| | """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) |
| | 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 |
| | |
| | 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") |
| |
|
