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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,
relative_l2,
resolve_device,
resolve_dtype,
seed_everything,
)
from data_utils import batched_predict, load_allen_cahn, sample_training_data # noqa: E402
from model.fpinn import build_model, loss_components, weighted_loss # noqa: E402
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
TASKS = ("forward", "inverse")
def main() -> None:
config_path = DEFAULT_CONFIG.resolve()
config = load_config(config_path)
common = config["common"]
task = str(common["task"]).lower()
if task not in TASKS:
raise ValueError("common.task must be one of: forward, inverse")
task_config = config["tasks"][task]
device = resolve_device(str(common["device"]))
dtype = resolve_dtype(str(common["dtype"]))
seed = int(common["seed"])
epochs = int(task_config["training"]["epochs"])
lbfgs_iters = int(task_config["training"]["lbfgs_iters"])
learning_rate = float(task_config["training"]["lr"])
if min(epochs, lbfgs_iters) < 0 or epochs + lbfgs_iters == 0:
raise ValueError("at least one optimizer iteration count must be positive")
if learning_rate <= 0:
raise ValueError("learning rate must be positive")
data_config = dict(config["data"])
data_path = project_path(data_config["mat_file"], PROJECT_ROOT)
weight_dir = project_path(common["weight_dir"], PROJECT_ROOT)
result_dir = project_path(common["result_dir"], PROJECT_ROOT)
checkpoint_path = weight_dir / task_config["output"]["checkpoint_name"]
history_path = result_dir / task_config["output"]["history_name"]
seed_everything(seed)
dataset = load_allen_cahn(data_path)
x_train, u_train = sample_training_data(
dataset,
int(data_config["n_train"]),
seed,
device,
dtype,
)
model = build_model(task, config["model"], task_config["pde"], dtype).to(
device=device, dtype=dtype
)
parameter_count = sum(parameter.numel() for parameter in model.parameters())
if task == "forward":
lambda_1 = float(task_config["pde"]["lambda_1"])
lambda_2 = float(task_config["pde"]["lambda_2"])
else:
lambda_1 = model.lambda_1
lambda_2 = model.lambda_2
print(f"Config: {config_path}")
print(f"Task: {task}")
print(f"Data: {data_path}")
print(f"Device: {device}")
print(f"Training points: {x_train.shape[0]}")
print(f"Parameters: {parameter_count:,}")
with torch.enable_grad():
sample = x_train[: min(10, x_train.shape[0])].detach().requires_grad_(True)
test_function = sample[:, 0:1].square() + sample[:, 1:2]
gradient = torch.autograd.grad(
test_function,
sample,
torch.ones_like(test_function),
create_graph=True,
)[0]
second = torch.autograd.grad(
gradient[:, 0:1],
sample,
torch.ones_like(gradient[:, 0:1]),
create_graph=True,
)[0][:, 0:1]
if not torch.allclose(gradient[:, 1:2], torch.ones_like(second), atol=1.0e-5):
raise RuntimeError("u_t autograd validation failed")
if not torch.allclose(second, torch.full_like(second, 2.0), atol=1.0e-5):
raise RuntimeError("u_xx autograd validation failed")
print("Autograd validation: passed")
def current_loss() -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
components = loss_components(
model, x_train, u_train, x_train, lambda_1, lambda_2
)
return weighted_loss(components, task_config["loss"]), components
def evaluate() -> float:
prediction = batched_predict(
model,
np.asarray(dataset["coordinates"]),
int(data_config["evaluation_batch_size"]),
device,
dtype,
)
return relative_l2(prediction, np.asarray(dataset["exact"]))
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
log_interval = int(task_config["training"]["log_interval"])
loss_history = []
l2_history = []
lambda_history = []
best_l2 = evaluate()
started = time.time()
for epoch in range(1, epochs + 1):
loss, components = current_loss()
if not torch.isfinite(loss):
raise FloatingPointError(f"FPINN loss became non-finite at epoch {epoch}")
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
loss_history.append(loss.item())
if task == "inverse":
lambda_history.append((model.lambda_1.item(), model.lambda_2.item()))
if epoch == 1 or epoch % log_interval == 0 or epoch == epochs:
error = evaluate()
l2_history.append((epoch, error))
best_l2 = min(best_l2, error)
lambda_text = (
""
if task == "forward"
else f" lambda=({model.lambda_1.item():.6e}, {model.lambda_2.item():.6e})"
)
print(
f"epoch={epoch:6d} loss={loss.item():.3e} "
f"data={components['data'].item():.3e} "
f"pde={components['pde'].item():.3e} l2={error:.3e}{lambda_text}"
)
if lbfgs_iters > 0:
lbfgs = torch.optim.LBFGS(
model.parameters(),
lr=1.0,
max_iter=lbfgs_iters,
max_eval=max(1, 2 * lbfgs_iters),
history_size=50,
tolerance_grad=1.0e-5,
tolerance_change=float(np.finfo(float).eps),
line_search_fn="strong_wolfe",
)
def closure() -> torch.Tensor:
lbfgs.zero_grad(set_to_none=True)
closure_loss, _ = current_loss()
if not torch.isfinite(closure_loss):
raise FloatingPointError("FPINN L-BFGS loss became non-finite")
closure_loss.backward()
return closure_loss
lbfgs.step(closure)
error = evaluate()
l2_history.append((epochs + lbfgs_iters, error))
best_l2 = min(best_l2, error)
print(f"L-BFGS relative L2={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 = {
"task": task,
"case": "allen_cahn",
"architecture": "fuzzy_pinn",
"model_state": model.state_dict(),
"model_config": config["model"],
"pde_config": task_config["pde"],
"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)
lambda_array = np.asarray(lambda_history, dtype=np.float64).reshape(-1, 2)
np.savez_compressed(
history_path,
loss=np.asarray(loss_history, dtype=np.float64),
l2_steps=l2_array[:, 0],
l2_values=l2_array[:, 1],
lambda_1=lambda_array[:, 0] if lambda_array.size else np.array([]),
lambda_2=lambda_array[:, 1] if lambda_array.size else np.array([]),
final_l2=final_l2,
best_l2=best_l2,
elapsed_seconds=elapsed,
parameter_count=parameter_count,
)
print(f"Final relative L2={final_l2:.6e}, elapsed={elapsed:.1f}s")
if task == "inverse":
print(
f"Recovered lambda_1={model.lambda_1.item():.8e}, "
f"lambda_2={model.lambda_2.item():.8e}"
)
print(f"Saved checkpoint: {checkpoint_path}")
print(f"Saved history: {history_path}")
if __name__ == "__main__":
main()
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