| """v37: Pinch-waist architecture — per-layer d_ff redistribution. |
| |
| Layer ablation on v29 showed: |
| L0 (+1.42 BPC when ablated), L9 (+0.41), L1 (+0.55), L8 (+0.30) |
| vs. middle layers at +0.10-0.17. |
| Hypothesis: allocate d_ff proportionally to ablation importance. Keep d_model |
| uniform (residual stream requires it), let only d_ff vary per layer. |
| |
| Total FFN params preserved: baseline 8 * (3*d*d_ff_uniform) = 8 * 393K = 3.14M. |
| Pinch-waist: wider d_ff at boundary layers, narrower in the middle. |
| """ |
| 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 |
|
|
|
|
| class BitFFNVar(nn.Module): |
| """BitFFN with configurable d_ff per instance.""" |
| def __init__(self, d_model, d_ff): |
| super().__init__() |
| self.gate = BitLinear(d_model, d_ff, binarize_input=True) |
| self.up = BitLinear(d_model, d_ff, binarize_input=True) |
| self.down = BitLinear(d_ff, d_model, binarize_input=True) |
|
|
| def forward(self, x): |
| return self.down(self.gate(x) * self.up(x)) |
|
|
|
|
| class BitBlockV37(nn.Module): |
| def __init__(self, d_model, n_heads, d_ff): |
| super().__init__() |
| self.attn = IntBinaryAttention(d_model, n_heads) |
| self.ffn = BitFFNVar(d_model, d_ff) |
|
|
| def forward(self, x): |
| a = self.attn(x) |
| f = self.ffn(x) |
| return sign_ste(x + a + f) |
|
|
|
|
| class BitLMv37(nn.Module): |
| """Variable d_ff per layer. `d_ffs` is a list of length n_layers.""" |
| def __init__(self, vocab_size=128, d_model=256, d_ffs=None, n_heads=8, |
| max_seq_len=256): |
| super().__init__() |
| self.vocab_size = vocab_size |
| self.d_model = d_model |
| self.n_layers = len(d_ffs) |
| self.max_seq_len = max_seq_len |
| self.d_ffs = d_ffs |
| self.embed = BinaryEmbedding(vocab_size, d_model) |
| self.blocks = nn.ModuleList([ |
| BitBlockV37(d_model, n_heads, d_ff) for d_ff in d_ffs |
| ]) |
| 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__': |
| from model_v16 import set_gumbel_tau |
| set_gumbel_tau(0.5) |
| |
| |
| d_ffs = [1024, 512, 256, 256, 256, 256, 512, 1024] |
| m = BitLMv37(vocab_size=128, d_model=256, d_ffs=d_ffs, n_heads=8, max_seq_len=256) |
| n = sum(p.numel() for p in m.parameters()) |
| print(f'v37 pinch-waist: {n:,} params ({n/1e6:.2f}M), d_ffs={d_ffs}') |
| 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') |
|
|
