File size: 8,506 Bytes
e9b4f6f | 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 | from __future__ import annotations
import logging
import os
import sys
import time
import importlib.util
from pathlib import Path
import torch
import torch.distributed as dist
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
os.chdir(ROOT)
from model import Transolver3D, Transolver3D_plus
import onescience
from onescience.distributed.manager import DistributedManager
from onescience.utils.YParams import YParams
def load_shapenet_car_datapipe():
module_path = Path(onescience.__file__).resolve().parent / "datapipes/cfd/ShapeNetCar.py"
spec = importlib.util.spec_from_file_location("_onescience_shapenetcar", module_path)
if spec is None or spec.loader is None:
raise ImportError(f"Unable to load ShapeNetCarDatapipe from {module_path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module.ShapeNetCarDatapipe
def setup_logging(rank: int) -> logging.Logger:
level = logging.INFO if rank == 0 else logging.WARNING
logging.basicConfig(
level=level,
format="%(asctime)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logging.getLogger().setLevel(level)
return logging.getLogger(__name__)
def build_model(model_name: str, model_params, device: torch.device) -> torch.nn.Module:
model_cls = {
"Transolver": Transolver3D,
"Transolver_plus": Transolver3D_plus,
}.get(model_name)
if model_cls is None:
raise NotImplementedError(f"Model {model_name} initialization not implemented.")
return model_cls(
n_hidden=model_params.n_hidden,
n_layers=model_params.n_layers,
space_dim=model_params.space_dim,
fun_dim=model_params.fun_dim,
n_head=model_params.n_head,
mlp_ratio=model_params.mlp_ratio,
out_dim=model_params.out_dim,
slice_num=model_params.slice_num,
unified_pos=model_params.unified_pos,
).to(device)
def resolve_device(gpuid: int) -> torch.device:
if torch.cuda.is_available() and int(gpuid) >= 0:
return torch.device(f"cuda:{gpuid}")
return torch.device("cpu")
def save_checkpoint(model, optimizer, scheduler, epoch: int, loss: float, ckp_dir: str, model_name: str) -> None:
Path(ckp_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,
},
Path(ckp_dir) / f"{model_name}.pth",
)
def main() -> None:
DistributedManager.initialize()
manager = DistributedManager()
logger = setup_logging(manager.rank)
config_file_path = str(ROOT / "conf/config.yaml")
cfg = YParams(config_file_path, "model")
cfg_data = YParams(config_file_path, "datapipe")
cfg_train = YParams(config_file_path, "training")
model_name = cfg.name
if model_name not in cfg.specific_params:
raise ValueError(f"Model '{model_name}' not found in model.specific_params.")
model_params = cfg.specific_params[model_name]
cfg_data.model_hparams = model_params
logger.info("Initializing ShapeNetCar datapipe...")
ShapeNetCarDatapipe = load_shapenet_car_datapipe()
datapipe = ShapeNetCarDatapipe(params=cfg_data, distributed=(manager.world_size > 1))
train_dataloader, train_sampler = datapipe.train_dataloader()
val_dataloader, val_sampler = datapipe.val_dataloader()
if manager.world_size > 1:
device = torch.device(f"cuda:{manager.local_rank}" if torch.cuda.is_available() else "cpu")
else:
device = resolve_device(cfg_train.gpuid)
model = build_model(model_name, model_params, device)
if manager.rank == 0:
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
logger.info("Model: %s, trainable params: %.2fM", model_name, total_params / 1e6)
if manager.world_size > 1:
model = DistributedDataParallel(
model,
device_ids=[manager.local_rank],
output_device=manager.local_rank,
find_unused_parameters=True,
)
optimizer = torch.optim.Adam(model.parameters(), lr=cfg_train.lr)
scheduler = torch.optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=cfg_train.lr,
total_steps=max(1, len(train_dataloader) * cfg_train.max_epoch),
)
if cfg_train.loss_criterion == "MSE":
loss_criterion = nn.MSELoss(reduction="none")
elif cfg_train.loss_criterion == "MAE":
loss_criterion = nn.L1Loss(reduction="none")
else:
raise ValueError(f"Unknown loss criterion: {cfg_train.loss_criterion}")
best_valid_loss = 1.0e6
best_loss_epoch = 0
logger.info("Starting training...")
for epoch in range(cfg_train.max_epoch):
epoch_start_time = time.time()
if manager.world_size > 1:
train_sampler.set_epoch(epoch)
if val_sampler is not None:
val_sampler.set_epoch(epoch)
model.train()
train_loss = train_loss_press = train_loss_velo = 0.0
for data in train_dataloader:
data = data.to(device)
optimizer.zero_grad()
out = model(data)
targets = data.y
loss_press = loss_criterion(out[data.surf, -1], targets[data.surf, -1]).mean()
loss_velo = loss_criterion(out[:, :-1], targets[:, :-1]).mean()
loss = loss_velo + cfg_train.loss_weight * loss_press
loss.backward()
optimizer.step()
scheduler.step()
train_loss += loss.item()
train_loss_press += loss_press.item()
train_loss_velo += loss_velo.item()
train_loss /= max(1, len(train_dataloader))
train_loss_press /= max(1, len(train_dataloader))
train_loss_velo /= max(1, len(train_dataloader))
valid_loss = valid_loss_press = valid_loss_velo = 0.0
if (epoch + 1) % cfg_train.val_iter == 0 or epoch == cfg_train.max_epoch - 1:
model.eval()
with torch.no_grad():
for data in val_dataloader:
data = data.to(device)
out = model(data)
targets = data.y
loss_press = loss_criterion(out[data.surf, -1], targets[data.surf, -1]).mean()
loss_velo = loss_criterion(out[:, :-1], targets[:, :-1]).mean()
loss = loss_velo + cfg_train.loss_weight * loss_press
if manager.world_size > 1:
dist.all_reduce(loss, op=dist.ReduceOp.AVG)
dist.all_reduce(loss_press, op=dist.ReduceOp.AVG)
dist.all_reduce(loss_velo, op=dist.ReduceOp.AVG)
valid_loss += loss.item()
valid_loss_press += loss_press.item()
valid_loss_velo += loss_velo.item()
valid_loss /= max(1, len(val_dataloader))
valid_loss_press /= max(1, len(val_dataloader))
valid_loss_velo /= max(1, len(val_dataloader))
if manager.rank == 0:
logger.info(
"Epoch [%d/%d] | Time: %.2fs | Train: %.6f (velo %.6f, press %.6f) | "
"Valid: %.6f (velo %.6f, press %.6f)",
epoch + 1,
cfg_train.max_epoch,
time.time() - epoch_start_time,
train_loss,
train_loss_velo,
train_loss_press,
valid_loss,
valid_loss_velo,
valid_loss_press,
)
if valid_loss > 0 and valid_loss < best_valid_loss:
best_valid_loss = valid_loss
best_loss_epoch = epoch
save_checkpoint(model, optimizer, scheduler, epoch, valid_loss, cfg_train.checkpoint_dir, model_name)
logger.info("New best checkpoint saved to %s/%s.pth", cfg_train.checkpoint_dir, model_name)
if epoch - best_loss_epoch > cfg_train.patience:
logger.warning("Validation loss has not improved for %d epochs. Stopping.", cfg_train.patience)
break
if __name__ == "__main__":
main()
|