File size: 4,534 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 | """v52: v48 BitNet + standard softmax attention (not Gumbel-argmax).
Our v16→v48 chain has always used Gumbel hard-attention where each query
attends to exactly ONE position. That's a severe restriction — every real
1-bit LLM paper uses vanilla softmax attention on float scores derived
from ±1 QK. Weights stay ±1; attention matrix A is a float softmax over
the ±1-derived integer scores.
Change from v48: gumbel_hard_attention(…) → softmax(scores, dim=-1).
Everything else identical.
"""
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_v47 import RMSNorm, BitLinearScaled, BitLinearScaledRaw, BitFFNScaled
class SoftmaxBinaryAttention(nn.Module):
"""±1 Q/K/V/O weights; vanilla softmax attention over scaled integer scores."""
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 = BitLinearScaled(d_model, d_model)
self.k_proj = BitLinearScaled(d_model, d_model)
self.v_proj = BitLinearScaled(d_model, d_model)
self.o_proj = BitLinearScaledRaw(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)
self.scale = 1.0 / math.sqrt(self.head_dim)
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) # ±1
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)) * self.scale # float
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) * self.scale
scores = scores - alibi
mask = torch.triu(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1)
scores = scores.masked_fill(mask, float('-inf'))
A = F.softmax(scores, dim=-1) # float softmax
O = torch.matmul(A, V) # float
O = O.transpose(1, 2).contiguous().view(B, T, D)
return self.o_proj(O)
class BitBlockV52(nn.Module):
def __init__(self, d_model, n_heads, d_ff):
super().__init__()
self.norm1 = RMSNorm(d_model)
self.attn = SoftmaxBinaryAttention(d_model, n_heads)
self.norm2 = RMSNorm(d_model)
self.ffn = BitFFNScaled(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 BitLMv52(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([
BitBlockV52(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__':
m = BitLMv52(d_model=512, n_layers=4, d_ff=192)
n = sum(p.numel() for p in m.parameters())
print(f'v52 softmax-attn: {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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