m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

unify ZeroCopy in analysis_test

Browse Source
revert-16045-imperative_remove_desc
luotao1 6 years ago
parent 9be825a982
commit fad06cb928
3 changed files with 96 additions and 69 deletions
Show Stats Download Patch File Download Diff File

15
paddle/fluid/inference/api/helper.h
Unescape View File

@ -127,9 +127,8 @@ static void TensorAssignData(PaddleTensor *tensor,
}
template <typename T>
static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
const std::vector<std::vector<T>> &data) {
int size{0};
static void ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
const std::vector<std::vector<T>> &data) {
auto *ptr = tensor->mutable_data<T>(PaddlePlace::kCPU);
int c = 0;
for (const auto &f : data) {
@ -137,7 +136,15 @@ static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
ptr[c++] = v;
}
}
return size;
}
template <typename T>
static void ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
const PaddleBuf &data) {
auto *ptr = tensor->mutable_data<T>(PaddlePlace::kCPU);
for (size_t i = 0; i < data.length() / sizeof(T); i++) {
ptr[i] = *(reinterpret_cast<T *>(data.data()) + i);
}
}
static bool CompareTensor(const PaddleTensor &a, const PaddleTensor &b) {

3
paddle/fluid/inference/tests/api/analyzer_pyramid_dnn_tester.cc
Unescape View File

@ -107,6 +107,9 @@ void SetConfig(AnalysisConfig *cfg) {
cfg->DisableGpu();
cfg->SwitchSpecifyInputNames();
cfg->SwitchIrOptim();
if (FLAGS_zero_copy) {
cfg->SwitchUseFeedFetchOps(false);
}
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {

147
paddle/fluid/inference/tests/api/tester_helper.h
Unescape View File

@ -51,6 +51,7 @@ DEFINE_bool(use_analysis, true,
DEFINE_bool(record_benchmark, false,
"Record benchmark after profiling the model");
DEFINE_double(accuracy, 1e-3, "Result Accuracy.");
DEFINE_bool(zero_copy, false, "Use ZeroCopy to speedup Feed/Fetch.");
DECLARE_bool(profile);
DECLARE_int32(paddle_num_threads);
@ -198,61 +199,104 @@ void GetInputPerBatch(const std::vector<std::vector<int64_t>> &in,
}
}
void TestOneThreadPrediction(
const PaddlePredictor::Config *config,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<PaddleTensor> *outputs, bool use_analysis = true) {
int batch_size = FLAGS_batch_size;
int num_times = FLAGS_repeat;
auto predictor = CreateTestPredictor(config, use_analysis);
void ConvertPaddleTensorToZeroCopyTensor(
PaddlePredictor *predictor, const std::vector<PaddleTensor> &inputs) {
for (size_t i = 0; i < inputs.size(); i++) {
auto input = inputs[i];
auto tensor = predictor->GetInputTensor(input.name);
tensor->Reshape(input.shape);
tensor->SetLoD({input.lod});
if (input.dtype == PaddleDType::INT64) {
ZeroCopyTensorAssignData<int64_t>(tensor.get(), input.data);
} else if (input.dtype == PaddleDType::FLOAT32) {
ZeroCopyTensorAssignData<float>(tensor.get(), input.data);
} else {
LOG(ERROR) << "unsupported feed type " << input.dtype;
}
}
}
// warmup run
LOG(INFO) << "Warm up run...";
{
Timer warmup_timer;
warmup_timer.tic();
void PredictionWarmUp(PaddlePredictor *predictor,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<PaddleTensor> *outputs, int num_threads,
int tid) {
int batch_size = FLAGS_batch_size;
LOG(INFO) << "Running thread " << tid << ", warm up run...";
if (FLAGS_zero_copy) {
ConvertPaddleTensorToZeroCopyTensor(predictor, inputs[0]);
}
Timer warmup_timer;
warmup_timer.tic();
if (!FLAGS_zero_copy) {
predictor->Run(inputs[0], outputs, batch_size);
PrintTime(batch_size, 1, 1, 0, warmup_timer.toc(), 1);
if (FLAGS_profile) {
paddle::platform::ResetProfiler();
}
} else {
predictor->ZeroCopyRun();
}
PrintTime(batch_size, 1, num_threads, tid, warmup_timer.toc(), 1);
if (FLAGS_profile) {
paddle::platform::ResetProfiler();
}
}
LOG(INFO) << "Run " << num_times << " times...";
{
Timer run_timer;
run_timer.tic();
void PredictionRun(PaddlePredictor *predictor,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<PaddleTensor> *outputs, int num_threads,
int tid) {
int batch_size = FLAGS_batch_size;
int num_times = FLAGS_repeat;
LOG(INFO) << "Thread " << tid << " run " << num_times << " times...";
Timer run_timer;
double elapsed_time = 0;
#ifdef WITH_GPERFTOOLS
ProfilerStart("paddle_inference.prof");
ProfilerStart("paddle_inference.prof");
#endif
for (int i = 0; i < num_times; i++) {
for (size_t j = 0; j < inputs.size(); j++) {
predictor->Run(inputs[j], outputs, batch_size);
if (!FLAGS_zero_copy) {
run_timer.tic();
for (size_t i = 0; i < inputs.size(); i++) {
for (int j = 0; j < num_times; j++) {
predictor->Run(inputs[i], outputs, batch_size);
}
}
elapsed_time = run_timer.toc();
} else {
for (size_t i = 0; i < inputs.size(); i++) {
ConvertPaddleTensorToZeroCopyTensor(predictor, inputs[i]);
run_timer.tic();
for (int j = 0; j < num_times; j++) {
predictor->ZeroCopyRun();
}
elapsed_time += run_timer.toc();
}
}
#ifdef WITH_GPERFTOOLS
ProfilerStop();
ProfilerStop();
#endif
double latency = run_timer.toc() / (num_times > 1 ? num_times : 1);
PrintTime(batch_size, num_times, 1, 0, latency, inputs.size());
if (FLAGS_record_benchmark) {
Benchmark benchmark;
benchmark.SetName(FLAGS_model_name);
benchmark.SetBatchSize(batch_size);
benchmark.SetLatency(latency);
benchmark.PersistToFile("benchmark_record.txt");
}
PrintTime(batch_size, num_times, num_threads, tid, elapsed_time / num_times,
inputs.size());
if (FLAGS_record_benchmark) {
Benchmark benchmark;
benchmark.SetName(FLAGS_model_name);
benchmark.SetBatchSize(batch_size);
benchmark.SetLatency(elapsed_time / num_times);
benchmark.PersistToFile("benchmark_record.txt");
}
}
void TestOneThreadPrediction(
const PaddlePredictor::Config *config,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<PaddleTensor> *outputs, bool use_analysis = true) {
auto predictor = CreateTestPredictor(config, use_analysis);
PredictionWarmUp(predictor.get(), inputs, outputs, 1, 0);
PredictionRun(predictor.get(), inputs, outputs, 1, 0);
}
void TestMultiThreadPrediction(
const PaddlePredictor::Config *config,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<PaddleTensor> *outputs, int num_threads,
bool use_analysis = true) {
int batch_size = FLAGS_batch_size;
int num_times = FLAGS_repeat;
std::vector<std::thread> threads;
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
predictors.emplace_back(CreateTestPredictor(config, use_analysis));
@ -260,7 +304,6 @@ void TestMultiThreadPrediction(
predictors.emplace_back(predictors.front()->Clone());
}
size_t total_time{0};
for (int tid = 0; tid < num_threads; ++tid) {
threads.emplace_back([&, tid]() {
// Each thread should have local inputs and outputs.
@ -273,34 +316,8 @@ void TestMultiThreadPrediction(
->SetMkldnnThreadID(static_cast<int>(tid) + 1);
}
#endif
// warmup run
LOG(INFO) << "Running thread " << tid << ", warm up run...";
{
Timer warmup_timer;
warmup_timer.tic();
predictor->Run(inputs[0], outputs, batch_size);
PrintTime(batch_size, 1, num_threads, tid, warmup_timer.toc(), 1);
if (FLAGS_profile) {
paddle::platform::ResetProfiler();
}
}
LOG(INFO) << "Thread " << tid << " run " << num_times << " times...";
{
Timer timer;
timer.tic();
for (int i = 0; i < num_times; i++) {
for (const auto &input : inputs) {
ASSERT_TRUE(predictor->Run(input, &outputs_tid));
}
}
auto time = timer.toc();
total_time += time;
PrintTime(batch_size, num_times, num_threads, tid, time / num_times,
inputs.size());
}
PredictionWarmUp(predictor.get(), inputs, outputs, num_threads, tid);
PredictionRun(predictor.get(), inputs, outputs, num_threads, tid);
});
}
for (int i = 0; i < num_threads; ++i) {

Write Preview
Loading…
Cancel
Save