add tensorrt ut and refine interface.

test=release/1.0.0

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -37,12 +37,16 @@ TEST(Analyzer, analysis_without_tensorrt) {
 TEST(Analyzer, analysis_with_tensorrt) {
   FLAGS_IA_enable_tensorrt_subgraph_engine = true;
   Argument argument;
+  argument.Set<int>("minimum_subgraph_size", new int(0));
+  argument.Set<int>("max_batch_size", new int(3));
+  argument.Set<int>("workspace_size", new int(1 << 20));
+  argument.Set<std::string>("precision_mode", new std::string("FP32"));
   argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
   Analyzer analyser;
   analyser.Run(&argument);
 }
 
-void TestWord2vecPrediction(const std::string &model_path) {
+void TestWord2vecPrediction(const std::string& model_path) {
   NativeConfig config;
   config.model_dir = model_path;
   config.use_gpu = false;
@@ -73,8 +77,8 @@ void TestWord2vecPrediction(const std::string &model_path) {
   // The outputs' buffers are in CPU memory.
   for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
     LOG(INFO) << "data: "
-              << static_cast<float *>(outputs.front().data.data())[i];
-    PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
+              << static_cast<float*>(outputs.front().data.data())[i];
+    PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
                    result[i]);
   }
 }

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc

@@ -97,8 +97,10 @@ void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
   }
 }
 
-void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
+void CreateTrtEngineOp(Node *node, Argument *argument,
                        framework::proto::BlockDesc *block) {
+  PADDLE_ENFORCE(argument->main_dfg.get());
+  const DataFlowGraph &graph = *(argument->main_dfg);
   static int counter{0};
   PADDLE_ENFORCE(node->IsFunctionBlock());
   framework::OpDesc desc;
@@ -204,7 +206,10 @@ void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
 
   PADDLE_ENFORCE(!block->vars().empty(), "the block has no var-desc");
   // Set attrs
+
   SetAttr(desc.Proto(), "subgraph", block->SerializeAsString());
+  SetAttr(desc.Proto(), "max_batch_size", argument->Get<int>("max_batch_size"));
+  SetAttr(desc.Proto(), "workspace_size", argument->Get<int>("workspace_size"));
   SetAttr(desc.Proto(), "engine_uniq_key", "trt-" + std::to_string(counter++));
   SetAttr(desc.Proto(), "parameters", ExtractParameters(graph.nodes.nodes()));
   SetAttr(desc.Proto(), "output_name_mapping", output_mapping);
@@ -248,7 +253,7 @@ void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
   *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());
+  CreateTrtEngineOp(node, argument_, block_desc.Proto());
   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");

paddle/fluid/inference/analysis/subgraph_splitter.cc

@@ -309,6 +309,8 @@ void SubGraphFuse::operator()() { ReplaceNodesWithSubGraphs(); }
 void SubGraphFuse::ReplaceNodesWithSubGraphs() {
   auto subgraphs = SubGraphSplitter(graph_, node_inside_subgraph_teller_)();
   for (auto &subgraph : subgraphs) {
+    if (subgraph.size() <= argument_->Get<int>("minimum_subgraph_size"))
+      continue;
     std::unordered_set<Node *> subgraph_uniq(subgraph.begin(), subgraph.end());
     // replace this sub-graph with the first node. Two steps: 1. Create a Block
     // Node that contains this subgraph 2. Mark the nodes inside the sub-graph

paddle/fluid/inference/analysis/subgraph_splitter.h

@@ -20,6 +20,7 @@ limitations under the License. */
 
 #include <vector>
 
+#include "paddle/fluid/inference/analysis/argument.h"
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
 #include "paddle/fluid/inference/analysis/node.h"
 
@@ -63,8 +64,11 @@ class SubGraphFuse {
  public:
   using NodeInsideSubgraphTeller = SubGraphSplitter::NodeInsideSubgraphTeller;
 
-  SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller)
-      : graph_(graph), node_inside_subgraph_teller_(teller) {}
+  SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller,
+               Argument *argument)
+      : graph_(graph),
+        node_inside_subgraph_teller_(teller),
+        argument_(argument) {}
 
   // The main method which run all the logic.
   void operator()();
@@ -76,6 +80,7 @@ class SubGraphFuse {
  private:
   DataFlowGraph *graph_;
   NodeInsideSubgraphTeller node_inside_subgraph_teller_;
+  Argument *argument_;
 };
 
 }  // namespace analysis

paddle/fluid/inference/analysis/subgraph_splitter_tester.cc

@@ -66,10 +66,12 @@ TEST(SubGraphSplitter, Split) {
 TEST(SubGraphSplitter, Fuse) {
   auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
   auto dfg = ProgramDescToDFG(desc);
+  Argument argument;
+  argument.Set<int>("minimum_subgraph_size", new int(3));
 
   size_t count0 = dfg.nodes.size();
 
-  SubGraphFuse fuse(&dfg, teller);
+  SubGraphFuse fuse(&dfg, teller, &argument);
   fuse();
 
   int count1 = 0;

paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc

@@ -24,7 +24,7 @@ TensorRTSubGraphPass::TensorRTSubGraphPass(
     : node_inside_subgraph_teller_(teller) {}
 
 void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
-  SubGraphFuse(graph, node_inside_subgraph_teller_)();
+  SubGraphFuse(graph, node_inside_subgraph_teller_, argument_)();
   VLOG(4) << "debug info "
           << graph->HumanReadableInfo(false /*show_values*/,
                                       true /*show_functions*/);

paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h

@@ -33,7 +33,10 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
 
   explicit TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
 
-  bool Initialize(Argument* argument) override { return true; }
+  bool Initialize(Argument* argument) override {
+    argument_ = argument;
+    return true;
+  }
 
   // This class get a sub-graph as input and determine whether to transform this
   // sub-graph into TensorRT.
@@ -46,6 +49,7 @@ class TensorRTSubGraphPass : public DataFlowGraphPass {
 
  private:
   NodeInsideSubgraphTeller node_inside_subgraph_teller_;
+  Argument* argument_;
 };
 
 }  // namespace analysis

paddle/fluid/inference/analysis/tensorrt_subgraph_pass_tester.cc

@@ -36,6 +36,10 @@ TEST(TensorRTSubGraphPass, main) {
   };
 
   Argument argument(FLAGS_inference_model_dir);
+  argument.Set<int>("minimum_subgraph_size", new int(0));
+  argument.Set<int>("max_batch_size", new int(3));
+  argument.Set<int>("workspace_size", new int(1 << 20));
+  argument.Set<std::string>("precision_mode", new std::string("FP32"));
 
   DFG_GraphvizDrawPass::Config config{FLAGS_dot_dir, "origin"};
   DFG_GraphvizDrawPass::Config config1{FLAGS_dot_dir, "fusion"};

paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc

@@ -35,8 +35,6 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
   bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
     FLAGS_IA_enable_tensorrt_subgraph_engine = true;
     VLOG(3) << "Predictor::init()";
-    FLAGS_tensorrt_max_batch_size = config_.max_batch_size;
-    FLAGS_tensorrt_workspace_size = config_.workspace_size;
     if (config_.use_gpu) {
       place_ = paddle::platform::CUDAPlace(config_.device);
     } else {
@@ -92,6 +90,14 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
   void OptimizeInferenceProgram() {
     // Analyze inference_program
     Argument argument;
+
+    argument.Set<int>("minimum_subgraph_size",
+                      new int(config_.minimum_subgraph_size));
+    argument.Set<int>("max_batch_size", new int(config_.max_batch_size));
+    argument.Set<int>("workspace_size", new int(config_.workspace_size));
+    argument.Set<std::string>("precision_mode",
+                              new std::string(config_.precision_mode));
+
     if (!config_.model_dir.empty()) {
       argument.fluid_model_dir.reset(new std::string(config_.model_dir));
     } else {

paddle/fluid/inference/api/paddle_inference_api.h

@@ -194,6 +194,14 @@ struct MixedRTConfig : public NativeConfig {
   // For workspace_size, refer it from here:
   // https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
   int workspace_size{1 << 30};
+  //  We transform the Ops that can be converted into TRT layer in the model,
+  //  and aggregate these Ops into subgraphs for TRT execution.
+  //  We set this variable to control the minimum number of nodes in the
+  //  subgraph, 3 as default value.
+  int minimum_subgraph_size = 3;
+  // Reserved configuration
+  // We just support "FP32" now, "FP16" and "INT8" will be supported.
+  std::string precision_mode = "FP32";
 };
 
 // NOTE WIP, not stable yet.

paddle/fluid/inference/tests/api/CMakeLists.txt

@@ -85,3 +85,13 @@ if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
                DEPS inference_anakin_api_shared dynload_cuda SERIAL)
    endif()
 endif()
+
+if(WITH_GPU AND TENSORRT_FOUND)
+   set(TRT_MODEL_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/trt")
+   if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
+       inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
+   endif()
+   cc_test(test_trt_models SRCS trt_models_tester.cc  
+     ARGS --dirname=${TRT_MODEL_INSTALL_DIR}/trt_test_models
+     DEPS paddle_inference_tensorrt_subgraph_engine)
+endif()

paddle/fluid/inference/tests/api/trt_models_tester.cc

+// Copyright (c) 2018 PaddlePaddle Authors. 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.
+
+#include <gflags/gflags.h>
+#include <glog/logging.h>
+#include <gtest/gtest.h>
+#include "paddle/fluid/inference/analysis/analyzer.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+
+namespace paddle {
+using paddle::contrib::MixedRTConfig;
+
+DEFINE_string(dirname, "", "Directory of the inference model.");
+
+NativeConfig GetConfigNative() {
+  NativeConfig config;
+  config.model_dir = FLAGS_dirname;
+  // LOG(INFO) << "dirname  " << config.model_dir;
+  config.fraction_of_gpu_memory = 0.45;
+  config.use_gpu = true;
+  config.device = 0;
+  return config;
+}
+
+MixedRTConfig GetConfigTRT() {
+  MixedRTConfig config;
+  config.model_dir = FLAGS_dirname;
+  config.use_gpu = true;
+  config.fraction_of_gpu_memory = 0.2;
+  config.device = 0;
+  config.max_batch_size = 3;
+  return config;
+}
+
+void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
+  NativeConfig config0 = GetConfigNative();
+  config0.model_dir = model_dirname;
+
+  MixedRTConfig config1 = GetConfigTRT();
+  config1.model_dir = model_dirname;
+  config1.max_batch_size = batch_size;
+
+  auto predictor0 =
+      CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
+  auto predictor1 =
+      CreatePaddlePredictor<MixedRTConfig,
+                            PaddleEngineKind::kAutoMixedTensorRT>(config1);
+  // Prepare inputs
+  int height = 224;
+  int width = 224;
+  float *data = new float[batch_size * 3 * height * width];
+  memset(data, 0, sizeof(float) * (batch_size * 3 * height * width));
+  data[0] = 1.0f;
+
+  // Prepare inputs
+  PaddleTensor tensor;
+  tensor.name = "input_0";
+  tensor.shape = std::vector<int>({batch_size, 3, height, width});
+  tensor.data = PaddleBuf(static_cast<void *>(data),
+                          sizeof(float) * (batch_size * 3 * height * width));
+  tensor.dtype = PaddleDType::FLOAT32;
+  std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
+
+  // Prepare outputs
+  std::vector<PaddleTensor> outputs0;
+  std::vector<PaddleTensor> outputs1;
+  CHECK(predictor0->Run(paddle_tensor_feeds, &outputs0));
+
+  CHECK(predictor1->Run(paddle_tensor_feeds, &outputs1, batch_size));
+
+  // Get output.
+  ASSERT_EQ(outputs0.size(), 1UL);
+  ASSERT_EQ(outputs1.size(), 1UL);
+
+  const size_t num_elements = outputs0.front().data.length() / sizeof(float);
+  const size_t num_elements1 = outputs1.front().data.length() / sizeof(float);
+  EXPECT_EQ(num_elements, num_elements1);
+
+  auto *data0 = static_cast<float *>(outputs0.front().data.data());
+  auto *data1 = static_cast<float *>(outputs1.front().data.data());
+
+  ASSERT_GT(num_elements, 0UL);
+  for (size_t i = 0; i < std::min(num_elements, num_elements1); i++) {
+    EXPECT_NEAR(data0[i], data1[i], 1e-3);
+  }
+}
+
+TEST(trt_models_test, main) {
+  std::vector<std::string> infer_models = {"mobilenet", "resnet50",
+                                           "resnext50"};
+  for (auto &model_dir : infer_models) {
+    CompareTensorRTWithFluid(1, FLAGS_dirname + "/" + model_dir);
+  }
+}
+}  // namespace paddle

paddle/fluid/operators/tensorrt_engine_op.cc

@@ -22,8 +22,6 @@
 namespace paddle {
 
 DEFINE_int32(tensorrt_engine_batch_size, 1, "the batch_size of TensorRT");
-DEFINE_int32(tensorrt_max_batch_size, 1, "TensorRT maximum batch size");
-DEFINE_int32(tensorrt_workspace_size, 16 << 20, "TensorRT workspace size");
 
 namespace operators {
 
@@ -34,6 +32,8 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Ys", "A list of outputs").AsDuplicable();
     AddAttr<std::string>("subgraph", "the subgraph.");
     AddAttr<std::string>("engine_uniq_key", "unique key for the TRT engine.");
+    AddAttr<int>("max_batch_size", "the maximum batch size.");
+    AddAttr<int>("workspace_size", "the workspace size.");
     AddComment("TensorRT engine operator.");
   }
 };

paddle/fluid/operators/tensorrt_engine_op.h

@@ -28,8 +28,6 @@
 namespace paddle {
 
 DECLARE_int32(tensorrt_engine_batch_size);
-DECLARE_int32(tensorrt_max_batch_size);
-DECLARE_int32(tensorrt_workspace_size);
 
 namespace operators {
 
@@ -92,14 +90,14 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     auto engine_name = context.Attr<std::string>("engine_uniq_key");
+    int max_batch_size = context.Attr<int>("max_batch_size");
     if (!Singleton<TRT_EngineManager>::Global().HasEngine(engine_name)) {
       Prepare(context);
     }
     auto* engine = Singleton<TRT_EngineManager>::Global().Get(engine_name);
     auto input_names = context.op().Inputs("Xs");
     PADDLE_ENFORCE(!input_names.empty(), "should pass more than one inputs");
-    PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size,
-                      FLAGS_tensorrt_max_batch_size);
+    PADDLE_ENFORCE_LE(FLAGS_tensorrt_engine_batch_size, max_batch_size);
 
     std::vector<std::string> output_maps =
         context.Attr<std::vector<std::string>>("output_name_mapping");
@@ -173,8 +171,9 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
     // Get the ProgramDesc and pass to convert.
     framework::proto::BlockDesc block_desc;
     block_desc.ParseFromString(context.Attr<std::string>("subgraph"));
-    int max_batch = FLAGS_tensorrt_max_batch_size;
-    auto max_workspace = FLAGS_tensorrt_workspace_size;
+    int max_batch_size = context.Attr<int>("max_batch_size");
+    int workspace_size = context.Attr<int>("workspace_size");
+
     auto params = context.Attr<std::vector<std::string>>("parameters");
     std::unordered_set<std::string> parameters;
     for (const auto& param : params) {
@@ -186,7 +185,7 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
 
     // 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*/,
+        max_batch_size, workspace_size, nullptr /*engine hold its own stream*/,
         context.Attr<std::string>("engine_uniq_key"),
         boost::get<platform::CUDAPlace>(context.GetPlace()).device);
 

paddle/fluid/operators/tensorrt_engine_op_test.cc

@@ -58,8 +58,6 @@ void AddTensorToBlockDesc(framework::proto::BlockDesc* block,
 using inference::analysis::SetAttr;
 
 TEST(TensorRTEngineOp, manual) {
-  FLAGS_tensorrt_engine_batch_size = 2;
-  FLAGS_tensorrt_max_batch_size = 2;
   framework::ProgramDesc program;
   auto* block_ = program.Proto()->add_blocks();
   block_->set_idx(0);
@@ -101,6 +99,8 @@ TEST(TensorRTEngineOp, manual) {
   engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z0"}));
   SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
                        block_->SerializeAsString());
+  SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", 2);
+  SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
   SetAttr<std::string>(engine_op_desc.Proto(), "engine_uniq_key", "a_engine");
   SetAttr<std::vector<std::string>>(engine_op_desc.Proto(), "parameters",
                                     std::vector<std::string>({}));
@@ -129,8 +129,6 @@ TEST(TensorRTEngineOp, manual) {
 }
 
 void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
-  FLAGS_tensorrt_engine_batch_size = batch_size;
-  FLAGS_tensorrt_max_batch_size = batch_size;
   framework::ProgramDesc program;
   framework::Scope scope;
   platform::CUDAPlace place;
@@ -195,8 +193,8 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
 
   SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
                        block_->SerializeAsString());
-  SetAttr<int>(engine_op_desc.Proto(), "max_batch", batch_size);
-  SetAttr<int>(engine_op_desc.Proto(), "max_workspace", 2 << 10);
+  SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", batch_size);
+  SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 2 << 10);
   SetAttr<std::vector<std::string>>(
       engine_op_desc.Proto(), "parameters",
       std::vector<std::string>({"y0", "y1", "y2", "y3"}));