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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 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))
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.factformer import FactFormer2D # noqa: E402
from onescience.datapipes.cfd import KolmogorovFlow2DDatapipe # 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()
)
stats_file = data.get("stats_file")
if stats_file:
data["stats_file"] = str(project_path(stats_file, 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 = {
"train_num": data["train_num"],
"test_num": data["test_num"],
"resolution": data["resolution"],
"interval": data["interval"],
"t_in": data["t_in"],
"t_out": data["t_out"],
"batch_size": loader["batch_size"],
"hidden_dim": model["hidden_dim"],
"depth": model["depth"],
"heads": model["heads"],
"mlp_ratio": model["mlp_ratio"],
"max_latent_steps": model["max_latent_steps"],
"epochs": training["epochs"],
"eval_interval": training["eval_interval"],
"train_latent_steps": training["train_latent_steps"],
}
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["heads"]):
raise ValueError("hidden_dim must be divisible by heads")
if int(training["train_latent_steps"]) > int(model["max_latent_steps"]):
raise ValueError("train_latent_steps cannot exceed model.max_latent_steps")
if int(training["train_latent_steps"]) > int(data["t_out"]):
raise ValueError("train_latent_steps cannot exceed datapipe.data.t_out")
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], spatial_shape: tuple[int, int]
) -> FactFormer2D:
return FactFormer2D(
in_dim=int(model_config["in_dim"]),
out_dim=int(model_config["out_dim"]),
spatial_shape=spatial_shape,
hidden_dim=int(model_config["hidden_dim"]),
depth=int(model_config["depth"]),
heads=int(model_config["heads"]),
mlp_ratio=int(model_config["mlp_ratio"]),
dropout=float(model_config["dropout"]),
activation=str(model_config["activation"]),
include_pos=bool(model_config["include_pos"]),
space_dim=int(model_config["space_dim"]),
latent_multiplier=float(model_config["latent_multiplier"]),
max_latent_steps=int(model_config["max_latent_steps"]),
)
def evaluate(
model: torch.nn.Module,
loader: torch.utils.data.DataLoader,
device: torch.device,
datapipe: KolmogorovFlow2DDatapipe,
t_out: int,
out_dim: int,
max_latent_steps: int,
max_batches: int | None,
distributed: bool,
rank: int,
) -> tuple[float, float]:
model.eval()
totals = torch.zeros(4, dtype=torch.float64, device=device)
with torch.no_grad():
iterator = tqdm(loader, desc="Evaluating", disable=rank != 0)
for batch_index, batch in enumerate(iterator):
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, max_latent_steps
)
prediction = datapipe.decode_solution(prediction)
target = datapipe.decode_solution(target)
totals[0] += F.mse_loss(prediction, target, reduction="sum")
totals[1] += target.numel()
totals[2] += relative_l2(prediction, target).sum()
totals[3] += target.shape[0]
if distributed:
torch.distributed.all_reduce(totals)
return (
(totals[0] / totals[1].clamp_min(1)).item(),
(totals[2] / totals[3].clamp_min(1)).item(),
)
def main() -> None:
config_path = DEFAULT_CONFIG.resolve()
config = load_config(config_path)
prepare_config(config)
validate_config(config)
common = config["common"]
datapipe_config = config["datapipe"]
model_config = config["model"]
training = config["training"]
seed_everything(int(common["seed"]))
DistributedManager.initialize()
dist = DistributedManager()
distributed = dist.world_size > 1
device = dist.device if str(common["device"]) == "auto" else resolve_device(
str(common["device"])
)
weight_dir = project_path(training["weight_dir"], PROJECT_ROOT).resolve()
checkpoint_path = weight_dir / str(training["checkpoint_name"])
if dist.rank == 0:
print(f"Config: {config_path}")
print(
"Data: "
f"{Path(datapipe_config['source']['data_dir']) / datapipe_config['source']['file_name']}"
)
print(f"Device: {device}")
print(
f"Samples: train={datapipe_config['data']['train_num']} "
f"test={datapipe_config['data']['test_num']} "
f"t_in={datapipe_config['data']['t_in']} "
f"t_out={datapipe_config['data']['t_out']}"
)
started = time.time()
try:
datapipe = KolmogorovFlow2DDatapipe(
to_attr_dict(datapipe_config), distributed=distributed
)
train_loader, train_sampler = datapipe.train_dataloader()
test_loader, _ = datapipe.test_dataloader()
spatial_shape = tuple(datapipe.spatial_shape)
model = build_model(model_config, spatial_shape).to(device)
if distributed:
device_ids = [dist.local_rank] if device.type == "cuda" else None
model = DDP(model, device_ids=device_ids)
if dist.rank == 0:
parameter_count = sum(parameter.numel() for parameter in model.parameters())
print(f"Spatial shape: {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"]),
)
data = datapipe_config["data"]
t_out = int(data["t_out"])
out_dim = int(data["out_dim"])
max_latent_steps = int(model_config["max_latent_steps"])
train_latent_steps = int(training["train_latent_steps"])
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):
if train_sampler is not None:
train_sampler.set_epoch(epoch)
model.train()
epoch_loss = 0.0
batch_count = 0
iterator = tqdm(train_loader, desc=f"Epoch {epoch}", disable=dist.rank != 0)
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)[..., : train_latent_steps * out_dim]
optimizer.zero_grad(set_to_none=True)
prediction = model(pos, state, latent_steps=train_latent_steps)
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
if dist.rank == 0:
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,
datapipe,
t_out,
out_dim,
max_latent_steps,
max_eval_batches,
distributed,
dist.rank,
)
if dist.rank == 0:
print(
f"epoch={epoch:4d} train_mse={epoch_loss / batch_count:.6e} "
f"val_mse={validation_mse:.6e} "
f"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)
model_to_save = model.module if isinstance(model, DDP) else model
torch.save(
{
"epoch": epoch,
"model_state": model_to_save.state_dict(),
"model_config": to_plain_dict(model_config),
"datapipe_config": to_plain_dict(datapipe_config),
"training_config": to_plain_dict(training),
"spatial_shape": 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
stop = torch.tensor(
[int(stale_evaluations >= int(training["patience"]))], device=device
)
if distributed:
torch.distributed.broadcast(stop, src=0)
if stop.item():
if dist.rank == 0:
print("Early stopping triggered")
break
if dist.rank == 0:
print(f"Elapsed: {time.time() - started:.1f}s")
finally:
DistributedManager.cleanup()
if __name__ == "__main__":
main()
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