409 lines
13 KiB
409 lines
13 KiB
// 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>
|
|
|
|
#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/paddle_inference_pass.h"
|
|
|
|
#include "paddle/fluid/inference/api/helper.h"
|
|
#include "paddle/fluid/inference/tests/api/config_printer.h"
|
|
#include "paddle/fluid/inference/tests/test_helper.h"
|
|
#include "paddle/fluid/platform/profiler.h"
|
|
|
|
DEFINE_string(infer_model, "", "model path");
|
|
DEFINE_string(infer_data, "", "data file");
|
|
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.");
|
|
|
|
DECLARE_bool(profile);
|
|
|
|
namespace paddle {
|
|
namespace inference {
|
|
|
|
void PrintConfig(const PaddlePredictor::Config *config, bool use_analysis) {
|
|
if (use_analysis) {
|
|
LOG(INFO) << *reinterpret_cast<const contrib::AnalysisConfig *>(config);
|
|
return;
|
|
}
|
|
LOG(INFO) << *reinterpret_cast<const NativeConfig *>(config);
|
|
}
|
|
|
|
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) {
|
|
EXPECT_NEAR(pdata_ref[j], pdata[j], 1e-3);
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
std::unique_ptr<PaddlePredictor> CreateTestPredictor(
|
|
const PaddlePredictor::Config *config, bool use_analysis = true) {
|
|
if (use_analysis) {
|
|
return CreatePaddlePredictor<contrib::AnalysisConfig>(
|
|
*(reinterpret_cast<const contrib::AnalysisConfig *>(config)));
|
|
}
|
|
return CreatePaddlePredictor<NativeConfig>(
|
|
*(reinterpret_cast<const NativeConfig *>(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") {
|
|
// 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();
|
|
|
|
int dim1 = feed_target_shapes[0][1];
|
|
int dim2 = feed_target_shapes[0][2];
|
|
int dim3 = feed_target_shapes[0][3];
|
|
|
|
PaddleTensor input;
|
|
std::vector<int> shape({FLAGS_batch_size, dim1, dim2, dim3});
|
|
input.shape = shape;
|
|
input.dtype = PaddleDType::FLOAT32;
|
|
|
|
// fill input data, for profile easily, do not use random data here.
|
|
size_t size = FLAGS_batch_size * dim1 * dim2 * dim3;
|
|
input.data.Resize(size * sizeof(float));
|
|
float *input_data = static_cast<float *>(input.data.data());
|
|
for (size_t i = 0; i < size; i++) {
|
|
*(input_data + i) = static_cast<float>(i) / size;
|
|
}
|
|
|
|
std::vector<PaddleTensor> input_slots;
|
|
input_slots.assign({input});
|
|
(*inputs).emplace_back(input_slots);
|
|
}
|
|
|
|
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 !defined(_WIN32)
|
|
if (FLAGS_profile) {
|
|
paddle::platform::ResetProfiler();
|
|
}
|
|
#endif
|
|
}
|
|
|
|
LOG(INFO) << "Run " << num_times << " times...";
|
|
{
|
|
Timer run_timer;
|
|
run_timer.tic();
|
|
for (int i = 0; i < num_times; i++) {
|
|
for (size_t j = 0; j < inputs.size(); j++) {
|
|
predictor->Run(inputs[j], outputs, batch_size);
|
|
}
|
|
}
|
|
PrintTime(batch_size, num_times, 1, 0, run_timer.toc() / num_times,
|
|
inputs.size());
|
|
}
|
|
}
|
|
|
|
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));
|
|
for (int tid = 1; tid < num_threads; ++tid) {
|
|
predictors.emplace_back(predictors.front()->Clone());
|
|
}
|
|
|
|
size_t total_time{0};
|
|
for (int tid = 0; tid < num_threads; ++tid) {
|
|
threads.emplace_back([&, tid]() {
|
|
#ifdef PADDLE_WITH_MKLDNN
|
|
platform::set_cur_thread_id(static_cast<int>(tid) + 1);
|
|
#endif
|
|
// Each thread should have local inputs and outputs.
|
|
// The inputs of each thread are all the same.
|
|
std::vector<PaddleTensor> outputs_tid;
|
|
auto &predictor = predictors[tid];
|
|
|
|
// 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 !defined(_WIN32)
|
|
if (FLAGS_profile) {
|
|
paddle::platform::ResetProfiler();
|
|
}
|
|
#endif
|
|
}
|
|
|
|
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 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);
|
|
}
|
|
|
|
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() == typeid(float)) {
|
|
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() == typeid(int64_t)) {
|
|
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
|