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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Baseline main-task eval: MATH (EM, greedy) and PPL (LM perplexity).
Outputs:
runs/<DATE>/baseline_eval/main/{math,ppl}/metrics.json
runs/<DATE>/baseline_eval/main/{math,ppl}/preds.jsonl
"""
import argparse, json, os, math, re, time, pathlib, statistics
from typing import List, Dict
import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer
def read_jsonl(path: str) -> List[Dict]:
with open(path, "r", encoding="utf-8") as f:
return [json.loads(line) for line in f if line.strip()]
def write_json(path: str, obj):
pathlib.Path(path).parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
json.dump(obj, f, indent=2, ensure_ascii=False)
def write_jsonl(path: str, rows: List[Dict]):
pathlib.Path(path).parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
for r in rows:
f.write(json.dumps(r, ensure_ascii=False) + "\n")
def now_ts() -> str:
import time
return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
# ---------- PPL ----------
@torch.no_grad()
def sequence_nll(model, tok, text: str, device: torch.device) -> float:
enc = tok(text, return_tensors="pt")
input_ids = enc.input_ids.to(device)
attn_mask = enc.attention_mask.to(device)
out = model(input_ids=input_ids, attention_mask=attn_mask)
logits = out.logits # [1, T, V]
logprobs = F.log_softmax(logits[:, :-1, :], dim=-1)
tgt = input_ids[:, 1:]
nll = -(logprobs.gather(-1, tgt.unsqueeze(-1)).squeeze(-1)).sum().item()
return float(nll), int(tgt.numel())
def eval_ppl(model, tok, rows: List[Dict], device, outdir: str):
nll_sum = 0.0
tok_count = 0
preds = []
for r in rows:
nll, n = sequence_nll(model, tok, r["text"], device)
nll_sum += nll; tok_count += n
preds.append({**r, "nll": nll, "tokens": n})
ppl = math.exp(nll_sum / max(1, tok_count))
write_json(os.path.join(outdir, "ppl", "metrics.json"), {
"timestamp": now_ts(), "count": len(rows), "tokens": tok_count, "ppl": ppl
})
write_jsonl(os.path.join(outdir, "ppl", "preds.jsonl"), preds)
# ---------- MATH ----------
def canon_num(s: str) -> str:
# Extract the last integer/decimal (simple heuristic for our 5 examples)
# Remove commas/whitespace; keep leading minus; allow ^digits not needed here
s = s.strip()
# pick last number-like pattern
nums = re.findall(r"-?\d+(?:\.\d+)?", s.replace(",", ""))
return nums[-1] if nums else s.strip().lower()
@torch.no_grad()
def greedy_generate(model, tok, prompt: str, device, max_new_tokens: int) -> str:
enc = tok(prompt, return_tensors="pt").to(device)
out = model.generate(
**enc,
do_sample=False, temperature=0.0, top_p=1.0,
max_new_tokens=max_new_tokens,
eos_token_id=tok.eos_token_id
)
text = tok.decode(out[0], skip_special_tokens=True)
# return only the newly generated tail (after prompt)
prompt_text = tok.decode(enc.input_ids[0], skip_special_tokens=True)
if text.startswith(prompt_text):
return text[len(prompt_text):].strip()
return text.strip()
def eval_math(model, tok, rows: List[Dict], device, outdir: str, max_new_tokens: int):
correct = 0
preds = []
for r in rows:
q = r["question"]; gold = r["gold"]
gen = greedy_generate(model, tok, q, device, max_new_tokens=max_new_tokens)
pred = canon_num(gen); gold_c = canon_num(gold)
is_ok = int(pred == gold_c)
correct += is_ok
preds.append({**r, "gen": gen, "pred": pred, "gold_canon": gold_c, "correct": is_ok})
acc = correct / max(1,len(rows))
sd = math.sqrt(acc*(1-acc)/max(1,len(rows)))
ci = 1.96 * sd
write_json(os.path.join(outdir, "math", "metrics.json"), {
"timestamp": now_ts(), "count": len(rows),
"acc": acc, "acc_ci95": ci
})
write_jsonl(os.path.join(outdir, "math", "preds.jsonl"), preds)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model", type=str, required=True)
ap.add_argument("--math", type=str, required=True)
ap.add_argument("--ppl", type=str, required=True)
ap.add_argument("--out", type=str, required=True)
ap.add_argument("--dtype", type=str, default="bfloat16", choices=["float16","bfloat16","float32"])
ap.add_argument("--max_new_tokens_math", type=int, default=512)
args = ap.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = {"float16":torch.float16, "bfloat16":torch.bfloat16, "float32":torch.float32}[args.dtype]
tok = AutoTokenizer.from_pretrained(args.model, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
args.model,
torch_dtype=dtype if device.type=="cuda" else torch.float32,
device_map=None
).to(device)
model.eval()
# MATH
math_rows = read_jsonl(args.math)
eval_math(model, tok, math_rows, device, args.out, max_new_tokens=args.max_new_tokens_math)
# PPL
ppl_rows = read_jsonl(args.ppl)
eval_ppl(model, tok, ppl_rows, device, args.out)
print("[DONE] Main baseline written to", args.out)
if __name__ == "__main__":
main()
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