vm_backup/code/model_v22.py

"""v22: Track III.C — multi-bit integer FFN accumulator.

Architecture:
    v18 FFN: x(±1) → gate,up (±1) → XNOR → down (±1) → sign → ±1
    v22 FFN: x(±1) → up_raw (integer popcount) → CLIP[-B,+B] → down (±1 weights × small int) → sign → ±1

The hidden FFN activation is a small signed integer (3 bits for B=7, 4 for B=15)
instead of 1 bit. The down-projection becomes a signed-integer adder tree:
    z_i = Σ_j (W_down[i,j] ∈ {±1}) · (y_j ∈ [−B, +B])
which is still strictly integer arithmetic — conditionally negate y_j, sum.
No float multiply anywhere. Hardware cost: adder width grows from 0 (popcount)
to log₂(d_ff · B). For d_ff=512, B=7: 13-bit INT adder tree of depth 9.

Per ParetoQ / BitNet a4.8: this is the single highest-impact change for closing
the FP32 gap under strict ±1 weights. Expected 0.20-0.35 BPC drop at equal params.

Inference path:
  - All weights still 1-bit ±1
  - Intermediate FFN activation is 3-bit signed int (B=7 fits in 4 bits incl. sign)
  - All other activations still ±1
  - No float on the hot path
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F

from model import sign_ste, sign_ste_clipped, BitLinear, BinaryEmbedding
from model_v18 import IntBinaryAttention
from model_v16 import set_gumbel_tau


class IntFFN(nn.Module):
    """Gated FFN (SwiGLU analog) with clipped-integer `up` activation.

    Forward:
      g = sign(popcount(W_gate @ x)) in ±1     (unchanged from v18)
      u_int = clip(popcount(W_up @ x) * scale, -B, +B)   # small signed integer
      h = g * u_int                             # ±B range, gated by ±1
      z = sign(popcount-with-integer(W_down @ h)) in ±1
    """
    def __init__(self, d_model, d_ff, B=7):
        super().__init__()
        self.d_model = d_model
        self.d_ff = d_ff
        self.B = B
        # Gate: standard ±1 BitLinear producing sign mask
        self.gate = BitLinear(d_model, d_ff, binarize_input=True)
        # Up: raw popcount, no final sign — we clip instead
        self.up_w = nn.Parameter(torch.randn(d_ff, d_model) * 0.02)
        self.up_shift = nn.Parameter(torch.zeros(d_ff))
        self.up_scale = nn.Parameter(torch.tensor(1.0 / math.sqrt(d_model)))
        # Down: ±1 weights, integer activation input
        self.down_w = nn.Parameter(torch.randn(d_model, d_ff) * 0.02)
        self.down_threshold = nn.Parameter(torch.zeros(d_model))

    def forward(self, x):
        # x is ±1 of shape (..., d_model)
        g = self.gate(x)  # ±1

        W_up = sign_ste(self.up_w)
        x_bin = sign_ste_clipped(x)
        up_raw = F.linear(x_bin, W_up)  # integer popcount
        up_scaled = up_raw * self.up_scale - self.up_shift
        up_clipped = torch.clamp(up_scaled, -self.B, self.B)
        up_int = up_scaled + (up_clipped - up_scaled).detach()  # STE through clip

        # XNOR-style gate: ±1 gate multiplied by signed integer gives range ±B
        h = g * up_int  # signed values in [-B, +B]

        # Down projection: ±1 weights, multi-bit input
        W_down = sign_ste(self.down_w)
        down_raw = F.linear(h, W_down)  # signed integer adder tree
        # Normalize by sqrt(d_ff * B) to keep pre-sign in ~unit scale
        scale = 1.0 / math.sqrt(self.d_ff * max(self.B, 1))
        down_final = down_raw * scale - self.down_threshold
        return sign_ste_clipped(down_final)


class BitBlockV22(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, B=7):
        super().__init__()
        self.attn = IntBinaryAttention(d_model, n_heads)
        self.ffn = IntFFN(d_model, d_ff, B=B)

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


class BitLMv22(nn.Module):
    def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8, d_ff=512,
                 max_seq_len=256, B=7):
        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.B = B
        self.embed = BinaryEmbedding(vocab_size, d_model)
        self.blocks = nn.ModuleList([
            BitBlockV22(d_model, n_heads, d_ff, B=B) 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 B in [3, 7, 15]:
        m = BitLMv22(B=B)
        n = sum(p.numel() for p in m.parameters())
        print(f'v22 B={B}: {n:,} params ({n/1e6:.2f}M)')
        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')