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import argparse
import os
import sys
import time
from pathlib import Path
import numpy as np
import torch
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
sys.path.insert(0, str(Path(__file__).resolve().parent))
from common import ( # noqa: E402
load_config,
project_path,
resolve_device,
resolve_dtype,
seed_everything,
)
from model.sa_pinn import build_model, loss_components, weighted_loss # noqa: E402
from problems import ( # noqa: E402
CASES,
build_equation,
generate_data,
point_counts,
relative_l2,
to_tensors,
)
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Train a self-adaptive PINN")
parser.add_argument(
"--config",
type=Path,
default=Path(os.environ.get("SAPINN_CONFIG", DEFAULT_CONFIG)),
help="YAML configuration file",
)
parser.add_argument("--case", choices=CASES, default="laplace")
parser.add_argument("--device", help="Override common.device, for example cpu or cuda:0")
parser.add_argument("--seed", type=int, help="Override common.seed")
parser.add_argument("--epochs", type=int, help="Override Adam iterations")
parser.add_argument("--lr", type=float, help="Override network learning rate")
parser.add_argument("--attention-lr", type=float, help="Override attention learning rate")
parser.add_argument("--lbfgs-iters", type=int, help="Override L-BFGS iterations")
parser.add_argument("--n-sol", type=int, help="Override data or initial-condition points")
parser.add_argument("--n-pde", type=int, help="Override PDE collocation points")
parser.add_argument("--n-bnd", type=int, help="Override boundary points")
parser.add_argument("--test-res", type=int, help="Override test resolution per axis")
parser.add_argument("--weight-dir", type=Path, help="Override checkpoint output directory")
parser.add_argument("--result-dir", type=Path, help="Override history output directory")
return parser.parse_args()
def main() -> None:
args = parse_args()
config_path = args.config.expanduser().resolve()
config = load_config(config_path)
common = config["common"]
case_config = config["cases"][args.case]
device = resolve_device(args.device or str(common["device"]))
dtype = resolve_dtype(str(common["dtype"]))
seed = args.seed if args.seed is not None else int(common["seed"])
epochs = (
args.epochs if args.epochs is not None else int(case_config["training"]["epochs"])
)
lbfgs_iters = (
args.lbfgs_iters
if args.lbfgs_iters is not None
else int(case_config["training"]["lbfgs_iters"])
)
network_lr = (
args.lr if args.lr is not None else float(case_config["training"]["lr"])
)
attention_lr = (
args.attention_lr
if args.attention_lr is not None
else float(case_config["attention"]["lr"])
)
if min(epochs, lbfgs_iters) < 0 or epochs + lbfgs_iters == 0:
raise ValueError("at least one optimizer iteration count must be positive")
if min(network_lr, attention_lr) <= 0:
raise ValueError("network and attention learning rates must be positive")
data_config = dict(case_config["data"])
for key, value in (
("n_sol", args.n_sol),
("n_pde", args.n_pde),
("n_bnd", args.n_bnd),
("test_res", args.test_res),
):
if value is not None:
data_config[key] = value
weight_dir = project_path(args.weight_dir or common["weight_dir"], PROJECT_ROOT)
result_dir = project_path(args.result_dir or common["result_dir"], PROJECT_ROOT)
checkpoint_path = weight_dir / case_config["output"]["checkpoint_name"]
history_path = result_dir / case_config["output"]["history_name"]
seed_everything(seed)
data = generate_data(args.case, data_config, seed)
counts = point_counts(data)
tensors = to_tensors(data, device, dtype)
attention_enabled = bool(case_config["attention"]["enabled"])
model = build_model(
case_config["model"], counts, attention_enabled, dtype
).to(device=device, dtype=dtype)
equation = build_equation(args.case, data_config)
parameter_count = sum(parameter.numel() for parameter in model.parameters())
print(f"Config: {config_path}")
print(f"Case: {args.case}")
print(f"Device: {device}")
print(f"Points: {counts}")
print(f"Parameters: {parameter_count:,}")
def loss_value() -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
components = loss_components(model, equation, tensors)
return weighted_loss(components, case_config["loss"]), components
def evaluate() -> float | None:
with torch.no_grad():
prediction = model(tensors["x_test"]).cpu().numpy()
return relative_l2(prediction, data["u_exact"])
network_optimizer = torch.optim.Adam(model.network_parameters(), lr=network_lr)
attention_parameters = list(model.attention_parameters())
attention_optimizer = (
torch.optim.Adam(attention_parameters, lr=attention_lr)
if attention_parameters
else None
)
history = []
l2_history = []
log_interval = int(case_config["training"]["log_interval"])
started = time.time()
for epoch in range(1, epochs + 1):
network_optimizer.zero_grad(set_to_none=True)
if attention_optimizer is not None:
attention_optimizer.zero_grad(set_to_none=True)
loss, components = loss_value()
if not torch.isfinite(loss):
raise FloatingPointError(f"SA-PINN loss became non-finite at epoch {epoch}")
loss.backward()
network_optimizer.step()
if attention_optimizer is not None:
for parameter in attention_parameters:
if parameter.grad is not None:
parameter.grad.neg_()
attention_optimizer.step()
history.append(loss.item())
if epoch == 1 or epoch % log_interval == 0 or epoch == epochs:
error = evaluate()
if error is not None:
l2_history.append((epoch, error))
attention = model.att_pde()
error_text = "N/A" if error is None else f"{error:.3e}"
print(
f"epoch={epoch:6d} loss={loss.item():.3e} "
f"data={components['data'].item():.3e} "
f"boundary={components['boundary'].item():.3e} "
f"pde={components['pde'].item():.3e} l2={error_text} "
f"attention_std={attention.std(unbiased=False).item():.3e}"
)
if lbfgs_iters > 0:
model.set_attention_trainable(False)
lbfgs = torch.optim.LBFGS(
model.network_parameters(),
lr=0.8,
max_iter=lbfgs_iters,
max_eval=max(1, 2 * lbfgs_iters),
tolerance_grad=1.0e-7,
tolerance_change=1.0e-9,
history_size=50,
line_search_fn="strong_wolfe",
)
def closure() -> torch.Tensor:
lbfgs.zero_grad(set_to_none=True)
closure_loss, _ = loss_value()
if not torch.isfinite(closure_loss):
raise FloatingPointError("SA-PINN L-BFGS loss became non-finite")
closure_loss.backward()
return closure_loss
lbfgs.step(closure)
model.set_attention_trainable(True)
error = evaluate()
if error is not None:
l2_history.append((epochs + lbfgs_iters, error))
print(f"L-BFGS L2={'N/A' if error is None else f'{error:.6e}'}")
final_l2 = evaluate()
elapsed = time.time() - started
weight_dir.mkdir(parents=True, exist_ok=True)
result_dir.mkdir(parents=True, exist_ok=True)
checkpoint = {
"case": args.case,
"architecture": "sa_pinn",
"model_state": model.state_dict(),
"model_config": case_config["model"],
"attention_enabled": attention_enabled,
"point_counts": counts,
"data_config": data_config,
"seed": seed,
"epochs": epochs,
"lbfgs_iters": lbfgs_iters,
"final_l2": final_l2,
}
torch.save(checkpoint, checkpoint_path)
l2_array = np.asarray(l2_history, dtype=np.float64).reshape(-1, 2)
np.savez_compressed(
history_path,
loss=np.asarray(history, dtype=np.float64),
l2_steps=l2_array[:, 0] if l2_array.size else np.array([]),
l2_values=l2_array[:, 1] if l2_array.size else np.array([]),
elapsed_seconds=elapsed,
final_l2=np.nan if final_l2 is None else final_l2,
)
print(f"Final L2={'N/A' if final_l2 is None else f'{final_l2:.6e}'}")
print(f"Saved checkpoint: {checkpoint_path}")
print(f"Saved history: {history_path}")
if __name__ == "__main__":
main()
|