|
|
|
|
@ -144,6 +144,9 @@ void SetConfig(AnalysisConfig *cfg, bool use_mkldnn = false) {
|
|
|
|
|
cfg->SwitchSpecifyInputNames();
|
|
|
|
|
cfg->SwitchIrDebug();
|
|
|
|
|
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
|
|
|
|
|
if (FLAGS_zero_copy) {
|
|
|
|
|
cfg->SwitchUseFeedFetchOps(false);
|
|
|
|
|
}
|
|
|
|
|
if (use_mkldnn) {
|
|
|
|
|
cfg->EnableMKLDNN();
|
|
|
|
|
}
|
|
|
|
|
@ -184,10 +187,10 @@ TEST(Analyzer_seq_pool1, compare_determine) {
|
|
|
|
|
input_slots_all);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void analysis_fuse_statis(bool use_zerocopy) {
|
|
|
|
|
// Check the fuse status
|
|
|
|
|
TEST(Analyzer_seq_pool1, fuse_statis) {
|
|
|
|
|
AnalysisConfig cfg;
|
|
|
|
|
SetConfig(&cfg);
|
|
|
|
|
cfg.SwitchUseFeedFetchOps(!use_zerocopy);
|
|
|
|
|
int num_ops;
|
|
|
|
|
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
|
|
|
|
|
auto fuse_statis = GetFuseStatis(predictor.get(), &num_ops);
|
|
|
|
|
@ -203,137 +206,17 @@ void analysis_fuse_statis(bool use_zerocopy) {
|
|
|
|
|
EXPECT_EQ(num_ops, 171);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// Check the fuse status
|
|
|
|
|
TEST(Analyzer_seq_pool1, fuse_statis) { analysis_fuse_statis(false); }
|
|
|
|
|
|
|
|
|
|
void PrepareZeroCopyInputs(
|
|
|
|
|
const std::unique_ptr<PaddlePredictor> &predictor,
|
|
|
|
|
std::vector<std::unique_ptr<ZeroCopyTensor>> *inputs) {
|
|
|
|
|
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
|
|
|
|
|
// only feed one batch
|
|
|
|
|
const auto &one_batch = data.NextBatch();
|
|
|
|
|
inputs->clear();
|
|
|
|
|
for (size_t i = 0; i < one_batch.size(); ++i) {
|
|
|
|
|
auto &slot = one_batch[i];
|
|
|
|
|
auto tensor = predictor->GetInputTensor(slot.name + "_embed");
|
|
|
|
|
tensor->Reshape(slot.shape);
|
|
|
|
|
tensor->SetLoD({slot.lod});
|
|
|
|
|
ZeroCopyTensorAssignData<float>(tensor.get(), slot.data);
|
|
|
|
|
inputs->emplace_back(std::move(tensor));
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// return the output values
|
|
|
|
|
std::vector<float> zerocopy_profile(int repeat_times) {
|
|
|
|
|
AnalysisConfig config;
|
|
|
|
|
SetConfig(&config);
|
|
|
|
|
config.SwitchUseFeedFetchOps(false);
|
|
|
|
|
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
|
|
|
|
|
std::vector<std::unique_ptr<ZeroCopyTensor>> inputs;
|
|
|
|
|
PrepareZeroCopyInputs(predictor, &inputs);
|
|
|
|
|
auto output_tensor = predictor->GetOutputTensor(out_var_name);
|
|
|
|
|
Timer timer;
|
|
|
|
|
LOG(INFO) << "Warm up run...";
|
|
|
|
|
timer.tic();
|
|
|
|
|
predictor->ZeroCopyRun();
|
|
|
|
|
PrintTime(FLAGS_batch_size, 1, 1, 0, timer.toc(), 1);
|
|
|
|
|
if (FLAGS_profile) {
|
|
|
|
|
paddle::platform::ResetProfiler();
|
|
|
|
|
}
|
|
|
|
|
LOG(INFO) << "Run " << repeat_times << " times...";
|
|
|
|
|
timer.tic();
|
|
|
|
|
for (int i = 0; i < repeat_times; i++) {
|
|
|
|
|
predictor->ZeroCopyRun();
|
|
|
|
|
}
|
|
|
|
|
PrintTime(FLAGS_batch_size, repeat_times, 1, 0, timer.toc() / repeat_times,
|
|
|
|
|
1);
|
|
|
|
|
|
|
|
|
|
LOG(INFO) << "ZeroCopy output: " << DescribeZeroCopyTensor(*output_tensor);
|
|
|
|
|
PaddlePlace place;
|
|
|
|
|
int output_size{0};
|
|
|
|
|
auto *pdata = output_tensor->data<float>(&place, &output_size);
|
|
|
|
|
std::vector<float> res(output_size);
|
|
|
|
|
for (int i = 0; i < output_size; ++i) {
|
|
|
|
|
res[i] = pdata[i];
|
|
|
|
|
}
|
|
|
|
|
return res;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
TEST(Analyzer_seq_pool1, zerocopy_profile) { zerocopy_profile(FLAGS_repeat); }
|
|
|
|
|
|
|
|
|
|
TEST(Analyzer_seq_pool1, zerocopy_profile_threads) {
|
|
|
|
|
AnalysisConfig config;
|
|
|
|
|
SetConfig(&config);
|
|
|
|
|
config.SwitchUseFeedFetchOps(false);
|
|
|
|
|
|
|
|
|
|
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
|
|
|
|
|
predictors.emplace_back(CreatePaddlePredictor<AnalysisConfig>(config));
|
|
|
|
|
for (int tid = 1; tid < FLAGS_num_threads; tid++) {
|
|
|
|
|
predictors.emplace_back(predictors.front()->Clone());
|
|
|
|
|
}
|
|
|
|
|
double total_time_of_threads{0};
|
|
|
|
|
std::vector<std::thread> threads;
|
|
|
|
|
|
|
|
|
|
for (int tid = 0; tid < FLAGS_num_threads; tid++) {
|
|
|
|
|
threads.emplace_back([&, tid] {
|
|
|
|
|
auto &predictor = predictors[tid];
|
|
|
|
|
std::vector<std::unique_ptr<ZeroCopyTensor>> inputs;
|
|
|
|
|
PrepareZeroCopyInputs(predictor, &inputs);
|
|
|
|
|
auto output_tensor = predictor->GetOutputTensor(out_var_name);
|
|
|
|
|
Timer timer;
|
|
|
|
|
double total_time{0};
|
|
|
|
|
|
|
|
|
|
LOG(INFO) << "Warm up run...";
|
|
|
|
|
timer.tic();
|
|
|
|
|
predictor->ZeroCopyRun();
|
|
|
|
|
PrintTime(FLAGS_batch_size, 1, FLAGS_num_threads, tid, timer.toc(), 1);
|
|
|
|
|
if (FLAGS_profile) {
|
|
|
|
|
paddle::platform::ResetProfiler();
|
|
|
|
|
}
|
|
|
|
|
int repeat_times = FLAGS_repeat;
|
|
|
|
|
LOG(INFO) << "Run " << repeat_times << " times...";
|
|
|
|
|
timer.tic();
|
|
|
|
|
|
|
|
|
|
for (int i = 0; i < repeat_times; i++) {
|
|
|
|
|
predictor->ZeroCopyRun();
|
|
|
|
|
}
|
|
|
|
|
total_time += timer.toc();
|
|
|
|
|
total_time_of_threads += total_time;
|
|
|
|
|
|
|
|
|
|
LOG(INFO) << "thread time: " << total_time / repeat_times;
|
|
|
|
|
});
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
for (auto &t : threads) {
|
|
|
|
|
t.join();
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
LOG(INFO) << "average time: "
|
|
|
|
|
<< total_time_of_threads / FLAGS_num_threads / FLAGS_repeat;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
TEST(Analyzer_seq_pool1, zerocopy_fuse_statis) { analysis_fuse_statis(true); }
|
|
|
|
|
// Compare result of AnalysisConfig and AnalysisConfig + ZeroCopy
|
|
|
|
|
TEST(Analyzer_seq_pool1, compare_zero_copy) {
|
|
|
|
|
AnalysisConfig cfg;
|
|
|
|
|
SetConfig(&cfg);
|
|
|
|
|
|
|
|
|
|
TEST(Analyzer_seq_pool1, zerocopy_compare_native) {
|
|
|
|
|
AnalysisConfig config;
|
|
|
|
|
SetConfig(&config);
|
|
|
|
|
config.SwitchUseFeedFetchOps(true);
|
|
|
|
|
auto predictor = CreatePaddlePredictor<NativeConfig>(config.ToNativeConfig());
|
|
|
|
|
std::vector<PaddleTensor> native_outputs;
|
|
|
|
|
std::vector<std::vector<PaddleTensor>> input_slots_all;
|
|
|
|
|
SetInput(&input_slots_all);
|
|
|
|
|
ASSERT_TRUE(predictor->Run(input_slots_all[0], &native_outputs));
|
|
|
|
|
EXPECT_EQ(native_outputs.size(), 1UL);
|
|
|
|
|
|
|
|
|
|
auto zerocopy_output = zerocopy_profile(1);
|
|
|
|
|
EXPECT_EQ(zerocopy_output.size() * sizeof(float),
|
|
|
|
|
native_outputs.front().data.length());
|
|
|
|
|
auto *native_data = static_cast<float *>(native_outputs.front().data.data());
|
|
|
|
|
for (size_t i = 0; i < zerocopy_output.size(); ++i) {
|
|
|
|
|
EXPECT_LT(
|
|
|
|
|
std::fabs((zerocopy_output[i] - native_data[i]) / zerocopy_output[i]),
|
|
|
|
|
1e-3);
|
|
|
|
|
}
|
|
|
|
|
std::vector<std::string> outputs_name;
|
|
|
|
|
outputs_name.emplace_back(out_var_name);
|
|
|
|
|
CompareAnalysisAndZeroCopy(reinterpret_cast<PaddlePredictor::Config *>(&cfg),
|
|
|
|
|
input_slots_all, outputs_name);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
} // namespace analysis
|
|
|
|
|
|
Tao Luo
7 years ago
committed by
GitHub