luotao1
7 years ago
parent
e69d9c845b
commit
20b40cb06a
@ -0,0 +1,126 @@
|
||||
// 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 <thread> // NOLINT
|
||||
#include <vector>
|
||||
#include "paddle/fluid/framework/ir/fuse_pass_base.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/platform/profiler.h"
|
||||
|
||||
DEFINE_string(infer_model, "", "model path");
|
||||
DEFINE_string(infer_data, "", "data file");
|
||||
DEFINE_int32(batch_size, 1, "batch size.");
|
||||
DEFINE_int32(burning, 0, "Burning before repeat.");
|
||||
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.");
|
||||
|
||||
namespace paddle {
|
||||
namespace inference {
|
||||
|
||||
void CompareResult(const std::vector<PaddleTensor> &outputs,
|
||||
const std::vector<PaddleTensor> &base_outputs) {
|
||||
PADDLE_ENFORCE_GT(outputs.size(), 0);
|
||||
PADDLE_ENFORCE_EQ(outputs.size(), base_outputs.size());
|
||||
for (size_t i = 0; i < outputs.size(); i++) {
|
||||
auto &out = outputs[i];
|
||||
auto &base_out = base_outputs[i];
|
||||
size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1,
|
||||
[](int a, int b) { return a * b; });
|
||||
size_t size1 = std::accumulate(base_out.shape.begin(), base_out.shape.end(),
|
||||
1, [](int a, int b) { return a * b; });
|
||||
PADDLE_ENFORCE_EQ(size, size1);
|
||||
PADDLE_ENFORCE_GT(size, 0);
|
||||
float *data = static_cast<float *>(out.data.data());
|
||||
float *base_data = static_cast<float *>(base_out.data.data());
|
||||
for (size_t i = 0; i < size; i++) {
|
||||
EXPECT_NEAR(data[i], base_data[i], 1e-3);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void TestOneThreadPrediction(
|
||||
AnalysisConfig config, const std::vector<std::vector<PaddleTensor>> inputs,
|
||||
std::vector<PaddleTensor> *outputs) {
|
||||
int batch_size = FLAGS_batch_size;
|
||||
int num_times = FLAGS_repeat;
|
||||
auto predictor =
|
||||
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
|
||||
config);
|
||||
Timer timer;
|
||||
timer.tic();
|
||||
for (int i = 0; i < num_times; i++) {
|
||||
for (size_t j = 0; j < inputs.size(); j++) {
|
||||
predictor->Run(inputs[j], outputs);
|
||||
}
|
||||
}
|
||||
PrintTime(batch_size, num_times, 1, 0, timer.toc() / num_times,
|
||||
inputs.size());
|
||||
}
|
||||
|
||||
void TestMultiThreadPrediction(
|
||||
AnalysisConfig config, const std::vector<std::vector<PaddleTensor>> inputs,
|
||||
std::vector<PaddleTensor> *outputs, int num_threads) {
|
||||
int batch_size = FLAGS_batch_size;
|
||||
int num_times = FLAGS_repeat;
|
||||
std::vector<std::thread> threads;
|
||||
std::vector<std::unique_ptr<PaddlePredictor>> predictors;
|
||||
// TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
|
||||
// because AttentionLSTM's hard code nodeid will be damanged.
|
||||
for (int tid = 0; tid < num_threads; ++tid) {
|
||||
predictors.emplace_back(
|
||||
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
|
||||
config));
|
||||
}
|
||||
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<std::vector<PaddleTensor>> inputs_tid = inputs;
|
||||
std::vector<PaddleTensor> outputs_tid;
|
||||
Timer timer;
|
||||
timer.tic();
|
||||
for (int i = 0; i < num_times; i++) {
|
||||
for (size_t j = 0; j < inputs_tid.size(); j++) {
|
||||
predictors[tid]->Run(inputs_tid[j], &outputs_tid);
|
||||
}
|
||||
}
|
||||
PrintTime(batch_size, num_times, num_threads, tid,
|
||||
timer.toc() / num_times, inputs_tid.size());
|
||||
});
|
||||
}
|
||||
for (int i = 0; i < num_threads; ++i) {
|
||||
threads[i].join();
|
||||
}
|
||||
}
|
||||
|
||||
void TestPrediction(AnalysisConfig config,
|
||||
const std::vector<std::vector<PaddleTensor>> inputs,
|
||||
std::vector<PaddleTensor> *outputs, int num_threads) {
|
||||
if (num_threads == 1) {
|
||||
TestOneThreadPrediction(config, inputs, outputs);
|
||||
} else {
|
||||
TestMultiThreadPrediction(config, inputs, outputs, num_threads);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace inference
|
||||
} // namespace paddle
|
||||
Loading…
Reference in new issue