Merge remote-tracking branch 'origin/master' into matmul-shape

.circleci/config.yml

@@ -3,27 +3,12 @@ jobs:
   build:
     docker:
       - image: circleci/python
+    resource_class: medium+
 
     steps:
       - run:
           name: Installing GCC, CMake, Ninja, Protobuf
           command: sudo apt-get update && sudo apt-get install -y gcc g++ cmake ninja-build protobuf-compiler
-      # Use cached mlir installation if possible.
-      - restore_cache:
-          key: V2-LLVM-PROJECT-{{ arch }}
-      - run:
-          name: Install MLIR
-          command: |
-            # Check whether cache restoration succeeds by checking whether
-            # mlir-opt executable exists.
-            if [ ! -f llvm-project/build/bin/mlir-opt ]; then
-              export MAKEFLAGS=-j4
-              source utils/install-mlir.sh
-            fi
-      - save_cache:
-          key: V2-LLVM-PROJECT-{{ arch }}
-          paths:
-            - llvm-project
       - checkout:
           path: ONNF
       - run:
@@ -31,9 +16,30 @@ jobs:
           command: |
             cd ONNF
             git submodule update --init --recursive
+      # Use cached mlir installation if possible.
+      - restore_cache:
+          key: V4-LLVM-PROJECT-{{ arch }}
+      - run:
+          name: Install MLIR
+          command: |
+            # Check whether cache restoration succeeds by checking whether
+            # mlir-opt executable exists.
+            if [ ! -f llvm-project/build/bin/mlir-opt ]; then
+              source ONNF/utils/install-mlir.sh
+            fi
+      - save_cache:
+          key: V4-LLVM-PROJECT-{{ arch }}
+          paths:
+            - llvm-project
       - run:
           name: Install ONNF
           command: source ONNF/utils/install-onnf.sh
+      - run:
+          name: Run End-To-End Tests
+          command: |
+            sudo pip install -q onnx
+            cd ONNF/build
+            cmake --build . --target run-onnx-backend-test
       - run:
           name: Run DocCheck
           command: cd ONNF/build && cmake --build . --target check-doc

.gitmodules

@@ -7,3 +7,6 @@
 [submodule "third_party/pybind11"]
 	path = third_party/pybind11
 	url = https://github.com/pybind/pybind11.git
+[submodule "third_party/variant"]
+	path = third_party/variant
+	url = git@github.com:mpark/variant.git

CMakeLists.txt

@@ -22,9 +22,9 @@ include(MLIR.cmake)
 add_subdirectory(third_party/onnx)
 add_subdirectory(third_party/benchmark)
 add_subdirectory(third_party/pybind11)
+add_subdirectory(third_party/variant)
 
 set(CMAKE_CXX_STANDARD 14)
 add_subdirectory(src)
 add_subdirectory(doc)
 add_subdirectory(test)
-

README.md

@@ -20,7 +20,8 @@ cmake -G Ninja ../llvm \
    -DLLVM_ENABLE_ASSERTIONS=ON \
    -DLLVM_ENABLE_RTTI=ON
 
-cmake --build . --target check-mlir -- ${MAKEFLAGS}
+cmake --build . --target
+cmake --build . --target check-mlir
 ```
 
 Two environment variables need to be set:
@@ -42,6 +43,7 @@ cmake ..
 cmake --build . --target onnf
 
 # Run FileCheck tests:
+export LIT_OPTS=-v
 cmake --build . --target check-mlir-lit
 ```
 

SharingWork.md

@@ -27,6 +27,7 @@ ONNX operations for which some work is needed.
 | Selu        | Tung                  | v              | v                     |                                          |
 | Sigmoid     | Tung                  | v              | v                     |                                          |
 | Sinh        | Tung                  | v              | v                     |                                          |
+| Softmax     | Tung                  | v              | v                     |                                          |
 | Sub         | Tung                  | v              | v                     | M                                        |
 | Sum         | Tung                  | v              | v                     | M                                        |
 | Tanh        | Tung                  | v              | v                     |                                          |

src/CMakeLists.txt

@@ -69,8 +69,9 @@ add_subdirectory(runtime)
 add_executable(onnf main.cpp)
 
 target_link_libraries(onnf builder ${MLIRLibs} onnf_transform onnf_shape_inference onnf_lower_frontend)
-set_target_properties(onnf PROPERTIES LINK_FLAGS "-lz")
 whole_archive_link_mlir(onnf ${MLIRWholeArchiveLibs})
+find_package(ZLIB REQUIRED)
+target_link_libraries(onnf ${ZLIB_LIBRARIES})
 
 target_include_directories(onnf PRIVATE ${CMAKE_SOURCE_DIR})
 target_include_directories(onnf PRIVATE ${CMAKE_BINARY_DIR})

src/builder/CMakeLists.txt

@@ -7,8 +7,9 @@ add_library(builder
 target_include_directories(builder PRIVATE ${CMAKE_SOURCE_DIR})
 target_include_directories(builder PRIVATE ${CMAKE_BINARY_DIR})
 
-target_link_libraries(builder compiler onnx ${MLIRLibs} curses)
+target_link_libraries(builder compiler onnx ${MLIRLibs} curses mpark_variant)
 target_include_directories(builder
                            PRIVATE
                            ${CMAKE_SOURCE_DIR}/third_party/onnx
+                           ${CMAKE_SOURCE_DIR}/third_party/variant
                            ${CMAKE_SOURCE_DIR})

src/builder/frontend_dialect_transformer.cpp

@@ -14,11 +14,16 @@
 //
 //===----------------------------------------------------------------------===//
 
+#include <map>
 #include <numeric>
 #include <regex>
 #include <string>
 #include <tuple>
-#include <map>
+
+// Using backported variant.
+// bstd = backported standard library.
+#include <mpark/variant.hpp>
+namespace bstd = mpark;
 
 #include "mlir/Analysis/Verifier.h"
 #include "mlir/Dialect/StandardOps/Ops.h"
@@ -42,15 +47,15 @@
 namespace onnf {
 namespace {
 
-void replaceAll(
+void replaceAll(std::string &str, const std::string &from,
-    std::string& str, const std::string& from, const std::string& to) {
+                const std::string &to) {
   if (from.empty())
     return;
   size_t start_pos = 0;
   while ((start_pos = str.find(from, start_pos)) != std::string::npos) {
     str.replace(start_pos, from.length(), to);
-    start_pos += to.length();  // In case 'to' contains 'from', like replacing
+    start_pos += to.length(); // In case 'to' contains 'from', like replacing
-                               // 'x' with 'yx'
+                              // 'x' with 'yx'
   }
 }
 
@@ -71,10 +76,10 @@ struct OnnxOnnfSymbolMapping {
    *  @param name onnx tensor name.
    *  @return onnf tensor corresponding to `name`.
    */
-  mlir::Value GetTensorByOnnxName(std::string name) {
+  mlir::Value GetTensorByOnnxName(const std::string &name) {
     assert(onnx_name2onnf_tensor.find(legalize_name(name)) !=
-                            onnx_name2onnf_tensor.end() &&
+               onnx_name2onnf_tensor.end() &&
-                        "Tensor not found");
+           "Tensor not found");
     return onnx_name2onnf_tensor.at(legalize_name(name));
   }
 
@@ -83,9 +88,9 @@ struct OnnxOnnfSymbolMapping {
    *  @param name onnx tensor name.
    *  @param tensor MLIR Value  pointer.
    */
-  void AddMapping(std::string name, mlir::Value tensor) {
+  void AddMapping(const std::string &name, mlir::Value tensor) {
     assert(onnx_name2onnf_tensor.count(legalize_name(name)) == 0 &&
-                        "Tensor already exists.");
+           "Tensor already exists.");
     onnx_name2onnf_tensor.emplace(legalize_name(name), tensor);
   }
 
@@ -124,34 +129,34 @@ private:
   // Convert type to MLIR type.
   // A complete list of types can be found in:
   // <onnf-build-folder>/third_party/onnx/onnx/onnx.pb.h
-  mlir::Type TypeConvert(onnx::TensorProto_DataType intype) {
+  mlir::Type convertONNXTypeToMLIRType(onnx::TensorProto_DataType onnxType) {
-    switch (intype) {
+    switch (onnxType) {
-      case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT16:
+    case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT16:
-        return builder_.getF16Type();
+      return builder_.getF16Type();
-      case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT:
+    case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT:
-        return builder_.getF32Type();
+      return builder_.getF32Type();
-      case onnx::TensorProto_DataType::TensorProto_DataType_DOUBLE:
+    case onnx::TensorProto_DataType::TensorProto_DataType_DOUBLE:
-        return builder_.getF64Type();
+      return builder_.getF64Type();
-      case onnx::TensorProto_DataType::TensorProto_DataType_INT8:
+    case onnx::TensorProto_DataType::TensorProto_DataType_INT8:
-      case onnx::TensorProto_DataType::TensorProto_DataType_UINT8:
+    case onnx::TensorProto_DataType::TensorProto_DataType_UINT8:
-        return builder_.getIntegerType(8);
+      return builder_.getIntegerType(8);
-      case onnx::TensorProto_DataType::TensorProto_DataType_INT16:
+    case onnx::TensorProto_DataType::TensorProto_DataType_INT16:
-      case onnx::TensorProto_DataType::TensorProto_DataType_UINT16:
+    case onnx::TensorProto_DataType::TensorProto_DataType_UINT16:
-        return builder_.getIntegerType(16);
+      return builder_.getIntegerType(16);
-      case onnx::TensorProto_DataType::TensorProto_DataType_INT32:
+    case onnx::TensorProto_DataType::TensorProto_DataType_INT32:
-      case onnx::TensorProto_DataType::TensorProto_DataType_UINT32:
+    case onnx::TensorProto_DataType::TensorProto_DataType_UINT32:
-        return builder_.getIntegerType(32);
+      return builder_.getIntegerType(32);
-      case onnx::TensorProto_DataType::TensorProto_DataType_INT64:
+    case onnx::TensorProto_DataType::TensorProto_DataType_INT64:
-      case onnx::TensorProto_DataType::TensorProto_DataType_UINT64:
+    case onnx::TensorProto_DataType::TensorProto_DataType_UINT64:
-        return builder_.getIntegerType(64);
+      return builder_.getIntegerType(64);
-      case onnx::TensorProto_DataType::TensorProto_DataType_BOOL:
+    case onnx::TensorProto_DataType::TensorProto_DataType_BOOL:
-        return builder_.getI1Type();
+      return builder_.getI1Type();
-      case onnx::TensorProto_DataType::TensorProto_DataType_STRING:
+    case onnx::TensorProto_DataType::TensorProto_DataType_STRING:
-      case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX64:
+    case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX64:
-      case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX128:
+    case onnx::TensorProto_DataType::TensorProto_DataType_COMPLEX128:
-      case onnx::TensorProto_DataType::TensorProto_DataType_UNDEFINED:
+    case onnx::TensorProto_DataType::TensorProto_DataType_UNDEFINED:
-        assert(false && "Unsupported data type encountered.");
+      assert(false && "Unsupported data type encountered.");
-        return nullptr;
+      return nullptr;
     }
   }
 
@@ -169,8 +174,8 @@ private:
     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(
+        assert(dim_numeric_size != 0 &&
-            dim_numeric_size != 0 && "Parsed an input tensor with a dimension size of zero");
+               "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.
@@ -184,7 +189,7 @@ private:
     }
 
     mlir::Type elementType =
-        TypeConvert(input.type().tensor_type().elem_type());
+        convertONNXTypeToMLIRType(input.type().tensor_type().elem_type());
     llvm::ArrayRef<int64_t> tensor_dims(dims.data(), dims.size());
     arg_types.emplace_back(
         mlir::RankedTensorType::get(tensor_dims, elementType));
@@ -200,288 +205,111 @@ private:
   void ImportInputTensorSymbol(const onnx::ValueInfoProto &input,
                                mlir::Value symbol) {
     auto input_tensor_legalized_name = legalize_name(input.name());
-    assert(
+    assert(!frontend_symbols_.ContainKey(input_tensor_legalized_name) &&
-        !frontend_symbols_.ContainKey(input_tensor_legalized_name) &&
+           "Found duplicate legalized input tensor names.");
-        "Found duplicate legalized input tensor names.");
     frontend_symbols_.AddMapping(input_tensor_legalized_name, symbol);
   }
 
-  template <typename T>
+  typedef bstd::variant<int64_t, std::vector<int64_t>, float,
-  T get_attr_generic(onnx::NodeProto &node, std::string name,
+                        std::vector<float>, std::string,
-                     std::function<T(onnx::AttributeProto &)> attr_getter,
+                        std::vector<std::string>>
-                     T default_val) {
+      AttrValueType;
+
+  struct ONNXAttrVisitor {
+    ONNXAttrVisitor(std::string name, mlir::OpBuilder &builder)
+        : _builder(builder), _name(std::move(name)) {}
+
+    // Op builder.
+    mlir::OpBuilder &_builder;
+
+    // Name of the attribute being inspected.
+    std::string _name;
+
+    mlir::NamedAttribute operator()(int64_t const &r) {
+      auto val = _builder.getI32IntegerAttr(r);
+      return _builder.getNamedAttr(_name, val);
+    }
+
+    mlir::NamedAttribute operator()(std::vector<int64_t> const &ints) {
+      auto val = _builder.getI64ArrayAttr(ints);
+      return _builder.getNamedAttr(_name, val);
+    }
+
+    mlir::NamedAttribute operator()(float const &r) {
+      auto val = _builder.getF32FloatAttr(r);
+      return _builder.getNamedAttr(_name, val);
+    }
+
+    mlir::NamedAttribute operator()(std::vector<float> const &floats) {
+      auto val = _builder.getF32ArrayAttr(floats);
+      return _builder.getNamedAttr(_name, val);
+    }
+
+    mlir::NamedAttribute operator()(std::string const &s) {
+      auto val = _builder.getStringAttr(s);
+      return _builder.getNamedAttr(_name, val);
+    }
+
+    mlir::NamedAttribute operator()(std::vector<std::string> const &r) {
+      assert(false && "type of attribute value is not implemented");
+      auto val = _builder.getI32IntegerAttr(1);
+      return _builder.getNamedAttr(_name, val);
+    };
+  };
+
+  mlir::NamedAttribute convertNameValuePairToNamedAttribute(
+      std::pair<std::string, AttrValueType> nameAndVal) {
+    auto visitor = ONNXAttrVisitor(nameAndVal.first, builder_);
+    return mpark::visit(visitor, nameAndVal.second);
+  }
+
+  static std::pair<std::string, AttrValueType>
+  convertAttributeProtoToNameValuePair(onnx::AttributeProto &attr) {
+    AttrValueType val;
+    switch (attr.type()) {
+    case onnx::AttributeProto::FLOAT:
+      return std::make_pair(attr.name(), AttrValueType(attr.f()));
+    case onnx::AttributeProto::INT:
+      return std::make_pair(attr.name(), AttrValueType(attr.i()));
+    case onnx::AttributeProto::STRING:
+      return std::make_pair(attr.name(), AttrValueType(attr.s()));
+    case onnx::AttributeProto::FLOATS:
+      val = AttrValueType(
+          std::vector<float>(attr.floats().begin(), attr.floats().end()));
+      return std::make_pair(attr.name(), val);
+    case onnx::AttributeProto::INTS:
+      val = AttrValueType(
+          std::vector<int64_t>(attr.ints().begin(), attr.ints().end()));
+      return std::make_pair(attr.name(), val);
+    default:
+      assert(false && "datatype for attribute is not implemented");
+      break;
+    }
+  }
+
+  std::vector<mlir::NamedAttribute> ImportNodeAttributes(
+      const onnx::NodeProto &node,
+      std::initializer_list<std::pair<std::string, AttrValueType>>
+          defaultAttrList) {
+    std::vector<mlir::NamedAttribute> attributes;
+    std::set<std::string> definedAttributeSet;
     for (int i = 0; i < node.attribute_size(); ++i) {
       auto attr = node.attribute(i);
-      if (attr.name() == name) {
+      auto nameValPair = convertAttributeProtoToNameValuePair(attr);
-        return attr_getter(attr);
+      attributes.push_back(convertNameValuePairToNamedAttribute(nameValPair));
-      }
+      definedAttributeSet.insert(attr.name());
     }
-    return default_val;
+    for (const auto &defaultAttr : defaultAttrList) {
-  }
+      if (definedAttributeSet.find(defaultAttr.first) ==
-
+          definedAttributeSet.end())
-  template <typename T>
+        attributes.push_back(convertNameValuePairToNamedAttribute(defaultAttr));
-  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!");
+    return attributes;
   }
 
-  auto get_attr_ints(onnx::NodeProto &node, std::string name,
+  void ImportNodeGeneric(const onnx::NodeProto &node) {
-                     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()) {
+    for (const auto &item : node.input()) {
       if (frontend_symbols_.ContainKey(legalize_name(item))) {
         inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
       }
@@ -511,12 +339,12 @@ private:
    * default}
    */
   template <typename T>
-  void ImportNodeOneOut(
+  void
-      onnx::NodeProto node, int nIn, int nOut,
+  ImportNodeOneOut(const onnx::NodeProto &node, int nIn, int nOut,
-      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+                   std::initializer_list<std::pair<std::string, AttrValueType>>
-          attrs) {
+                       defaultAttrList) {
     std::vector<mlir::Value> inputs;
-    for (auto item : node.input()) {
+    for (const auto &item : node.input()) {
       if (frontend_symbols_.ContainKey(legalize_name(item))) {
         inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
       }
@@ -528,22 +356,7 @@ private:
           mlir::UnrankedTensorType::get(builder_.getF32Type()));
     }
 
-    std::vector<mlir::NamedAttribute> attributes;
+    auto attributes = ImportNodeAttributes(node, defaultAttrList);
-    // 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();
 
@@ -559,11 +372,11 @@ private:
 
   template <typename T>
   void ImportNodeMultipleOuts(
-      onnx::NodeProto node, int nIn, int nOut,
+      const onnx::NodeProto &node, int nIn, int nOut,
-      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+      std::initializer_list<std::pair<std::string, AttrValueType>>
-          attrs) {
+          defaultAttrList) {
     std::vector<mlir::Value> inputs;
-    for (auto item : node.input()) {
+    for (const auto &item : node.input()) {
       if (frontend_symbols_.ContainKey(legalize_name(item))) {
         inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
       }
@@ -575,21 +388,7 @@ private:
           mlir::UnrankedTensorType::get(builder_.getF32Type()));
     }
 
-    std::vector<mlir::NamedAttribute> attributes;
+    auto attributes = ImportNodeAttributes(node, defaultAttrList);
-    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();
 
@@ -610,10 +409,10 @@ private:
    * c++ does not allow template specialization inside a class scope
    * a specialized function is used
    */
-  void ImportNodeConv(
+  void
-      onnx::NodeProto node, int nOut,
+  ImportNodeConv(onnx::NodeProto node, int nIn, int nOut,
-      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+                 std::initializer_list<std::pair<std::string, AttrValueType>>
-          attrs) {
+                     defaultAttrList) {
     // 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
@@ -627,29 +426,32 @@ private:
     int nOps = node.input().size();
 
     if (nOps == 2)
-      ImportNodeOneOut<mlir::ONNXConvNoBiasOp>(node, nOps, nOut, attrs);
+      ImportNodeOneOut<mlir::ONNXConvNoBiasOp>(
+          node, nOps, nOut, defaultAttrList);
     else
-      ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut, attrs);
+      ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut, defaultAttrList);
   }
 
   /*!
    * Special handle for MaxPool operations.
    */
   void ImportNodeMaxPool(
-      onnx::NodeProto node, int nIn,
+      onnx::NodeProto node, int nIn, int nOut,
-      std::initializer_list<std::tuple<std::string, std::string, std::string>>
+      std::initializer_list<std::pair<std::string, AttrValueType>>
-          attrs) {
+          defaultAttrList) {
     int nOuts = node.output().size();
     if (nOuts == 1) {
-      ImportNodeOneOut<mlir::ONNXMaxPoolSingleOutOp>(node, nIn, nOuts, attrs);
+      ImportNodeOneOut<mlir::ONNXMaxPoolSingleOutOp>(
+          node, nIn, nOuts, defaultAttrList);
     } else {
-      ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(node, nIn, nOuts, attrs);
+      ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(
+          node, nIn, nOuts, defaultAttrList);
     }
   }
 
-  void ImportNode(onnx::NodeProto node) {
+  void ImportNode(const onnx::NodeProto &node) {
     std::vector<mlir::Value> inputs;
-    for (auto item : node.input()) {
+    for (const auto &item : node.input()) {
       if (frontend_symbols_.ContainKey(legalize_name(item))) {
         inputs.push_back(frontend_symbols_.GetTensorByOnnxName(item));
       }
@@ -689,9 +491,8 @@ private:
                           llvm::SmallVectorImpl<mlir::Type> &ret_types,
                           llvm::SmallVectorImpl<mlir::Value> &ret_vals) {
     auto output_tensor_legalized_name = legalize_name(output.name());
-    assert(
+    assert(frontend_symbols_.ContainKey(output_tensor_legalized_name) &&
-        frontend_symbols_.ContainKey(output_tensor_legalized_name) &&
+           "Output tensor not found");
-        "Output tensor not found");
 
     auto tensor_val =
         frontend_symbols_.GetTensorByOnnxName(output_tensor_legalized_name);
@@ -750,9 +551,9 @@ private:
     funcType = builder_.getFunctionType(arg_types, ret_types);
     mainFunc.setType(funcType);
   }
-};  // FrontendGenImpl class
+}; // FrontendGenImpl class
-}  // namespace
+} // namespace
-}  // namespace onnf
+} // namespace onnf
 
 namespace onnf {
 
@@ -775,4 +576,4 @@ void ImportFrontendModelFile(std::string model_fname,
   FrontendGenImpl myONNXGen(context);
   module = myONNXGen.ImportONNXModel(model);
 }
-}  // namespace onnf
+} // namespace onnf

src/builder/op_build_table.inc

@@ -16,13 +16,13 @@
       });
     }else if (OpName == "ArgMax") {
        ImportNodeOneOut<mlir::ONNXArgMaxOp>(node, 1, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
-        ,{"keepdims","int","1"}
+        ,{"keepdims", 1}
       });
     }else if (OpName == "ArgMin") {
        ImportNodeOneOut<mlir::ONNXArgMinOp>(node, 1, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
-        ,{"keepdims","int","1"}
+        ,{"keepdims", 1}
       });
     }else if (OpName == "Asin") {
        ImportNodeOneOut<mlir::ONNXAsinOp>(node, 1, 1, {
@@ -38,25 +38,22 @@
       });
     }else if (OpName == "AveragePool") {
        ImportNodeOneOut<mlir::ONNXAveragePoolOp>(node, 1, 1, {
-         {"auto_pad","str","NOTSET"}
+         {"auto_pad", "NOTSET"}
-        ,{"ceil_mode","int","0"}
+        ,{"ceil_mode", 0}
-        ,{"count_include_pad","int","0"}
+        ,{"count_include_pad", 0}
-        ,{"kernel_shape","ints", ""}
+        ,{"kernel_shape", std::vector<int64_t> {}}
-        ,{"pads","", ""}
-        ,{"strides","", ""}
       });
     }else if (OpName == "BatchNormalization") {
        ImportNodeMultipleOuts<mlir::ONNXBatchNormalizationOp>(node, 5, 5, {
-         {"epsilon","float","1e-05"}
+         {"epsilon", (float)1e-05}
-        ,{"momentum","float","0.9"}
+        ,{"momentum", (float)0.9}
       });
     }else if (OpName == "BitShift") {
        ImportNodeOneOut<mlir::ONNXBitShiftOp>(node, 2, 1, {
-         {"direction","", ""}
       });
     }else if (OpName == "Cast") {
        ImportNodeOneOut<mlir::ONNXCastOp>(node, 1, 1, {
-         {"to","int", "0"}
+         {"to", 0}
       });
     }else if (OpName == "Ceil") {
        ImportNodeOneOut<mlir::ONNXCeilOp>(node, 1, 1, {
@@ -66,54 +63,35 @@
       });
     }else if (OpName == "Compress") {
        ImportNodeOneOut<mlir::ONNXCompressOp>(node, 2, 1, {
-         {"axis","", ""}
       });
     }else if (OpName == "Concat") {
        ImportNodeOneOut<mlir::ONNXConcatOp>(node, 1, 1, {
-         {"axis","int", "0"}
+         {"axis", 0}
       });
     }else if (OpName == "ConcatFromSequence") {
        ImportNodeOneOut<mlir::ONNXConcatFromSequenceOp>(node, 1, 1, {
-         {"axis","", ""}
+         {"new_axis", 0}
-        ,{"new_axis","int","0"}
       });
     }else if (OpName == "Constant") {
        ImportNodeOneOut<mlir::ONNXConstantOp>(node, 0, 1, {
-         {"sparse_value","", ""}
-        ,{"value","", ""}
       });
     }else if (OpName == "ConstantOfShape") {
        ImportNodeOneOut<mlir::ONNXConstantOfShapeOp>(node, 1, 1, {
-         {"value","", ""}
       });
     }else if (OpName == "Conv") {
-       ImportNodeConv(node, 1, {
+       ImportNodeConv(node, 3, 1, {
-         {"auto_pad","str","NOTSET"}
+         {"auto_pad", "NOTSET"}
-        ,{"dilations","", ""}
+        ,{"group", 1}
-        ,{"group","int", "1"}
-        ,{"kernel_shape","", ""}
-        ,{"pads","", ""}
-        ,{"strides","", ""}
       });
     }else if (OpName == "ConvInteger") {
        ImportNodeOneOut<mlir::ONNXConvIntegerOp>(node, 4, 1, {
-         {"auto_pad","str","NOTSET"}
+         {"auto_pad", "NOTSET"}
-        ,{"dilations","", ""}
+        ,{"group", 1}
-        ,{"group","int","1"}
-        ,{"kernel_shape","", ""}
-        ,{"pads","", ""}
-        ,{"strides","", ""}
       });
     }else if (OpName == "ConvTranspose") {
        ImportNodeOneOut<mlir::ONNXConvTransposeOp>(node, 3, 1, {
-         {"auto_pad","str","NOTSET"}
+         {"auto_pad", "NOTSET"}
-        ,{"dilations","", ""}
+        ,{"group", 1}
-        ,{"group","int","1"}
-        ,{"kernel_shape","", ""}
-        ,{"output_padding","", ""}
-        ,{"output_shape","", ""}
-        ,{"pads","", ""}
-        ,{"strides","", ""}
       });
     }else if (OpName == "Cos") {
        ImportNodeOneOut<mlir::ONNXCosOp>(node, 1, 1, {
@@ -123,13 +101,12 @@
       });
     }else if (OpName == "CumSum") {
        ImportNodeOneOut<mlir::ONNXCumSumOp>(node, 2, 1, {
-         {"exclusive","int","0"}
+         {"exclusive", 0}
-        ,{"reverse","int","0"}
+        ,{"reverse", 0}
       });
     }else if (OpName == "DepthToSpace") {
        ImportNodeOneOut<mlir::ONNXDepthToSpaceOp>(node, 1, 1, {
-         {"blocksize","", ""}
+         {"mode", "DCR"}
-        ,{"mode","str","DCR"}
       });
     }else if (OpName == "DequantizeLinear") {
        ImportNodeOneOut<mlir::ONNXDequantizeLinearOp>(node, 3, 1, {
@@ -142,14 +119,14 @@
       });
     }else if (OpName == "Dropout") {
        ImportNodeMultipleOuts<mlir::ONNXDropoutOp>(node, 1, 2, {
-         {"ratio","float","0.5"}
+         {"ratio", (float)0.5}
       });
     }else if (OpName == "DynamicQuantizeLinear") {
        ImportNodeMultipleOuts<mlir::ONNXDynamicQuantizeLinearOp>(node, 1, 3, {
       });
     }else if (OpName == "Elu") {
        ImportNodeOneOut<mlir::ONNXEluOp>(node, 1, 1, {
-         {"alpha","float","1.0"}
+         {"alpha", (float)1.0}
       });
     }else if (OpName == "Equal") {
        ImportNodeOneOut<mlir::ONNXEqualOp>(node, 2, 1, {
@@ -165,50 +142,44 @@
       });
     }else if (OpName == "EyeLike") {
        ImportNodeOneOut<mlir::ONNXEyeLikeOp>(node, 1, 1, {
-         {"dtype","", ""}
+         {"k", 0}
-        ,{"k","int","0"}
       });
     }else if (OpName == "Flatten") {
        ImportNodeOneOut<mlir::ONNXFlattenOp>(node, 1, 1, {
-         {"axis","int","1"}
+         {"axis", 1}
       });
     }else if (OpName == "Floor") {
        ImportNodeOneOut<mlir::ONNXFloorOp>(node, 1, 1, {
       });
     }else if (OpName == "GRU") {
        ImportNodeMultipleOuts<mlir::ONNXGRUOp>(node, 6, 2, {
-         {"activation_alpha","", ""}
+         {"direction", "forward"}
-        ,{"activation_beta","", ""}
+        ,{"linear_before_reset", 0}
-        ,{"activations","", ""}
-        ,{"clip","", ""}
-        ,{"direction","str","forward"}
-        ,{"hidden_size","", ""}
-        ,{"linear_before_reset","int","0"}
       });
     }else if (OpName == "Gather") {
        ImportNodeOneOut<mlir::ONNXGatherOp>(node, 2, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
       });
     }else if (OpName == "GatherElements") {
        ImportNodeOneOut<mlir::ONNXGatherElementsOp>(node, 2, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
       });
     }else if (OpName == "GatherND") {
        ImportNodeOneOut<mlir::ONNXGatherNDOp>(node, 2, 1, {
       });
     }else if (OpName == "Gemm") {
        ImportNodeOneOut<mlir::ONNXGemmOp>(node, 3, 1, {
-         {"alpha","float","1.0"}
+         {"alpha", (float)1.0}
-        ,{"beta","float","1.0"}
+        ,{"beta", (float)1.0}
-        ,{"transA","int","0"}
+        ,{"transA", 0}
-        ,{"transB","int","0"}
+        ,{"transB", 0}
       });
     }else if (OpName == "GlobalAveragePool") {
        ImportNodeOneOut<mlir::ONNXGlobalAveragePoolOp>(node, 1, 1, {
       });
     }else if (OpName == "GlobalLpPool") {
        ImportNodeOneOut<mlir::ONNXGlobalLpPoolOp>(node, 1, 1, {
-         {"p","int","2"}
+         {"p", 2}
       });
     }else if (OpName == "GlobalMaxPool") {
        ImportNodeOneOut<mlir::ONNXGlobalMaxPoolOp>(node, 1, 1, {
@@ -218,53 +189,45 @@
       });
     }else if (OpName == "HardSigmoid") {
        ImportNodeOneOut<mlir::ONNXHardSigmoidOp>(node, 1, 1, {
-         {"alpha","float","0.2"}
+         {"alpha", (float)0.2}
-        ,{"beta","float","0.5"}
+        ,{"beta", (float)0.5}
       });
     }else if (OpName == "Hardmax") {
        ImportNodeOneOut<mlir::ONNXHardmaxOp>(node, 1, 1, {
-         {"axis","int","1"}
+         {"axis", 1}
       });
     }else if (OpName == "Identity") {
        ImportNodeOneOut<mlir::ONNXIdentityOp>(node, 1, 1, {
       });
     }else if (OpName == "If") {
        ImportNodeOneOut<mlir::ONNXIfOp>(node, 1, 1, {
-         {"else_branch","", ""}
-        ,{"then_branch","", ""}
       });
     }else if (OpName == "InstanceNormalization") {
        ImportNodeOneOut<mlir::ONNXInstanceNormalizationOp>(node, 3, 1, {
-         {"epsilon","float","1e-05"}
+         {"epsilon", (float)1e-05}
       });
     }else if (OpName == "IsInf") {
        ImportNodeOneOut<mlir::ONNXIsInfOp>(node, 1, 1, {
-         {"detect_negative","int","1"}
+         {"detect_negative", 1}
-        ,{"detect_positive","int","1"}
+        ,{"detect_positive", 1}
       });
     }else if (OpName == "IsNaN") {
        ImportNodeOneOut<mlir::ONNXIsNaNOp>(node, 1, 1, {
       });
     }else if (OpName == "LRN") {
        ImportNodeOneOut<mlir::ONNXLRNOp>(node, 1, 1, {
-         {"alpha","float","0.0001"}
+         {"alpha", (float)0.0001}
-        ,{"beta","float","0.75"}
+        ,{"beta", (float)0.75}
-        ,{"bias","float","1.0"}
+        ,{"bias", (float)1.0}
-        ,{"size","int", ""}
       });
     }else if (OpName == "LSTM") {
        ImportNodeMultipleOuts<mlir::ONNXLSTMOp>(node, 8, 3, {
-         {"activation_alpha","", ""}
+         {"direction", "forward"}
-        ,{"activation_beta","", ""}
+        ,{"input_forget", 0}
-        ,{"activations","", ""}
-        ,{"clip","", ""}
-        ,{"direction","str","forward"}
-        ,{"hidden_size","", ""}
-        ,{"input_forget","int","0"}
       });
     }else if (OpName == "LeakyRelu") {
        ImportNodeOneOut<mlir::ONNXLeakyReluOp>(node, 1, 1, {
-         {"alpha","float","0.01"}
+         {"alpha", (float)0.01}
       });
     }else if (OpName == "Less") {
        ImportNodeOneOut<mlir::ONNXLessOp>(node, 2, 1, {
@@ -274,24 +237,20 @@
       });
     }else if (OpName == "LogSoftmax") {
        ImportNodeOneOut<mlir::ONNXLogSoftmaxOp>(node, 1, 1, {
-         {"axis","int","1"}
+         {"axis", 1}
       });
     }else if (OpName == "Loop") {
        ImportNodeOneOut<mlir::ONNXLoopOp>(node, 3, 1, {
-         {"body","", ""}
       });
     }else if (OpName == "LpNormalization") {
        ImportNodeOneOut<mlir::ONNXLpNormalizationOp>(node, 1, 1, {
-         {"axis","int","-1"}
+         {"axis", -1}
-        ,{"p","int","2"}
+        ,{"p", 2}
       });
     }else if (OpName == "LpPool") {
        ImportNodeOneOut<mlir::ONNXLpPoolOp>(node, 1, 1, {
-         {"auto_pad","str","NOTSET"}
+         {"auto_pad", "NOTSET"}
-        ,{"kernel_shape","", ""}
+        ,{"p", 2}
-        ,{"p","int","2"}
-        ,{"pads","", ""}
-        ,{"strides","", ""}
       });
     }else if (OpName == "MatMul") {
        ImportNodeOneOut<mlir::ONNXMatMulOp>(node, 2, 1, {
@@ -303,55 +262,47 @@
        ImportNodeOneOut<mlir::ONNXMaxOp>(node, 1, 1, {
       });
     }else if (OpName == "MaxPool") {
-       ImportNodeMaxPool(node, 1, {
+       ImportNodeMaxPool(node, 1, 2, {
-         {"auto_pad","str","NOTSET"}
+         {"auto_pad", "NOTSET"}
-        ,{"ceil_mode","int","0"}
+        ,{"ceil_mode", 0}
-        ,{"dilations","", ""}
+        ,{"kernel_shape", std::vector<int64_t> {}}
-        ,{"kernel_shape","ints", ""}
+        ,{"storage_order", 0}
-        ,{"pads","", ""}
-        ,{"storage_order","int","0"}
-        ,{"strides","", ""}
       });
     }else if (OpName == "MaxRoiPool") {
        ImportNodeOneOut<mlir::ONNXMaxRoiPoolOp>(node, 2, 1, {
-         {"pooled_shape","", ""}
+         {"spatial_scale", (float)1.0}
-        ,{"spatial_scale","float","1.0"}
       });
     }else if (OpName == "MaxUnpool") {
        ImportNodeOneOut<mlir::ONNXMaxUnpoolOp>(node, 3, 1, {
-         {"kernel_shape","", ""}
-        ,{"pads","", ""}
-        ,{"strides","", ""}
       });
     }else if (OpName == "Mean") {
        ImportNodeOneOut<mlir::ONNXMeanOp>(node, 1, 1, {
       });
     }else if (OpName == "MeanVarianceNormalization") {
        ImportNodeOneOut<mlir::ONNXMeanVarianceNormalizationOp>(node, 1, 1, {
-         {"axes","ints","{'0', '2', '3'}"}
+         {"axes", std::vector<int64_t>{0, 2, 3}}
       });
     }else if (OpName == "Min") {
        ImportNodeOneOut<mlir::ONNXMinOp>(node, 1, 1, {
       });
     }else if (OpName == "Mod") {
        ImportNodeOneOut<mlir::ONNXModOp>(node, 2, 1, {
-         {"fmod","int","0"}
+         {"fmod", 0}
       });
     }else if (OpName == "Mul") {
        ImportNodeOneOut<mlir::ONNXMulOp>(node, 2, 1, {
       });
     }else if (OpName == "Multinomial") {
        ImportNodeOneOut<mlir::ONNXMultinomialOp>(node, 1, 1, {
-         {"dtype","int","6"}
+         {"dtype", 6}
-        ,{"sample_size","int","1"}
+        ,{"sample_size", 1}
-        ,{"seed","", ""}
       });
     }else if (OpName == "Neg") {
        ImportNodeOneOut<mlir::ONNXNegOp>(node, 1, 1, {
       });
     }else if (OpName == "NonMaxSuppression") {
        ImportNodeOneOut<mlir::ONNXNonMaxSuppressionOp>(node, 5, 1, {
-         {"center_point_box","int","0"}
+         {"center_point_box", 0}
       });
     }else if (OpName == "NonZero") {
        ImportNodeOneOut<mlir::ONNXNonZeroOp>(node, 1, 1, {
@@ -361,7 +312,7 @@
       });
     }else if (OpName == "OneHot") {
        ImportNodeOneOut<mlir::ONNXOneHotOp>(node, 3, 1, {
-         {"axis","int","-1"}
+         {"axis", -1}
       });
     }else if (OpName == "Or") {
        ImportNodeOneOut<mlir::ONNXOrOp>(node, 2, 1, {
@@ -371,19 +322,15 @@
       });
     }else if (OpName == "Pad") {
        ImportNodeOneOut<mlir::ONNXPadOp>(node, 3, 1, {
-         {"mode","str","constant"}
+         {"mode", "constant"}
       });
     }else if (OpName == "Pow") {
        ImportNodeOneOut<mlir::ONNXPowOp>(node, 2, 1, {
       });
     }else if (OpName == "QLinearConv") {
        ImportNodeOneOut<mlir::ONNXQLinearConvOp>(node, 9, 1, {
-         {"auto_pad","str","NOTSET"}
+         {"auto_pad", "NOTSET"}
-        ,{"dilations","", ""}
+        ,{"group", 1}
-        ,{"group","int","1"}
-        ,{"kernel_shape","", ""}
-        ,{"pads","", ""}
-        ,{"strides","", ""}
       });
     }else if (OpName == "QLinearMatMul") {
        ImportNodeOneOut<mlir::ONNXQLinearMatMulOp>(node, 8, 1, {
@@ -393,42 +340,32 @@
       });
     }else if (OpName == "RNN") {
        ImportNodeMultipleOuts<mlir::ONNXRNNOp>(node, 6, 2, {
-         {"activation_alpha","floats", "{}"}
+         {"activation_alpha", std::vector<float> {}}
-        ,{"activation_beta","floats", "{}"}
+        ,{"activation_beta", std::vector<float> {}}
-        ,{"activations","", "{Tannh, Tanh}"}
+        ,{"activations", std::vector<std::string>{"Tanh", "Tanh"}}
-        ,{"clip","", ""}
+        ,{"direction", "forward"}
-        ,{"direction","str","forward"}
-        ,{"hidden_size","", ""}
       });
     }else if (OpName == "RandomNormal") {
        ImportNodeOneOut<mlir::ONNXRandomNormalOp>(node, 0, 1, {
-         {"dtype","int","1"}
+         {"dtype", 1}
-        ,{"mean","float","0.0"}
+        ,{"mean", (float)0.0}
-        ,{"scale","float","1.0"}
+        ,{"scale", (float)1.0}
-        ,{"seed","", ""}
-        ,{"shape","", ""}
       });
     }else if (OpName == "RandomNormalLike") {
        ImportNodeOneOut<mlir::ONNXRandomNormalLikeOp>(node, 1, 1, {
-         {"dtype","", ""}
+         {"mean", (float)0.0}
-        ,{"mean","float","0.0"}
+        ,{"scale", (float)1.0}
-        ,{"scale","float","1.0"}
-        ,{"seed","", ""}
       });
     }else if (OpName == "RandomUniform") {
        ImportNodeOneOut<mlir::ONNXRandomUniformOp>(node, 0, 1, {
-         {"dtype","int","1"}
+         {"dtype", 1}
-        ,{"high","float","1.0"}
+        ,{"high", (float)1.0}
-        ,{"low","float","0.0"}
+        ,{"low", (float)0.0}
-        ,{"seed","", ""}
-        ,{"shape","", ""}
       });
     }else if (OpName == "RandomUniformLike") {
        ImportNodeOneOut<mlir::ONNXRandomUniformLikeOp>(node, 1, 1, {
-         {"dtype","", ""}
+         {"high", (float)1.0}
-        ,{"high","float","1.0"}
+        ,{"low", (float)0.0}
-        ,{"low","float","0.0"}
-        ,{"seed","", ""}
       });
     }else if (OpName == "Range") {
        ImportNodeOneOut<mlir::ONNXRangeOp>(node, 3, 1, {
@@ -438,53 +375,43 @@
       });
     }else if (OpName == "ReduceL1") {
        ImportNodeOneOut<mlir::ONNXReduceL1Op>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceL2") {
        ImportNodeOneOut<mlir::ONNXReduceL2Op>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceLogSum") {
        ImportNodeOneOut<mlir::ONNXReduceLogSumOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceLogSumExp") {
        ImportNodeOneOut<mlir::ONNXReduceLogSumExpOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceMax") {
        ImportNodeOneOut<mlir::ONNXReduceMaxOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceMean") {
        ImportNodeOneOut<mlir::ONNXReduceMeanOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceMin") {
        ImportNodeOneOut<mlir::ONNXReduceMinOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceProd") {
        ImportNodeOneOut<mlir::ONNXReduceProdOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceSum") {
        ImportNodeOneOut<mlir::ONNXReduceSumOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "ReduceSumSquare") {
        ImportNodeOneOut<mlir::ONNXReduceSumSquareOp>(node, 1, 1, {
-         {"axes","", ""}
+         {"keepdims", 1}
-        ,{"keepdims","int","1"}
       });
     }else if (OpName == "Relu") {
        ImportNodeOneOut<mlir::ONNXReluOp>(node, 1, 1, {
@@ -494,53 +421,47 @@
       });
     }else if (OpName == "Resize") {
        ImportNodeOneOut<mlir::ONNXResizeOp>(node, 4, 1, {
-         {"coordinate_transformation_mode","str","half_pixel"}
+         {"coordinate_transformation_mode", "half_pixel"}
-        ,{"cubic_coeff_a","float","-0.75"}
+        ,{"cubic_coeff_a", (float)-0.75}
-        ,{"exclude_outside","int","0"}
+        ,{"exclude_outside", 0}
-        ,{"extrapolation_value","float","0.0"}
+        ,{"extrapolation_value", (float)0.0}
-        ,{"mode","str","nearest"}
+        ,{"mode", "nearest"}
-        ,{"nearest_mode","str","round_prefer_floor"}
+        ,{"nearest_mode", "round_prefer_floor"}
       });
     }else if (OpName == "ReverseSequence") {
        ImportNodeOneOut<mlir::ONNXReverseSequenceOp>(node, 2, 1, {
-         {"batch_axis","int","1"}
+         {"batch_axis", 1}
-        ,{"time_axis","int","0"}
+        ,{"time_axis", 0}
       });
     }else if (OpName == "RoiAlign") {
        ImportNodeOneOut<mlir::ONNXRoiAlignOp>(node, 3, 1, {
-         {"mode","str","avg"}
+         {"mode", "avg"}
-        ,{"output_height","int","1"}
+        ,{"output_height", 1}
-        ,{"output_width","int","1"}
+        ,{"output_width", 1}
-        ,{"sampling_ratio","int","0"}
+        ,{"sampling_ratio", 0}
-        ,{"spatial_scale","float","1.0"}
+        ,{"spatial_scale", (float)1.0}
       });
     }else if (OpName == "Round") {
        ImportNodeOneOut<mlir::ONNXRoundOp>(node, 1, 1, {
       });
     }else if (OpName == "Scan") {
        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") {
        ImportNodeOneOut<mlir::ONNXScatterOp>(node, 3, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
       });
     }else if (OpName == "ScatterElements") {
        ImportNodeOneOut<mlir::ONNXScatterElementsOp>(node, 3, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
       });
     }else if (OpName == "ScatterND") {
        ImportNodeOneOut<mlir::ONNXScatterNDOp>(node, 3, 1, {
       });
     }else if (OpName == "Selu") {
        ImportNodeOneOut<mlir::ONNXSeluOp>(node, 1, 1, {
-         {"alpha","float","1.67326"}
+         {"alpha", (float)1.67326}
-        ,{"gamma","float","1.0507"}
+        ,{"gamma", (float)1.0507}
       });
     }else if (OpName == "SequenceAt") {
        ImportNodeOneOut<mlir::ONNXSequenceAtOp>(node, 2, 1, {
@@ -550,7 +471,6 @@
       });
     }else if (OpName == "SequenceEmpty") {
        ImportNodeOneOut<mlir::ONNXSequenceEmptyOp>(node, 0, 1, {
-         {"dtype","", ""}
       });
     }else if (OpName == "SequenceErase") {
        ImportNodeOneOut<mlir::ONNXSequenceEraseOp>(node, 2, 1, {
@@ -566,8 +486,8 @@
       });
     }else if (OpName == "Shrink") {
        ImportNodeOneOut<mlir::ONNXShrinkOp>(node, 1, 1, {
-         {"bias","float","0.0"}
+         {"bias", (float)0.0}
-        ,{"lambd","float","0.5"}
+        ,{"lambd", (float)0.5}
       });
     }else if (OpName == "Sigmoid") {
        ImportNodeOneOut<mlir::ONNXSigmoidOp>(node, 1, 1, {
@@ -589,7 +509,7 @@
       });
     }else if (OpName == "Softmax") {
        ImportNodeOneOut<mlir::ONNXSoftmaxOp>(node, 1, 1, {
-         {"axis","int","1"}
+         {"axis", 1}
       });
     }else if (OpName == "Softplus") {
        ImportNodeOneOut<mlir::ONNXSoftplusOp>(node, 1, 1, {
@@ -599,31 +519,26 @@
       });
     }else if (OpName == "SpaceToDepth") {
        ImportNodeOneOut<mlir::ONNXSpaceToDepthOp>(node, 1, 1, {
-         {"blocksize","", ""}
       });
     }else if (OpName == "Split") {
        ImportNodeOneOut<mlir::ONNXSplitOp>(node, 1, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
-        ,{"split","", ""}
       });
     }else if (OpName == "SplitToSequence") {
        ImportNodeOneOut<mlir::ONNXSplitToSequenceOp>(node, 2, 1, {
-         {"axis","int","0"}
+         {"axis", 0}
-        ,{"keepdims","int","1"}
+        ,{"keepdims", 1}
       });
     }else if (OpName == "Sqrt") {
        ImportNodeOneOut<mlir::ONNXSqrtOp>(node, 1, 1, {
       });
     }else if (OpName == "Squeeze") {
        ImportNodeOneOut<mlir::ONNXSqueezeOp>(node, 1, 1, {
-         {"axes","", ""}
       });
     }else if (OpName == "StringNormalizer") {
        ImportNodeOneOut<mlir::ONNXStringNormalizerOp>(node, 1, 1, {
-         {"case_change_action","str","NONE"}
+         {"case_change_action", "NONE"}
-        ,{"is_case_sensitive","int","0"}
+        ,{"is_case_sensitive", 0}
-        ,{"locale","", ""}
-        ,{"stopwords","", ""}
       });
     }else if (OpName == "Sub") {
        ImportNodeOneOut<mlir::ONNXSubOp>(node, 2, 1, {
@@ -639,45 +554,34 @@
       });
     }else if (OpName == "TfIdfVectorizer") {
        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") {
        ImportNodeOneOut<mlir::ONNXThresholdedReluOp>(node, 1, 1, {
-         {"alpha","float","1.0"}
+         {"alpha", (float)1.0}
       });
     }else if (OpName == "Tile") {
        ImportNodeOneOut<mlir::ONNXTileOp>(node, 2, 1, {
       });
     }else if (OpName == "TopK") {
        ImportNodeMultipleOuts<mlir::ONNXTopKOp>(node, 2, 2, {
-         {"axis","int","-1"}
+         {"axis", -1}
-        ,{"largest","int","1"}
+        ,{"largest", 1}
-        ,{"sorted","int","1"}
+        ,{"sorted", 1}
       });
     }else if (OpName == "Transpose") {
        ImportNodeOneOut<mlir::ONNXTransposeOp>(node, 1, 1, {
-         {"perm","", ""}
       });
     }else if (OpName == "Unique") {
        ImportNodeMultipleOuts<mlir::ONNXUniqueOp>(node, 1, 4, {
-         {"axis","", ""}
+         {"sorted", 1}
-        ,{"sorted","int","1"}
       });
     }else if (OpName == "Unsqueeze") {
        ImportNodeOneOut<mlir::ONNXUnsqueezeOp>(node, 1, 1, {
-         {"axes","ints", ""}
+         {"axes", std::vector<int64_t> {}}
       });
     }else if (OpName == "Upsample") {
        ImportNodeOneOut<mlir::ONNXUpsampleOp>(node, 2, 1, {
-         {"mode","str","nearest"}
+         {"mode", "nearest"}
       });
     }else if (OpName == "Where") {
        ImportNodeOneOut<mlir::ONNXWhereOp>(node, 3, 1, {

src/dialect/onnx/gen_doc.py

@@ -267,7 +267,7 @@ def gen_schema(schema) :
                         'Add', 'Mul', 'Div', 'Sub', 'And', 'Or', 'Xor',
                         'Sum', 'Max', 'Min', 'MatMul', 'Gemm', 'LeakyRelu',
                         'Elu', 'Selu', 'HardSigmoid', 'Reshape', 'Reciprocal',
-                        'Identity', 'Cos', 'Log', 'Transpose']
+                        'Identity', 'Cos', 'Log', 'Transpose', 'Softmax']
     CanonicalList=['Add', 'Identity']
     line_indent = '  '
 
@@ -368,17 +368,17 @@ def gen_code(schema,fefile) :
         ("MaxPool", "ImportNodeMaxPool"),
         #("Transpose", "ImportNodeTranspose")
         ])
-    special_type = dict([
+    list_str = 'std::vector'
-        ("AveragePool "+"kernel_shape", '"ints", ""'),
+    empty_ints = list_str+'<int> {}' 
-        ("MaxPool "+"kernel_shape", '"ints", ""'),
+    empty_floats = list_str+'<float> {}' 
-        ("Cast "+"to", '"int", "0"'),
+    special_default = dict([
-        ("Concat "+"axis", '"int", "0"'),
+        ("AveragePool "+"kernel_shape", empty_ints),
-        ("Conv "+"group", '"int", "1"'),
+        ("MaxPool "+"kernel_shape", empty_ints),
-        ("Unsqueeze "+"axes", '"ints", ""'),
+        ("Cast "+"to", '0'),
-        ("RNN "+"activation_alpha", '"floats", "{}"'),
+        ("Concat "+"axis", '0'),
-        ("RNN "+"activation_beta", '"floats", "{}"'),
+        ("Unsqueeze "+"axes", empty_ints),
-        ("RNN "+"activations", '"", "{Tannh, Tanh}"'),
+        ("RNN "+"activation_alpha", empty_floats),
-        ("LRN "+"size", '"int", ""')
+        ("RNN "+"activation_beta", empty_floats)
         ])
     line_indent = '  '
     fefile.write('    '+'}else if (OpName == "'+schema.name+'") {\n')
@@ -400,21 +400,9 @@ def gen_code(schema,fefile) :
     if schema.attributes:
         first_attr = True
         for _, attr in sorted(schema.attributes.items()):
-            attr_line = line_indent+line_indent+line_indent+line_indent
+            #only generate default attr list
-            if not first_attr:
+            if schema.name+' '+attr.name in special_default:
-                attr_line += ',{'
+                attr_value = special_default[schema.name+' '+attr.name]
-            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)
 
@@ -430,28 +418,35 @@ def gen_code(schema,fefile) :
                     return str(value)
 
                 if isinstance(default_value, list):
+
                     value = default_value[0]
                     default_value = [format_value(val) for val in default_value]
+                    attr_option_str = '{}'.format(default_value)
+                    attr_option_str = attr_option_str.replace('[', '{', 1)
+                    attr_option_str = attr_option_str.replace(']', '}', 1)
                     # TODO the list type is homogenous or htergeneous?
                    
                     if isinstance(value, float) : 
-                        attr_type_str = '"floats"'
+                        attr_type_str = list_str+'<float>'
+                        attr_option_str = attr_option_str.replace("'", '')
                     elif isinstance(value, int) :
-                        attr_type_str = '"ints"'
+                        attr_type_str = list_str+'<int>'
+                        attr_option_str = attr_option_str.replace("'", '')
                     elif isinstance(value, str) :
-                        attr_type_str = '"strs"'
+                        attr_type_str = list_str+'<std::string>'
+                        attr_option_str = attr_option_str.replace("'", '"')
                     elif isinstance(value, (bytes, bytearray)) :
-                        attr_type_str = '"strs"'
+                        attr_type_str = list_str+'<std::string>'
+                        attr_option_str = attr_option_str.replace("'", '"')
                     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"'
+                        attr_type_str = '(float)'
+                        attr_option_str = default_value
                     elif isinstance(default_value, int) :
-                        attr_type_str = '"int"'
+                        attr_option_str = default_value
+                        attr_type_str=''
                     elif isinstance(default_value, str) :
                         attr_type_str = '"str"'
                     elif isinstance(default_value, (bytes, bytearray)) :
@@ -459,11 +454,25 @@ def gen_code(schema,fefile) :
                     else :
                         attr_type_str = '"unknown"'
                     default_value = format_value(default_value)
-                    attr_option_str = '"{}"'.format(default_value)
+                    if attr_type_str == '"str"' :
-                attr_line += attr_type_str+','+attr_option_str
+                        attr_option_str = '"'+default_value+'"'
+                        attr_type_str=''
+                    else :
+                        attr_option_str = default_value
+                attr_value = attr_type_str+attr_option_str
             else:
-                #TODO why?
+                #no default value
-                attr_line += '"", ""'
+                continue
+
+            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+'", '
+            attr_line += attr_value
             attr_line += '}\n'
             fefile.write(attr_line)
     fefile.write(line_indent+line_indent+line_indent+'});\n')

src/dialect/onnx/onnx_ops.cpp

@@ -13,6 +13,7 @@
 #include "mlir/IR/Function.h"
 #include "mlir/IR/IntegerSet.h"
 #include "mlir/IR/Matchers.h"
+#include "mlir/IR/Module.h"
 #include "mlir/IR/OpImplementation.h"
 #include "mlir/IR/PatternMatch.h"
 #include "llvm/ADT/SetVector.h"
@@ -157,6 +158,14 @@ void ONNXReciprocalOp::inferShapes() {
   getResult().setType(getOperand().getType());
 }
 
+//===----------------------------------------------------------------------===//
+// Softmax
+/// Infer the output shape of the ONNXSoftmaxOp. This method is required by
+/// the shape inference interface.
+void ONNXSoftmaxOp::inferShapes() {
+  getResult().setType(getOperand().getType());
+}
+
 //===----------------------------------------------------------------------===//
 // Add
 /// Infer the output shape of the ONNXAddOp. This method is required by the
@@ -484,13 +493,38 @@ void ONNXTransposeOp::inferShapes() {
 
   // Naive transposition which handles the default case of
   // reversing the shape of the tensor (similar to numpy.transpose).
-  // TODO: Once attributes are supported we can handle the case where the
-  // transposition uses a permutation vector to interchange the axes.
   auto arrayTy = getOperand().getType().cast<RankedTensorType>();
-  SmallVector<int64_t, 2> dims(llvm::reverse(arrayTy.getShape()));
+  SmallVector<int64_t, 2> dims;
+
+  if (auto permutation = getAttrOfType<ArrayAttr>(
+          ONNXTransposeOp::getPermAttrName())) {
+    // Perform transposition according to perm attribute.
+    for (auto perm : permutation.getValue())
+      dims.emplace_back(arrayTy.getShape()[perm.cast<IntegerAttr>().getInt()]);
+  } else {
+    // Default
+    for (auto dim : llvm::reverse(arrayTy.getShape()))
+      dims.emplace_back(dim);
+  }
+
   getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
 }
 
+LogicalResult verify(ONNXTransposeOp op) {
+  auto module = op.getParentOfType<ModuleOp>();
+  if (!module)
+    op.emitError("Expected to belong to a module.");
+
+  if (auto permutation = op.getAttrOfType<ArrayAttr>(
+          ONNXTransposeOp::getPermAttrName())) {
+    for (auto perm : permutation.getValue())
+      if (perm.cast<IntegerAttr>().getInt() < 0)
+        op.emitError("Cannot tranpose, permuation contains negative index.");
+  }
+
+  return success();
+}
+
 //===----------------------------------------------------------------------===//
 // TableGen'd op method definitions
 //===----------------------------------------------------------------------===//

src/dialect/onnx/onnxop.inc

@@ -2831,7 +2831,7 @@ def ONNXSliceOp:ONNX_Op<"Slice",
 }
 
 def ONNXSoftmaxOp:ONNX_Op<"Softmax", 
-    [NoSideEffect]> {
+    [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
   let summary = "ONNX Softmax operation";
   let description = [{
     "The operator computes the softmax (normalized exponential) values for each layer in the batch"
@@ -3098,6 +3098,12 @@ def ONNXTransposeOp:ONNX_Op<"Transpose",
   }];
   let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$data);
   let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
+
+  let extraClassDeclaration = [{
+    static StringRef getPermAttrName() { return "perm"; }
+  }];
+
+  let verifier = [{ return ::verify(*this); }];
 }
 
 def ONNXUniqueOp:ONNX_Op<"Unique", 

src/main.cpp

@@ -135,10 +135,13 @@ int main(int argc, char *argv[]) {
   if (mlir::failed(pm.run(*module)))
     return 4;
 
-  module->dump();
 
-  // Write LLVM bitcode to disk.
+  if (emissionTarget == EmitLLVMBC) {
-  if (emissionTarget == EmitLLVMBC)
+      // Write LLVM bitcode to disk.
-    EmitLLVMBitCode(module);
+      EmitLLVMBitCode(module);
+      printf("LLVM bitcode written to ./model.bc");
+  } else
+    module->dump();
+
   return 0;
 }

src/pass/lower_frontend_to_krnl.cpp

@@ -420,8 +420,8 @@ Value mapToLowerScalarOp<ONNXHardSigmoidOp>(
   //                                  Constant 1)
   auto loc = op->getLoc();
   Value operand = operands[0];
-  auto alphaAttr = op->getAttrOfType<FloatAttr>("HardSigmoid.alpha");
+  auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
-  auto betaAttr = op->getAttrOfType<FloatAttr>("HardSigmoid.beta");
+  auto betaAttr = op->getAttrOfType<FloatAttr>("beta");
   auto elementType = result_types[0];
 
   auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
@@ -455,7 +455,7 @@ Value mapToLowerScalarOp<ONNXEluOp>(Operation *op, ArrayRef<Type> result_types,
   Value operand = operands[0];
   auto elementType = result_types[0];
 
-  auto alphaAttr = op->getAttrOfType<FloatAttr>("Elu.alpha");
+  auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
   auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
   auto one = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 1));
   auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
@@ -508,7 +508,7 @@ Value mapToLowerScalarOp<ONNXLeakyReluOp>(Operation *op,
   Value operand = operands[0];
   auto elementType = result_types[0];
 
-  auto alphaAttr = op->getAttrOfType<FloatAttr>("LeakyRelu.alpha");
+  auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
   auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
   auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
   auto lessThanZero =
@@ -533,8 +533,8 @@ Value mapToLowerScalarOp<ONNXSeluOp>(Operation *op, ArrayRef<Type> result_types,
   //                                         alpha)))
   auto loc = op->getLoc();
   Value operand = operands[0];
-  auto alphaAttr = op->getAttrOfType<FloatAttr>("Selu.alpha");
+  auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
-  auto gammaAttr = op->getAttrOfType<FloatAttr>("Selu.gamma");
+  auto gammaAttr = op->getAttrOfType<FloatAttr>("gamma");
   auto elementType = result_types[0];
 
   auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
@@ -824,6 +824,225 @@ struct ONNXElementwiseVariadicOpLowering : public ConversionPattern {
   }
 };
 
+struct ONNXSoftmaxOpLowering : public ConversionPattern {
+  ONNXSoftmaxOpLowering(MLIRContext *ctx)
+      : ConversionPattern(mlir::ONNXSoftmaxOp::getOperationName(), 1, ctx) {}
+  PatternMatchResult
+  matchAndRewrite(Operation *op, ArrayRef<Value> operands,
+                  ConversionPatternRewriter &rewriter) const final {
+    // softmax(x) = let max_x = max(x) in
+    //                let exp_x = exp(x - max_x) in
+    //                  let sum = sum(exp_x) in
+    //                    exp_x / sum
+    auto tensorType = (*op->result_type_begin()).cast<RankedTensorType>();
+    int64_t rank = tensorType.getRank();
+    int64_t axis = op->getAttrOfType<IntegerAttr>("axis").getInt();
+    axis = axis >= 0 ? axis : rank + axis;
+    assert(axis >= -rank && axis <= rank - 1);
+
+    auto loc = op->getLoc();
+
+    // Insert an allocation and deallocation for the result of this operation.
+    auto memRefType = convertTensorToMemRef(tensorType);
+    auto elementType = memRefType.getElementType();
+
+    Value alloc;
+    bool insertDealloc = checkInsertDealloc(op);
+    if (hasAllConstantDimensions(memRefType))
+      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc);
+    else
+      alloc = insertAllocAndDealloc(memRefType, loc, rewriter, insertDealloc,
+                                    operands[0]);
+
+    // Shape of the result
+    auto memRefShape = memRefType.getShape();
+
+    // Insert allocations and deallocations for sum and max.
+    MemRefType scalarMemRefType = MemRefType::get({}, elementType, {}, 0);
+    Value sumOp = insertAllocAndDealloc(scalarMemRefType, loc, rewriter, true);
+    Value maxOp = insertAllocAndDealloc(scalarMemRefType, loc, rewriter, true);
+    Value zero =
+        rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
+    Value negInfinity = rewriter.create<ConstantOp>(
+        loc,
+        FloatAttr::get(elementType, -std::numeric_limits<float>::infinity()));
+
+    // Define loops.
+    auto loopsOp = rewriter.create<KrnlDefineLoopsOp>(loc, rank);
+    std::vector<Value> originalLoops;
+    originalLoops.reserve(rank);
+    for (auto result : loopsOp.getResults()) {
+      originalLoops.push_back(result);
+    }
+
+    // Define loop optimization.
+    auto optimizedLoopsOp = rewriter.create<KrnlOptimizeLoopsOp>(loc, rank);
+    std::vector<Value> optimizedLoops;
+    optimizedLoops.reserve(rank);
+    for (auto result : optimizedLoopsOp.getResults()) {
+      optimizedLoops.push_back(result);
+    }
+    Block &optimizationBlock = optimizedLoopsOp.region().front();
+
+    // Coerce the input into a 2-D tensor. `axis` will be the coercing point.
+    // This coercing follows the softmax definition in ONNX:
+    // https://github.com/onnx/onnx/blob/master/docs/Operators.md#Softmax
+    // Here, we create an outer loop and inner loop for handling the two
+    // dimensions. The outer loop is only created once `axis` is not zero.
+
+    // Define an outer loop with respect to axis.
+    std::vector<Value> outerLoops, optimizedOuterLoops;
+    outerLoops.reserve(axis);
+    optimizedOuterLoops.reserve(axis);
+    for (int i = 0; i < axis; ++i) {
+      outerLoops.push_back(originalLoops[i]);
+      optimizedOuterLoops.push_back(optimizedLoops[i]);
+    }
+    KrnlIterateOperandPack outerPack(rewriter, outerLoops, optimizedOuterLoops);
+    for (int i = 0; i < axis; ++i) {
+      if (memRefShape[i] < 0) {
+        outerPack.pushConstantBound(0);
+        outerPack.pushOperandBound(
+            rewriter.create<DimOp>(loc, operands[0], i).getResult());
+      } else {
+        outerPack.pushConstantBound(0);
+        outerPack.pushConstantBound(memRefShape[i]);
+      }
+    }
+    // Define an inner loop with respect to axis.
+    std::vector<Value> innerLoops, optimizedInnerLoops;
+    innerLoops.reserve(rank - axis);
+    optimizedInnerLoops.reserve(rank - axis);
+    for (int i = axis; i < rank; ++i) {
+      innerLoops.push_back(originalLoops[i]);
+      optimizedInnerLoops.push_back(optimizedLoops[i]);
+    }
+    KrnlIterateOperandPack innerPack(rewriter, innerLoops, optimizedInnerLoops);
+    for (int i = axis; i < rank; ++i) {
+      if (memRefShape[i] < 0) {
+        innerPack.pushConstantBound(0);
+        innerPack.pushOperandBound(
+            rewriter.create<DimOp>(loc, operands[0], i).getResult());
+      } else {
+        innerPack.pushConstantBound(0);
+        innerPack.pushConstantBound(memRefShape[i]);
+      }
+    }
+
+    KrnlIterateOp outerIterateOp, maxIterateOp, sumIterateOp, softmaxIterateOp;
+    SmallVector<Value, 4> outerLoopIVs;
+    if (axis != 0) {
+      outerIterateOp = rewriter.create<KrnlIterateOp>(loc, outerPack);
+
+      // No optimization
+      rewriter.setInsertionPointToEnd(&optimizationBlock);
+      rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
+      rewriter.setInsertionPoint(optimizedLoopsOp);
+
+      // Insert instructions inside the outer loop.
+      Block &outerIterationBlock = outerIterateOp.bodyRegion().front();
+      rewriter.setInsertionPointToStart(&outerIterationBlock);
+      for (auto arg : outerIterationBlock.getArguments())
+        outerLoopIVs.push_back(arg);
+
+      // Reset accumulators.
+      rewriter.create<StoreOp>(loc, zero, sumOp);
+      rewriter.create<StoreOp>(loc, negInfinity, maxOp);
+
+      // Create an inner loop to compute max.
+      maxIterateOp = rewriter.create<KrnlIterateOp>(loc, innerPack);
+      // Create an inner loop to compute sum.
+      sumIterateOp = rewriter.create<KrnlIterateOp>(loc, innerPack);
+      // Create an inner loop to compute softmax.
+      softmaxIterateOp = rewriter.create<KrnlIterateOp>(loc, innerPack);
+    } else {
+      // Reset accumulators.
+      rewriter.create<StoreOp>(loc, zero, sumOp);
+      rewriter.create<StoreOp>(loc, negInfinity, maxOp);
+
+      // Create an inner loop to compute max.
+      maxIterateOp = rewriter.create<KrnlIterateOp>(loc, innerPack);
+      // Create an inner loop to compute sum.
+      sumIterateOp = rewriter.create<KrnlIterateOp>(loc, innerPack);
+      // Create an inner loop to compute softmax.
+      softmaxIterateOp = rewriter.create<KrnlIterateOp>(loc, innerPack);
+
+      // No optimization
+      rewriter.setInsertionPointToEnd(&optimizationBlock);
+      rewriter.create<KrnlReturnLoopsOp>(loc, originalLoops);
+      rewriter.setInsertionPoint(optimizedLoopsOp);
+    }
+
+    // Insert instructions inside the max loop.
+    Block &maxIterationBlock = maxIterateOp.bodyRegion().front();
+    rewriter.setInsertionPointToStart(&maxIterationBlock);
+
+    // Get induction variables.
+    SmallVector<Value, 4> maxLoopIVs;
+    for (auto arg : outerLoopIVs)
+      maxLoopIVs.push_back(arg);
+    for (auto arg : maxIterationBlock.getArguments())
+      maxLoopIVs.push_back(arg);
+
+    // Compute the max value.
+    Value max = rewriter.create<LoadOp>(loc, maxOp);
+    Value nextMax = rewriter.create<LoadOp>(loc, operands[0], maxLoopIVs);
+    auto maxCond =
+        rewriter.create<CmpFOp>(loc, CmpFPredicate::OGT, max, nextMax);
+    max = rewriter.create<SelectOp>(loc, maxCond, max, nextMax);
+    rewriter.create<StoreOp>(loc, max, maxOp);
+
+    // Get the max.
+    rewriter.setInsertionPoint(sumIterateOp);
+    max = rewriter.create<LoadOp>(loc, maxOp);
+
+    // Insert instructions inside the sum loop.
+    Block &sumIterationBlock = sumIterateOp.bodyRegion().front();
+    rewriter.setInsertionPointToStart(&sumIterationBlock);
+
+    // Get induction variables.
+    SmallVector<Value, 4> sumLoopIVs;
+    for (auto arg : outerLoopIVs)
+      sumLoopIVs.push_back(arg);
+    for (auto arg : sumIterationBlock.getArguments())
+      sumLoopIVs.push_back(arg);
+
+    // Sum up values.
+    Value sum = rewriter.create<LoadOp>(loc, sumOp);
+    Value next = rewriter.create<LoadOp>(loc, operands[0], sumLoopIVs);
+    Value sub = rewriter.create<SubFOp>(loc, next, max);
+    Value exp = rewriter.create<ExpOp>(loc, sub);
+    sum = rewriter.create<AddFOp>(loc, sum, exp);
+    rewriter.create<StoreOp>(loc, sum, sumOp);
+    // Store intermediate values in the result to avoid recomputation.
+    rewriter.create<StoreOp>(loc, exp, alloc, sumLoopIVs);
+
+    // Get the sum.
+    rewriter.setInsertionPoint(softmaxIterateOp);
+    sum = rewriter.create<LoadOp>(loc, sumOp);
+
+    // Insert instructions inside the softmax loop.
+    Block &softmaxIterationBlock = softmaxIterateOp.bodyRegion().front();
+    rewriter.setInsertionPointToStart(&softmaxIterationBlock);
+
+    // Get induction variables.
+    SmallVector<Value, 4> softmaxLoopIVs;
+    for (auto arg : outerLoopIVs)
+      softmaxLoopIVs.push_back(arg);
+    for (auto arg : softmaxIterationBlock.getArguments())
+      softmaxLoopIVs.push_back(arg);
+
+    // Compute softmax.
+    Value expLoadedVal = rewriter.create<LoadOp>(loc, alloc, softmaxLoopIVs);
+    Value result = rewriter.create<DivFOp>(loc, expLoadedVal, sum);
+    rewriter.create<StoreOp>(loc, result, alloc, softmaxLoopIVs);
+
+    rewriter.replaceOp(op, alloc);
+
+    return matchSuccess();
+  }
+};
+
 struct ONNXReshapeOpLowering : public ConversionPattern {
   ONNXReshapeOpLowering(MLIRContext *ctx)
       : ConversionPattern(mlir::ONNXReshapeOp::getOperationName(), 1, ctx) {}
@@ -1005,7 +1224,8 @@ void FrontendToKrnlLoweringPass::runOnModule() {
                   ONNXElementwiseVariadicOpLowering<mlir::ONNXSumOp>,
                   ONNXElementwiseVariadicOpLowering<mlir::ONNXMaxOp>,
                   ONNXElementwiseVariadicOpLowering<mlir::ONNXMinOp>,
-                  ONNXReshapeOpLowering, ONNXEntryPointLowering>(&getContext());
+                  ONNXReshapeOpLowering, ONNXEntryPointLowering,
+                  ONNXSoftmaxOpLowering>(&getContext());
 
   // With the target and rewrite patterns defined, we can now attempt the
   // conversion. The conversion will signal failure if any of our `illegal`

src/pass/shape_inference_pass.cpp

@@ -116,7 +116,8 @@ public:
         op->getName().getStringRef() != "onnx.Gemm" &&
         op->getName().getStringRef() != "onnx.GemmNoBias" &&
         op->getName().getStringRef() != "onnx.Reshape" &&
-        op->getName().getStringRef() != "onnx.Transpose")
+        op->getName().getStringRef() != "onnx.Transpose" &&
+        op->getName().getStringRef() != "onnx.Softmax")
       return false;
     return llvm::any_of(op->getResultTypes(), [](Type result_type) {
       return !result_type.isa<RankedTensorType>();

test/CMakeLists.txt

@@ -1 +1,2 @@
 add_subdirectory(mlir)
+add_subdirectory(backend)

test/backend/CMakeLists.txt

@@ -0,0 +1,10 @@
+configure_file(test.py test.py COPYONLY)
+configure_file(test_config.py.in test_config.py)
+
+find_package(PythonInterp 3 REQUIRED)
+add_custom_target(run-onnx-backend-test
+        COMMAND ${PYTHON_EXECUTABLE}
+        ${CMAKE_CURRENT_BINARY_DIR}/test.py)
+
+add_dependencies(run-onnx-backend-test onnf)
+add_dependencies(run-onnx-backend-test pyruntime)

test/backend/test.py

@@ -3,46 +3,51 @@ from __future__ import division
 from __future__ import print_function
 from __future__ import unicode_literals
 
-import itertools
 import os
+import sys
 import unittest
 import onnx.backend.base
 import onnx.backend.test
 
 from onnx.backend.base import Device, DeviceType
-import onnx.shape_inference
-import onnx.version_converter
 import subprocess
+import test_config
+
+VERBOSE = bool(os.environ.get("VERBOSE"))
+
+CXX = test_config.CXX_PATH
+ONNF = os.path.join(test_config.ONNF_BUILD_PATH, "bin/onnf")
+LLC = os.path.join(test_config.LLVM_PROJ_BUILD_PATH, "bin/llc")
+
+# Make lib folder under build directory visible in PYTHONPATH
+doc_check_base_dir = os.path.dirname(os.path.realpath(__file__))
+RUNTIME_DIR = os.path.join(test_config.ONNF_BUILD_PATH, "lib")
+sys.path.append(RUNTIME_DIR)
 from pyruntime import ExecutionSession
 
-CXX = os.getenv('CXX')
+
-ONNF = os.getenv('ONNF')
+def execute_commands(cmds):
-LLC = os.getenv('LLC')
+    if (VERBOSE):
-RT_DIR = os.getenv('RT_DIR')
+        print(" ".join(cmds))
-assert CXX and ONNF and LLC and RT_DIR, "tools path not set"
+    subprocess.run(cmds, stdout=subprocess.PIPE)
+
 
 class DummyBackend(onnx.backend.base.Backend):
     @classmethod
-    def prepare(
+    def prepare(cls, model, device='CPU', **kwargs):
-            cls,
-            model,
-            device='CPU',
-            **kwargs
-    ):
         super(DummyBackend, cls).prepare(model, device, **kwargs)
         # Save model to disk as temp_model.onnx.
         onnx.save(model, "temp_model.onnx")
         # Call frontend to process temp_model.onnx, bit code will be generated.
-        subprocess.run([ONNF, "temp_model.onnx"], stdout=subprocess.PIPE)
+        execute_commands([ONNF, "temp_model.onnx"])
         # Call llc to generate object file from bitcode.
-        subprocess.run([LLC, "-filetype=obj", "model.bc"],
+        execute_commands(
-                       stdout=subprocess.PIPE)
+            [LLC, "-filetype=obj", "-relocation-model=pic", "model.bc"])
         # Generate shared library from object file, linking with c runtime.
-        subprocess.run([
+        execute_commands([
-            CXX, "-shared", "model.o", "-o", "model.so", "-L" + RT_DIR,
+            CXX, "-shared", "-fPIC", "model.o", "-o", "model.so",
-            "-lcruntime"
+            "-L" + RUNTIME_DIR, "-lcruntime"
-        ],
+        ])
-                       stdout=subprocess.PIPE)
         return ExecutionSession("./model.so", "_dyn_entry_point_main_graph")
 
     @classmethod
@@ -125,6 +130,14 @@ test_to_enable = [
     "test_sigmoid_cpu",
     "test_sigmoid_example_cpu",
 
+    # Softmax Op:
+    "test_softmax_axis_0_cpu",
+    "test_softmax_axis_1_cpu",
+    "test_softmax_axis_2_cpu",
+    "test_softmax_default_axis_cpu",
+    "test_softmax_example_cpu",
+    "test_softmax_large_number_cpu",
+
     # Sum Op:
     #"test_sum_example_cpu", <- error
     "test_sum_one_input_cpu",
@@ -140,18 +153,15 @@ import inspect
 all_tests = inspect.getmembers(
     backend_test.test_cases["OnnxBackendNodeModelTest"])
 all_test_names = list(map(lambda x: x[0], all_tests))
+
+# Ensure that test names specified in test_to_enable actually exist.
 for test_name in test_to_enable:
-    assert test_name in all_test_names, "test name {} not found".format(test_name)
+    assert test_name in all_test_names, "test name {} not found, it is likely "
+    "that you may have misspelled the test name or the specified test does not "
+    "exist in the version of onnx package you installed.".format(
+        test_name)
     backend_test.include(r"^{}$".format(test_name))
 
-
-def tearDownModule():
-    print()
-    print("*" * 40)
-    print("A total of {} tests should have run".format(len(test_to_enable)))
-    print("*" * 40)
-
-
 # import all test cases at global scope to make them visible to python.unittest
 globals().update(backend_test.test_cases)
 

test/backend/test_config.py.in

@@ -0,0 +1,3 @@
+ONNF_BUILD_PATH = "@CMAKE_BINARY_DIR@"
+LLVM_PROJ_BUILD_PATH = "@LLVM_PROJ_BUILD@"
+CXX_PATH = "@CMAKE_CXX_COMPILER@"

test/mlir/krnl/ops.mlir

@@ -1,12 +1,12 @@
 // RUN: onnf-opt %s -mlir-print-op-generic | FileCheck -check-prefix=GENERIC %s
 // RUN: onnf-opt %s | FileCheck %s
 
-// GENERIC-DAG: #{{.*}} = () -> (0)
+// GENERIC-DAG: #{{.*}} = affine_map<() -> (0)>
-// GENERIC-DAG: #{{.*}} = () -> (10)
+// GENERIC-DAG: #{{.*}} = affine_map<() -> (10)>
-// GENERIC-DAG: #{{.*}} = () -> (1)
+// GENERIC-DAG: #{{.*}} = affine_map<() -> (1)>
-// GENERIC-DAG: #{{.*}} = () -> (11)
+// GENERIC-DAG: #{{.*}} = affine_map<() -> (11)>
-// GENERIC-DAG: #{{.*}} = (d0, d1) -> (d0 - d1)
+// GENERIC-DAG: #{{.*}} = affine_map<(d0, d1) -> (d0 - d1)>
-// GENERIC-DAG: #{{.*}} = (d0, d1) -> (d0 + d1)
+// GENERIC-DAG: #{{.*}} = affine_map<(d0, d1) -> (d0 + d1)>
 
 func @simple_iterate(%N : index) {
   %ii, %ij, %ik = krnl.define_loops 3
@@ -55,18 +55,18 @@ func @affine_map_bound(%N : index) {
   // GENERIC: "krnl.iterate"(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}) ( {
   // GENERIC-NEXT: ^bb0(%{{.*}}: index, %{{.*}}: index):
   // CHECK: krnl.iterate(%{{.*}}, %{{.*}}) with (%{{.*}} -> %{{.*}} = 0 to 10, %{{.*}} -> %{{.*}} = 0 to 10) {
-  krnl.iterate(%oi, %oj) with (%ii -> %i = ()->(0)() to ()->(10)(), %ij -> %j = 0 to 10) {
+  krnl.iterate(%oi, %oj) with (%ii -> %i = affine_map<()->(0)>() to affine_map<()->(10)>(), %ij -> %j = 0 to 10) {
     // GENERIC: "krnl.iterate"(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}) ( {
     // GENERIC-NEXT: ^bb0(%{{.*}}: index):
     // CHECK: krnl.iterate(%{{.*}}) with (%{{.*}} -> %{{.*}} = #{{.*}}(%{{.*}}, %{{.*}}) to #{{.*}}(%{{.*}}, %{{.*}})) {
-    krnl.iterate(%ok) with (%ik -> %k = (d0, d1)->(d0 - d1)(%i, %j) to (d0, d1)->(d0 + d1)(%i, %j)) {
+    krnl.iterate(%ok) with (%ik -> %k = affine_map<(d0, d1)->(d0 - d1)>(%i, %j) to affine_map<(d0, d1)->(d0 + d1)>(%i, %j)) {
 
     }
 
     // GENERIC: "krnl.iterate"(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}, %{{.*}}) ( {
     // GENERIC-NEXT: ^bb0(%{{.*}}: index):
     // CHECK: krnl.iterate(%{{.*}}) with (%{{.*}} -> %{{.*}} = max #map{{.*}}(%{{.*}}, %{{.*}}) to min #map{{.*}}(%{{.*}}, %{{.*}})[%{{.*}}]) {
-    krnl.iterate(%ok) with (%ik -> %k = max (d0, d1)->(d0 - d1, 0)(%i, %j) to min (d0, d1)[s0]->(d0 + d1, s0)(%i, %j)[%N]) {
+    krnl.iterate(%ok) with (%ik -> %k = max affine_map<(d0, d1)->(d0 - d1, 0)>(%i, %j) to min affine_map<(d0, d1)[s0]->(d0 + d1, s0)>(%i, %j)[%N]) {
 
     }
   }

test/mlir/onnx/onnx_lowering.mlir

@@ -385,7 +385,7 @@ func @test_min(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>) -> tensor<*
 }
 
 func @test_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.Elu"(%arg0) {Elu.alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.Elu"(%arg0) {alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_elu
@@ -411,7 +411,7 @@ func @test_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
 }
 
 func @test_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.LeakyRelu"(%arg0) {LeakyRelu.alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.LeakyRelu"(%arg0) {alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_leakyrelu
@@ -434,7 +434,7 @@ func @test_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
 }
 
 func @test_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.Selu"(%arg0) {Selu.alpha=1.0:f32, Selu.gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.Selu"(%arg0) {alpha=1.0:f32, gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_selu
@@ -461,7 +461,7 @@ func @test_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
 }
 
 func @test_hardsigmoid(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.HardSigmoid"(%arg0) {HardSigmoid.alpha=1.0:f32, HardSigmoid.beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.HardSigmoid"(%arg0) {alpha=1.0:f32, beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_hardsigmoid
@@ -533,3 +533,49 @@ func @test_add_with_broadcasting(%arg0 : tensor<?xf32>, %arg1 : tensor<?x10xf32>
   // CHECK: }
   // CHECK: return [[RES]] : memref<?x10xf32>
 }
+
+func @test_softmax(%arg0 : tensor<10x10xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Softmax"(%arg0) {axis=1:i32} : (tensor<10x10xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+
+  // CHECK-LABEL: test_softmax
+  // CHECK: [[MAX:%.+]] = alloc() : memref<f32>
+  // CHECK: [[SUM:%.+]] = alloc() : memref<f32>
+  // CHECK: [[RES:%.+]] = alloc() : memref<10x10xf32>
+  // CHECK: [[CST:%.+]] = constant 0.000000e+00 : f32
+  // CHECK: [[CST_0:%.+]] = constant 0xFF800000 : f32
+  // CHECK: [[DEF_LOOPS:%.+]]:2 = krnl.define_loops 2
+  // CHECK: [[OPT_LOOPS:%.+]]:2 = krnl.optimize_loops  {
+  // CHECK:  krnl.return_loops [[DEF_LOOPS]]#0, %3#1
+  // CHECK: } : () -> (!krnl.loop, !krnl.loop)
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#0) with ([[DEF_LOOPS]]#0 -> %arg1 = 0 to 10) {
+  // CHECK: store [[CST]], [[SUM]][] : memref<f32>
+  // CHECK: store [[CST_0]], [[MAX]][] : memref<f32>
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
+  // CHECK:   [[LOAD1:%.+]] = load [[MAX]][] : memref<f32>
+  // CHECK:   [[LOAD2:%.+]] = load %arg0[%arg1, %arg2] : memref<10x10xf32>
+  // CHECK:   [[COND:%.+]] = cmpf "ogt", [[LOAD1]], [[LOAD2]] : f32
+  // CHECK:   [[SELECT:%.+]] = select [[COND]], [[LOAD1]], [[LOAD2]] : f32
+  // CHECK:   store [[SELECT]], [[MAX]][] : memref<f32>
+  // CHECK: }
+  // CHECK: %5 = load [[MAX]][] : memref<f32>
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
+  // CHECK:   [[LOAD1]] = load [[SUM]][] : memref<f32>
+  // CHECK:   [[LOAD2]] = load %arg0[%arg1, %arg2] : memref<10x10xf32>
+  // CHECK:   [[SUB:%.+]] = subf [[LOAD2]], %5 : f32
+  // CHECK:   [[EXP:%.+]] = exp [[SUB]] : f32
+  // CHECK:   [[ADD:%.+]] = addf [[LOAD1]], [[EXP]] : f32
+  // CHECK:   store [[ADD]], [[SUM]][] : memref<f32>
+  // CHECK:   store %10, [[RES]][%arg1, %arg2] : memref<10x10xf32>
+  // CHECK: }
+  // CHECK: %6 = load [[SUM]][] : memref<f32>
+  // CHECK: krnl.iterate([[OPT_LOOPS]]#1) with ([[DEF_LOOPS]]#1 -> %arg2 = 0 to 10) {
+  // CHECK:   [[LOAD1]] = load [[RES]][%arg1, %arg2] : memref<10x10xf32>
+  // CHECK:   [[DIV:%.+]] = divf [[LOAD1]], %6 : f32
+  // CHECK:   store [[DIV]], [[RES]][%arg1, %arg2] : memref<10x10xf32>
+  // CHECK: }
+  // CHECK: }
+  // CHECK: dealloc [[SUM]] : memref<f32>
+  // CHECK: dealloc [[MAX]] : memref<f32>
+  // CHECK: return [[RES]] : memref<10x10xf32>
+}

test/mlir/onnx/onnx_lowering_with_dealloc.mlir

@@ -648,8 +648,8 @@ func @test_min_min(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>) -> tens
 }
 
 func @test_elu_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.Elu"(%arg0) {Elu.alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.Elu"(%arg0) {alpha=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
-  %1 = "onnx.Elu"(%0) {Elu.alpha=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
+  %1 = "onnx.Elu"(%0) {alpha=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
   "std.return"(%1) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_elu_elu
@@ -701,8 +701,8 @@ func @test_elu_elu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
 }
 
 func @test_leakyrelu_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.LeakyRelu"(%arg0) {LeakyRelu.alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.LeakyRelu"(%arg0) {alpha=1.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
-  %1 = "onnx.LeakyRelu"(%0) {LeakyRelu.alpha=1.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
+  %1 = "onnx.LeakyRelu"(%0) {alpha=1.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
   "std.return"(%1) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_leakyrelu_leakyrelu
@@ -748,8 +748,8 @@ func @test_leakyrelu_leakyrelu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
 }
 
 func @test_selu_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.Selu"(%arg0) {Selu.alpha=1.0:f32, Selu.gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.Selu"(%arg0) {alpha=1.0:f32, gamma=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
-  %1 = "onnx.Selu"(%0) {Selu.alpha=1.0:f32, Selu.gamma=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
+  %1 = "onnx.Selu"(%0) {alpha=1.0:f32, gamma=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
   "std.return"(%1) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_selu_selu
@@ -803,8 +803,8 @@ func @test_selu_selu(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
 }
 
 func @test_hardsigmoid_hardsigmoid(%arg0 : tensor<?x10xf32>) -> tensor<*xf32> {
-  %0 = "onnx.HardSigmoid"(%arg0) {HardSigmoid.alpha=1.0:f32, HardSigmoid.beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
+  %0 = "onnx.HardSigmoid"(%arg0) {alpha=1.0:f32, beta=2.0:f32} : (tensor<?x10xf32>) -> tensor<*xf32>
-  %1 = "onnx.HardSigmoid"(%0) {HardSigmoid.alpha=1.0:f32, HardSigmoid.beta=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
+  %1 = "onnx.HardSigmoid"(%0) {alpha=1.0:f32, beta=2.0:f32} : (tensor<*xf32>) -> tensor<*xf32>
   "std.return"(%1) : (tensor<*xf32>) -> ()
 
   // CHECK-LABEL: test_hardsigmoid_hardsigmoid

test/mlir/onnx/onnx_shape_inference.mlir

@@ -11,8 +11,26 @@ func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
   // CHECK: return [[RES]] : tensor<32x1x5x5xf32>
 }
 
+// CHECK-LABEL: test_default_transpose
+// CHECK: [[RES:%.+]] = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<32x1x5x5xf32>
+// CHECK: return [[RES]] : tensor<32x1x5x5xf32>
+
+/// Test shape inference for transposition when perm attribute is specified.
+func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
+  %0 = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
+  "std.return"(%0) : (tensor<*xf32>) -> ()
+}
+
+// CHECK-LABEL: test_transpose
+// CHECK: [[RES_ATTR:%.+]] = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<1x5x32x5xf32>
+// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>
+
+//===----------------------------------------------------------------------===//
 /// Test the shape inferencing scheme for the matmul operation.
+//===----------------------------------------------------------------------===//
+
 /// MatMul: 1-D x 1-D
+
 func @test_matmul_1(%arg0 : tensor<32xf32>, %arg1 : tensor<32xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -23,6 +41,7 @@ func @test_matmul_1(%arg0 : tensor<32xf32>, %arg1 : tensor<32xf32>) -> tensor<*x
 }
 
 /// MatMul: K-D x 2-D (K > 2)
+
 func @test_matmul_2(%arg0 : tensor<16x?x64x42xf32>, %arg1 : tensor<42x32xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x64x42xf32>, tensor<42x32xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -33,6 +52,7 @@ func @test_matmul_2(%arg0 : tensor<16x?x64x42xf32>, %arg1 : tensor<42x32xf32>) -
 }
 
 /// MatMul: 2-D x K-D (K > 2)
+
 func @test_matmul_3(%arg0 : tensor<64x42xf32>, %arg1 : tensor<16x?x42x32xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<16x?x42x32xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -43,6 +63,7 @@ func @test_matmul_3(%arg0 : tensor<64x42xf32>, %arg1 : tensor<16x?x42x32xf32>) -
 }
 
 /// MatMul: 2-D x K-D (K > 2)
+
 func @test_matmul_4(%arg0 : tensor<64x42xf32>, %arg1 : tensor<?x?x?x?xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<64x42xf32>, tensor<?x?x?x?xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -53,6 +74,7 @@ func @test_matmul_4(%arg0 : tensor<64x42xf32>, %arg1 : tensor<?x?x?x?xf32>) -> t
 }
 
 /// MatMul: K1-D x K2-D (K1 > 2, K2 > 2)
+
 func @test_matmul_5(%arg0 : tensor<16x?x?x42xf32>, %arg1 : tensor<32x?x64x42x32xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<16x?x?x42xf32>, tensor<32x?x64x42x32xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -63,6 +85,7 @@ func @test_matmul_5(%arg0 : tensor<16x?x?x42xf32>, %arg1 : tensor<32x?x64x42x32x
 }
 
 /// MatMul: 1-D x 2-D
+
 func @test_matmul_6(%arg0 : tensor<32xf32>, %arg1 : tensor<32x64xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32xf32>, tensor<32x64xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -73,6 +96,7 @@ func @test_matmul_6(%arg0 : tensor<32xf32>, %arg1 : tensor<32x64xf32>) -> tensor
 }
 
 /// MatMul: 2-D x 1-D
+
 func @test_matmul_7(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()
@@ -83,6 +107,7 @@ func @test_matmul_7(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64xf32>) -> tensor
 }
 
 /// MatMul: 2-D x 2-D
+
 func @test_matmul_8(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64x128xf32>) -> tensor<*xf32> {
   %0 = "onnx.MatMul"(%arg0, %arg1) : (tensor<32x64xf32>, tensor<64x128xf32>) -> tensor<*xf32>
   "std.return"(%0) : (tensor<*xf32>) -> ()

third_party/variant

@@ -0,0 +1 @@
+Subproject commit 3c7fc8266bb46046b42c2dc2663f9f505f0cec28

utils/install-mlir.sh

@@ -9,4 +9,5 @@ cmake -G Ninja ../llvm \
    -DLLVM_ENABLE_ASSERTIONS=ON \
    -DLLVM_ENABLE_RTTI=ON
 
-cmake --build . --target check-mlir -- ${MAKEFLAGS}
+cmake --build . --target
+cmake --build . --target check-mlir

utils/install-onnf.sh

@@ -7,4 +7,5 @@ cmake ..
 cmake --build . --target onnf
 
 # Run FileCheck tests:
+export LIT_OPTS=-v
 cmake --build . --target check-mlir-lit