""" Modèle Transformer pour le Chess Challenge (1M paramètres). Ce module fournit une architecture transformer de style GPT conçue pour respecter la contrainte stricte de moins de 1 million de paramètres. Composants clés : - ChessConfig : Configuration des hyperparamètres. - ChessForCausalLM : Le modèle principal pour la prédiction du prochain coup. """ from __future__ import annotations import math from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from transformers import PretrainedConfig, PreTrainedModel, AutoConfig, AutoModelForCausalLM from transformers.modeling_outputs import CausalLMOutputWithPast # ----------------------------------------------------------------------------- # Configuration # ----------------------------------------------------------------------------- class ChessConfig(PretrainedConfig): """ Classe de configuration pour le modèle Chess Transformer. Conçue pour un budget de paramètres très serré (< 1M). Répartition du budget (avec les valeurs par défaut de ton ami) : - Vocabulaire (Embeddings) : 72 * 92 = ~6,6k - Embeddings de position : 256 * 92 = ~23,5k - Couches Transformer : 11 couches * (~85k par couche) = ~935k - Tête LM (liée aux embeddings) : 0 paramètre supplémentaire - Total : ~970k paramètres (juste en dessous de 1M). """ model_type = "chess_transformer" def __init__( self, vocab_size: int = 1200, n_embd: int = 128, n_layer: int = 6, 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) # ----------------------------------------------------------------------------- # Modules du Transformer # ----------------------------------------------------------------------------- class MultiHeadAttention(nn.Module): """ Module d'attention multi-têtes standard. Inclut le masquage causal pour empêcher le modèle de "voir le futur". """ def __init__(self, config: ChessConfig): super().__init__() assert config.n_embd % config.n_head == 0, \ f"n_embd ({config.n_embd}) doit être divisible par n_head ({config.n_head})" self.n_head = config.n_head self.n_embd = config.n_embd self.head_dim = config.n_embd // config.n_head # Projection combinée Q, K, V pour l'efficacité 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) # Masque causal (registre persistent=False pour ne pas le sauvegarder dans le checkpoint) 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: batch_size, seq_len, _ = x.size() # Calcul de Q, K, V qkv = self.c_attn(x) q, k, v = qkv.split(self.n_embd, dim=2) # Remodelage pour l'attention multi-têtes # (batch, seq_len, n_head, head_dim) -> (batch, n_head, seq_len, head_dim) q = q.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) k = k.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) v = v.view(batch_size, seq_len, self.n_head, self.head_dim).transpose(1, 2) # Attention produit scalaire (Scaled Dot-Product Attention) attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim) # Application du masque causal (le futur est masqué avec -inf) causal_mask = self.bias[:, :, :seq_len, :seq_len] attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf")) # Application du masque d'attention (pour le padding) if attention_mask is not None: # attention_mask shape: (batch_size, seq_len) -> (batch_size, 1, 1, seq_len) attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) attn_weights = attn_weights.masked_fill(attention_mask == 0, float("-inf")) attn_weights = F.softmax(attn_weights, dim=-1) attn_weights = self.dropout(attn_weights) # Application de l'attention aux valeurs attn_output = torch.matmul(attn_weights, v) # Remise en forme attn_output = attn_output.transpose(1, 2).contiguous().view( batch_size, seq_len, self.n_embd ) # Projection de sortie attn_output = self.c_proj(attn_output) return attn_output class FeedForward(nn.Module): """ Réseau de neurones Feed-Forward (MLP). Deux couches linéaires avec une activation GELU entre les deux. """ 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): """ Un bloc transformer unique contenant Attention et Feed-Forward. Utilise la "Pre-normalization" (LayerNorm avant l'attention/FFN) pour la stabilité. """ 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: # Connexion résiduelle + Pre-norm Attention x = x + self.attn(self.ln_1(x), attention_mask=attention_mask) # Connexion résiduelle + Pre-norm FFN x = x + self.mlp(self.ln_2(x)) return x # ----------------------------------------------------------------------------- # Modèle Principal # ----------------------------------------------------------------------------- class ChessForCausalLM(PreTrainedModel): """ Modèle final pour la prédiction de coups (Causal Language Modeling). Architecture : 1. Embeddings (Tokens + Position) 2. Empilement de blocs Transformer 3. Tête linéaire finale (Projection vers le vocabulaire) """ config_class = ChessConfig base_model_prefix = "transformer" supports_gradient_checkpointing = True # Ignore l'avertissement de clé manquante car lm_head partage les poids avec wte keys_to_ignore_on_load_missing = ["lm_head.weight"] def __init__(self, config: ChessConfig): super().__init__(config) # Embeddings 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) # Blocs Transformer self.h = nn.ModuleList([ TransformerBlock(config) for _ in range(config.n_layer) ]) # LayerNorm final self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) # Tête de sortie (sans biais pour économiser des paramètres) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) # Gestion du partage de poids (Weight Tying) if config.tie_weights: self._tied_weights_keys = ["lm_head.weight"] # Initialisation des poids self.post_init() # Forcer le lien des poids si configuré 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): """Lie les poids de l'embedding d'entrée et de la tête de sortie.""" 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): """Initialisation des poids style GPT-2.""" 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]: """ Passe avant (Forward pass). Calcule les logits et, si des étiquettes (labels) sont fournies, la perte (loss). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, seq_len = input_ids.size() device = input_ids.device # Création des IDs de position s'ils ne sont pas fournis if position_ids is None: position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1) # Calcul des embeddings (Token + Position) token_embeds = self.wte(input_ids) position_embeds = self.wpe(position_ids) hidden_states = self.drop(token_embeds + position_embeds) # Passage dans les blocs Transformer for block in self.h: hidden_states = block(hidden_states, attention_mask=attention_mask) # Normalisation finale hidden_states = self.ln_f(hidden_states) # Calcul des logits (prédiction du prochain token) logits = self.lm_head(hidden_states) # Calcul de la perte (Training Loss) loss = None if labels is not None: # On décale les logits et les labels d'un cran # (le modèle doit prédire le token t+1 à partir du token t) shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Perte CrossEntropy standard 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 = 1.0, top_k: Optional[int] = None, top_p: Optional[float] = None, ) -> int: """ Génère le prochain coup pour une séquence donnée. Utilisé pour l'inférence en jeu réel. """ self.eval() # Récupère les logits pour la dernière position uniquement outputs = self(input_ids) logits = outputs.logits[:, -1, :] / temperature # Filtrage Top-K if top_k is not None: indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = float("-inf") # Filtrage Top-P (Nucleus Sampling) if top_p is not None: sorted_logits, sorted_indices = torch.sort(logits, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # On retire les tokens qui sont au-dessus du seuil cumulatif sorted_indices_to_remove = cumulative_probs > top_p sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices_to_remove.scatter( dim=-1, index=sorted_indices, src=sorted_indices_to_remove ) logits[indices_to_remove] = float("-inf") # Échantillonnage final probs = F.softmax(logits, dim=-1) next_token = torch.multinomial(probs, num_samples=1) return next_token.item() # Enregistrement pour chargement automatique via AutoModel AutoConfig.register("chess_transformer", ChessConfig) AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)