Chess Challenge submission by MaximeMuhlethaler

config.json

@@ -13,6 +13,7 @@
   "pad_token_id": 0,
   "tie_weights": true,
   "transformers_version": "4.57.3",
+  "unk_token_id": 3,
   "vocab_size": 1200,
   "auto_map": {
     "AutoModelForCausalLM": "model.ChessForCausalLM",

model.py

@@ -1,46 +1,36 @@
 """
-Chess Transformer Model - Final Stable Version with Inference Patch
+Chess Transformer Model - The "Nuclear Patch" Edition
 """
 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
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
-
 class ChessConfig(PretrainedConfig):
     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,
+        vocab_size=1200,
+        n_embd=128,
+        n_layer=6,
+        n_head=4,
+        n_ctx=256,
+        n_inner=None,
+        dropout=0.1,
+        layer_norm_epsilon=1e-5,
+        tie_weights=True,
+        # Valeurs par défaut strictes
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        unk_token_id=3,
         **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
@@ -50,26 +40,25 @@ class ChessConfig(PretrainedConfig):
         self.dropout = dropout
         self.layer_norm_epsilon = layer_norm_epsilon
         self.tie_weights = tie_weights
-        self.tie_word_embeddings = bool(tie_weights)
-
+        
+        # On passe les IDs vitaux à kwargs pour le parent
+        kwargs["pad_token_id"] = pad_token_id
+        kwargs["bos_token_id"] = bos_token_id
+        kwargs["eos_token_id"] = eos_token_id
+        kwargs["unk_token_id"] = unk_token_id
+        
+        super().__init__(**kwargs)
 
 class MultiHeadAttention(nn.Module):
     def __init__(self, config: ChessConfig):
         super().__init__()
-        assert config.n_embd % config.n_head == 0
         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,
-        )
+        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, attention_mask=None):
         B, T, C = x.size()
@@ -78,31 +67,25 @@ class MultiHeadAttention(nn.Module):
         q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
         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)
-        
         att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-        att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float("-inf"))
-        
+        att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
         if attention_mask is not None:
-            att = att.masked_fill(attention_mask.view(B, 1, 1, T) == 0, float("-inf"))
-        
+            att = att.masked_fill(attention_mask.view(B, 1, 1, T) == 0, float('-inf'))
         att = F.softmax(att, dim=-1)
         att = self.dropout(att)
         y = att @ v
         y = y.transpose(1, 2).contiguous().view(B, T, C)
         return self.c_proj(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):
         return self.dropout(self.c_proj(F.gelu(self.c_fc(x))))
 
-
 class TransformerBlock(nn.Module):
     def __init__(self, config: ChessConfig):
         super().__init__()
@@ -110,18 +93,14 @@ class TransformerBlock(nn.Module):
         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, attention_mask=None):
-        x = x + self.attn(self.ln_1(x), attention_mask=attention_mask)
+        x = x + self.attn(self.ln_1(x), 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)
@@ -131,79 +110,50 @@ class ChessForCausalLM(PreTrainedModel):
         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.post_init()
-            self.tie_weights()
+        if config.tie_weights: self.post_init()
 
     def get_input_embeddings(self): return self.wte
     def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings
     def get_output_embeddings(self): return self.lm_head
     def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
 
-    def tie_weights(self):
-        if getattr(self.config, "tie_weights", False):
-            self._tie_or_clone_weights(self.lm_head, self.wte)
-
-    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]:
-        
-
+    def forward(self, input_ids, attention_mask=None, position_ids=None, labels=None, return_dict=None, **kwargs):
+        # 1. FIX TYPE RETOUR
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        
+        if return_dict is None: return_dict = True
+
         device = input_ids.device
         b, t = input_ids.size()
-        if position_ids is None:
-            position_ids = torch.arange(t, device=device).unsqueeze(0).expand(b, -1)
+        if position_ids is None: position_ids = torch.arange(t, device=device).unsqueeze(0)
         
         x = self.wte(input_ids) + self.wpe(position_ids)
         x = self.drop(x)
-        for block in self.h:
-            x = block(x, attention_mask)
+        for block in self.h: x = block(x, attention_mask)
         x = self.ln_f(x)
         logits = self.lm_head(x)
 
+        # ---------------------------------------------------------
+        # 2. PATCH NUCLÉAIRE : On bannit 0, 1, 2, 3 en dur
+        # ---------------------------------------------------------
         if labels is None:
-            bad_tokens = [
-                self.config.eos_token_id,
-                self.config.pad_token_id,
-                self.config.bos_token_id
-            ]
-            if hasattr(self.config, "unk_token_id") and self.config.unk_token_id is not None:
-                bad_tokens.append(self.config.unk_token_id)
+            # PAD=0, BOS=1, EOS=2, UNK=3 (Les standards de ton tokenizer)
+            nuclear_bad_ids = [0, 1, 2, 3] 
             
-            bad_tokens = [t for t in bad_tokens if t is not None]
-
-            if len(bad_tokens) > 0:
-                logits[:, :, bad_tokens] = float("-inf")
-
+            # On met -infini (impossible à choisir)
+            # Le slicing [:, :, ids] couvre tout le batch et toute la séquence
+            logits[:, :, nuclear_bad_ids] = float("-inf")
+        # ---------------------------------------------------------
 
         loss = None
         if labels is not None:
             shift_logits = logits[..., :-1, :].contiguous()
             shift_labels = labels[..., 1:].contiguous()
-            loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-        
+            loss = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)(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 ((loss,) + (logits,)) if loss is not None else (logits,)
         
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=None,
-            hidden_states=None,
-            attentions=None,
-        )
-
+        return CausalLMOutputWithPast(loss=loss, logits=logits)
 
 from transformers import AutoConfig, AutoModelForCausalLM
 AutoConfig.register("chess_transformer", ChessConfig)