| """v4 variant: softmax attention (concession #2 from proposal's degradation ladder). |
| |
| Keeps ±1 for everything except the attention score pass, where softmax-over-floats is |
| restored. Hypothesis test: is the bool-threshold attention the binding constraint? If v4 |
| dramatically outperforms v3, the answer is "yes, attention binarization was the problem." |
| """ |
| import math |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from model import ( |
| sign_ste, sign_ste_clipped, BitLinearRaw, BitLinear, |
| BitFFN, BinaryEmbedding, |
| ) |
|
|
|
|
| class SoftAttention(nn.Module): |
| """Same ±1 Q/K/V projections, but softmax (float) attention scores.""" |
| 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 = BitLinear(d_model, d_model, binarize_input=True) |
| self.k_proj = BitLinear(d_model, d_model, binarize_input=True) |
| self.v_proj = BitLinear(d_model, d_model, binarize_input=True) |
| self.o_proj = BitLinear(d_model, d_model, binarize_input=True) |
|
|
| 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) |
| y = F.scaled_dot_product_attention(Q, K, V, is_causal=True) |
| y = y.transpose(1, 2).contiguous().view(B, T, D) |
| return self.o_proj(y) |
|
|
|
|
| class BitBlockV4(nn.Module): |
| def __init__(self, d_model, n_heads, d_ff): |
| super().__init__() |
| self.attn = SoftAttention(d_model, n_heads) |
| self.ffn = BitFFN(d_model, d_ff) |
|
|
| def forward(self, x): |
| a = self.attn(x) |
| f = self.ffn(x) |
| return sign_ste(x + a + f) |
|
|
|
|
| class BitLMv4(nn.Module): |
| def __init__(self, vocab_size=128, d_model=256, n_layers=8, n_heads=8, d_ff=512, 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([ |
| BitBlockV4(d_model, n_heads, d_ff) 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__': |
| m = BitLMv4() |
| n = sum(p.numel() for p in m.parameters()) |
| print(f"v4 params: {n:,} ({n/1e6:.2f}M)") |
| x = torch.randint(0, 128, (2, 64)) |
| y = torch.randint(0, 128, (2, 64)) |
| logits, loss = m(x, y) |
| print("logits:", logits.shape, "loss:", loss.item()) |
| loss.backward() |
| print("backward OK") |
|
|
