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e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 e2703dc ede74c0 | 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 | from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
from torch.optim import Adam, LBFGS
from onescience.utils.pinnsformer_util import get_data, get_n_params, make_time_sequence
from common import (
build_model,
ensure_runtime_dirs,
initial_condition,
load_config,
project_path,
seed_everything,
select_device,
)
def init_weights(module: nn.Module) -> None:
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
module.bias.data.fill_(0.01)
def tensorize(array: np.ndarray, device: torch.device) -> torch.Tensor:
return torch.tensor(array, dtype=torch.float32, requires_grad=True, device=device)
def prepare_tensors(cfg: dict, device: torch.device, args: argparse.Namespace):
data_cfg = cfg["data"]
x_num = int(args.x_num or data_cfg["x_num"])
t_num = int(args.t_num or data_cfg["t_num"])
num_step = int(args.num_step or data_cfg["sequence"]["num_step"])
step = float(data_cfg["sequence"]["step"])
res, b_left, b_right, b_upper, b_lower = get_data(
data_cfg["x_range"],
data_cfg["t_range"],
x_num,
t_num,
)
tensors = []
for values in (res, b_left, b_right, b_upper, b_lower):
tensors.append(tensorize(make_time_sequence(values, num_step=num_step, step=step), device))
return tuple(tensors)
def loss_components(model: nn.Module, tensors: tuple[torch.Tensor, ...], cfg: dict):
res, b_left, b_right, b_upper, b_lower = tensors
x_res, t_res = res[:, :, 0:1], res[:, :, 1:2]
x_left, t_left = b_left[:, :, 0:1], b_left[:, :, 1:2]
x_upper, t_upper = b_upper[:, :, 0:1], b_upper[:, :, 1:2]
x_lower, t_lower = b_lower[:, :, 0:1], b_lower[:, :, 1:2]
pred_res = model(x_res, t_res)
pred_left = model(x_left, t_left)
pred_upper = model(x_upper, t_upper)
pred_lower = model(x_lower, t_lower)
u_t = torch.autograd.grad(
pred_res,
t_res,
grad_outputs=torch.ones_like(pred_res),
retain_graph=True,
create_graph=True,
)[0]
rate = float(cfg["equation"]["reaction_rate"])
target_ic = initial_condition(x_left[:, 0, :], cfg)
loss_res = torch.mean((u_t - rate * pred_res * (1 - pred_res)) ** 2)
loss_bc = torch.mean((pred_upper - pred_lower) ** 2)
loss_ic = torch.mean((pred_left[:, 0, :] - target_ic) ** 2)
loss = loss_res + loss_bc + loss_ic
return loss, (loss_res, loss_bc, loss_ic)
def build_optimizer(model: nn.Module, cfg: dict):
opt_cfg = cfg["training"]["optimizer"]
name = opt_cfg["name"].lower()
if name == "adam":
return Adam(model.parameters(), lr=float(opt_cfg.get("lr", 1e-3)))
if name == "lbfgs":
return LBFGS(
model.parameters(),
lr=float(opt_cfg.get("lr", 1.0)),
max_iter=int(opt_cfg.get("max_iter", 20)),
line_search_fn=opt_cfg.get("line_search_fn", "strong_wolfe"),
)
raise ValueError(f"Unsupported optimizer: {opt_cfg['name']}")
def save_checkpoint(path: Path, model: nn.Module, cfg: dict, loss_history: list[list[float]]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
torch.save(
{
"model_state_dict": model.state_dict(),
"config": cfg,
"loss_history": loss_history,
},
path,
)
def main() -> None:
parser = argparse.ArgumentParser(description="Train PINNsformer on the 1D reaction equation.")
parser.add_argument("--config", default=None, help="Path to config.yaml.")
parser.add_argument("--epochs", type=int, default=None, help="Override training epochs.")
parser.add_argument("--x-num", type=int, default=None, help="Override x grid count.")
parser.add_argument("--t-num", type=int, default=None, help="Override t grid count.")
parser.add_argument("--num-step", type=int, default=None, help="Override pseudo-sequence length.")
parser.add_argument("--device", default=None, help="Override runtime.device.")
args = parser.parse_args()
cfg = load_config(args.config)
ensure_runtime_dirs(cfg)
seed_everything(int(cfg["runtime"]["seed"]))
device = select_device(args.device or cfg["runtime"]["device"])
tensors = prepare_tensors(cfg, device, args)
model = build_model(cfg).to(device)
model.apply(init_weights)
optimizer = build_optimizer(model, cfg)
epochs = int(args.epochs or cfg["training"]["epochs"])
print(model)
print(f"parameters: {get_n_params(model)}")
print(f"device: {device}")
loss_history: list[list[float]] = []
for epoch in range(epochs):
latest: dict[str, float] = {}
def closure():
loss, parts = loss_components(model, tensors, cfg)
optimizer.zero_grad()
loss.backward()
latest["loss"] = float(loss.detach().cpu())
latest["loss_res"] = float(parts[0].detach().cpu())
latest["loss_bc"] = float(parts[1].detach().cpu())
latest["loss_ic"] = float(parts[2].detach().cpu())
return loss
if isinstance(optimizer, LBFGS):
optimizer.step(closure)
else:
closure()
optimizer.step()
loss_history.append([latest["loss_res"], latest["loss_bc"], latest["loss_ic"], latest["loss"]])
print(
f"epoch {epoch + 1}/{epochs} "
f"loss={latest['loss']:.6f} "
f"res={latest['loss_res']:.6f} "
f"bc={latest['loss_bc']:.6f} "
f"ic={latest['loss_ic']:.6f}"
)
checkpoint = project_path(cfg["training"]["checkpoint"])
save_checkpoint(checkpoint, model, cfg, loss_history)
np.save(project_path(cfg["paths"]["loss"]), np.asarray(loss_history, dtype=np.float32))
print(f"checkpoint saved to {checkpoint}")
if __name__ == "__main__":
main()
|