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bd3493c | 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 | """Train the official Aardvark Day-1 TAS model on official-schema tasks."""
from __future__ import annotations
import argparse
import json
import math
import random
import sys
from pathlib import Path
import numpy as np
import torch
import yaml
from torch.utils.data import DataLoader
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.aardvark_adapter import build_one_day_model
from model.sample_dataset import AardvarkTaskDataset, collate_tasks, discover_samples, split_samples
def parse_args() -> argparse.Namespace:
pre_parser = argparse.ArgumentParser(add_help=False)
pre_parser.add_argument("--config", type=Path, default=ROOT / "conf" / "config.yaml")
known, _ = pre_parser.parse_known_args()
config = yaml.safe_load(known.config.read_text())["training"]
parser = argparse.ArgumentParser(description=__doc__, parents=[pre_parser])
parser.add_argument("--data", type=Path, default=ROOT / config["data"])
parser.add_argument("--output-dir", type=Path, default=ROOT / config["output_dir"])
parser.add_argument("--epochs", type=int, default=config["epochs"])
parser.add_argument("--train-steps", type=int, default=config["train_steps"])
parser.add_argument("--validation-steps", type=int, default=config["validation_steps"])
parser.add_argument("--validation-fraction", type=float, default=config["validation_fraction"])
parser.add_argument("--batch-size", type=int, default=config["batch_size"])
parser.add_argument("--learning-rate", type=float, default=config["learning_rate"])
parser.add_argument("--weight-decay", type=float, default=config["weight_decay"])
parser.add_argument("--gradient-clip", type=float, default=config["gradient_clip"])
parser.add_argument("--patience", type=int, default=config["patience"])
parser.add_argument("--seed", type=int, default=config["seed"])
parser.add_argument("--train-modules", choices=("decoder", "all"), default=config["train_modules"])
parser.add_argument("--resume", type=Path)
return parser.parse_args()
def masked_metrics(prediction: torch.Tensor, target: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, int]:
valid = torch.isfinite(target)
count = int(valid.sum())
if count == 0:
raise RuntimeError("Aardvark task has no finite station targets")
error = prediction[valid] - target[valid]
return torch.sqrt(error.square().mean()), error.abs().mean(), count
def configure_trainable_parameters(model: torch.nn.Module, train_modules: str) -> list[torch.nn.Parameter]:
for parameter in model.parameters():
parameter.requires_grad_(train_modules == "all")
if train_modules == "decoder":
for parameter in model.sf_model.parameters():
parameter.requires_grad_(True)
parameters = [parameter for parameter in model.parameters() if parameter.requires_grad]
if not parameters:
raise RuntimeError("No trainable parameters selected")
return parameters
def checkpoint_payload(model, optimizer, scheduler, args, epoch, best_validation_rmse, history):
model_state = model.state_dict() if args.train_modules == "all" else model.sf_model.state_dict()
return {
"model": model_state,
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict(),
"epoch": epoch,
"best_validation_rmse": best_validation_rmse,
"history": history,
"train_modules": args.train_modules,
"config": {key: str(value) if isinstance(value, Path) else value for key, value in vars(args).items()},
"torch_rng_state": torch.get_rng_state(),
"numpy_rng_state": np.random.get_state(),
"python_rng_state": random.getstate(),
}
def main() -> None:
args = parse_args()
if not torch.cuda.is_available():
raise RuntimeError("The official Aardvark model requires CUDA")
if min(args.epochs, args.train_steps, args.validation_steps, args.batch_size) < 1:
raise ValueError("epochs, steps and batch_size must be at least 1")
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
args.output_dir.mkdir(parents=True, exist_ok=True)
samples = discover_samples(args.data)
train_samples, validation_samples = split_samples(samples, args.validation_fraction, args.seed)
train_loader = DataLoader(
AardvarkTaskDataset(train_samples, args.train_steps * args.batch_size),
batch_size=args.batch_size,
shuffle=True,
collate_fn=collate_tasks,
)
validation_loader = DataLoader(
AardvarkTaskDataset(validation_samples, args.validation_steps * args.batch_size),
batch_size=args.batch_size,
collate_fn=collate_tasks,
)
model = build_one_day_model(ROOT / "weights", ROOT / "official-src", "cuda")
model.return_gridded = False
parameters = configure_trainable_parameters(model, args.train_modules)
optimizer = torch.optim.AdamW(parameters, lr=args.learning_rate, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode="min",
factor=0.5,
patience=max(1, args.patience // 2),
)
start_epoch = 0
best_validation_rmse = math.inf
history: list[dict[str, float | int]] = []
if args.resume:
payload = torch.load(args.resume, map_location="cuda", weights_only=False)
if payload["train_modules"] != args.train_modules:
raise ValueError("--train-modules must match the resumed checkpoint")
target_model = model if args.train_modules == "all" else model.sf_model
target_model.load_state_dict(payload["model"])
optimizer.load_state_dict(payload["optimizer"])
scheduler.load_state_dict(payload["scheduler"])
start_epoch = int(payload["epoch"]) + 1
best_validation_rmse = float(payload["best_validation_rmse"])
history = payload["history"]
torch.set_rng_state(payload["torch_rng_state"])
np.random.set_state(payload["numpy_rng_state"])
random.setstate(payload["python_rng_state"])
epochs_without_improvement = 0
last_path = args.output_dir / "last.pth"
best_path = args.output_dir / "best.pth"
for epoch in range(start_epoch, args.epochs):
model.train()
if args.train_modules == "decoder":
model.se_model.eval()
model.forecast_model.eval()
model.sf_model.train()
train_rmse = []
train_mae = []
for task in train_loader:
optimizer.zero_grad(set_to_none=True)
prediction = model(task)
target = task["y_target"].to(prediction.device)
rmse, mae, _ = masked_metrics(prediction, target)
rmse.backward()
torch.nn.utils.clip_grad_norm_(parameters, args.gradient_clip)
optimizer.step()
train_rmse.append(float(rmse.detach()))
train_mae.append(float(mae.detach()))
model.eval()
validation_rmse = []
validation_mae = []
valid_stations = 0
with torch.inference_mode():
for task in validation_loader:
prediction = model(task)
rmse, mae, valid_stations = masked_metrics(prediction, task["y_target"].to(prediction.device))
validation_rmse.append(float(rmse))
validation_mae.append(float(mae))
record = {
"epoch": epoch,
"train_rmse": float(np.mean(train_rmse)),
"train_mae": float(np.mean(train_mae)),
"validation_rmse": float(np.mean(validation_rmse)),
"validation_mae": float(np.mean(validation_mae)),
"learning_rate": optimizer.param_groups[0]["lr"],
"valid_stations": valid_stations,
}
history.append(record)
scheduler.step(record["validation_rmse"])
improved = record["validation_rmse"] < best_validation_rmse
if improved:
best_validation_rmse = record["validation_rmse"]
epochs_without_improvement = 0
else:
epochs_without_improvement += 1
payload = checkpoint_payload(model, optimizer, scheduler, args, epoch, best_validation_rmse, history)
torch.save(payload, last_path)
if improved:
torch.save(payload, best_path)
(args.output_dir / "history.json").write_text(json.dumps(history, indent=2) + "\n")
print(json.dumps(record, sort_keys=True))
if epochs_without_improvement >= args.patience:
break
report = {
"status": "completed",
"epochs_completed": len(history),
"train_modules": args.train_modules,
"train_samples": [str(path) for path in train_samples],
"validation_samples": [str(path) for path in validation_samples],
"best_validation_rmse": best_validation_rmse,
"best_checkpoint": str(best_path),
"last_checkpoint": str(last_path),
}
(args.output_dir / "train.json").write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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