File size: 6,116 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 | """v53: Bit-plane binary weights. Every stored bit ±1; effective weight is
a K-bit signed integer (K ±1 matrices combined as Σ 2^k · sign(W_k)).
No softmax, no RMSNorm, no float scales, no float residual. Pure ±1 everywhere:
- Attention: Gumbel hard-argmax (v16 style) — one-hot per query
- Residual: sign_ste(x + a + f)
- Activations: ±1 via sign_ste
- Weights: K ±1 planes summed with powers-of-2 weights
Effective weight values: signed 2^K-level integer. K=4 gives 16 distinct values
per weight while still storing every bit strictly as ±1.
Config: d_model=224, n_layers=4, n_heads=8, d_ff=160, K=4 → 5.0M ±1 weights.
"""
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 BitPlaneLinear(nn.Module):
"""K stacked ±1 weight matrices; effective weight is signed K-bit integer."""
def __init__(self, in_features, out_features, K=4, binarize_input=True):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.K = K
self.binarize_input = binarize_input
# K latent-float weight matrices; each sign()'d at forward.
self.weights = nn.ParameterList([
nn.Parameter(torch.randn(out_features, in_features) * 0.02)
for _ in range(K)
])
self.threshold = nn.Parameter(torch.zeros(out_features))
# Accumulator max magnitude = sum_k 2^k * sqrt(in) ≈ (2^K - 1) sqrt(in)
self.scale = 1.0 / ((2 ** K - 1) * math.sqrt(in_features))
def forward(self, x):
if self.binarize_input:
x = sign_ste_clipped(x)
acc = 0
for k, w in enumerate(self.weights):
W = sign_ste(w)
acc = acc + (2 ** k) * F.linear(x, W)
s = acc * self.scale - self.threshold
return sign_ste_clipped(s)
class BitPlaneFFN(nn.Module):
def __init__(self, d_model, d_ff, K=4):
super().__init__()
self.gate = BitPlaneLinear(d_model, d_ff, K=K, binarize_input=True)
self.up = BitPlaneLinear(d_model, d_ff, K=K, binarize_input=True)
self.down = BitPlaneLinear(d_ff, d_model, K=K, binarize_input=True)
def forward(self, x):
return self.down(self.gate(x) * self.up(x))
class BitPlaneAttention(nn.Module):
"""Gumbel hard-argmax attention with bit-plane Q/K/V/O projections.
Attention matrix is one-hot per query (strict ±1 / {0,1})."""
def __init__(self, d_model, n_heads, K=4):
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 = BitPlaneLinear(d_model, d_model, K=K)
self.k_proj = BitPlaneLinear(d_model, d_model, K=K)
self.v_proj = BitPlaneLinear(d_model, d_model, K=K)
self.o_proj = BitPlaneLinear(d_model, d_model, K=K)
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) # one-hot per query
O = torch.matmul(A, V)
O = O.transpose(1, 2).contiguous().view(B, T, D)
return self.o_proj(O)
class BitBlockV53(nn.Module):
def __init__(self, d_model, n_heads, d_ff, K=4):
super().__init__()
self.attn = BitPlaneAttention(d_model, n_heads, K=K)
self.ffn = BitPlaneFFN(d_model, d_ff, K=K)
def forward(self, x):
a = self.attn(x)
f = self.ffn(x)
return sign_ste(x + a + f) # strict ±1 residual
class BitLMv53(nn.Module):
def __init__(self, vocab_size=128, d_model=224, n_layers=4, n_heads=8,
d_ff=160, K=4, 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.K = K
self.embed = BinaryEmbedding(vocab_size, d_model)
self.blocks = nn.ModuleList([
BitBlockV53(d_model, n_heads, d_ff, K=K) 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, K) in ((224, 160, 4), (200, 160, 4), (256, 128, 4), (224, 192, 4)):
m = BitLMv53(d_model=D, d_ff=d_ff, K=K)
n = sum(p.numel() for p in m.parameters())
print(f'D={D} d_ff={d_ff} K={K}: {n:,} ({n/1e6:.3f}M)')
m = BitLMv53()
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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