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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")
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