File size: 6,344 Bytes
28d9c3b | 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 | from __future__ import annotations
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
build_laplace_data,
load_config,
project_path,
relative_l2,
resolve_device,
resolve_dtype,
seed_everything,
)
from model.bpinn import ( # noqa: E402
build_model,
laplace1d_loss_components,
weighted_loss,
)
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
def main() -> None:
config_path = DEFAULT_CONFIG.resolve()
config = load_config(config_path)
common = config["common"]
device = resolve_device(str(common["device"]))
dtype = resolve_dtype(str(common["dtype"]))
seed = int(common["seed"])
epochs = int(config["training"]["epochs"])
lbfgs_iters = int(config["training"]["lbfgs_iters"])
learning_rate = float(config["training"]["lr"])
if min(epochs, lbfgs_iters) < 0 or epochs + lbfgs_iters == 0:
raise ValueError("at least one non-negative optimizer iteration count must be positive")
if learning_rate <= 0:
raise ValueError("learning rate must be positive")
data_config = dict(config["data"])
weight_dir = project_path(common["weight_dir"], PROJECT_ROOT)
result_dir = project_path(common["result_dir"], PROJECT_ROOT)
checkpoint_path = weight_dir / config["training"]["checkpoint_name"]
history_path = result_dir / config["inference"]["history_name"]
seed_everything(seed)
print(f"Config: {config_path}")
print(f"Device: {device}")
print(f"Data config: {data_config}")
data = build_laplace_data(data_config, seed, device, dtype)
with torch.enable_grad():
test_points = data["x_test"].detach().requires_grad_(True)
exact = torch.sin(torch.pi * test_points)
first = torch.autograd.grad(
exact, test_points, torch.ones_like(exact), create_graph=True
)[0]
second = torch.autograd.grad(
first, test_points, torch.ones_like(first), create_graph=True
)[0]
max_residual = torch.max(
torch.abs(second + torch.pi**2 * torch.sin(torch.pi * test_points))
).item()
if max_residual > 1.0e-8:
raise RuntimeError(f"Laplace autograd validation failed: {max_residual:.3e}")
print(f"PDE autograd validation: {max_residual:.3e}")
model = build_model(config["model"], dtype=dtype).to(device=device, dtype=dtype)
parameter_count = sum(parameter.numel() for parameter in model.parameters())
print(f"Parameters: {parameter_count:,}")
def loss_value() -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
components = laplace1d_loss_components(
model,
data["x_solution"],
data["u_solution"],
data["x_boundary"],
data["u_boundary"],
data["x_pde"],
)
return weighted_loss(components, config["loss"]), components
def evaluate() -> float:
with torch.no_grad():
prediction = model.predict_u(data["x_test"])
return relative_l2(prediction, data["u_test"])
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
loss_history = []
l2_history = []
log_interval = int(config["training"]["log_interval"])
best_l2 = evaluate()
started = time.time()
print(
f"Adam epochs={epochs} lr={learning_rate:g}, "
f"L-BFGS iterations={lbfgs_iters}"
)
for epoch in range(1, epochs + 1):
loss, components = loss_value()
if not torch.isfinite(loss):
raise FloatingPointError(f"BPINN loss became non-finite at epoch {epoch}")
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
loss_history.append(loss.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)
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:.3e}"
)
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,
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("BPINN 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 = {
"case": "laplace1d",
"architecture": "bpinn",
"model_state": model.state_dict(),
"model_config": config["model"],
"data_config": data_config,
"loss_weights": config["loss"],
"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(loss_history, dtype=np.float64),
l2_steps=l2_array[:, 0],
l2_values=l2_array[:, 1],
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")
print(f"Saved checkpoint: {checkpoint_path}")
print(f"Saved history: {history_path}")
if __name__ == "__main__":
main()
|