File size: 7,341 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | """v47: BitNet-style — per-channel float scale on every BitLinear + RMSNorm
between blocks + float residual stream.
Storage-wise still strict 1-bit per weight: the weight matrices are ±1. What we
add is float *auxiliary* parameters:
- per-output-channel scale α ∈ R^{d_out} per BitLinear
- RMSNorm γ ∈ R^{d_model} per block
These are the standard components of every "1-bit LLM" paper in the literature
(BitNet, OneBit). v17's maximalist design stripped them out. If the intern's
gain comes from restoring magnitude/normalization information (the only thing
strict ±1 maximalism destroys), this matches.
Float aux params: ~d_model floats per BitLinear + d_model per RMSNorm. For
v17-shape (d=512, L=4) that's ~20K floats, vs 5.5M ±1 weights. <0.4% overhead.
"""
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 BitLinearScaled(nn.Module):
"""±1 weights, XNOR-popcount matmul, per-channel float scale α.
forward: sign_ste_clipped(alpha * sign(W) @ sign(x) - threshold).
Every stored weight is ±1. α and threshold are float (trainable scalars
per output channel)."""
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 = nn.Parameter(torch.randn(out_features, in_features) * 0.02)
self.alpha = nn.Parameter(torch.full((out_features,), 1.0 / math.sqrt(in_features)))
self.threshold = nn.Parameter(torch.zeros(out_features))
def forward(self, x):
W = sign_ste(self.weight)
if self.binarize_input:
x = sign_ste_clipped(x)
s = F.linear(x, W) * self.alpha - self.threshold
return sign_ste_clipped(s)
class BitLinearScaledRaw(nn.Module):
"""Same as BitLinearScaled but returns the pre-sign (float/int) score.
Used where we want to sum raw values into the residual stream."""
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 = nn.Parameter(torch.randn(out_features, in_features) * 0.02)
self.alpha = nn.Parameter(torch.full((out_features,), 1.0 / math.sqrt(in_features)))
self.bias = nn.Parameter(torch.zeros(out_features))
def forward(self, x):
W = sign_ste(self.weight)
if self.binarize_input:
x = sign_ste_clipped(x)
return F.linear(x, W) * self.alpha + self.bias
class RMSNorm(nn.Module):
def __init__(self, d_model, eps=1e-6):
super().__init__()
self.gamma = nn.Parameter(torch.ones(d_model))
self.eps = eps
def forward(self, x):
rms = x.pow(2).mean(dim=-1, keepdim=True).add(self.eps).rsqrt()
return x * rms * self.gamma
class BitFFNScaled(nn.Module):
"""SwiGLU-ish: gate * up then down. Intermediate kept float (via scale)."""
def __init__(self, d_model, d_ff):
super().__init__()
self.gate = BitLinearScaled(d_model, d_ff, binarize_input=True)
self.up = BitLinearScaled(d_model, d_ff, binarize_input=True)
# down returns raw float into residual stream
self.down = BitLinearScaledRaw(d_ff, d_model, binarize_input=True)
def forward(self, x):
return self.down(self.gate(x) * self.up(x))
class IntBinaryAttentionScaled(nn.Module):
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 = BitLinearScaled(d_model, d_model)
self.k_proj = BitLinearScaled(d_model, d_model)
self.v_proj = BitLinearScaled(d_model, d_model)
# o_proj returns raw float for residual
self.o_proj = BitLinearScaledRaw(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 BitBlockV47(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.norm1 = RMSNorm(d_model)
self.attn = IntBinaryAttentionScaled(d_model, n_heads)
self.norm2 = RMSNorm(d_model)
self.ffn = BitFFNScaled(d_model, d_ff)
def forward(self, x):
x = x + self.attn(self.norm1(x))
x = x + self.ffn(self.norm2(x))
return x
class BitLMv47(nn.Module):
def __init__(self, vocab_size=128, d_model=512, 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([
BitBlockV47(d_model, n_heads, d_ff) for _ in range(n_layers)
])
self.norm_out = RMSNorm(d_model)
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)
x = self.norm_out(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)
m = BitLMv47(d_model=512, n_layers=4, d_ff=192)
n = sum(p.numel() for p in m.parameters())
float_p = sum(p.numel() for n_, p in m.named_parameters() if 'alpha' in n_ or 'gamma' in n_)
print(f'total: {n:,} ({n/1e6:.3f}M); float-aux: {float_p:,} ({float_p/n*100:.2f}%)')
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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