[MLIR] import attribute of onnx node (#383)

* add attributes as NamedAttribute

* support list value for attribute

* use std::tie to avoid c++17 feature

src/CMakeLists.txt

@@ -1,9 +1,12 @@
 add_executable(onnf main.cpp)
 
 target_link_libraries(onnf builder compiler ${MLIRLibs} ${Boost_LIBRARIES})
+set_target_properties(onnf PROPERTIES LINK_FLAGS "-lz")
+
 whole_archive_link_mlir(onnf ${MLIRWholeArchiveLibs})
 whole_archive_link_onnf(onnf onnf_transform)
 
+
 target_include_directories(onnf PRIVATE ${CMAKE_SOURCE_DIR})
 target_include_directories(onnf PRIVATE ${CMAKE_BINARY_DIR})
 

src/builder/CMakeLists.txt

@@ -1,5 +1,6 @@
 add_library(builder
         frontend_dialect_transformer.cpp
+        frontend_dialect_transformer.hpp
         op_build_table.inc
         )
 

src/builder/frontend_dialect_transformer.cpp

@@ -71,7 +71,10 @@ struct OnnxOnnfSymbolMapping {
    *  @param name onnx tensor name.
    *  @return onnf tensor corresponding to `name`.
    */
-  mlir::Value* GetTensorByOnnxName(std::string name) {
+  mlir::Value *GetTensorByOnnxName(std::string name) {
+    assert(onnx_name2onnf_tensor.find(legalize_name(name)) !=
+                            onnx_name2onnf_tensor.end() &&
+                        "Tensor not found");
     return onnx_name2onnf_tensor.at(legalize_name(name));
   }
 
@@ -80,7 +83,9 @@ struct OnnxOnnfSymbolMapping {
    *  @param name onnx tensor name.
    *  @param tensor MLIR Value* pointer.
    */
-  void AddMapping(std::string name, mlir::Value* tensor) {
+  void AddMapping(std::string name, mlir::Value *tensor) {
+    assert(onnx_name2onnf_tensor.count(legalize_name(name)) == 0 &&
+                        "Tensor already exists.");
     onnx_name2onnf_tensor.emplace(legalize_name(name), tensor);
   }
 
@@ -88,7 +93,7 @@ struct OnnxOnnfSymbolMapping {
     return onnx_name2onnf_tensor.count(name) != 0;
   }
 
- private:
+private:
   /*!
    *  mapping from onnx tensor names to MLIR tensor.
    */
@@ -96,8 +101,8 @@ struct OnnxOnnfSymbolMapping {
 };
 
 class FrontendGenImpl {
- public:
+public:
-  FrontendGenImpl(mlir::MLIRContext& context)
+  FrontendGenImpl(mlir::MLIRContext &context)
       : context_(context), builder_(&context) {
     module_ = mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));
   }
@@ -107,8 +112,8 @@ class FrontendGenImpl {
     return module_;
   }
 
- private:
+private:
-  mlir::MLIRContext& context_;
+  mlir::MLIRContext &context_;
   mlir::ModuleOp module_;
   mlir::OpBuilder builder_;
   // mapping between string name and symbol
@@ -145,55 +150,31 @@ class FrontendGenImpl {
       case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX64:
       case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX128:
       case onnx::TensorProto_DataType::TensorProto_DataType_UNDEFINED:
+        assert(false && "Unsupported data type encountered.");
         return nullptr;
     }
   }
 
-//if c++17 is used, these two def can be combined with 'if constexpr'
-//leave n there for possible future use
-//alternative is to use template and pass the outputTypes, inputs and attributes
-//as parameter
-
-#define MultipleOuts(name, nIn, nOut)\
-{ \
-  if (nIn == inputs.size() && nOut == outputTypes.size()) {\
-    auto op = builder_.create<mlir::ONNX##name##Op>(UnknownLoc(), outputTypes, inputs, attributes); \
-    for (int i = 0; i < node.output().size(); i++) { \
-      frontend_symbols_.AddMapping(\
-        legalize_name(node.output()[i]), op.getResult(i));\
-    }\
-    return;\
-  }\
-}
-
-#define OneOut(name, nIn, nOut)\
-{ \
-  if (nIn == inputs.size() && nOut == outputTypes.size()) {\
-    auto op = builder_.create<mlir::ONNX##name##Op>(UnknownLoc(), outputTypes, inputs, attributes); \
-      frontend_symbols_.AddMapping(\
-            legalize_name(node.output()[0]), op.getResult());\
-    return;\
-  }\
-}
-
   /*!
    * Import an onnx input tensor type by determining and recording its type
    * in a list of input tensor mlir types.
    * @param input onnx input tensor ValueInfoProto.
    * @param arg_types list of mlir types representing types of graph input.
    */
-  void ImportInputTensorType(const onnx::ValueInfoProto& input,
+  void ImportInputTensorType(const onnx::ValueInfoProto &input,
-      llvm::SmallVector<mlir::Type, 4>& arg_types) {
+                             llvm::SmallVector<mlir::Type, 4> &arg_types) {
     std::vector<int64_t> dims;
     auto shape_proto = input.type().tensor_type().shape();
     auto input_tensor_legalized_name = legalize_name(input.name());
     for (int i = 0; i < shape_proto.dim_size(); i++) {
       if (shape_proto.dim()[i].dim_value()) {
         int dim_numeric_size = shape_proto.dim()[i].dim_value();
+        assert(
+            dim_numeric_size != 0 && "Parsed an input tensor with a dimension size of zero");
         if (dim_numeric_size > 0) {
           dims.push_back(dim_numeric_size);
-        } else {  // If dim_value < 0, then dim is parametric.
+        } else { // If dim_value < 0, then dim is parametric.
-                  // TODO Verify the unknown dim size in MLIR
+                 // TODO Verify the unknown dim size in MLIR
           dims.push_back(-1);
         }
       } else {
@@ -216,8 +197,8 @@ class FrontendGenImpl {
    * @param input onnx input tensor ValueInfoProto.
    * @param symbol mlir input argument.
    */
-  void ImportInputTensorSymbol(
+  void ImportInputTensorSymbol(const onnx::ValueInfoProto &input,
-      const onnx::ValueInfoProto& input, mlir::Value* symbol) {
+                               mlir::Value *symbol) {
     auto input_tensor_legalized_name = legalize_name(input.name());
     assert(
         !frontend_symbols_.ContainKey(input_tensor_legalized_name) &&
@@ -225,32 +206,286 @@ class FrontendGenImpl {
     frontend_symbols_.AddMapping(input_tensor_legalized_name, symbol);
   }
 
-  void ImportNode(onnx::NodeProto node) {
+  template <typename T>
-    std::vector<mlir::Value*> inputs;
+  T get_attr_generic(onnx::NodeProto &node, std::string name,
+                     std::function<T(onnx::AttributeProto &)> attr_getter,
+                     T default_val) {
+    for (int i = 0; i < node.attribute_size(); ++i) {
+      auto attr = node.attribute(i);
+      if (attr.name() == name) {
+        return attr_getter(attr);
+      }
+    }
+    return default_val;
+  }
+
+  template <typename T>
+  T get_attr_generic(onnx::NodeProto &node, std::string name,
+                     std::function<T(onnx::AttributeProto &)> attr_getter) {
+    for (int i = 0; i < node.attribute_size(); ++i) {
+      auto attr = node.attribute(i);
+      if (attr.name() == name) {
+        return attr_getter(attr);
+      }
+    }
+    assert(false && "ONNX Node Attribute Not Found!");
+  }
+
+  auto get_attr_ints(onnx::NodeProto &node, std::string name,
+                     std::vector<int> default_val) {
+    std::function<std::vector<int>(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) {
+          std::vector<int> ints(attr.ints_size());
+          std::copy(attr.ints().begin(), attr.ints().end(), ints.begin());
+          return ints;
+        };
+    auto r = get_attr_generic(node, name, attr_getter, default_val);
+    auto dataType =
+        mlir::RankedTensorType::get(r.size(), builder_.getIntegerType(32));
+    auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
+    auto aname = node.op_type() + "." + name;
+    auto attr_output = builder_.getNamedAttr(aname, attr_v);
+    return attr_output;
+  }
+
+  auto get_attr_ints(onnx::NodeProto &node, std::string name) {
+    std::function<std::vector<int>(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) {
+          std::vector<int> ints(attr.ints_size());
+          std::copy(attr.ints().begin(), attr.ints().end(), ints.begin());
+          return ints;
+        };
+    auto r = get_attr_generic(node, name, attr_getter);
+    auto dataType =
+        mlir::RankedTensorType::get(r.size(), builder_.getIntegerType(32));
+    auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
+    auto aname = node.op_type() + "." + name;
+    auto attr_output = builder_.getNamedAttr(aname, attr_v);
+    return attr_output;
+  }
+
+  auto get_attr_floats(onnx::NodeProto &node, std::string name) {
+    std::function<std::vector<float>(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) {
+          std::vector<float> floats(attr.floats_size());
+          std::copy(attr.floats().begin(), attr.floats().end(), floats.begin());
+          return floats;
+        };
+    auto r = get_attr_generic(node, name, attr_getter);
+    auto dataType =
+        mlir::RankedTensorType::get(r.size(), builder_.getF32Type());
+    auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
+    auto aname = node.op_type() + "." + name;
+    auto attr_output = builder_.getNamedAttr(aname, attr_v);
+    return attr_output;
+  }
+
+  auto get_attr_floats(onnx::NodeProto &node, std::string name,
+                       std::vector<float> default_val) {
+    std::function<std::vector<float>(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) {
+          std::vector<float> floats(attr.floats_size());
+          std::copy(attr.floats().begin(), attr.floats().end(), floats.begin());
+          return floats;
+        };
+    auto r = get_attr_generic(node, name, attr_getter, default_val);
+    auto dataType =
+        mlir::RankedTensorType::get(r.size(), builder_.getF32Type());
+    auto attr_v = mlir::DenseElementsAttr::get(dataType, llvm::makeArrayRef(r));
+    auto aname = node.op_type() + "." + name;
+    auto attr_output = builder_.getNamedAttr(aname, attr_v);
+    return attr_output;
+  }
+
+  auto get_attr_int(onnx::NodeProto &node, std::string name) {
+    std::function<int(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) { return attr.i(); };
+    int r = get_attr_generic(node, name, attr_getter);
+    auto attr_v = builder_.getI32IntegerAttr(r);
+    auto aname = node.op_type() + "." + name;
+    auto attr_output = builder_.getNamedAttr(aname, attr_v);
+    return attr_output;
+  }
+
+  auto get_attr_int(onnx::NodeProto &node, std::string name, int default_val) {
+    std::function<int(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) { return attr.i(); };
+    int r = get_attr_generic(node, name, attr_getter, default_val);
+    auto attr_v = builder_.getI32IntegerAttr(r);
+    auto aname = node.op_type() + "." + name;
+    auto attr_output = builder_.getNamedAttr(aname, attr_v);
+    return attr_output;
+  }
+
+  auto get_attr_float(onnx::NodeProto &node, std::string name) {
+    std::function<float(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) { return attr.f(); };
+    auto r = get_attr_generic(node, name, attr_getter);
+    auto attr_v = builder_.getF32FloatAttr(r);
+    auto aname = node.op_type() + "." + name;
+    return builder_.getNamedAttr(aname, attr_v);
+  }
+
+  auto get_attr_float(onnx::NodeProto &node, std::string name,
+                      float default_val) {
+    std::function<float(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) { return attr.f(); };
+    auto r = get_attr_generic(node, name, attr_getter, default_val);
+    auto attr_v = builder_.getF32FloatAttr(r);
+    auto aname = node.op_type() + "." + name;
+    return builder_.getNamedAttr(aname, attr_v);
+  }
+
+  auto get_attr_string(onnx::NodeProto &node, std::string name) {
+    std::function<std::string(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) { return attr.s(); };
+    auto r = get_attr_generic(node, name, attr_getter);
+    auto attr_v = builder_.getStringAttr(r);
+    auto aname = node.op_type() + "." + name;
+    return builder_.getNamedAttr(aname, attr_v);
+  }
+
+  auto get_attr_string(onnx::NodeProto &node, std::string name,
+                       std::string default_val) {
+    std::function<std::string(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) { return attr.s(); };
+    auto r = get_attr_generic(node, name, attr_getter, default_val);
+    auto attr_v = builder_.getStringAttr(r);
+    auto aname = node.op_type() + "." + name;
+    return builder_.getNamedAttr(aname, attr_v);
+  }
+
+  /*
+    auto get_attr_strings(onnx::NodeProto &node, std::string name) {
+      std::function<std::vector<std::string>(onnx::AttributeProto &)>
+    attr_getter =
+          [](onnx::AttributeProto &attr) {
+            std::vector<std::string> strings(attr.strings_size());
+            std::copy(attr.strings().begin(), attr.strings().end(),
+    strings.begin()); return strings;
+          };
+      auto r = get_attr_generic(node, name, attr_getter);
+      return r;
+      return builder_.getNamedAttr(aname, attr_v);
+      auto dataType =
+          mlir::RankedTensorType::get(r.size(), builder_.get???Type());
+      auto attr_v = mlir::DenseElementsAttr::get(dataType,
+    llvm::makeArrayRef(r)); auto aname = node.op_type() + "." + name; auto
+    attr_output = builder_.getNamedAttr(aname, attr_v); return attr_output;
+    }
+  */
+
+  auto get_default_ints(std::string default_str) {
+    std::vector<int> r;
+    auto start = default_str.find("{");
+    while (true) {
+      auto end = default_str.find(",", start + 1);
+      if (end == std::string::npos) {
+        end = default_str.find("}", start + 1);
+        if (end != std::string::npos && end > start+1) {
+          r.push_back(std::stoi(default_str.substr(start + 1, end)));
+        }
+        break;
+      } else {
+        r.push_back(std::stoi(default_str.substr(start + 1, end)));
+      }
+      start = end + 1;
+    }
+    return r;
+  }
+
+  auto get_default_floats(std::string default_str) {
+    std::vector<float> r;
+    auto start = default_str.find("{");
+    while (true) {
+      auto end = default_str.find(",", start + 1);
+      if (end == std::string::npos) {
+        end = default_str.find("}", start + 1);
+        if (end != std::string::npos && end > start+1) {
+          r.push_back(std::stof(default_str.substr(start + 1, end)));
+        }
+        break;
+      } else {
+        r.push_back(std::stof(default_str.substr(start + 1, end)));
+      }
+      start = end + 1;
+    }
+    return r;
+  }
+
+  auto get_default_strings(std::string default_str) {
+    std::vector<std::string> r;
+    auto start = default_str.find("{");
+    while (true) {
+      auto end = default_str.find(",", start + 1);
+      if (end == std::string::npos) {
+        end = default_str.find("}", start + 1);
+        if (end != std::string::npos && end > start+1) {
+          r.push_back(default_str.substr(start + 1, end));
+        }
+        break;
+      } else {
+        r.push_back(default_str.substr(start + 1, end));
+      }
+      start = end + 1;
+    }
+    return r;
+  }
+
+  onnx::TensorProto get_attr_tensor(onnx::NodeProto &node, std::string name) {
+    std::function<onnx::TensorProto(onnx::AttributeProto &)> attr_getter =
+        [](onnx::AttributeProto &attr) { return attr.t(); };
+    return get_attr_generic(node, name, attr_getter);
+  }
+
+  auto ImportNodeAttr(onnx::NodeProto node, std::string attr_name,
+                      std::string type_name, std::string default_str) {
+    if (default_str == "") {
+      if (type_name == "int") {
+        return get_attr_int(node, attr_name);
+      } else if (type_name == "float") {
+        return get_attr_float(node, attr_name);
+      } else if (type_name == "str") {
+        return get_attr_string(node, attr_name);
+      } else if (type_name == "ints") {
+        return get_attr_ints(node, attr_name);
+      } else if (type_name == "floats") {
+        return get_attr_floats(node, attr_name);
+      } else {
+        assert(
+            false &&
+            "Got an empty initializer or initializer for this "
+            "datatype is not implemented. Something is wrong.");
+      }
+    } else {
+      // with default value
+      if (type_name == "int") {
+        return get_attr_int(node, attr_name, std::stoi(default_str));
+      } else if (type_name == "float") {
+        return get_attr_float(node, attr_name, std::stof(default_str));
+      } else if (type_name == "str") {
+        return get_attr_string(node, attr_name, default_str);
+      } else if (type_name == "ints") {
+        return get_attr_ints(node, attr_name, get_default_ints(default_str));
+      } else if (type_name == "floats") {
+        return get_attr_floats(node, attr_name,
+                               get_default_floats(default_str));
+      } else {
+        assert(
+            false &&
+            "Got an empty initializer or initializer for this "
+            "datatype is not implemented. Something is wrong.");
+      }
+    }
+  }
+
+  void ImportNodeGeneric(onnx::NodeProto node) {
+    std::vector<mlir::Value *> inputs;
     for (auto item : node.input()) {
       if (frontend_symbols_.ContainKey(legalize_name(item))) {
         inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
       }
     }
-
-    std::vector<mlir::Type> outputTypes;
-    for (auto item : node.output()) {
-      outputTypes.push_back(mlir::UnrankedTensorType::get(builder_.getF32Type()));
-    }
-
-    std::vector<mlir::NamedAttribute> attributes;
-    llvm::StringRef OpName = node.op_type();
-
-    //the following code is generated by gen_doc.py
-    //refer to dialect/onnx/onnx.td for details
-    //when the input or output of then op does not match the specification,
-    //the generic operator is used 
-    //one known reeason is the optional input
-
-#include "src/builder/op_build_table.inc"
-
-
-    // Old way of doing things.
     mlir::OperationState result(UnknownLoc(), "frontend." + node.op_type());
     for (auto item : node.output()) {
       result.addTypes(mlir::UnrankedTensorType::get(builder_.getF32Type()));
@@ -261,8 +496,165 @@ class FrontendGenImpl {
       auto r = op->getResult(i);
       frontend_symbols_.AddMapping(legalize_name(node.output()[i]), r);
     }
+  }
 
-    // TODO more info from node: attributes
+  // if c++17 is used, ImportNodeOneOut and ImportNodeMultipleOuts can be
+  // combined with 'if constexpr' the issue is the type of the output is
+  // different. alternative way to use variadic output for all the op
+
+  /*!
+   * Important onnx node which generates only one output
+   * @param node onnx node
+   * @param nIn number of expected inputs
+   * @param nOut number of expected outputs
+   * @param attrs  list of desription for attributes with format {name, type,
+   * default}
+   */
+  template <typename T>
+  void ImportNodeOneOut(
+      onnx::NodeProto node, int nIn, int nOut,
+      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+          attrs) {
+    std::vector<mlir::Value *> inputs;
+    for (auto item : node.input()) {
+      if (frontend_symbols_.ContainKey(legalize_name(item))) {
+        inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
+      }
+    }
+
+    std::vector<mlir::Type> outputTypes;
+    for (auto item : node.output()) {
+      outputTypes.push_back(
+          mlir::UnrankedTensorType::get(builder_.getF32Type()));
+    }
+
+    std::vector<mlir::NamedAttribute> attributes;
+    //for (auto [attr_name, attr_type, attr_default] : attrs) {
+    for (auto oneAttr: attrs) {
+      std::string attr_name;
+      std::string attr_type;
+      std::string attr_default;
+      std::tie (attr_name, attr_type, attr_default) = oneAttr; 
+      if (attr_type != "") {
+        auto attr = ImportNodeAttr(node, attr_name, attr_type, attr_default);
+        attributes.push_back(attr);
+      } else {
+        // TODO: the attributes need special handling
+        //std::cout << "missing " << node.op_type() << " " << attr_name << std::endl;
+      }
+    }
+
+    llvm::StringRef OpName = node.op_type();
+
+    if (nIn == inputs.size() && nOut == outputTypes.size()) {
+      auto op =
+          builder_.create<T>(UnknownLoc(), outputTypes, inputs, attributes);
+      frontend_symbols_.AddMapping(legalize_name(node.output()[0]),
+                                   op.getResult());
+    } else {
+      ImportNodeGeneric(node);
+    }
+  }
+
+  template <typename T>
+  void ImportNodeMultipleOuts(
+      onnx::NodeProto node, int nIn, int nOut,
+      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+          attrs) {
+    std::vector<mlir::Value *> inputs;
+    for (auto item : node.input()) {
+      if (frontend_symbols_.ContainKey(legalize_name(item))) {
+        inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
+      }
+    }
+
+    std::vector<mlir::Type> outputTypes;
+    for (auto item : node.output()) {
+      outputTypes.push_back(
+          mlir::UnrankedTensorType::get(builder_.getF32Type()));
+    }
+
+    std::vector<mlir::NamedAttribute> attributes;
+    for (auto oneAttr: attrs) {
+      std::string attr_name;
+      std::string attr_type;
+      std::string attr_default;
+      std::tie (attr_name, attr_type, attr_default) = oneAttr; 
+      if (attr_type != "") {
+        auto attr = ImportNodeAttr(node, attr_name, attr_type, attr_default);
+        attributes.push_back(attr);
+      } else {
+        // TODO: the attributes need special handling
+        //std::cout << "missing " << node.op_type() << " " << attr_name << std::endl;
+      }
+    }
+
+    llvm::StringRef OpName = node.op_type();
+
+    if (nIn == inputs.size() && nOut == outputTypes.size()) {
+      auto op =
+          builder_.create<T>(UnknownLoc(), outputTypes, inputs, attributes);
+      for (int i = 0; i < node.output().size(); i++) {
+        frontend_symbols_.AddMapping(legalize_name(node.output()[i]),
+                                     op.getResult(i));
+      }
+    } else {
+      ImportNodeGeneric(node);
+    }
+  }
+
+  /*!
+   * Special handle for Conv operations.
+   * c++ does not allow template specialization inside a class scope
+   * a specialized function is used
+   */
+  void ImportNodeConv(
+      onnx::NodeProto node, int nIn, int nOut,
+      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+          attrs) {
+
+    // Conv has attribute dilations, kernel_shape, pads, the default value of
+    // which  is determined by the shape of first argument. However, since the
+    // shape is unknown now, these attributes can be not generated auto
+    // dilations_attr = get_attr_ints(node, "dilations",
+    //    std::vector<int>(inputs[0]->getType().cast<RankedTensorType>.getDims()-2,
+    //    1));
+    // attributes.push_back(dilations_attr)
+    // similar situation for pads, strides in AveragePool
+    // axes of ReduceSum,  pads, strides, dilations and kernel_shape of MaxPool
+    // TODO: fix this after type inference
+
+    if (node.input().size() == 1) {
+      ImportNodeOneOut<mlir::ONNXConv1Op>(node, nIn, nOut, attrs);
+    } else {
+      ImportNodeOneOut<mlir::ONNXConv3Op>(node, nIn, nOut, attrs);
+    }
+  }
+
+  void ImportNode(onnx::NodeProto node) {
+    std::vector<mlir::Value *> inputs;
+    for (auto item : node.input()) {
+      if (frontend_symbols_.ContainKey(legalize_name(item))) {
+        inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
+      }
+    }
+
+    std::vector<mlir::Type> outputTypes;
+    for (auto item : node.output()) {
+      outputTypes.push_back(
+          mlir::UnrankedTensorType::get(builder_.getF32Type()));
+    }
+
+    std::vector<mlir::NamedAttribute> attributes;
+    llvm::StringRef OpName = node.op_type();
+
+    // the following code is generated by gen_doc.py
+    // refer to dialect/onnx/onnx.td for details
+    // when the input or output of then op does not match the specification,
+    // the generic operator is used
+    // one known reeason is the optional input
+
+#include "src/builder/op_build_table.inc"
   }
 
   /*!
@@ -277,9 +669,9 @@ class FrontendGenImpl {
    * @param ret_vals a vector of mlir Value* representing graph's
    *   output tensor.
    */
-  void ImportOutputTensor(const onnx::ValueInfoProto& output,
+  void ImportOutputTensor(const onnx::ValueInfoProto &output,
-      llvm::SmallVectorImpl<mlir::Type>& ret_types,
+                          llvm::SmallVectorImpl<mlir::Type> &ret_types,
-      llvm::SmallVectorImpl<mlir::Value*>& ret_vals) {
+                          llvm::SmallVectorImpl<mlir::Value *> &ret_vals) {
     auto output_tensor_legalized_name = legalize_name(output.name());
     assert(
         frontend_symbols_.ContainKey(output_tensor_legalized_name) &&
@@ -291,8 +683,8 @@ class FrontendGenImpl {
     ret_vals.push_back(tensor_val);
   }
 
-  void ImportGraph(
+  void ImportGraph(const onnx::GraphProto &graph,
-      const onnx::GraphProto& graph, const std::string& name = "main") {
+                   const std::string &name = "main") {
     // create a function for the graph
     // TODO:
     //  * get name and type for the function.
@@ -300,7 +692,7 @@ class FrontendGenImpl {
     llvm::SmallVector<mlir::Type, 4> arg_types;
 
     // Import the input tensor types.
-    for (const auto& input : graph.input()) {
+    for (const auto &input : graph.input()) {
       ImportInputTensorType(input, arg_types);
     }
 
@@ -308,7 +700,7 @@ class FrontendGenImpl {
     auto func_type = builder_.getFunctionType(arg_types, {});
     auto main_func =
         mlir::FuncOp::create(UnknownLoc(), name, func_type, /* attrs = */ {});
-    auto& entryBlock = *main_func.addEntryBlock();
+    auto &entryBlock = *main_func.addEntryBlock();
 
     builder_.setInsertionPointToStart(&entryBlock);
     module_.push_back(main_func);
@@ -319,14 +711,14 @@ class FrontendGenImpl {
 
     // import nodes in the graph
     auto node = graph.node();
-    for (const auto& item : node) {
+    for (const auto &item : node) {
       ImportNode(item);
     }
 
     llvm::SmallVector<mlir::Type, 4> ret_types;
-    llvm::SmallVector<mlir::Value*, 4> ret_vals;
+    llvm::SmallVector<mlir::Value *, 4> ret_vals;
     // Import the output tensors
-    for (const auto& output : graph.output()) {
+    for (const auto &output : graph.output()) {
       ImportOutputTensor(output, ret_types, ret_vals);
     }
 
@@ -337,7 +729,6 @@ class FrontendGenImpl {
     func_type = builder_.getFunctionType(arg_types, ret_types);
     main_func.setType(func_type);
   }
-
 };  // FrontendGenImpl class
 }  // namespace
 }  // namespace onnf
@@ -354,11 +745,13 @@ mlir::OwningModuleRef ImportFrontendModel(onnx::ModelProto model) {
 }
 
 void ImportFrontendModelFile(std::string model_fname,
-    mlir::MLIRContext& context, mlir::OwningModuleRef& module) {
+                             mlir::MLIRContext &context,
+                             mlir::OwningModuleRef &module) {
   onnx::ModelProto model;
   std::fstream input(model_fname, std::ios::in | std::ios::binary);
 
   auto parse_success = model.ParseFromIstream(&input);
+  assert(parse_success && "Onnx Model Parsing Failed.");
 
   FrontendGenImpl myONNXGen(context);
   module = myONNXGen.ImportONNXModel(model);

src/builder/op_build_table.inc

@@ -1,313 +1,688 @@
-    if (OpName == "Abs") {
+    if (OpName == "DUMMY") {
-       OneOut(Abs, 1, 1);
+    }else if (OpName == "Abs") {
+       ImportNodeOneOut<mlir::ONNXAbsOp>(node, 1, 1, {
+      });
     }else if (OpName == "Acos") {
-       OneOut(Acos, 1, 1);
+       ImportNodeOneOut<mlir::ONNXAcosOp>(node, 1, 1, {
+      });
     }else if (OpName == "Acosh") {
-       OneOut(Acosh, 1, 1);
+       ImportNodeOneOut<mlir::ONNXAcoshOp>(node, 1, 1, {
+      });
     }else if (OpName == "Add") {
-       OneOut(Add, 2, 1);
+       ImportNodeOneOut<mlir::ONNXAddOp>(node, 2, 1, {
+      });
     }else if (OpName == "And") {
-       OneOut(And, 2, 1);
+       ImportNodeOneOut<mlir::ONNXAndOp>(node, 2, 1, {
+      });
     }else if (OpName == "ArgMax") {
-       OneOut(ArgMax, 1, 1);
+       ImportNodeOneOut<mlir::ONNXArgMaxOp>(node, 1, 1, {
+         {"axis","int","0"}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ArgMin") {
-       OneOut(ArgMin, 1, 1);
+       ImportNodeOneOut<mlir::ONNXArgMinOp>(node, 1, 1, {
+         {"axis","int","0"}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "Asin") {
-       OneOut(Asin, 1, 1);
+       ImportNodeOneOut<mlir::ONNXAsinOp>(node, 1, 1, {
+      });
     }else if (OpName == "Asinh") {
-       OneOut(Asinh, 1, 1);
+       ImportNodeOneOut<mlir::ONNXAsinhOp>(node, 1, 1, {
+      });
     }else if (OpName == "Atan") {
-       OneOut(Atan, 1, 1);
+       ImportNodeOneOut<mlir::ONNXAtanOp>(node, 1, 1, {
+      });
     }else if (OpName == "Atanh") {
-       OneOut(Atanh, 1, 1);
+       ImportNodeOneOut<mlir::ONNXAtanhOp>(node, 1, 1, {
+      });
     }else if (OpName == "AveragePool") {
-       OneOut(AveragePool, 1, 1);
+       ImportNodeOneOut<mlir::ONNXAveragePoolOp>(node, 1, 1, {
+         {"auto_pad","str","NOTSET"}
+        ,{"ceil_mode","int","0"}
+        ,{"count_include_pad","int","0"}
+        ,{"kernel_shape","ints", ""}
+        ,{"pads","", ""}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "BatchNormalization") {
-       MultipleOuts(BatchNormalization, 5, 5);
+       ImportNodeMultipleOuts<mlir::ONNXBatchNormalizationOp>(node, 5, 5, {
+         {"epsilon","float","1e-05"}
+        ,{"momentum","float","0.9"}
+      });
     }else if (OpName == "BitShift") {
-       OneOut(BitShift, 2, 1);
+       ImportNodeOneOut<mlir::ONNXBitShiftOp>(node, 2, 1, {
+         {"direction","", ""}
+      });
     }else if (OpName == "Cast") {
-       OneOut(Cast, 1, 1);
+       ImportNodeOneOut<mlir::ONNXCastOp>(node, 1, 1, {
+         {"to","int", "0"}
+      });
     }else if (OpName == "Ceil") {
-       OneOut(Ceil, 1, 1);
+       ImportNodeOneOut<mlir::ONNXCeilOp>(node, 1, 1, {
+      });
     }else if (OpName == "Clip") {
-       OneOut(Clip, 3, 1);
+       ImportNodeOneOut<mlir::ONNXClipOp>(node, 3, 1, {
+      });
     }else if (OpName == "Compress") {
-       OneOut(Compress, 2, 1);
+       ImportNodeOneOut<mlir::ONNXCompressOp>(node, 2, 1, {
+         {"axis","", ""}
+      });
     }else if (OpName == "Concat") {
-       OneOut(Concat, 1, 1);
+       ImportNodeOneOut<mlir::ONNXConcatOp>(node, 1, 1, {
+         {"axis","int", "0"}
+      });
     }else if (OpName == "ConcatFromSequence") {
-       OneOut(ConcatFromSequence, 1, 1);
+       ImportNodeOneOut<mlir::ONNXConcatFromSequenceOp>(node, 1, 1, {
+         {"axis","", ""}
+        ,{"new_axis","int","0"}
+      });
     }else if (OpName == "Constant") {
-       OneOut(Constant, 0, 1);
+       ImportNodeOneOut<mlir::ONNXConstantOp>(node, 0, 1, {
+         {"sparse_value","", ""}
+        ,{"value","", ""}
+      });
     }else if (OpName == "ConstantOfShape") {
-       OneOut(ConstantOfShape, 1, 1);
+       ImportNodeOneOut<mlir::ONNXConstantOfShapeOp>(node, 1, 1, {
+         {"value","", ""}
+      });
     }else if (OpName == "Conv") {
-       OneOut(Conv, 3, 1);
+       ImportNodeConv(node, 3, 1, {
+         {"auto_pad","str","NOTSET"}
+        ,{"dilations","", ""}
+        ,{"group","int", "1"}
+        ,{"kernel_shape","", ""}
+        ,{"pads","", ""}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "ConvInteger") {
-       OneOut(ConvInteger, 4, 1);
+       ImportNodeOneOut<mlir::ONNXConvIntegerOp>(node, 4, 1, {
+         {"auto_pad","str","NOTSET"}
+        ,{"dilations","", ""}
+        ,{"group","int","1"}
+        ,{"kernel_shape","", ""}
+        ,{"pads","", ""}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "ConvTranspose") {
-       OneOut(ConvTranspose, 3, 1);
+       ImportNodeOneOut<mlir::ONNXConvTransposeOp>(node, 3, 1, {
+         {"auto_pad","str","NOTSET"}
+        ,{"dilations","", ""}
+        ,{"group","int","1"}
+        ,{"kernel_shape","", ""}
+        ,{"output_padding","", ""}
+        ,{"output_shape","", ""}
+        ,{"pads","", ""}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "Cos") {
-       OneOut(Cos, 1, 1);
+       ImportNodeOneOut<mlir::ONNXCosOp>(node, 1, 1, {
+      });
     }else if (OpName == "Cosh") {
-       OneOut(Cosh, 1, 1);
+       ImportNodeOneOut<mlir::ONNXCoshOp>(node, 1, 1, {
+      });
     }else if (OpName == "CumSum") {
-       OneOut(CumSum, 2, 1);
+       ImportNodeOneOut<mlir::ONNXCumSumOp>(node, 2, 1, {
+         {"exclusive","int","0"}
+        ,{"reverse","int","0"}
+      });
     }else if (OpName == "DepthToSpace") {
-       OneOut(DepthToSpace, 1, 1);
+       ImportNodeOneOut<mlir::ONNXDepthToSpaceOp>(node, 1, 1, {
+         {"blocksize","", ""}
+        ,{"mode","str","DCR"}
+      });
     }else if (OpName == "DequantizeLinear") {
-       OneOut(DequantizeLinear, 3, 1);
+       ImportNodeOneOut<mlir::ONNXDequantizeLinearOp>(node, 3, 1, {
+      });
     }else if (OpName == "Det") {
-       OneOut(Det, 1, 1);
+       ImportNodeOneOut<mlir::ONNXDetOp>(node, 1, 1, {
+      });
     }else if (OpName == "Div") {
-       OneOut(Div, 2, 1);
+       ImportNodeOneOut<mlir::ONNXDivOp>(node, 2, 1, {
+      });
     }else if (OpName == "Dropout") {
-       MultipleOuts(Dropout, 1, 2);
+       ImportNodeMultipleOuts<mlir::ONNXDropoutOp>(node, 1, 2, {
+         {"ratio","float","0.5"}
+      });
     }else if (OpName == "DynamicQuantizeLinear") {
-       MultipleOuts(DynamicQuantizeLinear, 1, 3);
+       ImportNodeMultipleOuts<mlir::ONNXDynamicQuantizeLinearOp>(node, 1, 3, {
+      });
     }else if (OpName == "Elu") {
-       OneOut(Elu, 1, 1);
+       ImportNodeOneOut<mlir::ONNXEluOp>(node, 1, 1, {
+         {"alpha","float","1.0"}
+      });
     }else if (OpName == "Equal") {
-       OneOut(Equal, 2, 1);
+       ImportNodeOneOut<mlir::ONNXEqualOp>(node, 2, 1, {
+      });
     }else if (OpName == "Erf") {
-       OneOut(Erf, 1, 1);
+       ImportNodeOneOut<mlir::ONNXErfOp>(node, 1, 1, {
+      });
     }else if (OpName == "Exp") {
-       OneOut(Exp, 1, 1);
+       ImportNodeOneOut<mlir::ONNXExpOp>(node, 1, 1, {
+      });
     }else if (OpName == "Expand") {
-       OneOut(Expand, 2, 1);
+       ImportNodeOneOut<mlir::ONNXExpandOp>(node, 2, 1, {
+      });
     }else if (OpName == "EyeLike") {
-       OneOut(EyeLike, 1, 1);
+       ImportNodeOneOut<mlir::ONNXEyeLikeOp>(node, 1, 1, {
+         {"dtype","", ""}
+        ,{"k","int","0"}
+      });
     }else if (OpName == "Flatten") {
-       OneOut(Flatten, 1, 1);
+       ImportNodeOneOut<mlir::ONNXFlattenOp>(node, 1, 1, {
+         {"axis","int","1"}
+      });
     }else if (OpName == "Floor") {
-       OneOut(Floor, 1, 1);
+       ImportNodeOneOut<mlir::ONNXFloorOp>(node, 1, 1, {
+      });
     }else if (OpName == "GRU") {
-       MultipleOuts(GRU, 6, 2);
+       ImportNodeMultipleOuts<mlir::ONNXGRUOp>(node, 6, 2, {
+         {"activation_alpha","", ""}
+        ,{"activation_beta","", ""}
+        ,{"activations","", ""}
+        ,{"clip","", ""}
+        ,{"direction","str","forward"}
+        ,{"hidden_size","", ""}
+        ,{"linear_before_reset","int","0"}
+      });
     }else if (OpName == "Gather") {
-       OneOut(Gather, 2, 1);
+       ImportNodeOneOut<mlir::ONNXGatherOp>(node, 2, 1, {
+         {"axis","int","0"}
+      });
     }else if (OpName == "GatherElements") {
-       OneOut(GatherElements, 2, 1);
+       ImportNodeOneOut<mlir::ONNXGatherElementsOp>(node, 2, 1, {
+         {"axis","int","0"}
+      });
     }else if (OpName == "GatherND") {
-       OneOut(GatherND, 2, 1);
+       ImportNodeOneOut<mlir::ONNXGatherNDOp>(node, 2, 1, {
+      });
     }else if (OpName == "Gemm") {
-       OneOut(Gemm, 3, 1);
+       ImportNodeOneOut<mlir::ONNXGemmOp>(node, 3, 1, {
+         {"alpha","float","1.0"}
+        ,{"beta","float","1.0"}
+        ,{"transA","int","0"}
+        ,{"transB","int","0"}
+      });
     }else if (OpName == "GlobalAveragePool") {
-       OneOut(GlobalAveragePool, 1, 1);
+       ImportNodeOneOut<mlir::ONNXGlobalAveragePoolOp>(node, 1, 1, {
+      });
     }else if (OpName == "GlobalLpPool") {
-       OneOut(GlobalLpPool, 1, 1);
+       ImportNodeOneOut<mlir::ONNXGlobalLpPoolOp>(node, 1, 1, {
+         {"p","int","2"}
+      });
     }else if (OpName == "GlobalMaxPool") {
-       OneOut(GlobalMaxPool, 1, 1);
+       ImportNodeOneOut<mlir::ONNXGlobalMaxPoolOp>(node, 1, 1, {
+      });
     }else if (OpName == "Greater") {
-       OneOut(Greater, 2, 1);
+       ImportNodeOneOut<mlir::ONNXGreaterOp>(node, 2, 1, {
+      });
     }else if (OpName == "HardSigmoid") {
-       OneOut(HardSigmoid, 1, 1);
+       ImportNodeOneOut<mlir::ONNXHardSigmoidOp>(node, 1, 1, {
+         {"alpha","float","0.2"}
+        ,{"beta","float","0.5"}
+      });
     }else if (OpName == "Hardmax") {
-       OneOut(Hardmax, 1, 1);
+       ImportNodeOneOut<mlir::ONNXHardmaxOp>(node, 1, 1, {
+         {"axis","int","1"}
+      });
     }else if (OpName == "Identity") {
-       OneOut(Identity, 1, 1);
+       ImportNodeOneOut<mlir::ONNXIdentityOp>(node, 1, 1, {
+      });
     }else if (OpName == "If") {
-       OneOut(If, 1, 1);
+       ImportNodeOneOut<mlir::ONNXIfOp>(node, 1, 1, {
+         {"else_branch","", ""}
+        ,{"then_branch","", ""}
+      });
     }else if (OpName == "InstanceNormalization") {
-       OneOut(InstanceNormalization, 3, 1);
+       ImportNodeOneOut<mlir::ONNXInstanceNormalizationOp>(node, 3, 1, {
+         {"epsilon","float","1e-05"}
+      });
     }else if (OpName == "IsInf") {
-       OneOut(IsInf, 1, 1);
+       ImportNodeOneOut<mlir::ONNXIsInfOp>(node, 1, 1, {
+         {"detect_negative","int","1"}
+        ,{"detect_positive","int","1"}
+      });
     }else if (OpName == "IsNaN") {
-       OneOut(IsNaN, 1, 1);
+       ImportNodeOneOut<mlir::ONNXIsNaNOp>(node, 1, 1, {
+      });
     }else if (OpName == "LRN") {
-       OneOut(LRN, 1, 1);
+       ImportNodeOneOut<mlir::ONNXLRNOp>(node, 1, 1, {
+         {"alpha","float","0.0001"}
+        ,{"beta","float","0.75"}
+        ,{"bias","float","1.0"}
+        ,{"size","int", ""}
+      });
     }else if (OpName == "LSTM") {
-       MultipleOuts(LSTM, 8, 3);
+       ImportNodeMultipleOuts<mlir::ONNXLSTMOp>(node, 8, 3, {
+         {"activation_alpha","", ""}
+        ,{"activation_beta","", ""}
+        ,{"activations","", ""}
+        ,{"clip","", ""}
+        ,{"direction","str","forward"}
+        ,{"hidden_size","", ""}
+        ,{"input_forget","int","0"}
+      });
     }else if (OpName == "LeakyRelu") {
-       OneOut(LeakyRelu, 1, 1);
+       ImportNodeOneOut<mlir::ONNXLeakyReluOp>(node, 1, 1, {
+         {"alpha","float","0.01"}
+      });
     }else if (OpName == "Less") {
-       OneOut(Less, 2, 1);
+       ImportNodeOneOut<mlir::ONNXLessOp>(node, 2, 1, {
+      });
     }else if (OpName == "Log") {
-       OneOut(Log, 1, 1);
+       ImportNodeOneOut<mlir::ONNXLogOp>(node, 1, 1, {
+      });
     }else if (OpName == "LogSoftmax") {
-       OneOut(LogSoftmax, 1, 1);
+       ImportNodeOneOut<mlir::ONNXLogSoftmaxOp>(node, 1, 1, {
+         {"axis","int","1"}
+      });
     }else if (OpName == "Loop") {
-       OneOut(Loop, 3, 1);
+       ImportNodeOneOut<mlir::ONNXLoopOp>(node, 3, 1, {
+         {"body","", ""}
+      });
     }else if (OpName == "LpNormalization") {
-       OneOut(LpNormalization, 1, 1);
+       ImportNodeOneOut<mlir::ONNXLpNormalizationOp>(node, 1, 1, {
+         {"axis","int","-1"}
+        ,{"p","int","2"}
+      });
     }else if (OpName == "LpPool") {
-       OneOut(LpPool, 1, 1);
+       ImportNodeOneOut<mlir::ONNXLpPoolOp>(node, 1, 1, {
+         {"auto_pad","str","NOTSET"}
+        ,{"kernel_shape","", ""}
+        ,{"p","int","2"}
+        ,{"pads","", ""}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "MatMul") {
-       OneOut(MatMul, 2, 1);
+       ImportNodeOneOut<mlir::ONNXMatMulOp>(node, 2, 1, {
+      });
     }else if (OpName == "MatMulInteger") {
-       OneOut(MatMulInteger, 4, 1);
+       ImportNodeOneOut<mlir::ONNXMatMulIntegerOp>(node, 4, 1, {
+      });
     }else if (OpName == "Max") {
-       OneOut(Max, 1, 1);
+       ImportNodeOneOut<mlir::ONNXMaxOp>(node, 1, 1, {
+      });
     }else if (OpName == "MaxPool") {
-       MultipleOuts(MaxPool, 1, 2);
+       ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(node, 1, 2, {
+         {"auto_pad","str","NOTSET"}
+        ,{"ceil_mode","int","0"}
+        ,{"dilations","", ""}
+        ,{"kernel_shape","ints", ""}
+        ,{"pads","", ""}
+        ,{"storage_order","int","0"}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "MaxRoiPool") {
-       OneOut(MaxRoiPool, 2, 1);
+       ImportNodeOneOut<mlir::ONNXMaxRoiPoolOp>(node, 2, 1, {
+         {"pooled_shape","", ""}
+        ,{"spatial_scale","float","1.0"}
+      });
     }else if (OpName == "MaxUnpool") {
-       OneOut(MaxUnpool, 3, 1);
+       ImportNodeOneOut<mlir::ONNXMaxUnpoolOp>(node, 3, 1, {
+         {"kernel_shape","", ""}
+        ,{"pads","", ""}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "Mean") {
-       OneOut(Mean, 1, 1);
+       ImportNodeOneOut<mlir::ONNXMeanOp>(node, 1, 1, {
+      });
     }else if (OpName == "MeanVarianceNormalization") {
-       OneOut(MeanVarianceNormalization, 1, 1);
+       ImportNodeOneOut<mlir::ONNXMeanVarianceNormalizationOp>(node, 1, 1, {
+         {"axes","ints","{'0', '2', '3'}"}
+      });
     }else if (OpName == "Min") {
-       OneOut(Min, 1, 1);
+       ImportNodeOneOut<mlir::ONNXMinOp>(node, 1, 1, {
+      });
     }else if (OpName == "Mod") {
-       OneOut(Mod, 2, 1);
+       ImportNodeOneOut<mlir::ONNXModOp>(node, 2, 1, {
+         {"fmod","int","0"}
+      });
     }else if (OpName == "Mul") {
-       OneOut(Mul, 2, 1);
+       ImportNodeOneOut<mlir::ONNXMulOp>(node, 2, 1, {
+      });
     }else if (OpName == "Multinomial") {
-       OneOut(Multinomial, 1, 1);
+       ImportNodeOneOut<mlir::ONNXMultinomialOp>(node, 1, 1, {
+         {"dtype","int","6"}
+        ,{"sample_size","int","1"}
+        ,{"seed","", ""}
+      });
     }else if (OpName == "Neg") {
-       OneOut(Neg, 1, 1);
+       ImportNodeOneOut<mlir::ONNXNegOp>(node, 1, 1, {
+      });
     }else if (OpName == "NonMaxSuppression") {
-       OneOut(NonMaxSuppression, 5, 1);
+       ImportNodeOneOut<mlir::ONNXNonMaxSuppressionOp>(node, 5, 1, {
+         {"center_point_box","int","0"}
+      });
     }else if (OpName == "NonZero") {
-       OneOut(NonZero, 1, 1);
+       ImportNodeOneOut<mlir::ONNXNonZeroOp>(node, 1, 1, {
+      });
     }else if (OpName == "Not") {
-       OneOut(Not, 1, 1);
+       ImportNodeOneOut<mlir::ONNXNotOp>(node, 1, 1, {
+      });
     }else if (OpName == "OneHot") {
-       OneOut(OneHot, 3, 1);
+       ImportNodeOneOut<mlir::ONNXOneHotOp>(node, 3, 1, {
+         {"axis","int","-1"}
+      });
     }else if (OpName == "Or") {
-       OneOut(Or, 2, 1);
+       ImportNodeOneOut<mlir::ONNXOrOp>(node, 2, 1, {
+      });
     }else if (OpName == "PRelu") {
-       OneOut(PRelu, 2, 1);
+       ImportNodeOneOut<mlir::ONNXPReluOp>(node, 2, 1, {
+      });
     }else if (OpName == "Pad") {
-       OneOut(Pad, 3, 1);
+       ImportNodeOneOut<mlir::ONNXPadOp>(node, 3, 1, {
+         {"mode","str","constant"}
+      });
     }else if (OpName == "Pow") {
-       OneOut(Pow, 2, 1);
+       ImportNodeOneOut<mlir::ONNXPowOp>(node, 2, 1, {
+      });
     }else if (OpName == "QLinearConv") {
-       OneOut(QLinearConv, 9, 1);
+       ImportNodeOneOut<mlir::ONNXQLinearConvOp>(node, 9, 1, {
+         {"auto_pad","str","NOTSET"}
+        ,{"dilations","", ""}
+        ,{"group","int","1"}
+        ,{"kernel_shape","", ""}
+        ,{"pads","", ""}
+        ,{"strides","", ""}
+      });
     }else if (OpName == "QLinearMatMul") {
-       OneOut(QLinearMatMul, 8, 1);
+       ImportNodeOneOut<mlir::ONNXQLinearMatMulOp>(node, 8, 1, {
+      });
     }else if (OpName == "QuantizeLinear") {
-       OneOut(QuantizeLinear, 3, 1);
+       ImportNodeOneOut<mlir::ONNXQuantizeLinearOp>(node, 3, 1, {
+      });
     }else if (OpName == "RNN") {
-       MultipleOuts(RNN, 6, 2);
+       ImportNodeMultipleOuts<mlir::ONNXRNNOp>(node, 6, 2, {
+         {"activation_alpha","floats", "{}"}
+        ,{"activation_beta","floats", "{}"}
+        ,{"activations","", "{Tannh, Tanh}"}
+        ,{"clip","", ""}
+        ,{"direction","str","forward"}
+        ,{"hidden_size","", ""}
+      });
     }else if (OpName == "RandomNormal") {
-       OneOut(RandomNormal, 0, 1);
+       ImportNodeOneOut<mlir::ONNXRandomNormalOp>(node, 0, 1, {
+         {"dtype","int","1"}
+        ,{"mean","float","0.0"}
+        ,{"scale","float","1.0"}
+        ,{"seed","", ""}
+        ,{"shape","", ""}
+      });
     }else if (OpName == "RandomNormalLike") {
-       OneOut(RandomNormalLike, 1, 1);
+       ImportNodeOneOut<mlir::ONNXRandomNormalLikeOp>(node, 1, 1, {
+         {"dtype","", ""}
+        ,{"mean","float","0.0"}
+        ,{"scale","float","1.0"}
+        ,{"seed","", ""}
+      });
     }else if (OpName == "RandomUniform") {
-       OneOut(RandomUniform, 0, 1);
+       ImportNodeOneOut<mlir::ONNXRandomUniformOp>(node, 0, 1, {
+         {"dtype","int","1"}
+        ,{"high","float","1.0"}
+        ,{"low","float","0.0"}
+        ,{"seed","", ""}
+        ,{"shape","", ""}
+      });
     }else if (OpName == "RandomUniformLike") {
-       OneOut(RandomUniformLike, 1, 1);
+       ImportNodeOneOut<mlir::ONNXRandomUniformLikeOp>(node, 1, 1, {
+         {"dtype","", ""}
+        ,{"high","float","1.0"}
+        ,{"low","float","0.0"}
+        ,{"seed","", ""}
+      });
     }else if (OpName == "Range") {
-       OneOut(Range, 3, 1);
+       ImportNodeOneOut<mlir::ONNXRangeOp>(node, 3, 1, {
+      });
     }else if (OpName == "Reciprocal") {
-       OneOut(Reciprocal, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReciprocalOp>(node, 1, 1, {
+      });
     }else if (OpName == "ReduceL1") {
-       OneOut(ReduceL1, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceL1Op>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceL2") {
-       OneOut(ReduceL2, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceL2Op>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceLogSum") {
-       OneOut(ReduceLogSum, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceLogSumOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceLogSumExp") {
-       OneOut(ReduceLogSumExp, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceLogSumExpOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceMax") {
-       OneOut(ReduceMax, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceMaxOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceMean") {
-       OneOut(ReduceMean, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceMeanOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceMin") {
-       OneOut(ReduceMin, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceMinOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceProd") {
-       OneOut(ReduceProd, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceProdOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceSum") {
-       OneOut(ReduceSum, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceSumOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "ReduceSumSquare") {
-       OneOut(ReduceSumSquare, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReduceSumSquareOp>(node, 1, 1, {
+         {"axes","", ""}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "Relu") {
-       OneOut(Relu, 1, 1);
+       ImportNodeOneOut<mlir::ONNXReluOp>(node, 1, 1, {
+      });
     }else if (OpName == "Reshape") {
-       OneOut(Reshape, 2, 1);
+       ImportNodeOneOut<mlir::ONNXReshapeOp>(node, 2, 1, {
+      });
     }else if (OpName == "Resize") {
-       OneOut(Resize, 4, 1);
+       ImportNodeOneOut<mlir::ONNXResizeOp>(node, 4, 1, {
+         {"coordinate_transformation_mode","str","half_pixel"}
+        ,{"cubic_coeff_a","float","-0.75"}
+        ,{"exclude_outside","int","0"}
+        ,{"extrapolation_value","float","0.0"}
+        ,{"mode","str","nearest"}
+        ,{"nearest_mode","str","round_prefer_floor"}
+      });
     }else if (OpName == "ReverseSequence") {
-       OneOut(ReverseSequence, 2, 1);
+       ImportNodeOneOut<mlir::ONNXReverseSequenceOp>(node, 2, 1, {
+         {"batch_axis","int","1"}
+        ,{"time_axis","int","0"}
+      });
     }else if (OpName == "RoiAlign") {
-       OneOut(RoiAlign, 3, 1);
+       ImportNodeOneOut<mlir::ONNXRoiAlignOp>(node, 3, 1, {
+         {"mode","str","avg"}
+        ,{"output_height","int","1"}
+        ,{"output_width","int","1"}
+        ,{"sampling_ratio","int","0"}
+        ,{"spatial_scale","float","1.0"}
+      });
     }else if (OpName == "Round") {
-       OneOut(Round, 1, 1);
+       ImportNodeOneOut<mlir::ONNXRoundOp>(node, 1, 1, {
+      });
     }else if (OpName == "Scan") {
-       OneOut(Scan, 1, 1);
+       ImportNodeOneOut<mlir::ONNXScanOp>(node, 1, 1, {
+         {"body","", ""}
+        ,{"num_scan_inputs","", ""}
+        ,{"scan_input_axes","", ""}
+        ,{"scan_input_directions","", ""}
+        ,{"scan_output_axes","", ""}
+        ,{"scan_output_directions","", ""}
+      });
     }else if (OpName == "Scatter") {
-       OneOut(Scatter, 3, 1);
+       ImportNodeOneOut<mlir::ONNXScatterOp>(node, 3, 1, {
+         {"axis","int","0"}
+      });
     }else if (OpName == "ScatterElements") {
-       OneOut(ScatterElements, 3, 1);
+       ImportNodeOneOut<mlir::ONNXScatterElementsOp>(node, 3, 1, {
+         {"axis","int","0"}
+      });
     }else if (OpName == "ScatterND") {
-       OneOut(ScatterND, 3, 1);
+       ImportNodeOneOut<mlir::ONNXScatterNDOp>(node, 3, 1, {
+      });
     }else if (OpName == "Selu") {
-       OneOut(Selu, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSeluOp>(node, 1, 1, {
+         {"alpha","float","1.67326"}
+        ,{"gamma","float","1.0507"}
+      });
     }else if (OpName == "SequenceAt") {
-       OneOut(SequenceAt, 2, 1);
+       ImportNodeOneOut<mlir::ONNXSequenceAtOp>(node, 2, 1, {
+      });
     }else if (OpName == "SequenceConstruct") {
-       OneOut(SequenceConstruct, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSequenceConstructOp>(node, 1, 1, {
+      });
     }else if (OpName == "SequenceEmpty") {
-       OneOut(SequenceEmpty, 0, 1);
+       ImportNodeOneOut<mlir::ONNXSequenceEmptyOp>(node, 0, 1, {
+         {"dtype","", ""}
+      });
     }else if (OpName == "SequenceErase") {
-       OneOut(SequenceErase, 2, 1);
+       ImportNodeOneOut<mlir::ONNXSequenceEraseOp>(node, 2, 1, {
+      });
     }else if (OpName == "SequenceInsert") {
-       OneOut(SequenceInsert, 3, 1);
+       ImportNodeOneOut<mlir::ONNXSequenceInsertOp>(node, 3, 1, {
+      });
     }else if (OpName == "SequenceLength") {
-       OneOut(SequenceLength, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSequenceLengthOp>(node, 1, 1, {
+      });
     }else if (OpName == "Shape") {
-       OneOut(Shape, 1, 1);
+       ImportNodeOneOut<mlir::ONNXShapeOp>(node, 1, 1, {
+      });
     }else if (OpName == "Shrink") {
-       OneOut(Shrink, 1, 1);
+       ImportNodeOneOut<mlir::ONNXShrinkOp>(node, 1, 1, {
+         {"bias","float","0.0"}
+        ,{"lambd","float","0.5"}
+      });
     }else if (OpName == "Sigmoid") {
-       OneOut(Sigmoid, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSigmoidOp>(node, 1, 1, {
+      });
     }else if (OpName == "Sign") {
-       OneOut(Sign, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSignOp>(node, 1, 1, {
+      });
     }else if (OpName == "Sin") {
-       OneOut(Sin, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSinOp>(node, 1, 1, {
+      });
     }else if (OpName == "Sinh") {
-       OneOut(Sinh, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSinhOp>(node, 1, 1, {
+      });
     }else if (OpName == "Size") {
-       OneOut(Size, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSizeOp>(node, 1, 1, {
+      });
     }else if (OpName == "Slice") {
-       OneOut(Slice, 5, 1);
+       ImportNodeOneOut<mlir::ONNXSliceOp>(node, 5, 1, {
+      });
     }else if (OpName == "Softmax") {
-       OneOut(Softmax, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSoftmaxOp>(node, 1, 1, {
+         {"axis","int","1"}
+      });
     }else if (OpName == "Softplus") {
-       OneOut(Softplus, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSoftplusOp>(node, 1, 1, {
+      });
     }else if (OpName == "Softsign") {
-       OneOut(Softsign, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSoftsignOp>(node, 1, 1, {
+      });
     }else if (OpName == "SpaceToDepth") {
-       OneOut(SpaceToDepth, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSpaceToDepthOp>(node, 1, 1, {
+         {"blocksize","", ""}
+      });
     }else if (OpName == "Split") {
-       OneOut(Split, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSplitOp>(node, 1, 1, {
+         {"axis","int","0"}
+        ,{"split","", ""}
+      });
     }else if (OpName == "SplitToSequence") {
-       OneOut(SplitToSequence, 2, 1);
+       ImportNodeOneOut<mlir::ONNXSplitToSequenceOp>(node, 2, 1, {
+         {"axis","int","0"}
+        ,{"keepdims","int","1"}
+      });
     }else if (OpName == "Sqrt") {
-       OneOut(Sqrt, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSqrtOp>(node, 1, 1, {
+      });
     }else if (OpName == "Squeeze") {
-       OneOut(Squeeze, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSqueezeOp>(node, 1, 1, {
+         {"axes","", ""}
+      });
     }else if (OpName == "StringNormalizer") {
-       OneOut(StringNormalizer, 1, 1);
+       ImportNodeOneOut<mlir::ONNXStringNormalizerOp>(node, 1, 1, {
+         {"case_change_action","str","NONE"}
+        ,{"is_case_sensitive","int","0"}
+        ,{"locale","", ""}
+        ,{"stopwords","", ""}
+      });
     }else if (OpName == "Sub") {
-       OneOut(Sub, 2, 1);
+       ImportNodeOneOut<mlir::ONNXSubOp>(node, 2, 1, {
+      });
     }else if (OpName == "Sum") {
-       OneOut(Sum, 1, 1);
+       ImportNodeOneOut<mlir::ONNXSumOp>(node, 1, 1, {
+      });
     }else if (OpName == "Tan") {
-       OneOut(Tan, 1, 1);
+       ImportNodeOneOut<mlir::ONNXTanOp>(node, 1, 1, {
+      });
     }else if (OpName == "Tanh") {
-       OneOut(Tanh, 1, 1);
+       ImportNodeOneOut<mlir::ONNXTanhOp>(node, 1, 1, {
+      });
     }else if (OpName == "TfIdfVectorizer") {
-       OneOut(TfIdfVectorizer, 1, 1);
+       ImportNodeOneOut<mlir::ONNXTfIdfVectorizerOp>(node, 1, 1, {
+         {"max_gram_length","", ""}
+        ,{"max_skip_count","", ""}
+        ,{"min_gram_length","", ""}
+        ,{"mode","", ""}
+        ,{"ngram_counts","", ""}
+        ,{"ngram_indexes","", ""}
+        ,{"pool_int64s","", ""}
+        ,{"pool_strings","", ""}
+        ,{"weights","", ""}
+      });
     }else if (OpName == "ThresholdedRelu") {
-       OneOut(ThresholdedRelu, 1, 1);
+       ImportNodeOneOut<mlir::ONNXThresholdedReluOp>(node, 1, 1, {
+         {"alpha","float","1.0"}
+      });
     }else if (OpName == "Tile") {
-       OneOut(Tile, 2, 1);
+       ImportNodeOneOut<mlir::ONNXTileOp>(node, 2, 1, {
+      });
     }else if (OpName == "TopK") {
-       MultipleOuts(TopK, 2, 2);
+       ImportNodeMultipleOuts<mlir::ONNXTopKOp>(node, 2, 2, {
+         {"axis","int","-1"}
+        ,{"largest","int","1"}
+        ,{"sorted","int","1"}
+      });
     }else if (OpName == "Transpose") {
-       OneOut(Transpose, 1, 1);
+       ImportNodeOneOut<mlir::ONNXTransposeOp>(node, 1, 1, {
+         {"perm","", ""}
+      });
     }else if (OpName == "Unique") {
-       MultipleOuts(Unique, 1, 4);
+       ImportNodeMultipleOuts<mlir::ONNXUniqueOp>(node, 1, 4, {
+         {"axis","", ""}
+        ,{"sorted","int","1"}
+      });
     }else if (OpName == "Unsqueeze") {
-       OneOut(Unsqueeze, 1, 1);
+       ImportNodeOneOut<mlir::ONNXUnsqueezeOp>(node, 1, 1, {
+         {"axes","ints", ""}
+      });
     }else if (OpName == "Upsample") {
-       OneOut(Upsample, 2, 1);
+       ImportNodeOneOut<mlir::ONNXUpsampleOp>(node, 2, 1, {
+         {"mode","str","nearest"}
+      });
     }else if (OpName == "Where") {
-       OneOut(Where, 3, 1);
+       ImportNodeOneOut<mlir::ONNXWhereOp>(node, 3, 1, {
+      });
     }else if (OpName == "Xor") {
-       OneOut(Xor, 2, 1);
+       ImportNodeOneOut<mlir::ONNXXorOp>(node, 2, 1, {
+      });
     }

src/compiler/dialect/onnx/gen_doc.py

@@ -352,6 +352,121 @@ def gen_schema(schema) :
 
     return s
 
+"""
+special cases:
+* Split: attr split default value: sizeof(output1) namely 1
+* Conv: attr dilations default value is {num_dim of first input - 2, 1}
+* Conv: attr kernel_shape type is ints
+* Transpose: attr perm default value is {} empty int list
+"""
+
+def gen_code(schema,fefile) :
+    special_handler = dict([
+        ("Conv", "ImportNodeConv"),
+        #("Transpose", "ImportNodeTranspose")
+        ])
+    special_type = dict([
+        ("AveragePool "+"kernel_shape", '"ints", ""'),
+        ("MaxPool "+"kernel_shape", '"ints", ""'),
+        ("Cast "+"to", '"int", "0"'),
+        ("Concat "+"axis", '"int", "0"'),
+        ("Conv "+"group", '"int", "1"'),
+        ("Unsqueeze "+"axes", '"ints", ""'),
+        ("RNN "+"activation_alpha", '"floats", "{}"'),
+        ("RNN "+"activation_beta", '"floats", "{}"'),
+        ("RNN "+"activations", '"", "{Tannh, Tanh}"'),
+        ("LRN "+"size", '"int", ""')
+        ])
+    line_indent = '  '
+    fefile.write('    '+'}else if (OpName == "'+schema.name+'") {\n')
+    op_type_str='mlir::ONNX'+schema.name+'Op'
+    if schema.name in special_handler :
+        fefile.write('       '+special_handler[schema.name]+'(node, '
+          +str(len(schema.inputs))
+          +', ' +str(len(schema.outputs))+', {\n')
+    elif len(schema.outputs) > 1 :
+        fefile.write('       '+'ImportNodeMultipleOuts<'+op_type_str+'>(node, '
+          +str(len(schema.inputs))
+          +', ' +str(len(schema.outputs))+', {\n')
+    else :
+        fefile.write('       '+'ImportNodeOneOut<'+op_type_str+'>(node, '
+          +str(len(schema.inputs))
+          +', ' +str(len(schema.outputs))+', {\n')
+    
+
+    if schema.attributes:
+        first_attr = True
+        for _, attr in sorted(schema.attributes.items()):
+            attr_line = line_indent+line_indent+line_indent+line_indent
+            if not first_attr:
+                attr_line += ',{'
+            else :
+                attr_line += ' {'
+            first_attr = False
+
+            attr_line += '"'+attr.name+'",'
+
+            if schema.name+' '+attr.name in special_type:
+                attr_line += special_type[schema.name+' '+attr.name]
+            # option holds either required or default value
+            elif attr.required:
+                attr_line += '"", ""'
+      
+            elif attr.default_value.name:
+                default_value = helper.get_attribute_value(attr.default_value)
+
+                def format_value(value):  # type: (Any) -> Text
+                    if isinstance(value, float):
+                        formatted = str(np.round(value, 5))
+                        # use default formatting, unless too long.
+                        if (len(formatted) > 10):
+                            formatted = str("({:e})".format(value))
+                        return formatted
+                    elif isinstance(value, (bytes, bytearray)) and sys.version_info[0] == 3:
+                        return str(value.decode('utf-8'))
+                    return str(value)
+
+                if isinstance(default_value, list):
+                    value = default_value[0]
+                    default_value = [format_value(val) for val in default_value]
+                    # TODO the list type is homogenous or htergeneous?
+                   
+                    if isinstance(value, float) : 
+                        attr_type_str = '"floats"'
+                    elif isinstance(value, int) :
+                        attr_type_str = '"ints"'
+                    elif isinstance(value, str) :
+                        attr_type_str = '"strs"'
+                    elif isinstance(value, (bytes, bytearray)) :
+                        attr_type_str = '"strs"'
+                    else :
+                        attr_type_str = '"unknowns"'
+                    attr_option_str = '"{}"'.format(default_value)
+                    attr_option_str = attr_option_str.replace('[', '{', 1)
+                    attr_option_str = attr_option_str.replace(']', '}', 1)
+                else:
+                    if isinstance(default_value, float) : 
+                        attr_type_str = '"float"'
+                    elif isinstance(default_value, int) :
+                        attr_type_str = '"int"'
+                    elif isinstance(default_value, str) :
+                        attr_type_str = '"str"'
+                    elif isinstance(default_value, (bytes, bytearray)) :
+                        attr_type_str = '"str"'
+                    else :
+                        attr_type_str = '"unknown"'
+                    default_value = format_value(default_value)
+                    attr_option_str = '"{}"'.format(default_value)
+                attr_line += attr_type_str+','+attr_option_str
+            else:
+                #TODO why?
+                attr_line += '"", ""'
+            attr_line += '}\n'
+            fefile.write(attr_line)
+    fefile.write(line_indent+line_indent+line_indent+'});\n')
+          
+
+
 def main(args):  # type: (Type[Args]) -> None
     with io.open(args.changelog, 'w', newline='') as fout:
         fout.write('## Operator Changelog\n')
@@ -453,6 +568,7 @@ def main(args):  # type: (Type[Args]) -> None
         fefile=io.open('op_build_table.inc', 'w', newline='')
         firstfunc = True
 
+        fefile.write('    '+'if (OpName == "DUMMY") {\n')
         for domain, supportmap in operator_schemas:
             s = '## {}\n'.format(display_domain_short(domain))
             fout.write(s)
@@ -461,21 +577,8 @@ def main(args):  # type: (Type[Args]) -> None
                 for op_type, schema, versions in namemap:
                     # op_type
                     #print("check point 1", schema.name, len(schema.inputs), len(schema.outputs))
-                    if firstfunc :
+                    gen_code(schema, fefile)
-                        fefile.write('    '+'if (OpName == "'+schema.name+'") {\n')
+
-                        firstfunc = False
-                    else :
-                        fefile.write('    '+'}else if (OpName == "'+schema.name+'") {\n')
-                    if len(schema.outputs) > 1 :
-                        fefile.write('       '+'MultipleOuts('+schema.name+', '
-                          +str(schema.since_version)+', '
-                          +str(len(schema.inputs))+', '
-                          +str(len(schema.outputs))+');\n')
-                    else :
-                        fefile.write('       '+'OneOut('+schema.name+', '
-                          +str(schema.since_version)+', '
-                          +str(len(schema.inputs))+', '
-                          +str(len(schema.outputs))+');\n')
                     r = gen_schema(schema)
                     tdfile.write(r)
                     s = ('### {}<a name="{}"></a><a name="{}">**{}**' + (' (deprecated)' if schema.deprecated else '') + '</a>\n').format(

src/compiler/dialect/onnx/onnx.td

@@ -71,4 +71,26 @@ def ONNXFullGemmOp: ONNX_Op<"FullGemm",
   let results = (outs AnyTensor);
 }
 
+def ONNXConv1Op:ONNX_Op<"Conv1", 
+    [NoSideEffect]> {
+  let summary = "ONNX Conv operation";
+  let description = [{
+    "The convolution operator consumes an input tensor and a filter, and"
+    "computes the output."
+  }];
+  let arguments = (ins AnyTensor:$X);
+  let results = (outs AnyTensor);
+}
+
+def ONNXConv3Op:ONNX_Op<"Conv3", 
+    [NoSideEffect]> {
+  let summary = "ONNX Conv operation";
+  let description = [{
+    "The convolution operator consumes an input tensor and a filter, and"
+    "computes the output."
+  }];
+  let arguments = (ins AnyTensor:$X, AnyTensor:$W, AnyTensor:$B);
+  let results = (outs AnyTensor);
+}
+
 #endif // ONNX_OPS