1. we delelte mul op, 2.modify fc and action op 3. modify the test inferface

Author: NHZlX

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

@@ -1,14 +1,12 @@
 # Add TRT tests
 nv_library(tensorrt_converter
-  SRCS mul_op.cc conv2d_op.cc fc_op.cc
+  SRCS conv2d_op.cc fc_op.cc
   DEPS tensorrt_engine mul_op)
 
 nv_test(test_op_converter SRCS test_op_converter.cc DEPS
   ${FLUID_CORE_MODULES} tensorrt_engine tensorrt_converter)
 
 nv_test(test_io_converter SRCS test_io_converter.cc io_converter.cc DEPS dynload_cuda dynamic_loader lod_tensor)
-nv_test(test_trt_mul_op SRCS test_mul_op.cc mul_op.cc
-        DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
 nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
         DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
 nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc

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

@@ -32,13 +32,13 @@ void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides,
   for (int h = 0; h < shape.h(); ++h) {
     for (int w = 0; w < shape.w(); ++w) {
       odata[h * ostrides.h() + w * ostrides.w()] =
-          idata[h * ostrides.h() + w * ostrides.w()];
+          idata[h * istrides.h() + w * istrides.w()];
     }
   }
 }
-
+// indata c * k
 // Reorder the data layout from CK to KC.
-void ReorderCKtoKC(TensorRTEngine::Weight& iweights,
+void ReorderCKtoKC(const TensorRTEngine::Weight& iweights,
                    TensorRTEngine::Weight* oweights) {
   int c = iweights.dims[0];
   int k = iweights.dims[1];
@@ -79,9 +79,8 @@ class FcOpConverter : public OpConverter {
 
     framework::LoDTensor tmp;
     tmp.Resize(Y_t->dims());
-    memcpy(tmp.mutable_data<float>(platform::CPUPlace()), Y_t->data<float>(),
-           Y_t->dims()[0] * Y_t->dims()[1]);
-
+    memcpy(tmp.mutable_data<float>(platform::CPUPlace()), weight_data,
+           Y_t->dims()[0] * Y_t->dims()[1] * sizeof(float));
     TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT,
                                   static_cast<void*>(weight_data),
                                   Y_t->memory_size() / sizeof(float)};
@@ -93,7 +92,7 @@ class FcOpConverter : public OpConverter {
 
     // The data layout of TRT FC layer's weight is different from fluid's FC,
     // need to reorder the elements.
-    ReorderCKtoKC(tmp_weight, &weight);
+    ReorderCKtoKC(weight, &tmp_weight);
 
     // Currently, the framework can only handle one fluid op -> one TRT layer,
     // but fc fuses `mul` and `bias` (2 fluid ops), so here is a trick, just
@@ -103,7 +102,7 @@ class FcOpConverter : public OpConverter {
 
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected,
                                        *const_cast<nvinfer1::ITensor*>(X),
-                                       n_output, weight.get(), bias.get());
+                                       n_output, tmp_weight.get(), bias.get());
 
     auto output_name = op_desc.Output("Out").front();
     engine_->SetITensor(output_name, layer->getOutput(0));
@@ -117,5 +116,5 @@ class FcOpConverter : public OpConverter {
 }  // namespace inference
 }  // namespace paddle
 
-REGISTER_TRT_OP_CONVERTER(fc, FcOpConverter);
+REGISTER_TRT_OP_CONVERTER(mul, FcOpConverter);
 USE_OP(mul);

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

@@ -23,7 +23,7 @@ namespace tensorrt {
 TEST(ReluOpConverter, main) {
   framework::Scope scope;
   std::unordered_set<std::string> parameters;
-  TRTConvertValidation validator(10, parameters, scope, 1000);
+  TRTConvertValidation validator(1, parameters, scope, 1000);
   validator.DeclInputVar("relu-X", nvinfer1::Dims2(10, 6));
   validator.DeclOutputVar("relu-Out", nvinfer1::Dims2(10, 6));
 
@@ -37,7 +37,7 @@ TEST(ReluOpConverter, main) {
   validator.SetOp(*desc.Proto());
   LOG(INFO) << "execute";
 
-  validator.Execute(10);
+  validator.Execute(1);
 }
 
 }  // namespace tensorrt

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

@@ -23,11 +23,12 @@ namespace tensorrt {
 TEST(fc_op, test) {
   std::unordered_set<std::string> parameters({"mul-Y"});
   framework::Scope scope;
-  TRTConvertValidation validator(20, parameters, scope, 1000);
+  TRTConvertValidation validator(1, parameters, scope, 1000);
 
-  validator.DeclInputVar("mul-X", nvinfer1::Dims4(8, 3, 1, 1));
-  validator.DeclParamVar("mul-Y", nvinfer1::Dims2(3, 2));
-  validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(8, 2));
+  validator.DeclInputVar("mul-X", nvinfer1::Dims4(1, 10, 1, 1));
+  validator.DeclParamVar("mul-Y", nvinfer1::Dims2(10, 2));
+  // validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
+  validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(1, 2));
 
   // Prepare Op description
   framework::OpDesc desc;
@@ -38,7 +39,7 @@ TEST(fc_op, test) {
 
   validator.SetOp(*desc.Proto());
 
-  validator.Execute(10);
+  validator.Execute(1);
 }
 
 }  // namespace tensorrt

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

@@ -39,7 +39,7 @@ namespace tensorrt {
 float random(float low, float high) {
   static std::random_device rd;
   static std::mt19937 mt(rd());
-  std::uniform_real_distribution<double> dist(1.0, 10.0);
+  std::uniform_real_distribution<double> dist(low, high);
   return dist(mt);
 }
 
@@ -49,6 +49,7 @@ void RandomizeTensor(framework::LoDTensor* tensor, const platform::Place& place,
   size_t num_elements = analysis::AccuDims(dims, dims.size());
   PADDLE_ENFORCE_GT(num_elements, 0);
   auto* data = tensor->mutable_data<float>(place);
+
   for (size_t i = 0; i < num_elements; i++) {
     *(data + i) = random(0., 1.);
   }
@@ -68,7 +69,7 @@ class TRTConvertValidation {
                        int workspace_size = 1 << 10)
       : parameters_(parameters), scope_(scope) {
     // create engine.
-    engine_.reset(new TensorRTEngine(10, 1 << 10, &stream_));
+    engine_.reset(new TensorRTEngine(batch_size, workspace_size, &stream_));
     engine_->InitNetwork();
 
     PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
@@ -142,8 +143,7 @@ class TRTConvertValidation {
     for (const auto& output : op_desc_->OutputArgumentNames()) {
       std::vector<float> fluid_out;
       std::vector<float> trt_out(output_space_size);
-      engine_->GetOutputInCPU(output, &trt_out[0],
-                              output_space_size * sizeof(float));
+      engine_->GetOutputInCPU(output, &trt_out[0]);
       cudaStreamSynchronize(*engine_->stream());
 
       auto* var = scope_.FindVar(output);