| """Run the LR pilots or one planned training phase. Usage: train.py pilots | train.py RUN_NAME""" |
| import json, sys |
| import numpy as np |
| from datasets import load_from_disk |
| from common import * |
| import engine |
|
|
| def exports(run): |
| folder = CKPT/run |
| return sorted([p for p in folder.glob('step-*') if (p/'model.safetensors').exists()], key=lambda p: int(p.name.split('-')[-1])) |
|
|
| def resolve_start(spec): |
| if spec == 'init': return init_path() |
| kind, run = spec.split(':') |
| assert kind == 'final' and (CKPT/run/'complete.json').exists(), f'{run} not complete' |
| return exports(run)[-1] |
|
|
| def index_sets(run, seed): |
| ds = load_from_disk(str(DATA/'train_all')) |
| lengths = np.array(ds['length']); rows = np.array(ds['source_row']) |
| aug = rows >= 1_000_000 |
| short_orig = np.flatnonzero(~aug & (lengths <= 4096)); long_orig = np.flatnonzero(~aug & (lengths > 4096)); aug_idx = np.flatnonzero(aug) |
| rng = np.random.default_rng(seed) |
| name = run['indices'] |
| if name == 'short_all': return np.sort(np.concatenate([short_orig, aug_idx])) |
| if name == 'long_mix': |
| replay = rng.choice(short_orig, int(len(long_orig)*run['short_replay']), replace=False) |
| aug_replay = rng.choice(aug_idx, int(len(long_orig)*run['aug_replay']), replace=False) |
| return np.sort(np.concatenate([long_orig, replay, aug_replay])) |
| if name == 'pilot': |
| return np.sort(rng.choice(np.concatenate([short_orig, aug_idx]), run['rows'], replace=False)) |
| raise ValueError(name) |
|
|
| def pilot_lr(): |
| result = json.loads((CKPT/'pilot_choice.json').read_text()) |
| return result['lr'] |
|
|
| def resolve_lr(spec): |
| if isinstance(spec, (int, float)): return float(spec) |
| if spec == 'pilot': return pilot_lr() |
| base, factor = spec.split('*'); assert base == 'pilot' |
| return pilot_lr()*float(factor) |
|
|
| def run_phase(run): |
| start = resolve_start(run['start_from']) |
| config = {**run, 'lr_resolved': resolve_lr(run['lr']), 'start_path': str(start)} |
| indices = index_sets(run, SEED + 101) |
| return engine.train_phase(run['name'], start, indices, epochs=run['epochs'], lr=config['lr_resolved'], |
| teacher_logits=DATA/'teacher_logits_all.npy', alpha=run['alpha'], temperature=run['temperature'], |
| evals_per_epoch=run.get('evals_per_epoch', 4), seed_offset=run.get('seed_offset', 0), |
| deny_weight=run.get('deny_weight', 1.0), config=config, export=run.get('export', True)) |
|
|
| def pilots(): |
| if (CKPT/'pilot_choice.json').exists(): print(json.loads((CKPT/'pilot_choice.json').read_text())); return |
| p = plan()['pilot']; results = [] |
| val = load_from_disk(str(DATA/'validation')); monitor = np.load(DATA/'validation_partitions.npz')['monitor'] |
| lengths = np.array(val.select(monitor.tolist())['length']); labels = np.array(val.select(monitor.tolist())['labels']) |
| short = lengths <= 4096 |
| for lr in p['lrs']: |
| run = {'name': f'pilot_lr{lr:g}', 'indices': 'pilot', 'rows': p['rows'], 'epochs': p['epochs'], 'lr': lr, |
| 'alpha': p['alpha'], 'temperature': p['temperature'], 'start_from': 'init', 'evals_per_epoch': 1, 'export': False} |
| done = run_phase(run) |
| last = exports(run['name'])[-1] |
| logits = np.load(last/'monitor_logits.npz')['logits'] |
| m_short = engine.metrics(labels[short], logits[short]); m_all = engine.metrics(labels, logits) |
| results.append({'lr': lr, 'short_nll': m_short['nll'], 'short_accuracy': m_short['accuracy'], 'short_balanced_accuracy': m_short['balanced_accuracy'], |
| 'all_nll': m_all['nll'], 'all_accuracy': m_all['accuracy'], 'steps': done['steps'], 'export': str(last)}) |
| event('pilot_result', **results[-1]) |
| best = min(results, key=lambda r: r['short_nll']) |
| atomic_json(CKPT/'pilot_choice.json', {'lr': best['lr'], 'results': results, 'criterion': p['criterion']}) |
| event('pilot_chosen', lr=best['lr']) |
|
|
| if __name__ == '__main__': |
| if sys.argv[1] == 'pilots': pilots() |
| else: run_phase(next(r for r in plan()['runs'] if r['name'] == sys.argv[1])) |
|
|
