Spaces:
Running
Running
File size: 11,339 Bytes
224d30c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | """
eval_set entry point for L2-Bench evaluation.
Usage:
uv run python -m l2_bench_eval.eval \
--model bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0 \
--log-dir logs/run-001
# Smoke test with 2 samples
uv run python -m l2_bench_eval.eval \
--model bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0 \
--log-dir logs/smoke-test \
--sample-limit 2
# Run with custom solver config and scorer
uv run python -m l2_bench_eval.eval \
--model bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0 \
--log-dir logs/run-001 \
--epochs 2 --sample-limit 10 \
--solver-max-tokens 8192 --solver-temperature 0.5 \
--scorer-model bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0 \
--csv-path path/to/data.csv --resources-dir path/to/resources
Version | Date | Author | Change comment
--------|------------|-----------|---------------
1.0.0 | 2026-07-29 | M. Ku | Initial open-source release
"""
import argparse
from pathlib import Path
from dotenv import load_dotenv
from inspect_ai import eval_set
from inspect_ai.model import GenerateConfig, get_model
from pydantic import BaseModel
from l2_bench_eval import config
from l2_bench_eval.bedrock_patch import patch_bedrock_timeout
from l2_bench_eval.score import ScorerSetting
from l2_bench_eval.task import create_l2_bench_eval_task
class EvalRunParams(BaseModel):
"""Parameters for a single evaluation run.
Attributes
----------
solver_model_name : str
Model identifier passed to ``get_model`` (e.g. ``bedrock/...``).
solver_model_base_url : str or None
Optional base URL override for the solver model API.
solver_model_config : GenerateConfig
Generation configuration for the solver model.
log_dir : str
Directory where eval logs are written.
epochs : int
Number of evaluation epochs.
retry_on_error : int or None
Number of retries on transient errors (``None`` disables retries).
continue_on_fail : bool
If ``True``, keep running remaining samples after a failure.
scorer_setting : ScorerSetting or None
Optional scorer model and generation configuration.
csv_path : Path or None
Path to the tasks CSV file. Uses the repo default when ``None``.
resources_dir : Path or None
Path to the task resources directory. Uses the repo default when ``None``.
first_n_samples : int or None
Limit evaluation to the first *n* samples.
sample_range : tuple of (int, int) or None
Slice range ``(start, end)`` applied to the dataset. Overrides
``first_n_samples`` when set.
"""
solver_model_name: str
solver_model_base_url: str | None = None
solver_model_config: GenerateConfig = GenerateConfig(max_tokens=4096, temperature=0.0)
log_dir: str
epochs: int = 1
retry_on_error: int | None = 1
continue_on_fail: bool = True
scorer_setting: ScorerSetting | None = None
csv_path: Path | None = None
resources_dir: Path | None = None
first_n_samples: int | None = None
sample_range: tuple[int, int] | None = None # will override first_n_samples
task_ids: list[int] | None = None # will override sample_range
def run_eval(params: EvalRunParams):
"""Execute an L2-Bench evaluation run.
Parameters
----------
params : EvalRunParams
Fully-populated run parameters including model, scorer, and dataset
settings.
"""
patch_bedrock_timeout(read_timeout=600)
solver_model = get_model(
model=params.solver_model_name,
base_url=params.solver_model_base_url,
config=params.solver_model_config,
)
task = create_l2_bench_eval_task(
scorer_setting=params.scorer_setting,
csv_path=params.csv_path,
resources_dir=params.resources_dir,
first_n_samples=params.first_n_samples,
sample_range=params.sample_range,
task_ids=params.task_ids
)
eval_set(
tasks=[task],
model=solver_model,
log_dir=params.log_dir,
epochs=params.epochs,
retry_on_error=params.retry_on_error,
continue_on_fail=params.continue_on_fail
)
def main():
"""CLI entry point for L2-Bench evaluation."""
parser = argparse.ArgumentParser(description="Run L2-Bench eval_set")
parser.add_argument("--model", required=True, help="Solver model name")
parser.add_argument("--log-dir", required=True, help="Log directory")
parser.add_argument("--epochs", type=int, default=1)
parser.add_argument("--sample-limit", type=int, default=0, help="0 = no limit")
parser.add_argument("--env-file", type=Path, default=Path.cwd() / ".env", help="Path to .env file")
parser.add_argument("--solver-max-tokens", type=int, default=4096, help="Solver max output tokens")
parser.add_argument("--solver-temperature", type=float, default=0.0, help="Solver sampling temperature")
parser.add_argument("--solver-top-p", type=float, default=None, help="Solver top-p (nucleus sampling)")
parser.add_argument("--solver-top-k", type=int, default=None, help="Solver top-k sampling")
parser.add_argument("--solver-frequency-penalty", type=float, default=None, help="Solver frequency penalty")
parser.add_argument("--solver-presence-penalty", type=float, default=None, help="Solver presence penalty")
parser.add_argument("--solver-seed", type=int, default=None, help="Solver random seed")
parser.add_argument("--solver-stop-seqs", nargs="*", default=None, help="Solver stop sequences")
parser.add_argument("--solver-num-choices", type=int, default=None, help="Solver number of choices")
parser.add_argument("--solver-best-of", type=int, default=None, help="Solver best-of sampling count")
parser.add_argument("--solver-max-retries", type=int, default=None, help="Solver max retries")
parser.add_argument("--solver-timeout", type=int, default=None, help="Solver timeout in seconds")
parser.add_argument("--solver-max-connections", type=int, default=None, help="Solver max connections")
parser.add_argument("--solver-reasoning-tokens", type=int, default=None, help="Solver reasoning/thinking token budget")
parser.add_argument("--solver-reasoning-effort", choices=["none", "minimal", "low", "medium", "high", "xhigh"], default=None, help="Solver reasoning effort level")
parser.add_argument("--scorer-model", default=config.DEFAULT_JUDGE_MODEL, help="Judge model name")
parser.add_argument("--scorer-max-tokens", type=int, default=None, help="Scorer max output tokens")
parser.add_argument("--scorer-temperature", type=float, default=None, help="Judge sampling temperature. Leave unset when a reasoning budget is in use")
parser.add_argument("--scorer-top-p", type=float, default=None, help="Scorer top-p (nucleus sampling)")
parser.add_argument("--scorer-top-k", type=int, default=None, help="Scorer top-k sampling")
parser.add_argument("--scorer-frequency-penalty", type=float, default=None, help="Scorer frequency penalty")
parser.add_argument("--scorer-presence-penalty", type=float, default=None, help="Scorer presence penalty")
parser.add_argument("--scorer-seed", type=int, default=None, help="Scorer random seed")
parser.add_argument("--scorer-stop-seqs", nargs="*", default=None, help="Scorer stop sequences")
parser.add_argument("--scorer-num-choices", type=int, default=None, help="Scorer number of choices")
parser.add_argument("--scorer-best-of", type=int, default=None, help="Scorer best-of sampling count")
parser.add_argument("--scorer-max-retries", type=int, default=None, help="Scorer max API retries")
parser.add_argument("--scorer-timeout", type=int, default=None, help="Scorer timeout in seconds")
parser.add_argument("--scorer-max-connections", type=int, default=None, help="Scorer max connections")
parser.add_argument("--scorer-reasoning-tokens", type=int, default=config.DEFAULT_JUDGE_REASONING_TOKENS, help="Judge reasoning/thinking token budget")
parser.add_argument("--scorer-reasoning-effort", choices=["none", "minimal", "low", "medium", "high", "xhigh"], default=None, help="Scorer reasoning effort level")
parser.add_argument(
"--task-ids", nargs="*", type=int, default=None,
help="List of task IDs to evaluate",
)
parser.add_argument("--continue-on-fail", action=argparse.BooleanOptionalAction, default=True)
parser.add_argument("--csv-path", type=Path, default=None)
parser.add_argument("--resources-dir", type=Path, default=None)
parser.add_argument(
"--prompt-version", default=config.DEFAULT_JUDGE_PROMPT_VERSION,
help="Judge prompt version. v1 is the production judge; v2-v4 are the paper's ablations",
)
parser.add_argument(
"--judge-verdict-retries", type=int, default=config.DEFAULT_JUDGE_MAX_RETRIES,
help="Times to re-prompt the judge when it returns an unparseable verdict",
)
args = parser.parse_args()
load_dotenv(args.env_file)
params = EvalRunParams(
solver_model_name=args.model,
solver_model_config=GenerateConfig(
max_tokens=args.solver_max_tokens,
temperature=args.solver_temperature,
top_p=args.solver_top_p,
top_k=args.solver_top_k,
frequency_penalty=args.solver_frequency_penalty,
presence_penalty=args.solver_presence_penalty,
seed=args.solver_seed,
stop_seqs=args.solver_stop_seqs,
num_choices=args.solver_num_choices,
best_of=args.solver_best_of,
max_retries=args.solver_max_retries,
timeout=args.solver_timeout,
max_connections=args.solver_max_connections,
reasoning_tokens=args.solver_reasoning_tokens,
reasoning_effort=args.solver_reasoning_effort,
),
log_dir=args.log_dir,
epochs=args.epochs,
continue_on_fail=args.continue_on_fail,
scorer_setting=ScorerSetting(
model=args.scorer_model,
max_retries=args.judge_verdict_retries,
scorer_model_config=GenerateConfig(
max_tokens=args.scorer_max_tokens,
temperature=args.scorer_temperature,
top_p=args.scorer_top_p,
top_k=args.scorer_top_k,
frequency_penalty=args.scorer_frequency_penalty,
presence_penalty=args.scorer_presence_penalty,
seed=args.scorer_seed,
stop_seqs=args.scorer_stop_seqs,
num_choices=args.scorer_num_choices,
best_of=args.scorer_best_of,
max_retries=args.scorer_max_retries,
timeout=args.scorer_timeout,
max_connections=args.scorer_max_connections,
reasoning_tokens=args.scorer_reasoning_tokens,
reasoning_effort=args.scorer_reasoning_effort,
),
prompt_version=args.prompt_version,
),
csv_path=args.csv_path,
resources_dir=args.resources_dir,
first_n_samples=args.sample_limit if args.sample_limit > 0 else None,
task_ids=args.task_ids,
)
run_eval(params)
if __name__ == "__main__":
main()
|