File size: 4,032 Bytes
2ea2835 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | import argparse
import os
import torch.distributed as dist
from omegaconf import OmegaConf
from utils.config import load_config
from utils.optional_wandb import wandb
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config_path", type=str, required=True)
parser.add_argument("--no_save", action="store_true")
parser.add_argument("--no_visualize", action="store_true")
parser.add_argument(
"--max_steps", "--training-updates",
type=int,
default=None,
help="Optional runtime override for training_updates (useful for smoke tests)",
)
parser.add_argument(
"--fresh-video-local-samples", "--distribution-samples-per-rank",
type=int,
default=None,
help=(
"Optional smoke-test override for the number of fresh videos "
"collected per rank and optimizer update."
),
)
parser.add_argument(
"--total-batch-size", "--distribution-batch-size",
type=int,
default=None,
help="Optional matching logical global-batch override.",
)
parser.add_argument(
"--eval-interval",
type=int,
default=None,
help="Optional runtime eval interval override; 0 disables eval.",
)
parser.add_argument("--logdir", type=str, default="", help="Path to the directory to save logs")
parser.add_argument("--wandb-save-dir", type=str, default="", help="Path to the directory to save wandb logs")
parser.add_argument("--disable-wandb", action="store_true")
parser.add_argument(
"--preflight-only",
action="store_true",
help="Build and validate the complete distributed trainer, then exit before updates.",
)
args = parser.parse_args()
config = load_config(args.config_path, "configs/default_config.yaml")
config.no_save = args.no_save
config.no_visualize = args.no_visualize
if args.max_steps is not None:
if args.max_steps <= 0:
parser.error("--max_steps must be positive")
config.max_steps = args.max_steps
if args.fresh_video_local_samples is not None:
if args.fresh_video_local_samples <= 0:
parser.error("--fresh-video-local-samples must be positive")
config.fresh_video_local_samples = args.fresh_video_local_samples
if args.total_batch_size is not None:
if args.total_batch_size <= 0:
parser.error("--total-batch-size must be positive")
config.total_batch_size = args.total_batch_size
if args.eval_interval is not None:
if args.eval_interval < 0:
parser.error("--eval-interval must be non-negative")
config.eval_interval = args.eval_interval
# get the filename of config_path
config_name = os.path.basename(args.config_path).split(".")[0]
config.config_name = config_name
# Preserve the exact YAML identity for strict offline-reference checks.
# Runtime-only smoke overrides remain separate and do not change the
# generator trajectory contract encoded by this file.
config.config_path = os.path.realpath(args.config_path)
config.logdir = args.logdir
config.wandb_save_dir = args.wandb_save_dir
config.disable_wandb = args.disable_wandb
if config.trainer != "score_distillation":
raise ValueError("This release uses the distribution matching trainer.")
from trainer.distribution_matching import DistributionMatchingTrainer
trainer = DistributionMatchingTrainer(config)
try:
if args.preflight_only:
if dist.is_available() and dist.is_initialized():
dist.barrier()
if not dist.is_initialized() or dist.get_rank() == 0:
print("Distributed trainer preflight complete; no update or checkpoint written.")
else:
trainer.train()
finally:
wandb.finish()
if dist.is_available() and dist.is_initialized():
dist.destroy_process_group()
if __name__ == "__main__":
main()
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