forked from mrq/ai-voice-cloning
92 lines
3.6 KiB
Python
Executable File
92 lines
3.6 KiB
Python
Executable File
import os
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import sys
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import argparse
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import yaml
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"""
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if 'BITSANDBYTES_OVERRIDE_LINEAR' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_LINEAR'] = '0'
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if 'BITSANDBYTES_OVERRIDE_EMBEDDING' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_EMBEDDING'] = '1'
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if 'BITSANDBYTES_OVERRIDE_ADAM' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_ADAM'] = '1'
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if 'BITSANDBYTES_OVERRIDE_ADAMW' not in os.environ:
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os.environ['BITSANDBYTES_OVERRIDE_ADAMW'] = '1'
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"""
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml', nargs='+') # ugh
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--mode', type=str, default='none', help='mode')
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args = parser.parse_args()
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args.opt = " ".join(args.opt) # absolutely disgusting
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with open(args.opt, 'r') as file:
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opt_config = yaml.safe_load(file)
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if "ext" in opt_config and "bitsandbytes" in opt_config["ext"] and not opt_config["ext"]["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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# this is some massive kludge that only works if it's called from a shell and not an import/PIP package
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# it's smart-yet-irritating module-model loader breaks when trying to load something specifically when not from a shell
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sys.path.insert(0, './modules/dlas/codes/')
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# this is also because DLAS is not written as a package in mind
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# it'll gripe when it wants to import from train.py
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sys.path.insert(0, './modules/dlas/')
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# for PIP, replace it with:
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# sys.path.insert(0, os.path.dirname(os.path.realpath(dlas.__file__)))
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# sys.path.insert(0, f"{os.path.dirname(os.path.realpath(dlas.__file__))}/../")
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# don't even really bother trying to get DLAS PIP'd
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# without kludge, it'll have to be accessible as `codes` and not `dlas`
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import torch
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import datetime
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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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from torch.distributed.run import main
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# this is effectively just copy pasted and cleaned up from the __main__ section of training.py
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# I'll clean it up better
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def train(yaml, launcher='none'):
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opt = option.parse(yaml, is_train=True)
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if launcher == 'none' and opt['gpus'] > 1:
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return main([f"--nproc_per_node={opt['gpus']}", "--master_port=1234", "./src/train.py", "-opt", yaml, "--launcher=pytorch"])
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trainer = tr.Trainer()
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#### distributed training settings
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if launcher == 'none': # disabled distributed training
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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(yaml, opt, launcher, '')
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trainer.do_training()
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if __name__ == "__main__":
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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(args.opt, args.launcher) |