File size: 2,694 Bytes
c6c9cdf | 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 | """TinyStoriesGPT — 24.59M-param BPE GPT trained on roneneldan/TinyStories.
Architecture: weight-tied decoder-only GPT, RMSNorm, fused qkv, GELU FFN.
Not a transformers model — load with this class + safetensors.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class RMSNorm(nn.Module):
def __init__(self, d):
super().__init__()
self.w = nn.Parameter(torch.ones(d))
def forward(self, x):
return self.w * x * torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + 1e-6)
class Block(nn.Module):
def __init__(self, d, h, ffn):
super().__init__()
self.ln1 = RMSNorm(d)
self.ln2 = RMSNorm(d)
self.qkv = nn.Linear(d, 3*d, bias=False)
self.proj = nn.Linear(d, d, bias=False)
self.fc1 = nn.Linear(d, ffn, bias=False)
self.fc2 = nn.Linear(ffn, d, bias=False)
self.h, self.d = h, d
def forward(self, x):
B, T, D = x.shape
h = self.ln1(x)
qkv = self.qkv(h).view(B, T, 3, self.h, D//self.h).transpose(2,1)
q, k, v = qkv[:,0], qkv[:,1], qkv[:,2]
q, k, v = q.transpose(1,2), k.transpose(1,2), v.transpose(1,2)
att = F.scaled_dot_product_attention(q, k, v, is_causal=True)
att = att.transpose(1,2).reshape(B, T, D)
x = x + self.proj(att)
x = x + self.fc2(F.gelu(self.fc1(self.ln2(x))))
return x
class TinyStoriesGPT(nn.Module):
def __init__(self, vocab_size=8192, d=384, n_layers=12, n_heads=8, ffn=1536, seq=512):
super().__init__()
self.tok = nn.Embedding(vocab_size, d)
self.pos = nn.Embedding(seq, d)
self.blocks = nn.ModuleList([Block(d, n_heads, ffn) for _ in range(n_layers)])
self.ln_f = RMSNorm(d)
self.vocab_size = vocab_size
def forward(self, x, targets=None):
b, t = x.shape
h = self.tok(x) + self.pos(torch.arange(t, device=x.device))
for blk in self.blocks:
h = blk(h)
h = self.ln_f(h)
logits = h @ self.tok.weight.t()
if targets is not None:
return F.cross_entropy(logits.float().view(-1, self.vocab_size), targets.view(-1))
return logits
@classmethod
def from_pretrained(cls, path, device="cpu"):
import json
from safetensors.torch import load_file
cfg = json.load(open(f"{path}/config.json"))
model = cls(vocab_size=cfg["vocab_size"], d=cfg["D"], n_layers=cfg["L"],
n_heads=cfg["H"], ffn=cfg["FFN"], seq=cfg["max_position_embeddings"])
sd = load_file(f"{path}/model.safetensors")
model.load_state_dict(sd)
model = model.to(device).eval()
return model
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