Microbenchmark for encoding+decoding ModelProto and GraphProto with a…

… single operator (onnx#609)

To build: cmake -GNinja -DCMAKE_BUILD_TYPE=Release -DONNX_BUILD_BENCHMARKS=ON -DBUILD_PYTHON=OFF ..

.gitmodules

@@ -2,3 +2,6 @@
 	path = third_party/pybind11
 	url = https://github.com/pybind/pybind11.git
 	branch = master
+[submodule "third_party/benchmark"]
+	path = third_party/benchmark
+	url = https://github.com/google/benchmark.git

CMakeLists.txt

@@ -3,6 +3,7 @@ cmake_minimum_required(VERSION 3.1)
 
 # Project
 project(onnx C CXX)
+option(ONNX_BUILD_BENCHMARKS "Build ONNX micro-benchmarks" OFF)
 
 option(BUILD_PYTHON "Build Python binaries" ON)
 
@@ -213,6 +214,19 @@ if(BUILD_PYTHON)
   endif()
 endif()
 
+if(ONNX_BUILD_BENCHMARKS)
+  if(NOT TARGET benchmark)
+    # We will not need to test benchmark lib itself.
+    set(BENCHMARK_ENABLE_TESTING OFF CACHE BOOL "Disable benchmark testing as we don't need it.")
+    # We will not need to install benchmark since we link it statically.
+    set(BENCHMARK_ENABLE_INSTALL OFF CACHE BOOL "Disable benchmark install to avoid overwriting vendor install.")
+    add_subdirectory(${PROJECT_SOURCE_DIR}/third_party/benchmark)
+  endif()
+
+  add_executable(protobuf-bench tools/protobuf-bench.cc)
+  target_link_libraries(protobuf-bench onnx_proto benchmark)
+endif()
+
 # Export include directories
 set(ONNX_INCLUDE_DIRS "${ONNX_ROOT}" "${CMAKE_CURRENT_BINARY_DIR}")
 set(ONNX_INCLUDE_DIRS ${ONNX_INCLUDE_DIRS} PARENT_SCOPE)

tools/protobuf-bench.cc

@@ -0,0 +1,146 @@
+#include <benchmark/benchmark.h>
+
+#include <onnx/onnx.pb.h>
+
+using namespace ONNX_NAMESPACE;
+
+
+inline void createValueInfo4D(
+    ValueInfoProto& value_info,
+    const std::string& name,
+    int64_t n,
+    int64_t c,
+    int64_t h,
+    int64_t w) {
+  value_info.set_name(name);
+
+  TypeProto_Tensor* tensor_type =
+      value_info.mutable_type()->mutable_tensor_type();
+  tensor_type->set_elem_type(TensorProto_DataType_FLOAT);
+
+  TensorShapeProto* shape = tensor_type->mutable_shape();
+  shape->add_dim()->set_dim_value(n);
+  shape->add_dim()->set_dim_value(c);
+  shape->add_dim()->set_dim_value(h);
+  shape->add_dim()->set_dim_value(w);
+}
+
+inline void createValueInfo2D(
+    ValueInfoProto& value_info,
+    const std::string& name,
+    int64_t h,
+    int64_t w) {
+  value_info.set_name(name);
+
+  TypeProto* type = value_info.mutable_type();
+
+  TypeProto_Tensor* tensor_type = type->mutable_tensor_type();
+  tensor_type->set_elem_type(TensorProto_DataType_FLOAT);
+  TensorShapeProto* shape = tensor_type->mutable_shape();
+  shape->add_dim()->set_dim_value(h);
+  shape->add_dim()->set_dim_value(w);
+}
+
+inline void createConv2D(
+    NodeProto& node,
+    const std::string& input,
+    const std::string& weights,
+    const std::string& bias,
+    const std::string& output,
+    uint32_t kernel_size) {
+  node.set_op_type("Conv");
+  node.add_input(input);
+  node.add_input(weights);
+  node.add_input(bias);
+  node.add_output(output);
+
+  {
+    AttributeProto* kernel = node.add_attribute();
+    kernel->set_name("kernel_shape");
+    kernel->set_type(AttributeProto::INTS);
+    kernel->add_ints(kernel_size);
+    kernel->add_ints(kernel_size);
+  }
+  {
+    AttributeProto* dilation = node.add_attribute();
+    dilation->set_name("dilations");
+    dilation->set_type(AttributeProto::INTS);
+    dilation->add_ints(1);
+    dilation->add_ints(1);
+  }
+  {
+    AttributeProto* stride = node.add_attribute();
+    stride->set_name("strides");
+    stride->set_type(AttributeProto::INTS);
+    stride->add_ints(1);
+    stride->add_ints(1);
+  }
+  {
+    AttributeProto* group = node.add_attribute();
+    group->set_name("group");
+    group->set_type(AttributeProto::INTS);
+    group->set_i(1);
+  }
+  {
+    AttributeProto* padding = node.add_attribute();
+    padding->set_name("pads");
+    padding->set_type(AttributeProto::INTS);
+    /* Use "same" padding */
+    padding->add_ints(kernel_size / 2);
+    padding->add_ints(kernel_size / 2);
+    padding->add_ints(kernel_size - 1 - kernel_size / 2);
+    padding->add_ints(kernel_size - 1 - kernel_size / 2);
+  }
+}
+
+static void ConvGraph(benchmark::State& state) {
+  while (state.KeepRunning()) {
+    std::string data;
+    GraphProto graph;
+
+    createConv2D(*graph.add_node(), "input", "weights", "bias", "output", 3);
+
+    createValueInfo4D(*graph.add_input(), "input", 1, 3, 224, 224);
+    createValueInfo4D(*graph.add_input(), "weights", 16, 16, 3, 3);
+    createValueInfo2D(*graph.add_input(), "bias", 1, 16);
+    createValueInfo4D(*graph.add_output(), "output", 16, 3, 224, 224);
+
+    graph.SerializeToString(&data);
+
+    GraphProto decodedGraph;
+    decodedGraph.ParseFromString(data);
+  }
+
+  state.SetItemsProcessed(int64_t(state.iterations()));
+}
+BENCHMARK(ConvGraph)->Unit(benchmark::kMicrosecond);
+
+static void ConvModel(benchmark::State& state) {
+  while (state.KeepRunning()) {
+    std::string data;
+    ModelProto model;
+    model.set_ir_version(IR_VERSION);
+    OperatorSetIdProto* op_set_id = model.add_opset_import();
+    op_set_id->set_domain("");
+    op_set_id->set_version(4);
+
+    GraphProto* graph = model.mutable_graph();
+
+    createConv2D(*graph->add_node(), "input", "weights", "bias", "output", 3);
+
+    createValueInfo4D(*graph->add_input(), "input", 1, 3, 224, 224);
+    createValueInfo4D(*graph->add_input(), "weights", 16, 16, 3, 3);
+    createValueInfo2D(*graph->add_input(), "bias", 1, 16);
+    createValueInfo4D(*graph->add_output(), "output", 16, 3, 224, 224);
+
+    model.SerializeToString(&data);
+
+    ModelProto decodedModel;
+    decodedModel.ParseFromString(data);
+  }
+
+  state.SetItemsProcessed(int64_t(state.iterations()));
+}
+BENCHMARK(ConvModel)->Unit(benchmark::kMicrosecond);
+
+BENCHMARK_MAIN();