diff --git a/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc b/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc index 21fd8d2df49698d7fa38d906f7921f092ca916a3..c6b7c05f784b7c44fe30dd69529fe48405538ab6 100644 --- a/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc +++ b/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc @@ -114,7 +114,7 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node, // it is either an OP's input or an OP's output. auto &subgraph_nodes = *Agent(node).subgraph(); - for (size_t index = 0; index < block_desc.OpSize(); index++) { + for (size_t index = 0; index < block_desc.OpSize(); ++index) { framework::proto::OpDesc *op = block_desc.Op(index)->Proto(); auto correspond_node = subgraph_nodes[index]; PADDLE_ENFORCE_EQ(correspond_node->Name(), op->type()); diff --git a/paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc b/paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc index 38e9b1c5e7c19c89f94ce55324507b02da0c5160..267737e95cb51f9a2d72b70b171be779d6f52c8d 100644 --- a/paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc +++ b/paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc @@ -45,7 +45,8 @@ void IrAnalysisComposePass::InitTensorRTAttrs(Argument *argument) { std::unordered_set teller_set( {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid", "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad", - "elementwise_add", "dropout", "split", "prelu", "conv2d_transpose"}); + "elementwise_add", "elementwise_mul", "dropout", "split", "prelu", + "conv2d_transpose"}); if (!node->IsOp()) return false; if (teller_set.count(node->Op()->Type())) { diff --git a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt index 85ad5ffe7875cdc205b5bdff28cc90ef01b236a4..8dd6e8453f971b980a3dcb95c10734537e8333dd 100644 --- a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt +++ b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt @@ -1,9 +1,9 @@ # Add TRT tests nv_library(tensorrt_converter - SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc -batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc -pad_op.cc split_op.cc prelu_op.cc - DEPS tensorrt_engine tensorrt_plugin operator scope framework_proto op_registry) + SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc + batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc + pad_op.cc split_op.cc prelu_op.cc + DEPS tensorrt_engine tensorrt_plugin operator scope framework_proto op_registry) nv_test(test_op_converter SRCS test_op_converter.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_converter) @@ -20,7 +20,8 @@ nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op SERIAL) nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc - DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine elementwise_add_op SERIAL) + DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin + elementwise_add_op elementwise_mul_op SERIAL) nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine softmax_op SERIAL) nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc @@ -33,7 +34,7 @@ nv_test(test_trt_pad_op SRCS test_pad_op.cc pad_op.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pad_op SERIAL) nv_test(test_trt_split_op SRCS test_split_op.cc split_op.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin - split_op concat_op SERIAL) + split_op concat_op SERIAL) nv_test(test_trt_prelu_op SRCS test_prelu_op.cc prelu_op.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin prelu_op SERIAL) diff --git a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc index 1af091fabd2aea03a85b2d19fd556b18cdd65e3b..6975086193d991dc9f53b2d9d988f960c8ad118d 100644 --- a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc @@ -4,7 +4,7 @@ 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 + 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, @@ -13,11 +13,25 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" +#include "paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h" namespace paddle { namespace inference { namespace tensorrt { +static bool CheckDims(const nvinfer1::Dims& dims_x, + const nvinfer1::Dims& dims_y) { + if (dims_x.nbDims != dims_y.nbDims) { + return false; + } + for (int i = 0; i < dims_x.nbDims; i++) { + if (dims_x.d[i] != dims_y.d[i]) { + return false; + } + } + return true; +} + class ElementwiseWeightOpConverter : public OpConverter { public: ElementwiseWeightOpConverter() {} @@ -26,7 +40,7 @@ class ElementwiseWeightOpConverter : public OpConverter { // Here the two nullptr looks strange, that's because the // framework::OpDesc's constructor is strange. framework::OpDesc op_desc(op, nullptr); - VLOG(3) << "convert a fluid elementwise op to tensorrt IScaleLayer"; + VLOG(3) << "Convert a fluid elementwise op to TensorRT IScaleLayer"; PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1); PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); // Y is a weight @@ -106,10 +120,12 @@ class ElementwiseTensorOpConverter : public OpConverter { ElementwiseTensorOpConverter() {} void operator()(const framework::proto::OpDesc& op, const framework::Scope& scope, bool test_mode) override { + auto op_pair = ops.find(op_type_); + PADDLE_ENFORCE(op_pair != ops.end(), "Wrong elementwise op type!"); + // Here the two nullptr looks strange, that's because the // framework::OpDesc's constructor is strange. framework::OpDesc op_desc(op, nullptr); - VLOG(3) << "convert a fluid elementwise op to tensorrt IScaleLayer"; PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1); PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1); // Y is a weight @@ -120,29 +136,35 @@ class ElementwiseTensorOpConverter : public OpConverter { nvinfer1::Dims dims_x = X->getDimensions(); nvinfer1::Dims dims_y = Y->getDimensions(); - // The two input tensor should have the same dims - PADDLE_ENFORCE(dims_x.nbDims >= 3); - if (dims_x.nbDims == dims_y.nbDims) { - for (int i = 0; i < dims_x.nbDims; i++) { - if (dims_x.d[i] != dims_y.d[i]) - PADDLE_THROW("TensorRT unsupported tensor shape for Elementwise op!"); - } - } else { - PADDLE_THROW("TensorRT unsupported tensor shape for Elementwise op!"); - } + int axis = boost::get(op_desc.GetAttr("axis")); + auto output_name = op_desc.Output("Out")[0]; + if (CheckDims(dims_x, dims_y)) { + // The two input tensor should have the same dims + VLOG(3) << "Convert a fluid elementwise op to TensorRT IElementWiseLayer"; - auto op_pair = ops.find(op_type_); - if (op_pair == ops.end()) { - PADDLE_THROW("Wrong elementwise op type!"); - } - nvinfer1::IElementWiseLayer* layer = TRT_ENGINE_ADD_LAYER( - engine_, ElementWise, *const_cast(X), - *const_cast(Y), op_pair->second); + nvinfer1::IElementWiseLayer* layer = TRT_ENGINE_ADD_LAYER( + engine_, ElementWise, *const_cast(X), + *const_cast(Y), op_pair->second); - auto output_name = op_desc.Output("Out")[0]; - layer->setName(("elementwise (Output: " + output_name + ")").c_str()); - layer->getOutput(0)->setName(output_name.c_str()); - engine_->SetITensor(output_name, layer->getOutput(0)); + layer->setName(("elementwise (Output: " + output_name + ")").c_str()); + layer->getOutput(0)->setName(output_name.c_str()); + engine_->SetITensor(output_name, layer->getOutput(0)); + } else { + VLOG(3) << "Convert a fluid elementwise op to TensorRT " + "ElementWisePluginLayer"; + + plugin::ElementWisePlugin* plugin = + new plugin::ElementWisePlugin(op_pair->second, dims_x, dims_y, axis); + plugin->AddInput(X); + plugin->AddInput(Y); + nvinfer1::IPluginLayer* layer = engine_->AddPlugin( + const_cast(plugin->GetInputs().data()), 2, + reinterpret_cast(plugin)); + + layer->setName(("elementwise (Output: " + output_name + ")").c_str()); + layer->getOutput(0)->setName(output_name.c_str()); + engine_->SetITensor(output_name, layer->getOutput(0)); + } if (test_mode) { // the test framework can not determine which is the // output, so place the declaration inside. engine_->DeclareOutput(output_name); diff --git a/paddle/fluid/inference/tensorrt/convert/op_converter.h b/paddle/fluid/inference/tensorrt/convert/op_converter.h index d309d94c560f2b484fac6b6cd40cc2704d641069..d61d635ed707bc455d495f2420925a3585234b5c 100644 --- a/paddle/fluid/inference/tensorrt/convert/op_converter.h +++ b/paddle/fluid/inference/tensorrt/convert/op_converter.h @@ -61,7 +61,7 @@ class OpConverter { // TODO(xingzhaolong): all mul, sub, div // static std::unordered_set add_weight_op_set {"add", "mul", // "sub", "div"}; - static std::unordered_set add_weight_op_set{"add"}; + static std::unordered_set add_weight_op_set{"add", "mul"}; PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1UL); int op_type_len = op_desc.Type().size(); std::string op_type = op_desc.Type().substr(op_type_len - 3, op_type_len); diff --git a/paddle/fluid/inference/tensorrt/convert/prelu_op.cc b/paddle/fluid/inference/tensorrt/convert/prelu_op.cc index 337885e6baa578d1f733e40f09f0586eba393333..dbdff85ddebc85bc51938a204a48affe485b8240 100644 --- a/paddle/fluid/inference/tensorrt/convert/prelu_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/prelu_op.cc @@ -54,7 +54,7 @@ class PReluOpConverter : public OpConverter { TensorRTEngine::Weight alpha_rt(nvinfer1::DataType::kFLOAT, static_cast(alpha_data), alpha_tensor_device->numel()); - PReluPlugin* plugin = new PReluPlugin(alpha_rt, mode); + plugin::PReluPlugin* plugin = new plugin::PReluPlugin(alpha_rt, mode); nvinfer1::IPluginLayer* layer = engine_->AddPlugin(&input, input_num, plugin); // keep alpha tensor to avoid release it's memory diff --git a/paddle/fluid/inference/tensorrt/convert/split_op.cc b/paddle/fluid/inference/tensorrt/convert/split_op.cc index 159854ab593fbbfa1e08a9ca148f1b3a636d668c..6620c76318f99092236d4009037f2ce01b295164 100644 --- a/paddle/fluid/inference/tensorrt/convert/split_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/split_op.cc @@ -50,7 +50,7 @@ class SplitOpConverter : public OpConverter { PADDLE_ENFORCE(output_lengths.size() == output_num); // - SplitPlugin* plugin = new SplitPlugin(axis, output_lengths); + plugin::SplitPlugin* plugin = new plugin::SplitPlugin(axis, output_lengths); nvinfer1::IPluginLayer* layer = engine_->AddPlugin(&input, input_num, plugin); diff --git a/paddle/fluid/inference/tensorrt/convert/test_elementwise_op.cc b/paddle/fluid/inference/tensorrt/convert/test_elementwise_op.cc index 7537d02a35b66a41c158cd8eb1b1e5d4107e7d84..cc967464a5f29151a061e99cda6870f9f370ec1b 100644 --- a/paddle/fluid/inference/tensorrt/convert/test_elementwise_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/test_elementwise_op.cc @@ -20,13 +20,12 @@ namespace paddle { namespace inference { namespace tensorrt { -TEST(elementwise_op, add_weight_test) { +TEST(elementwise_op, add_weight) { std::unordered_set parameters({"elementwise_add-Y"}); framework::Scope scope; TRTConvertValidation validator(10, parameters, scope, 1 << 15); validator.DeclInputVar("elementwise_add-X", nvinfer1::DimsCHW(10, 3, 3)); validator.DeclParamVar("elementwise_add-Y", nvinfer1::Dims3(10, 1, 1)); - // validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2)); validator.DeclOutputVar("elementwise_add-Out", nvinfer1::DimsCHW(10, 3, 3)); // Prepare Op description @@ -44,30 +43,65 @@ TEST(elementwise_op, add_weight_test) { validator.Execute(8); } -TEST(elementwise_op, add_tensor_test) { - std::unordered_set parameters; - framework::Scope scope; - TRTConvertValidation validator(8, parameters, scope, 1 << 15); - validator.DeclInputVar("elementwise_add-X", nvinfer1::DimsCHW(10, 3, 3)); - validator.DeclInputVar("elementwise_add-Y", nvinfer1::Dims3(10, 3, 3)); - // validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2)); - validator.DeclOutputVar("elementwise_add-Out", nvinfer1::DimsCHW(10, 3, 3)); - - // Prepare Op description - framework::OpDesc desc; - desc.SetType("elementwise_add"); - desc.SetInput("X", {"elementwise_add-X"}); - desc.SetInput("Y", {"elementwise_add-Y"}); - desc.SetOutput("Out", {"elementwise_add-Out"}); - - // the defalut axis of elementwise op is -1 - - validator.SetOp(*desc.Proto()); +TEST(elementwise_op, native) { + for (std::string type : {"add", "mul"}) { + int batch_size = 8; + std::unordered_set parameters; + framework::Scope scope; + TRTConvertValidation validator(batch_size, parameters, scope, 1 << 15); + validator.DeclInputVar("elementwise_" + type + "-X", + nvinfer1::DimsCHW(10, 3, 3)); + validator.DeclInputVar("elementwise_" + type + "-Y", + nvinfer1::Dims3(10, 3, 3)); + validator.DeclOutputVar("elementwise_" + type + "-Out", + nvinfer1::DimsCHW(10, 3, 3)); + + // Prepare Op description + framework::OpDesc desc; + desc.SetType("elementwise_" + type); + desc.SetInput("X", {"elementwise_" + type + "-X"}); + desc.SetInput("Y", {"elementwise_" + type + "-Y"}); + desc.SetOutput("Out", {"elementwise_" + type + "-Out"}); + + int axis = -1; + desc.SetAttr("axis", axis); + + validator.SetOp(*desc.Proto()); + validator.Execute(batch_size); + } +} - validator.Execute(8); +TEST(elementwise_op, plugin) { + for (std::string type : {"add", "mul"}) { + int batch_size = 8; + std::unordered_set parameters; + framework::Scope scope; + TRTConvertValidation validator(batch_size, parameters, scope, 1 << 15); + validator.DeclInputVar("elementwise_" + type + "-X", + nvinfer1::DimsCHW(10, 3, 3)); + validator.DeclInputVar("elementwise_" + type + "-Y", + nvinfer1::Dims3(10, 1, 1)); + validator.DeclOutputVar("elementwise_" + type + "-Out", + nvinfer1::DimsCHW(10, 3, 3)); + + // Prepare Op description + framework::OpDesc desc; + desc.SetType("elementwise_" + type); + desc.SetInput("X", {"elementwise_" + type + "-X"}); + desc.SetInput("Y", {"elementwise_" + type + "-Y"}); + desc.SetOutput("Out", {"elementwise_" + type + "-Out"}); + + int axis = -1; + desc.SetAttr("axis", axis); + + validator.SetOp(*desc.Proto()); + validator.Execute(batch_size); + } } } // namespace tensorrt } // namespace inference } // namespace paddle + USE_OP(elementwise_add); +USE_OP(elementwise_mul); diff --git a/paddle/fluid/inference/tensorrt/convert/test_mul_op.cc b/paddle/fluid/inference/tensorrt/convert/test_mul_op.cc index 3d34cd7d5d0deca4d83a3f5b5ed0fb396c6acd56..282f53559aa75b2c7c252450e392e1996f9b1d81 100644 --- a/paddle/fluid/inference/tensorrt/convert/test_mul_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/test_mul_op.cc @@ -1,16 +1,16 @@ /* 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 +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 + 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. */ +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 #include "paddle/fluid/framework/op_registry.h" diff --git a/paddle/fluid/inference/tensorrt/convert/ut_helper.h b/paddle/fluid/inference/tensorrt/convert/ut_helper.h index 0a6f171fc40a838fd81d6a51aca0430d5526f188..f313beb73bb0d21cab1d62859a46fcc76a373548 100644 --- a/paddle/fluid/inference/tensorrt/convert/ut_helper.h +++ b/paddle/fluid/inference/tensorrt/convert/ut_helper.h @@ -4,7 +4,7 @@ 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 + 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, diff --git a/paddle/fluid/inference/tensorrt/engine.cc b/paddle/fluid/inference/tensorrt/engine.cc index 208bd12b83aa19f01de9bcf4ada630c87defad5d..f739752cbc44805cb0fb3246385609cf16ba744a 100644 --- a/paddle/fluid/inference/tensorrt/engine.cc +++ b/paddle/fluid/inference/tensorrt/engine.cc @@ -257,9 +257,10 @@ void TensorRTEngine::freshDeviceId() { } nvinfer1::IPluginLayer *TensorRTEngine::AddPlugin( - nvinfer1::ITensor *const *inputs, int nbInputs, PluginTensorRT *plugin) { + nvinfer1::ITensor *const *inputs, int num_inputs, + plugin::PluginTensorRT *plugin) { owned_plugin_.emplace_back(plugin); - return infer_network_.get()->addPluginExt(inputs, nbInputs, *plugin); + return infer_network_.get()->addPluginExt(inputs, num_inputs, *plugin); } } // namespace tensorrt diff --git a/paddle/fluid/inference/tensorrt/engine.h b/paddle/fluid/inference/tensorrt/engine.h index 99420f19ba17d0bebf6dde3800d57c912256dc6b..f5b2c28ba9e6fefc1d6c14640d696c3bf3ac8249 100644 --- a/paddle/fluid/inference/tensorrt/engine.h +++ b/paddle/fluid/inference/tensorrt/engine.h @@ -128,7 +128,7 @@ class TensorRTEngine : public EngineBase { int GetRuntimeBatch(); int GetDevice() { return device_; } nvinfer1::IPluginLayer* AddPlugin(nvinfer1::ITensor* const* inputs, - int nbInputs, PluginTensorRT*); + int num_inputs, plugin::PluginTensorRT*); // A pointer to CPU memory is needed of the TRT weight. // Before TRT runs, fluid loads weight into GPU storage. @@ -171,7 +171,7 @@ class TensorRTEngine : public EngineBase { // The specific GPU id that the TensorRTEngine bounded to. int device_; - std::vector> owned_plugin_; + std::vector> owned_plugin_; // TensorRT related internal members template diff --git a/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt b/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt index b6811f9183aaa2313157bc5a8b2de1b7e447480f..40902694995f487343e3cfc727ad95daafa6b415 100644 --- a/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt +++ b/paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt @@ -1 +1,3 @@ -nv_library(tensorrt_plugin SRCS trt_plugin.cc split_op_plugin.cu prelu_op_plugin.cu DEPS enforce device_context) +nv_library(tensorrt_plugin + SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu prelu_op_plugin.cu + DEPS enforce device_context) diff --git a/paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.cu new file mode 100644 index 0000000000000000000000000000000000000000..9cd9026b7328083389b5af484bbb15c07b4908b0 --- /dev/null +++ b/paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.cu @@ -0,0 +1,138 @@ +/* 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 +#include "paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h" + +namespace paddle { +namespace inference { +namespace tensorrt { +namespace plugin { + +namespace details { + +template +struct Add { + __device__ T operator()(const T& a, const T& b) const { return a + b; } +}; + +template +struct Mul { + __device__ T operator()(const T& a, const T& b) const { return a * b; } +}; + +template +__global__ void ColumnWiseKernel(Operator op, const T* x, const T* y, T* out, + int batch_size, int num_rows, int num_cols) { + for (int batch_id = 0; batch_id < batch_size; ++batch_id) { + int row = blockIdx.x; + for (; row < num_rows; row += gridDim.x) { + T value_y = y[batch_id * num_rows + row]; + int col = threadIdx.x; + int offset = (batch_id * num_rows + row) * num_cols; + for (; col < num_cols; col += blockDim.x) { + T value_x = x[offset + col]; + out[offset + col] = op(value_x, value_y); + } + } + } +} + +template +static void ElementWise(Operator op, const T* x, const T* y, T* out, + int batch_size, int prev, int midd, int post, + cudaStream_t stream) { + const int kThreadsPerBlock = 1024; + const int kMaximumBlocks = 65535; + if (prev == 1) { + int num_threads = (post > kThreadsPerBlock) ? kThreadsPerBlock + : (((post + 31) >> 5) << 5); + int num_blocks = (midd < kMaximumBlocks) ? midd : kMaximumBlocks; + ColumnWiseKernel<<>>( + op, x, y, out, batch_size, midd, post); + } else if (post == 1) { + PADDLE_THROW("Not implemented."); + } else { + PADDLE_THROW("Not implemented."); + } +} + +} // namespace details + +nvinfer1::Dims ElementWisePlugin::getOutputDimensions( + int index, const nvinfer1::Dims* input_dims, int num_inputs) { + PADDLE_ENFORCE_EQ(index, 0); + PADDLE_ENFORCE_EQ(num_inputs, 2); + PADDLE_ENFORCE_NOT_NULL(input_dims); + return input_dims[0]; +} + +int ElementWisePlugin::initialize() { + PADDLE_ENFORCE_GT(dims_y_.nbDims, 0); + + axis_ = (axis_ == -1) ? dims_x_.nbDims - dims_y_.nbDims : axis_; + int trimed_nb_dims = dims_y_.nbDims; + for (; trimed_nb_dims > 0; --trimed_nb_dims) { + if (dims_y_.d[trimed_nb_dims - 1] != 1) { + break; + } + } + dims_y_.nbDims = trimed_nb_dims; + + PADDLE_ENFORCE_GE(dims_x_.nbDims, dims_y_.nbDims + axis_); + PADDLE_ENFORCE_LT(axis_, dims_x_.nbDims); + + prev_size_ = 1; + midd_size_ = 1; + post_size_ = 1; + for (int i = 0; i < axis_; ++i) { + prev_size_ *= dims_x_.d[i]; + } + + for (int i = 0; i < dims_y_.nbDims; ++i) { + PADDLE_ENFORCE_EQ(dims_x_.d[i + axis_], dims_y_.d[i], + "Broadcast dimension mismatch."); + midd_size_ *= dims_y_.d[i]; + } + + for (int i = axis_ + dims_y_.nbDims; i < dims_x_.nbDims; ++i) { + post_size_ *= dims_x_.d[i]; + } + return 0; +} + +int ElementWisePlugin::enqueue(int batch_size, const void* const* inputs, + void** outputs, void* workspace, + cudaStream_t stream) { + const float* x = reinterpret_cast(inputs[0]); + const float* y = reinterpret_cast(inputs[1]); + float* out = reinterpret_cast(outputs[0]); + + if (type_ == nvinfer1::ElementWiseOperation::kSUM) { + details::ElementWise(details::Add(), x, y, out, batch_size, + prev_size_, midd_size_, post_size_, stream); + } else if (type_ == nvinfer1::ElementWiseOperation::kPROD) { + details::ElementWise(details::Mul(), x, y, out, batch_size, + prev_size_, midd_size_, post_size_, stream); + } else { + PADDLE_THROW("Not implemented."); + } + + return cudaGetLastError() != cudaSuccess; +} + +} // namespace plugin +} // namespace tensorrt +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h b/paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h new file mode 100644 index 0000000000000000000000000000000000000000..9c461f7a5c44ebb9d4a755288c69abff55e2dea8 --- /dev/null +++ b/paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h @@ -0,0 +1,87 @@ +/* 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 +#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h" + +namespace paddle { +namespace inference { +namespace tensorrt { +namespace plugin { + +class ElementWisePlugin : public PluginTensorRT { + public: + ElementWisePlugin(nvinfer1::ElementWiseOperation type, + nvinfer1::Dims const &dims_x, nvinfer1::Dims const &dims_y, + int axis) + : type_(type), + dims_x_(dims_x), + dims_y_(dims_y), + axis_(axis), + prev_size_(1), + midd_size_(1), + post_size_(1) {} + + ElementWisePlugin(void const *serial_data, size_t serial_length) { + deserializeBase(serial_data, serial_length); + DeserializeValue(&serial_data, &serial_length, &axis_); + DeserializeValue(&serial_data, &serial_length, &dims_x_); + DeserializeValue(&serial_data, &serial_length, &dims_y_); + } + + ElementWisePlugin *clone() const override { + // return new ElementWisePlugin(dims_x_, dims_y_, axis_); + return nullptr; + } + + const char *getPluginType() const override { return "elementwise"; } + + nvinfer1::Dims getOutputDimensions(int index, + const nvinfer1::Dims *input_dims, + int num_inputs) override; + + int initialize() override; + + // execute the layer + int enqueue(int batch_size, const void *const *inputs, void **outputs, + void *workspace, cudaStream_t stream); + + protected: + size_t getSerializationSize() override { + return SerializedSize(axis_) + SerializedSize(dims_x_) + + SerializedSize(dims_y_) + getBaseSerializationSize(); + } + + void serialize(void *buffer) override { + serializeBase(buffer); + SerializeValue(&buffer, axis_); + SerializeValue(&buffer, dims_x_); + SerializeValue(&buffer, dims_y_); + } + + nvinfer1::ElementWiseOperation type_; + nvinfer1::Dims dims_x_; + nvinfer1::Dims dims_y_; + int axis_; + int prev_size_; + int midd_size_; + int post_size_; +}; + +} // namespace plugin +} // namespace tensorrt +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.cu index 0f1ca112955afeecbf82b26324b77aa8def2ad9f..e8f4254402a5d8a5e6c5a2384bf9fbe48341956e 100644 --- a/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.cu +++ b/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.cu @@ -20,6 +20,7 @@ namespace paddle { namespace inference { namespace tensorrt { +namespace plugin { static const int CUDA_NUM_THREADS = 1024; static const int CUDA_MAX_NUM_BLOCKS = 65535; @@ -126,6 +127,7 @@ int PReluPlugin::enqueue(int batchSize, const void *const *inputs, return cudaGetLastError() != cudaSuccess; } +} // namespace plugin } // namespace tensorrt } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.h b/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.h index aa0f865c89be2dc20d3a30314ec02fd0b425b2fe..0db56a310b072e64425f70ac23267ec72353e54b 100644 --- a/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.h +++ b/paddle/fluid/inference/tensorrt/plugin/prelu_op_plugin.h @@ -21,6 +21,7 @@ namespace paddle { namespace inference { namespace tensorrt { +namespace plugin { class PReluPlugin : public PluginTensorRT { TensorRTEngine::Weight alpha_; @@ -63,6 +64,7 @@ class PReluPlugin : public PluginTensorRT { void *workspace, cudaStream_t stream) override; }; +} // namespace plugin } // namespace tensorrt } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/serialize.h b/paddle/fluid/inference/tensorrt/plugin/serialize.h index 50c0b17d78327e22b0aa81fdac6958e80a30dfe8..ce859f16fc87479adf090687121ff06951b5684c 100644 --- a/paddle/fluid/inference/tensorrt/plugin/serialize.h +++ b/paddle/fluid/inference/tensorrt/plugin/serialize.h @@ -14,10 +14,15 @@ #pragma once -#include #include #include #include +#include "paddle/fluid/platform/enforce.h" + +namespace paddle { +namespace inference { +namespace tensorrt { +namespace plugin { template inline void SerializeValue(void** buffer, T const& value); @@ -26,7 +31,7 @@ template inline void DeserializeValue(void const** buffer, size_t* buffer_size, T* value); -namespace { +namespace details { template struct Serializer {}; @@ -36,10 +41,12 @@ struct Serializer::value || std::is_enum::value || std::is_pod::value>::type> { static size_t SerializedSize(T const& value) { return sizeof(T); } + static void Serialize(void** buffer, T const& value) { std::memcpy(*buffer, &value, sizeof(T)); reinterpret_cast(*buffer) += sizeof(T); } + static void Deserialize(void const** buffer, size_t* buffer_size, T* value) { assert(*buffer_size >= sizeof(T)); std::memcpy(value, *buffer, sizeof(T)); @@ -51,10 +58,12 @@ struct Serializer::value || template <> struct Serializer { static size_t SerializedSize(const char* value) { return strlen(value) + 1; } + static void Serialize(void** buffer, const char* value) { - std::strcpy(static_cast(*buffer), value); + std::strcpy(static_cast(*buffer), value); // NOLINT reinterpret_cast(*buffer) += strlen(value) + 1; } + static void Deserialize(void const** buffer, size_t* buffer_size, const char** value) { *value = static_cast(*buffer); @@ -73,39 +82,46 @@ struct Serializer, static size_t SerializedSize(std::vector const& value) { return sizeof(value.size()) + value.size() * sizeof(T); } + static void Serialize(void** buffer, std::vector const& value) { SerializeValue(buffer, value.size()); size_t nbyte = value.size() * sizeof(T); std::memcpy(*buffer, value.data(), nbyte); reinterpret_cast(*buffer) += nbyte; } + static void Deserialize(void const** buffer, size_t* buffer_size, std::vector* value) { size_t size; DeserializeValue(buffer, buffer_size, &size); value->resize(size); size_t nbyte = value->size() * sizeof(T); - assert(*buffer_size >= nbyte); + PADDLE_ENFORCE_GE(*buffer_size, nbyte); std::memcpy(value->data(), *buffer, nbyte); reinterpret_cast(*buffer) += nbyte; *buffer_size -= nbyte; } }; -} // namespace +} // namespace details template inline size_t SerializedSize(T const& value) { - return Serializer::SerializedSize(value); + return details::Serializer::SerializedSize(value); } template inline void SerializeValue(void** buffer, T const& value) { - return Serializer::Serialize(buffer, value); + return details::Serializer::Serialize(buffer, value); } template inline void DeserializeValue(void const** buffer, size_t* buffer_size, T* value) { - return Serializer::Deserialize(buffer, buffer_size, value); + return details::Serializer::Deserialize(buffer, buffer_size, value); } + +} // namespace plugin +} // namespace tensorrt +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.cu index bd6a44dcc14d50cddb879763a93abf4297494ec9..4adea2db1ee80fb20adba3cf4141a6485a1065a0 100644 --- a/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.cu +++ b/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.cu @@ -12,26 +12,26 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include -#include #include "paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h" namespace paddle { namespace inference { namespace tensorrt { +namespace plugin { -nvinfer1::Dims SplitPlugin::getOutputDimensions(int index, - const nvinfer1::Dims* inputDims, - int nbInputs) { - assert(nbInputs == 1); - assert(index < this->getNbOutputs()); - nvinfer1::Dims const& input_dims = inputDims[0]; - nvinfer1::Dims output_dims = input_dims; +nvinfer1::Dims SplitPlugin::getOutputDimensions( + int index, const nvinfer1::Dims* input_dims, int num_inputs) { + PADDLE_ENFORCE_EQ(num_inputs, 1); + PADDLE_ENFORCE_LT(index, this->getNbOutputs()); + + nvinfer1::Dims output_dims = input_dims[0]; output_dims.d[axis_] = output_length_.at(index); return output_dims; } int SplitPlugin::initialize() { + PADDLE_ENFORCE_LE(axis_, nvinfer1::Dims::MAX_DIMS); + std::vector segment_offsets(1, 0); for (int i = 0; i < this->getNbOutputs(); ++i) { segment_offsets.push_back(segment_offsets.back() + output_length_[i]); @@ -76,6 +76,7 @@ int SplitPlugin::enqueue(int batchSize, const void* const* inputs, return cudaGetLastError() != cudaSuccess; } -} // tensorrt -} // inference -} // paddle +} // namespace plugin +} // namespace tensorrt +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h b/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h index 7281e40c331550de472df49c57b1d9a5226842d5..b5b6e69992b057a1478f61457b4ae6f5f1619b4d 100644 --- a/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h +++ b/paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h @@ -14,61 +14,58 @@ #pragma once +#include #include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h" namespace paddle { namespace inference { namespace tensorrt { +namespace plugin { class SplitPlugin : public PluginTensorRT { - int axis_; - std::vector output_length_; - int nx_, ny_, nz_; - std::vector segment_offsets_; + public: + SplitPlugin(int axis, std::vector const &output_lengths) + : axis_(axis), output_length_(output_lengths) {} + + SplitPlugin(void const *serial_data, size_t serial_length) { + deserializeBase(serial_data, serial_length); + DeserializeValue(&serial_data, &serial_length, &axis_); + DeserializeValue(&serial_data, &serial_length, &output_length_); + } + + SplitPlugin *clone() const override { + return new SplitPlugin(axis_, output_length_); + } + + const char *getPluginType() const override { return "split"; } + int getNbOutputs() const override { return output_length_.size(); } + nvinfer1::Dims getOutputDimensions(int index, + const nvinfer1::Dims *input_dims, + int num_inputs) override; + + int initialize() override; + int enqueue(int batchSize, const void *const *inputs, void **outputs, + void *workspace, cudaStream_t stream) override; protected: - virtual size_t getSerializationSize() override { + size_t getSerializationSize() override { return SerializedSize(axis_) + SerializedSize(output_length_) + getBaseSerializationSize(); } - // TRT will call this func when we need to serialize the configuration of - // tensorrt. - // It should not be called by users. - virtual void serialize(void *buffer) override { + void serialize(void *buffer) override { serializeBase(buffer); SerializeValue(&buffer, axis_); SerializeValue(&buffer, output_length_); } - public: - SplitPlugin(int axis, std::vector const &output_lengths) - : axis_(axis), output_length_(output_lengths) { - assert(axis <= nvinfer1::Dims::MAX_DIMS); - } - - // It was used for tensorrt deserialization. - // It should not be called by users. - SplitPlugin(void const *serialData, size_t serialLength) { - deserializeBase(serialData, serialLength); - DeserializeValue(&serialData, &serialLength, &axis_); - DeserializeValue(&serialData, &serialLength, &output_length_); - } - - SplitPlugin *clone() const override { - return new SplitPlugin(axis_, output_length_); - } - - virtual const char *getPluginType() const override { return "split"; } - virtual int getNbOutputs() const override { return output_length_.size(); } - virtual nvinfer1::Dims getOutputDimensions(int index, - const nvinfer1::Dims *inputs, - int nbInputDims) override; - virtual int initialize() override; - virtual int enqueue(int batchSize, const void *const *inputs, void **outputs, - void *workspace, cudaStream_t stream) override; + int axis_; + std::vector output_length_; + int nx_, ny_, nz_; + std::vector segment_offsets_; }; -} // tensorrt -} // inference -} // paddle +} // namespace plugin +} // namespace tensorrt +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/trt_plugin.cc b/paddle/fluid/inference/tensorrt/plugin/trt_plugin.cc index 08016d84b15bc750738f3183d8d61a5c90862288..b0f4cff3ac184beeed2ebd3a4b7531d570c87075 100644 --- a/paddle/fluid/inference/tensorrt/plugin/trt_plugin.cc +++ b/paddle/fluid/inference/tensorrt/plugin/trt_plugin.cc @@ -17,6 +17,7 @@ namespace paddle { namespace inference { namespace tensorrt { +namespace plugin { void PluginTensorRT::serializeBase(void*& buffer) { SerializeValue(&buffer, input_dims_); @@ -25,12 +26,12 @@ void PluginTensorRT::serializeBase(void*& buffer) { SerializeValue(&buffer, data_format_); } -void PluginTensorRT::deserializeBase(void const*& serialData, - size_t& serialLength) { - DeserializeValue(&serialData, &serialLength, &input_dims_); - DeserializeValue(&serialData, &serialLength, &max_batch_size_); - DeserializeValue(&serialData, &serialLength, &data_type_); - DeserializeValue(&serialData, &serialLength, &data_format_); +void PluginTensorRT::deserializeBase(void const*& serial_data, + size_t& serial_length) { + DeserializeValue(&serial_data, &serial_length, &input_dims_); + DeserializeValue(&serial_data, &serial_length, &max_batch_size_); + DeserializeValue(&serial_data, &serial_length, &data_type_); + DeserializeValue(&serial_data, &serial_length, &data_format_); } size_t PluginTensorRT::getBaseSerializationSize() { @@ -44,18 +45,17 @@ bool PluginTensorRT::supportsFormat(nvinfer1::DataType type, (format == nvinfer1::PluginFormat::kNCHW)); } -void PluginTensorRT::configureWithFormat(const nvinfer1::Dims* inputDims, - int nbInputs, - const nvinfer1::Dims* outputDims, - int nbOutputs, nvinfer1::DataType type, - nvinfer1::PluginFormat format, - int maxBatchSize) { +void PluginTensorRT::configureWithFormat( + const nvinfer1::Dims* input_dims, int num_inputs, + const nvinfer1::Dims* output_dims, int num_outputs, nvinfer1::DataType type, + nvinfer1::PluginFormat format, int max_batch_size) { data_type_ = type; data_format_ = format; - input_dims_.assign(inputDims, inputDims + nbInputs); - max_batch_size_ = maxBatchSize; + input_dims_.assign(input_dims, input_dims + num_inputs); + max_batch_size_ = max_batch_size; } +} // namespace plugin } // namespace tensorrt } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/tensorrt/plugin/trt_plugin.h b/paddle/fluid/inference/tensorrt/plugin/trt_plugin.h index 4d85e955a49b7dcccae158ea06b76419419797cf..86084829e150f8a39610319a8f2138f2b2fdec68 100644 --- a/paddle/fluid/inference/tensorrt/plugin/trt_plugin.h +++ b/paddle/fluid/inference/tensorrt/plugin/trt_plugin.h @@ -14,23 +14,30 @@ #pragma once -#include +#include #include -#include #include #include -#include "NvInfer.h" #include "paddle/fluid/inference/tensorrt/plugin/serialize.h" +#include "paddle/fluid/platform/enforce.h" +#include "paddle/fluid/platform/profiler.h" + +DECLARE_bool(profile); namespace paddle { namespace inference { namespace tensorrt { +namespace plugin { class PluginTensorRT : public nvinfer1::IPluginExt { public: PluginTensorRT() {} + // It was used for TensorRT deserialization. + // It should not be called by users. PluginTensorRT(const void* serialized_data, size_t length) {} + virtual ~PluginTensorRT() {} + nvinfer1::Dims const& getInputDims(int index) const { return input_dims_.at(index); } @@ -38,43 +45,66 @@ class PluginTensorRT : public nvinfer1::IPluginExt { nvinfer1::DataType getDataType() const { return data_type_; } nvinfer1::PluginFormat getDataFormat() const { return data_format_; } virtual const char* getPluginVersion() const { return "1"; } + + void AddInput(nvinfer1::ITensor* input) { inputs_.push_back(input); } + std::vector& GetInputs() { return inputs_; } + + virtual nvinfer1::IPluginExt* clone() const = 0; + virtual const char* getPluginType() const = 0; + + // Following functions are inherit from nvinfer1::IPluginExt + // Get the number of outputs from the layer + int getNbOutputs() const { return 1; } + // Get the dimension of an output tensor + virtual nvinfer1::Dims getOutputDimensions(int index, + const nvinfer1::Dims* input_dims, + int num_inputs) = 0; + // Find the workspace size required by the layer size_t getWorkspaceSize(int) const override { return 0; } + + // Initialize the layer for execution. + // This is called when the engine is created. + int initialize() override { return 0; } + // Shutdown the layer. This is called when the engine is destroyed void terminate() override {} - virtual ~PluginTensorRT() {} + // Execute the layer + virtual int enqueue(int batch_size, const void* const* inputs, void** outputs, + void* workspace, cudaStream_t stream) = 0; + + // Find the size of the serialization buffer required + virtual size_t getSerializationSize() = 0; + // Serialize the layer config to buffer. + // TensorRT will call this func to serialize the configuration of TensorRT + // engine. It should not be called by users. + virtual void serialize(void* buffer) = 0; + // Check format support. The default is FLOAT32 and NCHW. bool supportsFormat(nvinfer1::DataType type, nvinfer1::PluginFormat format) const override; - void configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs, - const nvinfer1::Dims* outputDims, int nbOutputs, + // Configure the layer + void configureWithFormat(const nvinfer1::Dims* input_dims, int num_inputs, + const nvinfer1::Dims* output_dims, int num_outputs, nvinfer1::DataType type, nvinfer1::PluginFormat format, - int maxBatchSize) override; - - // *NOTE* The following functions need to be overrided in the subclass. - virtual nvinfer1::IPluginExt* clone() const = 0; - virtual const char* getPluginType() const = 0; - // Initialize the layer for execution. This is called when the engine is - // created. - int initialize() override { return 0; } - // Serialize the layer config to buffer. - virtual void serialize(void* buffer) = 0; - virtual size_t getSerializationSize() = 0; - virtual int enqueue(int batchSize, const void* const* inputs, void** outputs, - void* workspace, cudaStream_t stream) = 0; + int max_batch_size) override; protected: // Deserialize input_dims, max_batch_size, data_type, data_format - void deserializeBase(void const*& serialData, size_t& serialLength); + void deserializeBase(void const*& serial_data, // NOLINT + size_t& serial_length); // NOLINT size_t getBaseSerializationSize(); // Serialize input_dims, max_batch_size, data_type, data_format - void serializeBase(void*& buffer); + void serializeBase(void*& buffer); // NOLINT std::vector input_dims_; size_t max_batch_size_; nvinfer1::DataType data_type_; nvinfer1::PluginFormat data_format_; + + std::vector inputs_; }; +} // namespace plugin } // namespace tensorrt } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/tests/api/tester_helper.h b/paddle/fluid/inference/tests/api/tester_helper.h index a4046914132cc713a707fc2a4d12087383d77fe5..e66ae2805766754d9d07877c31889dd421daf9f1 100644 --- a/paddle/fluid/inference/tests/api/tester_helper.h +++ b/paddle/fluid/inference/tests/api/tester_helper.h @@ -51,7 +51,7 @@ void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) { LOG(INFO) << *reinterpret_cast(config); return; } - LOG(INFO) << *config; + LOG(INFO) << *reinterpret_cast(config); } void CompareResult(const std::vector &outputs,