mapping the variable name inside the subgraph

paddle/fluid/inference/analysis/analyzer.cc

@@ -24,7 +24,7 @@
 
 namespace paddle {
 
-DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, false,
+DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, true,
             "Enable subgraph to TensorRT engine for acceleration");
 
 DEFINE_string(inference_analysis_graphviz_log_root, "./",
@@ -42,10 +42,19 @@ class DfgPassManagerImpl final : public DfgPassManager {
     // TODO(Superjomn) set the key with pass reprs.
     AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
     if (FLAGS_inference_analysis_enable_tensorrt_subgraph_engine) {
-      auto trt_teller = [](const Node* node) {
+      auto trt_teller = [&](const Node* node) {
+        std::unordered_set<std::string> teller_set(
+            {"elementwise_add", "mul", "conv2d", "pool2d", "relu"});
         if (!node->IsFunction()) return false;
-        return static_cast<const Function*>(node)->func_type() == "mul";
+
+        const auto* func = static_cast<const Function*>(node);
+        if (teller_set.count(func->func_type()))
+          return true;
+        else {
+          return false;
+        }
       };
+
       AddPass("tensorrt-subgraph-marker",
               new TensorRTSubgraphNodeMarkPass(trt_teller));
       AddPass("tensorrt-subgraph", new TensorRTSubGraphPass(trt_teller));

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc

@@ -23,7 +23,7 @@
 namespace paddle {
 namespace inference {
 
-DEFINE_int32(tensorrt_max_batchsize, 300, "TensorRT maximum batch size");
+DEFINE_int32(tensorrt_max_batchsize, 3, "TensorRT maximum batch size");
 DEFINE_int32(tensorrt_workspace_size, 2048, "TensorRT workspace size");
 
 namespace analysis {
@@ -87,27 +87,90 @@ void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
 }
 
 void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
-                       const framework::proto::BlockDesc &block) {
+                       framework::proto::BlockDesc &block) {
   static int counter{0};
   PADDLE_ENFORCE(node->IsFunctionBlock());
   framework::OpDesc desc;
   auto *func = static_cast<FunctionBlock *>(node);
 
   // collect inputs
-  std::vector<std::string> io;
+  std::unordered_set<std::string> input_names;
   for (auto *x : func->inlinks) {
-    io.push_back(x->name());
+    input_names.insert(x->name());
   }
-  desc.SetInput("Xs", io);
+  desc.SetInput(
+      "Xs", std::vector<std::string>(input_names.begin(), input_names.end()));
 
-  // collect outputs
-  io.clear();
+  std::unordered_set<std::string> output_names;
   for (auto *x : func->outlinks) {
-    io.push_back(x->name());
+    output_names.insert(x->name());
   }
-  desc.SetOutput("Ys", io);
+
+  std::vector<std::string> output_temp(output_names.begin(),
+                                       output_names.end());
+  desc.SetOutput("Ys", output_temp);
   desc.SetType("tensorrt_engine");
 
+  std::unordered_map<std::string, std::string> output_name_map;
+  auto subgraph_nodes = func->subgraph;
+
+  for (int index = 0; index < block.ops_size(); index++) {
+    framework::proto::OpDesc *op = block.mutable_ops(index);
+    // auto &op = block.mutable_ops(index);
+    auto correspond_node = subgraph_nodes[index];
+    PADDLE_ENFORCE_EQ(correspond_node->name(), op->type());
+
+    std::unordered_map<std::string, size_t> var2id;
+    for (auto *in_var : correspond_node->inlinks) {
+      var2id[in_var->name()] = in_var->id();
+    }
+    // TODO(zhaolong): add comments
+    for (int i = 0; i < op->inputs_size(); i++) {
+      framework::proto::OpDesc_Var *in_var = op->mutable_inputs(i);
+      // auto &in_var = op->mutable_inputs(i);
+      std::vector<std::string> replaced_names;
+      for (int k = 0; k < in_var->arguments_size(); k++) {
+        std::string arg_value = in_var->arguments(k);
+        if (input_names.count(arg_value)) {
+          replaced_names.push_back(arg_value);
+        } else {
+          replaced_names.push_back(arg_value +
+                                   std::to_string(var2id[arg_value]));
+        }
+      }
+      in_var->clear_arguments();
+      for (size_t k = 0; k < replaced_names.size(); k++) {
+        in_var->add_arguments(replaced_names[k]);
+      }
+    }
+    var2id.clear();
+    for (auto out_var : correspond_node->outlinks) {
+      var2id[out_var->name()] = out_var->id();
+    }
+
+    for (int i = 0; i < op->outputs_size(); i++) {
+      framework::proto::OpDesc_Var *out_var = op->mutable_outputs(i);
+      std::vector<std::string> replaced_names;
+      for (int k = 0; k < out_var->arguments_size(); k++) {
+        std::string arg_value = out_var->arguments(k);
+        if (output_names.count(arg_value)) {
+          output_name_map[arg_value] =
+              arg_value + std::to_string(var2id[arg_value]);
+        }
+        replaced_names.push_back(arg_value + std::to_string(var2id[arg_value]));
+      }
+      out_var->clear_arguments();
+      for (size_t k = 0; k < replaced_names.size(); k++) {
+        out_var->add_arguments(replaced_names[k]);
+      }
+    }
+  }
+  std::vector<std::string> output_mapping;
+  for (auto name : output_names) {
+    PADDLE_ENFORCE(output_name_map.count(name) != 0);
+    output_mapping.push_back(output_name_map[name]);
+  }
+
   PADDLE_ENFORCE(!block.vars().empty(), "the block has no var-desc");
   // Set attrs
   SetAttr(desc.Proto(), "subgraph", block.SerializeAsString());
@@ -115,6 +178,7 @@ void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
   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()));
+  SetAttr(desc.Proto(), "output_name_mapping", output_mapping);
   node->SetPbMsg(desc.Proto()->SerializeAsString());
 }
 
@@ -146,11 +210,13 @@ void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
   LOG(INFO) << "transformed variable size: "
             << block_desc.Proto()->vars().size();
   // copy ops.
+
   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());

paddle/fluid/inference/analysis/subgraph_splitter.cc

@@ -76,7 +76,7 @@ void UnionFindCombine(const node_map_t &node_map, size_t a, size_t b) {
 
 std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
   std::vector<Node *> marked_nodes;
-  for (auto &node : GraphTraits<DataFlowGraph>(graph_).nodes()) {
+  for (auto &node : GraphTraits<DataFlowGraph>(graph_).nodes_in_TS()) {
     if (node.attr(kMarkerAttrName).Bool()) {
       marked_nodes.push_back(&node);
     }

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

 # Add TRT tests
 nv_library(tensorrt_converter
   SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
+activation_op.cc
   DEPS tensorrt_engine operator scope framework_proto op_registry)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -55,7 +55,6 @@ class OpConverter {
         it = Registry<OpConverter>::Lookup("fc");
       }
     }
-
     if (op_desc.Type().find("elementwise") != std::string::npos) {
       static std::unordered_set<std::string> add_tensor_op_set{
           "add", "mul", "sub", "div", "max", "min", "pow"};
@@ -72,6 +71,8 @@ class OpConverter {
                        "Unsupported elementwise type" + op_type);
         it =
             Registry<OpConverter>::Lookup("elementwise_" + op_type + "_weight");
+        PADDLE_ENFORCE_NOT_NULL(it, "no OpConverter for optype [%s]",
+                                op_desc.Type());
       } else {
         PADDLE_ENFORCE(add_tensor_op_set.count(op_type) > 0,
                        "Unsupported elementwise type" + op_type);

paddle/fluid/operators/tensorrt_engine_op.cc

@@ -55,18 +55,8 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t> &shape) {
                     "TensorRT' tensor input requires at least 2 dimensions");
   PADDLE_ENFORCE_LE(shape.size(), 4UL,
                     "TensorRT' tensor input requires at most 4 dimensions");
-
-  switch (shape.size()) {
-    case 2:
-      return nvinfer1::Dims2(1, shape[1]);
-    case 3:
-      return nvinfer1::Dims3(1, shape[1], shape[2]);
-    case 4:
-      return nvinfer1::Dims4(1, shape[1], shape[2], shape[3]);
-    default:
-      return nvinfer1::Dims();
-  }
-  return nvinfer1::Dims();
+  PADDLE_ENFORCE_EQ(shape.size(), 4UL);
+  return nvinfer1::DimsCHW(shape[1], shape[2], shape[3]);
 }
 
 }  // namespace
@@ -86,6 +76,9 @@ void TensorRTEngineKernel<DeviceContext, T>::Prepare(
     parameters.insert(param);
   }
 
+  std::vector<std::string> output_maps =
+      context.Attr<std::vector<std::string>>("output_name_mapping");
+
   // TODO(Superjomn) replace this with a different stream
   auto *engine = Singleton<TRT_EngineManager>::Global().Create(
       max_batch, max_workspace, nullptr /*engine hold its own stream*/,
@@ -97,6 +90,7 @@ void TensorRTEngineKernel<DeviceContext, T>::Prepare(
   // Add inputs
   VLOG(4) << "declare inputs";
   for (auto &input : context.Inputs("Xs")) {
+    if (parameters.count(input)) continue;
     VLOG(4) << "declare input " << input;
     auto *var = block.FindVar(input);
     // TensorRT engine need to create parameters. The parameter's description
@@ -122,7 +116,7 @@ void TensorRTEngineKernel<DeviceContext, T>::Prepare(
       block_desc, parameters, context.scope(), engine);
 
   // Add outputs
-  for (auto &output : context.Outputs("Ys")) {
+  for (auto &output : output_maps) {
     engine->DeclareOutput(output);
   }
 

paddle/fluid/operators/tensorrt_engine_op.h

@@ -66,8 +66,17 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
     PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size,
                       context.Attr<int>("max_batch"));
 
+    std::vector<std::string> output_maps =
+        context.Attr<std::vector<std::string>>("output_name_mapping");
+
+    auto params = context.Attr<std::vector<std::string>>("parameters");
+    std::unordered_set<std::string> parameters;
+    for (const auto& param : params) {
+      parameters.insert(param);
+    }
     // Convert input tensor from fluid to engine.
     for (const auto& x : context.Inputs("Xs")) {
+      if (parameters.count(x)) continue;
       // convert input and copy to TRT engine's buffer
       auto& t = inference::analysis::GetFromScope<framework::LoDTensor>(
           context.scope(), x);
@@ -82,10 +91,12 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
     // Execute the engine.
     PADDLE_ENFORCE_GT(FLAGS_tensorrt_engine_batch_size, 0);
     engine->Execute(FLAGS_tensorrt_engine_batch_size);
+
     // Convert output tensor from engine to fluid
+    int output_index = 0;
     for (const auto& y : context.Outputs("Ys")) {
       // convert output and copy to fluid.
-      nvinfer1::ITensor* trt_t = engine->GetITensor(y);
+      nvinfer1::ITensor* trt_t = engine->GetITensor(output_maps[output_index]);
       auto dims = trt_t->getDimensions();
       // Use the output ITensor's dims to reshape the Fluid Tensor.
       std::vector<int> ddim(dims.d, dims.d + dims.nbDims);
@@ -102,7 +113,7 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
       // TODO(Superjomn) change this float to dtype size.
       auto size = inference::analysis::AccuDims(dims.d, dims.nbDims) *
                   FLAGS_tensorrt_engine_batch_size;
-      engine->GetOutputInCPU(y,
+      engine->GetOutputInCPU(output_maps[output_index],
                              fluid_t->mutable_data<float>(platform::CPUPlace()),
                              size * sizeof(float));
       //} else {
@@ -110,6 +121,7 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
       // y, fluid_t->mutable_data<float>(platform::CUDAPlace()),
       // size * sizeof(float));
       //}
+      output_index += 1;
     }
 
     cudaStreamSynchronize(*engine->stream());