File size: 6,592 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 | """v50: v45 (doubled-binary {-2,0,+2}) stacked on v47 (BitNet scaffolding).
Each BitLinear stores TWO ±1 weight matrices; effective weight per entry is
their sum → {-2, 0, +2}. Strictly 1-bit per stored parameter (no ternary,
no quantized weights). Adds the BitNet amenities:
- per-output-channel float scale α
- RMSNorm between blocks
- float residual stream
Matched to v17 at 5.5M via doubled-binary shape (d=336, d_ff=224, L=4).
"""
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
class DoubledScaledBitLinear(nn.Module):
"""Two ±1 weights summed (effective {-2,0,2}); per-channel float scale."""
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)
self.alpha = nn.Parameter(torch.full((out_features,), 1.0 / (2.0 * math.sqrt(in_features))))
self.threshold = nn.Parameter(torch.zeros(out_features))
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)
s = F.linear(x, W) * self.alpha - self.threshold
return sign_ste_clipped(s)
class DoubledScaledBitLinearRaw(nn.Module):
"""Same as DoubledScaledBitLinear but returns pre-sign float score."""
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)
self.alpha = nn.Parameter(torch.full((out_features,), 1.0 / (2.0 * math.sqrt(in_features))))
self.bias = nn.Parameter(torch.zeros(out_features))
def forward(self, x):
W = sign_ste(self.weight_a) + sign_ste(self.weight_b)
if self.binarize_input:
x = sign_ste_clipped(x)
return F.linear(x, W) * self.alpha + self.bias
class DoubledScaledFFN(nn.Module):
def __init__(self, d_model, d_ff):
super().__init__()
self.gate = DoubledScaledBitLinear(d_model, d_ff, binarize_input=True)
self.up = DoubledScaledBitLinear(d_model, d_ff, binarize_input=True)
self.down = DoubledScaledBitLinearRaw(d_ff, d_model, binarize_input=True)
def forward(self, x):
return self.down(self.gate(x) * self.up(x))
class DoubledScaledAttention(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 = DoubledScaledBitLinear(d_model, d_model)
self.k_proj = DoubledScaledBitLinear(d_model, d_model)
self.v_proj = DoubledScaledBitLinear(d_model, d_model)
self.o_proj = DoubledScaledBitLinearRaw(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 BitBlockV50(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.norm1 = RMSNorm(d_model)
self.attn = DoubledScaledAttention(d_model, n_heads)
self.norm2 = RMSNorm(d_model)
self.ffn = DoubledScaledFFN(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 BitLMv50(nn.Module):
def __init__(self, vocab_size=128, d_model=336, n_layers=4, n_heads=8,
d_ff=224, 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([
BitBlockV50(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)
for (D, d_ff) in ((320, 224), (336, 224), (336, 240), (352, 224)):
m = BitLMv50(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 = BitLMv50()
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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