"""v49: BitNet b1.58 — ternary weights {-1, 0, +1} + everything from v47. Quantization: each matrix's latent float W is quantized per-matrix via W_q = clamp(round(W / α), -1, +1) where α = mean(|W|) giving weights in {-1, 0, +1}. Commonly called "1-bit LLM" even though each weight is log2(3) ≈ 1.58 bits. This gives expressivity our strict ±1 lacks: the "skip" state. Our v43 doubled-binary {-2, 0, +2} approximated this with two ±1 weights; ternary does it natively in one weight. All else identical to v47: per-channel α scale + RMSNorm + float residual. """ import math import torch import torch.nn as nn import torch.nn.functional as F from model import sign_ste, sign_ste_clipped, BinaryEmbedding from model_v16 import gumbel_hard_attention from model_v47 import RMSNorm def ternary_ste(w): """Per-matrix ternary quantization with STE: {-1, 0, +1} × α.""" alpha = w.abs().mean().clamp(min=1e-8) w_scaled = w / alpha w_q = w_scaled.round().clamp(-1, 1) return w + (w_q - w).detach() # STE: forward = w_q, backward = identity class TernaryBitLinear(nn.Module): """Ternary weights {-1, 0, +1} with per-channel float scale + threshold.""" def __init__(self, in_features, out_features, binarize_input=True): super().__init__() self.in_features = in_features self.out_features = out_features self.binarize_input = binarize_input self.weight = nn.Parameter(torch.randn(out_features, in_features) * 0.02) self.alpha = nn.Parameter(torch.full((out_features,), 1.0 / math.sqrt(in_features))) self.threshold = nn.Parameter(torch.zeros(out_features)) def forward(self, x): W = ternary_ste(self.weight) if self.binarize_input: x = sign_ste_clipped(x) s = F.linear(x, W) * self.alpha - self.threshold return sign_ste_clipped(s) class TernaryBitLinearRaw(nn.Module): """Ternary weights; returns the pre-sign float score (for residual sums).""" def __init__(self, in_features, out_features, binarize_input=True): super().__init__() self.in_features = in_features self.out_features = out_features self.binarize_input = binarize_input self.weight = nn.Parameter(torch.randn(out_features, in_features) * 0.02) self.alpha = nn.Parameter(torch.full((out_features,), 1.0 / math.sqrt(in_features))) self.bias = nn.Parameter(torch.zeros(out_features)) def forward(self, x): W = ternary_ste(self.weight) if self.binarize_input: x = sign_ste_clipped(x) return F.linear(x, W) * self.alpha + self.bias class TernaryFFN(nn.Module): def __init__(self, d_model, d_ff): super().__init__() self.gate = TernaryBitLinear(d_model, d_ff, binarize_input=True) self.up = TernaryBitLinear(d_model, d_ff, binarize_input=True) self.down = TernaryBitLinearRaw(d_ff, d_model, binarize_input=True) def forward(self, x): return self.down(self.gate(x) * self.up(x)) class TernaryAttention(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 = TernaryBitLinear(d_model, d_model) self.k_proj = TernaryBitLinear(d_model, d_model) self.v_proj = TernaryBitLinear(d_model, d_model) self.o_proj = TernaryBitLinearRaw(d_model, d_model) slopes = torch.tensor([1 << i for i in range(n_heads)], dtype=torch.long) self.register_buffer('alibi_slopes_int', slopes) 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)) pos = torch.arange(T, device=x.device) dist = (pos.unsqueeze(0) - pos.unsqueeze(1)).abs() alibi = self.alibi_slopes_int.view(1, H, 1, 1).to(scores.dtype) \ * dist.view(1, 1, T, T).to(scores.dtype) scores = scores - alibi mask = torch.triu(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1) A = gumbel_hard_attention(scores, mask=mask) O = torch.matmul(A, V) O = O.transpose(1, 2).contiguous().view(B, T, D) return self.o_proj(O) class BitBlockV49(nn.Module): def __init__(self, d_model, n_heads, d_ff): super().__init__() self.norm1 = RMSNorm(d_model) self.attn = TernaryAttention(d_model, n_heads) self.norm2 = RMSNorm(d_model) self.ffn = TernaryFFN(d_model, d_ff) def forward(self, x): x = x + self.attn(self.norm1(x)) x = x + self.ffn(self.norm2(x)) return x class BitLMv49(nn.Module): def __init__(self, vocab_size=128, d_model=512, n_layers=4, n_heads=8, d_ff=192, 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([ BitBlockV49(d_model, n_heads, d_ff) for _ in range(n_layers) ]) self.norm_out = RMSNorm(d_model) 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) x = self.norm_out(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 if __name__ == '__main__': from model_v16 import set_gumbel_tau set_gumbel_tau(0.5) m = BitLMv49(d_model=512, n_layers=4, d_ff=192) n = sum(p.numel() for p in m.parameters()) print(f'total: {n:,} ({n/1e6:.3f}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')