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import sys
import time
from pathlib import Path
from typing import Any
import torch
import torch.nn.functional as F
from tqdm import tqdm
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
sys.path.insert(0, str(Path(__file__).resolve().parent))
LOCAL_WORKSPACE = PROJECT_ROOT.parents[1]
if (LOCAL_WORKSPACE / "onescience" / "src" / "onescience" / "datapipes").is_dir():
sys.path.insert(0, str(LOCAL_WORKSPACE))
from common import ( # noqa: E402
load_config,
project_path,
relative_l2,
resolve_device,
rollout,
seed_everything,
to_attr_dict,
to_plain_dict,
)
from model.ono import ONO # noqa: E402
from onescience.datapipes.cfd import NavierStokesDatapipe # noqa: E402
from onescience.distributed.manager import DistributedManager # noqa: E402
DEFAULT_CONFIG = PROJECT_ROOT / "conf" / "config.yaml"
def prepare_config(config: dict[str, Any]) -> None:
datapipe = config["datapipe"]
data = datapipe["data"]
model = config["model"]
datapipe["source"]["data_dir"] = str(
project_path(datapipe["source"]["data_dir"], PROJECT_ROOT).resolve()
)
model["in_dim"] = int(data["t_in"]) * int(data["out_dim"])
model["out_dim"] = int(data["out_dim"])
def validate_config(config: dict[str, Any]) -> None:
data = config["datapipe"]["data"]
loader = config["datapipe"]["dataloader"]
model = config["model"]
training = config["training"]
positive_values = {
"ntrain": data["ntrain"],
"ntest": data["ntest"],
"t_in": data["t_in"],
"t_out": data["t_out"],
"downsamplex": data["downsamplex"],
"downsampley": data["downsampley"],
"batch_size": loader["batch_size"],
"hidden_dim": model["hidden_dim"],
"num_layers": model["num_layers"],
"num_heads": model["num_heads"],
"mlp_ratio": model["mlp_ratio"],
"psi_dim": model["psi_dim"],
"epochs": training["epochs"],
"eval_interval": training["eval_interval"],
}
for name, value in positive_values.items():
if int(value) < 1:
raise ValueError(f"{name} must be positive, got {value}")
if int(loader["num_workers"]) < 0:
raise ValueError("num_workers cannot be negative")
if int(model["hidden_dim"]) % int(model["num_heads"]):
raise ValueError("hidden_dim must be divisible by num_heads")
for name in ("max_train_batches", "max_eval_batches"):
value = training.get(name)
if value is not None and int(value) < 1:
raise ValueError(f"{name} must be positive when set")
def build_model(model_config: dict[str, Any]) -> ONO:
return ONO(
in_dim=int(model_config["in_dim"]),
out_dim=int(model_config["out_dim"]),
hidden_dim=int(model_config["hidden_dim"]),
num_layers=int(model_config["num_layers"]),
num_heads=int(model_config["num_heads"]),
space_dim=int(model_config["space_dim"]),
include_pos=bool(model_config["include_pos"]),
dropout=float(model_config["dropout"]),
activation=str(model_config["activation"]),
mlp_ratio=int(model_config["mlp_ratio"]),
attn_type=str(model_config["attn_type"]),
psi_dim=int(model_config["psi_dim"]),
)
def evaluate(
model: torch.nn.Module,
loader: torch.utils.data.DataLoader,
device: torch.device,
t_out: int,
out_dim: int,
max_batches: int | None,
) -> tuple[float, float]:
model.eval()
squared_error = 0.0
element_count = 0
relative_error = 0.0
sample_count = 0
with torch.no_grad():
for batch_index, batch in enumerate(tqdm(loader, desc="Evaluating")):
if max_batches is not None and batch_index >= max_batches:
break
pos = batch["pos"].to(device)
state = batch["x"].to(device)
target = batch["y"].to(device)
prediction = rollout(model, pos, state, t_out, out_dim)
squared_error += F.mse_loss(prediction, target, reduction="sum").item()
element_count += target.numel()
relative_error += relative_l2(prediction, target).sum().item()
sample_count += target.shape[0]
return (
squared_error / max(element_count, 1),
relative_error / max(sample_count, 1),
)
def main() -> None:
config_path = DEFAULT_CONFIG.resolve()
config = load_config(config_path)
prepare_config(config)
validate_config(config)
common = config["common"]
data_config = config["datapipe"]
model_config = config["model"]
training = config["training"]
device = resolve_device(str(common["device"]))
seed_everything(int(common["seed"]))
weight_dir = project_path(training["weight_dir"], PROJECT_ROOT).resolve()
checkpoint_path = weight_dir / str(training["checkpoint_name"])
print(f"Config: {config_path}")
print(f"Data: {Path(data_config['source']['data_dir']) / data_config['source']['file_name']}")
print(f"Device: {device}")
print(
f"Samples: train={data_config['data']['ntrain']} test={data_config['data']['ntest']} "
f"t_in={data_config['data']['t_in']} t_out={data_config['data']['t_out']}"
)
DistributedManager.initialize()
started = time.time()
try:
datapipe = NavierStokesDatapipe(to_attr_dict(data_config), distributed=False)
train_loader, _ = datapipe.train_dataloader()
test_loader, _ = datapipe.test_dataloader()
model = build_model(model_config).to(device)
parameter_count = sum(parameter.numel() for parameter in model.parameters())
print(f"Spatial shape: {datapipe.spatial_shape}")
print(f"Parameters: {parameter_count:,}")
optimizer = torch.optim.AdamW(
model.parameters(),
lr=float(training["lr"]),
weight_decay=float(training["weight_decay"]),
)
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=int(training["step_size"]),
gamma=float(training["gamma"]),
)
t_out = int(data_config["data"]["t_out"])
out_dim = int(data_config["data"]["out_dim"])
max_train_batches = training.get("max_train_batches")
max_train_batches = None if max_train_batches is None else int(max_train_batches)
max_eval_batches = training.get("max_eval_batches")
max_eval_batches = None if max_eval_batches is None else int(max_eval_batches)
best_relative_l2 = float("inf")
stale_evaluations = 0
for epoch in range(1, int(training["epochs"]) + 1):
model.train()
epoch_loss = 0.0
batch_count = 0
iterator = tqdm(train_loader, desc=f"Epoch {epoch}")
for batch_index, batch in enumerate(iterator):
if max_train_batches is not None and batch_index >= max_train_batches:
break
pos = batch["pos"].to(device)
state = batch["x"].to(device)
target = batch["y"].to(device)
optimizer.zero_grad(set_to_none=True)
prediction = rollout(
model,
pos,
state,
t_out,
out_dim,
teacher_forcing=bool(training["teacher_forcing"]),
target=target,
)
loss = F.mse_loss(prediction, target)
if not torch.isfinite(loss):
raise FloatingPointError(f"Non-finite training loss at epoch {epoch}")
loss.backward()
if training.get("max_grad_norm") is not None:
torch.nn.utils.clip_grad_norm_(
model.parameters(), float(training["max_grad_norm"])
)
optimizer.step()
epoch_loss += loss.item()
batch_count += 1
iterator.set_postfix(loss=f"{loss.item():.3e}")
scheduler.step()
if batch_count == 0:
raise RuntimeError("No training batches were processed")
should_evaluate = (
epoch % int(training["eval_interval"]) == 0
or epoch == int(training["epochs"])
)
if not should_evaluate:
continue
validation_mse, validation_relative_l2 = evaluate(
model,
test_loader,
device,
t_out,
out_dim,
max_eval_batches,
)
print(
f"epoch={epoch:4d} train_mse={epoch_loss / batch_count:.6e} "
f"val_mse={validation_mse:.6e} val_relative_l2={validation_relative_l2:.6e}"
)
if validation_relative_l2 < best_relative_l2:
best_relative_l2 = validation_relative_l2
stale_evaluations = 0
weight_dir.mkdir(parents=True, exist_ok=True)
torch.save(
{
"epoch": epoch,
"model_state": model.state_dict(),
"model_config": to_plain_dict(model_config),
"datapipe_config": to_plain_dict(data_config),
"training_config": to_plain_dict(training),
"spatial_shape": tuple(datapipe.spatial_shape),
"normalizer": datapipe.get_normalizer_state(),
"best_relative_l2": best_relative_l2,
},
checkpoint_path,
)
print(f"Saved checkpoint: {checkpoint_path}")
else:
stale_evaluations += 1
if stale_evaluations >= int(training["patience"]):
print("Early stopping triggered")
break
print(f"Elapsed: {time.time() - started:.1f}s")
finally:
DistributedManager.cleanup()
if __name__ == "__main__":
main()
|