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import os
import shutil
import subprocess
import time
from collections import deque
from copy import deepcopy
from dataclasses import asdict, dataclass, field
from typing import Any, Dict, List
import json
import torch
from swift.llm import ExportArguments
from swift.utils import find_free_port, get_device_count, get_logger
logger = get_logger()
@dataclass
class Experiment:
name: str
cmd: str
group: str
requirements: Dict = field(default_factory=dict)
eval_requirements: Dict = field(default_factory=dict)
eval_dataset: List = field(default_factory=list)
args: Dict = field(default_factory=dict)
env: Dict = field(default_factory=dict)
record: Dict = field(default_factory=dict)
create_time: float = None
runtime: Dict = field(default_factory=dict)
input_args: Any = None
do_eval = False
def __init__(self,
name,
cmd,
group,
requirements=None,
eval_requirements=None,
eval_dataset=None,
args=None,
input_args=None,
**kwargs):
self.name = name
self.cmd = cmd
self.group = group
self.requirements = requirements or {}
self.args = args or {}
self.record = {}
self.env = {}
self.runtime = {}
self.input_args = input_args
self.eval_requirements = eval_requirements or {}
self.eval_dataset = eval_dataset or []
if self.cmd == 'eval':
self.do_eval = True
def load(self, _json):
self.name = _json['name']
self.cmd = _json['cmd']
self.requirements = _json['requirements']
self.args = _json['args']
self.record = _json['record']
self.env = _json['env']
self.create_time = _json['create_time']
@property
def priority(self):
return self.requirements.get('gpu', 0)
def to_dict(self):
_dict = asdict(self)
_dict.pop('runtime')
_dict.pop('input_args')
return _dict
class ExpManager:
RESULT_FILE = 'result.jsonl'
def __init__(self):
self.exps = []
def assert_gpu_not_overlap(self):
all_gpus = set()
for exp in self.exps:
gpus = exp.runtime['env']['CUDA_VISIBLE_DEVICES'].split(',')
if all_gpus & set(gpus):
raise ValueError(f'GPU overlap: {self.exps}!')
all_gpus.update(gpus)
def run(self, exp: Experiment):
if os.path.exists(os.path.join(exp.input_args.save_dir, exp.name + '.json')):
with open(os.path.join(exp.input_args.save_dir, exp.name + '.json'), 'r', encoding='utf-8') as f:
_json = json.load(f)
if exp.eval_dataset and 'eval_result' not in _json['record']:
if not exp.do_eval:
logger.info(f'Experiment {exp.name} need eval, load from file.')
exp.load(_json)
exp.do_eval = True
else:
logger.warn(f'Experiment {exp.name} already done, skip')
return
if exp.do_eval:
runtime = self._build_eval_cmd(exp)
exp.runtime = runtime
envs = deepcopy(runtime.get('env', {}))
envs.update(os.environ)
logger.info(f'Running cmd: {runtime["running_cmd"]}, env: {runtime.get("env", {})}')
os.makedirs('exp', exist_ok=True)
log_file = os.path.join('exp', f'{exp.name}.eval.log')
exp.handler = subprocess.Popen(runtime['running_cmd'] + f' > {log_file} 2>&1', env=envs, shell=True)
self.exps.append(exp)
self.assert_gpu_not_overlap()
return
if any([exp.name == e.name for e in self.exps]):
raise ValueError(f'Why exp name duplicate? {exp.name}')
elif exp.cmd == 'export' and any([exp.cmd == 'export' for exp in self.exps]): # noqa
raise AssertionError('Cannot run parallel export task.')
else:
exp.create_time = time.time()
runtime = self._build_cmd(exp)
exp.runtime = runtime
envs = deepcopy(runtime.get('env', {}))
envs.update(os.environ)
logger.info(f'Running cmd: {runtime["running_cmd"]}, env: {runtime.get("env", {})}')
os.makedirs('exp', exist_ok=True)
log_file = os.path.join('exp', f'{exp.name}.{exp.cmd}.log')
exp.handler = subprocess.Popen(runtime['running_cmd'] + f' > {log_file} 2>&1', env=envs, shell=True)
self.exps.append(exp)
self.assert_gpu_not_overlap()
def _build_eval_cmd(self, exp: Experiment):
gpu = exp.eval_requirements.get('gpu', None)
env = {}
allocated = []
if gpu:
allocated = self._find_free_gpu(int(gpu))
assert allocated, 'No free gpu for now!'
allocated = [str(gpu) for gpu in allocated]
env['CUDA_VISIBLE_DEVICES'] = ','.join(allocated)
best_model_checkpoint = exp.record.get('best_model_checkpoint')
eval_dataset = exp.eval_dataset
if best_model_checkpoint is not None:
if not os.path.exists(os.path.join(best_model_checkpoint, 'args.json')):
cmd = f'swift eval --ckpt_dir {best_model_checkpoint} ' \
+ f'--infer_backend pt --train_type full --eval_dataset {" ".join(eval_dataset)}'
else:
cmd = f'swift eval --model {exp.args.get("model")} --infer_backend pt ' \
f'--eval_dataset {" ".join(eval_dataset)}'
return {
'running_cmd': cmd,
'gpu': allocated,
'env': env,
}
def _build_cmd(self, exp: Experiment):
gpu = exp.requirements.get('gpu', None)
env = {}
allocated = []
if gpu:
allocated = self._find_free_gpu(int(gpu))
assert allocated, 'No free gpu for now!'
allocated = [str(gpu) for gpu in allocated]
env['CUDA_VISIBLE_DEVICES'] = ','.join(allocated)
if int(exp.requirements.get('ddp', 1)) > 1:
env['NPROC_PER_NODE'] = exp.requirements.get('ddp')
env['MASTER_PORT'] = str(find_free_port())
if exp.cmd == 'sft':
from swift.llm import TrainArguments
args = exp.args
sft_args = TrainArguments(**args)
args['output_dir'] = sft_args.output_dir
args['logging_dir'] = sft_args.logging_dir
args['add_version'] = False
os.makedirs(sft_args.output_dir, exist_ok=True)
os.makedirs(sft_args.logging_dir, exist_ok=True)
cmd = 'swift sft '
for key, value in args.items():
cmd += f' --{key} {value}'
elif exp.cmd == 'rlhf':
from swift.llm import RLHFArguments
args = exp.args
rlhf_args = RLHFArguments(**args)
args['output_dir'] = rlhf_args.output_dir
args['logging_dir'] = rlhf_args.logging_dir
args['add_version'] = False
os.makedirs(rlhf_args.output_dir, exist_ok=True)
os.makedirs(rlhf_args.logging_dir, exist_ok=True)
cmd = 'swift rlhf '
for key, value in args.items():
cmd += f' --{key} {value}'
elif exp.cmd == 'export':
args = exp.args
cmd = 'swift export '
for key, value in args.items():
cmd += f' --{key} {value}'
else:
raise ValueError(f'Unsupported cmd type: {exp.cmd}')
return {
'running_cmd': cmd,
'gpu': allocated,
'env': env,
'logging_dir': args.get('logging_dir'),
'output_dir': args.get('output_dir', args.get('ckpt_dir'))
}
def _find_free_gpu(self, n):
all_gpus = set()
for exp in self.exps:
all_gpus.update(exp.runtime.get('gpu', set()))
all_gpus = {int(g) for g in all_gpus}
free_gpu = set(range(get_device_count())) - all_gpus
if len(free_gpu) < n:
return None
return list(free_gpu)[:n]
def prepare_experiments(self, args: Any):
experiments = []
for config_file in args.config:
with open(config_file, 'r', encoding='utf-8') as f:
group = os.path.basename(config_file)
group = group[:-5]
content = json.load(f)
exps = content['experiment']
for exp in exps:
main_cfg = deepcopy(content)
name = exp['name']
cmd = main_cfg['cmd']
run_args = main_cfg['args']
env = main_cfg.get('env', {})
requirements = main_cfg.get('requirements', {})
eval_requirements = main_cfg.get('eval_requirements', {})
eval_dataset = main_cfg.get('eval_dataset', {})
if 'args' in exp:
run_args.update(exp['args'])
if 'requirements' in exp:
requirements.update(exp['requirements'])
if 'env' in exp:
env.update(exp['env'])
experiments.append(
Experiment(
group=group,
name=name,
cmd=cmd,
args=run_args,
env=env,
requirements=requirements,
eval_requirements=eval_requirements,
eval_dataset=eval_dataset,
input_args=args))
return experiments
@staticmethod
def _get_metric(exp: Experiment):
if exp.do_eval:
if os.path.isfile(os.path.join('exp', f'{exp.name}.eval.log')):
with open(os.path.join('exp', f'{exp.name}.eval.log'), 'r', encoding='utf-8') as f:
for line in f.readlines():
if 'Final report:' in line:
return json.loads(line.split('Final report:')[1].replace('\'', '"'))
elif exp.cmd == 'export':
exp_args = ExportArguments(**exp.args)
if exp_args.quant_bits > 0:
if exp_args.ckpt_dir is None:
path = f'{exp_args.model_type}-{exp_args.quant_method}-int{exp_args.quant_bits}'
else:
ckpt_dir, ckpt_name = os.path.split(exp_args.ckpt_dir)
path = os.path.join(ckpt_dir, f'{ckpt_name}-{exp_args.quant_method}-int{exp_args.quant_bits}')
else:
ckpt_dir, ckpt_name = os.path.split(exp_args.ckpt_dir)
path = os.path.join(ckpt_dir, f'{ckpt_name}-merged')
if os.path.exists(path):
shutil.rmtree(exp.name, ignore_errors=True)
os.makedirs(exp.name, exist_ok=True)
shutil.move(path, os.path.join(exp.name, path))
return {
'best_model_checkpoint': os.path.join(exp.name, path),
}
else:
logging_dir = exp.runtime.get('logging_dir')
logging_file = os.path.join(logging_dir, '..', 'logging.jsonl')
if os.path.isfile(logging_file):
with open(logging_file, 'r', encoding='utf-8') as f:
for line in f.readlines():
if 'model_info' in line:
return json.loads(line)
return None
@staticmethod
def write_record(exp: Experiment):
target_dir = exp.input_args.save_dir
file = os.path.join(target_dir, exp.name + '.json')
with open(file, 'w', encoding='utf-8') as f:
f.write(json.dumps(exp.to_dict()) + '\n')
def _poll(self):
while True:
time.sleep(5)
has_finished = False
for exp in self.exps:
rt = exp.handler.poll()
if rt is None:
continue
has_finished = True
if rt == 0:
if not exp.do_eval:
all_metric = self._get_metric(exp)
if all_metric:
exp.record.update(all_metric)
if exp.eval_dataset:
exp.do_eval = True
self.exp_queue.appendleft(exp)
self.write_record(exp)
else:
logger.error(f'Running {exp.name} task, but no result found')
else:
all_metric = self._get_metric(exp)
exp.record['eval_result'] = all_metric
if all_metric:
self.write_record(exp)
else:
logger.error(f'Running {exp.name} eval task, but no eval result found')
logger.info(f'Running {exp.name} finished with return code: {rt}')
if has_finished:
self.exps = [exp for exp in self.exps if exp.handler.poll() is None]
break
def begin(self, args: Any):
exps = self.prepare_experiments(args)
logger.info(f'all exps: {exps}')
exps.sort(key=lambda e: e.priority)
self.exp_queue = deque()
for exp in exps:
self.exp_queue.append(exp)
while len(self.exp_queue) or len(self.exps) > 0:
while len(self.exp_queue):
try:
logger.info(f'Running exp: {self.exp_queue[0].name}')
self.run(self.exp_queue[0])
except Exception as e:
if not isinstance(e, AssertionError):
logger.error(f'Adding exp {self.exp_queue[0].name} error because of:')
logger.error(e)
self.exp_queue.popleft()
else:
logger.info(f'Adding exp {self.exp_queue[0].name} error because of:', str(e))
if 'no free gpu' in str(e).lower():
break
else:
continue
else:
self.exp_queue.popleft()
self._poll()
logger.info(f'Run task finished because of exp queue: {self.exp_queue} and exps: {self.exps}')
def find_all_config(dir_or_file: str):
if os.path.isfile(dir_or_file):
return [dir_or_file]
else:
configs = []
for dirpath, dirnames, filenames in os.walk(dir_or_file):
for name in filenames:
if name.endswith('.json') and 'ipynb' not in dirpath:
configs.append(os.path.join(dirpath, name))
return configs
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