"""v40: BitProto — attention augmented with learnable ±1 prototype keys/values. v39 (separate Hopfield head in parallel with attention) was plateauing around 1.98 BPC: the extra head competes with real attention for residual bandwidth and steals d_ff budget. v40 integrates prototypes *inside* the existing attention mechanism: K_ext = [K_from_x | K_proto] (T + M columns per head) V_ext = [V_from_x | V_proto] A = Gumbel-argmax over (T + M) options O = A @ V_ext No separate head, no extra residual summand. Prototypes live per-head, per-layer. They're non-causal (always visible). ALiBi bias is zero for prototype columns. Everything remains strictly ±1 on the forward path. Prototypes are latent floats, sign()'d at forward. Adds only 2·n_proto·d_model params per layer (16K for n_proto=32, d_model=256). """ 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_v16 import gumbel_hard_attention class IntBinaryAttentionWithProto(nn.Module): """IntBinaryAttention + M learnable ±1 prototype K/V per head.""" def __init__(self, d_model, n_heads, n_proto=32): 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.n_proto = n_proto self.q_proj = BitLinear(d_model, d_model) self.k_proj = BitLinear(d_model, d_model) self.v_proj = BitLinear(d_model, d_model) self.o_proj = BitLinear(d_model, d_model) # Integer ALiBi slopes (power-of-2). slopes = torch.tensor([1 << i for i in range(n_heads)], dtype=torch.long) self.register_buffer('alibi_slopes_int', slopes) # Per-head prototypes: (M, H, Dh). Latent float; sign() at forward. self.key_proto = nn.Parameter(torch.randn(n_proto, n_heads, self.head_dim) * 0.02) self.val_proto = nn.Parameter(torch.randn(n_proto, n_heads, self.head_dim) * 0.02) def forward(self, x): B, T, D = x.shape H, Dh, M = self.n_heads, self.head_dim, self.n_proto Q = self.q_proj(x).view(B, T, H, Dh).transpose(1, 2) # (B, H, T, Dh) 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) # Binarize + broadcast prototypes. Kp = sign_ste(self.key_proto).permute(1, 0, 2) # (H, M, Dh) Vp = sign_ste(self.val_proto).permute(1, 0, 2) Kp = Kp.unsqueeze(0).expand(B, H, M, Dh) Vp = Vp.unsqueeze(0).expand(B, H, M, Dh) K_ext = torch.cat([K, Kp], dim=2) # (B, H, T+M, Dh) V_ext = torch.cat([V, Vp], dim=2) scores = torch.matmul(Q, K_ext.transpose(-2, -1)) # (B, H, T, T+M) # ALiBi over T-part only; 0 bias for prototypes. pos = torch.arange(T, device=x.device) dist = (pos.unsqueeze(0) - pos.unsqueeze(1)).abs() # (T, T) alibi_t = self.alibi_slopes_int.view(1, H, 1, 1).to(scores.dtype) \ * dist.view(1, 1, T, T).to(scores.dtype) alibi_p = torch.zeros(1, H, T, M, dtype=scores.dtype, device=x.device) alibi = torch.cat([alibi_t, alibi_p], dim=-1) scores = scores - alibi # Causal mask over T-part; prototypes always visible. causal = torch.triu(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1) visible_p = torch.zeros(T, M, device=x.device, dtype=torch.bool) mask = torch.cat([causal, visible_p], dim=-1) # (T, T+M) A = gumbel_hard_attention(scores, mask=mask) # (B, H, T, T+M) O = torch.matmul(A, V_ext) O = O.transpose(1, 2).contiguous().view(B, T, D) return self.o_proj(O) class BitBlockV40(nn.Module): def __init__(self, d_model, n_heads, d_ff, n_proto): super().__init__() self.attn = IntBinaryAttentionWithProto(d_model, n_heads, n_proto) self.ffn = BitFFN(d_model, d_ff) def forward(self, x): a = self.attn(x) f = self.ffn(x) return sign_ste(x + a + f) class BitLMv40(nn.Module): def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8, d_ff=444, n_proto=32, max_seq_len=256): super().__init__() self.vocab_size = vocab_size self.d_model = d_model self.n_layers = n_layers self.n_heads = n_heads self.max_seq_len = max_seq_len self.n_proto = n_proto self.embed = BinaryEmbedding(vocab_size, d_model) self.blocks = nn.ModuleList([ BitBlockV40(d_model, n_heads, d_ff, n_proto) 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__': from model_v16 import set_gumbel_tau set_gumbel_tau(0.5) for d_ff in (440, 444, 448): m = BitLMv40(d_ff=d_ff) n = sum(p.numel() for p in m.parameters()) print(f'd_ff={d_ff}: {n:,} ({n/1e6:.3f}M)') m = BitLMv40() 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')