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4d90337 | 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 | #!/usr/bin/env python3
"""Train the compact chess policy/value network on prepared arrays."""
from __future__ import annotations
import argparse
import time
from pathlib import Path
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, Dataset
from model import ChessNet
class PreparedDataset(Dataset):
def __init__(self, root: Path, split: str):
self.planes = np.load(root / f"{split}_planes.npy", mmap_mode="r")
self.legal = np.load(root / f"{split}_legal.npy", mmap_mode="r")
self.move = np.load(root / f"{split}_move.npy", mmap_mode="r")
self.value = np.load(root / f"{split}_value.npy", mmap_mode="r")
self.wdl = np.load(root / f"{split}_wdl.npy", mmap_mode="r")
def __len__(self):
return len(self.move)
def __getitem__(self, index):
planes = np.array(self.planes[index], dtype=np.float32)
planes[18] /= 100.0
legal = np.unpackbits(np.array(self.legal[index], dtype=np.uint8)).astype(np.bool_)
return (
torch.from_numpy(planes),
torch.from_numpy(legal),
torch.tensor(int(self.move[index]), dtype=torch.long),
torch.tensor(float(self.value[index]), dtype=torch.float32),
torch.tensor(int(self.wdl[index]), dtype=torch.long),
)
def run_epoch(model, loader, optimizer, scaler, device, train: bool):
model.train(train)
total_loss = 0.0
correct1 = correct5 = total = 0
value_abs = 0.0
wdl_correct = 0
for planes, legal, target, value, wdl in loader:
planes = planes.to(device, non_blocking=True)
legal = legal.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
value = value.to(device, non_blocking=True)
wdl = wdl.to(device, non_blocking=True)
with torch.set_grad_enabled(train):
with torch.autocast("cuda", dtype=torch.float16, enabled=(device.type == "cuda")):
logits, pred_value, pred_wdl = model(planes, legal)
policy_loss = nn.functional.cross_entropy(logits.float(), target)
value_loss = nn.functional.mse_loss(pred_value.float(), value)
wdl_loss = nn.functional.cross_entropy(pred_wdl.float(), wdl)
loss = policy_loss + 0.5 * value_loss + 0.25 * wdl_loss
if train:
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
total_loss += float(loss.detach()) * len(target)
with torch.no_grad():
top5 = torch.topk(logits, 5, dim=1).indices
correct1 += int((top5[:, 0] == target).sum())
correct5 += int((top5 == target[:, None]).any(dim=1).sum())
value_abs += float((pred_value.float() - value).abs().sum())
wdl_correct += int((pred_wdl.argmax(dim=1) == wdl).sum())
total += len(target)
return {
"loss": total_loss / max(total, 1),
"top1": correct1 / max(total, 1),
"top5": correct5 / max(total, 1),
"value_mae": value_abs / max(total, 1),
"wdl_acc": wdl_correct / max(total, 1),
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--prepared", default="prepared")
parser.add_argument("--epochs", type=int, default=10)
parser.add_argument("--batch", type=int, default=256)
parser.add_argument("--channels", type=int, default=128)
parser.add_argument("--blocks", type=int, default=10)
parser.add_argument("--lr", type=float, default=1e-3)
parser.add_argument("--workers", type=int, default=4)
parser.add_argument("--device", default="cuda")
parser.add_argument("--out", default="checkpoints/chessnet.pt")
parser.add_argument("--max-steps", type=int, default=0)
args = parser.parse_args()
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
root = Path(args.prepared)
train_ds = PreparedDataset(root, "train")
val_ds = PreparedDataset(root, "val")
train_loader = DataLoader(
train_ds, batch_size=args.batch, shuffle=True,
num_workers=args.workers, pin_memory=True, drop_last=True,
)
val_loader = DataLoader(
val_ds, batch_size=args.batch, shuffle=False,
num_workers=args.workers, pin_memory=True,
)
model = ChessNet(channels=args.channels, blocks=args.blocks).to(device)
params = sum(p.numel() for p in model.parameters())
print(f"device={device} params={params:,} train={len(train_ds):,} val={len(val_ds):,}")
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=0.01)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs)
scaler = torch.amp.GradScaler("cuda", enabled=(device.type == "cuda"))
best = -1.0
out_path = Path(args.out)
out_path.parent.mkdir(parents=True, exist_ok=True)
for epoch in range(1, args.epochs + 1):
started = time.time()
train_stats = run_epoch(model, train_loader, optimizer, scaler, device, True)
if args.max_steps:
break
val_stats = run_epoch(model, val_loader, optimizer, scaler, device, False)
scheduler.step()
elapsed = time.time() - started
print(
f"epoch {epoch:02d} {elapsed:6.1f}s | "
f"train loss {train_stats['loss']:.3f} top1 {train_stats['top1']:.3f} | "
f"val loss {val_stats['loss']:.3f} top1 {val_stats['top1']:.3f} "
f"top5 {val_stats['top5']:.3f} value_mae {val_stats['value_mae']:.3f} "
f"wdl {val_stats['wdl_acc']:.3f}",
flush=True,
)
if val_stats["top1"] > best:
best = val_stats["top1"]
torch.save(
{
"model": model.state_dict(),
"channels": args.channels,
"blocks": args.blocks,
"epoch": epoch,
"val": val_stats,
},
out_path,
)
print(f"best top1={best:.3f}; saved {out_path}")
if __name__ == "__main__":
main()
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