File size: 3,155 Bytes
4754707 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | """v46: Float residual stream. Every WEIGHT is ±1; the residual stream between
blocks is not sign()'d.
v18/v17 force x = sign(x + a + f) at every block. That means the entire model
lives in a ±1 vector space between layers — a crushing constraint. Each
BitLinear binarizes its input anyway via sign_ste_clipped, so removing the
outer sign on the residual costs nothing in "1-bit compute": every matmul is
still XNOR-popcount, every weight is still ±1.
What changes: residual stream accumulates as int/float across depth. The
model now has float-valued activations (small dynamic range: sum of L ±1
vectors). Storage is still strictly 1-bit per parameter.
This is what BitNet/1-bit-LLMs actually do. Our "maximalist" framing was
pessimistic about this tradeoff.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from model import sign_ste, BitLinear, BitFFN, BinaryEmbedding
from model_v18 import IntBinaryAttention
class BitBlockV46(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.attn = IntBinaryAttention(d_model, n_heads)
self.ffn = BitFFN(d_model, d_ff)
def forward(self, x):
a = self.attn(x) # BitLinear inside binarizes x → ±1 internally
f = self.ffn(x)
return x + a + f # NO sign on residual — float/int accumulation
class BitLMv46(nn.Module):
def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8,
d_ff=512, max_seq_len=256):
super().__init__()
self.vocab_size = vocab_size
self.d_model = d_model
self.n_layers = n_layers
self.max_seq_len = max_seq_len
self.embed = BinaryEmbedding(vocab_size, d_model)
self.blocks = nn.ModuleList([
BitBlockV46(d_model, n_heads, d_ff) for _ in range(n_layers)
])
self.out_codebook = nn.Parameter(torch.randn(vocab_size, d_model) * 0.02)
self.logit_scale = nn.Parameter(torch.tensor(1.0 / math.sqrt(d_model)))
self.out_bias = nn.Parameter(torch.zeros(vocab_size))
def forward(self, idx, targets=None):
x = self.embed(idx)
for blk in self.blocks:
x = blk(x)
# Head: binarize residual once, then match against binary codebook
x = sign_ste(x)
W_out = sign_ste(self.out_codebook)
scores = torch.matmul(x, W_out.t())
logits = scores * self.logit_scale + self.out_bias
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, self.vocab_size), targets.view(-1))
return logits, loss
if __name__ == '__main__':
from model_v16 import set_gumbel_tau
set_gumbel_tau(0.5)
for (D, L, d_ff) in ((256, 8, 512), (512, 4, 192), (336, 4, 192)):
m = BitLMv46(d_model=D, n_layers=L, d_ff=d_ff)
n = sum(p.numel() for p in m.parameters())
print(f'D={D} L={L} d_ff={d_ff}: {n:,} ({n/1e6:.3f}M)')
m = BitLMv46()
x = torch.randint(0, 128, (2, 64))
y = torch.randint(0, 128, (2, 64))
logits, loss = m(x, y)
loss.backward()
print(f'loss={loss.item():.3f}, backward OK')
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