1 files changed, 95 insertions, 0 deletions
diff --git a/ep_run/casc_gen.py b/ep_run/casc_gen.py
new file mode 100644
index 0000000..e535df8
--- /dev/null
+++ b/ep_run/casc_gen.py
@@ -0,0 +1,95 @@
+"""Generate TinyStories samples from a cascade (OLMo2-arch) checkpoint — plain forward, standard LLM inference."""
+import argparse, pickle, sys
+import torch, torch.nn as nn, torch.nn.functional as F
+from pathlib import Path
+
+ap = argparse.ArgumentParser()
+ap.add_argument('--ckpt', required=True)
+ap.add_argument('--n', type=int, default=3)
+ap.add_argument('--len', type=int, default=180)
+ap.add_argument('--temp', type=float, default=0.8)
+ap.add_argument('--topk', type=int, default=40)
+ap.add_argument('--prompt', default='Once upon a time')
+args = ap.parse_args()
+
+DD = Path('/home/yurenh2/ept/ep_run/data/tinystories_bpe')
+vocab = pickle.load(open(DD / 'meta.pkl', 'rb'))['vocab_size']
+from tokenizers import Tokenizer
+tk = Tokenizer.from_file(str(DD / 'tokenizer.json'))
+
+class RMSNorm(nn.Module):
+    def __init__(self, C, eps=1e-6):
+        super().__init__(); self.g = nn.Parameter(torch.ones(C)); self.eps = eps
+    def forward(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.g
+
+class SwiGLU(nn.Module):
+    def __init__(self, C):
+        super().__init__()
+        h = ((8 * C // 3) + 63) // 64 * 64
+        self.w1 = nn.Linear(C, h, bias=False); self.w3 = nn.Linear(C, h, bias=False)
+        self.w2 = nn.Linear(h, C, bias=False)
+    def forward(self, x): return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+class Olmo2Attn(nn.Module):
+    def __init__(self, C, H, T):
+        super().__init__()
+        self.H, self.hd = H, C // H
+        self.qkv = nn.Linear(C, 3 * C, bias=False); self.proj = nn.Linear(C, C, bias=False)
+        self.qn, self.kn = RMSNorm(C), RMSNorm(C)
+        inv = 1.0 / (500000.0 ** (torch.arange(0, self.hd, 2).float() / self.hd))
+        fr = torch.outer(torch.arange(T).float(), inv)
+        self.register_buffer('rc', fr.cos(), persistent=False)
+        self.register_buffer('rs', fr.sin(), persistent=False)
+    def rope(self, x):
+        T = x.shape[2]
+        x1, x2 = x[..., ::2], x[..., 1::2]
+        c, s = self.rc[None, None, :T], self.rs[None, None, :T]
+        return torch.stack((x1 * c - x2 * s, x1 * s + x2 * c), dim=-1).flatten(-2)
+    def forward(self, x):
+        B, T, C = x.shape
+        q, k, v = self.qkv(x).split(C, dim=2)
+        q, k = self.qn(q), self.kn(k)
+        q = self.rope(q.view(B, T, self.H, self.hd).transpose(1, 2))
+        k = self.rope(k.view(B, T, self.H, self.hd).transpose(1, 2))
+        v = v.view(B, T, self.H, self.hd).transpose(1, 2)
+        y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
+        return self.proj(y.transpose(1, 2).contiguous().view(B, T, C))
+
+class Olmo2Block(nn.Module):
+    def __init__(self, C, H, T):
+        super().__init__()
+        self.attn = Olmo2Attn(C, H, T); self.ff = SwiGLU(C)
+        self.na, self.nf = RMSNorm(C), RMSNorm(C)
+    def forward(self, z):
+        z = z + self.na(self.attn(z))
+        return z + self.nf(self.ff(z))
+
+dev = 'cuda' if torch.cuda.is_available() else 'cpu'
+ck = torch.load(args.ckpt, map_location=dev, weights_only=False)
+cfg = ck['config']; C, H, T, L = cfg['C'], cfg['H'], cfg['T'], cfg['L']
+print(f"[gen] {args.ckpt} | step {ck.get('step')} val {ck.get('val'):.4f} | L{L} C{C}", flush=True)
+assert ck.get('wout') is not None, 'need untied head (olmo2 ckpt)'
+tok = nn.Embedding(vocab, C).to(dev); tok.load_state_dict(ck['tok'])
+blocks = nn.ModuleList([Olmo2Block(C, H, T) for _ in range(L)]).to(dev)
+blocks.load_state_dict(ck['blocks'])
+W_out = ck['wout'].to(dev)
+ln_f = RMSNorm(C).to(dev); ln_f.load_state_dict(ck['lnf'])
+for m in [tok, blocks, ln_f]: m.eval()
+
+@torch.no_grad()
+def gen_one(seed):
+    torch.manual_seed(seed)
+    ids = tk.encode(args.prompt).ids
+    for _ in range(args.len):
+        x = torch.tensor(ids[-T:], device=dev)[None]
+        z = tok(x)
+        for b in blocks: z = b(z)
+        logits = (ln_f(z[:, -1]) @ W_out.t()) / args.temp
+        v, _ = torch.topk(logits, args.topk)
+        logits[logits < v[:, -1:]] = -float('inf')
+        ids.append(int(torch.multinomial(F.softmax(logits, -1), 1)))
+    return tk.decode(ids)
+
+for i in range(args.n):
+    print(f'--- sample {i+1} ---'); print(gen_one(1234 + i), flush=True)