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Revert "[Benchmark]Benchmark cpp for YOLOv5" (#1250)

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Revert "[Benchmark]Benchmark cpp for YOLOv5 (#1224)"

This reverts commit c487359e33.
This commit is contained in:
Jason
2023-02-07 22:14:48 +08:00
committed by GitHub
parent c487359e33
commit c25aa71fa9
27 changed files with 44 additions and 422 deletions
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benchmark/python/benchmark_uie.py
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import numpy as np
import os
import time
import distutils.util
import sys
import json
import fastdeploy as fd
from fastdeploy.text import UIEModel, SchemaLanguage
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_dir",
required=True,
help="The directory of model and tokenizer.")
parser.add_argument(
"--data_path", required=True, help="The path of uie data.")
parser.add_argument(
"--device",
type=str,
default='cpu',
choices=['gpu', 'cpu'],
help="Type of inference device, support 'cpu' or 'gpu'.")
parser.add_argument(
"--backend",
type=str,
default='paddle',
choices=['ort', 'paddle', 'trt', 'paddle_trt', 'ov'],
help="The inference runtime backend.")
parser.add_argument(
"--device_id", type=int, default=0, help="device(gpu) id")
parser.add_argument(
"--batch_size", type=int, default=1, help="The batch size of data.")
parser.add_argument(
"--max_length",
type=int,
default=128,
help="The max length of sequence.")
parser.add_argument(
"--cpu_num_threads",
type=int,
default=8,
help="The number of threads when inferring on cpu.")
parser.add_argument(
"--enable_trt_fp16",
type=distutils.util.strtobool,
default=False,
help="whether enable fp16 in trt backend")
parser.add_argument(
"--epoch", type=int, default=1, help="The epoch of test")
parser.add_argument(
"--enable_collect_memory_info",
type=ast.literal_eval,
default=False,
help="whether enable collect memory info")
return parser.parse_args()
def build_option(args):
option = fd.RuntimeOption()
if args.device == 'cpu':
option.use_cpu()
option.set_cpu_thread_num(args.cpu_num_threads)
else:
option.use_gpu(args.device_id)
if args.backend == 'paddle':
option.use_paddle_backend()
elif args.backend == 'ort':
option.use_ort_backend()
elif args.backend == 'ov':
option.use_openvino_backend()
else:
option.use_trt_backend()
if args.backend == 'paddle_trt':
option.enable_paddle_to_trt()
option.enable_paddle_trt_collect_shape()
trt_file = os.path.join(args.model_dir, "infer.trt")
option.set_trt_input_shape(
'input_ids',
min_shape=[1, 1],
opt_shape=[args.batch_size, args.max_length // 2],
max_shape=[args.batch_size, args.max_length])
option.set_trt_input_shape(
'token_type_ids',
min_shape=[1, 1],
opt_shape=[args.batch_size, args.max_length // 2],
max_shape=[args.batch_size, args.max_length])
option.set_trt_input_shape(
'pos_ids',
min_shape=[1, 1],
opt_shape=[args.batch_size, args.max_length // 2],
max_shape=[args.batch_size, args.max_length])
option.set_trt_input_shape(
'att_mask',
min_shape=[1, 1],
opt_shape=[args.batch_size, args.max_length // 2],
max_shape=[args.batch_size, args.max_length])
if args.enable_trt_fp16:
option.enable_trt_fp16()
trt_file = trt_file + ".fp16"
option.set_trt_cache_file(trt_file)
return option
class StatBase(object):
"""StatBase"""
nvidia_smi_path = "nvidia-smi"
gpu_keys = ('index', 'uuid', 'name', 'timestamp', 'memory.total',
'memory.free', 'memory.used', 'utilization.gpu',
'utilization.memory')
nu_opt = ',nounits'
cpu_keys = ('cpu.util', 'memory.util', 'memory.used')
class Monitor(StatBase):
"""Monitor"""
def __init__(self, use_gpu=False, gpu_id=0, interval=0.1):
self.result = {}
self.gpu_id = gpu_id
self.use_gpu = use_gpu
self.interval = interval
self.cpu_stat_q = multiprocessing.Queue()
def start(self):
cmd = '%s --id=%s --query-gpu=%s --format=csv,noheader%s -lms 50' % (
StatBase.nvidia_smi_path, self.gpu_id, ','.join(StatBase.gpu_keys),
StatBase.nu_opt)
if self.use_gpu:
self.gpu_stat_worker = subprocess.Popen(
cmd,
stderr=subprocess.STDOUT,
stdout=subprocess.PIPE,
shell=True,
close_fds=True,
preexec_fn=os.setsid)
# cpu stat
pid = os.getpid()
self.cpu_stat_worker = multiprocessing.Process(
target=self.cpu_stat_func,
args=(self.cpu_stat_q, pid, self.interval))
self.cpu_stat_worker.start()
def stop(self):
try:
if self.use_gpu:
os.killpg(self.gpu_stat_worker.pid, signal.SIGUSR1)
# os.killpg(p.pid, signal.SIGTERM)
self.cpu_stat_worker.terminate()
self.cpu_stat_worker.join(timeout=0.01)
except Exception as e:
print(e)
return
# gpu
if self.use_gpu:
lines = self.gpu_stat_worker.stdout.readlines()
lines = [
line.strip().decode("utf-8") for line in lines
if line.strip() != ''
]
gpu_info_list = [{
k: v
for k, v in zip(StatBase.gpu_keys, line.split(', '))
} for line in lines]
if len(gpu_info_list) == 0:
return
result = gpu_info_list[0]
for item in gpu_info_list:
for k in item.keys():
if k not in ["name", "uuid", "timestamp"]:
result[k] = max(int(result[k]), int(item[k]))
else:
result[k] = max(result[k], item[k])
self.result['gpu'] = result
# cpu
cpu_result = {}
if self.cpu_stat_q.qsize() > 0:
cpu_result = {
k: v
for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
}
while not self.cpu_stat_q.empty():
item = {
k: v
for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
}
for k in StatBase.cpu_keys:
cpu_result[k] = max(cpu_result[k], item[k])
cpu_result['name'] = cpuinfo.get_cpu_info()['brand_raw']
self.result['cpu'] = cpu_result
def output(self):
return self.result
def cpu_stat_func(self, q, pid, interval=0.0):
"""cpu stat function"""
stat_info = psutil.Process(pid)
while True:
# pid = os.getpid()
cpu_util, mem_util, mem_use = stat_info.cpu_percent(
), stat_info.memory_percent(), round(stat_info.memory_info().rss /
1024.0 / 1024.0, 4)
q.put([cpu_util, mem_util, mem_use])
time.sleep(interval)
return
def get_dataset(data_path, max_seq_len=512):
json_lines = []
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
json_line = json.loads(line)
content = json_line['content'].strip()
prompt = json_line['prompt']
# Model Input is aslike: [CLS] Prompt [SEP] Content [SEP]
# It include three summary tokens.
if max_seq_len <= len(prompt) + 3:
raise ValueError(
"The value of max_seq_len is too small, please set a larger value"
)
json_lines.append(json_line)
return json_lines
if __name__ == '__main__':
args = parse_arguments()
runtime_option = build_option(args)
model_path = os.path.join(args.model_dir, "inference.pdmodel")
param_path = os.path.join(args.model_dir, "inference.pdiparams")
vocab_path = os.path.join(args.model_dir, "vocab.txt")
gpu_id = args.device_id
enable_collect_memory_info = args.enable_collect_memory_info
dump_result = dict()
end2end_statis = list()
cpu_mem = list()
gpu_mem = list()
gpu_util = list()
if args.device == "cpu":
file_path = args.model_dir + "_model_" + args.backend + "_" + \
args.device + "_" + str(args.cpu_num_threads) + ".txt"
else:
if args.enable_trt_fp16:
file_path = args.model_dir + "_model_" + \
args.backend + "_fp16_" + args.device + ".txt"
else:
file_path = args.model_dir + "_model_" + args.backend + "_" + args.device + ".txt"
f = open(file_path, "w")
f.writelines("===={}====: \n".format(os.path.split(file_path)[-1][:-4]))
ds = get_dataset(args.data_path)
schema = ["时间"]
uie = UIEModel(
model_path,
param_path,
vocab_path,
position_prob=0.5,
max_length=args.max_length,
batch_size=args.batch_size,
schema=schema,
runtime_option=runtime_option,
schema_language=SchemaLanguage.ZH)
try:
if enable_collect_memory_info:
import multiprocessing
import subprocess
import psutil
import signal
import cpuinfo
enable_gpu = args.device == "gpu"
monitor = Monitor(enable_gpu, gpu_id)
monitor.start()
uie.enable_record_time_of_runtime()
for ep in range(args.epoch):
for i, sample in enumerate(ds):
curr_start = time.time()
uie.set_schema([sample['prompt']])
result = uie.predict([sample['content']])
end2end_statis.append(time.time() - curr_start)
runtime_statis = uie.print_statis_info_of_runtime()
warmup_iter = args.epoch * len(ds) // 5
end2end_statis_repeat = end2end_statis[warmup_iter:]
if enable_collect_memory_info:
monitor.stop()
mem_info = monitor.output()
dump_result["cpu_rss_mb"] = mem_info['cpu'][
'memory.used'] if 'cpu' in mem_info else 0
dump_result["gpu_rss_mb"] = mem_info['gpu'][
'memory.used'] if 'gpu' in mem_info else 0
dump_result["gpu_util"] = mem_info['gpu'][
'utilization.gpu'] if 'gpu' in mem_info else 0
dump_result["runtime"] = runtime_statis["avg_time"] * 1000
dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
time_cost_str = f"Runtime(ms): {dump_result['runtime']}\n" \
f"End2End(ms): {dump_result['end2end']}\n"
f.writelines(time_cost_str)
print(time_cost_str)
if enable_collect_memory_info:
mem_info_str = f"cpu_rss_mb: {dump_result['cpu_rss_mb']}\n" \
f"gpu_rss_mb: {dump_result['gpu_rss_mb']}\n" \
f"gpu_util: {dump_result['gpu_util']}\n"
f.writelines(mem_info_str)
print(mem_info_str)
except:
f.writelines("!!!!!Infer Failed\n")
f.close()