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Paddle/paddle/fluid/inference/tests/api/tester_helper.h

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// Copyright (c) 2018 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.
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <string>
#include <thread> // NOLINT
#include <vector>
#ifdef WITH_GPERFTOOLS
#include <gperftools/profiler.h>
#endif
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/tests/api/config_printer.h"
#include "paddle/fluid/inference/tests/test_helper.h"
#include "paddle/fluid/inference/utils/benchmark.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_string(model_name, "", "model name");
DEFINE_string(infer_model, "", "model path");
DEFINE_string(infer_data, "", "data file");
DEFINE_string(refer_result, "", "reference result for comparison");
DEFINE_int32(batch_size, 1, "batch size.");
DEFINE_int32(repeat, 1, "Running the inference program repeat times.");
DEFINE_bool(test_all_data, false, "Test the all dataset in data file.");
DEFINE_int32(num_threads, 1, "Running the inference program in multi-threads.");
DEFINE_bool(use_analysis, true,
"Running the inference program in analysis mode.");
DEFINE_bool(record_benchmark, false,
"Record benchmark after profiling the model");
DEFINE_double(accuracy, 1e-3, "Result Accuracy.");
DECLARE_bool(profile);
DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace inference {
void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) {
const auto *analysis_config =
reinterpret_cast<const AnalysisConfig *>(config);
if (use_analysis) {
LOG(INFO) << *analysis_config;
return;
}
LOG(INFO) << analysis_config->ToNativeConfig();
}
void CompareResult(const std::vector<PaddleTensor> &outputs,
const std::vector<PaddleTensor> &ref_outputs) {
EXPECT_GT(outputs.size(), 0UL);
EXPECT_EQ(outputs.size(), ref_outputs.size());
for (size_t i = 0; i < outputs.size(); i++) {
auto &out = outputs[i];
auto &ref_out = ref_outputs[i];
size_t size = VecReduceToInt(out.shape);
size_t ref_size = VecReduceToInt(ref_out.shape);
EXPECT_GT(size, 0UL);
EXPECT_EQ(size, ref_size);
EXPECT_EQ(out.dtype, ref_out.dtype);
switch (out.dtype) {
case PaddleDType::INT64: {
int64_t *pdata = static_cast<int64_t *>(out.data.data());
int64_t *pdata_ref = static_cast<int64_t *>(ref_out.data.data());
for (size_t j = 0; j < size; ++j) {
EXPECT_EQ(pdata_ref[j], pdata[j]);
}
break;
}
case PaddleDType::FLOAT32: {
float *pdata = static_cast<float *>(out.data.data());
float *pdata_ref = static_cast<float *>(ref_out.data.data());
for (size_t j = 0; j < size; ++j) {
CHECK_LE(std::abs(pdata_ref[j] - pdata[j]), FLAGS_accuracy);
}
break;
}
}
}
}
std::unique_ptr<PaddlePredictor> CreateTestPredictor(
const PaddlePredictor::Config *config, bool use_analysis = true) {
const auto *analysis_config =
reinterpret_cast<const AnalysisConfig *>(config);
if (use_analysis) {
return CreatePaddlePredictor<AnalysisConfig>(*analysis_config);
}
auto native_config = analysis_config->ToNativeConfig();
return CreatePaddlePredictor<NativeConfig>(native_config);
}
size_t GetSize(const PaddleTensor &out) { return VecReduceToInt(out.shape); }
std::unordered_map<std::string, int> GetFuseStatis(PaddlePredictor *predictor,
int *num_ops) {
std::unordered_map<std::string, int> res;
auto *analysis_predictor = static_cast<AnalysisPredictor *>(predictor);
auto *fusion_status =
analysis_predictor->analysis_argument().fusion_statis_ptr();
if (!fusion_status) {
return res;
}
for (auto &item : *fusion_status) {
LOG(INFO) << "fused " << item.first << " " << item.second;
}
int num = 0;
for (auto &node :
analysis_predictor->analysis_argument().main_graph().Nodes()) {
if (node->IsOp()) {
++num;
}
}
*num_ops = num;
return *fusion_status;
}
void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
const std::string &dirname, bool is_combined = true,
std::string model_filename = "model",
std::string params_filename = "params",
const std::vector<std::string> *feed_names = nullptr,
const int continuous_inuput_index = 0) {
// Set fake_image_data
PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "Only have single batch of data.");
std::vector<std::vector<int64_t>> feed_target_shapes = GetFeedTargetShapes(
dirname, is_combined, model_filename, params_filename);
std::ostringstream os;
for (size_t i = 0; i < feed_target_shapes.size(); ++i) {
os << "feed target " << i << ": {" << feed_target_shapes[i][0];
for (size_t j = 1; j < feed_target_shapes[i].size(); ++j) {
os << ", " << feed_target_shapes[i][j];
}
os << "}\n";
}
LOG(INFO) << os.str();
if (feed_names) {
PADDLE_ENFORCE_EQ(feed_names->size(), feed_target_shapes.size());
}
std::vector<PaddleTensor> input_slots(feed_target_shapes.size());
for (size_t i = 0; i < feed_target_shapes.size(); ++i) {
const auto &feed_shape = feed_target_shapes[i];
auto &input = input_slots[i];
std::vector<int> shape({FLAGS_batch_size});
for (size_t s = 1; s < feed_shape.size(); ++s) {
shape.push_back(static_cast<int>(feed_shape[s]));
}
if (feed_names) {
input.name = (*feed_names)[i];
}
input.shape = shape;
input.dtype = PaddleDType::FLOAT32;
size_t len = std::accumulate(shape.begin(), shape.end(), 1,
[](int a, int b) { return a * b; });
input.data.Resize(len * sizeof(float));
input.lod.assign({{0, static_cast<size_t>(FLAGS_batch_size)}});
float *input_data = static_cast<float *>(input.data.data());
// fill input data, for profile easily, do not use random data here.
for (size_t j = 0; j < len; ++j) {
*(input_data + j) =
static_cast<float>((j + continuous_inuput_index) % len) / len;
}
}
(*inputs).emplace_back(input_slots);
}
void GetInputPerBatch(const std::vector<std::vector<int64_t>> &in,
std::vector<std::vector<int64_t>> *out,
std::vector<size_t> *lod, size_t batch_iter,
size_t batch_end) {
lod->clear();
lod->push_back(0);
for (auto it = in.begin() + batch_iter; it < in.begin() + batch_end; it++) {
out->push_back(*it);
lod->push_back(lod->back() + (*it).size()); // calculate lod
}
}
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);
// warmup run
LOG(INFO) << "Warm up run...";
{
Timer warmup_timer;
warmup_timer.tic();
predictor->Run(inputs[0], outputs, batch_size);
PrintTime(batch_size, 1, 1, 0, warmup_timer.toc(), 1);
if (FLAGS_profile) {
paddle::platform::ResetProfiler();
}
}
LOG(INFO) << "Run " << num_times << " times...";
{
Timer run_timer;
run_timer.tic();
#ifdef WITH_GPERFTOOLS
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);
}
}
#ifdef WITH_GPERFTOOLS
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");
}
}
}
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;
auto main_predictor = CreateTestPredictor(config, use_analysis);
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.
// The inputs of each thread are all the same.
std::vector<PaddleTensor> outputs_tid;
// To ensure the thread binding correctly,
// please clone inside the threadpool.
auto predictor = main_predictor->Clone();
#ifdef PADDLE_WITH_MKLDNN
if (use_analysis) {
static_cast<AnalysisPredictor *>(predictor.get())
->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());
}
});
}
for (int i = 0; i < num_threads; ++i) {
threads[i].join();
}
}
void TestPrediction(const PaddlePredictor::Config *config,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<PaddleTensor> *outputs, int num_threads,
bool use_analysis = FLAGS_use_analysis) {
PrintConfig(config, use_analysis);
if (num_threads == 1) {
TestOneThreadPrediction(config, inputs, outputs, use_analysis);
} else {
TestMultiThreadPrediction(config, inputs, outputs, num_threads,
use_analysis);
}
}
void CompareDeterministic(
const PaddlePredictor::Config *config,
const std::vector<std::vector<PaddleTensor>> &inputs) {
int batch_size = FLAGS_batch_size;
int num_times = FLAGS_repeat;
auto predictor = CreateTestPredictor(config, FLAGS_use_analysis);
std::vector<PaddleTensor> warmup_outputs, outputs;
// run num_times to Compare Deterministic Result.
for (size_t j = 0; j < inputs.size(); j++) {
// warmup run
predictor->Run(inputs[j], &warmup_outputs, batch_size);
for (int i = 0; i < num_times; i++) {
predictor->Run(inputs[j], &outputs, batch_size);
CompareResult(outputs, warmup_outputs);
}
}
}
void CompareNativeAndAnalysis(
const PaddlePredictor::Config *config,
const std::vector<std::vector<PaddleTensor>> &inputs) {
PrintConfig(config, true);
std::vector<PaddleTensor> native_outputs, analysis_outputs;
TestOneThreadPrediction(config, inputs, &native_outputs, false);
TestOneThreadPrediction(config, inputs, &analysis_outputs, true);
CompareResult(analysis_outputs, native_outputs);
}
void CompareNativeAndAnalysis(
PaddlePredictor *native_pred, PaddlePredictor *analysis_pred,
const std::vector<std::vector<PaddleTensor>> &inputs) {
int batch_size = FLAGS_batch_size;
std::vector<PaddleTensor> native_outputs, analysis_outputs;
native_pred->Run(inputs[0], &native_outputs, batch_size);
analysis_pred->Run(inputs[0], &analysis_outputs, batch_size);
CompareResult(analysis_outputs, native_outputs);
}
template <typename T>
std::string LoDTensorSummary(const framework::LoDTensor &tensor) {
std::stringstream ss;
ss << "\n---- tensor ---" << '\n';
ss << "lod: [";
for (const auto &level : tensor.lod()) {
ss << "[ ";
for (auto i : level) {
ss << i << ", ";
}
ss << "]";
}
ss << "]\n";
ss << "shape: [";
int size = 1;
for (int i = 0; i < tensor.dims().size(); i++) {
int dim = tensor.dims()[i];
ss << dim << ", ";
size *= dim;
}
ss << "]\n";
ss << "data: ";
for (int i = 0; i < std::min(20, size); i++) {
ss << tensor.data<T>()[i] << " ";
}
ss << "\n";
return ss.str();
}
static bool CompareLoD(const framework::LoD &a, const framework::LoD &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("lod size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
auto &al = a[i];
auto &bl = b[i];
if (al.size() != bl.size()) {
LOG(ERROR) << string::Sprintf("level size %d != %d", al.size(),
bl.size());
return false;
}
}
return true;
}
static bool CompareShape(const std::vector<int64_t> &a,
const std::vector<int64_t> &b) {
if (a.size() != b.size()) {
LOG(ERROR) << string::Sprintf("shape size not match %d != %d", a.size(),
b.size());
return false;
}
for (size_t i = 0; i < a.size(); i++) {
if (a[i] != b[i]) {
LOG(ERROR) << string::Sprintf("shape %d-th element not match %d != %d", i,
a[i], b[i]);
return false;
}
}
return true;
}
static bool CompareTensorData(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
auto a_shape = framework::vectorize(a.dims());
auto b_shape = framework::vectorize(b.dims());
size_t a_size = std::accumulate(a_shape.begin(), a_shape.end(), 1,
[](int a, int b) { return a * b; });
size_t b_size = std::accumulate(b_shape.begin(), b_shape.end(), 1,
[](int a, int b) { return a * b; });
if (a_size != b_size) {
LOG(ERROR) << string::Sprintf("tensor data size not match, %d != %d",
a_size, b_size);
}
for (size_t i = 0; i < a_size; i++) {
if (a.type() == framework::proto::VarType::FP32) {
const auto *a_data = a.data<float>();
const auto *b_data = b.data<float>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
} else if (a.type() == framework::proto::VarType::INT64) {
const auto *a_data = a.data<int64_t>();
const auto *b_data = b.data<int64_t>();
if (std::abs(a_data[i] - b_data[i]) > 1e-3) {
LOG(ERROR) << string::Sprintf(
"tensor data %d-th element not match, %f != %f", i, a_data[i],
b_data[i]);
return false;
}
}
}
return true;
}
static bool CompareTensor(const framework::LoDTensor &a,
const framework::LoDTensor &b) {
if (!CompareLoD(a.lod(), b.lod())) {
return false;
}
if (!CompareShape(framework::vectorize(a.dims()),
framework::vectorize(b.dims()))) {
return false;
}
if (!CompareTensorData(a, b)) {
return false;
}
return true;
}
} // namespace inference
} // namespace paddle
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