File size: 4,005 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 | """v37: Pinch-waist architecture — per-layer d_ff redistribution.
Layer ablation on v29 showed:
L0 (+1.42 BPC when ablated), L9 (+0.41), L1 (+0.55), L8 (+0.30)
vs. middle layers at +0.10-0.17.
Hypothesis: allocate d_ff proportionally to ablation importance. Keep d_model
uniform (residual stream requires it), let only d_ff vary per layer.
Total FFN params preserved: baseline 8 * (3*d*d_ff_uniform) = 8 * 393K = 3.14M.
Pinch-waist: wider d_ff at boundary layers, narrower in the middle.
"""
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
class BitFFNVar(nn.Module):
"""BitFFN with configurable d_ff per instance."""
def __init__(self, d_model, d_ff):
super().__init__()
self.gate = BitLinear(d_model, d_ff, binarize_input=True)
self.up = BitLinear(d_model, d_ff, binarize_input=True)
self.down = BitLinear(d_ff, d_model, binarize_input=True)
def forward(self, x):
return self.down(self.gate(x) * self.up(x))
class BitBlockV37(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.attn = IntBinaryAttention(d_model, n_heads)
self.ffn = BitFFNVar(d_model, d_ff)
def forward(self, x):
a = self.attn(x)
f = self.ffn(x)
return sign_ste(x + a + f)
class BitLMv37(nn.Module):
"""Variable d_ff per layer. `d_ffs` is a list of length n_layers."""
def __init__(self, vocab_size=128, d_model=256, d_ffs=None, n_heads=8,
max_seq_len=256):
super().__init__()
self.vocab_size = vocab_size
self.d_model = d_model
self.n_layers = len(d_ffs)
self.max_seq_len = max_seq_len
self.d_ffs = d_ffs
self.embed = BinaryEmbedding(vocab_size, d_model)
self.blocks = nn.ModuleList([
BitBlockV37(d_model, n_heads, d_ff) for d_ff in d_ffs
])
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__':
from model_v16 import set_gumbel_tau
set_gumbel_tau(0.5)
# Pinch-waist matching 5M baseline total FFN params (8 * 512 = 4096 total ffn-width)
# Distribution: [1024, 512, 256, 256, 256, 256, 512, 1024] = 4096 ✓
d_ffs = [1024, 512, 256, 256, 256, 256, 512, 1024]
m = BitLMv37(vocab_size=128, d_model=256, d_ffs=d_ffs, n_heads=8, max_seq_len=256)
n = sum(p.numel() for p in m.parameters())
print(f'v37 pinch-waist: {n:,} params ({n/1e6:.2f}M), d_ffs={d_ffs}')
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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