| 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]): |
| 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 |
|
|
