File size: 2,915 Bytes
158258b | 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 | #!/bin/bash
# Reproduce upstream's published baseline scores, to check this runner's evaluation path against
# the official one before spending GPU-days on agent runs.
#
# Targets come from third_party/PostTrainBench/scripts/baselines.json ("zeroshot"). The cells below
# are chosen to exercise every benchmark's own code path with a non-zero, discriminating target:
# gsm8k/humaneval use the 3000-token generation budget, gpqamain/aime2025 the 12000-token one, and
# bfcl the tool-calling harness.
#
# bash scripts/ptb_validate_baselines.sh # submit
# bash scripts/ptb_validate_baselines.sh --check # compare finished runs against baselines.json
set -euo pipefail
cd /home/jiaruiliu/local/git-repos/auto-impl
OUT_ROOT="${OUT_ROOT:-/home/jiaruiliu/ptb-data/baseline_validation}"
# task model expected
CELLS=(
"gsm8k Qwen/Qwen3-1.7B-Base 0.1266"
"gsm8k Qwen/Qwen3-4B-Base 0.4185"
"humaneval Qwen/Qwen3-4B-Base 0.3659"
"gpqamain Qwen/Qwen3-1.7B-Base 0.1406"
"aime2025 Qwen/Qwen3-4B-Base 0.0333"
"bfcl google/gemma-3-4b-pt 0.0600"
)
if [[ "${1:-}" == "--check" ]]; then
# Judge by |delta| against the run's own stderr, not a fixed tolerance: inspect_ai samples with
# the model's HF generation config rather than greedily, so repeating an eval moves the score.
# On aime2025 (30 questions) a single problem is already 0.033, which is why a fixed band
# flagged runs that are in fact indistinguishable from upstream's.
printf "%-10s %-30s %8s %8s %9s %9s %7s %s\n" TASK MODEL EXPECTED ACTUAL STDERR DELTA "|d|/se" VERDICT
for cell in "${CELLS[@]}"; do
read -r task model expected <<< "$cell"
out="$OUT_ROOT/${task}_$(echo "$model" | tr '/' '_')"
python3 -c "
import json, math, os
p = '$out/metrics.json'
if not os.path.exists(p):
print(f'{\"$task\":<10} {\"$model\":<30} {$expected:8.4f} {\"-\":>8} {\"-\":>9} {\"-\":>9} {\"-\":>7} pending')
else:
d = json.load(open(p)); a = d.get('accuracy', d.get('score')); se = d.get('stderr')
if se:
z = abs(a - $expected) / se
v = 'consistent' if z < 2 else ('suspect' if z < 3 else 'DIVERGENT')
print(f'{\"$task\":<10} {\"$model\":<30} {$expected:8.4f} {a:8.4f} {se:9.4f} {a-$expected:+9.4f} {z:7.2f} {v}')
else:
print(f'{\"$task\":<10} {\"$model\":<30} {$expected:8.4f} {a:8.4f} {\"n/a\":>9} {a-$expected:+9.4f} {\"n/a\":>7} (no stderr)')
"
done
exit 0
fi
for cell in "${CELLS[@]}"; do
read -r task model expected <<< "$cell"
out="$OUT_ROOT/${task}_$(echo "$model" | tr '/' '_')"
echo "submitting $task $model (expect $expected)"
sbatch --job-name="ptbval-${task}" \
${DEPENDENCY:+--dependency="$DEPENDENCY"} \
--export="ALL,TASK=${task},MODEL=${model},LIMIT=-1,OUT=${out}" \
scripts/ptb_eval.sbatch
done
|