[mir_onnx] Rework attribute parsing (#6651)

* Make attribute parsing functions templated and extract them to a separate file.
* Move definition of `getKernelStridesPadding` into cpp file.

Signed-off-by: Sergei Barannikov <s.barannikov@samsung.com>

18 files changed, 191 insertions, 126 deletions

diff --git a/compiler/mir-onnx-importer/AttributeHelpers.h b/compiler/mir-onnx-importer/AttributeHelpers.h
new file mode 100644
index 000000000..24e14d46f
--- /dev/null
+++ b/compiler/mir-onnx-importer/AttributeHelpers.h
@@ -0,0 +1,97 @@
+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MIR_ONNX_ATTRIBUTE_HELPERS_H
+#define MIR_ONNX_ATTRIBUTE_HELPERS_H
+
+#include "onnx/onnx.pb.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace mir_onnx
+{
+
+template <typename T> T getAttributeValue(const onnx::AttributeProto &attribute) = delete;
+
+template <> inline float getAttributeValue(const onnx::AttributeProto &attribute)
+{
+  assert(attribute.type() == onnx::AttributeProto::FLOAT);
+  return attribute.f();
+}
+
+template <> inline std::int64_t getAttributeValue(const onnx::AttributeProto &attribute)
+{
+  assert(attribute.type() == onnx::AttributeProto::INT);
+  return attribute.i();
+}
+
+template <> inline std::string getAttributeValue(const onnx::AttributeProto &attribute)
+{
+  assert(attribute.type() == onnx::AttributeProto::STRING);
+  return attribute.s();
+}
+
+template <> inline onnx::TensorProto getAttributeValue(const onnx::AttributeProto &attribute)
+{
+  assert(attribute.type() == onnx::AttributeProto::TENSOR);
+  return attribute.t();
+}
+
+template <>
+inline std::vector<std::int64_t> getAttributeValue(const onnx::AttributeProto &attribute)
+{
+  assert(attribute.type() == onnx::AttributeProto::INTS);
+  return {attribute.ints().cbegin(), attribute.ints().cend()};
+}
+
+inline const onnx::AttributeProto *findAttribute(const onnx::NodeProto &node,
+                                                 const std::string &name)
+{
+  const auto &attributes = node.attribute();
+  const auto it = std::find_if(
+      attributes.cbegin(), attributes.cend(),
+      [&name](const onnx::AttributeProto &attribute) { return attribute.name() == name; });
+  if (it == attributes.cend())
+    return nullptr;
+  return &*it;
+}
+
+template <typename T> T getAttributeValue(const onnx::NodeProto &node, const std::string &name)
+{
+  const auto *attribute = findAttribute(node, name);
+  if (attribute == nullptr)
+    throw std::runtime_error("Cannot find attribute '" + name + "' in node '" + node.name() + "'.");
+  return getAttributeValue<T>(*attribute);
+}
+
+template <typename T>
+T getAttributeValue(const onnx::NodeProto &node, const std::string &name, T default_value)
+{
+  const auto *attribute = findAttribute(node, name);
+  if (attribute == nullptr)
+    return std::move(default_value);
+  return getAttributeValue<T>(*attribute);
+}
+
+} // namespace mir_onnx
+
+#endif // MIR_ONNX_ATTRIBUTE_HELPERS_H
diff --git a/compiler/mir-onnx-importer/CMakeLists.txt b/compiler/mir-onnx-importer/CMakeLists.txt
index 1a3347695..10de9dfed 100644
--- a/compiler/mir-onnx-importer/CMakeLists.txt
+++ b/compiler/mir-onnx-importer/CMakeLists.txt
@@ -20,6 +20,7 @@ target_include_directories(mir_onnx_proto PUBLIC ${MIR_ONNX_PROTO_INCLUDE_DIRS})
 target_link_libraries(mir_onnx_proto PUBLIC libprotobuf)
 
 set(MIR_ONNX_IMPORTER_SOURCES
+        AttributeHelpers.h
         ONNXHelpers.cpp
         ONNXHelpers.h
         ONNXImporterImpl.cpp
diff --git a/compiler/mir-onnx-importer/ONNXHelpers.cpp b/compiler/mir-onnx-importer/ONNXHelpers.cpp
index 4e4f1b0a2..2035918e1 100644
--- a/compiler/mir-onnx-importer/ONNXHelpers.cpp
+++ b/compiler/mir-onnx-importer/ONNXHelpers.cpp
@@ -15,6 +15,9 @@
  */
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
+
+#include "mir/ShapeRange.h"
 
 namespace mir_onnx
 {
@@ -124,4 +127,35 @@ mir::TensorVariant createTensor(const onnx::TensorProto *tensor)
   return mir::TensorVariant(type, shape, src_data);
 }
 
+void getKernelStridesPadding(const onnx::NodeProto &onnx_node, KernelStridesPadding &cdata)
+{
+  const auto kernel_shape = getAttributeValue<std::vector<std::int64_t>>(onnx_node, "kernel_shape");
+  assert(!kernel_shape.empty());
+  const auto strides = getAttributeValue<std::vector<std::int64_t>>(onnx_node, "strides");
+  assert(!strides.empty());
+  const auto *pads_attribute = findAttribute(onnx_node, "pads");
+
+  cdata.kernel_shape = mir::Shape(kernel_shape.size());
+  for (std::size_t i = 0; i < kernel_shape.size(); ++i)
+  {
+    cdata.kernel_shape.dim(i) = kernel_shape[i];
+  }
+
+  cdata.strides_shape = mir::Shape(strides.size());
+  for (std::size_t i = 0; i < strides.size(); ++i)
+  {
+    cdata.strides_shape.dim(i) = strides[i];
+  }
+
+  if (pads_attribute != nullptr)
+  {
+    const auto pads = getAttributeValue<std::vector<std::int64_t>>(*pads_attribute);
+    assert(pads.size() == 4);
+    cdata.padding_before[0] = pads[0];
+    cdata.padding_before[1] = pads[1];
+    cdata.padding_after[0] = pads[2];
+    cdata.padding_after[1] = pads[3];
+  }
+}
+
 } // namespace mir_onnx
diff --git a/compiler/mir-onnx-importer/ONNXHelpers.h b/compiler/mir-onnx-importer/ONNXHelpers.h
index ef00f4303..8ccf1fcd4 100644
--- a/compiler/mir-onnx-importer/ONNXHelpers.h
+++ b/compiler/mir-onnx-importer/ONNXHelpers.h
@@ -19,63 +19,16 @@
 
 #include "mir/Graph.h"
 #include "mir/TensorVariant.h"
-#include "mir/ShapeRange.h"
-
 #include "mir/ops/TransposeOp.h"
 
 #include "onnx/onnx.pb.h"
 
-#include <cassert>
-
 namespace mir_onnx
 {
 
 mir::TensorVariant fixGroupedKernel(int groups, const mir::TensorVariant &folded_kernel);
 mir::TensorVariant createTensor(const onnx::TensorProto *tensor);
 
-inline const onnx::AttributeProto *findAttribute(const onnx::NodeProto &onnx_node,
-                                                 const std::string &name)
-{
-  for (auto &att : onnx_node.attribute())
-  {
-    if (att.name() == name)
-    {
-      return &att;
-    }
-  }
-  return nullptr;
-}
-
-inline int64_t getIntAttribute(const onnx::NodeProto &onnx_node, const std::string &name,
-                               const int64_t default_value)
-{
-  auto result = findAttribute(onnx_node, name);
-  if (!result)
-    return default_value;
-  assert(result->type() == onnx::AttributeProto_AttributeType::AttributeProto_AttributeType_INT);
-  return result->i();
-}
-
-inline std::string getStringAttribute(const onnx::NodeProto &onnx_node, const std::string &name,
-                                      const std::string &default_value)
-{
-  auto result = findAttribute(onnx_node, name);
-  if (!result)
-    return default_value;
-  assert(result->type() == onnx::AttributeProto_AttributeType::AttributeProto_AttributeType_STRING);
-  return result->s();
-}
-
-inline float getFloatAttribute(const onnx::NodeProto &onnx_node, const std::string &name,
-                               const float default_value)
-{
-  auto result = findAttribute(onnx_node, name);
-  if (!result)
-    return default_value;
-  assert(result->type() == onnx::AttributeProto_AttributeType::AttributeProto_AttributeType_FLOAT);
-  return result->f();
-}
-
 struct KernelStridesPadding
 {
   mir::Shape kernel_shape;
@@ -84,36 +37,7 @@ struct KernelStridesPadding
   std::vector<int32_t> padding_after{0, 0};
 };
 
-inline void getKernelStridesPadding(const onnx::NodeProto &onnx_node, KernelStridesPadding &cdata)
-{
-  auto *kshape = findAttribute(onnx_node, "kernel_shape");
-  assert(kshape && kshape->ints_size());
-  auto *strides = findAttribute(onnx_node, "strides");
-  assert(strides && strides->ints_size());
-  auto *pads = findAttribute(onnx_node, "pads");
-
-  cdata.kernel_shape = mir::Shape(kshape->ints_size());
-  for (int i = 0; i < kshape->ints_size(); ++i)
-  {
-    cdata.kernel_shape.dim(i) = kshape->ints(i);
-  }
-  cdata.strides_shape = mir::Shape(strides->ints_size());
-  for (int i = 0; i < strides->ints_size(); ++i)
-  {
-    cdata.strides_shape.dim(i) = strides->ints(i);
-  }
-
-  if (pads)
-  {
-    assert(pads->ints_size() == 4);
-    cdata.padding_before[0] = pads->ints(0);
-    cdata.padding_before[1] = pads->ints(1);
-    // TODO: ONNX padding could be for the beginning and ending along each axis that's why we
-    // should select the interesting ones.
-    cdata.padding_after[0] = pads->ints(2);
-    cdata.padding_after[1] = pads->ints(3);
-  }
-}
+void getKernelStridesPadding(const onnx::NodeProto &onnx_node, KernelStridesPadding &cdata);
 
 template <typename OpType, typename... Types>
 mir::Operation *createOp(mir::Graph *graph, Types &&... args)
diff --git a/compiler/mir-onnx-importer/Op/Add.cpp b/compiler/mir-onnx-importer/Op/Add.cpp
index a9542de6e..e00e4f4ff 100644
--- a/compiler/mir-onnx-importer/Op/Add.cpp
+++ b/compiler/mir-onnx-importer/Op/Add.cpp
@@ -17,6 +17,7 @@
 #include "Add.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/AddOp.h"
 
diff --git a/compiler/mir-onnx-importer/Op/AveragePool.cpp b/compiler/mir-onnx-importer/Op/AveragePool.cpp
index ca1593f59..e1ea2a159 100644
--- a/compiler/mir-onnx-importer/Op/AveragePool.cpp
+++ b/compiler/mir-onnx-importer/Op/AveragePool.cpp
@@ -17,6 +17,7 @@
 #include "AveragePool.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/PoolOp.h"
 
@@ -40,7 +41,7 @@ void AveragePoolNodeConverter::convert(const onnx::NodeProto &onnx_node,
 void AveragePoolNodeConverter::convertV1(const onnx::NodeProto &onnx_node,
                                          ConverterContext *context) const
 {
-  const auto auto_pad = getStringAttribute(onnx_node, "auto_pad", "NOTSET");
+  const auto auto_pad = getAttributeValue<std::string>(onnx_node, "auto_pad", "NOTSET");
   // auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.
   if (auto_pad != "NOTSET")
     throw std::runtime_error("Supported only explicit padding!");
@@ -69,7 +70,7 @@ void AveragePoolNodeConverter::convertV1(const onnx::NodeProto &onnx_node,
 void AveragePoolNodeConverter::convertV7(const onnx::NodeProto &onnx_node,
                                          ConverterContext *context) const
 {
-  const auto count_include_pad = getIntAttribute(onnx_node, "count_include_pad", 0);
+  const auto count_include_pad = getAttributeValue<int64_t>(onnx_node, "count_include_pad", 0);
   if (count_include_pad != 0)
     throw std::runtime_error("Not supported count_include_pad attribute!");
 
@@ -79,7 +80,7 @@ void AveragePoolNodeConverter::convertV7(const onnx::NodeProto &onnx_node,
 void AveragePoolNodeConverter::convertV10(const onnx::NodeProto &onnx_node,
                                           ConverterContext *context) const
 {
-  const auto ceil_mode = getIntAttribute(onnx_node, "ceil_mode", 0);
+  const auto ceil_mode = getAttributeValue<int64_t>(onnx_node, "ceil_mode", 0);
   if (ceil_mode != 0)
     throw std::runtime_error("Not supported ceil_mode attribute!");
 
diff --git a/compiler/mir-onnx-importer/Op/BatchNormalization.cpp b/compiler/mir-onnx-importer/Op/BatchNormalization.cpp
index a6baf5e89..9cc02e3a1 100644
--- a/compiler/mir-onnx-importer/Op/BatchNormalization.cpp
+++ b/compiler/mir-onnx-importer/Op/BatchNormalization.cpp
@@ -17,6 +17,7 @@
 #include "BatchNormalization.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ShapeRange.h"
 #include "mir/Tensor.h"
@@ -56,7 +57,7 @@ void BatchNormalizationNodeConverter::convertV1(const onnx::NodeProto &onnx_node
 void BatchNormalizationNodeConverter::convertV6(const onnx::NodeProto &onnx_node,
                                                 ConverterContext *context) const
 {
-  const auto is_test = getIntAttribute(onnx_node, "is_test", 0);
+  const auto is_test = getAttributeValue<int64_t>(onnx_node, "is_test", 0);
   if (is_test != 0)
     throw std::runtime_error("Not supported is_test attribute!");
 
@@ -66,7 +67,7 @@ void BatchNormalizationNodeConverter::convertV6(const onnx::NodeProto &onnx_node
 void BatchNormalizationNodeConverter::convertV7(const onnx::NodeProto &onnx_node,
                                                 ConverterContext *context) const
 {
-  const auto spatial = getIntAttribute(onnx_node, "spatial", 1);
+  const auto spatial = getAttributeValue<int64_t>(onnx_node, "spatial", 1);
   if (spatial != 1)
     throw std::runtime_error("Not supported spatial attribute!");
 
@@ -89,7 +90,7 @@ void BatchNormalizationNodeConverter::convertV9(const onnx::NodeProto &onnx_node
   auto var = inputs[4];
 
   // 1e-05f is the default epsilon.
-  const float epsilon = getFloatAttribute(onnx_node, "epsilon", 1e-05f);
+  const auto epsilon = getAttributeValue<float>(onnx_node, "epsilon", 1e-05f);
 
   // Y = (X - mean) * scale / sqrt(var + epsilon) + bias =
   //   = (X + C1) * C2 + bias
diff --git a/compiler/mir-onnx-importer/Op/Concat.cpp b/compiler/mir-onnx-importer/Op/Concat.cpp
index 59ae59036..229cb76d8 100644
--- a/compiler/mir-onnx-importer/Op/Concat.cpp
+++ b/compiler/mir-onnx-importer/Op/Concat.cpp
@@ -17,6 +17,7 @@
 #include "Concat.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/ConcatOp.h"
 
@@ -40,7 +41,7 @@ void ConcatNodeConverter::convertV1(const onnx::NodeProto &onnx_node,
   std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
   mir::Graph *graph = context->getGraph();
 
-  const auto axis = getIntAttribute(onnx_node, "axis", 1);
+  const auto axis = getAttributeValue<int64_t>(onnx_node, "axis", 1);
 
   auto result = createOp<mir::ops::ConcatOp>(graph, inputs, axis)->getOutput(0);
 
diff --git a/compiler/mir-onnx-importer/Op/Constant.cpp b/compiler/mir-onnx-importer/Op/Constant.cpp
index 7c73cb6c9..3a1a9e95b 100644
--- a/compiler/mir-onnx-importer/Op/Constant.cpp
+++ b/compiler/mir-onnx-importer/Op/Constant.cpp
@@ -17,6 +17,7 @@
 #include "Constant.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/TensorVariant.h"
 #include "mir/ops/ConstantOp.h"
@@ -41,16 +42,13 @@ void ConstantNodeConverter::convert(const onnx::NodeProto &onnx_node,
 void ConstantNodeConverter::convertV1(const onnx::NodeProto &onnx_node,
                                       ConverterContext *context) const
 {
-  const auto *value_attr = findAttribute(onnx_node, "value");
-  if (value_attr == nullptr)
-    throw std::runtime_error("Not enough value attribute in Constant operation!");
-  assert(value_attr->type() == onnx::AttributeProto_AttributeType_TENSOR);
+  std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
+  mir::Graph *graph = context->getGraph();
 
+  const auto onnx_tensor = getAttributeValue<onnx::TensorProto>(onnx_node, "value");
   const auto &name = onnx_node.output(0);
-  const auto &onnx_tensor = value_attr->t();
   auto mir_tensor = createTensor(&onnx_tensor);
 
-  mir::Graph *graph = context->getGraph();
   auto result = graph->create<mir::ops::ConstantOp>(name, mir_tensor)->getOutput(0);
 
   context->setNodeOutputs(onnx_node, {result});
diff --git a/compiler/mir-onnx-importer/Op/Conv.cpp b/compiler/mir-onnx-importer/Op/Conv.cpp
index 6d9916f67..e25f6b1f5 100644
--- a/compiler/mir-onnx-importer/Op/Conv.cpp
+++ b/compiler/mir-onnx-importer/Op/Conv.cpp
@@ -17,6 +17,7 @@
 #include "Conv.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/TensorUtil.h"
 
@@ -39,7 +40,7 @@ void ConvNodeConverter::convert(const onnx::NodeProto &onnx_node, ConverterConte
 
 void ConvNodeConverter::convertV1(const onnx::NodeProto &onnx_node, ConverterContext *context) const
 {
-  const auto auto_pad = getStringAttribute(onnx_node, "auto_pad", "NOTSET");
+  const auto auto_pad = getAttributeValue<std::string>(onnx_node, "auto_pad", "NOTSET");
   // auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.
   if (auto_pad != "NOTSET")
     throw std::runtime_error("Supported only explicit padding!");
@@ -64,8 +65,8 @@ void ConvNodeConverter::convertV1(const onnx::NodeProto &onnx_node, ConverterCon
   auto out_channels = kernel_tensor.getShape().dim(3);
 
   // 1 is the default number of groups.
-  int num_groups = getIntAttribute(onnx_node, "group", 1);
-  bool is_depthwise = (num_groups != 1) && (in_group_size == 1) && (out_channels == num_groups);
+  const auto group = getAttributeValue<std::int64_t>(onnx_node, "group", 1);
+  bool is_depthwise = (group != 1) && (in_group_size == 1) && (out_channels == group);
 
   mir::Operation::Output *result;
   auto transposed_input = convertONNXToMIR(graph, inputs[0]);
@@ -82,8 +83,8 @@ void ConvNodeConverter::convertV1(const onnx::NodeProto &onnx_node, ConverterCon
   else
   {
     // first we need to convert kernel of grouped convolution to appropriate ordinary kernel
-    if (num_groups != 1)
-      kernel_tensor = fixGroupedKernel(num_groups, kernel_tensor);
+    if (group != 1)
+      kernel_tensor = fixGroupedKernel(group, kernel_tensor);
     kernel_tensor = mir::transposeTensor<3, 0, 1, 2>(kernel_tensor);
     auto kernel = createOp<mir::ops::ConstantOp>(graph, kernel_tensor)->getOutput(0);
     result = createOp<mir::ops::Conv2DOp>(graph, transposed_input, kernel, cdata.strides_shape,
diff --git a/compiler/mir-onnx-importer/Op/Gather.cpp b/compiler/mir-onnx-importer/Op/Gather.cpp
index 425e47869..517007ed0 100644
--- a/compiler/mir-onnx-importer/Op/Gather.cpp
+++ b/compiler/mir-onnx-importer/Op/Gather.cpp
@@ -17,6 +17,7 @@
 #include "Gather.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/GatherOp.h"
 
@@ -29,7 +30,7 @@ void GatherNodeConverter::convert(const onnx::NodeProto &onnx_node, ConverterCon
   mir::Graph *graph = context->getGraph();
 
   // 0 is the default axis number.
-  int axis = getIntAttribute(onnx_node, "axis", 0);
+  const auto axis = getAttributeValue<std::int64_t>(onnx_node, "axis", 0);
 
   auto result = createOp<mir::ops::GatherOp>(graph, inputs[0], inputs[1], axis)->getOutput(0);
 
diff --git a/compiler/mir-onnx-importer/Op/Gemm.cpp b/compiler/mir-onnx-importer/Op/Gemm.cpp
index 7fd78cf84..793dada25 100644
--- a/compiler/mir-onnx-importer/Op/Gemm.cpp
+++ b/compiler/mir-onnx-importer/Op/Gemm.cpp
@@ -17,6 +17,7 @@
 #include "Gemm.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/TensorVariant.h"
 
@@ -41,12 +42,12 @@ void GemmNodeConverter::convert(const onnx::NodeProto &onnx_node, ConverterConte
   auto c = inputs[2];
 
   // 1.0f is the default factor.
-  const float alpha_val = getFloatAttribute(onnx_node, "alpha", 1.0f);
-  const float beta_val = getFloatAttribute(onnx_node, "beta", 1.0f);
+  const auto alpha_val = getAttributeValue<float>(onnx_node, "alpha", 1.0f);
+  const auto beta_val = getAttributeValue<float>(onnx_node, "beta", 1.0f);
 
   // 0 means that no transpose is needed. It is the default value.
-  const bool trans_a = getIntAttribute(onnx_node, "transA", 0);
-  const bool trans_b = getIntAttribute(onnx_node, "transB", 0);
+  const auto trans_a = getAttributeValue<std::int64_t>(onnx_node, "transA", 0);
+  const auto trans_b = getAttributeValue<std::int64_t>(onnx_node, "transB", 0);
 
   // Transpose the A and B matrices as needed.
   if (trans_a)
diff --git a/compiler/mir-onnx-importer/Op/MaxPool.cpp b/compiler/mir-onnx-importer/Op/MaxPool.cpp
index 0da6e3562..48e35eb8c 100644
--- a/compiler/mir-onnx-importer/Op/MaxPool.cpp
+++ b/compiler/mir-onnx-importer/Op/MaxPool.cpp
@@ -17,6 +17,7 @@
 #include "MaxPool.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/PoolOp.h"
 
@@ -40,7 +41,7 @@ void MaxPoolNodeConverter::convert(const onnx::NodeProto &onnx_node,
 void MaxPoolNodeConverter::convertV1(const onnx::NodeProto &onnx_node,
                                      ConverterContext *context) const
 {
-  const auto auto_pad = getStringAttribute(onnx_node, "auto_pad", "NOTSET");
+  const auto auto_pad = getAttributeValue<std::string>(onnx_node, "auto_pad", "NOTSET");
   // auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID.
   if (auto_pad != "NOTSET")
     throw std::runtime_error("Supported only explicit padding!");
@@ -69,7 +70,7 @@ void MaxPoolNodeConverter::convertV1(const onnx::NodeProto &onnx_node,
 void MaxPoolNodeConverter::convertV8(const onnx::NodeProto &onnx_node,
                                      ConverterContext *context) const
 {
-  const auto storage_order = getIntAttribute(onnx_node, "storage_order", 0);
+  const auto storage_order = getAttributeValue<int64_t>(onnx_node, "storage_order", 0);
   if (storage_order != 0)
     throw std::runtime_error("Not supported storage order attribute!");
 
@@ -79,7 +80,7 @@ void MaxPoolNodeConverter::convertV8(const onnx::NodeProto &onnx_node,
 void MaxPoolNodeConverter::convertV10(const onnx::NodeProto &onnx_node,
                                       ConverterContext *context) const
 {
-  const auto ceil_mode = getIntAttribute(onnx_node, "ceil_mode", 0);
+  const auto ceil_mode = getAttributeValue<int64_t>(onnx_node, "ceil_mode", 0);
   if (ceil_mode != 0)
     throw std::runtime_error("Not supported ceil_mode attribute!");
 
diff --git a/compiler/mir-onnx-importer/Op/Pad.cpp b/compiler/mir-onnx-importer/Op/Pad.cpp
index b4cec79e8..f3fdeefa6 100644
--- a/compiler/mir-onnx-importer/Op/Pad.cpp
+++ b/compiler/mir-onnx-importer/Op/Pad.cpp
@@ -17,6 +17,7 @@
 #include "Pad.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/PadOp.h"
 
@@ -42,24 +43,21 @@ void PadNodeConverter::convertPadAttrName(const std::string pad_attr_name,
   mir::Graph *graph = context->getGraph();
 
   // 0.0f is the default value to be filled into padded cells.
-  const float value = getFloatAttribute(onnx_node, "value", 0.0f);
-  const auto *pads_attr = findAttribute(onnx_node, pad_attr_name);
-  assert(pads_attr);
+  const float value = getAttributeValue<float>(onnx_node, "value", 0.0f);
+  const auto pads = getAttributeValue<std::vector<int64_t>>(onnx_node, "pads");
   // "constant" is the default mode.
-  auto mode = getStringAttribute(onnx_node, "mode", "constant");
+  auto mode = getAttributeValue<std::string>(onnx_node, "mode", "constant");
   if (mode != "constant")
-    throw std::runtime_error("Not supported Pad mode attribue!");
+    throw std::runtime_error("Not supported Pad mode attribute!");
 
   const mir::Scalar scalar(reinterpret_cast<const char *>(&value), mir::DTYPE::FLOAT32,
                            sizeof(float));
-  assert(pads_attr->ints_size() > 0);
-  int axis_size = pads_attr->ints_size() / 2;
-  std::vector<std::pair<int32_t, int32_t>> vec(axis_size);
-  auto *data = pads_attr->ints().data();
-  for (int i = 0; i < axis_size; i++)
+  assert(!pads.empty());
+  const auto axis_size = pads.size() / 2;
+  std::vector<std::pair<int32_t, int32_t>> vec;
+  for (std::size_t i = 0; i < axis_size; i++)
   {
-    auto pair = std::make_pair(data[i], data[axis_size + i]);
-    vec[i] = pair;
+    vec.emplace_back(pads[i], pads[axis_size + i]);
   }
   auto result =
       createOp<mir::ops::PadOp>(graph, inputs[0], inputs[0]->getShape().rank(), vec, scalar)
diff --git a/compiler/mir-onnx-importer/Op/Reshape.cpp b/compiler/mir-onnx-importer/Op/Reshape.cpp
index f8fd69cfa..ac6a16d1a 100644
--- a/compiler/mir-onnx-importer/Op/Reshape.cpp
+++ b/compiler/mir-onnx-importer/Op/Reshape.cpp
@@ -17,8 +17,10 @@
 #include "Reshape.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/Tensor.h"
+#include "mir/ShapeRange.h"
 
 #include "mir/ops/ConstantOp.h"
 #include "mir/ops/ReshapeOp.h"
diff --git a/compiler/mir-onnx-importer/Op/Softmax.cpp b/compiler/mir-onnx-importer/Op/Softmax.cpp
index 21e4c93e6..84db24ea9 100644
--- a/compiler/mir-onnx-importer/Op/Softmax.cpp
+++ b/compiler/mir-onnx-importer/Op/Softmax.cpp
@@ -17,6 +17,7 @@
 #include "Softmax.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/SoftmaxOp.h"
 
@@ -30,7 +31,7 @@ void SoftmaxNodeConverter::convert(const onnx::NodeProto &onnx_node,
   mir::Graph *graph = context->getGraph();
 
   // 1 is the default axis number.
-  int axis = getIntAttribute(onnx_node, "axis", 1);
+  const auto axis = getAttributeValue<std::int64_t>(onnx_node, "axis", 1);
 
   auto result = createOp<mir::ops::SoftmaxOp>(graph, inputs[0], axis)->getOutput(0);
 
diff --git a/compiler/mir-onnx-importer/Op/Unsqueeze.cpp b/compiler/mir-onnx-importer/Op/Unsqueeze.cpp
index 0fcb307a7..e1a80619b 100644
--- a/compiler/mir-onnx-importer/Op/Unsqueeze.cpp
+++ b/compiler/mir-onnx-importer/Op/Unsqueeze.cpp
@@ -17,6 +17,7 @@
 #include "Unsqueeze.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/ops/ReshapeOp.h"
 
@@ -28,16 +29,16 @@ void UnsqueezeNodeConverter::convert(const onnx::NodeProto &onnx_node,
 {
   std::vector<mir::Operation::Output *> inputs = context->getNodeInputs(onnx_node);
   mir::Graph *graph = context->getGraph();
-  auto *axes = findAttribute(onnx_node, "axes");
-  assert(axes && axes->ints_size());
+  const auto axes = getAttributeValue<std::vector<std::int64_t>>(onnx_node, "axes");
+  assert(!axes.empty());
   const mir::Shape &input_shape = inputs[0]->getShape();
-  const int out_rank = input_shape.rank() + axes->ints_size();
+  const int out_rank = input_shape.rank() + static_cast<int>(axes.size());
   mir::Shape out_shape(out_rank);
-  auto ints_iterator = axes->ints().begin();
+  auto ints_iterator = axes.cbegin();
   int j = 0;
   for (int i = 0; i < out_rank; i++)
   {
-    if (ints_iterator < axes->ints().end() && i == *ints_iterator)
+    if (ints_iterator < axes.cend() && i == *ints_iterator)
     {
       out_shape.dim(i) = 1;
       ints_iterator++;
diff --git a/compiler/mir-onnx-importer/Op/Upsample.cpp b/compiler/mir-onnx-importer/Op/Upsample.cpp
index a7e4f78e2..4bc8326ab 100644
--- a/compiler/mir-onnx-importer/Op/Upsample.cpp
+++ b/compiler/mir-onnx-importer/Op/Upsample.cpp
@@ -17,6 +17,7 @@
 #include "Upsample.h"
 
 #include "ONNXHelpers.h"
+#include "AttributeHelpers.h"
 
 #include "mir/Tensor.h"
 
@@ -49,11 +50,11 @@ void UpsampleNodeConverter::convertV1(const onnx::NodeProto &onnx_node,
   mir::Graph *graph = context->getGraph();
 
   // "nearest" is the default mode.
-  std::string mode = getStringAttribute(onnx_node, "mode", "nearest");
+  std::string mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
   assert(mode == "nearest" && "Unsupported upscale mode!");
 
-  const float h_scale = getFloatAttribute(onnx_node, "height_scale", 0.0f); // required
-  const float w_scale = getFloatAttribute(onnx_node, "width_scale", 0.0f);  // required
+  const float h_scale = getAttributeValue<float>(onnx_node, "height_scale", 0.0f); // required
+  const float w_scale = getAttributeValue<float>(onnx_node, "width_scale", 0.0f);  // required
   if (h_scale < 1.0f || w_scale < 1.0f)
     throw std::runtime_error("Wrong scale attributes!");
 
@@ -81,7 +82,7 @@ void UpsampleNodeConverter::convertV7(const onnx::NodeProto &onnx_node,
   mir::Graph *graph = context->getGraph();
 
   // "nearest" is the default mode.
-  std::string mode = getStringAttribute(onnx_node, "mode", "nearest");
+  std::string mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
   assert(mode == "nearest" && "Unsupported upscale mode!");
 
   const auto *scales_attr = findAttribute(onnx_node, "scales");
@@ -116,7 +117,7 @@ void UpsampleNodeConverter::convertV9(const onnx::NodeProto &onnx_node,
   mir::Graph *graph = context->getGraph();
 
   // "nearest" is the default mode.
-  std::string mode = getStringAttribute(onnx_node, "mode", "nearest");
+  const auto mode = getAttributeValue<std::string>(onnx_node, "mode", "nearest");
   assert(mode == "nearest" && "Unsupported upscale mode!");
 
   // relies on attributes being lifted to constants (ONNX optimization pass)