vm_backup/code/model_v11.py

"""v11: top-k binary attention (ternary {-1,0,+1} attention matrix).

Issue 1 isolation test. Exactly v3 architecture except the attention matrix A
selects only the top-k positions per query (ternary: {-1,0,+1} where 0 = ignore).

Hypothesis: if the POC plateau at 3.20 BPC is caused by binary attention's
inability to express selective sparsity, then restoring sparse selection via
top-k should close most of the v3→v4 gap (0.48 BPC) while adding only the
minimal concession of ternary attention weights (per-position A ∈ {−1, 0, +1}).
Everything else (weights, Q/K/V/O projections, FFN, residuals, embeddings)
stays strict ±1.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F

from model import sign_ste, sign_ste_clipped, BitLinear, BitFFN, BinaryEmbedding


class TopKBinaryAttention(nn.Module):
    def __init__(self, d_model, n_heads, topk=8):
        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.topk = topk

        self.q_proj = BitLinear(d_model, d_model, binarize_input=True)
        self.k_proj = BitLinear(d_model, d_model, binarize_input=True)
        self.v_proj = BitLinear(d_model, d_model, binarize_input=True)
        self.o_proj = BitLinear(d_model, d_model, binarize_input=True)

        slopes = torch.tensor([2.0 ** (i - 2) for i in range(n_heads)])
        self.register_buffer('alibi_slopes', slopes)
        self.register_buffer('_causal_mask', torch.empty(0), persistent=False)

    def _get_mask(self, T, device):
        if self._causal_mask.shape[-1] < T or self._causal_mask.device != device:
            m = torch.triu(torch.ones(T, T, device=device, dtype=torch.bool), diagonal=1)
            self._causal_mask = m
        return self._causal_mask[:T, :T]

    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)
        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))  # (B,H,T,T) integer popcount
        scores_f = scores / math.sqrt(Dh)

        pos = torch.arange(T, device=x.device).float()
        dist = (pos.unsqueeze(0) - pos.unsqueeze(1)).abs()
        alibi_bias = self.alibi_slopes.view(1, H, 1, 1) * dist.view(1, 1, T, T) / math.sqrt(Dh)
        scores_f = scores_f - alibi_bias

        mask = self._get_mask(T, x.device)
        scores_f = scores_f.masked_fill(mask, -1e9)

        # Per-query top-k selection. k is clamped to number of valid (unmasked) keys.
        # For query position i, exactly min(i+1, topk) keys are valid.
        k = min(self.topk, T)
        _, topk_idx = torch.topk(scores_f, k=k, dim=-1)  # (B,H,T,k)

        # Build mask_on: 1 at top-k positions, 0 elsewhere
        mask_on = torch.zeros_like(scores_f, dtype=scores_f.dtype)
        mask_on.scatter_(-1, topk_idx, 1.0)

        # Ternary attention: sign(scores) * mask_on, giving {-1, 0, +1}.
        # STE: forward ternary, backward identity through the float scores.
        sign_scores = torch.where(scores_f >= 0, torch.ones_like(scores_f), -torch.ones_like(scores_f))
        A_ternary = sign_scores * mask_on  # {-1, 0, +1}
        # Also zero out attention on causally-masked positions explicitly.
        A_ternary = A_ternary.masked_fill(mask, 0.0)
        # STE pass-through
        A = scores_f + (A_ternary - scores_f).detach()

        O = torch.matmul(A, V)
        O = O.transpose(1, 2).contiguous().view(B, T, D)
        return self.o_proj(O)


class BitBlockV11(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, topk=8):
        super().__init__()
        self.attn = TopKBinaryAttention(d_model, n_heads, topk=topk)
        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 BitLMv11(nn.Module):
    def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8, d_ff=512, max_seq_len=256, topk=8):
        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([
            BitBlockV11(d_model, n_heads, d_ff, topk=topk) 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__':
    m = BitLMv11(topk=8)
    n = sum(p.numel() for p in m.parameters())
    print(f"v11 params: {n:,} ({n/1e6:.2f}M)")
    x = torch.randint(0, 128, (2, 64))
    y = torch.randint(0, 128, (2, 64))
    logits, loss = m(x, y)
    print("logits:", logits.shape, "loss:", loss.item())
    loss.backward()
    print("backward OK")