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"""Parallax-Chess v3: Predict move as single index (from_sq * 64 + to_sq).
Fixes the v2 bug where only from-square was supervised.
"""
import sys, math, time, json, random
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import chess
ROOT = Path(__file__).parent.parent
sys.path.insert(0, str(ROOT / "scripts"))
sys.path.insert(0, str(ROOT / "src"))
sys.path.insert(0, str(ROOT.parent))
from centauri.models.base import SmallLMConfig
from centauri.models.torch.small_lm import SmallLM
from chess_encoding import encode_board, VOCAB_SIZE
random.seed(42)
# Move index: from_sq * 64 + to_sq for non-promo, then promo moves
# Total: 64*64 = 4096 normal + 4*8*8 = 256 promo = 4352 total
MOVE_INDEX_SIZE = 4352 # 64*64 + 4*64 (promotions)
def move_to_index(move):
"""Convert chess.Move to index 0..4351."""
if move.promotion:
promo_map = {chess.QUEEN: 0, chess.ROOK: 1, chess.BISHOP: 2, chess.KNIGHT: 3}
return 4096 + promo_map[move.promotion] * 64 + move.to_square
return move.from_square * 64 + move.to_square
def index_to_move(idx, board):
"""Convert index back to chess.Move, only if legal."""
if idx >= 4096:
promo_offset = idx - 4096
promo_type = promo_offset // 64
to_sq = promo_offset % 64
promo_map = {0: chess.QUEEN, 1: chess.ROOK, 2: chess.BISHOP, 3: chess.KNIGHT}
promotion = promo_map[promo_type]
# Find from_square (must be a pawn on correct file)
for from_sq in chess.SQUARES:
piece = board.piece_at(from_sq)
if piece and piece.piece_type == chess.PAWN and piece.color == board.turn:
move = chess.Move(from_sq, to_sq, promotion=promotion)
if move in board.legal_moves:
return move
return None
else:
from_sq = idx // 64
to_sq = idx % 64
move = chess.Move(from_sq, to_sq)
if move in board.legal_moves:
return move
return None
class ChessDataset(Dataset):
"""Load SF-evaluated positions."""
def __init__(self, data_paths):
self.samples = []
for path in data_paths:
path = Path(path)
if not path.exists():
continue
with open(path, "r", encoding="utf-8") as f:
for line in f:
try:
item = json.loads(line)
board = chess.Board(item["fen"])
move_key = "best_move" if "best_move" in item else "move"
move = chess.Move.from_uci(item[move_key])
if move not in board.legal_moves:
continue
board_tokens = encode_board(board)
move_idx = move_to_index(move)
if "eval_cp" in item:
value = max(-1.0, min(1.0, item["eval_cp"] / 1000.0))
elif "result" in item:
r = item["result"]
value = 1.0 if r == "1-0" else -1.0 if r == "0-1" else 0.0
else:
value = 0.0
self.samples.append((board_tokens, move_idx, value))
if random.random() < 0.5:
flipped = flip_board(board_tokens)
flipped_move = flip_move_index(move_idx)
self.samples.append((flipped, flipped_move, value))
except:
pass
print("Loaded %d samples" % len(self.samples))
def __len__(self):
return len(self.samples)
def __getitem__(self, i):
board_tokens, move_idx, value = self.samples[i]
return (
torch.tensor(board_tokens, dtype=torch.long),
torch.tensor(move_idx, dtype=torch.long),
torch.tensor(value, dtype=torch.float),
)
def flip_board(board_tokens):
flipped = list(board_tokens)
for r in range(8):
for f in range(8):
flipped[r * 8 + (7 - f)] = board_tokens[r * 8 + f]
return flipped
def flip_sq(sq):
"""Mirror a square horizontally: file f -> 7-f."""
rank = sq // 8
file = sq % 8
return rank * 8 + (7 - file)
def flip_move_index(idx):
"""Flip move index when board is mirrored horizontally."""
if idx >= 4096:
promo_offset = idx - 4096
promo_type = promo_offset // 64
to_sq = promo_offset % 64
return 4096 + promo_type * 64 + flip_sq(to_sq)
from_sq = idx // 64
to_sq = idx % 64
return flip_sq(from_sq) * 64 + flip_sq(to_sq)
class ParallaxChessV3(nn.Module):
"""Board -> move index (4352 classes) + value."""
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.board_encoder = nn.Embedding(VOCAB_SIZE, cfg.d_model)
from centauri.models.torch.small_lm import SmallLM
self.backbone = SmallLM(cfg)
self.policy_head = nn.Linear(cfg.d_model, MOVE_INDEX_SIZE)
self.value_head = nn.Sequential(
nn.Linear(cfg.d_model, 256), nn.ReLU(), nn.Dropout(0.1),
nn.Linear(256, 1), nn.Tanh())
def forward(self, board_tokens):
x = self.board_encoder(board_tokens)
freq, _ = __import__('centauri.models.torch.small_lm', fromlist=['precompute_rope']).precompute_rope(
self.cfg, x.device, x.dtype)
for layer in self.backbone.layers:
x, _ = layer(x, freq, None)
pooled = x.mean(dim=1)
return {"logits": self.policy_head(pooled), "value": self.value_head(pooled).squeeze(-1)}
def predict_move(self, board, device=None):
if device is None:
device = next(self.parameters()).device
board_tokens = encode_board(board)
inp = torch.tensor([board_tokens], dtype=torch.long).to(device)
with torch.no_grad():
out = self(inp)
probs = torch.softmax(out["logits"], dim=-1)
# Score only legal moves
best_move, best_score = None, -float("inf")
for move in board.legal_moves:
idx = move_to_index(move)
score = probs[0, idx].item()
if score > best_score:
best_score = score
best_move = move
return best_move
def evaluate(self, board, device=None):
if device is None:
device = next(self.parameters()).device
board_tokens = encode_board(board)
inp = torch.tensor([board_tokens], dtype=torch.long).to(device)
with torch.no_grad():
return self(inp)["value"].item() * 1000
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", type=int, default=64)
parser.add_argument("--lr", type=float, default=1e-3)
parser.add_argument("--max_steps", type=int, default=80000)
parser.add_argument("--save_dir", default=str(ROOT / "checkpoints" / "parallax_chess_v3"))
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Device:", device)
cfg_dict = {
"vocab_size": VOCAB_SIZE, "d_model": 512, "n_heads": 8, "n_kv_heads": 4,
"n_layers": 8, "intermediate_size": 2048, "max_seq_len": 128,
"norm_type": "rms", "rope_type": "neox", "n_experts": 0,
"n_loops": 1, "loop_mode": "per_layer",
}
cfg = SmallLMConfig(**cfg_dict)
model = ParallaxChessV3(cfg).to(device)
n_params = sum(p.numel() for p in model.parameters())
print("Params: %d (%.1fM)" % (n_params, n_params / 1e6))
data_paths = [
ROOT / "data" / "chess_train_sf.jsonl",
ROOT / "data" / "chess_train_large.jsonl",
]
ds = ChessDataset(data_paths)
loader = DataLoader(ds, batch_size=args.batch_size, shuffle=True, num_workers=0, pin_memory=True)
save_dir = Path(args.save_dir)
save_dir.mkdir(parents=True, exist_ok=True)
step = 0
for f in sorted(save_dir.glob("step_*.pt")):
try:
ckpt = torch.load(str(f), map_location=device)
model.load_state_dict(ckpt["model"])
step = ckpt.get("step", 0)
print("Resumed from %s at step %d" % (f.name, step))
break
except:
pass
total_steps = min(args.max_steps, len(ds) // args.batch_size * 5)
opt = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=0.01, betas=(0.9, 0.98))
def lr_schedule(s):
warmup = 2000
if s < warmup:
return s / warmup
progress = (s - warmup) / max(1, total_steps - warmup)
return max(0.05, 0.5 * (1.0 + math.cos(math.pi * progress)))
sched = torch.optim.lr_scheduler.LambdaLR(opt, lr_schedule)
model.train()
t0 = time.time()
for epoch in range(999):
for board_tokens, move_indices, values in loader:
board_tokens = board_tokens.to(device)
move_indices = move_indices.to(device)
values = values.to(device)
out = model(board_tokens)
policy_loss = F.cross_entropy(out["logits"], move_indices)
value_loss = F.mse_loss(out["value"], values)
loss = policy_loss + 1.0 * value_loss
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
opt.step()
opt.zero_grad()
sched.step()
if step % 100 == 0:
lr = sched.get_last_lr()[0]
print("Step %6d | P:%.4f V:%.4f | Loss:%.4f | LR %.1e" % (
step, policy_loss.item(), value_loss.item(), loss.item(), lr))
if step > 0 and step % 10000 == 0:
ckpt = save_dir / ("step_%d.pt" % step)
torch.save({"model": model.state_dict(), "step": step, "config": cfg_dict, "n_params": n_params}, str(ckpt))
print("Saved %s" % ckpt.name)
step += 1
if step >= total_steps:
break
final = save_dir / "final.pt"
torch.save({"model": model.state_dict(), "step": step, "config": cfg_dict, "n_params": n_params}, str(final))
print("Done! %s (%d steps)" % (final, step))
if __name__ == "__main__":
main()
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