File size: 7,574 Bytes
c5f49b9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | import argparse
import json
import os
import re
from datetime import datetime
import torch
from GPT_model import GPT, block_size
from chat import collect_banned_token_ids, generate, load_tokenizer, DEFAULT_SYSTEM_PROMPT
def parse_args():
p = argparse.ArgumentParser(description="Batch prompt evaluator for Jarvis chat checkpoints")
p.add_argument("--ckpt", default="cpu_gpt_jarvis_rebuild_l6_v2048_best.pth")
p.add_argument("--prompts-file", default=os.path.join("data", "jarvis_eval_prompts.txt"))
p.add_argument("--out-prefix", default="jarvis_eval")
p.add_argument("--num-prompts", type=int, default=120)
p.add_argument("--temperature", type=float, default=0.62)
p.add_argument("--top-k", type=int, default=32)
p.add_argument("--top-p", type=float, default=0.9)
p.add_argument("--repetition-penalty", type=float, default=1.12)
p.add_argument("--no-repeat-ngram", type=int, default=3)
p.add_argument("--max-new-tokens", type=int, default=96)
p.add_argument("--min-new-tokens", type=int, default=10)
p.add_argument("--max-context-tokens", type=int, default=block_size)
p.add_argument("--system-prompt", default=DEFAULT_SYSTEM_PROMPT)
p.add_argument("--ban-empty-tokens", action=argparse.BooleanOptionalAction, default=True)
p.add_argument("--threads", type=int, default=max(1, min(6, (os.cpu_count() or 4) - 2)))
p.add_argument("--interop-threads", type=int, default=1)
p.add_argument("--seed", type=int, default=1337)
p.add_argument("--int8", action=argparse.BooleanOptionalAction, default=False)
return p.parse_args()
def parse_prompts(path):
if not os.path.exists(path):
raise FileNotFoundError(f"Prompts file not found: {path}")
text = open(path, "r", encoding="utf-8", errors="ignore").read()
chunks = [c.strip() for c in re.split(r"\n\s*\n", text) if c.strip()]
prompts = []
for chunk in chunks:
match = re.search(r"User:\s*(.*?)(?:\nAssistant:|$)", chunk, flags=re.S)
if not match:
continue
user = re.sub(r"\s+", " ", match.group(1)).strip()
if user:
prompts.append(user)
return prompts
def likely_gibberish(text):
if not text:
return True
words = re.findall(r"[A-Za-z]{18,}", text)
weird_words = [w for w in words if len(set(w.lower())) > 12]
if len(weird_words) >= 3:
return True
alpha = sum(ch.isalpha() for ch in text)
printable = sum((31 < ord(ch) < 127) or ch in "\n\t\r" for ch in text)
if printable < max(1, int(0.9 * len(text))):
return True
if alpha < 10:
return True
return False
def repetition_score(text):
tokens = re.findall(r"\w+", text.lower())
if len(tokens) < 6:
return 0.0
trigrams = [" ".join(tokens[i : i + 3]) for i in range(len(tokens) - 2)]
if not trigrams:
return 0.0
unique = len(set(trigrams))
return 1.0 - (unique / len(trigrams))
def load_model(ckpt_path, vocab_size, use_int8):
if not os.path.exists(ckpt_path):
raise FileNotFoundError(f"Checkpoint not found: {ckpt_path}")
model = GPT(vocab_size).to("cpu")
ckpt = torch.load(ckpt_path, map_location="cpu")
ckpt_vocab = ckpt.get("vocab_size")
if ckpt_vocab is not None and int(ckpt_vocab) != vocab_size:
raise RuntimeError(
f"Checkpoint/tokenizer mismatch: ckpt vocab_size={ckpt_vocab}, tokenizer vocab_size={vocab_size}"
)
model.load_state_dict(ckpt["model"], strict=True)
model.eval()
if use_int8:
model = torch.ao.quantization.quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8
)
model.eval()
return model, ckpt
def main():
args = parse_args()
torch.manual_seed(args.seed)
torch.set_num_threads(args.threads)
torch.set_num_interop_threads(args.interop_threads)
tokenizer = load_tokenizer()
prompts = parse_prompts(args.prompts_file)
if not prompts:
raise RuntimeError("No valid prompts found.")
prompts = prompts[: max(1, args.num_prompts)]
model, ckpt = load_model(args.ckpt, len(tokenizer.vocab), args.int8)
max_ctx = max(32, min(args.max_context_tokens, block_size))
banned_token_ids = collect_banned_token_ids(tokenizer, args.ban_empty_tokens)
bootstrap = ""
if args.system_prompt.strip():
bootstrap = f"User: {args.system_prompt.strip()}\nAssistant: Understood.\n"
bootstrap_tokens = tokenizer.encode(bootstrap)
rows = []
empty_count = 0
gibberish_count = 0
repetition_scores = []
lengths = []
for i, user in enumerate(prompts, start=1):
turn_prefix = f"\nUser: {user}\nAssistant:"
prompt_tokens = (bootstrap_tokens + tokenizer.encode(turn_prefix))[-max_ctx:]
reply, _ = generate(
model=model,
tokenizer=tokenizer,
prompt_tokens=prompt_tokens,
max_new_tokens=args.max_new_tokens,
min_new_tokens=args.min_new_tokens,
temperature=args.temperature,
top_k=args.top_k,
top_p=args.top_p,
repetition_penalty=args.repetition_penalty,
no_repeat_ngram=args.no_repeat_ngram,
max_context_tokens=max_ctx,
banned_token_ids=banned_token_ids,
)
reply = reply.strip()
if not reply:
reply = "(empty)"
empty_count += 1
if likely_gibberish(reply):
gibberish_count += 1
rep = repetition_score(reply)
repetition_scores.append(rep)
lengths.append(len(reply))
rows.append(
{
"idx": i,
"user": user,
"assistant": reply,
"repetition_score": round(rep, 4),
}
)
avg_len = sum(lengths) / max(1, len(lengths))
avg_rep = sum(repetition_scores) / max(1, len(repetition_scores))
summary = {
"timestamp": datetime.now().isoformat(timespec="seconds"),
"checkpoint": args.ckpt,
"ckpt_step": ckpt.get("step"),
"ckpt_best_val": ckpt.get("best_val"),
"prompts_file": args.prompts_file,
"num_prompts": len(rows),
"empty_count": empty_count,
"empty_rate": round(empty_count / max(1, len(rows)), 4),
"likely_gibberish_count": gibberish_count,
"likely_gibberish_rate": round(gibberish_count / max(1, len(rows)), 4),
"avg_response_chars": round(avg_len, 2),
"avg_repetition_score": round(avg_rep, 4),
"decode": {
"temperature": args.temperature,
"top_k": args.top_k,
"top_p": args.top_p,
"repetition_penalty": args.repetition_penalty,
"no_repeat_ngram": args.no_repeat_ngram,
},
}
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
out_json = f"{args.out_prefix}_{ts}.json"
out_txt = f"{args.out_prefix}_{ts}.txt"
with open(out_json, "w", encoding="utf-8") as f:
json.dump({"summary": summary, "samples": rows}, f, indent=2)
with open(out_txt, "w", encoding="utf-8") as f:
f.write(json.dumps(summary, indent=2))
f.write("\n\n")
for row in rows:
f.write(f"[{row['idx']}] User: {row['user']}\n")
f.write(f"[{row['idx']}] Assistant: {row['assistant']}\n")
f.write(f"[{row['idx']}] repetition_score={row['repetition_score']}\n\n")
print(json.dumps(summary, indent=2))
print(f"Saved: {out_json}")
print(f"Saved: {out_txt}")
if __name__ == "__main__":
main()
|