File size: 3,546 Bytes
4754707 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | """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 # tie
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)")
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