File size: 6,949 Bytes
256a159 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import os
import os.path as osp
import re
import subprocess
import sys
import time
from concurrent.futures import ThreadPoolExecutor
from functools import partial
from threading import Lock
from typing import Any, Dict, List, Tuple
import mmengine
import numpy as np
from mmengine.config import ConfigDict
from tqdm import tqdm
from opencompass.registry import RUNNERS, TASKS
from opencompass.utils import get_logger
from .base import BaseRunner
def get_command_template(gpu_ids: List[int]) -> str:
"""Format command template given available gpu ids."""
if sys.platform == 'win32': # Always return win32 for Windows
# use command in Windows format
tmpl = 'set CUDA_VISIBLE_DEVICES=' + ','.join(str(i) for i in gpu_ids)
tmpl += ' & {task_cmd}'
else:
tmpl = 'CUDA_VISIBLE_DEVICES=' + ','.join(str(i) for i in gpu_ids)
tmpl += ' {task_cmd}'
return tmpl
@RUNNERS.register_module()
class LocalRunner(BaseRunner):
"""Local runner. Start tasks by local python.
Args:
task (ConfigDict): Task type config.
max_num_workers (int): Max number of workers to run in parallel.
Defaults to 16.
max_workers_per_gpu (int): Max number of workers to run for one GPU.
Defaults to 1.
debug (bool): Whether to run in debug mode.
lark_bot_url (str): Lark bot url.
"""
def __init__(self,
task: ConfigDict,
max_num_workers: int = 16,
debug: bool = False,
max_workers_per_gpu: int = 1,
lark_bot_url: str = None):
super().__init__(task=task, debug=debug, lark_bot_url=lark_bot_url)
self.max_num_workers = max_num_workers
self.max_workers_per_gpu = max_workers_per_gpu
def launch(self, tasks: List[Dict[str, Any]]) -> List[Tuple[str, int]]:
"""Launch multiple tasks.
Args:
tasks (list[dict]): A list of task configs, usually generated by
Partitioner.
Returns:
list[tuple[str, int]]: A list of (task name, exit code).
"""
status = []
import torch
if 'CUDA_VISIBLE_DEVICES' in os.environ:
all_gpu_ids = [
int(i) for i in re.findall(r'(?<!-)\d+',
os.getenv('CUDA_VISIBLE_DEVICES'))
]
else:
all_gpu_ids = list(range(torch.cuda.device_count()))
if self.debug:
for task in tasks:
task = TASKS.build(dict(cfg=task, type=self.task_cfg['type']))
task_name = task.name
num_gpus = task.num_gpus
assert len(all_gpu_ids) >= num_gpus
# get cmd
mmengine.mkdir_or_exist('tmp/')
param_file = f'tmp/{os.getpid()}_params.py'
try:
task.cfg.dump(param_file)
# if use torchrun, restrict it behaves the same as non
# debug mode, otherwise, the torchrun will use all the
# available resources which might cause inconsistent
# behavior.
if len(all_gpu_ids) > num_gpus and num_gpus > 0:
get_logger().warning(f'Only use {num_gpus} GPUs for '
f'total {len(all_gpu_ids)} '
'available GPUs in debug mode.')
tmpl = get_command_template(all_gpu_ids[:num_gpus])
cmd = task.get_command(cfg_path=param_file, template=tmpl)
# run in subprocess if starts with torchrun etc.
if 'python3 ' in cmd or 'python ' in cmd:
task.run()
else:
subprocess.run(cmd, shell=True, text=True)
finally:
os.remove(param_file)
status.append((task_name, 0))
else:
if len(all_gpu_ids) > 0:
gpus = np.zeros(max(all_gpu_ids) + 1, dtype=np.uint)
gpus[all_gpu_ids] = self.max_workers_per_gpu
else:
gpus = np.array([], dtype=np.uint)
pbar = tqdm(total=len(tasks))
lock = Lock()
def submit(task, index):
task = TASKS.build(dict(cfg=task, type=self.task_cfg['type']))
num_gpus = task.num_gpus
assert len(gpus) >= num_gpus
while True:
lock.acquire()
if sum(gpus > 0) >= num_gpus:
gpu_ids = np.where(gpus)[0][:num_gpus]
gpus[gpu_ids] -= 1
lock.release()
break
lock.release()
time.sleep(1)
if num_gpus > 0:
tqdm.write(f'launch {task.name} on GPU ' +
','.join(map(str, gpu_ids)))
else:
tqdm.write(f'launch {task.name} on CPU ')
res = self._launch(task, gpu_ids, index)
pbar.update()
with lock:
gpus[gpu_ids] += 1
return res
with ThreadPoolExecutor(
max_workers=self.max_num_workers) as executor:
status = executor.map(submit, tasks, range(len(tasks)))
return status
def _launch(self, task, gpu_ids, index):
"""Launch a single task.
Args:
task (BaseTask): Task to launch.
Returns:
tuple[str, int]: Task name and exit code.
"""
task_name = task.name
# Dump task config to file
mmengine.mkdir_or_exist('tmp/')
param_file = f'tmp/{os.getpid()}_{index}_params.py'
try:
task.cfg.dump(param_file)
tmpl = get_command_template(gpu_ids)
get_cmd = partial(task.get_command,
cfg_path=param_file,
template=tmpl)
cmd = get_cmd()
logger = get_logger()
logger.debug(f'Running command: {cmd}')
# Run command
out_path = task.get_log_path(file_extension='out')
mmengine.mkdir_or_exist(osp.split(out_path)[0])
stdout = open(out_path, 'w', encoding='utf-8')
result = subprocess.run(cmd,
shell=True,
text=True,
stdout=stdout,
stderr=stdout)
if result.returncode != 0:
logger.warning(f'task {task_name} fail, see\n{out_path}')
finally:
# Clean up
os.remove(param_file)
return task_name, result.returncode
|