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https://github.com/PaddlePaddle/FastDeploy.git
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[Benchmark]Benchmark cpp for YOLOv5 (#1224)
* add GPL lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * support yolov8 * add pybind for yolov8 * add yolov8 readme * add cpp benchmark * add cpu and gpu mem * public part split * add runtime mode * fixed bugs * add cpu_thread_nums * deal with comments * deal with comments * deal with comments * rm useless code * add FASTDEPLOY_DECL * add FASTDEPLOY_DECL
This commit is contained in:
WJJ1995
321
benchmark/python/benchmark_uie.py
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321
benchmark/python/benchmark_uie.py
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import numpy as np
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import os
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import time
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import distutils.util
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import sys
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import json
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import fastdeploy as fd
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from fastdeploy.text import UIEModel, SchemaLanguage
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model_dir",
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required=True,
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help="The directory of model and tokenizer.")
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parser.add_argument(
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"--data_path", required=True, help="The path of uie data.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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choices=['gpu', 'cpu'],
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help="Type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--backend",
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type=str,
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default='paddle',
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choices=['ort', 'paddle', 'trt', 'paddle_trt', 'ov'],
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help="The inference runtime backend.")
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parser.add_argument(
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"--device_id", type=int, default=0, help="device(gpu) id")
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parser.add_argument(
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"--batch_size", type=int, default=1, help="The batch size of data.")
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parser.add_argument(
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"--max_length",
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type=int,
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default=128,
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help="The max length of sequence.")
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parser.add_argument(
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"--cpu_num_threads",
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type=int,
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default=8,
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help="The number of threads when inferring on cpu.")
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parser.add_argument(
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"--enable_trt_fp16",
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type=distutils.util.strtobool,
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default=False,
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help="whether enable fp16 in trt backend")
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parser.add_argument(
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"--epoch", type=int, default=1, help="The epoch of test")
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parser.add_argument(
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"--enable_collect_memory_info",
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type=ast.literal_eval,
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default=False,
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help="whether enable collect memory info")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device == 'cpu':
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option.use_cpu()
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option.set_cpu_thread_num(args.cpu_num_threads)
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else:
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option.use_gpu(args.device_id)
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if args.backend == 'paddle':
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option.use_paddle_backend()
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elif args.backend == 'ort':
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option.use_ort_backend()
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elif args.backend == 'ov':
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option.use_openvino_backend()
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else:
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option.use_trt_backend()
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if args.backend == 'paddle_trt':
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option.enable_paddle_to_trt()
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option.enable_paddle_trt_collect_shape()
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trt_file = os.path.join(args.model_dir, "infer.trt")
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option.set_trt_input_shape(
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'input_ids',
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min_shape=[1, 1],
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opt_shape=[args.batch_size, args.max_length // 2],
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max_shape=[args.batch_size, args.max_length])
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option.set_trt_input_shape(
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'token_type_ids',
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min_shape=[1, 1],
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opt_shape=[args.batch_size, args.max_length // 2],
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max_shape=[args.batch_size, args.max_length])
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option.set_trt_input_shape(
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'pos_ids',
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min_shape=[1, 1],
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opt_shape=[args.batch_size, args.max_length // 2],
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max_shape=[args.batch_size, args.max_length])
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option.set_trt_input_shape(
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'att_mask',
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min_shape=[1, 1],
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opt_shape=[args.batch_size, args.max_length // 2],
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max_shape=[args.batch_size, args.max_length])
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if args.enable_trt_fp16:
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option.enable_trt_fp16()
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trt_file = trt_file + ".fp16"
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option.set_trt_cache_file(trt_file)
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return option
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class StatBase(object):
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"""StatBase"""
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nvidia_smi_path = "nvidia-smi"
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gpu_keys = ('index', 'uuid', 'name', 'timestamp', 'memory.total',
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'memory.free', 'memory.used', 'utilization.gpu',
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'utilization.memory')
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nu_opt = ',nounits'
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cpu_keys = ('cpu.util', 'memory.util', 'memory.used')
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class Monitor(StatBase):
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"""Monitor"""
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def __init__(self, use_gpu=False, gpu_id=0, interval=0.1):
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self.result = {}
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self.gpu_id = gpu_id
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self.use_gpu = use_gpu
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self.interval = interval
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self.cpu_stat_q = multiprocessing.Queue()
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def start(self):
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cmd = '%s --id=%s --query-gpu=%s --format=csv,noheader%s -lms 50' % (
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StatBase.nvidia_smi_path, self.gpu_id, ','.join(StatBase.gpu_keys),
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StatBase.nu_opt)
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if self.use_gpu:
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self.gpu_stat_worker = subprocess.Popen(
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cmd,
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stderr=subprocess.STDOUT,
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stdout=subprocess.PIPE,
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shell=True,
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close_fds=True,
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preexec_fn=os.setsid)
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# cpu stat
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pid = os.getpid()
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self.cpu_stat_worker = multiprocessing.Process(
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target=self.cpu_stat_func,
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args=(self.cpu_stat_q, pid, self.interval))
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self.cpu_stat_worker.start()
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def stop(self):
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try:
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if self.use_gpu:
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os.killpg(self.gpu_stat_worker.pid, signal.SIGUSR1)
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# os.killpg(p.pid, signal.SIGTERM)
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self.cpu_stat_worker.terminate()
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self.cpu_stat_worker.join(timeout=0.01)
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except Exception as e:
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print(e)
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return
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# gpu
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if self.use_gpu:
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lines = self.gpu_stat_worker.stdout.readlines()
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lines = [
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line.strip().decode("utf-8") for line in lines
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if line.strip() != ''
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]
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gpu_info_list = [{
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k: v
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for k, v in zip(StatBase.gpu_keys, line.split(', '))
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} for line in lines]
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if len(gpu_info_list) == 0:
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return
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result = gpu_info_list[0]
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for item in gpu_info_list:
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for k in item.keys():
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if k not in ["name", "uuid", "timestamp"]:
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result[k] = max(int(result[k]), int(item[k]))
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else:
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result[k] = max(result[k], item[k])
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self.result['gpu'] = result
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# cpu
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cpu_result = {}
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if self.cpu_stat_q.qsize() > 0:
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cpu_result = {
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k: v
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for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
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}
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while not self.cpu_stat_q.empty():
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item = {
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k: v
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for k, v in zip(StatBase.cpu_keys, self.cpu_stat_q.get())
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}
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for k in StatBase.cpu_keys:
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cpu_result[k] = max(cpu_result[k], item[k])
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cpu_result['name'] = cpuinfo.get_cpu_info()['brand_raw']
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self.result['cpu'] = cpu_result
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def output(self):
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return self.result
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def cpu_stat_func(self, q, pid, interval=0.0):
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"""cpu stat function"""
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stat_info = psutil.Process(pid)
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while True:
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# pid = os.getpid()
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cpu_util, mem_util, mem_use = stat_info.cpu_percent(
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), stat_info.memory_percent(), round(stat_info.memory_info().rss /
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1024.0 / 1024.0, 4)
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q.put([cpu_util, mem_util, mem_use])
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time.sleep(interval)
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return
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def get_dataset(data_path, max_seq_len=512):
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json_lines = []
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with open(data_path, 'r', encoding='utf-8') as f:
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for line in f:
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json_line = json.loads(line)
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content = json_line['content'].strip()
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prompt = json_line['prompt']
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# Model Input is aslike: [CLS] Prompt [SEP] Content [SEP]
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# It include three summary tokens.
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if max_seq_len <= len(prompt) + 3:
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raise ValueError(
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"The value of max_seq_len is too small, please set a larger value"
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)
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json_lines.append(json_line)
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return json_lines
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if __name__ == '__main__':
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args = parse_arguments()
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runtime_option = build_option(args)
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model_path = os.path.join(args.model_dir, "inference.pdmodel")
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param_path = os.path.join(args.model_dir, "inference.pdiparams")
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vocab_path = os.path.join(args.model_dir, "vocab.txt")
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gpu_id = args.device_id
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enable_collect_memory_info = args.enable_collect_memory_info
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dump_result = dict()
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end2end_statis = list()
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cpu_mem = list()
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gpu_mem = list()
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gpu_util = list()
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if args.device == "cpu":
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file_path = args.model_dir + "_model_" + args.backend + "_" + \
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args.device + "_" + str(args.cpu_num_threads) + ".txt"
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else:
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if args.enable_trt_fp16:
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file_path = args.model_dir + "_model_" + \
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args.backend + "_fp16_" + args.device + ".txt"
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else:
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file_path = args.model_dir + "_model_" + args.backend + "_" + args.device + ".txt"
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f = open(file_path, "w")
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f.writelines("===={}====: \n".format(os.path.split(file_path)[-1][:-4]))
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ds = get_dataset(args.data_path)
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schema = ["时间"]
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uie = UIEModel(
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model_path,
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param_path,
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vocab_path,
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position_prob=0.5,
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max_length=args.max_length,
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batch_size=args.batch_size,
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schema=schema,
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runtime_option=runtime_option,
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schema_language=SchemaLanguage.ZH)
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try:
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if enable_collect_memory_info:
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import multiprocessing
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import subprocess
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import psutil
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import signal
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import cpuinfo
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enable_gpu = args.device == "gpu"
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monitor = Monitor(enable_gpu, gpu_id)
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monitor.start()
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uie.enable_record_time_of_runtime()
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for ep in range(args.epoch):
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for i, sample in enumerate(ds):
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curr_start = time.time()
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uie.set_schema([sample['prompt']])
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result = uie.predict([sample['content']])
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end2end_statis.append(time.time() - curr_start)
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runtime_statis = uie.print_statis_info_of_runtime()
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warmup_iter = args.epoch * len(ds) // 5
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end2end_statis_repeat = end2end_statis[warmup_iter:]
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if enable_collect_memory_info:
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monitor.stop()
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mem_info = monitor.output()
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dump_result["cpu_rss_mb"] = mem_info['cpu'][
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'memory.used'] if 'cpu' in mem_info else 0
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dump_result["gpu_rss_mb"] = mem_info['gpu'][
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'memory.used'] if 'gpu' in mem_info else 0
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dump_result["gpu_util"] = mem_info['gpu'][
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'utilization.gpu'] if 'gpu' in mem_info else 0
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dump_result["runtime"] = runtime_statis["avg_time"] * 1000
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dump_result["end2end"] = np.mean(end2end_statis_repeat) * 1000
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time_cost_str = f"Runtime(ms): {dump_result['runtime']}\n" \
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f"End2End(ms): {dump_result['end2end']}\n"
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f.writelines(time_cost_str)
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print(time_cost_str)
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if enable_collect_memory_info:
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mem_info_str = f"cpu_rss_mb: {dump_result['cpu_rss_mb']}\n" \
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f"gpu_rss_mb: {dump_result['gpu_rss_mb']}\n" \
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f"gpu_util: {dump_result['gpu_util']}\n"
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f.writelines(mem_info_str)
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print(mem_info_str)
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except:
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f.writelines("!!!!!Infer Failed\n")
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f.close()
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