Add self-contained loader + TinyStoriesGPT class

load_model.py

@@ -0,0 +1,96 @@
+"""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  # weight-tied
+    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))