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