File size: 3,879 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 | """v51: v48 + SiLU-gated FFN (true SwiGLU in binary form).
v47/v48 FFN: `down(sign(gate(x)) * sign(up(x)))` — the gate*up is XNOR of two
±1 vectors. That throws away 1 bit of gate information per channel.
v51 FFN: SwiGLU-style. `down(silu(gate_raw(x)) * sign(up(x)))` where:
- gate_raw returns the pre-sign float (α·popcount - threshold)
- silu of that is a float
- up returns ±1
- product is float
- down is a DoubledScaled... wait no, keep it single ±1 per weight.
Keeps weights strictly ±1 per stored parameter. The FFN's forward path now
produces float activations through the gate branch, matching standard
SwiGLU. This is how BitNet-1.58b (and BitNet v1) actually structure FFN.
"""
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, BitLinearScaled, BitLinearScaledRaw, IntBinaryAttentionScaled
class BitFFNSwiGLU(nn.Module):
"""SwiGLU: silu(gate_raw) * sign(up) → down. gate has float output; up is ±1.
down's input is float; it still uses ±1 weights (XNOR-popcount on int8-ish input).
"""
def __init__(self, d_model, d_ff):
super().__init__()
# gate returns raw float (no final sign). up returns ±1.
self.gate = BitLinearScaledRaw(d_model, d_ff, binarize_input=True)
self.up = BitLinearScaled(d_model, d_ff, binarize_input=True)
# down takes float input; still binarizes internally.
self.down = BitLinearScaledRaw(d_ff, d_model, binarize_input=True)
def forward(self, x):
g = F.silu(self.gate(x)) # float
u = self.up(x) # ±1
return self.down(g * u) # float, returned as raw (into residual)
class BitBlockV51(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 = BitFFNSwiGLU(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 BitLMv51(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([
BitBlockV51(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 = BitLMv51(d_model=512, n_layers=4, d_ff=192)
n = sum(p.numel() for p in m.parameters())
print(f'v51 SwiGLU: {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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