File size: 3,717 Bytes
213304a | 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 99 100 101 102 103 104 105 106 107 108 109 110 | #!/usr/bin/env python3
"""Custom loader for Compactbot/swordies-22m.
Swordies-22M is a from-scratch BPE GPT (NOT a transformers model). This file
reconstructs the architecture from config.json and loads model.safetensors.
Usage:
from load_model import load_model
model = load_model("model.safetensors")
logits = model(token_ids) # token_ids: int64 [B, T], vocab 8192
probs = torch.softmax(logits, -1)
Tensor layout (57 tensors, F32, weight-tied):
tok.weight [8192, 448] (also the lm_head, tied)
pos.weight [512, 448]
blocks.{0..8}.ln1.w [448]
blocks.{0..8}.ln2.w [448]
blocks.{0..8}.qkv.weight [448, 1344] (fused q|k|v, no bias)
blocks.{0..8}.proj.weight [448, 448]
blocks.{0..8}.fc1.weight [448, 1408]
blocks.{0..8}.fc2.weight [1408, 448]
ln_f.w [448]
"""
import json, os
import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors import safe_open
VOCAB = 8192
D = 448
L = 9
H = 7
FFN = 1408
SEQ = 512
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):
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, Dd = x.shape
h = self.ln1(x)
qkv = self.qkv(h).view(B, T, 3, self.h, Dd // 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, Dd)
x = x + self.proj(att)
x = x + self.fc2(F.gelu(self.fc1(self.ln2(x))))
return x
class SwordiesGPT(nn.Module):
def __init__(self):
super().__init__()
self.tok = nn.Embedding(VOCAB, D)
self.pos = nn.Embedding(SEQ, D)
self.blocks = nn.ModuleList([Block(D, H) for _ in range(L)])
self.ln_f = RMSNorm(D)
def forward(self, idx, targets=None):
B, T = idx.shape
x = self.tok(idx) + self.pos(torch.arange(T, device=idx.device))
for b in self.blocks:
x = b(x)
x = self.ln_f(x)
logits = x @ self.tok.weight.t()
if targets is not None:
return F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
return logits
def load_model(path, device="cpu"):
"""Load model.safetensors into a SwordiesGPT and return it (eval mode)."""
model = SwordiesGPT().to(device)
with safe_open(path, framework="pt") as f:
state = {k: f.get_tensor(k) for k in f.keys()}
missing, unexpected = model.load_state_dict(state, strict=True)
model.eval()
n = sum(p.numel() for p in model.parameters())
assert n == 22487360, f"param mismatch: {n}"
return model
if __name__ == "__main__":
here = os.path.dirname(os.path.abspath(__file__))
m = load_model(os.path.join(here, "model.safetensors"))
x = torch.randint(0, VOCAB, (1, 64), dtype=torch.int64)
with torch.no_grad():
lg = m(x)
print("loaded OK; params =", sum(p.numel() for p in m.parameters()))
print("logits shape", tuple(lg.shape), "finite:", bool(torch.isfinite(lg).all())) |