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paddle/fluid/inference/tests/api/analyzer_detect_functional_tester.cc
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| /* Copyright (c) 2020 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. */ | ||
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| #include <gtest/gtest.h> | ||
| #include <fstream> | ||
| #include <iostream> | ||
| #include "paddle/fluid/inference/tests/api/tester_helper.h" | ||
| #include "paddle/fluid/platform/device_context.h" | ||
| #include "paddle/fluid/platform/place.h" | ||
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| DEFINE_string(infer_shape, "", "data shape file"); | ||
| DEFINE_int32(sample, 20, "number of sample"); | ||
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| namespace paddle { | ||
| namespace inference { | ||
| namespace analysis { | ||
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| struct Record { | ||
| std::vector<float> data; | ||
| std::vector<int32_t> shape; | ||
| }; | ||
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| Record ProcessALine(const std::string &line, const std::string &shape_line) { | ||
| VLOG(3) << "process a line"; | ||
| std::vector<std::string> columns; | ||
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| Record record; | ||
| std::vector<std::string> data_strs; | ||
| split(line, ' ', &data_strs); | ||
| for (auto &d : data_strs) { | ||
| record.data.push_back(std::stof(d)); | ||
| } | ||
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| std::vector<std::string> shape_strs; | ||
| split(shape_line, ' ', &shape_strs); | ||
| for (auto &s : shape_strs) { | ||
| record.shape.push_back(std::stoi(s)); | ||
| } | ||
| return record; | ||
| } | ||
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| void SetConfig(AnalysisConfig *cfg) { | ||
| cfg->SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params"); | ||
| cfg->DisableGpu(); | ||
| cfg->SwitchIrDebug(); | ||
| cfg->SwitchSpecifyInputNames(false); | ||
| cfg->SetCpuMathLibraryNumThreads(FLAGS_cpu_num_threads); | ||
| } | ||
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| void SetInput(std::vector<std::vector<PaddleTensor>> *inputs, | ||
| const std::string &line, const std::string &shape_line) { | ||
| auto record = ProcessALine(line, shape_line); | ||
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| PaddleTensor input; | ||
| input.shape = record.shape; | ||
| input.dtype = PaddleDType::FLOAT32; | ||
| size_t input_size = record.data.size() * sizeof(float); | ||
| input.data.Resize(input_size); | ||
| memcpy(input.data.data(), record.data.data(), input_size); | ||
| std::vector<PaddleTensor> input_slots; | ||
| input_slots.assign({input}); | ||
| (*inputs).emplace_back(input_slots); | ||
| } | ||
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| #ifdef PADDLE_WITH_MKLDNN | ||
| int GetNumCachedObjects(void) { | ||
| auto &pool = platform::DeviceContextPool::Instance(); | ||
| platform::CPUPlace place; | ||
| auto onednn_dev_ctx = | ||
| dynamic_cast<platform::MKLDNNDeviceContext *>(pool.Get(place)); | ||
| return onednn_dev_ctx->GetCachedObjectsNumber(); | ||
| } | ||
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| void validate_cache_onednn(int cache_capacity = 1) { | ||
| AnalysisConfig cfg; | ||
| SetConfig(&cfg); | ||
| cfg.EnableMKLDNN(); | ||
| cfg.SetMkldnnCacheCapacity(cache_capacity); | ||
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| // we will use two predictors (same model) | ||
| auto first_predictor = CreatePaddlePredictor<AnalysisConfig>(cfg); | ||
| std::vector<std::vector<PaddleTensor>> ref_outputs; | ||
| std::vector<std::vector<PaddleTensor>> input_slots_all; | ||
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| std::ifstream file(FLAGS_infer_data); | ||
| std::ifstream infer_file(FLAGS_infer_shape); | ||
| std::vector<std::string> lines; | ||
| std::vector<std::string> shape_lines; | ||
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| // Let's work with 4 samples | ||
| auto num_samples = 4; | ||
| ref_outputs.resize(num_samples); | ||
| lines.resize(num_samples); | ||
| shape_lines.resize(num_samples); | ||
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| // Let's remeber number of cached objects before | ||
| // exection and after every single execution | ||
| std::vector<int> cache_filling; | ||
| cache_filling.push_back(GetNumCachedObjects()); | ||
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| // compute sequenctilly prediction | ||
| for (int i = 0; i < num_samples; ++i) { | ||
| std::getline(file, lines[i]); | ||
| std::getline(infer_file, shape_lines[i]); | ||
| SetInput(&input_slots_all, lines[i], shape_lines[i]); | ||
| first_predictor->Run(input_slots_all[i], &ref_outputs[i], FLAGS_batch_size); | ||
| // record number of cached objects | ||
| cache_filling.push_back(GetNumCachedObjects()); | ||
| } | ||
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| file.close(); | ||
| infer_file.close(); | ||
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| first_predictor.reset(nullptr); | ||
| cache_filling.push_back(GetNumCachedObjects()); | ||
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| // Compare results | ||
| // First and last value should be equal e.g. before using cache (empty) and | ||
| // after releasing executor | ||
| PADDLE_ENFORCE_EQ( | ||
| cache_filling[0], cache_filling[cache_filling.size() - 1], | ||
| paddle::platform::errors::Fatal("Cache size before execution and after " | ||
| "releasing Executor do not match")); | ||
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| // Iterate to check if cache is not increasing | ||
| // over exceeding cache capacity | ||
| if (cache_capacity != 0) { | ||
| for (int i = cache_capacity + 1; i < num_samples + 1; ++i) { | ||
| PADDLE_ENFORCE_EQ( | ||
| cache_filling[cache_capacity], cache_filling[i], | ||
| paddle::platform::errors::Fatal("Cache capacity should not increase " | ||
| "after full capacity is used")); | ||
| } | ||
| } | ||
| } | ||
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| TEST(Analyzer_detect, validate_cache_onednn) { | ||
| validate_cache_onednn(2 /*cache_capacity */); | ||
| } | ||
| #endif | ||
|
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| } // namespace analysis | ||
| } // namespace inference | ||
| } // namespace paddle |
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