vm_backup/code/model_v25.py

"""v25: Track VII.A — Gumbel-routed ±1 MoE FFN.

Reuses the same Gumbel-softmax hard-argmax machinery we already know trains
well for attention; applies it to expert routing. Token → router scores (one
per expert) → Gumbel one-hot selection at training → pure argmax at inference.

Each of E experts is a standard v18 BitFFN. Matches v21's total active
per-token compute when `experts = 4, d_ff_per_expert = d_ff/4` (standard MoE
"fixed active FLOPs" setup), at cost of 4× more total parameters. We instead
use matched-total-params (each expert has d_ff = d_model), which means total
params equal v21 but active per-token FLOPs drop 4×.

Routing is pure-integer at inference:
  scores = popcount(W_router ⊕ x)   # (E,) integer per token per layer
  expert = argmax(scores)            # integer compare tree
  y = experts[expert](x)

All weights ±1. All activations ±1. Only train-time float: Gumbel-softmax's
softmax (same concession v18 already pays for attention).
"""
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
from model_v18 import IntBinaryAttention
from model_v16 import set_gumbel_tau, _get_tau


def gumbel_route(scores, mask=None):
    """Gumbel hard routing; soft-to-hard STE at train, argmax at eval."""
    tau = _get_tau(scores.device)
    if scores.requires_grad:
        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:
        y = torch.zeros_like(scores)
        y.scatter_(-1, scores.argmax(-1, keepdim=True), 1.0)
        return y


class MoEFFN(nn.Module):
    def __init__(self, d_model, d_ff, E=4):
        super().__init__()
        self.E = E
        self.d_model = d_model
        # Router: ±1 weight mapping x → E scores
        self.router_w = nn.Parameter(torch.randn(E, d_model) * 0.02)
        # Experts: each is a standard BitFFN
        self.experts = nn.ModuleList([BitFFN(d_model, d_ff) for _ in range(E)])

    def forward(self, x):
        # x: (B, T, D) ±1
        B, T, D = x.shape
        # Route
        W_r = sign_ste(self.router_w)  # (E, D) ±1
        x_bin = sign_ste_clipped(x)
        scores = F.linear(x_bin, W_r)  # (B, T, E) integer popcount
        route = gumbel_route(scores)    # (B, T, E) soft-to-hard

        # Compute all E experts (simple implementation; real MoE would dispatch).
        # (B, T, D) each
        outs = torch.stack([exp(x) for exp in self.experts], dim=-2)  # (B, T, E, D)
        # Mix by route weights: (B, T, E, 1) * (B, T, E, D) -> sum over E
        return (route.unsqueeze(-1) * outs).sum(dim=-2)


class BitBlockV25(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, E=4):
        super().__init__()
        self.attn = IntBinaryAttention(d_model, n_heads)
        self.ffn = MoEFFN(d_model, d_ff, E=E)

    def forward(self, x):
        a = self.attn(x)
        f = self.ffn(x)
        return sign_ste(x + a + f)


class BitLMv25(nn.Module):
    def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8, d_ff=512,
                 max_seq_len=256, E=4):
        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.E = E
        self.embed = BinaryEmbedding(vocab_size, d_model)
        self.blocks = nn.ModuleList([
            BitBlockV25(d_model, n_heads, d_ff, E=E) 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(0.5)
    for E in [2, 4, 8]:
        m = BitLMv25(E=E)
        n = sum(p.numel() for p in m.parameters())
        print(f'v25 E={E}: {n:,} params ({n/1e6:.2f}M)')