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 AutoConfig, AutoModelForCausalLM, PretrainedConfig, PreTrainedModel from transformers.modeling_outputs import CausalLMOutputWithPast class ChessConfig(PretrainedConfig): model_type = "chess_square_transformer" def __init__( self, vocab_size: int = 72, n_embd: int = 128, n_layer: int = 5, n_head: int = 4, n_ctx: int = 256, n_inner: Optional[int] = None, dropout: float = 0.1, layer_norm_epsilon: float = 1e-5, 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, ) 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 3 * n_embd self.dropout = dropout self.layer_norm_epsilon = layer_norm_epsilon self.tie_weights = tie_weights self.tie_word_embeddings = bool(tie_weights) class MultiHeadAttention(nn.Module): def __init__(self, config: ChessConfig): super().__init__() if config.n_embd % config.n_head != 0: raise ValueError("n_embd must be divisible by n_head") 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.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 forward(self, x: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: bsz, seq_len, _ = x.size() qkv = self.c_attn(x) q, k, v = qkv.split(self.n_embd, dim=2) q = q.view(bsz, seq_len, self.n_head, self.head_dim).transpose(1, 2) k = k.view(bsz, seq_len, self.n_head, self.head_dim).transpose(1, 2) v = v.view(bsz, seq_len, self.n_head, self.head_dim).transpose(1, 2) attn = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) causal_mask = self.bias[:, :, :seq_len, :seq_len] attn = attn.masked_fill(causal_mask == 0, float("-inf")) if attention_mask is not None: attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attn = attn.masked_fill(attention_mask == 0, float("-inf")) attn = F.softmax(attn, dim=-1) attn = self.dropout(attn) y = torch.matmul(attn, v) y = y.transpose(1, 2).contiguous().view(bsz, seq_len, self.n_embd) y = self.c_proj(y) return y class FeedForward(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__() self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.attn = MultiHeadAttention(config) self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) self.mlp = FeedForward(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"] def __init__(self, config: ChessConfig): super().__init__(config) self.wte = nn.Embedding(config.vocab_size, config.n_embd) 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)]) 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) elif isinstance(module, nn.LayerNorm): torch.nn.init.ones_(module.weight) torch.nn.init.zeros_(module.bias) 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 bsz, seq_len = input_ids.size() device = input_ids.device if position_ids is None: position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(bsz, -1) tok = self.wte(input_ids) pos = self.wpe(position_ids) x = self.drop(tok + pos) 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, ) AutoConfig.register("chess_square_transformer", ChessConfig) AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)