+    test_case.run();
+}
+
 NGRAPH_TEST(onnx_${BACKEND_NAME}, model_sum)
 {
-    // Simple Sum test
+    // Simple Sum test for opset8.
     auto function =
         onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/sum.prototxt"));
 
     auto test_case = ngraph::test::NgraphTestCase(function, "${BACKEND_NAME}");
-    test_case.add_input<float>({3.f, 0.f, 2.f});
+    test_case.add_input<float>({3.f});
     test_case.add_input<float>({1.f, 3.f, 4.f});
     test_case.add_input<float>({2.f, 6.f, 6.f});
-    test_case.add_expected_output<float>(Shape{3}, {6.f, 9.f, 12.f});
+    test_case.add_expected_output<float>(Shape{3}, {6.f, 12.f, 13.f});
     test_case.run();
 }
 
@@ -486,10 +500,10 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_cum_sum_3d_exclusive_reverse)
     test_case.run();
 }
 
-NGRAPH_TEST(onnx_${BACKEND_NAME}, model_min_two_inputs)
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_min_two_inputs_opset1)
 {
     auto function = onnx_import::import_onnx_model(
-        file_util::path_join(SERIALIZED_ZOO, "onnx/min_two_inputs.prototxt"));
+        file_util::path_join(SERIALIZED_ZOO, "onnx/min_two_inputs_opset1.prototxt"));
 
     // input data shape (3, )
     Inputs inputs;
@@ -501,6 +515,37 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_min_two_inputs)
     EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
 }
 
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_min_two_inputs)
+{
+    auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/min_two_inputs.prototxt"));
+
+    // input data shape (3, )
+    Inputs inputs;
+    inputs.emplace_back(std::vector<float>{2.f});
+    inputs.emplace_back(std::vector<float>{1.f, 4.f, 4.f});
+
+    Outputs expected_outputs{{1.f, 2.f, 2.f}};
+    Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
+    EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
+}
+
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_max_opset1)
+{
+    auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/max_opset1.prototxt"));
+
+    // input data shape (3, )
+    Inputs inputs;
+    inputs.emplace_back(std::vector<float>{3.f, 2.f, 1.f});
+    inputs.emplace_back(std::vector<float>{1.f, 4.f, 4.f});
+    inputs.emplace_back(std::vector<float>{2.f, 5.f, 3.f});
+
+    Outputs expected_outputs{{3.f, 5.f, 4.f}};
+    Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
+    EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
+}
+
 NGRAPH_TEST(onnx_${BACKEND_NAME}, model_max)
 {
     auto function =
@@ -508,8 +553,9 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_max)
 
     // input data shape (3, )
     Inputs inputs;
-    inputs.emplace_back(std::vector<float>{3.f, 2.f, 1.f});
+
     inputs.emplace_back(std::vector<float>{1.f, 4.f, 4.f});
+    inputs.emplace_back(std::vector<float>{3.f});
     inputs.emplace_back(std::vector<float>{2.f, 5.f, 3.f});
 
     Outputs expected_outputs{{3.f, 5.f, 4.f}};
@@ -517,10 +563,10 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_max)
     EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
 }
 
-NGRAPH_TEST(onnx_${BACKEND_NAME}, model_mean)
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_mean_opset1)
 {
-    auto function =
-        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/mean.prototxt"));
+    auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/mean_opset1.prototxt"));
 
     // input data shape (3, )
     Inputs inputs;
@@ -533,6 +579,22 @@ NGRAPH_TEST(onnx_${BACKEND_NAME}, model_mean)
     EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
 }
 
+NGRAPH_TEST(onnx_${BACKEND_NAME}, model_mean)
+{
+    auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/mean.prototxt"));
+
+    // input data shape (3, )
+    Inputs inputs;
+    inputs.emplace_back(std::vector<float>{3.f});
+    inputs.emplace_back(std::vector<float>{1.f, 2.f, 5.f});
+    inputs.emplace_back(std::vector<float>{2.f, 7.f, 7.f});
+
+    Outputs expected_outputs{{2.f, 4.f, 5.f}};
+    Outputs outputs{execute(function, inputs, "${BACKEND_NAME}")};
+    EXPECT_TRUE(test::all_close_f(expected_outputs.front(), outputs.front()));
+}
+
 NGRAPH_TEST(onnx_${BACKEND_NAME}, model_gemm_abc)
 {
     auto function = onnx_import::import_onnx_model(