Delete sample.py

sample.py

@@ -1,85 +0,0 @@
-"""
-Sample from a trained model (London GPT)
-"""
-import os
-import pickle
-import torch
-from contextlib import nullcontext
-from model import GPTConfig, GPT
-from tokenizers import ByteLevelBPETokenizer
-
-# ─── tokenizer setup ────────────────────────────────────────────────
-tok_folder = "tokenizer_london"
-vocab_path = os.path.join(tok_folder, "vocab.json")
-merges_path = os.path.join(tok_folder, "merges.txt")
-if not (os.path.isfile(vocab_path) and os.path.isfile(merges_path)):
-    raise FileNotFoundError(f"Cannot find tokenizer files in {tok_folder}: {vocab_path}, {merges_path}")
-
-tokenizer = ByteLevelBPETokenizer(vocab_path, merges_path)
-encode = lambda s: tokenizer.encode(s).ids
-decode = lambda ids: tokenizer.decode(ids)
-# ────────────────────────────────────────────────────────────────────
-
-# ─── experiment settings (you can override via CLI) ────────────────
-import sys
-import argparse
-parser = argparse.ArgumentParser()
-parser.add_argument("--out_dir",        default="out_london")
-parser.add_argument("--device",         default="cpu")
-parser.add_argument("--start",          default="\n")
-parser.add_argument("--num_samples",    type=int, default=10)
-parser.add_argument("--max_new_tokens", type=int, default=500)
-parser.add_argument("--temperature",    type=float, default=0.8)
-parser.add_argument("--top_k",          type=int, default=200)
-parser.add_argument("--seed",           type=int, default=1337)
-parser.add_argument("--compile",        action="store_true")
-args = parser.parse_args()
-
-out_dir        = args.out_dir
-start          = args.start
-num_samples    = args.num_samples
-max_new_tokens = args.max_new_tokens
-temperature    = args.temperature
-top_k          = args.top_k
-seed           = args.seed
-compile_flag   = args.compile
-device_str     = args.device
-# ────────────────────────────────────────────────────────────────────
-
-# reproducibility & device
-torch.manual_seed(seed)
-device = torch.device(device_str)
-ctx = nullcontext() if device.type == "cpu" else torch.amp.autocast(device_type=device.type, dtype=torch.float32)
-
-# ─── load model checkpoint ──────────────────────────────────────────
-ckpt_path = os.path.join(out_dir, "ckpt.pt")
-if not os.path.isfile(ckpt_path):
-    raise FileNotFoundError(f"Checkpoint not found: {ckpt_path}")
-ckpt = torch.load(ckpt_path, map_location=device)
-gptconf = GPTConfig(**ckpt["model_args"])
-model   = GPT(gptconf)
-sd      = ckpt["model"]
-# strip prefix if present
-for k in list(sd.keys()):
-    if k.startswith("_orig_mod."):
-        sd[k[len("_orig_mod."):]] = sd.pop(k)
-model.load_state_dict(sd)
-model.eval().to(device)
-if compile_flag:
-    model = torch.compile(model)
-# ────────────────────────────────────────────────────────────────────
-
-# prepare prompt tensor
-if start.startswith("FILE:"):
-    with open(start[5:], "r", encoding="utf-8") as f:
-        start = f.read()
-ids = encode(start)
-x   = torch.tensor([ids], dtype=torch.long, device=device)
-
-# ─── generation ─────────────────────────────────────────────────────
-with torch.no_grad(), ctx:
-    for i in range(num_samples):
-        y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
-        print(decode(y[0].tolist()))
-        print("---------------")
-# ────────────────────────────────────────────────────────────────────