Merge pull request #14934 from NHZlX/fix_trt_thread_pool_bug_to_1.2

cherry-pick """fix pool2d  and thread bug of paddle-trt """ to release1.2

Author: panyx0718

cmake/inference_lib.cmake

@@ -182,6 +182,13 @@ if (WITH_ANAKIN AND WITH_MKL)
     list(APPEND inference_deps anakin_inference_lib)
 endif ()
 
+if (TENSORRT_FOUND)
+    copy(tensorrt_lib DEPS ${inference_deps} 
+        SRCS ${TENSORRT_ROOT}/include/Nv*.h ${TENSORRT_ROOT}/lib/libnvinfer*
+        DSTS ${FLUID_INSTALL_DIR}/third_party/install/tensorrt/include ${FLUID_INSTALL_DIR}/third_party/install/tensorrt/lib)
+endif ()
+
+
 set(module "inference")
 copy(inference_lib DEPS ${inference_deps}
   SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*

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

@@ -63,7 +63,6 @@ std::unique_ptr<framework::ir::Graph> analysis::TensorRtSubgraphPass::ApplyImpl(
 void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
                                             Graph *graph) const {
   auto *op_desc = node->Op();
-  static int counter{0};
   auto &subgraph = *Agent(node).subgraph();
   PADDLE_ENFORCE(!subgraph.empty());
 
@@ -191,8 +190,6 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
           block_desc.Proto()->SerializeAsString());
   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(), "engine_uniq_key",
-          "trt-" + std::to_string(counter++));
   SetAttr(op_desc->Proto(), "parameters", ExtractParameters(graph->Nodes()));
   SetAttr(op_desc->Proto(), "output_name_mapping", output_mapping);
 }

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

@@ -103,6 +103,7 @@ class OpConverter {
   void ConvertBlock(const framework::proto::BlockDesc& block,
                     const std::unordered_set<std::string>& parameters,
                     const framework::Scope& scope, TensorRTEngine* engine) {
+    std::unique_lock<std::mutex> lk(mut_);
     for (int i = 0; i < block.ops_size(); i++) {
       const auto& op = block.ops(i);
       ConvertOp(op, parameters, scope, engine);
@@ -125,6 +126,7 @@ class OpConverter {
   std::unordered_map<std::string, OpConverter*> converters_;
   // fluid inference scope
   framework::Scope* scope_{nullptr};
+  std::mutex mut_;
 };
 
 }  // namespace tensorrt

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -109,8 +109,12 @@ class Pool2dOpConverter : public OpConverter {
     }
 
     if (pool_type == "max") {
-      nvinfer1::DimsHW pre_pad(paddings[0], paddings[1]);
-      nvinfer1::DimsHW post_pad(paddings[0], paddings[1]);
+      // Under ceil mode, the pre_pad and post_pad are used to
+      // record the the padding size. In some ceil mode cases,
+      // we do not need padding, so we initialize the two vars to 0.
+
+      nvinfer1::DimsHW pre_pad(0, 0);
+      nvinfer1::DimsHW post_pad(0, 0);
       if (ceil_mode) {
         // If ceil mode is true, we will pad the appropriate size to the input.
         DealCeilMode(input_shape, ksize, strides, paddings, &pre_pad, &post_pad,

paddle/fluid/operators/tensorrt/CMakeLists.txt

@@ -1,5 +1,5 @@
 op_library(tensorrt_engine_op DEPS tensorrt_engine tensorrt_converter)
-file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(tensorrt_engine);\n")
+file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(tensorrt_engine);\n")
 nv_test(test_tensorrt_engine_op SRCS tensorrt_engine_op_test.cc
   DEPS tensorrt_engine_op
   analysis)

paddle/fluid/operators/tensorrt/tensorrt_engine_op.cc

@@ -21,8 +21,6 @@
 
 namespace paddle {
 
-DEFINE_int32(tensorrt_engine_batch_size, 1, "the batch_size of TensorRT");
-
 namespace operators {
 
 class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -31,7 +29,6 @@ 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>("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.");
@@ -50,6 +47,6 @@ 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.cu.cc

@@ -27,8 +27,6 @@
 
 namespace paddle {
 
-DECLARE_int32(tensorrt_engine_batch_size);
-
 namespace operators {
 
 using FluidDT = framework::proto::VarType_Type;
@@ -49,7 +47,7 @@ TRT_DT FluidDataType2TRT(FluidDT type) {
   return TRT_DT::kINT32;
 }
 
-nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t>& shape) {
+nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t> &shape) {
   PADDLE_ENFORCE_GT(shape.size(), 1UL,
                     "TensorRT' tensor input requires at least 2 dimensions");
   PADDLE_ENFORCE_LE(shape.size(), 4UL,
@@ -63,171 +61,153 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t>& shape) {
 }  // namespace // NOLINT
 
 using inference::Singleton;
-using inference::tensorrt::TRT_EngineManager;
+using inference::tensorrt::TensorRTEngine;
+
+class TensorRTEngineOp : public framework::OperatorBase {
+ private:
+  std::vector<std::string> input_names_;
+  std::unordered_set<std::string> param_names_;
+  mutable std::unique_ptr<TensorRTEngine> trt_engine_;
+  int max_batch_size_;
+  int workspace_size_;
 
-class TensorRTEngineOp : public framework::OperatorWithKernel {
  public:
-  using framework::OperatorWithKernel::OperatorWithKernel;
+  TensorRTEngineOp(const std::string &type,
+                   const framework::VariableNameMap &inputs,
+                   const framework::VariableNameMap &outputs,
+                   const framework::AttributeMap &attrs)
+      : framework::OperatorBase(type, inputs, outputs, attrs) {
+    input_names_ = Inputs("Xs");
+    max_batch_size_ = Attr<int>("max_batch_size");
+    workspace_size_ = Attr<int>("workspace_size");
+
+    auto params = Attr<std::vector<std::string>>("parameters");
+    for (const auto &param : params) {
+      param_names_.insert(param);
+    }
+  }
 
  protected:
-  void InferShape(framework::InferShapeContext* ctx) const override {}
-
-  framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
-    auto input0 = ctx.Inputs("Xs").front();
-    framework::OpKernelType kt = framework::OpKernelType(
-        framework::ToDataType(ctx.scope()
-                                  .FindVar(input0)
-                                  ->GetMutable<framework::LoDTensor>()
-                                  ->type()),
-        ctx.GetPlace());
-    return kt;
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &dev_place) const override {
+    RunTrt(scope, dev_place);
   }
-};
 
-template <typename DeviceContext, typename T>
-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);
+  void RunTrt(const framework::Scope &scope,
+              const platform::Place &dev_place) const {
+    int runtime_batch = 1;
+    if (trt_engine_.get() == nullptr) {
+      trt_engine_.reset(new TensorRTEngine(
+          max_batch_size_, workspace_size_, nullptr,
+          boost::get<platform::CUDAPlace>(dev_place).device));
+      Prepare(scope, dev_place, trt_engine_.get());
     }
-    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, max_batch_size);
+
+    auto *engine = trt_engine_.get();
+    PADDLE_ENFORCE(!input_names_.empty(), "should pass more than one inputs");
 
     std::vector<std::string> output_maps =
-        context.Attr<std::vector<std::string>>("output_name_mapping");
+        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;
+    for (const auto &x : Inputs("Xs")) {
+      if (param_names_.count(x)) continue;
       // convert input and copy to TRT engine's buffer
-      auto& t = inference::analysis::GetFromScope<framework::LoDTensor>(
-          context.scope(), x);
+      auto &t =
+          inference::analysis::GetFromScope<framework::LoDTensor>(scope, x);
+      auto t_shape = framework::vectorize(t.dims());
+      runtime_batch = t_shape[0];
       if (platform::is_cpu_place(t.place())) {
-        engine->SetInputFromCPU(x, static_cast<const void*>(t.data<void>()),
+        engine->SetInputFromCPU(x, static_cast<const void *>(t.data<void>()),
                                 t.memory_size());
       } else {
-        engine->SetInputFromGPU(x, static_cast<const void*>(t.data<void>()),
+        engine->SetInputFromGPU(x, static_cast<const void *>(t.data<void>()),
                                 t.memory_size());
       }
     }
+
+    PADDLE_ENFORCE_LE(runtime_batch, max_batch_size_);
     // Execute the engine.
-    PADDLE_ENFORCE_GT(FLAGS_tensorrt_engine_batch_size, 0);
-    engine->Execute(FLAGS_tensorrt_engine_batch_size);
+    engine->Execute(runtime_batch);
 
     // Convert output tensor from engine to fluid
     int output_index = 0;
     VLOG(4) << "TensorRT Engine Op Outputs:";
-    for (const auto& y : context.Outputs("Ys")) {
+    for (const auto &y : Outputs("Ys")) {
       VLOG(4) << y;
       // convert output and copy to fluid.
-      nvinfer1::ITensor* trt_t = engine->GetITensor(output_maps[output_index]);
+      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.
       // The ITensor doesn't contain the batch size dim.
       std::vector<int> ddim;
-      ddim.push_back(FLAGS_tensorrt_engine_batch_size);
+      ddim.push_back(runtime_batch);
       for (int i = 0; i < dims.nbDims; i++) {
         ddim.push_back(dims.d[i]);
       }
 
-      auto* fluid_v = context.scope().FindVar(y);
+      auto *fluid_v = scope.FindVar(y);
       PADDLE_ENFORCE_NOT_NULL(fluid_v, "no output variable called %s", y);
-      auto* fluid_t = fluid_v->GetMutable<framework::LoDTensor>();
+      auto *fluid_t = fluid_v->GetMutable<framework::LoDTensor>();
 
       fluid_t->Resize(framework::make_ddim(ddim));
 
-      // TODO(Superjomn) find some way to determine which device to output the
-      // tensor.
-      // if (platform::is_cpu_place(fluid_t->place())) {
       // TODO(Superjomn) change this float to dtype size.
-      auto size = inference::analysis::AccuDims(dims.d, dims.nbDims) *
-                  FLAGS_tensorrt_engine_batch_size;
+      auto size =
+          inference::analysis::AccuDims(dims.d, dims.nbDims) * runtime_batch;
       engine->GetOutputInGPU(
           output_maps[output_index],
           fluid_t->mutable_data<float>(platform::CUDAPlace(
-              boost::get<platform::CUDAPlace>(context.GetPlace()).device)),
+              boost::get<platform::CUDAPlace>(dev_place).device)),
           size * sizeof(float));
-
       output_index += 1;
     }
 
     cudaStreamSynchronize(*engine->stream());
   }
 
- protected:
-  void Prepare(const framework::ExecutionContext& context) const {
+  void Prepare(const framework::Scope &scope, const platform::Place &dev_place,
+               TensorRTEngine *engine) const {
     VLOG(4) << "Prepare engine";
-    // Get the ProgramDesc and pass to convert.
     framework::proto::BlockDesc block_desc;
-    block_desc.ParseFromString(context.Attr<std::string>("subgraph"));
-    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) {
-      parameters.insert(param);
-    }
+    block_desc.ParseFromString(Attr<std::string>("subgraph"));
 
     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_size, workspace_size, nullptr /*engine hold its own stream*/,
-        context.Attr<std::string>("engine_uniq_key"),
-        boost::get<platform::CUDAPlace>(context.GetPlace()).device);
+        Attr<std::vector<std::string>>("output_name_mapping");
 
     engine->InitNetwork();
 
     framework::BlockDesc block(nullptr /*programdesc*/, &block_desc);
     VLOG(4) << "parsed var size " << block.AllVars().size();
     // Add inputs
     VLOG(4) << "declare inputs";
-    for (auto& input : context.Inputs("Xs")) {
-      if (parameters.count(input)) continue;
+    for (auto &input : Inputs("Xs")) {
+      if (param_names_.count(input)) continue;
       VLOG(4) << "declare input " << input;
-      auto* var = block.FindVar(input);
+
+      auto &t =
+          inference::analysis::GetFromScope<framework::LoDTensor>(scope, input);
+      auto t_shape = framework::vectorize(t.dims());
+
+      auto *var = block.FindVar(input);
       // TensorRT engine need to create parameters. The parameter's description
       // should be set in
       PADDLE_ENFORCE(var, "no variable called %s", input);
       PADDLE_ENFORCE_EQ(var->GetType(), FluidDT::VarType_Type_LOD_TENSOR,
                         "TensorRT engine only takes LoDTensor as input");
-      auto shape = var->GetShape();
-      // For the special batch_size placeholder -1, drop it and pass the real
-      // shape of data.
-      // TODO(Superjomn) fix this with batch broadcast, or it can't handle
-      // variational batch size.
-      if (shape[0] == -1) {
-        shape[0] = FLAGS_tensorrt_engine_batch_size;
-      }
+
       engine->DeclareInput(
           input, FluidDataType2TRT(
                      var->Proto()->type().lod_tensor().tensor().data_type()),
-          Vec2TRT_Dims(shape));
+          Vec2TRT_Dims(t_shape));
     }
-
     inference::Singleton<inference::tensorrt::OpConverter>::Global()
-        .ConvertBlock(block_desc, parameters, context.scope(), engine);
+        .ConvertBlock(block_desc, param_names_, scope, engine);
 
     // Add outputs
-    for (auto& output : output_maps) {
-      if (!engine->HasDeclared(output)) {
-        engine->DeclareOutput(output);
-      }
+    for (auto &output : output_maps) {
+      engine->DeclareOutput(output);
     }
-
     engine->FreezeNetwork();
   }
 };

paddle/fluid/operators/tensorrt/tensorrt_engine_op.h

@@ -24,8 +24,7 @@ limitations under the License. */
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
 #include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
 
-USE_CUDA_ONLY_OP(tensorrt_engine);
-
+USE_NO_KERNEL_OP(tensorrt_engine);
 namespace paddle {
 namespace operators {