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model.py

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+"""PureBit Transformer - Binary-level language model"""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+
+class Attention(nn.Module):
+    def __init__(self, d, heads=8):
+        super().__init__()
+        self.heads = heads
+        self.dk = d // heads
+        self.q_proj = nn.Linear(d, d, bias=False)
+        self.k_proj = nn.Linear(d, d, bias=False)
+        self.v_proj = nn.Linear(d, d, bias=False)
+        self.out_proj = nn.Linear(d, d, bias=False)
+    
+    def forward(self, x, mask=None):
+        B, N, D = x.shape
+        q = self.q_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
+        k = self.k_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
+        v = self.v_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
+        att = (q @ k.transpose(-1, -2)) / math.sqrt(self.dk)
+        if mask is not None:
+            att = att + mask
+        att = F.softmax(att, dim=-1)
+        out = (att @ v).transpose(1, 2).reshape(B, N, D)
+        return self.out_proj(out)
+
+class MLP(nn.Module):
+    def __init__(self, d, mult=4):
+        super().__init__()
+        self.fc1 = nn.Linear(d, d * mult, bias=False)
+        self.fc2 = nn.Linear(d * mult, d, bias=False)
+    
+    def forward(self, x):
+        return self.fc2(F.gelu(self.fc1(x)))
+
+class Block(nn.Module):
+    def __init__(self, d, heads=8):
+        super().__init__()
+        self.ln1 = nn.LayerNorm(d)
+        self.attn = Attention(d, heads)
+        self.ln2 = nn.LayerNorm(d)
+        self.mlp = MLP(d)
+    
+    def forward(self, x, mask):
+        x = x + self.attn(self.ln1(x), mask)
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+class PureBitTransformer(nn.Module):
+    """Transformer operating on raw binary bits (vocab_size=2)"""
+    def __init__(self, d=256, layers=6, heads=8, ctx=4096):
+        super().__init__()
+        self.ctx = ctx
+        self.emb = nn.Embedding(2, d)  # Binary: 0 or 1
+        self.blocks = nn.ModuleList([Block(d, heads) for _ in range(layers)])
+        self.ln = nn.LayerNorm(d)
+        self.head = nn.Linear(d, 2, bias=False)
+        self.head.weight = self.emb.weight  # Weight tying
+    
+    def forward(self, x):
+        B, N = x.shape
+        mask = torch.triu(torch.ones(N, N, device=x.device), 1) * -1e9
+        h = self.emb(x)
+        for b in self.blocks:
+            h = b(h, mask)
+        return self.head(self.ln(h))
+    
+    @torch.no_grad()
+    def generate(self, bits, max_new=256, temp=0.8):
+        """Generate new bits autoregressively"""
+        x = torch.tensor(bits, device=next(self.parameters()).device).unsqueeze(0)
+        for _ in range(max_new):
+            logits = self(x[:, -self.ctx:])[:, -1, :] / temp
+            next_bit = torch.multinomial(F.softmax(logits, -1), 1)
+            x = torch.cat([x, next_bit], 1)
+        return x[0].tolist()
+
+def text_to_bits(text):
+    """Convert UTF-8 text to list of bits"""
+    bits = []
+    for byte in text.encode('utf-8'):
+        for i in range(7, -1, -1):
+            bits.append((byte >> i) & 1)
+    return bits
+
+def bits_to_text(bits):
+    """Convert list of bits back to UTF-8 text"""
+    while len(bits) % 8 != 0:
+        bits = bits + [0]
+    bytes_out = []
+    for i in range(0, len(bits), 8):
+        byte = 0
+        for j in range(8):
+            byte = (byte << 1) | bits[i + j]
+        bytes_out.append(byte)
+    return bytes(bytes_out).decode('utf-8', errors='replace')
+
+def load_model(checkpoint_path, device='cuda'):
+    """Load model from checkpoint"""
+    ckpt = torch.load(checkpoint_path, map_location=device)
+    model = PureBitTransformer(d=256, layers=6, heads=8).to(device)
+    model.load_state_dict(ckpt['model'])
+    model.eval()
+    return model, ckpt
+
+if __name__ == "__main__":
+    model = PureBitTransformer()
+    params = sum(p.numel() for p in model.parameters())
+    print(f"PureBit Transformer: {params:,} parameters")