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feat: add --resume support to training script #315

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Merged
CalebisGross merged 1 commit into from
Mar 21, 2026
Merged

feat: add --resume support to training script #315

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45 changes: 42 additions & 3 deletions training/scripts/train_mnemonic_lm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -224,20 +224,44 @@ def train(config, args):
ckpt_dir = Path(f"checkpoints/{args.config}")
ckpt_dir.mkdir(parents=True, exist_ok=True)
global_step = 0
global_step_start = 0
lr = args.lr
losses = []

if args.resume:
print(f"\n Resuming from {args.resume}")
ckpt = torch.load(args.resume, map_location=device, weights_only=False)
raw_model = model._orig_mod if hasattr(model, '_orig_mod') else model
if isinstance(ckpt, dict) and 'model_state_dict' in ckpt:
raw_model.load_state_dict(ckpt['model_state_dict'])
optimizer.load_state_dict(ckpt['optimizer_state_dict'])
global_step = ckpt['global_step']
losses = ckpt.get('losses', [])
else:
# Legacy checkpoint: raw state_dict only
raw_model.load_state_dict(ckpt)
print(" Warning: legacy checkpoint (model weights only, no optimizer state)")
global_step_start = global_step
if losses:
print(f" Resumed at step {global_step}, loss={losses[-1]:.3f}")
else:
print(f" Resumed at step {global_step}")

model.train()
optimizer.zero_grad()
start_time = time.time()

try:
from tqdm import tqdm
pbar = tqdm(total=max_steps, desc="Training")
pbar = tqdm(total=max_steps, desc="Training", initial=global_step)
except ImportError:
pbar = None

for input_ids, targets in train_loader:
if global_step_start > 0 and global_step < global_step_start:
global_step += 1
continue

input_ids = input_ids.to(device)
targets = targets.to(device)

Expand Down Expand Up @@ -283,7 +307,14 @@ def train(config, args):

# Periodic checkpoint
if global_step % args.save_interval == 0:
torch.save(model.state_dict(), ckpt_dir / f"step_{global_step}.pt")
raw_model = model._orig_mod if hasattr(model, '_orig_mod') else model
torch.save({
'model_state_dict': raw_model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'global_step': global_step,
'losses': losses[-100:],
'args': vars(args),
}, ckpt_dir / f"step_{global_step}.pt")
print(f"\n Checkpoint saved at step {global_step}")

if global_step % 5000 == 0 and global_step > 0:
Expand Down Expand Up @@ -319,7 +350,14 @@ def train(config, args):
else:
print(" FAIL: Loss did not decrease!")

torch.save(model.state_dict(), ckpt_dir / "last.pt")
raw_model = model._orig_mod if hasattr(model, '_orig_mod') else model
torch.save({
'model_state_dict': raw_model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'global_step': global_step,
'losses': losses[-100:],
'args': vars(args),
}, ckpt_dir / "last.pt")
print(f" Checkpoint: {ckpt_dir}/last.pt")

if not args.no_wandb:
Expand Down Expand Up @@ -347,6 +385,7 @@ def main():
parser.add_argument("--compile", action="store_true")
parser.add_argument("--spoke-lr-mult", type=float, default=2.0, help="Spoke LR multiplier")
parser.add_argument("--tokenized-dir", type=str, default=None)
parser.add_argument("--resume", type=str, default=None, help="Path to checkpoint .pt file to resume from")
parser.add_argument("--smoke-test", action="store_true", help="Run 1000 steps to verify pipeline")
args = parser.parse_args()

Expand Down