Chess Challenge submission by MDaytek

README.md

@@ -14,7 +14,7 @@ Chess model submitted to the LLM Course Chess Challenge.
 ## Submission Info
 
 - **Submitted by**: [MDaytek](https://huggingface.co/MDaytek)
-- **Parameters**: 999,240
+- **Parameters**: 909,824
 - **Organization**: LLM-course
 
 ## Usage

config.json

@@ -1,14 +1,24 @@
 {
-  "dropout": 0.1,
-  "model_type": "chess_gpt",
-  "n_embd": 120,
-  "n_head": 8,
-  "n_layer": 5,
-  "n_positions": 256,
-  "transformers_version": "4.57.6",
-  "vocab_size": 402,
+  "architectures": [
+    "ChessForCausalLM"
+  ],
   "auto_map": {
     "AutoConfig": "model.ChessConfig",
     "AutoModelForCausalLM": "model.ChessForCausalLM"
-  }
-}
+  },
+  "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": 128,
+  "n_head": 4,
+  "n_inner": 384,
+  "n_layer": 4,
+  "pad_token_id": 0,
+  "tie_weights": true,
+  "transformers_version": "4.57.6",
+  "vocab_size": 1682
+}

model.py

@@ -2,104 +2,221 @@ import math
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from transformers import PreTrainedModel, PretrainedConfig
+from transformers import PretrainedConfig, PreTrainedModel
 from transformers.modeling_outputs import CausalLMOutputWithPast
 
 class ChessConfig(PretrainedConfig):
-    model_type = "chess_gpt"
-
+    model_type = "chess_transformer"
+    
     def __init__(
         self,
-        vocab_size: int = 402,
-        n_embd: int = 120,
-        n_layer: int = 5,
-        n_head: int = 8,
-        n_positions: int = 256,
-        dropout: float = 0.1,
+        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,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
         **kwargs,
     ):
-        super().__init__(**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_positions = n_positions
+        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 = True
 
-class CausalSelfAttention(nn.Module):
+class MultiHeadAttention(nn.Module):
     def __init__(self, config):
         super().__init__()
-        self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd)
-        self.proj = nn.Linear(config.n_embd, config.n_embd)
+        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):
+        batch_size, seq_len, _ = x.size()
+        
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        
+        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)
+        
+        attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        
+        causal_mask = self.bias[:, :, :seq_len, :seq_len]
+        attn_weights = attn_weights.masked_fill(causal_mask == 0, float("-inf"))
+        
+        if attention_mask is not None:
+            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)
+        
+        attn_output = torch.matmul(attn_weights, v)
+        attn_output = attn_output.transpose(1, 2).contiguous().view(
+            batch_size, seq_len, self.n_embd
+        )
+        attn_output = self.c_proj(attn_output)
+        
+        return attn_output
 
+class FeedForward(nn.Module):
+    def __init__(self, config):
+        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):
-        B, T, C = x.size()
-        q, k, v = self.qkv(x).chunk(3, dim=-1)
-        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)) / math.sqrt(self.head_dim)
-        mask = torch.triu(torch.ones(T, T, device=x.device), diagonal=1).bool()
-        att = att.masked_fill(mask, float("-inf"))
-        att = F.softmax(att, dim=-1)
-        att = self.dropout(att)
-        out = att @ v
-        out = out.transpose(1, 2).contiguous().view(B, T, C)
-        return self.proj(out)
+        x = self.c_fc(x)
+        x = F.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
 
-class Block(nn.Module):
+class TransformerBlock(nn.Module):
     def __init__(self, config):
         super().__init__()
-        self.ln1 = nn.LayerNorm(config.n_embd)
-        self.ln2 = nn.LayerNorm(config.n_embd)
-        self.attn = CausalSelfAttention(config)
-        self.mlp = nn.Sequential(
-            nn.Linear(config.n_embd, 4 * config.n_embd),
-            nn.GELU(),
-            nn.Linear(4 * config.n_embd, config.n_embd),
-            nn.Dropout(config.dropout),
-        )
-
-    def forward(self, x):
-        x = x + self.attn(self.ln1(x))
-        x = x + self.mlp(self.ln2(x))
+        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):
         super().__init__(config)
-        self.tok_emb = nn.Embedding(config.vocab_size, config.n_embd)
-        self.pos_emb = nn.Embedding(config.n_positions, config.n_embd)
-        self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
-        self.ln_f = nn.LayerNorm(config.n_embd)
+        
+        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)
-        self.apply(self._init_weights)
-
+        
+        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):
+        return self.wte
+    
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+        if getattr(self.config, "tie_weights", False):
+            self.tie_weights()
+    
+    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) or getattr(self.config, "tie_word_embeddings", False):
+            self._tie_or_clone_weights(self.lm_head, self.wte)
+    
     def _init_weights(self, module):
         if isinstance(module, nn.Linear):
-            nn.init.normal_(module.weight, mean=0.0, std=0.02)
-            if getattr(module, "bias", None) is not None:
-                nn.init.zeros_(module.bias)
+            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):
-            nn.init.normal_(module.weight, mean=0.0, std=0.02)
-
-    def forward(self, input_ids, labels=None, **kwargs):
-        B, T = input_ids.shape
-        pos = torch.arange(T, device=input_ids.device)
-        x = self.tok_emb(input_ids) + self.pos_emb(pos)
-        for block in self.blocks:
-            x = block(x)
-        logits = self.lm_head(self.ln_f(x))
+            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,
+        attention_mask=None,
+        position_ids=None,
+        labels=None,
+        return_dict=None,
+        **kwargs,
+    ):
+        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
+        
+        if position_ids is None:
+            position_ids = torch.arange(seq_len, device=device).unsqueeze(0).expand(batch_size, -1)
+        
+        token_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = self.drop(token_embeds + position_embeds)
+        
+        for block in self.h:
+            hidden_states = block(hidden_states, attention_mask=attention_mask)
+        
+        hidden_states = self.ln_f(hidden_states)
+        logits = self.lm_head(hidden_states)
+        
         loss = None
         if labels is not None:
-            loss = F.cross_entropy(
-                logits.reshape(-1, logits.size(-1)),
-                labels.reshape(-1),
-                ignore_index=-100,
+            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),
             )
-        return CausalLMOutputWithPast(loss=loss, logits=logits)
+        
+        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,
+        )

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:bcfed0c7dac79b06cf919234d499e3a5d1064c3aac0f0549f6fa4f2815b486c4
-size 4002592
+oid sha256:f9d316d492c66f7b5c220e5cdac553e5d1e3482e5444dac5dc81fb0aa45736ff
+size 3643696

special_tokens_map.json

@@ -3,4 +3,4 @@
   "eos_token": "[EOS]",
   "pad_token": "[PAD]",
   "unk_token": "[UNK]"
-}
+}

tokenizer.py

@@ -2,50 +2,4 @@ import json
 import os
 from transformers import PreTrainedTokenizer
 
-class ChessTokenizer(PreTrainedTokenizer):
-    tokenizer_class = "ChessTokenizer"
-
-    def __init__(self, vocab=None, **kwargs):
-        self.vocab = vocab or {}
-        self.id_to_token = {v: k for k, v in self.vocab.items()}
-        super().__init__(
-            pad_token="[PAD]",
-            unk_token="[UNK]",
-            bos_token="[BOS]",
-            eos_token="[EOS]",
-            **kwargs,
-        )
-
-    @property
-    def vocab_size(self):
-        return len(self.vocab)
-
-    def get_vocab(self):
-        return dict(self.vocab)
-
-    def _convert_token_to_id(self, token):
-        return self.vocab.get(token, self.vocab.get("[UNK]", 1))
-
-    def _convert_id_to_token(self, index):
-        return self.id_to_token.get(index, "[UNK]")
-
-    def save_vocabulary(self, save_directory, filename_prefix=None):
-        path = os.path.join(save_directory, "vocab.json")
-        with open(path, "w", encoding="utf-8") as f:
-            json.dump(self.vocab, f, indent=2, ensure_ascii=False)
-        return (path,)
-
-    @classmethod
-    def from_pretrained(cls, path, **kwargs):
-        if os.path.isdir(path):
-            vocab_path = os.path.join(path, "vocab.json")
-        else:
-            from huggingface_hub import hf_hub_download
-            vocab_path = hf_hub_download(repo_id=path, filename="vocab.json")
-        with open(vocab_path, "r", encoding="utf-8") as f:
-            vocab = json.load(f)
-        return cls(vocab=vocab, **kwargs)
-
-    @classmethod
-    def register_for_auto_class(cls):
-        return
+", "

tokenizer_config.json

@@ -1,13 +1,13 @@
 {
-  "tokenizer_class": "ChessTokenizer",
   "auto_map": {
     "AutoTokenizer": [
       "tokenizer.ChessTokenizer",
       null
     ]
   },
-  "unk_token": "[UNK]",
-  "pad_token": "[PAD]",
+  "tokenizer_class": "ChessTokenizer",
   "bos_token": "[BOS]",
-  "eos_token": "[EOS]"
+  "eos_token": "[EOS]",
+  "pad_token": "[PAD]",
+  "unk_token": "[UNK]"
 }

vocab.json

@@ -1,16 +1,16 @@
 {
   "[PAD]": 0,
-  "[UNK]": 1,
-  "[BOS]": 2,
-  "[EOS]": 3,
+  "[BOS]": 1,
+  "[EOS]": 2,
+  "[UNK]": 3,
   "WNg1f3": 4,
   "BNg8f6": 5,
   "WPe2e4": 6,
   "WPd2d4": 7,
-  "WNb1c3": 8,
-  "WKe1g1": 9,
+  "WKe1g1(o)": 8,
+  "WNb1c3": 9,
   "BNb8c6": 10,
-  "BKe8g8": 11,
+  "BKe8g8(o)": 11,
   "BPd7d5": 12,
   "BPe7e6": 13,
   "BPe7e5": 14,
@@ -23,382 +23,1662 @@
   "BPc7c5": 21,
   "BPa7a6": 22,
   "WPc2c4": 23,
-  "WNf3e5": 24,
+  "WPa2a3": 24,
   "BBf8e7": 25,
-  "WPa2a3": 26,
-  "WPg2g3": 27,
+  "WPg2g3": 26,
+  "WPe2e3": 27,
   "WPf2f4": 28,
-  "WPe2e3": 29,
+  "WPd2d3": 29,
   "BNb8d7": 30,
-  "WBf1c4": 31,
+  "BPb7b6": 31,
   "WRf1e1": 32,
-  "WPd2d3": 33,
+  "WPe4e5": 33,
   "WNb1d2": 34,
-  "WPe4e5": 35,
-  "BPb7b6": 36,
+  "BPf7f6": 35,
+  "WPb2b3": 36,
   "WBf1d3": 37,
-  "BPf7f6": 38,
-  "BNf6e4": 39,
-  "WPb2b3": 40,
-  "BPb7b5": 41,
+  "WBf1c4": 38,
+  "BPb7b5": 39,
+  "WPf2f3": 40,
+  "WBc1e3": 41,
   "WBc1g5": 42,
-  "WBc1e3": 43,
-  "WPf2f3": 44,
+  "WPe4d5(x)": 43,
+  "BBf8g7": 44,
   "BBc8g4": 45,
-  "WPe4d5": 46,
-  "BRf8e8": 47,
-  "BBf8g7": 48,
-  "WBf1e2": 49,
-  "BPf7f5": 50,
-  "WNf3d4": 51,
-  "WPb2b4": 52,
-  "WBc1f4": 53,
-  "BPc5d4": 54,
-  "BBc8b7": 55,
-  "WNc3d5": 56,
-  "BNc6d4": 57,
-  "WPg2g4": 58,
-  "BNf6d5": 59,
-  "WPa2a4": 60,
-  "BBc8d7": 61,
-  "BBf8d6": 62,
-  "WPh2h4": 63,
-  "BPa7a5": 64,
-  "WPd4d5": 65,
-  "BBc8e6": 66,
-  "WPd4e5": 67,
+  "BRf8e8": 46,
+  "BPf7f5": 47,
+  "WBf1e2": 48,
+  "WPb2b4": 49,
+  "BPc5d4(x)": 50,
+  "BBc8b7": 51,
+  "WPg2g4": 52,
+  "WPa2a4": 53,
+  "WPh2h4": 54,
+  "BPa7a5": 55,
+  "BBc8d7": 56,
+  "WNf3e5": 57,
+  "WBc1f4": 58,
+  "WPd4d5": 59,
+  "BBf8d6": 60,
+  "WPd4e5(x)": 61,
+  "BPh7h5": 62,
+  "BRa8c8": 63,
+  "BNg8e7": 64,
+  "BBc8e6": 65,
+  "BPe6e5": 66,
+  "BPe5d4(x)": 67,
   "WRa1d1": 68,
-  "BRa8c8": 69,
-  "BBf8c5": 70,
-  "BNc6e5": 71,
-  "WQd1e2": 72,
-  "BNg8e7": 73,
-  "BPh7h5": 74,
-  "WNc3e4": 75,
-  "BRa8d8": 76,
-  "BQd8e7": 77,
-  "BPe6e5": 78,
-  "BBc8f5": 79,
-  "BPe5d4": 80,
-  "WNf3g5": 81,
-  "WRa1c1": 82,
-  "WQd1f3": 83,
-  "BPd5e4": 84,
-  "WBf1b5": 85,
-  "WKe1c1": 86,
-  "WKg1h1": 87,
-  "BPc6c5": 88,
-  "BPg7g5": 89,
-  "WBc1d2": 90,
-  "WQd1d2": 91,
-  "BBf8b4": 92,
-  "WBf1g2": 93,
-  "BKg8h8": 94,
-  "BKg8g7": 95,
-  "WPc4d5": 96,
-  "WBc1b2": 97,
-  "BPe5e4": 98,
-  "BRa8b8": 99,
-  "WPh4h5": 100,
-  "BKe8c8": 101,
-  "BPd5d4": 102,
-  "BPe6d5": 103,
-  "BPd6d5": 104,
-  "WPc3c4": 105,
-  "WRa1b1": 106,
-  "BQd8f6": 107,
-  "BQd8c7": 108,
-  "BPb5b4": 109,
-  "BQd8d7": 110,
-  "BNf6g4": 111,
-  "WKg1g2": 112,
-  "BPd6e5": 113,
-  "BPb7c6": 114,
-  "BPc6d5": 115,
-  "WPf4f5": 116,
-  "WPg4g5": 117,
-  "WRf1d1": 118,
-  "WPc3d4": 119,
-  "BPa5a4": 120,
-  "WPe3e4": 121,
-  "WRa1e1": 122,
-  "BRf8d8": 123,
-  "WPb2c3": 124,
-  "WNg1e2": 125,
-  "WPh3h4": 126,
-  "BBg4f3": 127,
-  "WKg1h2": 128,
-  "BNd7e5": 129,
-  "BQd8b6": 130,
-  "BPc5c4": 131,
-  "WBg5f6": 132,
-  "WPd3d4": 133,
-  "BRa8e8": 134,
-  "WQd1c2": 135,
-  "BBe7f6": 136,
-  "BKg8h7": 137,
-  "BPg6g5": 138,
-  "WPc4c5": 139,
-  "BNf6d7": 140,
-  "WPa4a5": 141,
-  "BPa6a5": 142,
-  "WPb4b5": 143,
-  "BPh5h4": 144,
-  "WNd2f3": 145,
-  "BNd7f6": 146,
-  "BQd8d5": 147,
-  "WNc3e2": 148,
-  "BPg5g4": 149,
-  "BPf6f5": 150,
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