| """v55: XOR-residual binary transformer. |
| |
| Standard transformer: x_new = sign(x + attn(x) + ffn(x)) — majority vote. |
| v55: x_new = x ⊙ attn(x) ⊙ ffn(x) — elementwise XOR. |
| |
| For ±1 vectors, elementwise multiply IS XOR. Unlike majority-vote-sum, XOR |
| preserves all three bits of information in a different algebra: it's invertible |
| in a group-theoretic sense (commutative, associative, own inverse). |
| |
| This is a fundamentally different binary residual operator. Every residual |
| stream value is strictly ±1 at every step. Attention is still Gumbel hard-argmax. |
| FFN is still XNOR gated. Weights ±1. Nothing float anywhere. |
| |
| Config: v17 shape (d=512, L=4, d_ff=192, 5.52M), 10K steps. |
| """ |
| import math |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from model import sign_ste, BitLinear, BitFFN, BinaryEmbedding |
| from model_v18 import IntBinaryAttention |
|
|
|
|
| class BitBlockV55(nn.Module): |
| def __init__(self, d_model, n_heads, d_ff): |
| super().__init__() |
| self.attn = IntBinaryAttention(d_model, n_heads) |
| self.ffn = BitFFN(d_model, d_ff) |
|
|
| def forward(self, x): |
| a = self.attn(x) |
| f = self.ffn(x) |
| return x * a * f |
|
|
|
|
| class BitLMv55(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([ |
| BitBlockV55(d_model, n_heads, d_ff) 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 |
|
|
|
|
| if __name__ == '__main__': |
| from model_v16 import set_gumbel_tau |
| set_gumbel_tau(0.5) |
| m = BitLMv55(d_model=512, n_layers=4, d_ff=192) |
| n = sum(p.numel() for p in m.parameters()) |
| print(f'v55 XOR-res: {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') |
|
|
