diff --git a/paddle/fluid/inference/tests/api/CMakeLists.txt b/paddle/fluid/inference/tests/api/CMakeLists.txt index 95bbc74a5961eb28a0d8fbd7c680c0740fc68d8a..9aa9db031cd46c9d537bd686f0b23f4c9ae71de6 100644 --- a/paddle/fluid/inference/tests/api/CMakeLists.txt +++ b/paddle/fluid/inference/tests/api/CMakeLists.txt @@ -108,6 +108,10 @@ inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose inference_analysis_api_test_with_fake_data(test_analyzer_resnet50 "${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz") +# seq_pool1 +inference_analysis_api_test_with_fake_data(test_analyzer_seq_pool1 +"${INFERENCE_DEMO_INSTALL_DIR}/seq_pool1" analyzer_seq_pool1_tester.cc "seq_pool1.tar.gz") + # mobilenet with depthwise_conv op inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_conv "${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz") diff --git a/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc b/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc new file mode 100644 index 0000000000000000000000000000000000000000..2ae840fd11f627d845a20a59ab14118516311d22 --- /dev/null +++ b/paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc @@ -0,0 +1,117 @@ +/* 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 +#include "paddle/fluid/inference/tests/api/tester_helper.h" + +namespace paddle { +namespace inference { +namespace analysis { + +void SetConfig(AnalysisConfig *cfg) { + cfg->param_file = FLAGS_infer_model + "/params"; + cfg->prog_file = FLAGS_infer_model + "/model"; + cfg->use_gpu = false; + cfg->device = 0; + cfg->enable_ir_optim = true; + cfg->specify_input_name = true; + cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads); +} + +void SetInput(std::vector> *inputs) { + std::vector feed_names = { + "slot10000_embed", "slot10001_embed", "slot10004_embed", + "slot10005_embed", "slot10008_embed", "slot10009_embed", + "slot10012_embed", "slot10013_embed", "slot10108_embed", + "slot13324_embed", "slot13325_embed", "slot13326_embed", + "slot13327_embed", "slot13328_embed", "slot13329_embed", + "slot13330_embed", "slot13331_embed", "slot15501_embed", + "slot15502_embed", "slot15503_embed", "slot15504_embed", + "slot15505_embed", "slot15506_embed", "slot15507_embed", + "slot15508_embed", "slot15516_embed", "slot15519_embed", + "slot15523_embed", "slot15531_embed", "slot15533_embed", + "slot15548_embed", "slot15564_embed", "slot15565_embed", + "slot15566_embed", "slot15570_embed", "slot15571_embed", + "slot15572_embed", "slot15573_embed", "slot15574_embed", + "slot15575_embed", "slot15576_embed", "slot15577_embed", + "slot15579_embed", "slot15581_embed", "slot15582_embed", + "slot15583_embed", "slot15584_embed", "slot5016_embed", + "slot5021_embed", "slot6002_embed", "slot6003_embed", + "slot6004_embed", "slot6005_embed", "slot6006_embed", + "slot6007_embed", "slot6008_embed", "slot6009_embed", + "slot6011_embed", "slot6014_embed", "slot6015_embed", + "slot6023_embed", "slot6024_embed", "slot6025_embed", + "slot6027_embed", "slot6029_embed", "slot6031_embed", + "slot6034_embed", "slot6035_embed", "slot6036_embed", + "slot6037_embed", "slot6039_embed", "slot6048_embed", + "slot6050_embed", "slot6058_embed", "slot6059_embed", + "slot6060_embed", "slot6066_embed", "slot6067_embed", + "slot6068_embed", "slot6069_embed", "slot6070_embed", + "slot6071_embed", "slot6072_embed", "slot6073_embed", + "slot6182_embed", "slot6183_embed", "slot6184_embed", + "slot6185_embed", "slot6186_embed", "slot6188_embed", + "slot6189_embed", "slot6190_embed", "slot6201_embed", + "slot6202_embed", "slot6203_embed", "slot6247_embed", + "slot6248_embed", "slot6250_embed", "slot6251_embed", + "slot6807_embed", "slot6808_embed", "slot6809_embed", + "slot6810_embed", "slot6811_embed", "slot6812_embed", + "slot6813_embed", "slot6814_embed", "slot6815_embed", + "slot6816_embed", "slot6817_embed", "slot6818_embed", + "slot6819_embed", "slot6820_embed", "slot6822_embed", + "slot6823_embed", "slot6826_embed", "slot7002_embed", + "slot7003_embed", "slot7004_embed", "slot7005_embed", + "slot7006_embed", "slot7008_embed", "slot7009_embed", + "slot7010_embed", "slot7011_embed", "slot7013_embed", + "slot7014_embed", "slot7015_embed", "slot7016_embed", + "slot7017_embed", "slot7019_embed", "slot7100_embed", + "slot7506_embed", "slot7507_embed", "slot7514_embed", + "slot7515_embed", "slot7516_embed"}; + SetFakeImageInput(inputs, FLAGS_infer_model, true, "model", "params", + &feed_names); +} + +// Easy for profiling independently. +void profile(bool use_mkldnn = false) { + AnalysisConfig cfg; + SetConfig(&cfg); + + if (use_mkldnn) { + cfg.EnableMKLDNN(); + } + std::vector outputs; + + std::vector> input_slots_all; + SetInput(&input_slots_all); + TestPrediction(reinterpret_cast(&cfg), + input_slots_all, &outputs, FLAGS_num_threads); +} + +TEST(Analyzer_seq_pool1, profile) { profile(); } + +// Check the fuse status +TEST(Analyzer_seq_pool1, fuse_statis) { + AnalysisConfig cfg; + SetConfig(&cfg); + int num_ops; + auto predictor = CreatePaddlePredictor(cfg); + auto fuse_statis = GetFuseStatis( + static_cast(predictor.get()), &num_ops); + LOG(INFO) << "num_ops: " << num_ops; + EXPECT_EQ(num_ops, 314); +} + +} // namespace analysis +} // 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 b0c8f395ce05fbfceaec3d8b69367292eca714e4..ef7e2198c5db4e723406fe1a31d2c3dde5121931 100644 --- a/paddle/fluid/inference/tests/api/tester_helper.h +++ b/paddle/fluid/inference/tests/api/tester_helper.h @@ -132,7 +132,8 @@ std::unordered_map GetFuseStatis(PaddlePredictor *predictor, void SetFakeImageInput(std::vector> *inputs, const std::string &dirname, bool is_combined = true, std::string model_filename = "model", - std::string params_filename = "params") { + std::string params_filename = "params", + const std::vector *feed_names = nullptr) { // Set fake_image_data PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "Only have single batch of data."); std::vector> feed_target_shapes = GetFeedTargetShapes( @@ -146,26 +147,32 @@ void SetFakeImageInput(std::vector> *inputs, os << "}\n"; } LOG(INFO) << os.str(); - - int dim1 = feed_target_shapes[0][1]; - int dim2 = feed_target_shapes[0][2]; - int dim3 = feed_target_shapes[0][3]; - - PaddleTensor input; - std::vector shape({FLAGS_batch_size, dim1, dim2, dim3}); - input.shape = shape; - input.dtype = PaddleDType::FLOAT32; - - // fill input data, for profile easily, do not use random data here. - size_t size = FLAGS_batch_size * dim1 * dim2 * dim3; - input.data.Resize(size * sizeof(float)); - float *input_data = static_cast(input.data.data()); - for (size_t i = 0; i < size; i++) { - *(input_data + i) = static_cast(i) / size; + if (feed_names) { + PADDLE_ENFORCE_EQ(feed_names->size(), feed_target_shapes.size()); + } + std::vector input_slots(feed_target_shapes.size()); + for (size_t i = 0; i < feed_target_shapes.size(); ++i) { + const auto &feed_shape = feed_target_shapes[i]; + auto &input = input_slots[i]; + std::vector shape({FLAGS_batch_size}); + for (size_t s = 1; s < feed_shape.size(); ++s) { + shape.push_back(static_cast(feed_shape[s])); + } + if (feed_names) { + input.name = (*feed_names)[i]; + } + input.shape = shape; + input.dtype = PaddleDType::FLOAT32; + size_t len = std::accumulate(shape.begin(), shape.end(), 1, + [](int a, int b) { return a * b; }); + input.data.Resize(len * sizeof(float)); + input.lod.assign({{0, static_cast(FLAGS_batch_size)}}); + float *input_data = static_cast(input.data.data()); + // fill input data, for profile easily, do not use random data here. + for (size_t j = 0; j < len; ++j) { + *(input_data + j) = static_cast(j) / len; + } } - - std::vector input_slots; - input_slots.assign({input}); (*inputs).emplace_back(input_slots); }