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import random
import sys
from pathlib import Path
from timeit import default_timer
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm import tqdm
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
from model import HeteroGNS
from onescience.datapipes.cfd import BENODatapipe
from onescience.distributed.manager import DistributedManager
from onescience.utils.YParams import YParams
from onescience.utils.beno.utilities import LpLoss
def set_default_data_env():
os.environ.setdefault("ONESCIENCE_BENO_DATA_DIR", str(PROJECT_ROOT / "data"))
def resolve_path(path_value):
path = Path(path_value)
return path if path.is_absolute() else PROJECT_ROOT / path
def load_config():
set_default_data_env()
cfg = YParams(str(PROJECT_ROOT / "conf" / "config.yaml"), "root")
cfg.datapipe.source.data_dir = str(resolve_path(cfg.datapipe.source.data_dir))
cfg.datapipe.source.cache_dir = str(resolve_path(cfg.datapipe.source.cache_dir))
cfg.training.output_dir = str(resolve_path(cfg.training.output_dir))
return cfg
def seed_everything(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def activation_from_name(name):
name = str(name).lower()
if name == "relu":
return nn.ReLU
if name == "elu":
return nn.ELU
if name == "leakyrelu":
return nn.LeakyReLU
return nn.SiLU
def build_model(model_cfg):
return HeteroGNS(
nnode_in_features=model_cfg.nnode_in_features,
nnode_out_features=model_cfg.nnode_out_features,
nedge_in_features=model_cfg.nedge_in_features,
latent_dim=model_cfg.get("latent_dim", model_cfg.get("width", 128)),
nmessage_passing_steps=model_cfg.get("nmessage_passing_steps", 10),
nmlp_layers=model_cfg.nmlp_layers,
mlp_hidden_dim=model_cfg.get("mlp_hidden_dim", model_cfg.get("width", 128)),
activation=activation_from_name(model_cfg.act),
boundary_dim=model_cfg.boundary_dim,
trans_layer=model_cfg.trans_layer,
)
def select_device(device_name, dist):
device_name = str(device_name).lower()
if device_name == "cpu":
return torch.device("cpu")
if device_name == "cuda":
if not torch.cuda.is_available():
raise RuntimeError("training.device is cuda, but CUDA is not available.")
return torch.device(f"cuda:{dist.local_rank}")
return dist.device
def reduce_scalar(value, device, dist):
tensor = torch.tensor(value, device=device, dtype=torch.float32)
if dist.world_size > 1:
torch.distributed.all_reduce(tensor)
tensor /= dist.world_size
return tensor.item()
def evaluate(model, test_loader, device, u_normalizer, myloss, dist):
model.eval()
total_l2 = 0.0
with torch.no_grad():
for batch in test_loader:
batch = batch.to(device)
out = model(batch)
pred = u_normalizer.decode(
out.view(batch.num_graphs, -1),
sample_idx=batch["G1"].sample_idx.view(batch.num_graphs, -1),
)
total_l2 += myloss(pred, batch["G1+2"].y.view(batch.num_graphs, -1)).item()
total_l2 = reduce_scalar(total_l2, device, dist)
return total_l2
def main():
cfg = load_config()
seed_everything(int(cfg.training.seed))
DistributedManager.initialize()
dist = DistributedManager()
device = select_device(cfg.training.get("device", "auto"), dist)
output_dir = Path(cfg.training.output_dir)
if dist.rank == 0:
output_dir.mkdir(parents=True, exist_ok=True)
print(f"Config: {PROJECT_ROOT / 'conf' / 'config.yaml'}")
print(f"Data: {cfg.datapipe.source.data_dir}")
print(f"Checkpoint directory: {output_dir}")
datapipe = BENODatapipe(cfg, distributed=(dist.world_size > 1))
train_loader, train_sampler = datapipe.train_dataloader()
test_loader, _ = datapipe.test_dataloader()
if len(train_loader) == 0:
raise RuntimeError("Training loader is empty. Check ntrain and batch_size.")
u_normalizer = datapipe.u_normalizer.to(device)
model = build_model(cfg.model).to(device)
if dist.world_size > 1:
device_ids = [dist.local_rank] if device.type == "cuda" else None
model = DDP(model, device_ids=device_ids)
optimizer = torch.optim.Adam(
model.parameters(),
lr=cfg.training.optimizer.lr,
weight_decay=cfg.training.optimizer.weight_decay,
)
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(
optimizer,
T_0=cfg.training.scheduler.T_0,
T_mult=cfg.training.scheduler.T_mult,
)
myloss = LpLoss(size_average=False)
for epoch in range(int(cfg.training.epochs)):
if train_sampler:
train_sampler.set_epoch(epoch)
model.train()
train_mse = 0.0
train_l2 = 0.0
batches = 0
start = default_timer()
iterator = tqdm(train_loader, desc=f"Epoch {epoch}", disable=(dist.rank != 0))
for batch in iterator:
batch = batch.to(device)
optimizer.zero_grad(set_to_none=True)
out = model(batch)
loss = F.mse_loss(out.view(-1, 1), batch["G1+2"].y.view(-1, 1))
loss.backward()
optimizer.step()
with torch.no_grad():
pred_denorm = u_normalizer.decode(
out.view(batch.num_graphs, -1),
sample_idx=batch["G1"].sample_idx.view(batch.num_graphs, -1),
)
target_denorm = u_normalizer.decode(
batch["G1+2"].y.view(batch.num_graphs, -1),
sample_idx=batch["G1"].sample_idx.view(batch.num_graphs, -1),
)
l2 = myloss(pred_denorm, target_denorm)
train_mse += loss.item()
train_l2 += l2.item()
batches += 1
if dist.rank == 0:
iterator.set_postfix({"mse": f"{loss.item():.2e}", "l2": f"{l2.item():.2e}"})
scheduler.step()
train_mse = reduce_scalar(train_mse / batches, device, dist)
train_l2 = reduce_scalar(train_l2 / datapipe.train_dataset.ntrain, device, dist)
test_l2 = evaluate(model, test_loader, device, u_normalizer, myloss, dist)
test_l2 /= datapipe.test_dataset.ntest
if dist.rank == 0:
print(
f"Epoch {epoch:03d} | Train MSE: {train_mse:.6f} | "
f"Train L2: {train_l2:.6f} | Test L2: {test_l2:.6f} | "
f"Time: {default_timer() - start:.1f}s"
)
if (epoch + 1) % int(cfg.training.save_period) == 0:
model_to_save = model.module if hasattr(model, "module") else model
checkpoint_name = cfg.training.checkpoint_name.format(epoch=epoch)
ckpt_path = output_dir / checkpoint_name
torch.save(model_to_save.state_dict(), ckpt_path)
print(f"Saved checkpoint to {ckpt_path}")
dist.cleanup()
if __name__ == "__main__":
main()
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