File size: 6,377 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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | """v43: Doubled-Binary — each BitLinear has TWO ±1 weight matrices summed.
Effective weights take values in {-2, 0, +2}: ternary with a neutral/zero state.
This is still pure 1-bit-per-parameter (every stored weight is ±1 via sign STE).
Motivation: analysis on v29 showed 25–30% of latent weights have |w| < 0.01 —
the training signal wants them near zero, but sign() forces ±1 regardless. The
model is being forced to commit weights that "don't want to be committed,"
creating noise. Doubled binary lets two opposing ±1 values cancel (sum=0), so
the effective weight can be zero. Same 1-bit storage, more expressive.
v17 shape with 2x weight count: d_model=336 (from 512), n_layers=4, d_ff=192.
Target: 5.26M ≈ 5.52M v17 baseline.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from model import sign_ste, sign_ste_clipped, BinaryEmbedding
from model_v16 import gumbel_hard_attention
class DoubledBitLinearRaw(nn.Module):
"""Two ±1 weight matrices summed: effective W_eff in {-2, 0, +2}."""
def __init__(self, in_features, out_features, binarize_input=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.binarize_input = binarize_input
self.weight_a = nn.Parameter(torch.randn(out_features, in_features) * 0.02)
self.weight_b = nn.Parameter(torch.randn(out_features, in_features) * 0.02)
def forward(self, x):
W = sign_ste(self.weight_a) + sign_ste(self.weight_b) # {-2, 0, 2}
if self.binarize_input:
x = sign_ste_clipped(x)
return F.linear(x, W)
class DoubledBitLinear(nn.Module):
"""DoubledBitLinearRaw + learned threshold + sign. Returns ±1.
Sum of two ±1 matrices has effective values in {-2, 0, 2}. The raw popcount
output variance is ~2x standard BitLinear, so we scale by 1/(2·sqrt(in)).
"""
def __init__(self, in_features, out_features, binarize_input=True):
super().__init__()
self.raw = DoubledBitLinearRaw(in_features, out_features, binarize_input=binarize_input)
self.threshold = nn.Parameter(torch.zeros(out_features))
# Scale by 1/(2·sqrt(in)) since effective |w| can be 2 and sum over in_features.
self.scale = 1.0 / (2.0 * math.sqrt(in_features))
def forward(self, x):
s = self.raw(x) * self.scale - self.threshold
return sign_ste_clipped(s)
class DoubledBitFFN(nn.Module):
def __init__(self, d_model, d_ff):
super().__init__()
self.gate = DoubledBitLinear(d_model, d_ff, binarize_input=True)
self.up = DoubledBitLinear(d_model, d_ff, binarize_input=True)
self.down = DoubledBitLinear(d_ff, d_model, binarize_input=True)
def forward(self, x):
return self.down(self.gate(x) * self.up(x))
class DoubledIntBinaryAttention(nn.Module):
"""v18 attention with DoubledBitLinear Q/K/V/O."""
def __init__(self, d_model, n_heads):
super().__init__()
assert d_model % n_heads == 0
self.d_model = d_model
self.n_heads = n_heads
self.head_dim = d_model // n_heads
self.q_proj = DoubledBitLinear(d_model, d_model)
self.k_proj = DoubledBitLinear(d_model, d_model)
self.v_proj = DoubledBitLinear(d_model, d_model)
self.o_proj = DoubledBitLinear(d_model, d_model)
slopes = torch.tensor([1 << i for i in range(n_heads)], dtype=torch.long)
self.register_buffer('alibi_slopes_int', slopes)
def forward(self, x):
B, T, D = x.shape
H, Dh = self.n_heads, self.head_dim
Q = self.q_proj(x).view(B, T, H, Dh).transpose(1, 2)
K = self.k_proj(x).view(B, T, H, Dh).transpose(1, 2)
V = self.v_proj(x).view(B, T, H, Dh).transpose(1, 2)
scores = torch.matmul(Q, K.transpose(-2, -1))
pos = torch.arange(T, device=x.device)
dist = (pos.unsqueeze(0) - pos.unsqueeze(1)).abs()
alibi = self.alibi_slopes_int.view(1, H, 1, 1).to(scores.dtype) \
* dist.view(1, 1, T, T).to(scores.dtype)
scores = scores - alibi
mask = torch.triu(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1)
A = gumbel_hard_attention(scores, mask=mask)
O = torch.matmul(A, V)
O = O.transpose(1, 2).contiguous().view(B, T, D)
return self.o_proj(O)
class BitBlockV43(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.attn = DoubledIntBinaryAttention(d_model, n_heads)
self.ffn = DoubledBitFFN(d_model, d_ff)
def forward(self, x):
a = self.attn(x)
f = self.ffn(x)
return sign_ste(x + a + f)
class BitLMv43(nn.Module):
def __init__(self, vocab_size=128, d_model=336, n_layers=4, n_heads=8,
d_ff=192, 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([
BitBlockV43(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)
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, d_ff) in ((320, 240), (336, 192), (336, 208)):
m = BitLMv43(d_model=D, d_ff=d_ff)
n = sum(p.numel() for p in m.parameters())
print(f'D={D}, d_ff={d_ff}: {n:,} ({n/1e6:.3f}M)')
m = BitLMv43()
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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