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| import os |
| import torch |
| from . import training_stats |
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| def init(): |
| if 'MASTER_ADDR' not in os.environ: |
| os.environ['MASTER_ADDR'] = 'localhost' |
| if 'MASTER_PORT' not in os.environ: |
| os.environ['MASTER_PORT'] = '29500' |
| if 'RANK' not in os.environ: |
| os.environ['RANK'] = '0' |
| if 'LOCAL_RANK' not in os.environ: |
| os.environ['LOCAL_RANK'] = '0' |
| if 'WORLD_SIZE' not in os.environ: |
| os.environ['WORLD_SIZE'] = '1' |
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| backend = 'gloo' if os.name == 'nt' else 'nccl' |
| torch.distributed.init_process_group(backend=backend, init_method='env://') |
| torch.cuda.set_device(int(os.environ.get('LOCAL_RANK', '0'))) |
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| sync_device = torch.device('cuda') if get_world_size() > 1 else None |
| training_stats.init_multiprocessing(rank=get_rank(), sync_device=sync_device) |
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| def get_rank(): |
| return torch.distributed.get_rank() if torch.distributed.is_initialized() else 0 |
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| def get_world_size(): |
| return torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1 |
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| def should_stop(): |
| return False |
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| def update_progress(cur, total): |
| _ = cur, total |
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| def print0(*args, **kwargs): |
| if get_rank() == 0: |
| print(*args, **kwargs) |
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