File size: 15,259 Bytes
663d8ea |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 |
"""
Chess Transformer Model for the Chess Challenge.
Modern small-LLM upgrades:
- RoPE (rotary positional embeddings): no learned positional embeddings needed
- RMSNorm (optional, default True)
- SwiGLU MLP (optional, default True)
- Weight tying (default True)
- Safe loss ignore_index = -100 (HF convention)
"""
from __future__ import annotations
import math
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
class ChessConfig(PretrainedConfig):
model_type = "chess_transformer"
def __init__(
self,
vocab_size: int = 1200,
# Architecture (defaults tuned to be < 1M params for common vocabs)
n_embd: int = 112,
n_layer: int = 7,
n_head: int = 7,
# Context window
n_ctx: int = 512,
# MLP hidden size:
# - if mlp_type="swiglu", this is SwiGLU hidden size h
# - if mlp_type="gelu", this is FFN inner size
n_inner: Optional[int] = 192,
dropout: float = 0.05,
layer_norm_epsilon: float = 1e-6,
# Position encoding
use_rope: bool = True,
rope_theta: float = 10000.0,
# Normalization / MLP type
use_rmsnorm: bool = True,
mlp_type: str = "swiglu", # "swiglu" or "gelu"
# Weight tying
tie_weights: bool = True,
pad_token_id: int = 0,
bos_token_id: int = 1,
eos_token_id: int = 2,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
if n_embd % n_head != 0:
raise ValueError(f"n_embd ({n_embd}) must be divisible by n_head ({n_head})")
head_dim = n_embd // n_head
if use_rope and (head_dim % 2 != 0):
raise ValueError(
f"RoPE requires even head_dim, got head_dim={head_dim}. "
f"Choose n_embd/n_head even."
)
self.vocab_size = vocab_size
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_ctx = n_ctx
self.n_inner = n_inner if n_inner is not None else (2 * n_embd)
self.dropout = dropout
self.layer_norm_epsilon = layer_norm_epsilon
self.use_rope = use_rope
self.rope_theta = rope_theta
self.use_rmsnorm = use_rmsnorm
self.mlp_type = mlp_type
self.tie_weights = tie_weights
# HF uses this field for embedding tying behavior
self.tie_word_embeddings = bool(tie_weights)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
norm = torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
return x * norm * self.weight
def rotate_half(x: torch.Tensor) -> torch.Tensor:
x1 = x[..., 0::2]
x2 = x[..., 1::2]
out = torch.empty_like(x)
out[..., 0::2] = -x2
out[..., 1::2] = x1
return out
class RotaryEmbedding(nn.Module):
"""
RoPE cache builder. Applies RoPE to q,k with shape (B,H,T,D).
"""
def __init__(self, head_dim: int, theta: float = 10000.0):
super().__init__()
if head_dim % 2 != 0:
raise ValueError(f"RoPE requires even head_dim, got {head_dim}")
inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
self._cos_cached = None
self._sin_cached = None
self._seq_len_cached = 0
self._device_cached = None
self._dtype_cached = None
def _build_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.inv_freq) # (T, D/2)
cos = freqs.cos().to(dtype=dtype)
sin = freqs.sin().to(dtype=dtype)
self._cos_cached = cos
self._sin_cached = sin
self._seq_len_cached = seq_len
self._device_cached = device
self._dtype_cached = dtype
def forward(self, q: torch.Tensor, k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
# q,k: (B,H,T,D)
T = q.size(-2)
device = q.device
dtype = q.dtype
if (
self._cos_cached is None
or T > self._seq_len_cached
or device != self._device_cached
or dtype != self._dtype_cached
):
self._build_cache(T, device, dtype)
cos = self._cos_cached[:T] # (T, D/2)
sin = self._sin_cached[:T] # (T, D/2)
# broadcast to (1,1,T,D) via repeat_interleave on last dim
cos = torch.repeat_interleave(cos.unsqueeze(0).unsqueeze(0), 2, dim=-1)
sin = torch.repeat_interleave(sin.unsqueeze(0).unsqueeze(0), 2, dim=-1)
q_out = (q * cos) + (rotate_half(q) * sin)
k_out = (k * cos) + (rotate_half(k) * sin)
return q_out, k_out
class MultiHeadAttention(nn.Module):
def __init__(self, config: ChessConfig):
super().__init__()
self.n_head = config.n_head
self.n_embd = config.n_embd
self.head_dim = config.n_embd // config.n_head
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
self.c_proj = nn.Linear(config.n_embd, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
self.use_rope = bool(config.use_rope)
self.rope = RotaryEmbedding(self.head_dim, theta=config.rope_theta) if self.use_rope else None
# causal mask buffer (expandable)
self.register_buffer(
"bias",
torch.tril(torch.ones(config.n_ctx, config.n_ctx)).view(1, 1, config.n_ctx, config.n_ctx),
persistent=False,
)
def _ensure_causal_mask(self, seq_len: int, device: torch.device, dtype: torch.dtype):
if self.bias.size(-1) >= seq_len and self.bias.device == device:
return
self.bias = torch.tril(torch.ones(seq_len, seq_len, device=device, dtype=dtype)).view(1, 1, seq_len, seq_len)
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
B, T, _ = x.size()
qkv = self.c_attn(x)
q, k, v = qkv.split(self.n_embd, dim=2)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2) # (B,H,T,D)
k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
if self.use_rope:
q, k = self.rope(q, k)
attn = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
self._ensure_causal_mask(T, attn.device, attn.dtype)
causal_mask = self.bias[:, :, :T, :T]
mask_value = torch.finfo(attn.dtype).min
attn = attn.masked_fill(causal_mask == 0, mask_value)
# padding mask (1=keep, 0=mask)
if attention_mask is not None:
am = attention_mask.unsqueeze(1).unsqueeze(2) # (B,1,1,T)
attn = attn.masked_fill(am == 0, mask_value)
attn = F.softmax(attn, dim=-1)
attn = self.dropout(attn)
y = torch.matmul(attn, v) # (B,H,T,D)
y = y.transpose(1, 2).contiguous().view(B, T, self.n_embd)
y = self.c_proj(y)
y = self.dropout(y)
return y
class SwiGLU(nn.Module):
def __init__(self, config: ChessConfig):
super().__init__()
h = config.n_inner
self.w12 = nn.Linear(config.n_embd, 2 * h)
self.w3 = nn.Linear(h, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x12 = self.w12(x)
x1, x2 = x12.chunk(2, dim=-1)
x = F.silu(x1) * x2
x = self.w3(x)
x = self.dropout(x)
return x
class FeedForwardGELU(nn.Module):
def __init__(self, config: ChessConfig):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, config.n_inner)
self.c_proj = nn.Linear(config.n_inner, config.n_embd)
self.dropout = nn.Dropout(config.dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.c_fc(x)
x = F.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class TransformerBlock(nn.Module):
def __init__(self, config: ChessConfig):
super().__init__()
if config.use_rmsnorm:
self.ln_1 = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.ln_2 = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
else:
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = MultiHeadAttention(config)
if config.mlp_type.lower() == "swiglu":
self.mlp = SwiGLU(config)
else:
self.mlp = FeedForwardGELU(config)
def forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
x = x + self.mlp(self.ln_2(x))
return x
class ChessForCausalLM(PreTrainedModel):
config_class = ChessConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
keys_to_ignore_on_load_missing = ["lm_head.weight"]
_no_split_modules = ["TransformerBlock"]
def __init__(self, config: ChessConfig):
super().__init__(config)
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
# learned positional embeddings only if RoPE disabled
self.wpe = None
if not config.use_rope:
self.wpe = nn.Embedding(config.n_ctx, config.n_embd)
self.drop = nn.Dropout(config.dropout)
self.h = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layer)])
if config.use_rmsnorm:
self.ln_f = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
else:
self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
if config.tie_weights:
self._tied_weights_keys = ["lm_head.weight"]
self.post_init()
if config.tie_weights:
self.tie_weights()
def get_input_embeddings(self) -> nn.Module:
return self.wte
def set_input_embeddings(self, new_embeddings: nn.Module):
self.wte = new_embeddings
if getattr(self.config, "tie_weights", False):
self.tie_weights()
def get_output_embeddings(self) -> nn.Module:
return self.lm_head
def set_output_embeddings(self, new_embeddings: nn.Module):
self.lm_head = new_embeddings
def tie_weights(self):
if getattr(self.config, "tie_weights", False) or getattr(self.config, "tie_word_embeddings", False):
self._tie_or_clone_weights(self.lm_head, self.wte)
def _init_weights(self, module: nn.Module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
labels: Optional[torch.LongTensor] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[Tuple, CausalLMOutputWithPast]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
B, T = input_ids.size()
device = input_ids.device
x = self.wte(input_ids)
if self.wpe is not None:
if position_ids is None:
position_ids = torch.arange(T, device=device).unsqueeze(0).expand(B, -1)
x = x + self.wpe(position_ids)
x = self.drop(x)
for block in self.h:
x = block(x, attention_mask=attention_mask)
x = self.ln_f(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
)
if not return_dict:
output = (logits,)
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=None,
hidden_states=None,
attentions=None,
)
@torch.no_grad()
def generate_move(
self,
input_ids: torch.LongTensor,
temperature: float = 0.7,
top_k: Optional[int] = 50,
top_p: Optional[float] = None,
) -> int:
self.eval()
outputs = self(input_ids)
logits = outputs.logits[:, -1, :] / max(float(temperature), 1e-6)
if top_k is not None and top_k > 0:
k = min(int(top_k), logits.size(-1))
thresh = torch.topk(logits, k)[0][..., -1, None]
logits = logits.masked_fill(logits < thresh, torch.finfo(logits.dtype).min)
if top_p is not None:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
probs = F.softmax(sorted_logits, dim=-1)
cum = torch.cumsum(probs, dim=-1)
to_remove = cum > float(top_p)
to_remove[..., 1:] = to_remove[..., :-1].clone()
to_remove[..., 0] = 0
indices_to_remove = to_remove.scatter(dim=-1, index=sorted_indices, src=to_remove)
logits = logits.masked_fill(indices_to_remove, torch.finfo(logits.dtype).min)
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
return int(next_token.item())
# Register the model with Auto classes
from transformers import AutoConfig, AutoModelForCausalLM
AutoConfig.register("chess_transformer", ChessConfig)
AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)
|