File size: 10,652 Bytes
21e6c23 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 | from __future__ import annotations
import logging
import random
import sys
import time
from pathlib import Path
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data.distributed import DistributedSampler
from torch_geometric.loader import DataLoader as PyGDataLoader
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model import Transolver2D, Transolver2D_plus
from onescience.distributed.manager import DistributedManager
from onescience.utils.YParams import YParams
def resolve_path(path: str) -> Path:
value = Path(path).expanduser()
return value if value.is_absolute() else ROOT / value
def setup_logging(rank: int) -> logging.Logger:
logging.basicConfig(
level=logging.INFO if rank == 0 else logging.WARNING,
format="%(asctime)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
return logging.getLogger("transolver-train")
def load_graph(path: Path):
try:
return torch.load(path, weights_only=False)
except TypeError:
return torch.load(path)
class FakeAirfRANSDataset(torch.utils.data.Dataset):
def __init__(self, data_dir: Path, stats_dir: Path, split: str, coef_norm=None):
manifest_path = data_dir / "manifest.json"
if not manifest_path.exists():
raise FileNotFoundError(f"Fake manifest not found: {manifest_path}. Run scripts/fake_data.py first.")
import json
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
self.data_dir = data_dir
self.names = manifest[split]
self.coef_norm = coef_norm or (
np.load(stats_dir / "mean_in.npy"),
np.load(stats_dir / "std_in.npy"),
np.load(stats_dir / "mean_out.npy"),
np.load(stats_dir / "std_out.npy"),
)
def __len__(self) -> int:
return len(self.names)
def __getitem__(self, index: int):
data = load_graph(self.data_dir / f"{self.names[index]}.pt")
mean_in, std_in, mean_out, std_out = self.coef_norm
data.x = (data.x - torch.as_tensor(mean_in, dtype=data.x.dtype)) / torch.as_tensor(std_in + 1e-8, dtype=data.x.dtype)
data.y = (data.y - torch.as_tensor(mean_out, dtype=data.y.dtype)) / torch.as_tensor(std_out + 1e-8, dtype=data.y.dtype)
return data
class FakeAirfRANSDatapipe:
def __init__(self, cfg_data, distributed: bool):
self.cfg_data = cfg_data
self.distributed = distributed
data_dir = resolve_path(cfg_data.source.data_dir)
stats_dir = resolve_path(cfg_data.source.stats_dir)
self.train_dataset = FakeAirfRANSDataset(data_dir, stats_dir, cfg_data.data.splits.train_name)
self.coef_norm = self.train_dataset.coef_norm
self.val_dataset = FakeAirfRANSDataset(data_dir, stats_dir, cfg_data.data.splits.val_name, self.coef_norm)
self.test_dataset = FakeAirfRANSDataset(data_dir, stats_dir, cfg_data.data.splits.test_name, self.coef_norm)
def _loader(self, dataset, shuffle: bool):
sampler = DistributedSampler(dataset, shuffle=shuffle) if self.distributed else None
return (
PyGDataLoader(
dataset,
batch_size=self.cfg_data.dataloader.batch_size,
num_workers=self.cfg_data.dataloader.num_workers,
pin_memory=torch.cuda.is_available(),
shuffle=shuffle and sampler is None,
sampler=sampler,
),
sampler,
)
def train_dataloader(self):
return self._loader(self.train_dataset, True)
def val_dataloader(self):
return self._loader(self.val_dataset, False)
def build_model(cfg_model):
model_name = cfg_model.name
if model_name not in cfg_model.specific_params:
raise ValueError(f"Model '{model_name}' is missing from model.specific_params")
params = cfg_model.specific_params[model_name]
model_cls = Transolver2D_plus if model_name == "Transolver_plus" else Transolver2D
if model_name not in ("Transolver", "Transolver_plus"):
raise NotImplementedError("This refactored package localizes only Transolver and Transolver_plus.")
return model_cls(
n_hidden=params.n_hidden,
n_layers=params.n_layers,
space_dim=params.space_dim,
fun_dim=params.fun_dim,
n_head=params.n_head,
mlp_ratio=params.mlp_ratio,
out_dim=params.out_dim,
slice_num=params.slice_num,
unified_pos=bool(params.unified_pos),
)
def build_datapipe(cfg_data, model_params, distributed: bool):
cfg_data.model_hparams = model_params
if cfg_data.backend == "airfrans":
from onescience.datapipes.cfd import AirfRANSDatapipe
return AirfRANSDatapipe(params=cfg_data, distributed=distributed)
if cfg_data.backend == "fake_airfrans":
return FakeAirfRANSDatapipe(cfg_data, distributed=distributed)
raise ValueError(f"Unsupported datapipe.backend: {cfg_data.backend}")
def masked_mean(losses: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
if mask.any():
return losses[mask].mean()
return losses.mean() * 0.0
def save_checkpoint(model, optimizer, scheduler, epoch: int, loss: float, checkpoint_dir: Path, model_name: str) -> None:
checkpoint_dir.mkdir(parents=True, exist_ok=True)
model_to_save = model.module if hasattr(model, "module") else model
torch.save(
{
"model_state_dict": model_to_save.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"scheduler_state_dict": scheduler.state_dict(),
"epoch": epoch,
"loss": loss,
},
checkpoint_dir / f"{model_name}.pth",
)
def main() -> int:
config_path = ROOT / "conf" / "config.yaml"
cfg_model = YParams(str(config_path), "model")
cfg_data = YParams(str(config_path), "datapipe")
cfg_train = YParams(str(config_path), "training")
torch.manual_seed(int(cfg_train.seed))
np.random.seed(int(cfg_train.seed))
random.seed(int(cfg_train.seed))
DistributedManager.initialize()
manager = DistributedManager()
logger = setup_logging(manager.rank)
device_name = getattr(cfg_train, "device", "auto")
if device_name == "cpu":
device = torch.device("cpu")
elif device_name.startswith("cuda"):
device = torch.device(device_name if torch.cuda.is_available() else "cpu")
else:
device = torch.device(f"cuda:{cfg_train.gpuid}" if torch.cuda.is_available() and manager.world_size == 1 else manager.device)
model_params = cfg_model.specific_params[cfg_model.name]
datapipe = build_datapipe(cfg_data, model_params, distributed=(manager.world_size > 1))
train_loader, train_sampler = datapipe.train_dataloader()
val_loader, val_sampler = datapipe.val_dataloader()
if len(train_loader) == 0 or len(val_loader) == 0:
raise RuntimeError("Train and validation loaders must both be non-empty.")
model = build_model(cfg_model).to(device)
if manager.world_size > 1:
model = DistributedDataParallel(model, device_ids=[manager.local_rank], output_device=manager.local_rank)
optimizer = torch.optim.Adam(model.parameters(), lr=float(cfg_train.lr))
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=float(cfg_train.lr),
total_steps=max(1, len(train_loader) * int(cfg_train.max_epoch)),
)
criterion = nn.L1Loss(reduction="none") if cfg_train.loss_criterion == "MAE" else nn.MSELoss(reduction="none")
use_weighted_loss = cfg_train.loss_criterion == "MSE_weighted"
checkpoint_dir = resolve_path(cfg_train.checkpoint_dir)
best_valid_loss = float("inf")
best_epoch = -1
if manager.rank == 0:
params = sum(p.numel() for p in model.parameters() if p.requires_grad)
logger.info("Training %s with %.3fM parameters on %s", cfg_model.name, params / 1e6, device)
for epoch in range(int(cfg_train.max_epoch)):
start = time.time()
if train_sampler is not None:
train_sampler.set_epoch(epoch)
if val_sampler is not None:
val_sampler.set_epoch(epoch)
model.train()
train_loss = 0.0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad(set_to_none=True)
out = model(data)
losses = criterion(out, data.y)
surf_loss = masked_mean(losses, data.surf)
vol_loss = masked_mean(losses, ~data.surf)
loss = vol_loss + float(cfg_train.loss_weight) * surf_loss if use_weighted_loss else losses.mean()
loss.backward()
optimizer.step()
scheduler.step()
train_loss += float(loss.detach().cpu())
train_loss /= len(train_loader)
model.eval()
valid_loss = 0.0
with torch.no_grad():
for data in val_loader:
data = data.to(device)
out = model(data)
losses = criterion(out, data.y)
surf_loss = masked_mean(losses, data.surf)
vol_loss = masked_mean(losses, ~data.surf)
loss = vol_loss + float(cfg_train.loss_weight) * surf_loss if use_weighted_loss else losses.mean()
if manager.world_size > 1:
dist.all_reduce(loss, op=dist.ReduceOp.AVG)
valid_loss += float(loss.detach().cpu())
valid_loss /= len(val_loader)
if manager.rank == 0:
logger.info(
"Epoch %d/%d train=%.6f valid=%.6f time=%.2fs",
epoch + 1,
int(cfg_train.max_epoch),
train_loss,
valid_loss,
time.time() - start,
)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_epoch = epoch
save_checkpoint(model, optimizer, scheduler, epoch, valid_loss, checkpoint_dir, cfg_model.name)
logger.info("Saved checkpoint to %s", checkpoint_dir / f"{cfg_model.name}.pth")
if epoch - best_epoch > int(cfg_train.patience):
logger.info("Early stopping after %d epochs without improvement.", int(cfg_train.patience))
break
if manager.world_size > 1 and dist.is_available() and dist.is_initialized():
dist.barrier()
dist.destroy_process_group()
return 0
if __name__ == "__main__":
raise SystemExit(main())
|