| """FP32 baseline of matched parameter count. Standard transformer with RMSNorm + SwiGLU + RoPE-free. |
| Used only as a reference; this is not binary.""" |
| import math |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
|
|
| class RMSNorm(nn.Module): |
| def __init__(self, d, eps=1e-6): |
| super().__init__() |
| self.weight = nn.Parameter(torch.ones(d)) |
| self.eps = eps |
|
|
| def forward(self, x): |
| n = torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) |
| return x * n * self.weight |
|
|
|
|
| class MHA(nn.Module): |
| def __init__(self, d, h): |
| super().__init__() |
| self.d = d |
| self.h = h |
| self.dh = d // h |
| self.qkv = nn.Linear(d, 3 * d, bias=False) |
| self.o = nn.Linear(d, d, bias=False) |
|
|
| def forward(self, x): |
| B, T, D = x.shape |
| qkv = self.qkv(x).reshape(B, T, 3, self.h, self.dh).permute(2, 0, 3, 1, 4) |
| q, k, v = qkv[0], qkv[1], qkv[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(y) |
|
|
|
|
| class SwiGLU(nn.Module): |
| def __init__(self, d, d_ff): |
| super().__init__() |
| self.g = nn.Linear(d, d_ff, bias=False) |
| self.u = nn.Linear(d, d_ff, bias=False) |
| self.d = nn.Linear(d_ff, d, bias=False) |
|
|
| def forward(self, x): |
| return self.d(F.silu(self.g(x)) * self.u(x)) |
|
|
|
|
| class Block(nn.Module): |
| def __init__(self, d, h, d_ff): |
| super().__init__() |
| self.n1 = RMSNorm(d) |
| self.a = MHA(d, h) |
| self.n2 = RMSNorm(d) |
| self.f = SwiGLU(d, d_ff) |
|
|
| def forward(self, x): |
| x = x + self.a(self.n1(x)) |
| x = x + self.f(self.n2(x)) |
| return x |
|
|
|
|
| class FP32LM(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.max_seq_len = max_seq_len |
| self.embed = nn.Embedding(vocab_size, d_model) |
| self.pos = nn.Embedding(max_seq_len, d_model) |
| self.blocks = nn.ModuleList([Block(d_model, n_heads, d_ff) for _ in range(n_layers)]) |
| self.norm_f = RMSNorm(d_model) |
| self.head = nn.Linear(d_model, vocab_size, bias=False) |
| self.head.weight = self.embed.weight |
|
|
| def forward(self, idx, targets=None): |
| B, T = idx.shape |
| pos = torch.arange(T, device=idx.device) |
| x = self.embed(idx) + self.pos(pos) |
| for b in self.blocks: |
| x = b(x) |
| x = self.norm_f(x) |
| logits = self.head(x) |
| 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 = FP32LM() |
| n = sum(p.numel() for p in m.parameters()) |
| print(f"fp32 params: {n:,} ({n/1e6:.2f}M)") |
|
|
