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Add ernie c++ inference test (#21015)
* Add ernie unit test test=develop * Add ernie unit test test=develop * Add ernie unit test test=develop * remove ngraph * optimize gpu test test=develop * optimize codes test=develop
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paddle/fluid/inference/tests/api/analyzer_ernie_tester.cc
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// Copyright (c) 2019 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 "paddle/fluid/inference/tests/api/tester_helper.h" | ||
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namespace paddle { | ||
namespace inference { | ||
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using paddle::PaddleTensor; | ||
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template <typename T> | ||
void GetValueFromStream(std::stringstream *ss, T *t) { | ||
(*ss) >> (*t); | ||
} | ||
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template <> | ||
void GetValueFromStream<std::string>(std::stringstream *ss, std::string *t) { | ||
*t = ss->str(); | ||
} | ||
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// Split string to vector | ||
template <typename T> | ||
void Split(const std::string &line, char sep, std::vector<T> *v) { | ||
std::stringstream ss; | ||
T t; | ||
for (auto c : line) { | ||
if (c != sep) { | ||
ss << c; | ||
} else { | ||
GetValueFromStream<T>(&ss, &t); | ||
v->push_back(std::move(t)); | ||
ss.str({}); | ||
ss.clear(); | ||
} | ||
} | ||
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if (!ss.str().empty()) { | ||
GetValueFromStream<T>(&ss, &t); | ||
v->push_back(std::move(t)); | ||
ss.str({}); | ||
ss.clear(); | ||
} | ||
} | ||
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// Parse tensor from string | ||
template <typename T> | ||
bool ParseTensor(const std::string &field, paddle::PaddleTensor *tensor) { | ||
std::vector<std::string> data; | ||
Split(field, ':', &data); | ||
if (data.size() < 2) return false; | ||
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std::string shape_str = data[0]; | ||
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std::vector<int> shape; | ||
Split(shape_str, ' ', &shape); | ||
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std::string mat_str = data[1]; | ||
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std::vector<T> mat; | ||
Split(mat_str, ' ', &mat); | ||
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tensor->shape = shape; | ||
auto size = | ||
std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()) * | ||
sizeof(T); | ||
tensor->data.Resize(size); | ||
std::copy(mat.begin(), mat.end(), static_cast<T *>(tensor->data.data())); | ||
tensor->dtype = GetPaddleDType<T>(); | ||
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return true; | ||
} | ||
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// Parse input tensors from string | ||
bool ParseLine(const std::string &line, | ||
std::vector<paddle::PaddleTensor> *tensors) { | ||
std::vector<std::string> fields; | ||
Split(line, ';', &fields); | ||
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tensors->clear(); | ||
tensors->reserve(4); | ||
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int i = 0; | ||
for (; i < 3; i++) { | ||
paddle::PaddleTensor temp; | ||
ParseTensor<int64_t>(fields[i], &temp); | ||
temp.name = "placeholder_" + std::to_string(i); | ||
tensors->push_back(temp); | ||
} | ||
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// input_mask | ||
paddle::PaddleTensor input_mask; | ||
ParseTensor<float>(fields[i++], &input_mask); | ||
input_mask.name = "placeholder_3"; | ||
tensors->push_back(input_mask); | ||
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return true; | ||
} | ||
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bool LoadInputData(std::vector<std::vector<paddle::PaddleTensor>> *inputs) { | ||
if (FLAGS_infer_data.empty()) { | ||
LOG(ERROR) << "please set input data path"; | ||
return false; | ||
} | ||
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std::ifstream fin(FLAGS_infer_data); | ||
std::string line; | ||
int sample = 0; | ||
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// The unit-test dataset only have 10 samples, each sample have 5 feeds. | ||
while (std::getline(fin, line)) { | ||
std::vector<paddle::PaddleTensor> feed_data; | ||
ParseLine(line, &feed_data); | ||
inputs->push_back(std::move(feed_data)); | ||
sample++; | ||
if (!FLAGS_test_all_data && sample == FLAGS_batch_size) break; | ||
} | ||
LOG(INFO) << "number of samples: " << sample; | ||
return true; | ||
} | ||
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void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false, | ||
bool use_gpu = false) { | ||
cfg->SetModel(FLAGS_infer_model); | ||
if (use_mkldnn) { | ||
cfg->EnableMKLDNN(); | ||
} | ||
if (use_gpu) { | ||
cfg->EnableUseGpu(100, 0); | ||
} else { | ||
cfg->DisableGpu(); | ||
} | ||
cfg->SwitchSpecifyInputNames(); | ||
cfg->SwitchIrOptim(); | ||
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads); | ||
} | ||
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void profile(bool use_mkldnn = false, bool use_gpu = false) { | ||
AnalysisConfig config; | ||
SetConfig(&config, use_mkldnn, use_gpu); | ||
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std::vector<std::vector<PaddleTensor>> outputs; | ||
std::vector<std::vector<PaddleTensor>> inputs; | ||
LoadInputData(&inputs); | ||
TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&config), | ||
inputs, &outputs, FLAGS_num_threads); | ||
} | ||
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TEST(Analyzer_ernie, profile) { profile(); } | ||
#ifdef PADDLE_WITH_MKLDNN | ||
TEST(Analyzer_ernie, profile_mkldnn) { profile(true, false); } | ||
#endif | ||
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// Check the model by gpu | ||
#ifdef PADDLE_WITH_CUDA | ||
TEST(Analyzer_ernie, profile_gpu) { profile(false, true); } | ||
#endif | ||
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// Check the fuse status | ||
TEST(Analyzer_Ernie, fuse_statis) { | ||
AnalysisConfig cfg; | ||
SetConfig(&cfg); | ||
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int num_ops; | ||
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg); | ||
auto fuse_statis = GetFuseStatis( | ||
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops); | ||
ASSERT_TRUE(fuse_statis.count("fc_fuse")); | ||
ASSERT_EQ(fuse_statis.at("fc_fuse"), 74); | ||
LOG(INFO) << "num_ops: " << num_ops; | ||
EXPECT_EQ(num_ops, 295); | ||
} | ||
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// Compare result of NativeConfig and AnalysisConfig | ||
void compare(bool use_mkldnn = false) { | ||
AnalysisConfig cfg; | ||
SetConfig(&cfg, use_mkldnn, false); | ||
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std::vector<std::vector<PaddleTensor>> inputs; | ||
LoadInputData(&inputs); | ||
CompareNativeAndAnalysis( | ||
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), inputs); | ||
} | ||
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TEST(Analyzer_ernie, compare) { compare(); } | ||
#ifdef PADDLE_WITH_MKLDNN | ||
TEST(Analyzer_ernie, compare_mkldnn) { compare(true /* use_mkldnn */); } | ||
#endif | ||
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// Compare Deterministic result | ||
TEST(Analyzer_Ernie, compare_determine) { | ||
AnalysisConfig cfg; | ||
SetConfig(&cfg); | ||
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std::vector<std::vector<PaddleTensor>> input_slots_all; | ||
LoadInputData(&input_slots_all); | ||
CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg), | ||
input_slots_all); | ||
} | ||
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// Compare results | ||
TEST(Analyzer_Ernie, compare_results) { | ||
AnalysisConfig cfg; | ||
SetConfig(&cfg); | ||
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std::vector<std::vector<PaddleTensor>> input_slots_all; | ||
LoadInputData(&input_slots_all); | ||
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std::ifstream fin(FLAGS_refer_result); | ||
std::string line; | ||
std::vector<float> ref; | ||
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while (std::getline(fin, line)) { | ||
Split(line, ' ', &ref); | ||
} | ||
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auto predictor = CreateTestPredictor( | ||
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), | ||
FLAGS_use_analysis); | ||
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std::vector<PaddleTensor> outputs; | ||
for (size_t i = 0; i < input_slots_all.size(); i++) { | ||
outputs.clear(); | ||
predictor->Run(input_slots_all[i], &outputs); | ||
auto outputs_size = outputs.front().data.length() / (sizeof(float)); | ||
for (size_t j = 0; j < outputs_size; ++j) { | ||
EXPECT_NEAR(ref[i * outputs_size + j], | ||
static_cast<float *>(outputs[0].data.data())[j], | ||
FLAGS_accuracy); | ||
} | ||
} | ||
} | ||
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} // namespace inference | ||
} // namespace paddle |