vm_backup/code/model_v16.py

"""v16: Gumbel hard-attention. Each query attends to exactly ONE key, selected via
Gumbel-softmax with temperature annealing from soft → hard.

Why this might work where v11 top-k failed: v11's STE through top-k gave gradient
that pushed scores up/down but the discrete selection didn't move easily. Gumbel
softmax gives a proper continuous-to-discrete bridge. At high temperature, attn
is like softmax (multiple positions active). At low temperature, attn is
one-hot (single position). Training anneals high → low.

At eval: pure argmax. Each query attends to exactly one position (attention as
pointer). This is ternary {-1, 0, +1} in the attention matrix: one +1 per row,
rest 0s, with optional sign flip carried via separate bit.
"""
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


# Module-level temperature as a mutable CUDA tensor so torch.compile doesn't
# retrace every step when we anneal tau.
_GUMBEL_TAU = torch.tensor([1.0])


def set_gumbel_tau(tau: float):
    """Mutate the tau tensor in place — keeps the same object identity so
    torch.compile doesn't see a new constant."""
    global _GUMBEL_TAU
    _GUMBEL_TAU.fill_(float(tau))


def _get_tau(device):
    """Return the current tau as a device-resident tensor."""
    global _GUMBEL_TAU
    if _GUMBEL_TAU.device != device:
        _GUMBEL_TAU = _GUMBEL_TAU.to(device)
    return _GUMBEL_TAU.clamp(min=0.05)


def gumbel_hard_attention(scores, mask=None):
    """scores: (B, H, T, T). mask: bool (T, T) with True for positions to zero out.
    Returns (B, H, T, T) attention matrix with one non-zero entry per row at train
    time (straight-through hard), and pure argmax at eval."""
    tau = _get_tau(scores.device)
    if mask is not None:
        scores = scores.masked_fill(mask, -1e9)
    if scores.requires_grad:
        # Gumbel-softmax sample, then straight-through hardify.
        g = -torch.log(-torch.log(torch.rand_like(scores).clamp(min=1e-9)) + 1e-9)
        y_soft = F.softmax((scores + g) / tau, dim=-1)
        y_hard = torch.zeros_like(y_soft)
        y_hard.scatter_(-1, y_soft.argmax(-1, keepdim=True), 1.0)
        return y_soft + (y_hard - y_soft).detach()
    else:
        # Eval: pure argmax
        y_hard = torch.zeros_like(scores)
        y_hard.scatter_(-1, scores.argmax(-1, keepdim=True), 1.0)
        return y_hard


class GumbelHardAttention(nn.Module):
    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 = 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)
        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)) / 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 = scores - alibi_bias

        mask = self._get_mask(T, x.device)
        A = gumbel_hard_attention(scores, mask=mask)  # 1-hot per row
        # A is float (soft at train, hard at eval). Multiply by sign of V to mimic
        # value aggregation; for pure strict ±1 we'd also sign V before, but V is
        # already ±1 by construction.
        O = torch.matmul(A, V)
        O = O.transpose(1, 2).contiguous().view(B, T, D)
        return self.o_proj(O)


class BitBlockV16(nn.Module):
    def __init__(self, d_model, n_heads, d_ff):
        super().__init__()
        self.attn = GumbelHardAttention(d_model, n_heads)
        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 BitLMv16(nn.Module):
    def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8, d_ff=512, 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([BitBlockV16(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

    @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__':
    set_gumbel_tau(1.0)
    m = BitLMv16()
    n = sum(p.numel() for p in m.parameters())
    print(f"v16 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")