| """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() |
|
|
|
|
| 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') |
|
|
