| """Load Compactbot/tinystories-50m. |
| |
| Custom small GPT (not a transformers model). Weights are plain state-dict |
| tensors in model.safetensors. Tokenizer is a HuggingFace `tokenizers` BPE |
| file (tokenizer.json). |
| |
| from load_model import TinyStoriesGPT, load |
| model, tok = load() |
| ids = tok.encode("Once upon a time,") |
| ... |
| """ |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from tokenizers import Tokenizer |
|
|
| D, L, H, FFN, VOCAB, SEQ = 512, 16, 8, 2048, 8192, 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 TinyStoriesGPT(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) |
| self.lm_head = self.tok |
| 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 = self.lm_head(x) |
| loss = None |
| if targets is not None: |
| loss = F.cross_entropy(logits.view(-1, VOCAB), targets.view(-1)) |
| return logits, loss |
| def generate(self, idx, max_new, temp=0.8, top_k=40): |
| for _ in range(max_new): |
| logits, _ = self(idx) |
| logits = logits[:, -1] / temp |
| if top_k: |
| v, _ = torch.topk(logits, top_k) |
| logits[logits < v[:, [-1]]] = float("-inf") |
| nxt = torch.multinomial(F.softmax(logits, -1), 1) |
| idx = torch.cat([idx, nxt], 1) |
| return idx |
|
|
|
|
| def load(weights="model.safetensors", tokenizer="tokenizer.json", device="cuda"): |
| from safetensors.torch import load_file |
| m = TinyStoriesGPT().to(device) |
| sd = load_file(weights) |
| m.load_state_dict(sd) |
| m.eval() |
| tok = Tokenizer.from_file(tokenizer) |
| return m, tok |
|
|
|
|
| if __name__ == "__main__": |
| m, tok = load() |
| ids = tok.encode("Once upon a time,") |
| ids = torch.tensor([ids], device="cuda") |
| out = m.generate(ids, 100, temp=0.8, top_k=40) |
| print(tok.decode(out[0].tolist(), skip_special_tokens=True)) |
|
|
