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0de2901 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | #!/usr/bin/env python3
"""
Supervised Cortex adapter tuning.
This trains only Cortex module parameters against the same multiple-choice
log-likelihood objective used by the benchmark runner. It is intended as a
small, explicit tuning step before expecting Cortex to outperform the base
model.
"""
import argparse
import os
import random
import sys
import time
import torch
# Ensure parent directory is on path for imports
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from benchmark.runner import BenchmarkRunner
from benchmark.tasks import TASK_REGISTRY
from benchmark.tuning import cortex_auxiliary_loss, multiple_choice_loss
def load_examples(task_names, n_per_task, seed):
examples = []
for task_name in task_names:
task_cls = TASK_REGISTRY[task_name]
task = task_cls() if callable(task_cls) else task_cls
task_examples = task.load_examples(n=n_per_task, seed=seed)
examples.extend((task_name, ex) for ex in task_examples)
print(f"Loaded {len(task_examples)} examples for {task_name}")
return examples
def main():
parser = argparse.ArgumentParser(description="Train Cortex modules on benchmark-style MC data")
parser.add_argument(
"--model", type=str, default="HuggingFaceTB/SmolLM2-135M",
help="HuggingFace model ID to tune",
)
parser.add_argument(
"--tasks", nargs="+", default=["hellaswag", "piqa", "arc-easy", "winogrande"],
help="Tasks to train on",
)
parser.add_argument(
"--n-train", type=int, default=8,
help="Examples per task for tuning",
)
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument("--weight-decay", type=float, default=0.01)
parser.add_argument("--max-grad-norm", type=float, default=1.0)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument(
"--device", type=str, default="auto",
help="Device: cuda, mps, cpu, or auto",
)
parser.add_argument(
"--dtype", type=str, default="float32",
choices=["float32", "float16", "bfloat16"],
)
parser.add_argument(
"--init-cortex-weights", type=str, default=None,
help="Optional Cortex weights to resume from",
)
parser.add_argument(
"--output", type=str, default="cortex_tuned.pt",
help="Path to save tuned Cortex weights",
)
parser.add_argument("--log-every", type=int, default=4)
args = parser.parse_args()
random.seed(args.seed)
torch.manual_seed(args.seed)
runner = BenchmarkRunner(
model_name=args.model,
device=args.device,
dtype=args.dtype,
cortex_weights=args.init_cortex_weights,
)
runner.inject_cortex()
model = runner.model
tokenizer = runner.tokenizer
surgeon = runner._surgeon
model.train()
examples = load_examples(args.tasks, args.n_train, args.seed)
if not examples:
raise RuntimeError("No training examples loaded")
trainable_params = list(surgeon.get_trainable_parameters())
optimizer = torch.optim.AdamW(
trainable_params,
lr=args.lr,
weight_decay=args.weight_decay,
)
print(f"Training on {len(examples)} examples for {args.epochs} epoch(s)")
start = time.time()
for epoch in range(args.epochs):
rng = random.Random(args.seed + epoch)
rng.shuffle(examples)
total_loss = 0.0
correct = 0
seen = 0
skipped = 0
for step, (task_name, example) in enumerate(examples, start=1):
optimizer.zero_grad(set_to_none=True)
loss, pred = multiple_choice_loss(model, tokenizer, example, runner.device)
if loss is None:
skipped += 1
continue
aux_loss = cortex_auxiliary_loss(model)
train_loss = loss + aux_loss
train_loss.backward()
if args.max_grad_norm > 0:
torch.nn.utils.clip_grad_norm_(trainable_params, args.max_grad_norm)
optimizer.step()
seen += 1
total_loss += float(train_loss.detach().cpu())
correct += int(pred == example["gold_idx"])
if step % args.log_every == 0 or step == len(examples):
avg_loss = total_loss / max(seen, 1)
acc = correct / max(seen, 1)
print(
f"epoch={epoch + 1} step={step}/{len(examples)} "
f"task={task_name} loss={avg_loss:.4f} acc={acc:.3f}"
)
avg_loss = total_loss / max(seen, 1)
acc = correct / max(seen, 1)
print(
f"Epoch {epoch + 1} done: loss={avg_loss:.4f} "
f"acc={acc:.3f} skipped={skipped}"
)
output_dir = os.path.dirname(args.output)
if output_dir:
os.makedirs(output_dir, exist_ok=True)
surgeon.save_cortex_modules(args.output)
elapsed = time.time() - start
print(f"Saved Cortex weights to {args.output} [{elapsed:.1f}s]")
if __name__ == "__main__":
main()
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