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tm_benchmark.cc
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tm_benchmark.cc
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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.
*/
/*
* Copyright (c) 2021, OPEN AI LAB
* Author: qtang@openailab.com
* Update: lswang@openailab.com
*/
#include "tengine/c_api.h"
#include "common/cmdline.hpp"
#include "common/timer.hpp"
#include <cstdio>
#include <string>
int benchmark_loop = 1;
int benchmark_threads = 1;
int benchmark_model = -1;
int benchmark_cluster = 0;
int benchmark_mask = 0xFFFF;
int benchmark_data_type = 0;
std::string benchmark_device = "";
context_t s_context;
int get_tenser_element_size(int data_type)
{
switch (data_type)
{
case TENGINE_DT_FP32:
case TENGINE_DT_INT32:
return 4;
case TENGINE_DT_FP16:
case TENGINE_DT_INT16:
return 2;
case TENGINE_DT_INT8:
case TENGINE_DT_UINT8:
return 1;
default:
return 0;
}
}
int benchmark_graph(options_t* opt, const char* name, const char* file, int height, int width, int channel, int batch)
{
// create graph, load tengine model xxx.tmfile
graph_t graph = create_graph(s_context, "tengine", file);
if (nullptr == graph)
{
fprintf(stderr, "Tengine Benchmark: Create graph failed.\n");
return -1;
}
// set the input shape to initial the graph, and pre-run graph to infer shape
int input_size = height * width * channel;
int shape[] = { batch, channel, height, width }; // nchw
std::vector<unsigned char> input_buffer(batch * input_size * get_tenser_element_size(benchmark_data_type));
memset(input_buffer.data(), 1, input_buffer.size());
tensor_t input_tensor = get_graph_input_tensor(graph, 0, 0);
if (input_tensor == nullptr)
{
fprintf(stderr, "Tengine Benchmark: Get input tensor failed.\n");
return -1;
}
if (set_tensor_shape(input_tensor, shape, 4) < 0)
{
fprintf(stderr, "Tengine Benchmark: Set input tensor shape failed.\n");
return -1;
}
if (prerun_graph_multithread(graph, *opt) < 0)
{
fprintf(stderr, "Tengine Benchmark: Pre-run graph failed.\n");
return -1;
}
// prepare process input data, set the data mem to input tensor
if (set_tensor_buffer(input_tensor, input_buffer.data(), (int)input_buffer.size()) < 0)
{
fprintf(stderr, "Tengine Benchmark: Set input tensor buffer failed\n");
return -1;
}
// warning up graph
if (run_graph(graph, 1) < 0)
{
fprintf(stderr, "Tengine Benchmark: Run graph failed.\n");
return -1;
}
std::vector<float> time_cost(benchmark_loop);
// run graph
for (int i = 0; i < benchmark_loop; i++)
{
Timer timer;
int ret = run_graph(graph, 1);
time_cost[i] = timer.TimeCost();
if (0 != ret)
{
fprintf(stderr, "Tengine Benchmark: Run graph failed\n");
return -1;
}
}
float min = time_cost[0], max = time_cost[0], sum = 0.f;
for (const auto& var : time_cost)
{
if (min > var)
{
min = var;
}
if (max < var)
{
max = var;
}
sum += var;
}
sum /= (float)time_cost.size();
fprintf(stderr, "%20s min = %7.2f ms max = %7.2f ms avg = %7.2f ms\n", name, min, max, sum);
// release tengine graph
release_graph_tensor(input_tensor);
postrun_graph(graph);
destroy_graph(graph);
return 0;
}
int main(int argc, char* argv[])
{
cmdline::parser cmd;
cmd.add<int>("loop_count", 'r', "benchmark loops count", false, 1);
cmd.add<int>("thread_count", 't', "benchmark threads count", false, 1);
cmd.add<int>("cpu_cluster", 'p', "cpu cluster [0:auto, 1:big, 2:middle, 3:little]", false, 0);
cmd.add<int>("model", 's', "benchmark which model, \"-1\" means all models", false, -1);
cmd.add<int>("cpu_mask", 'a', "benchmark on masked cpu core(s)", false, -1);
cmd.add<std::string>("device", 'd', "device name (should be upper-case)", false);
cmd.add<std::string>("model_file", 'm', "path to a model file", false);
cmd.add<std::string>("input_shape", 'i', "shape of input (n,c,h,w)", false);
cmd.add<int>("input_dtype", 'f', "data type of input", false);
cmd.parse_check(argc, argv);
benchmark_loop = cmd.get<int>("loop_count");
benchmark_threads = cmd.get<int>("thread_count");
benchmark_model = cmd.get<int>("model");
benchmark_cluster = cmd.get<int>("cpu_cluster");
benchmark_mask = cmd.get<int>("cpu_mask");
benchmark_device = cmd.get<std::string>("device");
std::string benchmark_model_file = cmd.get<std::string>("model_file");
std::string input_shape = cmd.get<std::string>("input_shape");
benchmark_data_type = cmd.get<int>("input_dtype");
if (benchmark_device.empty())
{
benchmark_device = "CPU";
}
else
{
for (int i = 0; i < benchmark_device.length(); i++)
{
benchmark_device[i] = ::toupper(benchmark_device[i]);
}
}
fprintf(stdout, "Tengine benchmark:\n");
fprintf(stdout, " loops: %d\n", benchmark_loop);
fprintf(stdout, " threads: %d\n", benchmark_threads);
fprintf(stdout, " cluster: %d\n", benchmark_cluster);
fprintf(stdout, " affinity: 0x%X\n", benchmark_mask);
fprintf(stdout, " device: %s\n", benchmark_device.c_str());
// initialize tengine
if (0 != init_tengine())
{
fprintf(stderr, "Tengine Benchmark: Initialize tengine failed.\n");
return -1;
}
fprintf(stdout, "Tengine-lite library version: %s\n", get_tengine_version());
s_context = create_context("ctx", benchmark_device.empty() ? 0 : 1);
if (!benchmark_device.empty())
{
int ret = set_context_device(s_context, benchmark_device.c_str(), nullptr, 0);
if (0 != ret)
{
fprintf(stderr, "Set context device failed: %d.\n", ret);
return false;
}
}
struct options opt;
opt.num_thread = benchmark_threads;
opt.precision = TENGINE_MODE_FP32;
opt.affinity = benchmark_mask;
switch (benchmark_cluster)
{
case 0:
opt.cluster = TENGINE_CLUSTER_ALL;
break;
case 1:
opt.cluster = TENGINE_CLUSTER_BIG;
break;
case 2:
opt.cluster = TENGINE_CLUSTER_MEDIUM;
break;
case 3:
opt.cluster = TENGINE_CLUSTER_LITTLE;
break;
default:
opt.cluster = TENGINE_CLUSTER_ALL;
}
// run benchmarks
switch(benchmark_model)
{
case 0:
benchmark_graph(&opt, "squeezenet_v1.1", "./models/squeezenet_v1.1_benchmark.tmfile", 227, 227, 3, 1);
break;
case 1:
benchmark_graph(&opt, "mobilenetv1", "./models/mobilenet_benchmark.tmfile", 224, 224, 3, 1);
break;
case 2:
benchmark_graph(&opt, "mobilenetv2", "./models/mobilenet_v2_benchmark.tmfile", 224, 224, 3, 1);
break;
case 3:
benchmark_graph(&opt, "mobilenetv3", "./models/mobilenet_v3_benchmark.tmfile", 224, 224, 3, 1);
break;
case 4:
benchmark_graph(&opt, "shufflenetv2", "./models/shufflenet_v2_benchmark.tmfile", 224, 224, 3, 1);
break;
case 5:
benchmark_graph(&opt, "resnet18", "./models/resnet18_benchmark.tmfile", 224, 224, 3, 1);
break;
case 6:
benchmark_graph(&opt, "resnet50", "./models/resnet50_benchmark.tmfile", 224, 224, 3, 1);
break;
case 7:
benchmark_graph(&opt, "googlenet", "./models/googlenet_benchmark.tmfile", 224, 224, 3, 1);
break;
case 8:
benchmark_graph(&opt, "inceptionv3", "./models/inception_v3_benchmark.tmfile", 299, 299, 3, 1);
break;
case 9:
benchmark_graph(&opt, "vgg16", "./models/vgg16_benchmark.tmfile", 224, 224, 3, 1);
break;
case 10:
benchmark_graph(&opt, "mssd", "./models/mssd_benchmark.tmfile", 300, 300, 3, 1);
break;
case 11:
benchmark_graph(&opt, "retinaface", "./models/retinaface_benchmark.tmfile", 320, 240, 3, 1);
break;
case 12:
benchmark_graph(&opt, "yolov3_tiny", "./models/yolov3_tiny_benchmark.tmfile", 416, 416, 3, 1);
break;
case 13:
benchmark_graph(&opt, "mobilefacenets", "./models/mobilefacenets_benchmark.tmfile", 112, 112, 3, 1);
break;
default:
if (!benchmark_model_file.empty()) {
int n = 1, c = 3, h = 224, w = 224;
if (!input_shape.empty()) {
char ch;
int count = sscanf(input_shape.c_str(), "%u%c%u%c%u%c%u", &n, &ch, &c, &ch, &h, &ch, &w);
if (count == 3) {
w = h, h = c, c = n, n = 1;
} else if (count == 2) {
w = c, h = n, c = 3, n = 1;
}
}
benchmark_graph(&opt, benchmark_model_file.c_str(), benchmark_model_file.c_str(), w, h, c, n);
break;
}
benchmark_graph(&opt, "squeezenet_v1.1", "./models/squeezenet_v1.1_benchmark.tmfile", 227, 227, 3, 1);
benchmark_graph(&opt, "mobilenetv1", "./models/mobilenet_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "mobilenetv2", "./models/mobilenet_v2_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "mobilenetv3", "./models/mobilenet_v3_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "shufflenetv2", "./models/shufflenet_v2_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "resnet18", "./models/resnet18_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "resnet50", "./models/resnet50_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "googlenet", "./models/googlenet_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "inceptionv3", "./models/inception_v3_benchmark.tmfile", 299, 299, 3, 1);
benchmark_graph(&opt, "vgg16", "./models/vgg16_benchmark.tmfile", 224, 224, 3, 1);
benchmark_graph(&opt, "mssd", "./models/mssd_benchmark.tmfile", 300, 300, 3, 1);
benchmark_graph(&opt, "retinaface", "./models/retinaface_benchmark.tmfile", 320, 240, 3, 1);
benchmark_graph(&opt, "yolov3_tiny", "./models/yolov3_tiny_benchmark.tmfile", 416, 416, 3, 1);
benchmark_graph(&opt, "mobilefacenets", "./models/mobilefacenets_benchmark.tmfile", 112, 112, 3, 1);
}
/* release tengine */
destroy_context(s_context);
release_tengine();
fprintf(stderr, "ALL TEST DONE.\n");
return 0;
}