提交 829bf871 编写于 作者: G GaoWei8 提交者: Yiqun Liu

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
上级 89bc18ee
......@@ -21,10 +21,14 @@ function(download_model_and_data install_dir model_name data_name)
download_data(${install_dir} ${data_name})
endfunction()
function(download_result install_dir result_name)
download_data(${install_dir} ${result_name})
endfunction()
function(inference_analysis_api_test target install_dir filename)
inference_analysis_test(${target} SRCS ${filename}
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} benchmark
ARGS --infer_model=${install_dir}/model --infer_data=${install_dir}/data.txt)
ARGS --infer_model=${install_dir}/model --infer_data=${install_dir}/data.txt --refer_result=${install_dir}/result.txt)
endfunction()
function(inference_analysis_api_int8_test_build TARGET_NAME filename)
......@@ -129,6 +133,12 @@ set(PYRAMID_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/pyramid_dnn")
download_model_and_data(${PYRAMID_DNN_INSTALL_DIR} "PyramidDNN_model.tar.gz" "PyramidDNN_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_pyramid_dnn ${PYRAMID_DNN_INSTALL_DIR} analyzer_pyramid_dnn_tester.cc)
#Ernie
set(ERNIE_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/Ernie")
download_model_and_data(${ERNIE_INSTALL_DIR} "Ernie_model.tar.gz" "Ernie_data.txt.tar.gz" "Ernie_result.txt.tar.gz")
download_result(${ERNIE_INSTALL_DIR} "Ernie_result.txt.tar.gz")
inference_analysis_api_test(test_analyzer_ernie ${ERNIE_INSTALL_DIR} analyzer_ernie_tester.cc)
# text_classification
set(TEXT_CLASSIFICATION_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/text_classification")
download_model_and_data(${TEXT_CLASSIFICATION_INSTALL_DIR} "text-classification-Senta.tar.gz" "text_classification_data.txt.tar.gz")
......
// 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.
#include "paddle/fluid/inference/tests/api/tester_helper.h"
namespace paddle {
namespace inference {
using paddle::PaddleTensor;
template <typename T>
void GetValueFromStream(std::stringstream *ss, T *t) {
(*ss) >> (*t);
}
template <>
void GetValueFromStream<std::string>(std::stringstream *ss, std::string *t) {
*t = ss->str();
}
// 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();
}
}
if (!ss.str().empty()) {
GetValueFromStream<T>(&ss, &t);
v->push_back(std::move(t));
ss.str({});
ss.clear();
}
}
// 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;
std::string shape_str = data[0];
std::vector<int> shape;
Split(shape_str, ' ', &shape);
std::string mat_str = data[1];
std::vector<T> mat;
Split(mat_str, ' ', &mat);
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>();
return true;
}
// Parse input tensors from string
bool ParseLine(const std::string &line,
std::vector<paddle::PaddleTensor> *tensors) {
std::vector<std::string> fields;
Split(line, ';', &fields);
tensors->clear();
tensors->reserve(4);
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);
}
// input_mask
paddle::PaddleTensor input_mask;
ParseTensor<float>(fields[i++], &input_mask);
input_mask.name = "placeholder_3";
tensors->push_back(input_mask);
return true;
}
bool LoadInputData(std::vector<std::vector<paddle::PaddleTensor>> *inputs) {
if (FLAGS_infer_data.empty()) {
LOG(ERROR) << "please set input data path";
return false;
}
std::ifstream fin(FLAGS_infer_data);
std::string line;
int sample = 0;
// 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;
}
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);
}
void profile(bool use_mkldnn = false, bool use_gpu = false) {
AnalysisConfig config;
SetConfig(&config, use_mkldnn, use_gpu);
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);
}
TEST(Analyzer_ernie, profile) { profile(); }
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_ernie, profile_mkldnn) { profile(true, false); }
#endif
// Check the model by gpu
#ifdef PADDLE_WITH_CUDA
TEST(Analyzer_ernie, profile_gpu) { profile(false, true); }
#endif
// Check the fuse status
TEST(Analyzer_Ernie, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
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);
}
// Compare result of NativeConfig and AnalysisConfig
void compare(bool use_mkldnn = false) {
AnalysisConfig cfg;
SetConfig(&cfg, use_mkldnn, false);
std::vector<std::vector<PaddleTensor>> inputs;
LoadInputData(&inputs);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), inputs);
}
TEST(Analyzer_ernie, compare) { compare(); }
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_ernie, compare_mkldnn) { compare(true /* use_mkldnn */); }
#endif
// Compare Deterministic result
TEST(Analyzer_Ernie, compare_determine) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
LoadInputData(&input_slots_all);
CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all);
}
// Compare results
TEST(Analyzer_Ernie, compare_results) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
LoadInputData(&input_slots_all);
std::ifstream fin(FLAGS_refer_result);
std::string line;
std::vector<float> ref;
while (std::getline(fin, line)) {
Split(line, ' ', &ref);
}
auto predictor = CreateTestPredictor(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
FLAGS_use_analysis);
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);
}
}
}
} // namespace inference
} // namespace paddle
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