| |
| """Minimal check that the log-likelihood scorer works at all. |
| |
| The first probe run had every model predicting the same option on P2 and |
| scoring exactly 0.0 unnormalised on an 8-way P1 -- both impossible for a real |
| model, so the scorer is suspect rather than the models. This scores pairs |
| whose answer is not in doubt and prints the raw numbers, so the failure is |
| visible instead of inferred. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import json |
| import os |
| import sys |
|
|
| sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) |
|
|
| SANITY = [ |
| ("The capital of France is", [" Paris", " Tokyo"], 0), |
| ("2 + 2 =", [" 4", " 97"], 0), |
| ("The error 'CUDA out of memory' means the GPU has", [" run out of memory", " finished training successfully"], 0), |
| ] |
|
|
|
|
| def main() -> None: |
| model = sys.argv[1] |
| from transformers import AutoTokenizer |
| from vllm import LLM, SamplingParams |
|
|
| tok = AutoTokenizer.from_pretrained(model, local_files_only=True) |
| llm = LLM(model=model, max_model_len=4096, gpu_memory_utilization=0.85, |
| dtype="bfloat16", enforce_eager=True, trust_remote_code=True) |
| sp = SamplingParams(max_tokens=1, temperature=0.0, prompt_logprobs=0) |
|
|
| print("\n================ RAW API SHAPE ================") |
| out = llm.generate(["The capital of France is Paris"], sp)[0] |
| plp = out.prompt_logprobs |
| print("type(prompt_logprobs):", type(plp), "len:", len(plp) if plp else None) |
| print("prompt_token_ids len:", len(out.prompt_token_ids)) |
| for j in range(min(4, len(plp))): |
| e = plp[j] |
| print(f" [{j}] token={out.prompt_token_ids[j]!r} entry={type(e)} keys={list(e)[:3] if isinstance(e, dict) else e}") |
| if isinstance(e, dict) and e: |
| k = list(e)[0] |
| print(f" sample value type={type(e[k])} repr={e[k]!r}") |
|
|
| print("\n================ TOKENISATION ALIGNMENT ================") |
| ctx = "The capital of France is" |
| for opt in (" Paris", " Tokyo"): |
| a = tok.encode(ctx) |
| b = tok.encode(ctx + opt) |
| print(f" ctx={len(a)} toks, ctx+opt={len(b)} toks, prefix_matches={b[:len(a)] == a}, opt_toks={len(b)-len(a)}") |
|
|
| print("\n================ SANITY PAIRS ================") |
| for ctx, opts, gold in SANITY: |
| texts = [ctx + o for o in opts] |
| res = llm.generate(texts, sp) |
| n_ctx = len(tok.encode(ctx)) |
| rows = [] |
| for r, o in zip(res, opts): |
| plp = r.prompt_logprobs or [] |
| tot, cnt = 0.0, 0 |
| for j in range(n_ctx, len(plp)): |
| e = plp[j] |
| if not e: |
| continue |
| t = r.prompt_token_ids[j] |
| lp = e.get(t) |
| if lp is None: |
| continue |
| tot += lp.logprob if hasattr(lp, "logprob") else float(lp) |
| cnt += 1 |
| rows.append((o, tot, cnt, tot / max(cnt, 1))) |
| best = max(range(len(rows)), key=lambda i: rows[i][3]) |
| print(f" {ctx!r}") |
| for i, (o, tot, cnt, norm) in enumerate(rows): |
| mark = "<-- picked" if i == best else "" |
| star = "*" if i == gold else " " |
| print(f" {star}{o!r:45s} sum={tot:9.3f} n={cnt:2d} norm={norm:8.3f} {mark}") |
| print(f" => {'OK' if best == gold else 'WRONG'}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
