Manual Force Upload

configuration_chess.py

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+
+from transformers import PretrainedConfig
+
+class ChessConfig(PretrainedConfig):
+    model_type = "chess"
+    
+    def __init__(
+        self, 
+        vocab_size=1340, 
+        n_embd=128, 
+        n_layer=6, 
+        n_head=8, 
+        n_inner=256, 
+        n_ctx=256, 
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_inner = n_inner
+        self.n_ctx = n_ctx
+        self.num_hidden_layers = n_layer
+        self.hidden_size = n_embd
+        self.num_attention_heads = n_head

modeling_chess.py

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+
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, GenerationMixin
+from transformers.modeling_outputs import CausalLMOutput
+from .configuration_chess import ChessConfig 
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(config.n_embd)
+        self.attn = nn.MultiheadAttention(config.n_embd, config.n_head, batch_first=True)
+        self.ln2 = nn.LayerNorm(config.n_embd)
+        self.mlp = nn.Sequential(
+            nn.Linear(config.n_embd, config.n_inner), 
+            nn.GELU(), 
+            nn.Linear(config.n_inner, config.n_embd)
+        )
+        
+    def forward(self, x, mask=None):
+        attn_out, _ = self.attn(self.ln1(x), self.ln1(x), self.ln1(x), attn_mask=mask, need_weights=False)
+        return x + attn_out + self.mlp(self.ln2(x + attn_out))
+
+class ChessForCausalLM(PreTrainedModel, GenerationMixin):
+    config_class = ChessConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.token_emb = nn.Embedding(config.vocab_size, config.n_embd)
+        self.pos_emb = nn.Embedding(config.n_ctx, config.n_embd)
+        self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        
+        # Weight Tying (Important pour le comptage param)
+        self.lm_head.weight = self.token_emb.weight
+        
+        self.apply(self._init_weights)
+            
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)): 
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {"input_ids": input_ids}
+            
+    def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids) + self.pos_emb(torch.arange(T, device=input_ids.device))
+        mask = torch.triu(torch.ones(T, T, device=input_ids.device) * float('-inf'), diagonal=1)
+        for block in self.blocks: x = block(x, mask=mask)
+        logits = self.lm_head(self.ln_f(x))
+        loss = None
+        if labels is not None:
+            loss = nn.CrossEntropyLoss(ignore_index=-100)(logits.view(-1, logits.size(-1)), labels.view(-1))
+        return CausalLMOutput(loss=loss, logits=logits)