File size: 11,721 Bytes
09c43eb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | """Run the full Table 9 Darcy schedule from the package root."""
from __future__ import annotations
import csv
import json
import os
from pathlib import Path
import random
import time
import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset
from common import (HNO, atomic_checkpoint, atomic_json, compute_device, configuration,
decode_output, encode_input, load_checkpoint, objective, read_config,
read_split, relative_l2, set_seed, source_hashes, training_statistics)
def optimizer_and_schedule(model, c, steps):
t = c['training']
optimizer = torch.optim.AdamW(model.parameters(), lr=t['learning_rate'],
weight_decay=t['weight_decay'], betas=tuple(t['adam_betas']), eps=t['adam_eps'])
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=t['learning_rate'],
epochs=t['epochs'], steps_per_epoch=steps, **t['onecycle'])
return optimizer, scheduler
def evaluate(model, loader, stats, device):
model.eval()
accumulated = torch.zeros((), device=device, dtype=stats['target_mean'].dtype)
count = 0
with torch.no_grad():
for x, target in loader:
x, target = x.to(device), target.to(device)
prediction = decode_output(model(x), stats)
accumulated += relative_l2(prediction, target).sum()
count += len(x)
return accumulated.item() / count
def epoch_train(model, loader, stats, device, optimizer, scheduler, c, epoch, smoke=False):
model.train()
totals = torch.zeros(3, device=device, dtype=stats['target_mean'].dtype)
count = 0
for batch, (x, target) in enumerate(loader, start=1):
x, target = x.to(device), target.to(device)
optimizer.zero_grad(set_to_none=True)
prediction = decode_output(model(x), stats)
loss, primary, auxiliary = objective(prediction, target, c['training'])
loss.backward()
norm = torch.nn.utils.clip_grad_norm_(model.parameters(), c['training']['gradient_clip'], error_if_nonfinite=True)
optimizer.step()
scheduler.step()
totals += torch.stack((loss.detach(), primary.detach(), auxiliary.detach()))
count += len(x)
if batch % c['training']['print_every_batches'] == 0 or batch == len(loader):
print(f'{"SMOKE " if smoke else ""}epoch={epoch:03d} batch={batch:03d}/{len(loader)} '
f'loss={loss.item()/len(x):.8f} rel_l2={primary.item()/len(x):.8f} '
f'derivative={auxiliary.item()/len(x):.8f} grad_norm_before_clip={float(norm):.6g}', flush=True)
values = (totals / count).cpu().tolist()
if not all(np.isfinite(values)):
raise FloatingPointError('Nonfinite epoch losses')
return values
def rng_snapshot(generator):
ns = np.random.get_state()
return {'python': random.getstate(), 'numpy': (ns[0], ns[1].tolist(), ns[2], ns[3], ns[4]),
'torch': torch.get_rng_state(), 'accelerator': torch.cuda.get_rng_state_all(),
'loader': generator.get_state()}
def restore_rng(state, generator):
random.setstate(state['python'])
ns = state['numpy']
np.random.set_state((ns[0], np.asarray(ns[1], dtype=np.uint32), ns[2], ns[3], ns[4]))
torch.set_rng_state(state['torch'])
torch.cuda.set_rng_state_all(state['accelerator'])
generator.set_state(state['loader'])
def main():
parser = configuration('HNO Darcy Table 9 training')
parser.add_argument('--resume', help='Explicit last checkpoint to continue the same schedule')
parser.add_argument('--smoke', action='store_true', help='Six diagnostic checks; separate outputs, not a formal run')
args = parser.parse_args()
c = read_config(args.config)
device = compute_device(args.device)
torch.set_num_threads(c['training']['cpu_threads'])
set_seed(c['seed'])
torch.backends.cudnn.benchmark = True
results = Path(c['output']['results_dir'])
results.mkdir(parents=True, exist_ok=True)
print(f'SLURM_JOB_ID={os.environ.get("SLURM_JOB_ID")} node={os.uname().nodename} '
f'device={torch.cuda.get_device_name()} torch={torch.__version__} hip={torch.version.hip}', flush=True)
start = time.monotonic()
a_train, u_train = read_split(c, 'train')
a_eval, u_eval = read_split(c, 'eval')
stats_cpu = training_statistics(a_train, u_train)
if any(torch.linalg.vector_norm(u.flatten(1), dim=1).min() <= 1e-12 for u in [u_train, u_eval]):
raise ValueError('Zero-norm solution sample')
stats = {k: v.to(device) for k, v in stats_cpu.items()}
generator = torch.Generator().manual_seed(c['seed'])
train_loader = DataLoader(TensorDataset(encode_input(a_train, stats_cpu), u_train),
batch_size=c['training']['batch_size'], shuffle=True, generator=generator,
num_workers=c['training']['num_workers'], pin_memory=True)
eval_loader = DataLoader(TensorDataset(encode_input(a_eval, stats_cpu), u_eval),
batch_size=c['training']['batch_size'], shuffle=False,
num_workers=c['training']['num_workers'], pin_memory=True)
model = HNO(**c['model']).to(device)
parameters = sum(p.numel() for p in model.parameters())
print(f'Parameters={parameters:,}; train={len(a_train)} eval={len(a_eval)} '
f'grid={tuple(a_train.shape[1:])} tokens={model.patch_grid**2} '
f'target_dtype={u_train.dtype}; epochs={c["training"]["epochs"]}', flush=True)
optimizer, scheduler = optimizer_and_schedule(model, c, len(train_loader))
best, best_epoch, start_epoch = float('inf'), 0, 1
if args.resume:
model, stats, checkpoint = load_checkpoint(args.resume, device)
if checkpoint['training_config'] != c['training'] or checkpoint['model_config'] != c['model']:
raise ValueError('Resume requires identical architecture and training schedule')
optimizer, scheduler = optimizer_and_schedule(model, c, len(train_loader))
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
restore_rng(checkpoint['rng'], generator)
stats_cpu = checkpoint['normalization']
# Resume inputs must use saved statistics, including when data path changes.
train_loader.dataset.tensors = (encode_input(a_train, stats_cpu), u_train)
eval_loader.dataset.tensors = (encode_input(a_eval, stats_cpu), u_eval)
best, best_epoch = checkpoint['best_eval_relative_l2'], checkpoint['best_epoch']
start_epoch = checkpoint['epoch'] + 1
if args.smoke:
checks = {}
x, y = next(iter(eval_loader))
x, y = x.to(device), y.to(device)
prediction = decode_output(model(x), stats)
assert prediction.shape == y.shape and torch.isfinite(prediction).all()
checks['forward_pass'] = 'passed'
loss, _, _ = objective(prediction, y, c['training'])
loss.backward()
assert all(p.grad is not None and torch.isfinite(p.grad).all() for p in model.parameters())
checks['backward_pass'] = 'passed'
losses = epoch_train(model, train_loader, stats, device, optimizer, scheduler, c, 1, smoke=True)
checks['train_loop_dry_run'] = {'status': 'passed', 'n_samples':len(a_train), 'losses':losses}
score = evaluate(model, eval_loader, stats, device)
assert np.isfinite(score)
checks['validation_loop_dry_run'] = {'status': 'passed','n_samples':len(a_eval),'relative_l2':score}
assert torch.equal(relative_l2(y, y), torch.zeros(len(y), device=device, dtype=y.dtype))
assert torch.allclose(relative_l2(2*y, y), torch.ones(len(y), device=device,dtype=y.dtype))
checks['domain_metric_dry_run'] = 'passed: exact identity and scaling checks'
assert parameters == 815873, f'Unexpected parameter count: {parameters}'
assert len(train_loader) == 250 and len(eval_loader) == 50
checks['config_consistency'] = {'status':'passed','parameters':parameters,'training_batches':250,'evaluation_batches':50}
atomic_json(results/'smoke_checks.json', checks)
print('SMOKE_ALL_SIX_CHECKS_PASSED', flush=True)
return
manifest = {'job_id':os.environ.get('SLURM_JOB_ID'), 'node':os.uname().nodename,
'device':torch.cuda.get_device_name(), 'torch':str(torch.__version__), 'hip':torch.version.hip,
'parameters':parameters,'config':c,'source_sha256':source_hashes(),
'n_train':len(a_train),'n_eval':len(a_eval),'steps_per_epoch':len(train_loader),
'target_dtype':str(u_train.dtype), 'checkpoint_selection':'minimum evaluation relative L2; no separate validation set',
'normalization':{k:v.tolist() for k,v in stats_cpu.items()},'status':'running'}
atomic_json(results/'run_manifest.json',manifest)
fields=['epoch','global_step','train_loss','train_relative_l2','train_derivative','eval_relative_l2','learning_rate','seconds','best_epoch','best_relative_l2']
history=results/'training_history.csv'
with history.open('a' if args.resume else 'w', newline='') as f:
writer=csv.DictWriter(f,fieldnames=fields)
if not args.resume: writer.writeheader()
for epoch in range(start_epoch,c['training']['epochs']+1):
tic=time.monotonic()
losses=epoch_train(model,train_loader,stats,device,optimizer,scheduler,c,epoch)
score=evaluate(model,eval_loader,stats,device)
if not np.isfinite(score): raise FloatingPointError('Nonfinite evaluation metric')
improved=score<best
if improved: best,best_epoch=score,epoch
row=dict(zip(fields,[epoch,epoch*len(train_loader),*losses,score,optimizer.param_groups[0]['lr'],time.monotonic()-tic,best_epoch,best]))
writer.writerow(row); f.flush()
print(f'epoch={epoch:03d}/{c["training"]["epochs"]} step={row["global_step"]} '
f'train_loss={losses[0]:.8f} train_rel={losses[1]:.8f} derivative={losses[2]:.8f} '
f'eval_rel={score:.8f} best={best:.8f}@{best_epoch} '
f'lr={row["learning_rate"]:.8g} seconds={row["seconds"]:.2f}',flush=True)
saved={'format_version':1,'model_config':c['model'],'model_state':model.state_dict(),
'normalization':{k:v.detach().cpu() for k,v in stats.items()},'epoch':epoch,
'global_step':epoch*len(train_loader),'eval_relative_l2':score,
'best_eval_relative_l2':best,'best_epoch':best_epoch,'seed':c['seed'],
'training_config':c['training'],'config_at_training':c}
if improved: atomic_checkpoint(c['output']['checkpoint'],saved)
saved.update(optimizer=optimizer.state_dict(),scheduler=scheduler.state_dict(),rng=rng_snapshot(generator))
atomic_checkpoint(c['output']['last_checkpoint'],saved)
atomic_json(results/'training_progress.json',row)
manifest.update(status='completed',completed_epochs=c['training']['epochs'],
global_step=c['training']['epochs']*len(train_loader),best_epoch=best_epoch,
best_eval_relative_l2=best,elapsed_seconds=time.monotonic()-start)
atomic_json(results/'run_manifest.json',manifest)
atomic_json(results/'training_summary.json',{k:manifest[k] for k in ['status','completed_epochs','global_step','best_epoch','best_eval_relative_l2','elapsed_seconds']})
print(f'FORMAL_TRAINING_COMPLETED epochs={c["training"]["epochs"]} best={best:.9f} checkpoint={c["output"]["checkpoint"]}',flush=True)
if __name__=='__main__':
main()
|