From 9ee698e6059e15488100e1b905100031cfb357e5 Mon Sep 17 00:00:00 2001 From: Yan Chunwei Date: Wed, 22 Aug 2018 13:09:03 +0800 Subject: [PATCH] enhance/ditu rnn with fc fuse (#12831) * make fc fuse work with ditu rnn * add ditu rnn data download to CMAKE --- paddle/fluid/framework/ir/graph_helper.cc | 2 +- .../fluid/inference/analysis/CMakeLists.txt | 45 ++- paddle/fluid/inference/analysis/analyzer.cc | 3 +- paddle/fluid/inference/analysis/analyzer.h | 3 +- .../inference/analysis/analyzer_tester.cc | 266 +++++++++++++++++- paddle/fluid/inference/api/CMakeLists.txt | 5 +- paddle/fluid/inference/api/api_impl.cc | 7 +- paddle/fluid/inference/api/helper.h | 110 ++++++++ .../inference/api/paddle_inference_api.h | 2 + paddle/fluid/operators/mul_op.cc | 6 +- 10 files changed, 423 insertions(+), 26 deletions(-) create mode 100644 paddle/fluid/inference/api/helper.h diff --git a/paddle/fluid/framework/ir/graph_helper.cc b/paddle/fluid/framework/ir/graph_helper.cc index b1c19e65351..dc81a2cac58 100644 --- a/paddle/fluid/framework/ir/graph_helper.cc +++ b/paddle/fluid/framework/ir/graph_helper.cc @@ -104,7 +104,7 @@ std::map> BuildOperationAdjList( for (auto &adj_n : var->inputs) { PADDLE_ENFORCE(adj_n->NodeType() == ir::Node::Type::kOperation); adj_list[n].insert(adj_n); - VLOG(3) << "adj " << adj_n->Name() << reinterpret_cast(adj_n) + VLOG(4) << "adj " << adj_n->Name() << reinterpret_cast(adj_n) << " -> " << n->Name() << reinterpret_cast(n) << " via " << var->Name() << reinterpret_cast(var); } diff --git a/paddle/fluid/inference/analysis/CMakeLists.txt b/paddle/fluid/inference/analysis/CMakeLists.txt index f1271ddb75f..4feaed2b0d9 100644 --- a/paddle/fluid/inference/analysis/CMakeLists.txt +++ b/paddle/fluid/inference/analysis/CMakeLists.txt @@ -22,7 +22,7 @@ function (inference_analysis_test TARGET) if(WITH_TESTING) set(options "") set(oneValueArgs "") - set(multiValueArgs SRCS) + set(multiValueArgs SRCS EXTRA_DEPS) cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) set(mem_opt "") @@ -31,22 +31,43 @@ function (inference_analysis_test TARGET) endif() cc_test(${TARGET} SRCS "${analysis_test_SRCS}" - DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass + DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass ${analysis_test_EXTRA_DEPS} ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt}) set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec) endif(WITH_TESTING) endfunction(inference_analysis_test) -cc_test(test_analyzer SRCS analyzer_tester.cc DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis - # ir - fc_fuse_pass - graph_viz_pass - infer_clean_graph_pass - graph_pattern_detecter - pass - ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model) -#set_tests_properties(test_analyzer PROPERTIES DEPENDS test_word2vec) -#inference_api_test(test_analyzer SRC analyzer_tester.cc ARGS test_word2vec) +set(DITU_RNN_MODEL_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fmodel.tar.gz") +set(DITU_RNN_DATA_URL "http://paddle-inference-dist.bj.bcebos.com/ditu_rnn_fluid%2Fdata.txt.tar.gz") +set(DITU_INSTALL_DIR "${THIRD_PARTY_PATH}/install/ditu_rnn" CACHE PATH "Ditu RNN model and data root." FORCE) +set(DITU_RNN_MODEL ${DITU_INSTALL_DIR}/model) +set(DITU_RNN_DATA ${DITU_INSTALL_DIR}/data.txt) + +function (inference_download_and_uncompress target url gz_filename) + message(STATUS "Download inference test stuff ${gz_filename} from ${url}") + execute_process(COMMAND bash -c "mkdir -p ${DITU_INSTALL_DIR}") + execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && wget -q ${url}") + execute_process(COMMAND bash -c "cd ${DITU_INSTALL_DIR} && tar xzf ${gz_filename}") + message(STATUS "finish downloading ${gz_filename}") +endfunction(inference_download_and_uncompress) + +if (NOT EXISTS ${DITU_INSTALL_DIR}) + inference_download_and_uncompress(ditu_rnn_model ${DITU_RNN_MODEL_URL} "ditu_rnn_fluid%2Fmodel.tar.gz") + inference_download_and_uncompress(ditu_rnn_data ${DITU_RNN_DATA_URL} "ditu_rnn_fluid%2Fdata.txt.tar.gz") +endif() + +inference_analysis_test(test_analyzer SRCS analyzer_tester.cc + EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis + # ir + fc_fuse_pass + graph_viz_pass + infer_clean_graph_pass + graph_pattern_detecter + infer_clean_graph_pass + pass + ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model + --infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model + --infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt) inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc) inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc) diff --git a/paddle/fluid/inference/analysis/analyzer.cc b/paddle/fluid/inference/analysis/analyzer.cc index fc8b1f68647..7d163646094 100644 --- a/paddle/fluid/inference/analysis/analyzer.cc +++ b/paddle/fluid/inference/analysis/analyzer.cc @@ -23,8 +23,6 @@ #include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h" #include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h" -namespace paddle { - DEFINE_bool(IA_enable_tensorrt_subgraph_engine, false, "Enable subgraph to TensorRT engine for acceleration"); @@ -35,6 +33,7 @@ DEFINE_string(IA_graphviz_log_root, "./", DEFINE_string(IA_output_storage_path, "", "optimized model output path"); +namespace paddle { namespace inference { namespace analysis { diff --git a/paddle/fluid/inference/analysis/analyzer.h b/paddle/fluid/inference/analysis/analyzer.h index a72875d36f4..2e107c82dd5 100644 --- a/paddle/fluid/inference/analysis/analyzer.h +++ b/paddle/fluid/inference/analysis/analyzer.h @@ -39,8 +39,6 @@ limitations under the License. */ #include "paddle/fluid/inference/analysis/pass.h" #include "paddle/fluid/inference/analysis/pass_manager.h" -namespace paddle { - // TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this // flag if not available. DECLARE_bool(IA_enable_tensorrt_subgraph_engine); @@ -48,6 +46,7 @@ DECLARE_string(IA_graphviz_log_root); DECLARE_string(IA_output_storage_path); DECLARE_bool(IA_enable_ir); +namespace paddle { namespace inference { namespace analysis { diff --git a/paddle/fluid/inference/analysis/analyzer_tester.cc b/paddle/fluid/inference/analysis/analyzer_tester.cc index 3be336dd5c3..52f5c4f5aea 100644 --- a/paddle/fluid/inference/analysis/analyzer_tester.cc +++ b/paddle/fluid/inference/analysis/analyzer_tester.cc @@ -13,11 +13,17 @@ // limitations under the License. #include "paddle/fluid/inference/analysis/analyzer.h" + #include +#include #include "paddle/fluid/framework/ir/pass.h" #include "paddle/fluid/inference/analysis/ut_helper.h" +#include "paddle/fluid/inference/api/helper.h" #include "paddle/fluid/inference/api/paddle_inference_api.h" +DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN"); +DEFINE_string(infer_ditu_rnn_data, "", "data path for ditu RNN"); + namespace paddle { namespace inference { namespace analysis { @@ -38,7 +44,7 @@ TEST(Analyzer, analysis_with_tensorrt) { analyser.Run(&argument); } -void TestWord2vecPrediction(const std::string& model_path) { +void TestWord2vecPrediction(const std::string &model_path) { NativeConfig config; config.model_dir = model_path; config.use_gpu = false; @@ -69,12 +75,245 @@ void TestWord2vecPrediction(const std::string& model_path) { // The outputs' buffers are in CPU memory. for (size_t i = 0; i < std::min(5UL, num_elements); i++) { LOG(INFO) << "data: " - << static_cast(outputs.front().data.data())[i]; - PADDLE_ENFORCE(static_cast(outputs.front().data.data())[i], + << static_cast(outputs.front().data.data())[i]; + PADDLE_ENFORCE(static_cast(outputs.front().data.data())[i], result[i]); } } +namespace { + +struct DataRecord { + std::vector>> link_step_data_all; + std::vector> week_data_all, minute_data_all; + std::vector lod1, lod2, lod3; + std::vector> rnn_link_data, rnn_week_datas, + rnn_minute_datas; + size_t batch_iter{0}; + size_t batch_size{1}; + DataRecord() = default; + DataRecord(const std::string &path, int batch_size = 1) + : batch_size(batch_size) { + Load(path); + } + DataRecord NextBatch() { + DataRecord data; + size_t batch_end = batch_iter + batch_size; + // NOTE skip the final batch, if no enough data is provided. + if (batch_end <= link_step_data_all.size()) { + data.link_step_data_all.assign(link_step_data_all.begin() + batch_iter, + link_step_data_all.begin() + batch_end); + data.week_data_all.assign(week_data_all.begin() + batch_iter, + week_data_all.begin() + batch_end); + data.minute_data_all.assign(minute_data_all.begin() + batch_iter, + minute_data_all.begin() + batch_end); + // Prepare LoDs + data.lod1.push_back(0); + data.lod2.push_back(0); + data.lod3.push_back(0); + CHECK(!data.link_step_data_all.empty()) << "empty"; + CHECK(!data.week_data_all.empty()); + CHECK(!data.minute_data_all.empty()); + CHECK_EQ(data.link_step_data_all.size(), data.week_data_all.size()); + CHECK_EQ(data.minute_data_all.size(), data.link_step_data_all.size()); + for (size_t j = 0; j < data.link_step_data_all.size(); j++) { + for (const auto &d : data.link_step_data_all[j]) { + data.rnn_link_data.push_back(d); + } + data.rnn_week_datas.push_back(data.week_data_all[j]); + data.rnn_minute_datas.push_back(data.minute_data_all[j]); + // calculate lod + data.lod1.push_back(data.lod1.back() + + data.link_step_data_all[j].size()); + data.lod3.push_back(data.lod3.back() + 1); + for (size_t i = 1; i < data.link_step_data_all[j].size() + 1; i++) { + data.lod2.push_back(data.lod2.back() + + data.link_step_data_all[j].size()); + } + } + } + batch_iter += batch_size; + return data; + } + void Load(const std::string &path) { + std::ifstream file(path); + std::string line; + int num_lines = 0; + while (std::getline(file, line)) { + num_lines++; + std::vector data; + split(line, ':', &data); + std::vector> link_step_data; + std::vector link_datas; + split(data[0], '|', &link_datas); + for (auto &step_data : link_datas) { + std::vector tmp; + split_to_float(step_data, ',', &tmp); + link_step_data.push_back(tmp); + } + // load week data + std::vector week_data; + split_to_float(data[2], ',', &week_data); + // load minute data + std::vector minute_data; + split_to_float(data[1], ',', &minute_data); + link_step_data_all.push_back(std::move(link_step_data)); + week_data_all.push_back(std::move(week_data)); + minute_data_all.push_back(std::move(minute_data)); + } + } +}; +void PrepareInputs(std::vector *input_slots, DataRecord *data, + int batch_size) { + // DataRecord data(FLAGS_datapath, batch_size); + PaddleTensor lod_attention_tensor, init_zero_tensor, lod_tensor_tensor, + week_tensor, minute_tensor; + lod_attention_tensor.name = "data_lod_attention"; + init_zero_tensor.name = "cell_init"; + lod_tensor_tensor.name = "data"; + week_tensor.name = "week"; + minute_tensor.name = "minute"; + auto one_batch = data->NextBatch(); + // clang-format off + std::vector rnn_link_data_shape + ({static_cast(one_batch.rnn_link_data.size()), static_cast(one_batch.rnn_link_data.front().size())}); + lod_attention_tensor.shape.assign({1, 2}); + lod_attention_tensor.lod.assign({one_batch.lod1, one_batch.lod2}); + init_zero_tensor.shape.assign({batch_size, 15}); + init_zero_tensor.lod.assign({one_batch.lod3}); + lod_tensor_tensor.shape = rnn_link_data_shape; + lod_tensor_tensor.lod.assign({one_batch.lod1}); + week_tensor.shape.assign({(int) one_batch.rnn_week_datas.size(), (int) one_batch.rnn_week_datas.front().size()}); + week_tensor.lod.assign({one_batch.lod3}); + minute_tensor.shape.assign({(int) one_batch.rnn_minute_datas.size(), + (int) one_batch.rnn_minute_datas.front().size()}); + minute_tensor.lod.assign({one_batch.lod3}); + // assign data + TensorAssignData(&lod_attention_tensor, std::vector>({{0, 0}})); + std::vector tmp_zeros(batch_size * 15, 0.); + TensorAssignData(&init_zero_tensor, {tmp_zeros}); + TensorAssignData(&lod_tensor_tensor, one_batch.rnn_link_data); + TensorAssignData(&week_tensor, one_batch.rnn_week_datas); + TensorAssignData(&minute_tensor, one_batch.rnn_minute_datas); + // clang-format on + // Set inputs. + auto init_zero_tensor1 = init_zero_tensor; + init_zero_tensor1.name = "hidden_init"; + input_slots->assign({week_tensor, init_zero_tensor, minute_tensor, + init_zero_tensor1, lod_attention_tensor, + lod_tensor_tensor}); + for (auto &tensor : *input_slots) { + tensor.dtype = PaddleDType::FLOAT32; + } +} + +std::string DescribeTensor(const PaddleTensor &tensor) { + std::stringstream os; + os << "Tensor [" << tensor.name << "]\n"; + os << " - type: "; + switch (tensor.dtype) { + case PaddleDType::FLOAT32: + os << "float32"; + break; + case PaddleDType::INT64: + os << "int64"; + break; + default: + os << "unset"; + } + os << '\n'; + + os << " - shape: " << to_string(tensor.shape) << '\n'; + os << " - lod: "; + for (auto &l : tensor.lod) { + os << to_string(l) << "; "; + } + os << "\n"; + os << " - data: "; + + // clang-format off + int dim = std::accumulate(tensor.shape.begin(), + tensor.shape.end(), + 1, + [](int a, int b) { return a * b; }); // clang-format on + for (size_t i = 0; i < dim; i++) { + os << static_cast(tensor.data.data())[i] << " "; + } + os << '\n'; + return os.str(); +} + +} // namespace + +const float ditu_rnn_target_data[] = { + 104.711, 11.2431, 1.35422, 0, 0, 0, 0, 0, + 27.7039, 1.41486, 7.09526, 0, 0, 0, 0, 0, + 7.6481, 6.5324, 56.383, 2.88018, 8.92918, 132.007, 4.27429, 2.02934, + 14.1727, 10.7461, 25.0616, 16.0197, 14.4163, 16.9199, 6.75517, 0, + 80.0249, 4.77739, 0, 0, 0, 0, 0, 0, + 47.5643, 2.67029, 8.76252, 0, 0, 0, 0, 0, + 51.8822, 4.4411, 0, 0, 0, 0, 0, 0, + 10.7286, 12.0595, 10.6672, 0, 0, 0, 0, 0, + 93.5771, 3.84641, 0, 0, 0, 0, 0, 0, + 169.426, 0, 0, 0, 0, 0, 0, 0}; +// Test with a really complicate model. +void TestDituRNNPrediction(const std::string &model_path, + const std::string &data_path, int batch_size, + bool use_analysis, bool activate_ir, + int num_times = 1) { + FLAGS_IA_enable_ir = activate_ir; + FLAGS_IA_enable_tensorrt_subgraph_engine = false; + FLAGS_IA_output_storage_path = "./analysis.out"; + + std::string model_out; + if (use_analysis) { + Argument argument(model_path); + argument.model_output_store_path.reset(new std::string("./analysis.out")); + + Analyzer analyzer; + analyzer.Run(&argument); + + // Should get the transformed model stored to ./analysis.out + model_out = "./analysis.out"; + ASSERT_TRUE(PathExists(model_out)); + } else { + model_out = FLAGS_infer_ditu_rnn_model; + } + + NativeConfig config; + config.prog_file = model_out + "/__model__"; + config.param_file = model_out + "/param"; + config.use_gpu = false; + config.device = 0; + config.specify_input_name = true; + + auto predictor = + CreatePaddlePredictor(config); + std::vector input_slots; + DataRecord data(data_path, batch_size); + // Prepare inputs. + PrepareInputs(&input_slots, &data, batch_size); + std::vector outputs; + + Timer timer; + timer.tic(); + for (int i = 0; i < num_times; i++) { + predictor->Run(input_slots, &outputs); + } + LOG(INFO) << "time/batch: " << timer.toc() / num_times; + + for (auto &out : outputs) { + size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1, + [](int a, int b) { return a * b; }); + float *data = static_cast(out.data.data()); + for (int i = 0; + i < std::min(sizeof(ditu_rnn_target_data) / sizeof(float), size); + i++) { + EXPECT_NEAR(data[i], ditu_rnn_target_data[i], 1e-3); + } + } +} + // Turn on the IR pass supportion, run a real inference and check the result. TEST(Analyzer, SupportIRPass) { FLAGS_IA_enable_ir = true; @@ -94,6 +333,27 @@ TEST(Analyzer, SupportIRPass) { TestWord2vecPrediction("./analysis.out"); } +// Directly infer with the original model. +TEST(Analyzer, DituRNN_without_analysis) { + TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data, + 10, false, false); +} + +// Inference with the original model with the analysis turned on, the analysis +// module will transform the program to a data flow graph. +TEST(Analyzer, DituRNN_with_analysis) { + LOG(INFO) << "ditu rnn with analysis"; + TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data, + 10, true, false, 1); +} + +// Inference with analysis and IR. The IR module will fuse some large kernels. +TEST(Analyzer, DituRNN_with_analysis_with_IR) { + LOG(INFO) << "ditu rnn with analysis and IR fuse"; + TestDituRNNPrediction(FLAGS_infer_ditu_rnn_model, FLAGS_infer_ditu_rnn_data, + 10, true, true, 1); +} + } // namespace analysis } // namespace inference } // namespace paddle diff --git a/paddle/fluid/inference/api/CMakeLists.txt b/paddle/fluid/inference/api/CMakeLists.txt index ce6c8f0474d..6da9a6385f9 100644 --- a/paddle/fluid/inference/api/CMakeLists.txt +++ b/paddle/fluid/inference/api/CMakeLists.txt @@ -18,7 +18,10 @@ if(APPLE) endif(APPLE) -set(inference_deps paddle_inference_api paddle_fluid_api) +set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager + graph_viz_pass fc_fuse_pass + infer_clean_graph_pass + ) if(WITH_GPU AND TENSORRT_FOUND) set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine) diff --git a/paddle/fluid/inference/api/api_impl.cc b/paddle/fluid/inference/api/api_impl.cc index e31c637e969..32a691b81ff 100644 --- a/paddle/fluid/inference/api/api_impl.cc +++ b/paddle/fluid/inference/api/api_impl.cc @@ -137,8 +137,11 @@ bool NativePaddlePredictor::Run(const std::vector &inputs, return false; } for (size_t i = 0; i < feed_target_names_.size(); ++i) { - VLOG(4) << "setting " << i << "-th target"; - feed_targets[feed_target_names_[i]] = &feeds[i]; + if (config_.specify_input_name) { + feed_targets[inputs[i].name] = &feeds[i]; + } else { + feed_targets[feed_target_names_[i]] = &feeds[i]; + } } // get fetch variable std::map fetch_targets; diff --git a/paddle/fluid/inference/api/helper.h b/paddle/fluid/inference/api/helper.h new file mode 100644 index 00000000000..2c166cc0622 --- /dev/null +++ b/paddle/fluid/inference/api/helper.h @@ -0,0 +1,110 @@ +// 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 +#include +#include +#include +#include "paddle/fluid/inference/api/paddle_inference_api.h" + +namespace paddle { +namespace inference { + +// Timer for timer +class Timer { + public: + double start; + double startu; + void tic() { + struct timeval tp; + gettimeofday(&tp, NULL); + start = tp.tv_sec; + startu = tp.tv_usec; + } + double toc() { + struct timeval tp; + gettimeofday(&tp, NULL); + double used_time_ms = + (tp.tv_sec - start) * 1000.0 + (tp.tv_usec - startu) / 1000.0; + return used_time_ms; + } +}; + +void split(const std::string &str, char sep, std::vector *pieces) { + pieces->clear(); + if (str.empty()) { + return; + } + size_t pos = 0; + size_t next = str.find(sep, pos); + while (next != std::string::npos) { + pieces->push_back(str.substr(pos, next - pos)); + pos = next + 1; + next = str.find(sep, pos); + } + if (!str.substr(pos).empty()) { + pieces->push_back(str.substr(pos)); + } +} +void split_to_float(const std::string &str, char sep, std::vector *fs) { + std::vector pieces; + split(str, sep, &pieces); + std::transform(pieces.begin(), pieces.end(), std::back_inserter(*fs), + [](const std::string &v) { return std::stof(v); }); +} +template +std::string to_string(const std::vector &vec) { + std::stringstream ss; + for (const auto &c : vec) { + ss << c << " "; + } + return ss.str(); +} +template <> +std::string to_string>( + const std::vector> &vec) { + std::stringstream ss; + for (const auto &piece : vec) { + ss << to_string(piece) << "\n"; + } + return ss.str(); +} +template <> +std::string to_string>>( + const std::vector>> &vec) { + std::stringstream ss; + for (const auto &line : vec) { + for (const auto &rcd : line) { + ss << to_string(rcd) << ";\t"; + } + ss << '\n'; + } + return ss.str(); +} +// clang-format off +void TensorAssignData(PaddleTensor *tensor, const std::vector> &data) { + // Assign buffer + int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1, [](int a, int b) { return a * b; }); + tensor->data.Resize(sizeof(float) * dim); + int c = 0; + for (const auto &f : data) { + for (float v : f) { static_cast(tensor->data.data())[c++] = v; } + } +} + +} // namespace inference +} // namespace paddle diff --git a/paddle/fluid/inference/api/paddle_inference_api.h b/paddle/fluid/inference/api/paddle_inference_api.h index da6c2cfc218..3b36377274b 100644 --- a/paddle/fluid/inference/api/paddle_inference_api.h +++ b/paddle/fluid/inference/api/paddle_inference_api.h @@ -120,6 +120,8 @@ struct NativeConfig : public PaddlePredictor::Config { bool use_gpu{false}; int device{0}; float fraction_of_gpu_memory{-1.f}; // Negative to notify initialization. + // Specify the variable's name of each input. + bool specify_input_name{false}; std::string prog_file; std::string param_file; diff --git a/paddle/fluid/operators/mul_op.cc b/paddle/fluid/operators/mul_op.cc index 51993398bd3..2a8e4af516c 100644 --- a/paddle/fluid/operators/mul_op.cc +++ b/paddle/fluid/operators/mul_op.cc @@ -54,9 +54,9 @@ class MulOp : public framework::OperatorWithKernel { auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims); auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims); - PADDLE_ENFORCE_EQ( - x_mat_dims[1], y_mat_dims[0], - "First matrix's width must be equal with second matrix's height."); + PADDLE_ENFORCE_EQ(x_mat_dims[1], y_mat_dims[0], + "First matrix's width must be equal with second matrix's " + "height. %s, %s"); std::vector output_dims; output_dims.reserve( static_cast(x_num_col_dims + y_dims.size() - y_num_col_dims)); -- GitLab