lars
cf653f7664
Replaces CosineAnnealingLR with a LambdaLR that linearly ramps the LR from lr/warmup_epochs to lr over the first warmup_epochs steps, then applies cosine decay for the remainder. Default warmup_epochs=5; overridable via --warmup-epochs CLI flag. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
115 lines
3.5 KiB
Python
115 lines
3.5 KiB
Python
import argparse
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
|
|
from giant import config as gconfig
|
|
from giant.pipeline import run_train_job
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(description="Train GIANT surrogate model")
|
|
parser.add_argument("--config", default=None, help="Path to TOML config file")
|
|
parser.add_argument(
|
|
"--data", required=True, help="Path to parquet file or directory"
|
|
)
|
|
parser.add_argument("--mode", choices=["flow", "ddpm"])
|
|
parser.add_argument("--epochs", type=int)
|
|
parser.add_argument("--batch-size", type=int)
|
|
parser.add_argument("--lr", type=float)
|
|
parser.add_argument("--warmup-epochs", type=int, dest="warmup_epochs")
|
|
parser.add_argument("--hidden-dim", type=int)
|
|
parser.add_argument("--n-blocks", type=int)
|
|
parser.add_argument("--emb-dim", type=int)
|
|
parser.add_argument(
|
|
"--dropout", type=float, help="Dropout probability in ResBlocks (default: 0.1)"
|
|
)
|
|
parser.add_argument("--val-fraction", type=float)
|
|
parser.add_argument("--seed", type=int, help="Random seed for reproducibility")
|
|
parser.add_argument(
|
|
"--validate-every",
|
|
type=int,
|
|
help="Run marginal+KL validation every N epochs (0 disables)",
|
|
)
|
|
parser.add_argument(
|
|
"--shuffle-buffer",
|
|
type=int,
|
|
default=65536,
|
|
help="Rows held in RAM for shuffling per worker (default: 65536)",
|
|
)
|
|
parser.add_argument(
|
|
"--out",
|
|
default=None,
|
|
help="Checkpoint output directory (default: auto from hyperparams)",
|
|
)
|
|
parser.add_argument(
|
|
"--device", default=None, help="cpu | cuda | mps (default: auto)"
|
|
)
|
|
parser.add_argument("--num-workers", type=int)
|
|
parser.add_argument(
|
|
"--resume", default=None, help="Checkpoint .pt to resume training from"
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
cli_train = {
|
|
k: v
|
|
for k, v in {
|
|
"mode": args.mode,
|
|
"epochs": args.epochs,
|
|
"batch_size": args.batch_size,
|
|
"lr": args.lr,
|
|
"warmup_epochs": args.warmup_epochs,
|
|
"val_fraction": args.val_fraction,
|
|
"num_workers": args.num_workers,
|
|
"seed": args.seed,
|
|
"validate_every": args.validate_every,
|
|
}.items()
|
|
if v is not None
|
|
}
|
|
cli_model = {
|
|
k: v
|
|
for k, v in {
|
|
"hidden_dim": args.hidden_dim,
|
|
"n_blocks": args.n_blocks,
|
|
"emb_dim": args.emb_dim,
|
|
"dropout": args.dropout,
|
|
}.items()
|
|
if v is not None
|
|
}
|
|
config_path = Path(args.config) if args.config else None
|
|
cfg = gconfig.merge_cli_overrides(
|
|
gconfig.DEFAULT_CONFIG, config_path, cli_train, cli_model
|
|
)
|
|
t, m = cfg["train"], cfg["model"]
|
|
|
|
device = torch.device(args.device) if args.device else gconfig.auto_device()
|
|
out_dir = Path(
|
|
args.out
|
|
or (
|
|
f"checkpoints/{t['mode']}"
|
|
f"_h{m['hidden_dim']}"
|
|
f"_b{m['n_blocks']}"
|
|
f"_e{m['emb_dim']}"
|
|
f"_lr{t['lr']}"
|
|
f"_bs{t['batch_size']}"
|
|
)
|
|
)
|
|
|
|
print(f"device: {device}")
|
|
print(f"out_dir: {out_dir}")
|
|
|
|
run_train_job(
|
|
data=Path(args.data),
|
|
cfg=cfg,
|
|
out_dir=out_dir,
|
|
device=device,
|
|
shuffle_buffer=args.shuffle_buffer,
|
|
num_workers=t["num_workers"],
|
|
resume=Path(args.resume) if args.resume else None,
|
|
echo=print,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|