71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
import os
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import sys
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import argparse
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import yaml
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import datetime
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from torch.distributed.run import main as torchrun
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# I don't want this invoked from an import
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if __name__ != "__main__":
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raise Exception("Do not invoke this from an import")
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parser = argparse.ArgumentParser()
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parser.add_argument('--yaml', type=str, help='Path to training configuration file.', default='./training/voice/train.yml', nargs='+') # ugh
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='Job launcher')
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args = parser.parse_args()
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args.yaml = " ".join(args.yaml) # absolutely disgusting
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config_path = args.yaml
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with open(config_path, 'r') as file:
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opt_config = yaml.safe_load(file)
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# it'd be downright sugoi if I was able to install DLAS as a pip package
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sys.path.insert(0, './modules/dlas/codes/')
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sys.path.insert(0, './modules/dlas/')
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# yucky override
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if "bitsandbytes" in opt_config and not opt_config["bitsandbytes"]:
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os.environ['BITSANDBYTES_OVERRIDE_LINEAR'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_EMBEDDING'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_ADAM'] = '0'
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os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '0'
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import torch
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from codes import train as tr
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from utils import util, options as option
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# this is effectively just copy pasted and cleaned up from the __main__ section of training.py
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def train(config_path, launcher='none'):
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opt = option.parse(config_path, is_train=True)
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if launcher == 'none' and opt['gpus'] > 1:
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return torchrun([f"--nproc_per_node={opt['gpus']}", "./src/train.py", "--yaml", config_path, "--launcher=pytorch"])
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trainer = tr.Trainer()
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if launcher == 'none':
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opt['dist'] = False
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trainer.rank = -1
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if len(opt['gpu_ids']) == 1:
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torch.cuda.set_device(opt['gpu_ids'][0])
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print('Disabled distributed training.')
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else:
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opt['dist'] = True
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tr.init_dist('nccl', timeout=datetime.timedelta(seconds=5*60))
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trainer.world_size = torch.distributed.get_world_size()
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trainer.rank = torch.distributed.get_rank()
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torch.cuda.set_device(torch.distributed.get_rank())
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trainer.init(config_path, opt, launcher, '')
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trainer.do_training()
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try:
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import torch_intermediary
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if torch_intermediary.OVERRIDE_ADAM:
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print("Using BitsAndBytes optimizations")
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else:
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print("NOT using BitsAndBytes optimizations")
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except Exception as e:
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pass
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train(config_path, args.launcher) |