Chess Challenge submission by MaximeMuhlethaler

README.md

@@ -0,0 +1,23 @@
+---
+library_name: transformers
+tags:
+- chess
+- llm-course
+- chess-challenge
+license: mit
+---
+
+# chess-try
+
+Chess model submitted to the LLM Course Chess Challenge.
+
+## Submission Info
+- **Submitted by**: [MaximeMuhlethaler](https://huggingface.co/MaximeMuhlethaler)
+- **Parameters**: 924,000
+- **Organization**: LLM-course
+- **Architecture**: Custom Chess Transformer (Regex Tokenizer + EOS Protection)
+
+## Model Details
+- **Vocab size**: 1200
+- **Layers**: 6
+- **Heads**: 8

config.json

@@ -0,0 +1,21 @@
+{
+  "bos_token_id": 1,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "chess_transformer",
+  "n_ctx": 256,
+  "n_embd": 112,
+  "n_head": 8,
+  "n_inner": 336,
+  "n_layer": 6,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 1200,
+  "auto_map": {
+    "AutoModelForCausalLM": "model.ChessForCausalLM",
+    "AutoConfig": "model.ChessConfig"
+  }
+}

model.py

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+"""
+Chess Transformer Model - Final Stable Version with Inference Patch
+"""
+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,
+        **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__()
+        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,
+        )
+    
+    def forward(self, x, attention_mask=None):
+        B, T, C = 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)
+        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"))
+        
+        if attention_mask is not None:
+            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__()
+        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, attention_mask=None):
+        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.post_init()
+            self.tie_weights()
+
+    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]:
+        
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        
+        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)
+        
+        x = self.wte(input_ids) + self.wpe(position_ids)
+        x = self.drop(x)
+        for block in self.h:
+            x = block(x, attention_mask)
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+
+        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)
+            
+            bad_tokens = [t for t in bad_tokens if t is not None]
+
+            if len(bad_tokens) > 0:
+                logits[:, :, bad_tokens] = 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))
+        
+        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,
+        )
+
+
+from transformers import AutoConfig, AutoModelForCausalLM
+AutoConfig.register("chess_transformer", ChessConfig)
+AutoModelForCausalLM.register(ChessConfig, ChessForCausalLM)

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d638adc2fb527d4c06e3a92895c9d10523d568048b92991aa434b6bbbe3ef338
+size 3719211

special_tokens_map.json

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+{
+  "bos_token": "[BOS]",
+  "eos_token": "[EOS]",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
+}

tokenizer.py

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+"""
+Custom Chess Tokenizer for the Chess Challenge.
+
+This tokenizer treats each move as a single token using the extended UCI notation
+from the Lichess dataset (e.g., WPe2e4, BNg8f6).
+
+The dataset format uses:
+- W/B prefix for White/Black
+- Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
+- Source and destination squares (e.g., e2e4)
+- Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from transformers import PreTrainedTokenizer
+"""
+Custom Chess Tokenizer - Normalized Version
+"""
+import re
+
+MOVE_RE = re.compile(r"([a-h][1-8])([a-h][1-8])")
+PROMO_RE = re.compile(r"=([NBRQ])")
+
+def normalize_move(tok: str) -> str:
+    """Transforme 'WPe2e4(x)' en 'WPe2e4' pour réduire le vocabulaire."""
+    m = MOVE_RE.search(tok)
+    if not m:
+        return tok 
+    
+    fr, to = m.group(1), m.group(2)
+    
+
+    promo = ""
+    pm = PROMO_RE.search(tok)
+    if pm:
+        promo = "=" + pm.group(1)
+        
+
+    prefix = tok[:2] if len(tok) >= 2 else "WP"
+    return f"{prefix}{fr}{to}{promo}"
+
+class ChessTokenizer(PreTrainedTokenizer):
+    model_input_names = ["input_ids", "attention_mask"]
+    
+    PAD_TOKEN = "[PAD]"
+    BOS_TOKEN = "[BOS]"
+    EOS_TOKEN = "[EOS]"
+    UNK_TOKEN = "[UNK]"
+    
+    def __init__(self, vocab_file=None, vocab=None, **kwargs):
+        self._pad_token = self.PAD_TOKEN
+        self._bos_token = self.BOS_TOKEN
+        self._eos_token = self.EOS_TOKEN
+        self._unk_token = self.UNK_TOKEN
+        
+        # Nettoyage kwargs
+        for t in ["pad_token", "bos_token", "eos_token", "unk_token"]:
+            kwargs.pop(t, None)
+            
+        if vocab:
+            self._vocab = vocab
+        elif vocab_file:
+            with open(vocab_file, "r", encoding="utf-8") as f:
+                self._vocab = json.load(f)
+        else:
+            self._vocab = {t: i for i, t in enumerate([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])}
+            
+        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
+        super().__init__(pad_token=self.PAD_TOKEN, bos_token=self.BOS_TOKEN, eos_token=self.EOS_TOKEN, unk_token=self.UNK_TOKEN, **kwargs)
+
+    @property
+    def vocab_size(self):
+        return len(self._vocab)
+
+    def get_vocab(self):
+        return dict(self._vocab)
+
+    def _tokenize(self, text):
+
+        return [normalize_move(t) for t in text.strip().split()]
+
+    def _convert_token_to_id(self, token):
+        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN))
+
+    def _convert_id_to_token(self, index):
+        return self._ids_to_tokens.get(index, self.UNK_TOKEN)
+
+    def convert_tokens_to_string(self, tokens):
+        return " ".join(t for t in tokens if t not in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])
+    
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        if not os.path.exists(save_directory):
+            os.makedirs(save_directory)
+        path = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.json")
+        with open(path, "w") as f:
+            json.dump(self._vocab, f, indent=2)
+        return (path,)
+
+    @classmethod
+    def build_vocab_from_dataset(cls, dataset_name, min_frequency=2, max_vocab_size=1200, **kwargs):
+        """Construit un vocabulaire compact et dense."""
+        from datasets import load_dataset
+        from collections import Counter
+        
+        # On charge en streaming pour aller vite
+        ds = load_dataset(dataset_name, split="train", streaming=True)
+        ds = ds.take(50000) # 50k parties suffisent pour voir tous les coups possibles
+        
+        counter = Counter()
+        for ex in ds:
+            # On normalise avant de compter !
+            moves = [normalize_move(t) for t in ex["text"].split()]
+            counter.update(moves)
+            
+        # On garde les tokens spéciaux + les N plus fréquents
+        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
+        most_common = counter.most_common(max_vocab_size - len(special))
+        
+        vocab = {t: i for i, t in enumerate(special + [t for t, c in most_common])}
+        return cls(vocab=vocab)

tokenizer_config.json

@@ -0,0 +1,52 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[BOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[EOS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenizer.ChessTokenizer",
+      null
+    ]
+  },
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[EOS]",
+  "extra_special_tokens": {},
+  "model_max_length": 256,
+  "pad_token": "[PAD]",
+  "tokenizer_class": "ChessTokenizer",
+  "unk_token": "[UNK]",
+  "vocab_files_names": {},
+  "vocab_size": 1200
+}

vocab.json

@@ -0,0 +1,1202 @@
+{
+  "[PAD]": 0,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
+  "WNg1f3": 4,
+  "BNg8f6": 5,
+  "WPe2e4": 6,
+  "WPd2d4": 7,
+  "WNb1c3": 8,
+  "WKe1g1": 9,
+  "BNb8c6": 10,
+  "BKe8g8": 11,
+  "BPd7d5": 12,
+  "BPe7e6": 13,
+  "BPe7e5": 14,
+  "BPd7d6": 15,
+  "WPh2h3": 16,
+  "WPc2c3": 17,
+  "BPg7g6": 18,
+  "BPc7c6": 19,
+  "BPh7h6": 20,
+  "BPc7c5": 21,
+  "BPa7a6": 22,
+  "WPc2c4": 23,
+  "WNf3e5": 24,
+  "BBf8e7": 25,
+  "WPa2a3": 26,
+  "WPg2g3": 27,
+  "WPe2e3": 28,
+  "WPf2f4": 29,
+  "BNb8d7": 30,
+  "WBf1c4": 31,
+  "WPd2d3": 32,
+  "WRf1e1": 33,
+  "WNb1d2": 34,
+  "BPb7b6": 35,
+  "WPe4e5": 36,
+  "WBf1d3": 37,
+  "BPf7f6": 38,
+  "BNf6e4": 39,
+  "WPb2b3": 40,
+  "BPb7b5": 41,
+  "WBc1g5": 42,
+  "WBc1e3": 43,
+  "BBc8g4": 44,
+  "WPf2f3": 45,
+  "BRf8e8": 46,
+  "WPe4d5": 47,
+  "BBf8g7": 48,
+  "WBf1e2": 49,
+  "BPf7f5": 50,
+  "WNf3d4": 51,
+  "WPb2b4": 52,
+  "WBc1f4": 53,
+  "BBc8b7": 54,
+  "BPc5d4": 55,
+  "BNc6d4": 56,
+  "WNc3d5": 57,
+  "WPg2g4": 58,
+  "BNf6d5": 59,
+  "WPa2a4": 60,
+  "BBf8d6": 61,
+  "BBc8d7": 62,
+  "BPa7a5": 63,
+  "WPh2h4": 64,
+  "WPd4d5": 65,
+  "BBc8e6": 66,
+  "WPd4e5": 67,
+  "BRa8c8": 68,
+  "WRa1d1": 69,
+  "BBf8c5": 70,
+  "WQd1e2": 71,
+  "BNc6e5": 72,
+  "BNg8e7": 73,
+  "BPh7h5": 74,
+  "WNc3e4": 75,
+  "BRa8d8": 76,
+  "BPe6e5": 77,
+  "BQd8e7": 78,
+  "BBc8f5": 79,
+  "BPe5d4": 80,
+  "WNf3g5": 81,
+  "WRa1c1": 82,
+  "WQd1f3": 83,
+  "BPd5e4": 84,
+  "WKe1c1": 85,
+  "WKg1h1": 86,
+  "WBf1b5": 87,
+  "BPg7g5": 88,
+  "WQd1d2": 89,
+  "BPc6c5": 90,
+  "WBc1d2": 91,
+  "BBf8b4": 92,
+  "WBf1g2": 93,
+  "BKg8h8": 94,
+  "BKg8g7": 95,
+  "WBc1b2": 96,
+  "WPc4d5": 97,
+  "BPe5e4": 98,
+  "BRa8b8": 99,
+  "WPh4h5": 100,
+  "BKe8c8": 101,
+  "BPe6d5": 102,
+  "BPd6d5": 103,
+  "BPd5d4": 104,
+  "BQd8c7": 105,
+  "WPc3c4": 106,
+  "WRa1b1": 107,
+  "BQd8f6": 108,
+  "BPb5b4": 109,
+  "BQd8d7": 110,
+  "BNf6g4": 111,
+  "BPd6e5": 112,
+  "WKg1g2": 113,
+  "WPf4f5": 114,
+  "WPg4g5": 115,
+  "BPc6d5": 116,
+  "BPb7c6": 117,
+  "WRf1d1": 118,
+  "BRf8d8": 119,
+  "WRa1e1": 120,
+  "WPe3e4": 121,
+  "WNg1e2": 122,
+  "WPc3d4": 123,
+  "WPb2c3": 124,
+  "BPa5a4": 125,
+  "WKg1h2": 126,
+  "BBg4f3": 127,
+  "WPh3h4": 128,
+  "BNd7e5": 129,
+  "BQd8b6": 130,
+  "BNf6d7": 131,
+  "WBg5f6": 132,
+  "BPc5c4": 133,
+  "WPd3d4": 134,
+  "BRa8e8": 135,
+  "BKg8h7": 136,
+  "BBe7f6": 137,
+  "BPg6g5": 138,
+  "WPc4c5": 139,
+  "WQd1c2": 140,
+  "WNd2f3": 141,
+  "WPa4a5": 142,
+  "WPb4b5": 143,
+  "BNd7f6": 144,
+  "BPh5h4": 145,
+  "BPa6a5": 146,
+  "BQd8d5": 147,
+  "WNc3e2": 148,
+  "WKg1f2": 149,
+  "BPg5g4": 150,
+  "WPg3g4": 151,
+  "WPf3f4": 152,
+  "BPf6f5": 153,
+  "BPh6h5": 154,
+  "BKg8f7": 155,
+  "WKg1f1": 156,
+  "WNc3b5": 157,
+  "BKg8f8": 158,
+  "BPd5c4": 159,
+  "BPf5f4": 160,
+  "BNf6h5": 161,
+  "WNd2e4": 162,
+  "WPd4c5": 163,
+  "WBe2f3": 164,
+  "WBb5c6": 165,
+  "WPf4e5": 166,
+  "BBb4c3": 167,
+  "BNc6e7": 168,
+  "WQd1d3": 169,
+  "BPb6b5": 170,
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