File size: 6,577 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 | """v49: BitNet b1.58 — ternary weights {-1, 0, +1} + everything from v47.
Quantization: each matrix's latent float W is quantized per-matrix via
W_q = clamp(round(W / α), -1, +1) where α = mean(|W|)
giving weights in {-1, 0, +1}. Commonly called "1-bit LLM" even though each
weight is log2(3) ≈ 1.58 bits. This gives expressivity our strict ±1 lacks:
the "skip" state. Our v43 doubled-binary {-2, 0, +2} approximated this with
two ±1 weights; ternary does it natively in one weight.
All else identical to v47: per-channel α scale + RMSNorm + float residual.
"""
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
from model_v47 import RMSNorm
def ternary_ste(w):
"""Per-matrix ternary quantization with STE: {-1, 0, +1} × α."""
alpha = w.abs().mean().clamp(min=1e-8)
w_scaled = w / alpha
w_q = w_scaled.round().clamp(-1, 1)
return w + (w_q - w).detach() # STE: forward = w_q, backward = identity
class TernaryBitLinear(nn.Module):
"""Ternary weights {-1, 0, +1} with per-channel float scale + threshold."""
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 = ternary_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 TernaryBitLinearRaw(nn.Module):
"""Ternary weights; returns the pre-sign float score (for residual sums)."""
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 = ternary_ste(self.weight)
if self.binarize_input:
x = sign_ste_clipped(x)
return F.linear(x, W) * self.alpha + self.bias
class TernaryFFN(nn.Module):
def __init__(self, d_model, d_ff):
super().__init__()
self.gate = TernaryBitLinear(d_model, d_ff, binarize_input=True)
self.up = TernaryBitLinear(d_model, d_ff, binarize_input=True)
self.down = TernaryBitLinearRaw(d_ff, d_model, binarize_input=True)
def forward(self, x):
return self.down(self.gate(x) * self.up(x))
class TernaryAttention(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 = TernaryBitLinear(d_model, d_model)
self.k_proj = TernaryBitLinear(d_model, d_model)
self.v_proj = TernaryBitLinear(d_model, d_model)
self.o_proj = TernaryBitLinearRaw(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 BitBlockV49(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.norm1 = RMSNorm(d_model)
self.attn = TernaryAttention(d_model, n_heads)
self.norm2 = RMSNorm(d_model)
self.ffn = TernaryFFN(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 BitLMv49(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([
BitBlockV49(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 = BitLMv49(d_model=512, n_layers=4, d_ff=192)
n = sum(p.numel() for p in m.parameters())
print(f'total: {n:,} ({n/1e6:.3f}M)')
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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