vm_backup/code/model_v40.py

"""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')