Merge pull request #15242 from NHZlX/trt_int8_ultimate_version

add trt int8 support

paddle/fluid/framework/ir/graph_traits.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/framework/ir/graph_traits.h"
 
+#include <set>
 #include <vector>
 
 namespace paddle {
@@ -79,7 +80,7 @@ NodesTSIterator::NodesTSIterator(const std::vector<Node *> &source) {
   }
 
   std::unordered_set<Node *> visited;
-  std::unordered_set<Node *> to_visit{source.begin(), source.end()};
+  std::set<Node *> to_visit{source.begin(), source.end()};
 
   std::vector<Node *> inlink_visited;
   while (!to_visit.empty()) {

paddle/fluid/inference/analysis/argument.h

@@ -28,6 +28,7 @@
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/inference/api/paddle_analysis_config.h"
 #include "paddle/fluid/platform/variant.h"
 
 namespace paddle {
@@ -130,6 +131,8 @@ struct Argument {
   DECL_ARGUMENT_FIELD(tensorrt_max_batch_size, TensorRtMaxBatchSize, int);
   DECL_ARGUMENT_FIELD(tensorrt_workspace_size, TensorRtWorkspaceSize, int);
   DECL_ARGUMENT_FIELD(tensorrt_min_subgraph_size, TensorRtMinSubgraphSize, int);
+  DECL_ARGUMENT_FIELD(tensorrt_precision_mode, TensorRtPrecisionMode,
+                      contrib::AnalysisConfig::Precision);
 
   // Memory optimized related.
   DECL_ARGUMENT_FIELD(enable_memory_optim, EnableMemoryOptim, bool);

paddle/fluid/inference/analysis/helper.cc

@@ -36,6 +36,14 @@ void SetAttr<int>(framework::proto::OpDesc *op, const std::string &name,
   attr->set_i(data);
 }
 template <>
+void SetAttr<bool>(framework::proto::OpDesc *op, const std::string &name,
+                   const bool &data) {
+  auto *attr = op->add_attrs();
+  attr->set_name(name);
+  attr->set_type(paddle::framework::proto::AttrType::BOOLEAN);
+  attr->set_b(data);
+}
+template <>
 void SetAttr<int64_t>(framework::proto::OpDesc *op, const std::string &name,
                       const int64_t &data) {
   auto *attr = op->add_attrs();

paddle/fluid/inference/analysis/helper.h

@@ -17,6 +17,7 @@ limitations under the License. */
 #include <sys/stat.h>
 #include <cstdio>
 #include <fstream>
+#include <set>
 #include <string>
 #include <typeindex>
 #include <unordered_map>
@@ -29,9 +30,14 @@ limitations under the License. */
 #include "paddle/fluid/platform/port.h"
 
 #ifdef _WIN32
+#include <direct.h>
+#include <io.h>
 #define GCC_ATTRIBUTE(attr__) ;
+#define MKDIR(path) _mkdir(path)
 #else
+#include <unistd.h>
 #define GCC_ATTRIBUTE(attr__) __attribute__((attr__));
+#define MKDIR(path) mkdir(path, S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH)
 #endif
 #define __SHOULD_USE_RESULT__ GCC_ATTRIBUTE(warn_unused_result)
 
@@ -163,6 +169,54 @@ static bool PathExists(const std::string &path) {
   return false;
 }
 
+static std::string GetDirRoot(const std::string &path) {
+  char sep = '/';
+
+#ifdef _WIN32
+  sep = '\\';
+#endif
+
+  size_t i = path.rfind(sep, path.length());
+  if (i != std::string::npos) {
+    return (path.substr(0, i));
+  }
+  return path;
+}
+
+static std::string GetOrCreateModelOptCacheDir(const std::string &model_root) {
+  std::string opt_cache_dir = model_root + "/_opt_cache/";
+  if (!PathExists(opt_cache_dir)) {
+    PADDLE_ENFORCE(MKDIR(opt_cache_dir.c_str()) != -1,
+                   "Can not create optimize cache directory: %s, Make sure you "
+                   "have permission to write",
+                   opt_cache_dir);
+  }
+  return opt_cache_dir;
+}
+
+static std::string GetTrtCalibPath(const std::string &model_root,
+                                   const std::string &engine_key) {
+  return model_root + "/trt_calib_" + engine_key;
+}
+
+// If there is no calib table data file in model_opt_cache_dir, return "".
+static std::string GetTrtCalibTableData(const std::string &model_opt_cache_dir,
+                                        const std::string &engine_key,
+                                        bool enable_int8) {
+  std::string trt_calib_table_path =
+      GetTrtCalibPath(model_opt_cache_dir, engine_key);
+  if (enable_int8 && FileExists(trt_calib_table_path)) {
+    VLOG(3) << "Calibration table file: " << trt_calib_table_path
+            << "is found here";
+    std::ifstream infile(trt_calib_table_path, std::ios::in);
+    std::stringstream buffer;
+    buffer << infile.rdbuf();
+    std::string calibration_data(buffer.str());
+    return calibration_data;
+  }
+  return "";
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -67,6 +67,20 @@ void IRPassManager::CreatePasses(Argument *argument,
       pass->Set("max_batch_size", new int(argument->tensorrt_max_batch_size()));
       pass->Set("min_subgraph_size",
                 new int(argument->tensorrt_min_subgraph_size()));
+      pass->Set("program",
+                new framework::ProgramDesc *(&argument->main_program()));
+
+      bool enable_int8 = argument->tensorrt_precision_mode() ==
+                         contrib::AnalysisConfig::Precision::kInt8;
+
+      pass->Set("enable_int8", new bool(enable_int8));
+      std::string model_opt_cache_dir =
+          argument->Has("model_dir")
+              ? argument->model_dir()
+              : GetDirRoot(argument->model_program_path());
+      pass->Set(
+          "model_opt_cache_dir",
+          new std::string(GetOrCreateModelOptCacheDir(model_opt_cache_dir)));
     }
 
     // graph_ = pass->Apply(std::move(graph_));
@@ -91,11 +105,14 @@ std::unique_ptr<Graph> IRPassManager::Apply(std::unique_ptr<Graph> graph) {
 }
 
 framework::proto::ProgramDesc IRPassManager::AcquireProgram(
-    std::unique_ptr<Graph> *graph, const ProgramDesc &program) const {
+    std::unique_ptr<Graph> *graph, ProgramDesc *program) const {
   auto pass =
       framework::ir::PassRegistry::Instance().Get("graph_to_program_pass");
 
-  ProgramDesc desc(program);
+  // Direct using ProgramDesc desc(argument->main_program()) may cause
+  // incomplete copies of information.
+  ProgramDesc desc;
+  desc.CopyFrom(*program->Proto());
   pass->SetNotOwned("program", &desc);
   auto *the_graph = graph->release();
   *graph = pass->Apply(std::unique_ptr<Graph>(the_graph));

paddle/fluid/inference/analysis/ir_pass_manager.h

@@ -29,6 +29,7 @@
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/analysis/argument.h"
+#include "paddle/fluid/inference/analysis/helper.h"
 
 namespace paddle {
 namespace inference {
@@ -42,8 +43,8 @@ class IRPassManager final {
 
   std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph);
 
-  framework::proto::ProgramDesc AcquireProgram(
-      std::unique_ptr<Graph> *graph, const ProgramDesc &program) const;
+  framework::proto::ProgramDesc AcquireProgram(std::unique_ptr<Graph> *graph,
+                                               ProgramDesc *program) const;
 
   framework::ir::Graph &graph() const { return *graph_; }
 

paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include <algorithm>
+#include <set>
 #include <string>
 #include <vector>
 
@@ -67,12 +68,33 @@ std::unique_ptr<framework::ir::Graph> analysis::TensorRtSubgraphPass::ApplyImpl(
   return graph;
 }
 
+std::string GenerateEngineKey(const std::set<std::string> &engine_inputs,
+                              const std::set<std::string> &engine_outputs) {
+  std::string engine_hash_key = "";
+  for (auto name : engine_inputs) {
+    engine_hash_key += name;
+  }
+  for (auto name : engine_outputs) {
+    engine_hash_key += name;
+  }
+  auto engine_key = std::to_string(std::hash<std::string>()(engine_hash_key));
+  return engine_key;
+}
+
 void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
                                             Graph *graph) const {
   auto *op_desc = node->Op();
   auto &subgraph = *Agent(node).subgraph();
   PADDLE_ENFORCE(!subgraph.empty());
 
+  framework::ProgramDesc *program_desc =
+      Get<framework::ProgramDesc *>("program");
+  // Add new block for TensorRTEngineOP
+  const framework::BlockDesc &main_block =
+      program_desc->Block(framework::kRootBlockIndex);
+  // const framework::BlockDesc& main_block = program_desc->Block(0);
+  framework::BlockDesc *new_block = program_desc->AppendBlock(main_block);
+
   // An fake block desc.
   framework::proto::BlockDesc block_proto;
   framework::BlockDesc block_desc(nullptr, &block_proto);
@@ -82,13 +104,18 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
                           subgraph.size());
 
   for (auto *node : subgraph) {
+    auto *new_block_op = new_block->AppendOp();
     auto *op = block_desc.AppendOp();
+    *new_block_op->Proto() = *node->Op()->Proto();
     *op->Proto() = *node->Op()->Proto();
   }
 
-  // collect inputs
-  std::unordered_set<std::string> input_names;
-  std::unordered_set<std::string> input_names_with_id;
+  // Then, we will use the input_names_with_id and output_names_with_id to
+  // generate the eigine key.
+  // So, We use set instead of unordered_set here to ensure that the engine key
+  // is unique.
+  std::set<std::string> input_names;
+  std::set<std::string> input_names_with_id;
   for (auto *x : node->inputs) {
     input_names.insert(x->Name());
     input_names_with_id.insert(x->Name() + std::to_string(x->id()));
@@ -96,8 +123,8 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
   op_desc->SetInput(
       "Xs", std::vector<std::string>(input_names.begin(), input_names.end()));
 
-  std::unordered_set<std::string> output_names;
-  std::unordered_set<std::string> output_names_with_id;
+  std::set<std::string> output_names;
+  std::set<std::string> output_names_with_id;
   for (auto *x : node->outputs) {
     output_names.insert(x->Name());
     output_names_with_id.insert(x->Name() + std::to_string(x->id()));
@@ -182,7 +209,6 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
   // to Tensor.
   std::vector<std::string> output_mapping;
   for (auto name : output_names) {
-    // LOG(INFO) << name << " " << output_name_map.size();
     PADDLE_ENFORCE(output_name_map.count(name) != 0);
     output_mapping.push_back(output_name_map[name]);
   }
@@ -193,16 +219,29 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
       *vars->Add() = *node->Var()->Proto();
     }
   }
+
   PADDLE_ENFORCE(!block_desc.Proto()->vars().empty(),
                  "the block has no var-desc");
   PADDLE_ENFORCE(!output_mapping.empty());
-  // Set attrs
+  op_desc->SetBlockAttr("sub_block", new_block);
   SetAttr(op_desc->Proto(), "subgraph",
           block_desc.Proto()->SerializeAsString());
+  // Set attrs
   SetAttr(op_desc->Proto(), "max_batch_size", Get<int>("max_batch_size"));
   SetAttr(op_desc->Proto(), "workspace_size", Get<int>("workspace_size"));
   SetAttr(op_desc->Proto(), "parameters", ExtractParameters(graph->Nodes()));
   SetAttr(op_desc->Proto(), "output_name_mapping", output_mapping);
+
+  auto enable_int8 = Get<bool>("enable_int8");
+  auto engine_key =
+      GenerateEngineKey(input_names_with_id, output_names_with_id);
+
+  std::string calibration_data = GetTrtCalibTableData(
+      Get<std::string>("model_opt_cache_dir"), engine_key, enable_int8);
+  SetAttr(op_desc->Proto(), "calibration_data", calibration_data);
+
+  SetAttr(op_desc->Proto(), "enable_int8", enable_int8);
+  SetAttr(op_desc->Proto(), "engine_key", engine_key);
 }
 
 std::vector<std::string> ExtractParameters(

paddle/fluid/inference/analysis/passes/ir_graph_to_program_pass.cc

@@ -31,7 +31,11 @@ void IrGraphToProgramPass::RunImpl(Argument *argument) {
   }
 
   std::unique_ptr<Graph> graph(argument->main_graph_ptr());
-  framework::ProgramDesc desc(argument->main_program());
+
+  // Direct using ProgramDesc desc(argument->main_program()) may cause
+  // incomplete copies of information.
+  framework::ProgramDesc desc;
+  desc.CopyFrom(*argument->main_program().Proto());
   pass->SetNotOwned("program", &desc);
   auto thegraph = pass->Apply(std::move(graph));
   thegraph.release();  // the argument still own the graph.

paddle/fluid/inference/api/analysis_config.cc

@@ -102,6 +102,7 @@ contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) {
   CP_MEMBER(tensorrt_workspace_size_);
   CP_MEMBER(tensorrt_max_batchsize_);
   CP_MEMBER(tensorrt_min_subgraph_size_);
+  CP_MEMBER(tensorrt_precision_mode_);
   // MKLDNN releated.
   CP_MEMBER(use_mkldnn_);
   CP_MEMBER(mkldnn_enabled_op_types_);
@@ -141,9 +142,9 @@ void contrib::AnalysisConfig::EnableMKLDNN() {
   Update();
 }
 
-void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size,
-                                                   int max_batch_size,
-                                                   int min_subgraph_size) {
+void contrib::AnalysisConfig::EnableTensorRtEngine(
+    int workspace_size, int max_batch_size, int min_subgraph_size,
+    contrib::AnalysisConfig::Precision precision_mode) {
 #ifdef PADDLE_WITH_CUDA
   if (!use_gpu()) {
     LOG(ERROR) << "To use TensorRT engine, please call EnableGpu() first";
@@ -154,6 +155,7 @@ void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size,
   tensorrt_workspace_size_ = workspace_size;
   tensorrt_max_batchsize_ = max_batch_size;
   tensorrt_min_subgraph_size_ = min_subgraph_size;
+  tensorrt_precision_mode_ = precision_mode;
 
   Update();
 #else

paddle/fluid/inference/api/analysis_predictor.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/inference/api/analysis_predictor.h"
 #include <glog/logging.h>
 #include <algorithm>
+#include <fstream>
 #include <memory>
 #include <string>
 #include <vector>
@@ -25,6 +26,7 @@
 #include "paddle/fluid/framework/naive_executor.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/framework/var_type_traits.h"
+#include "paddle/fluid/inference/analysis/helper.h"
 #include "paddle/fluid/inference/analysis/passes/memory_optimize_pass.h"
 #include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
@@ -37,6 +39,8 @@
 
 #if PADDLE_WITH_TENSORRT
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/trt_int8_calibrator.h"
+
 #endif
 
 DECLARE_bool(profile);
@@ -44,6 +48,12 @@ DECLARE_bool(profile);
 namespace paddle {
 
 using contrib::AnalysisConfig;
+using inference::Singleton;
+#if PADDLE_WITH_TENSORRT
+using inference::tensorrt::TRTInt8Calibrator;
+using inference::tensorrt::TRTCalibratorEngine;
+using inference::tensorrt::TRTCalibratorEngineManager;
+#endif
 
 namespace {
 bool IsPersistable(const framework::VarDesc *var) {
@@ -339,6 +349,8 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
         !config_.params_file().empty(),
         "Either model_dir or (param_file, prog_file) should be set.");
     PADDLE_ENFORCE(!config_.prog_file().empty());
+    std::string dir = inference::analysis::GetDirRoot(config_.prog_file());
+
     argument_.SetModelProgramPath(config_.prog_file());
     argument_.SetModelParamsPath(config_.params_file());
   }
@@ -349,6 +361,7 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
     argument_.SetTensorRtWorkspaceSize(config_.tensorrt_workspace_size_);
     argument_.SetTensorRtMaxBatchSize(config_.tensorrt_max_batchsize_);
     argument_.SetTensorRtMinSubgraphSize(config_.tensorrt_min_subgraph_size_);
+    argument_.SetTensorRtPrecisionMode(config_.tensorrt_precision_mode_);
   }
 
   if (config_.use_mkldnn_) {
@@ -569,7 +582,67 @@ bool AnalysisPredictor::LoadParameters() {
   return true;
 }
 
+#if PADDLE_WITH_TENSORRT
+bool AnalysisPredictor::SaveTrtCalibToDisk() {
+  PADDLE_ENFORCE(config_.tensorrt_engine_enabled(),
+                 "This func can be invoked only in trt mode");
+  auto &block = inference_program_->Block(0);
+  for (auto &op_desc : block.AllOps()) {
+    if (op_desc->Type() == "tensorrt_engine") {
+      std::string engine_name =
+          boost::get<std::string>(op_desc->GetAttr("engine_key"));
+      if (!Singleton<TRTCalibratorEngineManager>::Global().Has(engine_name)) {
+        LOG(ERROR) << "You should run the predictor(with trt) on the real data "
+                      "to generate calibration info";
+        return false;
+      }
+      TRTCalibratorEngine *calib_engine =
+          Singleton<TRTCalibratorEngineManager>::Global().Get(engine_name);
+      LOG(INFO) << "Wait for calib threads done.";
+      calib_engine->calib_->waitAndSetDone();
+      LOG(INFO) << "Generating TRT Calibration table data, this may cost a lot "
+                   "of time...";
+      calib_engine->thr_->join();
+      std::string calibration_table_data =
+          calib_engine->calib_->getCalibrationTableAsString();
+
+      if (calibration_table_data.empty()) {
+        LOG(ERROR) << "the calibration table is empty.";
+        return false;
+      }
+
+      std::string model_opt_cache_dir =
+          argument_.Has("model_dir")
+              ? argument_.model_dir()
+              : inference::analysis::GetDirRoot(argument_.model_program_path());
+
+      std::string calibration_table_data_path =
+          inference::analysis::GetTrtCalibPath(
+              inference::analysis::GetOrCreateModelOptCacheDir(
+                  model_opt_cache_dir),
+              engine_name);
+
+      std::ofstream ofile(calibration_table_data_path, std::ios::out);
+      LOG(INFO) << "Write Paddle-TRT INT8 calibration table data to file "
+                << calibration_table_data_path;
+      ofile << calibration_table_data;
+      ofile.close();
+    }
+  }
+  // Free all calibrator resources.
+  Singleton<TRTCalibratorEngineManager>::Global().DeleteALL();
+  return true;
+}
+#endif
+
 AnalysisPredictor::~AnalysisPredictor() {
+#if PADDLE_WITH_TENSORRT
+  if (config_.tensorrt_engine_enabled() &&
+      config_.tensorrt_precision_mode_ == AnalysisConfig::Precision::kInt8 &&
+      Singleton<TRTCalibratorEngineManager>::Global().Has()) {
+    SaveTrtCalibToDisk();
+  }
+#endif
   if (FLAGS_profile) {
     platform::DisableProfiler(platform::EventSortingKey::kTotal,
                               "./profile.log");

paddle/fluid/inference/api/analysis_predictor.h

@@ -97,6 +97,21 @@ class AnalysisPredictor : public PaddlePredictor {
   void GetFetchOne(const framework::LoDTensor &fetchs,
                    PaddleTensor *output_data);
 
+#if PADDLE_WITH_TENSORRT
+  // When we use Paddle-TRT INT8 engine, we need to generate calibration table
+  // data first,
+  // the calibration table contains the range for each op's input and output,
+  // this whole process can be divided into several steps:
+  //
+  // 1. Builds a 32-bit engine, runs it on the calibration set, and records a
+  // histogram for each
+  // tensor of the distribution of activation values.
+  // 2. Builds a calibration table from the histograms.
+  //
+  // After step 2, we need to store the calibration table on disk
+  bool SaveTrtCalibToDisk();
+#endif
+
 // Some more detailed tests, they are made the friends of the predictor, so that
 // the all the details can be tested.
 #if PADDLE_WITH_TESTING

paddle/fluid/inference/api/paddle_analysis_config.h

@@ -42,6 +42,10 @@ struct AnalysisConfig {
   explicit AnalysisConfig(const std::string& model_dir);
   explicit AnalysisConfig(const std::string& prog_file,
                           const std::string& params_file);
+  enum class Precision {
+    kFloat32 = 0,
+    kInt8,
+  };
 
   /** Set model with a directory.
    */
@@ -135,7 +139,8 @@ struct AnalysisConfig {
    * subgraph is less than this, it will not transfer to TensorRT engine.
    */
   void EnableTensorRtEngine(int workspace_size = 1 << 20,
-                            int max_batch_size = 1, int min_subgraph_size = 3);
+                            int max_batch_size = 1, int min_subgraph_size = 3,
+                            Precision precision = Precision::kFloat32);
   /** A boolean state telling whether the TensorRT engine is used.
    */
   bool tensorrt_engine_enabled() const { return use_tensorrt_; }
@@ -229,6 +234,7 @@ struct AnalysisConfig {
   //  We set this variable to control the minimum number of nodes in the
   //  subgraph, 3 as default value.
   int tensorrt_min_subgraph_size_{3};
+  Precision tensorrt_precision_mode_;
 
   // memory reuse related.
   bool enable_memory_optim_{false};

paddle/fluid/inference/tensorrt/CMakeLists.txt

-nv_library(tensorrt_engine SRCS engine.cc DEPS ${GLOB_OPERATOR_DEPS} framework_proto device_context)
+nv_library(tensorrt_engine SRCS engine.cc trt_int8_calibrator.cc DEPS ${GLOB_OPERATOR_DEPS} framework_proto device_context)
 nv_library(tensorrt_op_teller SRCS op_teller.cc DEPS framework_proto)
 nv_test(test_tensorrt SRCS test_tensorrt.cc DEPS dynload_cuda device_context dynamic_loader)
 nv_test(test_tensorrt_engine SRCS test_engine.cc DEPS dynload_cuda tensorrt_engine)

paddle/fluid/inference/tensorrt/engine.cc

@@ -69,6 +69,13 @@ void TensorRTEngine::FreezeNetwork() {
   // build engine.
   infer_builder_->setMaxBatchSize(max_batch_);
   infer_builder_->setMaxWorkspaceSize(max_workspace_);
+  if (enable_int8_) {
+    infer_builder_->setInt8Mode(true);
+    PADDLE_ENFORCE(
+        calibrator_ != nullptr,
+        "The precision mode is 'INT8', the calibrator should not be nullptr");
+    infer_builder_->setInt8Calibrator(calibrator_);
+  }
 
   infer_engine_.reset(infer_builder_->buildCudaEngine(*infer_network_));
   PADDLE_ENFORCE(infer_engine_ != nullptr, "build cuda engine failed!");

paddle/fluid/inference/tensorrt/engine.h

@@ -23,12 +23,14 @@ limitations under the License. */
 #include "paddle/fluid/inference/engine.h"
 #include "paddle/fluid/inference/tensorrt/helper.h"
 #include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
+#include "paddle/fluid/inference/tensorrt/trt_int8_calibrator.h"
 #include "paddle/fluid/inference/utils/singleton.h"
 
 namespace paddle {
 namespace inference {
 namespace tensorrt {
 
+class TRTInt8Calibrator;
 /*
  * TensorRT Engine.
  *
@@ -55,13 +57,16 @@ class TensorRTEngine : public EngineBase {
   };
 
   TensorRTEngine(int max_batch, int max_workspace, cudaStream_t stream,
-                 int device = 0,
+                 int device = 0, bool enable_int8 = false,
+                 TRTInt8Calibrator* calibrator = nullptr,
                  nvinfer1::ILogger& logger = NaiveLogger::Global())
       : max_batch_(max_batch),
         max_workspace_(max_workspace),
         stream_(stream),
-        logger_(logger),
-        device_(device) {}
+        device_(device),
+        enable_int8_(enable_int8),
+        calibrator_(calibrator),
+        logger_(logger) {}
 
   virtual ~TensorRTEngine();
 
@@ -139,8 +144,8 @@ class TensorRTEngine : public EngineBase {
   // In the normal case, the paddle-trt exists bug when runing the googlenet.
   // When there are more than two convolutions of 1 * 1 with the same input, the
   // paddle-tensorrt will do the merging optimization, which fuse those conv
-  // into
-  // one conv, and then trigger bug. So,  We should use strategy to avoid this
+  // into one conv, and then trigger bug. So,  We should use strategy to avoid
+  // this
   // optimization for the time being. This bug will be fixed in the future.
   std::unordered_map<std::string /*name*/, int /*ITensor_quote_num*/>
       itensor_quote_num;
@@ -153,9 +158,14 @@ class TensorRTEngine : public EngineBase {
   // the max memory size the engine uses
   int max_workspace_;
 
+  cudaStream_t stream_;
+  // The specific GPU id that the TensorRTEngine bounded to.
+  int device_;
+
+  bool enable_int8_;
+  TRTInt8Calibrator* calibrator_;
   // batch size of the current data, will be updated each Executation.
   int batch_size_{-1};
-  cudaStream_t stream_;
 
   nvinfer1::ILogger& logger_;
 
@@ -165,8 +175,6 @@ class TensorRTEngine : public EngineBase {
   std::unordered_map<std::string /*name*/, nvinfer1::ITensor* /*ITensor*/>
       itensor_map_;
 
-  // The specific GPU id that the TensorRTEngine bounded to.
-  int device_;
   std::vector<std::unique_ptr<plugin::PluginTensorRT>> owned_plugin_;
 
   // TensorRT related internal members

paddle/fluid/inference/tensorrt/trt_int8_calibrator.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 "paddle/fluid/inference/tensorrt/trt_int8_calibrator.h"
+#include "glog/logging.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+// set the batch size before constructing the thread to execute engine
+int TRTInt8Calibrator::getBatchSize() const { return batch_size_; }
+
+TRTInt8Calibrator::TRTInt8Calibrator(
+    const std::unordered_map<std::string, size_t>& buffers, int batch_size,
+    std::string engine_name, const platform::Place place)
+    : batch_size_(batch_size), engine_name_(engine_name) {
+  int i = 0;
+  VLOG(4) << "Init a new calibrator: " << engine_name_;
+  for (const auto it : buffers) {
+    framework::Tensor temp_tensor;
+    std::string input_name = it.first;
+    int data_size = it.second;
+    int num_ele = data_size / sizeof(int16_t);
+    framework::DDim data_shape = framework::make_ddim({num_ele});
+    temp_tensor.Resize(data_shape);
+    data_tensors_.push_back(temp_tensor);
+    data_buffers_[input_name] = std::pair<void*, size_t>(
+        static_cast<void*>(temp_tensor.mutable_data<int16_t>(place)), num_ele);
+    i += 1;
+  }
+}
+
+TRTInt8Calibrator::TRTInt8Calibrator(const std::string& calib_data)
+    : batch_size_(0),
+      calib_running_(false),
+      data_is_set_(false),
+      done_(true),
+      calibration_table_(calib_data) {}
+
+void TRTInt8Calibrator::waitAndSetDone() {
+  std::unique_lock<std::mutex> lk(mut_);
+  while ((calib_running_ || data_is_set_) && !done_) cond_.wait(lk);
+  if (!done_) {
+    done_ = true;
+    cond_.notify_all();
+  }
+}
+
+// There might be more than one input for trt subgraph,
+// So, we use a map to store input information.
+bool TRTInt8Calibrator::setBatch(
+    const std::unordered_map<std::string, void*>& data) {
+  VLOG(3) << "set batch: " << engine_name_;
+  std::unique_lock<std::mutex> lk(mut_);
+  //  There is a producer and a consumer. The producer set the batch data and
+  //  the consumer get the batch data. The size of the data pool is one.
+  //  So, the producer has to wait for the consumer to finish processing before
+  //  they can set the data.
+  while ((calib_running_ || data_is_set_) && (!done_)) cond_.wait(lk);
+  // The done_ is set to true using waitAndSetDone, When all calibration data
+  // are processed.
+  if (done_) return false;
+
+  // Sets the batch.
+  for (const auto& it : data) {
+    auto dataptr = data_buffers_.find(it.first);
+    if (dataptr == data_buffers_.end()) {
+      LOG(FATAL) << "FATAL " << engine_name_ << " input name '" << it.first
+                 << "' does not match with the buffer names";
+    }
+    const auto& d = dataptr->second;
+    PADDLE_ENFORCE(
+        cudaMemcpy(d.first, it.second, d.second, cudaMemcpyDeviceToDevice),
+        "Fail to cudaMemcpy %s for %s", engine_name_, it.first);
+  }
+
+  data_is_set_ = true;
+  cond_.notify_all();
+  return true;
+}
+
+bool TRTInt8Calibrator::getBatch(void** bindings, const char** names,
+                                 int num_bindings) {
+  VLOG(4) << "get batch: " << engine_name_;
+  std::unique_lock<std::mutex> lk(mut_);
+  // The consumer has just finished processing a data.
+  // The producer can set the data again.
+  calib_running_ = false;
+  cond_.notify_all();
+
+  // As long as there is data in the pool, the consumer can get it.
+  while (!data_is_set_ && !done_) cond_.wait(lk);
+  if (done_) return false;
+
+  // Gets the batch
+  for (int i = 0; i < num_bindings; i++) {
+    auto it = data_buffers_.find(names[i]);
+    if (it == data_buffers_.end()) {
+      LOG(FATAL) << "Calibration engine asked for unknown tensor name '"
+                 << names[i] << "' at position " << i;
+    }
+    bindings[i] = it->second.first;
+  }
+
+  data_is_set_ = false;
+  calib_running_ = true;
+  VLOG(4) << "get batch done: " << engine_name_;
+  return true;
+}
+
+void TRTInt8Calibrator::setDone() {
+  std::unique_lock<std::mutex> lk(mut_);
+  done_ = true;
+  cond_.notify_all();
+}
+
+const void* TRTInt8Calibrator::readCalibrationCache(size_t& length) {
+  if (calibration_table_.empty()) return nullptr;
+  length = calibration_table_.size();
+  return calibration_table_.data();
+}
+
+void TRTInt8Calibrator::writeCalibrationCache(const void* ptr,
+                                              std::size_t length) {
+  calibration_table_ = std::string((const char*)ptr, length);
+  VLOG(4) << "Got calibration data for " << engine_name_ << " " << ptr
+          << " length=" << length;
+}
+TRTInt8Calibrator::~TRTInt8Calibrator() {
+  VLOG(4) << "Destroying calibrator for " << engine_name_;
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/tensorrt/trt_int8_calibrator.h

+// 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.
+
+#pragma once
+#include <atomic>
+#include <memory>
+#include <mutex>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <NvInfer.h>
+#include <cuda_runtime_api.h>
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/inference/tensorrt/engine.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class TensorRTEngine;
+
+struct TRTInt8Calibrator : public nvinfer1::IInt8EntropyCalibrator {
+ public:
+  TRTInt8Calibrator(const std::unordered_map<std::string, size_t>& buffers,
+                    int batch_size, std::string engine_name,
+                    const platform::Place place);
+
+  explicit TRTInt8Calibrator(const std::string& calibration_data);
+  ~TRTInt8Calibrator();
+
+  int getBatchSize() const override;
+
+  bool getBatch(void* bindings[], const char* names[],
+                int num_bindings) override;
+
+  bool setBatch(const std::unordered_map<std::string, void*>& data);
+  void setDone();
+  void waitAndSetDone();
+
+  const void* readCalibrationCache(std::size_t& length) override;
+  void writeCalibrationCache(const void* ptr, std::size_t length) override;
+  const std::string& getCalibrationTableAsString() {
+    return calibration_table_;
+  }
+
+ private:
+  const int batch_size_;
+
+  bool calib_running_{true};
+  bool data_is_set_{false};
+  bool done_{false};
+
+  std::mutex mut_;
+  std::condition_variable cond_;
+
+  std::unordered_map<std::string, std::pair<void*, size_t>> data_buffers_;
+  std::vector<framework::Tensor> data_tensors_;
+
+  std::string engine_name_;
+  std::string calibration_table_;
+};
+
+class TRTCalibratorEngine {
+ public:
+  TRTCalibratorEngine() {}
+  std::unique_ptr<TRTInt8Calibrator> calib_;
+  std::unique_ptr<std::thread> thr_;
+  std::unique_ptr<TensorRTEngine> engine_;
+};
+/*
+ * Manager to control the TensorRT Int8 calibration creation and deltetion.
+ */
+class TRTCalibratorEngineManager {
+ public:
+  bool Has() const { return res_.size() > 0; }
+  bool Has(const std::string& name) const {
+    if (res_.count(name) == 0) return false;
+    return res_.at(name).get() != nullptr;
+  }
+
+  // Get Int8Calibrator via name
+  TRTCalibratorEngine* Get(const std::string& name) const {
+    return res_.at(name).get();
+  }
+
+  // Look up or create a calibrator.
+  TRTCalibratorEngine* LookupOrCreate(const std::string& engine_name) {
+    if (res_.count(engine_name) == 0) {
+      auto* p = new TRTCalibratorEngine;
+      res_[engine_name].reset(p);
+    }
+    return res_.at(engine_name).get();
+  }
+
+  // Create an Int8Calibrator
+  TRTCalibratorEngine* Create(const std::string& engine_name) {
+    auto* p = new TRTCalibratorEngine;
+    res_[engine_name].reset(p);
+    return p;
+  }
+
+  void DeleteALL() {
+    for (auto& item : res_) {
+      item.second.reset(nullptr);
+    }
+  }
+
+ private:
+  std::unordered_map<std::string, std::unique_ptr<TRTCalibratorEngine>> res_;
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/operators/tensorrt/tensorrt_engine_op.cc

@@ -29,8 +29,14 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Xs", "A list of inputs.").AsDuplicable();
     AddOutput("Ys", "A list of outputs").AsDuplicable();
     AddAttr<std::string>("subgraph", "the subgraph.");
+    AddAttr<std::string>("calibration_data", "the calibration data for int8");
+    AddAttr<std::string>(
+        "engine_key",
+        "The engine_key here is used to distinguish different TRT Engines");
     AddAttr<int>("max_batch_size", "the maximum batch size.");
     AddAttr<int>("workspace_size", "the workspace size.");
+    AddAttr<framework::BlockDesc *>("sub_block", "the trt block");
+    AddAttr<bool>("enable_int8", "whether swith to int8 mode");
     AddComment("TensorRT engine operator.");
   }
 };
@@ -47,6 +53,6 @@ class TensorRTEngineInferVarType : public framework::VarTypeInference {
 namespace ops = paddle::operators;
 
 REGISTER_OPERATOR(tensorrt_engine, ops::TensorRTEngineOp,
-                  ops::TensorRTEngineOpMaker);
+                  ops::TensorRTEngineOpMaker, ops::TensorRTEngineOpMaker);
 
 #endif  // PADDLE_WITH_CUDA

paddle/fluid/operators/tensorrt/tensorrt_engine_op.h

@@ -17,8 +17,10 @@
 #ifdef PADDLE_WITH_CUDA
 
 #include <string>
+#include <unordered_map>
 #include <vector>
 
+#include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/inference/analysis/helper.h"
@@ -62,6 +64,9 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t> &shape) {
 
 using inference::Singleton;
 using inference::tensorrt::TensorRTEngine;
+using inference::tensorrt::TRTInt8Calibrator;
+using inference::tensorrt::TRTCalibratorEngine;
+using inference::tensorrt::TRTCalibratorEngineManager;
 
 class TensorRTEngineOp : public framework::OperatorBase {
  private:
@@ -70,6 +75,11 @@ class TensorRTEngineOp : public framework::OperatorBase {
   mutable std::unique_ptr<TensorRTEngine> trt_engine_;
   int max_batch_size_;
   int workspace_size_;
+  std::unique_ptr<TRTInt8Calibrator> calibrator_;
+  bool enable_int8_;
+  std::string calibration_data_;
+  std::string engine_key_;
+  bool calibration_mode_;
 
  public:
   TensorRTEngineOp(const std::string &type,
@@ -80,19 +90,96 @@ class TensorRTEngineOp : public framework::OperatorBase {
     input_names_ = Inputs("Xs");
     max_batch_size_ = Attr<int>("max_batch_size");
     workspace_size_ = Attr<int>("workspace_size");
+    enable_int8_ = Attr<bool>("enable_int8");
+    calibration_data_ = Attr<std::string>("calibration_data");
+    engine_key_ = Attr<std::string>("engine_key");
 
     auto params = Attr<std::vector<std::string>>("parameters");
     for (const auto &param : params) {
       param_names_.insert(param);
     }
+    // calibration_mode is ture represents we need to
+    // generate the calibration table data.
+    calibration_mode_ = (enable_int8_ && calibration_data_.size() == 0);
+
+    VLOG(4) << "calibration_mode: " << calibration_mode_;
+    if (enable_int8_ && calibration_data_.size()) {
+      calibrator_.reset(new TRTInt8Calibrator(calibration_data_));
+    }
   }
 
  protected:
+  void RunNativeImpl(const framework::Scope &scope,
+                     const platform::Place &dev_place) const {
+    framework::Executor executor(dev_place);
+    auto *block = Attr<framework::BlockDesc *>("sub_block");
+    auto *program = block->Program();
+    auto &current_scope = scope.NewScope();
+    auto ctx = executor.Prepare(*program, block->ID());
+    executor.RunPreparedContext(ctx.get(), &current_scope, false, true, true);
+  }
+
   void RunImpl(const framework::Scope &scope,
                const platform::Place &dev_place) const override {
+    if (calibration_mode_ == true) {
+      RunCalibration(scope, dev_place);
+      return;
+    }
     RunTrt(scope, dev_place);
   }
 
+  void RunCalibration(const framework::Scope &scope,
+                      const platform::Place &dev_place) const {
+    // This process will builds a 32-bit trt engine, runs it on the calibration
+    // set, and records a histogram for each
+    // tensor of the distribution of activation values.
+    LOG_FIRST_N(INFO, 1) << "The TRT engine: " << engine_key_
+                         << " is running calibration trt int8... ";
+    int runtime_batch = 1;
+    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
+    auto &dev_ctx = *pool.Get(dev_place);
+    auto stream =
+        reinterpret_cast<const platform::CUDADeviceContext &>(dev_ctx).stream();
+    if (!Singleton<TRTCalibratorEngineManager>::Global().Has(engine_key_)) {
+      TRTCalibratorEngine *calib_res =
+          Singleton<TRTCalibratorEngineManager>::Global().Create(engine_key_);
+      std::unordered_map<std::string, size_t> calib_buffers;
+      for (auto &x : input_names_) {
+        if (param_names_.count(x)) continue;
+        auto &t =
+            inference::analysis::GetFromScope<framework::LoDTensor>(scope, x);
+        calib_buffers[x] = t.memory_size();
+        auto t_shape = framework::vectorize(t.dims());
+        runtime_batch = t_shape[0];
+      }
+      calib_res->calib_.reset(new TRTInt8Calibrator(
+          calib_buffers, runtime_batch, engine_key_, dev_place));
+      calib_res->thr_.reset(new std::thread([&]() {
+        calib_res->engine_.reset(new TensorRTEngine(
+            max_batch_size_, workspace_size_, stream,
+            boost::get<platform::CUDAPlace>(dev_place).device, enable_int8_,
+            calib_res->calib_.get()));
+        VLOG(3) << "start the calib trt engine thread";
+        Prepare(scope, dev_place, calib_res->engine_.get());
+      }));
+    }
+
+    TRTInt8Calibrator *temp_calibrator =
+        Singleton<TRTCalibratorEngineManager>::Global()
+            .Get(engine_key_)
+            ->calib_.get();
+    std::unordered_map<std::string, void *> calib_data;
+
+    for (auto &x : Inputs("Xs")) {
+      if (param_names_.count(x)) continue;
+      auto &t =
+          inference::analysis::GetFromScope<framework::LoDTensor>(scope, x);
+      calib_data.emplace(x, t.data<void>());
+    }
+    temp_calibrator->setBatch(calib_data);
+    RunNativeImpl(scope, dev_place);
+  }
+
   void RunTrt(const framework::Scope &scope,
               const platform::Place &dev_place) const {
     int runtime_batch = 1;
@@ -101,9 +188,10 @@ class TensorRTEngineOp : public framework::OperatorBase {
     auto stream =
         reinterpret_cast<const platform::CUDADeviceContext &>(dev_ctx).stream();
     if (trt_engine_.get() == nullptr) {
-      trt_engine_.reset(new TensorRTEngine(
-          max_batch_size_, workspace_size_, stream,
-          boost::get<platform::CUDAPlace>(dev_place).device));
+      trt_engine_.reset(
+          new TensorRTEngine(max_batch_size_, workspace_size_, stream,
+                             boost::get<platform::CUDAPlace>(dev_place).device,
+                             enable_int8_, calibrator_.get()));
       Prepare(scope, dev_place, trt_engine_.get());
     }
 
@@ -173,7 +261,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
 
   void Prepare(const framework::Scope &scope, const platform::Place &dev_place,
                TensorRTEngine *engine) const {
-    VLOG(4) << "Prepare engine";
+    LOG(INFO) << "Prepare TRT engine (Optimize model structure, Select OP "
+                 "kernel etc). This process may cost a lot of time.";
     framework::proto::BlockDesc block_desc;
     block_desc.ParseFromString(Attr<std::string>("subgraph"));
 

paddle/fluid/operators/tensorrt/tensorrt_engine_op_test.cc

@@ -96,19 +96,20 @@ TEST(TensorRTEngineOp, manual) {
   engine_op_desc.SetType("tensorrt_engine");
   engine_op_desc.SetInput("Xs", std::vector<std::string>({"x"}));
   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", 1 << 20);
-  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>({}));
-  SetAttr<std::vector<std::string>>(engine_op_desc.Proto(),
-                                    "output_name_mapping",
-                                    std::vector<std::string>({"z0"}));
+
+  engine_op_desc.SetBlockAttr("sub_block", &block_desc);
+  engine_op_desc.SetAttr("max_batch_size", static_cast<int>(2));
+  engine_op_desc.SetAttr("workspace_size", static_cast<int>(1 << 20));
+  engine_op_desc.SetAttr("parameters", std::vector<std::string>({}));
+  engine_op_desc.SetAttr("engine_key", std::string("a_engine"));
+  engine_op_desc.SetAttr("calibration_data", std::string(""));
+  engine_op_desc.SetAttr("enable_int8", static_cast<bool>(false));
+  engine_op_desc.SetAttr("output_name_mapping",
+                         std::vector<std::string>({"z0"}));
+  engine_op_desc.SetAttr("subgraph", std::string(block_->SerializeAsString()));
 
   LOG(INFO) << "create engine op";
-  auto engine_op = framework::OpRegistry::CreateOp(*engine_op_desc.Proto());
+  auto engine_op = framework::OpRegistry::CreateOp(engine_op_desc);
   LOG(INFO) << "engine_op " << engine_op.get();
 
   framework::Scope scope;
@@ -190,20 +191,19 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
   engine_op_desc.SetInput("Xs", std::vector<std::string>({"x0"}));
   engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z3"}));
 
-  SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
-                       block_->SerializeAsString());
-  SetAttr<int>(engine_op_desc.Proto(), "max_batch_size", batch_size);
-  SetAttr<int>(engine_op_desc.Proto(), "workspace_size", 1 << 20);
-  SetAttr<std::vector<std::string>>(
-      engine_op_desc.Proto(), "parameters",
-      std::vector<std::string>({"y0", "y1", "y2", "y3"}));
-  SetAttr<std::string>(engine_op_desc.Proto(), "engine_uniq_key", "b_engine");
-
-  SetAttr<std::vector<std::string>>(engine_op_desc.Proto(),
-                                    "output_name_mapping",
-                                    std::vector<std::string>({"z3"}));
-
-  auto engine_op = framework::OpRegistry::CreateOp(*engine_op_desc.Proto());
+  engine_op_desc.SetBlockAttr("sub_block", &block_desc);
+  engine_op_desc.SetAttr("max_batch_size", static_cast<int>(batch_size));
+  engine_op_desc.SetAttr("workspace_size", static_cast<int>(1 << 20));
+  engine_op_desc.SetAttr("parameters",
+                         std::vector<std::string>({"y0", "y1", "y2", "y3"}));
+  engine_op_desc.SetAttr("engine_key", std::string("b_engine"));
+  engine_op_desc.SetAttr("calibration_data", std::string(""));
+  engine_op_desc.SetAttr("enable_int8", static_cast<bool>(false));
+  engine_op_desc.SetAttr("output_name_mapping",
+                         std::vector<std::string>({"z3"}));
+  engine_op_desc.SetAttr("subgraph", std::string(block_->SerializeAsString()));
+
+  auto engine_op = framework::OpRegistry::CreateOp(engine_op_desc);
 
   // Execute them.
   engine_op->Run(scope, place);

paddle/fluid/pybind/inference_api.cc

@@ -180,8 +180,14 @@ void BindNativePredictor(py::module *m) {
 }
 
 void BindAnalysisConfig(py::module *m) {
-  py::class_<AnalysisConfig>(*m, "AnalysisConfig")
-      .def(py::init<const AnalysisConfig &>())
+  py::class_<AnalysisConfig> analysis_config(*m, "AnalysisConfig");
+
+  py::enum_<AnalysisConfig::Precision>(analysis_config, "Precision")
+      .value("Float32", AnalysisConfig::Precision::kFloat32)
+      .value("Int8", AnalysisConfig::Precision::kInt8)
+      .export_values();
+
+  analysis_config.def(py::init<const AnalysisConfig &>())
       .def(py::init<const std::string &>())
       .def(py::init<const std::string &, const std::string &>())
       .def("set_model", (void (AnalysisConfig::*)(const std::string &)) &
@@ -215,7 +221,8 @@ void BindAnalysisConfig(py::module *m) {
       .def("specify_input_name", &AnalysisConfig::specify_input_name)
       .def("enable_tensorrt_engine", &AnalysisConfig::EnableTensorRtEngine,
            py::arg("workspace_size") = 1 << 20, py::arg("max_batch_size") = 1,
-           py::arg("min_subgraph_size") = 3)
+           py::arg("min_subgraph_size") = 3,
+           py::arg("precision_mode") = AnalysisConfig::Precision::kFloat32)
       .def("tensorrt_engine_enabled", &AnalysisConfig::tensorrt_engine_enabled)
       .def("switch_ir_debug", &AnalysisConfig::SwitchIrDebug,
            py::arg("x") = true)