Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into update_py_reader

benchmark/paddle/image/run.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function train() {

benchmark/paddle/image/run_mkl_infer.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function clock_to_seconds() {

benchmark/paddle/image/run_mkl_train.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function train() {

benchmark/paddle/image/run_openblas_infer.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function clock_to_seconds() {

benchmark/paddle/image/run_openblas_train.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function train() {

benchmark/paddle/rnn/run.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function train() {

benchmark/tensorflow/image/run.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function test() {

benchmark/tensorflow/image/run_multi.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function test() {

benchmark/tensorflow/rnn/run.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function test() {

benchmark/tensorflow/rnn/run_multi.sh

@@ -1,3 +1,5 @@
+#!/bin/bash
+
 set -e
 
 function test() {

paddle/fluid/inference/analysis/analyzer.cc

@@ -22,8 +22,6 @@
 #include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
 
 namespace paddle {
-namespace inference {
-namespace analysis {
 
 DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, false,
             "Enable subgraph to TensorRT engine for acceleration");
@@ -31,6 +29,9 @@ DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, false,
 DEFINE_string(inference_analysis_graphviz_log_root, "./",
               "Graphviz debuger for data flow graphs.");
 
+namespace inference {
+namespace analysis {
+
 class DfgPassManagerImpl final : public DfgPassManager {
  public:
   DfgPassManagerImpl() {

paddle/fluid/inference/analysis/analyzer.h

@@ -45,14 +45,15 @@ limitations under the License. */
 #include "paddle/fluid/inference/analysis/pass_manager.h"
 
 namespace paddle {
-namespace inference {
-namespace analysis {
 
 // TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
 // flag if not available.
 DECLARE_bool(inference_analysis_enable_tensorrt_subgraph_engine);
 DECLARE_string(inference_analysis_graphviz_log_root);
 
+namespace inference {
+namespace analysis {
+
 class Analyzer : public OrderedRegistry<PassManager> {
  public:
   // Register all the pass-managers.

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -13,13 +13,21 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/analysis/analyzer.h"
+#include <google/protobuf/text_format.h>
 #include "paddle/fluid/inference/analysis/ut_helper.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-TEST_F(DFG_Tester, main) {
+TEST_F(DFG_Tester, analysis_without_tensorrt) {
+  FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = false;
+  Analyzer analyser;
+  analyser.Run(&argument);
+}
+
+TEST_F(DFG_Tester, analysis_with_tensorrt) {
+  FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = true;
   Analyzer analyser;
   analyser.Run(&argument);
 }

paddle/fluid/inference/analysis/data_flow_graph.cc

@@ -222,10 +222,19 @@ Node *GraphTraits<DataFlowGraph>::NodesDFSIterator::operator->() {
   return stack_.top();
 }
 
+inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
+  return node.inlinks.size() == n;
+}
+
 GraphTraits<DataFlowGraph>::NodesTSIterator::NodesTSIterator(
     const std::vector<Node *> &source) {
   PADDLE_ENFORCE(!source.empty(),
                  "Start points of topological sorting should not be empty!");
+  // CHECK all the inputs' in-degree is 0
+  for (auto *node : source) {
+    PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
+  }
+
   std::unordered_set<Node *> visited;
   std::unordered_set<Node *> to_visit{source.begin(), source.end()};
 
@@ -233,6 +242,11 @@ GraphTraits<DataFlowGraph>::NodesTSIterator::NodesTSIterator(
   while (!to_visit.empty()) {
     std::vector<Node *> queue(to_visit.begin(), to_visit.end());
     for (auto *p : queue) {
+      if (p->deleted()) {
+        visited.insert(p);
+        to_visit.erase(p);
+        continue;
+      }
       inlink_visited.clear();
 
       std::copy_if(p->inlinks.begin(), p->inlinks.end(),
@@ -292,6 +306,37 @@ Node *GraphTraits<DataFlowGraph>::NodesTSIterator::operator->() {
   return sorted_[cursor_];
 }
 
+std::pair<std::vector<Node *>, std::vector<Node *>>
+ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) {  // NOLINT
+  std::unordered_set<Node *> nodes(graph.begin(), graph.end());
+  std::unordered_set<Node *> inputs;
+  std::unordered_set<Node *> outputs;
+  // Input a Value, check whether its inlink is in the subgraph.
+  auto inlink_in_subgraph = [&](Node *n) {
+    for (auto *in : n->inlinks) {
+      if (nodes.count(in)) return true;
+    }
+    return false;
+  };
+  for (auto &node : graph) {
+    for (auto *in : node->inlinks) {
+      // The Value that is written by nodes inside a sub-graph shouldn't be the
+      // input of the sub-graph.
+      if (!nodes.count(in) && in->type() == Node::Type::kValue &&
+          !inlink_in_subgraph(in)) {
+        inputs.insert(in);
+      }
+    }
+    for (auto *out : node->outlinks) {
+      if (!nodes.count(out) && out->type() == Node::Type::kValue) {
+        outputs.insert(out);
+      }
+    }
+  }
+  return std::make_pair(std::vector<Node *>(inputs.begin(), inputs.end()),
+                        std::vector<Node *>(outputs.begin(), outputs.end()));
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph.h

@@ -133,7 +133,7 @@ struct GraphTraits<DataFlowGraph> {
 
    private:
     std::vector<Node *> sorted_;
-    int cursor_{0};
+    size_t cursor_{0};
   };
 
   explicit GraphTraits(DataFlowGraph *graph) : graph_(graph) {}
@@ -173,36 +173,8 @@ struct GraphTraits<DataFlowGraph> {
 // Extract the inputs and outputs of a graph. The inputs and outputs of a
 // sub-graph is the inputs nodes and output nodes that doesn't inside the
 // sub-graph.
-static std::pair<std::vector<Node *>, std::vector<Node *>>
-ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) {  // NOLINT
-  std::unordered_set<Node *> nodes(graph.begin(), graph.end());
-  std::unordered_set<Node *> inputs;
-  std::unordered_set<Node *> outputs;
-  // Input a Value, check whether its inlink is in the subgraph.
-  auto inlink_in_subgraph = [&](Node *n) {
-    for (auto *in : n->inlinks) {
-      if (nodes.count(in)) return true;
-    }
-    return false;
-  };
-  for (auto &node : graph) {
-    for (auto *in : node->inlinks) {
-      // The Value that is written by nodes inside a sub-graph shouldn't be the
-      // input of the sub-graph.
-      if (!nodes.count(in) && in->type() == Node::Type::kValue &&
-          !inlink_in_subgraph(in)) {
-        inputs.insert(in);
-      }
-    }
-    for (auto *out : node->outlinks) {
-      if (!nodes.count(out) && out->type() == Node::Type::kValue) {
-        outputs.insert(out);
-      }
-    }
-  }
-  return std::make_pair(std::vector<Node *>(inputs.begin(), inputs.end()),
-                        std::vector<Node *>(outputs.begin(), outputs.end()));
-}
+std::pair<std::vector<Node *>, std::vector<Node *>>
+ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph);
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc

@@ -22,14 +22,18 @@
 
 namespace paddle {
 namespace inference {
+
+DEFINE_int32(tensorrt_max_batchsize, 300, "TensorRT maximum batch size");
+DEFINE_int32(tensorrt_workspace_size, 2048, "TensorRT workspace size");
+
 namespace analysis {
 
 using framework::proto::ProgramDesc;
 
 std::vector<std::string> ExtractParameters(
-    const std::vector<std::unique_ptr<Node>>& nodes);
+    const std::vector<std::unique_ptr<Node>> &nodes);
 
-bool DataFlowGraphToFluidPass::Initialize(Argument* argument) {
+bool DataFlowGraphToFluidPass::Initialize(Argument *argument) {
   ANALYSIS_ARGUMENT_CHECK_FIELD(argument)
   ANALYSIS_ARGUMENT_CHECK_FIELD(argument->origin_program_desc)
   PADDLE_ENFORCE(!argument->transformed_program_desc);
@@ -47,76 +51,77 @@ bool DataFlowGraphToFluidPass::Initialize(Argument* argument) {
 
 bool DataFlowGraphToFluidPass::Finalize() { return true; }
 
-void DataFlowGraphToFluidPass::Run(DataFlowGraph* graph) {
-  auto traits = GraphTraits<DataFlowGraph>(graph);
-  for (auto it = traits.nodes().begin(); it != traits.nodes().end(); ++it) {
-    if (it->deleted()) continue;
+void DataFlowGraphToFluidPass::Run(DataFlowGraph *graph) {
+  LOG(INFO) << "graph.inputs " << graph->inputs.size();
+  for (auto &node : GraphTraits<DataFlowGraph>(graph).nodes_in_TS()) {
+    if (node.deleted()) continue;
 
-    switch (it->type()) {
+    switch (node.type()) {
       case Node::Type::kFunction: {
-        LOG(INFO) << "add function " << it->repr();
-        AddFluidOp(&(*it));
+        LOG(INFO) << "add function " << node.repr();
+        AddFluidOp(&node);
       } break;
       case Node::Type::kFunctionBlock: {
-        LOG(INFO) << "add engine op " << it->repr() << " , "
-                  << static_cast<FunctionBlock*>(&(*it))->subgraph.size();
-        AddEngineOp(&(*it));
+        LOG(INFO) << "add engine op " << node.repr() << " , "
+                  << static_cast<FunctionBlock *>(&node)->subgraph.size();
+        AddEngineOp(&node);
       } break;
       default:
         continue;
     }
   }
+
+  PADDLE_ENFORCE(argument_->transformed_program_desc.get());
 }
 
-void DataFlowGraphToFluidPass::AddFluidOp(Node* node) {
-  auto* ori_op = static_cast<framework::proto::OpDesc*>(node->pb_desc());
+void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
+  auto *ori_op = static_cast<framework::proto::OpDesc *>(node->pb_desc());
   // currently only the main block is analyzed.
-  auto* main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
-  auto* op = main_block->add_ops();
+  auto *main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
+  auto *op = main_block->add_ops();
   *op = *ori_op;  // copy the attributes, by default, these will not be changed
-                  // by analysis phrase.
+  // by analysis phrase.
   // The inputs and outputs of the existing ops are not changed by tensorrt
   // subgraph pass.
   // NOTE It might be changed by other passes in the long run.
 }
 
-void CreateTrtEngineOp(Node* node, const DataFlowGraph& graph,
-                       const framework::proto::BlockDesc& block) {
+void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
+                       const framework::proto::BlockDesc &block) {
   static int counter{0};
   PADDLE_ENFORCE(node->IsFunctionBlock());
   framework::OpDesc desc;
-  auto* func = static_cast<FunctionBlock*>(node);
+  auto *func = static_cast<FunctionBlock *>(node);
 
   // collect inputs
   std::vector<std::string> io;
-  for (auto* x : func->inlinks) {
+  for (auto *x : func->inlinks) {
     io.push_back(x->name());
   }
   desc.SetInput("Xs", io);
 
   // collect outputs
   io.clear();
-  for (auto* x : func->outlinks) {
+  for (auto *x : func->outlinks) {
     io.push_back(x->name());
   }
   desc.SetOutput("Ys", io);
-
   desc.SetType("tensorrt_engine");
+
+  PADDLE_ENFORCE(!block.vars().empty(), "the block has no var-desc");
   // Set attrs
   SetAttr(desc.Proto(), "subgraph", block.SerializeAsString());
-  SetAttr(desc.Proto(), "engine_unique_key",
-          "trt-" + std::to_string(counter++));
-  SetAttr(desc.Proto(), "max_batch", 100);  // TODO(Superjomn) add config latter
-  SetAttr(desc.Proto(), "max_workspace",
-          1024);  // TODO(Superjomn) add config latter
+  SetAttr(desc.Proto(), "engine_uniq_key", "trt-" + std::to_string(counter++));
+  SetAttr(desc.Proto(), "max_batch", FLAGS_tensorrt_max_batchsize);
+  SetAttr(desc.Proto(), "max_workspace", FLAGS_tensorrt_workspace_size);
   SetAttr(desc.Proto(), "parameters", ExtractParameters(graph.nodes.nodes()));
   node->SetPbMsg(desc.Proto()->SerializeAsString());
 }
 
 std::vector<std::string> ExtractParameters(
-    const std::vector<std::unique_ptr<Node>>& nodes) {
+    const std::vector<std::unique_ptr<Node>> &nodes) {
   std::vector<std::string> parameters;
-  for (const auto& node : nodes) {
+  for (const auto &node : nodes) {
     if (!node->IsValue()) continue;
     PADDLE_ENFORCE(!node->pb_msg().empty(), "pb_msg should be set first");
     framework::proto::VarDesc var;
@@ -128,21 +133,30 @@ std::vector<std::string> ExtractParameters(
   return parameters;
 }
 
-void DataFlowGraphToFluidPass::AddEngineOp(Node* node) {
+void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
   // TODO(Superjomn) Here need to expose some arguments for default setting.
   PADDLE_ENFORCE(node->IsFunctionBlock());
-  auto* block_node = static_cast<FunctionBlock*>(node);
+  auto *block_node = static_cast<FunctionBlock *>(node);
   framework::proto::BlockDesc proto;
   framework::BlockDesc block_desc(nullptr, &proto);
+  block_desc.Proto()->set_parent_idx(-1);
+  block_desc.Proto()->set_idx(0);
+  LOG(INFO) << "origin variable size: "
+            << argument_->origin_program_desc->blocks(0).vars().size();
+  LOG(INFO) << "transformed variable size: "
+            << block_desc.Proto()->vars().size();
   // copy ops.
-  for (auto* node : block_node->subgraph) {
-    auto* op = block_desc.AppendOp();
+  for (auto *node : block_node->subgraph) {
+    auto *op = block_desc.AppendOp();
     PADDLE_ENFORCE(!node->pb_msg().empty());
     op->Proto()->ParseFromString(node->pb_msg());
   }
+  *block_desc.Proto()->mutable_vars() =
+      argument_->origin_program_desc->blocks(0).vars();
+  PADDLE_ENFORCE(!block_desc.Proto()->vars().empty());
   CreateTrtEngineOp(node, *argument_->main_dfg, *block_desc.Proto());
-  auto* main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
-  auto* op = main_block->add_ops();
+  auto *main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
+  auto *op = main_block->add_ops();
   PADDLE_ENFORCE(!node->pb_msg().empty(), "failed to set desc for block");
   op->ParseFromString(node->pb_msg());
 }
@@ -151,7 +165,7 @@ namespace {
 class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
  public:
   using Config = DFG_GraphvizDrawPass::Config;
-  explicit DFG_DebuggerPass(const Config& config)
+  explicit DFG_DebuggerPass(const Config &config)
       : DFG_GraphvizDrawPass(config) {}
 
   std::string repr() const override { return "dfg-to-fluid-debuger-pass"; }
@@ -160,7 +174,7 @@ class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
 };
 }  // namespace
 
-Pass* DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
+Pass *DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
   return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
       FLAGS_inference_analysis_graphviz_log_root,
       "data_flow_graph_to_fluid_graphviz_debugger"));

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h

@@ -26,6 +26,10 @@
 
 namespace paddle {
 namespace inference {
+
+DECLARE_int32(tensorrt_max_batchsize);
+DECLARE_int32(tensorrt_workspace_size);
+
 namespace analysis {
 class DataFlowGraphToFluidPass final : public DataFlowGraphPass {
  public:

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass_tester.cc

@@ -40,7 +40,7 @@ TEST_F(DFG_Tester, dfg_graphviz_draw_pass_tester) {
     no++;
   }
   // DFG is sensitive to ProgramDesc, be careful to change the existing models.
-  ASSERT_EQ(no, 82);
+  ASSERT_EQ(no, 83);
 }
 
 }  // namespace analysis

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc

@@ -28,7 +28,6 @@ bool FluidToDataFlowGraphPass::Initialize(Argument *argument) {
   ANALYSIS_ARGUMENT_CHECK_FIELD(argument->origin_program_desc);
   PADDLE_ENFORCE(argument);
   if (!argument->main_dfg) {
-    LOG(INFO) << "Init DFG";
     argument->main_dfg.reset(new DataFlowGraph);
   }
   desc_ = argument->origin_program_desc.get();
@@ -51,6 +50,7 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
     v->SetPbMsg(var.SerializeAsString());
     var2id[var.name()] = v->id();
   }
+
   for (int i = 0; i < main_block.ops_size(); i++) {
     const auto &op = main_block.ops(i);
     auto *o = graph->nodes.Create(Node::Type::kFunction);
@@ -62,19 +62,31 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
     o->SetPbMsg(op.SerializeAsString());
 
     // set inputs and outputs
-    // TODO(Superjomn) make sure the InputNames is the real variable name.
+    std::unordered_set<Node *> inlinks;
     for (int j = 0; j < op.inputs_size(); j++) {
       auto &in_var = op.inputs(j);
       for (int k = 0; k < in_var.arguments_size(); k++) {
         auto *in = graph->nodes.GetMutable(var2id.at(in_var.arguments(k)));
         in->outlinks.push_back(o);
         o->inlinks.push_back(in);
+        inlinks.insert(in);
       }
     }
     for (int j = 0; j < op.outputs_size(); j++) {
       auto &out_var = op.outputs(j);
       for (int k = 0; k < out_var.arguments_size(); k++) {
         auto *out = graph->nodes.GetMutable(var2id[out_var.arguments(k)]);
+        if (inlinks.count(out)) {
+          // Loop found, for example, a = op(a), use SSA, change to a1 = op(a).
+          auto *out_alias = graph->nodes.Create(Node::Type::kValue);
+          out_alias->SetName(out->name());
+          out_alias->SetPbDesc(out->pb_desc());
+          out_alias->SetPbMsg(out->pb_msg());
+          var2id[out_alias->name()] = out_alias->id();  // update a -> a0
+          LOG(INFO) << "loop found in graph, create SSA alias node ["
+                    << out_alias->repr() << "] for [" << out->repr() << "]";
+          out = out_alias;
+        }
         out->inlinks.push_back(o);
         o->outlinks.push_back(out);
       }

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc

@@ -24,12 +24,12 @@ namespace analysis {
 TEST_F(DFG_Tester, Init) {
   FluidToDataFlowGraphPass pass;
   pass.Initialize(&argument);
-  DataFlowGraph graph;
-  pass.Run(&graph);
+  pass.Run(argument.main_dfg.get());
   // Analysis is sensitive to ProgramDesc, careful to change the original model.
-  ASSERT_EQ(graph.nodes.size(), 37UL);
+  ASSERT_EQ(argument.main_dfg->nodes.size(), 38UL);
   pass.Finalize();
-  LOG(INFO) << '\n' << graph.DotString();
+  ASSERT_FALSE(argument.main_dfg->DotString().empty());
+  EXPECT_FALSE(argument.main_dfg->inputs.empty());
 }
 
 }  // namespace analysis

paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc

@@ -25,6 +25,9 @@ TensorRTSubGraphPass::TensorRTSubGraphPass(
 
 void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
   SubGraphFuse(graph, node_inside_subgraph_teller_)();
+  VLOG(4) << "debug info "
+          << graph->HumanReadableInfo(false /*show_values*/,
+                                      true /*show_functions*/);
 }
 
 }  // namespace analysis

paddle/fluid/inference/api/CMakeLists.txt

@@ -82,7 +82,7 @@ inference_api_test(test_api_impl
 if(WITH_GPU AND TENSORRT_FOUND)
 cc_library(paddle_inference_tensorrt_subgraph_engine
         SRCS api_tensorrt_subgraph_engine.cc
-        DEPS paddle_inference_api analysis tensorrt_engine paddle_fluid_api)
+        DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter)
 
 inference_api_test(test_api_tensorrt_subgraph_engine ARGS test_word2vec)
 endif()

paddle/fluid/inference/api/api_anakin_engine.cc

@@ -39,7 +39,7 @@ bool PaddleInferenceAnakinPredictor::Init(const AnakinConfig &config) {
 
 bool PaddleInferenceAnakinPredictor::Run(
     const std::vector<PaddleTensor> &inputs,
-    std::vector<PaddleTensor> *output_data) {
+    std::vector<PaddleTensor> *output_data, int batch_size) {
   for (const auto &input : inputs) {
     if (input.dtype != PaddleDType::FLOAT32) {
       LOG(ERROR) << "Only support float type inputs. " << input.name

paddle/fluid/inference/api/api_anakin_engine.h

@@ -37,7 +37,8 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
   // NOTE Unlike the native engine, the buffers of anakin engine's output_data
   // should be allocated first.
   bool Run(const std::vector<PaddleTensor>& inputs,
-           std::vector<PaddleTensor>* output_data) override;
+           std::vector<PaddleTensor>* output_data,
+           int batch_size = -1) override;
 
   std::unique_ptr<PaddlePredictor> Clone() override;
 

paddle/fluid/inference/api/api_impl.cc

@@ -108,7 +108,8 @@ NativePaddlePredictor::~NativePaddlePredictor() {
 }
 
 bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
-                                std::vector<PaddleTensor> *output_data) {
+                                std::vector<PaddleTensor> *output_data,
+                                int batch_size) {
   VLOG(3) << "Predictor::predict";
   Timer timer;
   timer.tic();