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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 | """v22: Track III.C — multi-bit integer FFN accumulator.
Architecture:
v18 FFN: x(±1) → gate,up (±1) → XNOR → down (±1) → sign → ±1
v22 FFN: x(±1) → up_raw (integer popcount) → CLIP[-B,+B] → down (±1 weights × small int) → sign → ±1
The hidden FFN activation is a small signed integer (3 bits for B=7, 4 for B=15)
instead of 1 bit. The down-projection becomes a signed-integer adder tree:
z_i = Σ_j (W_down[i,j] ∈ {±1}) · (y_j ∈ [−B, +B])
which is still strictly integer arithmetic — conditionally negate y_j, sum.
No float multiply anywhere. Hardware cost: adder width grows from 0 (popcount)
to log₂(d_ff · B). For d_ff=512, B=7: 13-bit INT adder tree of depth 9.
Per ParetoQ / BitNet a4.8: this is the single highest-impact change for closing
the FP32 gap under strict ±1 weights. Expected 0.20-0.35 BPC drop at equal params.
Inference path:
- All weights still 1-bit ±1
- Intermediate FFN activation is 3-bit signed int (B=7 fits in 4 bits incl. sign)
- All other activations still ±1
- No float on the hot path
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from model import sign_ste, sign_ste_clipped, BitLinear, BinaryEmbedding
from model_v18 import IntBinaryAttention
from model_v16 import set_gumbel_tau
class IntFFN(nn.Module):
"""Gated FFN (SwiGLU analog) with clipped-integer `up` activation.
Forward:
g = sign(popcount(W_gate @ x)) in ±1 (unchanged from v18)
u_int = clip(popcount(W_up @ x) * scale, -B, +B) # small signed integer
h = g * u_int # ±B range, gated by ±1
z = sign(popcount-with-integer(W_down @ h)) in ±1
"""
def __init__(self, d_model, d_ff, B=7):
super().__init__()
self.d_model = d_model
self.d_ff = d_ff
self.B = B
# Gate: standard ±1 BitLinear producing sign mask
self.gate = BitLinear(d_model, d_ff, binarize_input=True)
# Up: raw popcount, no final sign — we clip instead
self.up_w = nn.Parameter(torch.randn(d_ff, d_model) * 0.02)
self.up_shift = nn.Parameter(torch.zeros(d_ff))
self.up_scale = nn.Parameter(torch.tensor(1.0 / math.sqrt(d_model)))
# Down: ±1 weights, integer activation input
self.down_w = nn.Parameter(torch.randn(d_model, d_ff) * 0.02)
self.down_threshold = nn.Parameter(torch.zeros(d_model))
def forward(self, x):
# x is ±1 of shape (..., d_model)
g = self.gate(x) # ±1
W_up = sign_ste(self.up_w)
x_bin = sign_ste_clipped(x)
up_raw = F.linear(x_bin, W_up) # integer popcount
up_scaled = up_raw * self.up_scale - self.up_shift
up_clipped = torch.clamp(up_scaled, -self.B, self.B)
up_int = up_scaled + (up_clipped - up_scaled).detach() # STE through clip
# XNOR-style gate: ±1 gate multiplied by signed integer gives range ±B
h = g * up_int # signed values in [-B, +B]
# Down projection: ±1 weights, multi-bit input
W_down = sign_ste(self.down_w)
down_raw = F.linear(h, W_down) # signed integer adder tree
# Normalize by sqrt(d_ff * B) to keep pre-sign in ~unit scale
scale = 1.0 / math.sqrt(self.d_ff * max(self.B, 1))
down_final = down_raw * scale - self.down_threshold
return sign_ste_clipped(down_final)
class BitBlockV22(nn.Module):
def __init__(self, d_model, n_heads, d_ff, B=7):
super().__init__()
self.attn = IntBinaryAttention(d_model, n_heads)
self.ffn = IntFFN(d_model, d_ff, B=B)
def forward(self, x):
a = self.attn(x)
f = self.ffn(x)
return sign_ste(x + a + f)
class BitLMv22(nn.Module):
def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8, d_ff=512,
max_seq_len=256, B=7):
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.B = B
self.embed = BinaryEmbedding(vocab_size, d_model)
self.blocks = nn.ModuleList([
BitBlockV22(d_model, n_heads, d_ff, B=B) 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
@torch.no_grad()
def generate(self, idx, max_new_tokens=200, temperature=1.0, top_k=None):
self.eval()
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.max_seq_len:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / max(temperature, 1e-5)
if top_k is not None:
v, _ = torch.topk(logits, top_k)
logits[logits < v[:, [-1]]] = -float('inf')
probs = F.softmax(logits, dim=-1)
nxt = torch.multinomial(probs, num_samples=1)
idx = torch.cat([idx, nxt], dim=1)
return idx
if __name__ == '__main__':
set_gumbel_tau(0.5)
for B in [3, 7, 15]:
m = BitLMv22(B=B)
n = sum(p.numel() for p in m.parameters())
print(f'v22 B={B}: {n:,} params ({n/1e6:.2f}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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