566 lines
20 KiB
Python
566 lines
20 KiB
Python
#!/usr/bin/env python3
|
||
"""
|
||
Unified benchmark runner for EvalScope.
|
||
|
||
A single entry point for lite / mid / full / group1 / group2 / group3 evaluations.
|
||
All tunable parameters can be controlled via command-line arguments.
|
||
|
||
Examples:
|
||
# Full evaluation (all benchmarks, multi-run for stability)
|
||
python bash/run.py \
|
||
--model DeepSeek-V4-Flash-Int8 \
|
||
--api-url http://localhost:30000/v1 \
|
||
--dataset-dir /data1/sora/evalscope \
|
||
--output-dir /data1/sora/evalscope/output \
|
||
--suite full \
|
||
--limit none
|
||
|
||
# Lite smoke test (~5h with full samples)
|
||
python bash/run.py --suite lite --limit none
|
||
|
||
# Run only selected benchmarks
|
||
python bash/run.py --datasets aime24,gsm8k,arc --limit 20
|
||
|
||
# Custom judge model
|
||
python bash/run.py \
|
||
--judge-model deepseek-v4-pro \
|
||
--judge-api-url https://api.deepseek.com/v1 \
|
||
--judge-api-key sk-xxx
|
||
"""
|
||
|
||
import argparse
|
||
import json
|
||
import sys
|
||
from copy import deepcopy
|
||
from pathlib import Path
|
||
|
||
import yaml
|
||
|
||
from evalscope import run_task, TaskConfig
|
||
from evalscope.api.agent import NativeAgentConfig
|
||
from evalscope.config import SandboxTaskConfig
|
||
|
||
SCRIPT_DIR = Path(__file__).parent.resolve()
|
||
PROJECT_ROOT = SCRIPT_DIR.parent
|
||
|
||
# ============================================================
|
||
# Default configuration (override via CLI)
|
||
# ============================================================
|
||
|
||
DEFAULT_MODEL = 'DeepSeek-V4-Flash-Int8'
|
||
DEFAULT_API_URL = 'http://localhost:30000/v1'
|
||
DEFAULT_DATASET_DIR = str(PROJECT_ROOT)
|
||
DEFAULT_OUTPUT_DIR = str(PROJECT_ROOT / 'output')
|
||
DEFAULT_CONFIG = str(PROJECT_ROOT / 'config' / 'dpv4-int8_nothinking.yaml')
|
||
DEFAULT_TOKENIZER_PATH = '/data1/models/DeepSeek-V4-Flash-INT8'
|
||
DEFAULT_LIMIT = None
|
||
DEFAULT_SEED = 42
|
||
DEFAULT_BATCH_SIZE = 4
|
||
DEFAULT_ENABLE_THINKING = False
|
||
|
||
DEFAULT_JUDGE_MODEL = 'DeepSeek/DeepSeek-V4-Pro'
|
||
DEFAULT_JUDGE_API_URL = 'https://api.vectron.meta-stone.com/v1'
|
||
DEFAULT_JUDGE_API_KEY = 'sk-dbd8a665f7634081b87ec409c7636500'
|
||
DEFAULT_JUDGE_MAX_TOKENS = 10240
|
||
|
||
# 长文本 middle-truncation 上限（token 数）。当前默认 128k。
|
||
DEFAULT_TRUNCATION_TOKENS = 32768 * 4
|
||
|
||
# ============================================================
|
||
# Benchmark suites
|
||
# ============================================================
|
||
|
||
# 多次采样配置：总样本数控制在 ~400-500
|
||
MULTI_RUN_CONFIG = {
|
||
'aime24': 12,
|
||
'aime25': 12,
|
||
'aime26': 12,
|
||
'hmmt26': 12,
|
||
'live_code_bench': 5,
|
||
'imo_answerbench': 4,
|
||
'humaneval': 3,
|
||
'gpqa_diamond': 2,
|
||
}
|
||
|
||
# 能力域完整列表
|
||
ALL_MULTI_RUN = [
|
||
'humaneval', 'live_code_bench',
|
||
'aime24', 'aime25', 'aime26', 'hmmt26',
|
||
'imo_answerbench', 'gpqa_diamond',
|
||
]
|
||
ALL_SINGLE_RUN = [
|
||
'bigcodebench', 'bfcl_v3', 'competition_math', 'gsm8k', 'hle', 'super_gpqa',
|
||
'arc', 'bbh', 'cmmlu', 'drop', 'hellaswag', 'mmlu', 'mmlu_pro',
|
||
'simple_qa', 'trivia_qa', 'winogrande',
|
||
'openai_mrcr', 'longbench_v2',
|
||
]
|
||
ALL_AGENT = ['tau2_bench', 'general_fc']
|
||
|
||
# 分组基于 CSV 单次时间 + multi-run 后的 wall time 平衡：
|
||
# Group1: ~61h | Group2: ~62h | Group3: ~55h
|
||
SUITES = {
|
||
'full': {
|
||
'multi': ALL_MULTI_RUN,
|
||
'single': ALL_SINGLE_RUN,
|
||
'agent': ALL_AGENT,
|
||
},
|
||
'lite': {
|
||
'multi': ['aime24', 'humaneval'],
|
||
'single': ['gsm8k', 'arc', 'longbench_v2'],
|
||
'agent': ['general_fc'],
|
||
},
|
||
'mid': {
|
||
'multi': ['aime24', 'humaneval'],
|
||
'single': [
|
||
'live_code_bench', 'bigcodebench', 'competition_math', 'gsm8k',
|
||
'gpqa_diamond', 'mmlu_pro', 'simple_qa', 'longbench_v2', 'openai_mrcr',
|
||
],
|
||
'agent': ['general_fc', 'tau2_bench'],
|
||
},
|
||
# 多机组分组，基于 CSV 实测完整时间（已含 multi-run）平衡：
|
||
# Group1: ~22.7h | Group2: ~25.6h | Group3: ~27.0h | 合计 ~75.2h
|
||
'group1': {
|
||
'multi': ['live_code_bench', 'aime24', 'aime25', 'aime26', 'hmmt26', 'imo_answerbench', 'humaneval'],
|
||
'single': ['bigcodebench', 'competition_math', 'gsm8k', 'drop', 'arc', 'hellaswag', 'winogrande'],
|
||
'agent': [],
|
||
},
|
||
'group2': {
|
||
'multi': [],
|
||
'single': ['hle', 'mmlu_pro', 'trivia_qa'],
|
||
'agent': [],
|
||
},
|
||
'group3': {
|
||
'multi': ['gpqa_diamond'],
|
||
'single': ['openai_mrcr', 'longbench_v2', 'bfcl_v3', 'mmlu', 'cmmlu', 'bbh', 'simple_qa'],
|
||
'agent': ['tau2_bench', 'general_fc'],
|
||
},
|
||
}
|
||
|
||
# ============================================================
|
||
# Fixed configuration
|
||
# ============================================================
|
||
|
||
MATH_DATASETS = {
|
||
'aime24', 'aime25', 'aime26', 'hmmt26',
|
||
'gsm8k', 'competition_math', 'imo_answerbench',
|
||
}
|
||
|
||
MATH_PROMPT_TEMPLATE = (
|
||
"{question}\n"
|
||
"Please reason step by step, and put your final answer within \\boxed{{}}."
|
||
)
|
||
|
||
SANDBOX_DATASETS = {'humaneval', 'bigcodebench'}
|
||
SANDBOX_CONFIGS = {
|
||
'bigcodebench': {
|
||
'image': 'bigcodebench-sandbox:latest',
|
||
'working_dir': '/tmp',
|
||
'tools_config': {
|
||
'shell_executor': {},
|
||
'python_executor': {}
|
||
}
|
||
},
|
||
'humaneval': {
|
||
'image': 'python:3.11-slim',
|
||
'tools_config': {
|
||
'shell_executor': {},
|
||
'python_executor': {}
|
||
}
|
||
},
|
||
}
|
||
|
||
|
||
# ============================================================
|
||
# CLI parser
|
||
# ============================================================
|
||
|
||
def build_parser():
|
||
parser = argparse.ArgumentParser(
|
||
description='Unified EvalScope benchmark runner',
|
||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||
epilog='Suites: full, lite, mid, group1, group2, group3',
|
||
)
|
||
|
||
# Model / API
|
||
parser.add_argument('--model', default=DEFAULT_MODEL,
|
||
help='Served model name (default: %(default)s)')
|
||
parser.add_argument('--api-url', default=DEFAULT_API_URL,
|
||
help='OpenAI-compatible API URL (default: %(default)s)')
|
||
|
||
# Paths
|
||
parser.add_argument('--dataset-dir', default=DEFAULT_DATASET_DIR,
|
||
help='Parent directory containing datasets/ subdir (default: %(default)s)')
|
||
parser.add_argument('--output-dir', default=DEFAULT_OUTPUT_DIR,
|
||
help='Output root directory (default: %(default)s)')
|
||
parser.add_argument('--config', default=DEFAULT_CONFIG,
|
||
help='YAML config path (default: %(default)s)')
|
||
parser.add_argument('--tokenizer-path', default=DEFAULT_TOKENIZER_PATH,
|
||
help='Local tokenizer path for middle-truncation (default: %(default)s)')
|
||
|
||
# Run control
|
||
parser.add_argument('--suite', default='full', choices=list(SUITES.keys()),
|
||
help='Benchmark suite to run (default: %(default)s)')
|
||
parser.add_argument('--datasets', '--benchmarks', dest='datasets', default=None,
|
||
help='Override suite with comma-separated benchmark names, e.g. aime24,gsm8k')
|
||
parser.add_argument('--exclude', default=None,
|
||
help='Comma-separated benchmarks to exclude from the chosen suite')
|
||
parser.add_argument('--limit', default=None,
|
||
help='Max samples per benchmark; "none"/"all" for no limit (default: none)')
|
||
parser.add_argument('--seed', type=int, default=DEFAULT_SEED,
|
||
help='Random seed (default: %(default)s)')
|
||
parser.add_argument('--batch-size', type=int, default=DEFAULT_BATCH_SIZE,
|
||
help='Evaluation batch size (default: %(default)s)')
|
||
|
||
# Decoding / thinking
|
||
parser.add_argument('--thinking', action='store_true', default=None,
|
||
help='Enable thinking mode (sglang chat_template_kwargs.thinking=True)')
|
||
parser.add_argument('--no-thinking', dest='thinking', action='store_false',
|
||
help='Disable thinking mode (default)')
|
||
|
||
# Judge model
|
||
parser.add_argument('--judge-model', default=DEFAULT_JUDGE_MODEL,
|
||
help='Judge model name (default: %(default)s)')
|
||
parser.add_argument('--judge-api-url', default=DEFAULT_JUDGE_API_URL,
|
||
help='Judge model API URL (default: %(default)s)')
|
||
parser.add_argument('--judge-api-key', default=DEFAULT_JUDGE_API_KEY,
|
||
help='Judge model API key')
|
||
parser.add_argument('--judge-max-tokens', type=int, default=DEFAULT_JUDGE_MAX_TOKENS,
|
||
help='Judge model max_tokens (default: %(default)s)')
|
||
|
||
# Truncation
|
||
parser.add_argument('--truncation-tokens', type=int, default=DEFAULT_TRUNCATION_TOKENS,
|
||
help='Middle-truncation token budget for long-context benchmarks (default: %(default)s)')
|
||
|
||
return parser
|
||
|
||
|
||
# ============================================================
|
||
# Middle-truncation helpers
|
||
# ============================================================
|
||
|
||
_TOKENIZER = None
|
||
|
||
|
||
def get_tokenizer(tokenizer_path: str):
|
||
global _TOKENIZER
|
||
if _TOKENIZER is None:
|
||
from transformers import AutoTokenizer
|
||
try:
|
||
_TOKENIZER = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True)
|
||
except Exception:
|
||
_TOKENIZER = AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-V4-Flash', trust_remote_code=True)
|
||
return _TOKENIZER
|
||
|
||
|
||
def truncate_middle(text: str, max_tokens: int, tokenizer_path: str) -> str:
|
||
if max_tokens <= 0:
|
||
return text
|
||
tokenizer = get_tokenizer(tokenizer_path)
|
||
token_ids = tokenizer.encode(text, add_special_tokens=False)
|
||
if len(token_ids) <= max_tokens:
|
||
return text
|
||
keep_head = max_tokens // 2
|
||
keep_tail = max_tokens - keep_head
|
||
truncated_ids = token_ids[:keep_head] + token_ids[-keep_tail:]
|
||
return tokenizer.decode(truncated_ids, skip_special_tokens=True)
|
||
|
||
|
||
def _patch_adapters_for_truncation(tokenizer_path: str, truncation_tokens: int):
|
||
from evalscope.benchmarks.longbench_v2.longbench_v2_adapter import LongBenchV2Adapter
|
||
from evalscope.benchmarks.openai_mrcr.openai_mrcr_adapter import OpenAIMRCRAdapter
|
||
|
||
_orig_longbench_format = LongBenchV2Adapter.format_prompt_template
|
||
|
||
def _patched_longbench_format(self, sample):
|
||
if sample.metadata and 'context' in sample.metadata:
|
||
sample.metadata['context'] = truncate_middle(sample.metadata['context'], truncation_tokens, tokenizer_path)
|
||
return _orig_longbench_format(self, sample)
|
||
|
||
LongBenchV2Adapter.format_prompt_template = _patched_longbench_format
|
||
|
||
_orig_mrcr_record = OpenAIMRCRAdapter.record_to_sample
|
||
|
||
def _patched_mrcr_record(self, record):
|
||
per_msg_max_tok = 8192
|
||
if 'prompt' in record:
|
||
try:
|
||
prompt_data = json.loads(record['prompt'])
|
||
if not isinstance(prompt_data, list) or len(prompt_data) == 0:
|
||
return _orig_mrcr_record(self, record)
|
||
|
||
tokenizer = get_tokenizer(tokenizer_path)
|
||
total_tok = sum(
|
||
len(tokenizer.encode(msg.get('content', '') if isinstance(msg, dict) else '', add_special_tokens=False))
|
||
for msg in prompt_data
|
||
)
|
||
if total_tok <= truncation_tokens:
|
||
return _orig_mrcr_record(self, record)
|
||
|
||
desired_idx = record.get('desired_msg_index', 0)
|
||
if not isinstance(desired_idx, int) or desired_idx < 0 or desired_idx >= len(prompt_data):
|
||
desired_idx = 0
|
||
|
||
n = len(prompt_data)
|
||
keep = set()
|
||
keep.update(range(min(2, n)))
|
||
keep.update(range(max(0, n - 2), n))
|
||
window = 2
|
||
keep.update(range(max(0, desired_idx - window), min(n, desired_idx + window + 1)))
|
||
keep = sorted(keep)
|
||
|
||
new_prompt = []
|
||
for idx in keep:
|
||
msg = prompt_data[idx]
|
||
if isinstance(msg, dict):
|
||
msg = dict(msg)
|
||
content = msg.get('content', '')
|
||
if len(tokenizer.encode(content, add_special_tokens=False)) > per_msg_max_tok:
|
||
msg['content'] = truncate_middle(content, per_msg_max_tok, tokenizer_path)
|
||
new_prompt.append(msg)
|
||
|
||
record = dict(record)
|
||
record['prompt'] = json.dumps(new_prompt)
|
||
except (json.JSONDecodeError, TypeError):
|
||
pass
|
||
return _orig_mrcr_record(self, record)
|
||
|
||
OpenAIMRCRAdapter.record_to_sample = _patched_mrcr_record
|
||
|
||
|
||
# ============================================================
|
||
# Helpers
|
||
# ============================================================
|
||
|
||
def load_dataset_configs(config_path: str):
|
||
if not Path(config_path).exists():
|
||
raise FileNotFoundError(f'Config file not found: {config_path}')
|
||
with open(config_path, 'r', encoding='utf-8') as f:
|
||
return yaml.safe_load(f)
|
||
|
||
|
||
def configure_thinking(generation_config: dict, enable: bool) -> dict:
|
||
extra_body = generation_config.get('extra_body', {})
|
||
chat_template_kwargs = extra_body.get('chat_template_kwargs', {})
|
||
if enable:
|
||
chat_template_kwargs['thinking'] = True
|
||
else:
|
||
chat_template_kwargs.pop('thinking', None)
|
||
if chat_template_kwargs:
|
||
extra_body['chat_template_kwargs'] = chat_template_kwargs
|
||
if extra_body:
|
||
generation_config['extra_body'] = extra_body
|
||
return generation_config
|
||
|
||
|
||
def build_agent_config(agent_cfg: dict) -> NativeAgentConfig:
|
||
agent_cfg = deepcopy(agent_cfg or {})
|
||
known_fields = {'mode', 'strategy', 'tools', 'max_steps', 'mcp_servers', 'environment', 'environment_extra'}
|
||
kwargs = agent_cfg.pop('kwargs', {})
|
||
for key in list(agent_cfg.keys()):
|
||
if key not in known_fields:
|
||
kwargs[key] = agent_cfg.pop(key)
|
||
if kwargs:
|
||
agent_cfg['kwargs'] = kwargs
|
||
return NativeAgentConfig(**agent_cfg)
|
||
|
||
|
||
def build_task_config(
|
||
dataset_name: str,
|
||
ds_cfg: dict,
|
||
batch_size: int,
|
||
enable_thinking: bool,
|
||
seed: int,
|
||
limit,
|
||
output_dir: str,
|
||
model: str,
|
||
api_url: str,
|
||
dataset_dir: str,
|
||
judge_model_args: dict,
|
||
run_idx: int = 0,
|
||
) -> TaskConfig:
|
||
if run_idx > 0:
|
||
work_dir = Path(output_dir) / dataset_name / f'seed_{seed}_run_{run_idx}'
|
||
else:
|
||
work_dir = Path(output_dir) / dataset_name / f'seed_{seed}'
|
||
work_dir.mkdir(parents=True, exist_ok=True)
|
||
work_dir = str(work_dir)
|
||
|
||
generation_config = configure_thinking(deepcopy(ds_cfg['generation_config']), enable_thinking)
|
||
dataset_args = deepcopy(ds_cfg.get('dataset_args', {}))
|
||
dataset_args.setdefault('shuffle', True)
|
||
|
||
if dataset_name in MATH_DATASETS:
|
||
dataset_args['prompt_template'] = MATH_PROMPT_TEMPLATE
|
||
|
||
dataset_args_dict = {dataset_name: dataset_args}
|
||
|
||
agent_config = None
|
||
if 'agent_config' in ds_cfg:
|
||
agent_config = build_agent_config(ds_cfg['agent_config'])
|
||
|
||
return TaskConfig(
|
||
model=model,
|
||
api_url=api_url,
|
||
eval_type='openai_api',
|
||
dataset_dir=dataset_dir,
|
||
judge_model_args=judge_model_args,
|
||
seed=seed,
|
||
limit=limit,
|
||
collect_perf=True,
|
||
no_timestamp=True,
|
||
work_dir=work_dir,
|
||
use_cache=work_dir,
|
||
datasets=[dataset_name],
|
||
generation_config=generation_config,
|
||
dataset_args=dataset_args_dict,
|
||
agent_config=agent_config,
|
||
eval_batch_size=batch_size,
|
||
sandbox=SandboxTaskConfig(
|
||
enabled=True,
|
||
engine='docker',
|
||
default_config=SANDBOX_CONFIGS.get(dataset_name, {
|
||
'image': 'python:3.11-slim',
|
||
'tools_config': {
|
||
'shell_executor': {},
|
||
'python_executor': {}
|
||
}
|
||
})
|
||
) if dataset_name in SANDBOX_DATASETS else None,
|
||
)
|
||
|
||
|
||
# ============================================================
|
||
# Main
|
||
# ============================================================
|
||
|
||
def main():
|
||
parser = build_parser()
|
||
args = parser.parse_args()
|
||
|
||
# Resolve limit
|
||
limit = args.limit
|
||
if limit is not None:
|
||
if str(limit).lower() in ('none', 'all'):
|
||
limit = None
|
||
else:
|
||
limit = int(limit)
|
||
|
||
enable_thinking = DEFAULT_ENABLE_THINKING if args.thinking is None else args.thinking
|
||
|
||
# Resolve suite or custom datasets
|
||
if args.datasets:
|
||
custom = [d.strip() for d in args.datasets.split(',') if d.strip()]
|
||
multi_run = [d for d in custom if d in MULTI_RUN_CONFIG]
|
||
single_run = [d for d in custom if d not in MULTI_RUN_CONFIG]
|
||
agent = [d for d in custom if d in ALL_AGENT]
|
||
single_run = [d for d in single_run if d not in ALL_AGENT]
|
||
else:
|
||
suite = SUITES[args.suite]
|
||
multi_run = list(suite['multi'])
|
||
single_run = list(suite['single'])
|
||
agent = list(suite['agent'])
|
||
|
||
# Apply --exclude
|
||
if args.exclude:
|
||
exclude = {d.strip() for d in args.exclude.split(',') if d.strip()}
|
||
multi_run = [d for d in multi_run if d not in exclude]
|
||
single_run = [d for d in single_run if d not in exclude]
|
||
agent = [d for d in agent if d not in exclude]
|
||
|
||
judge_model_args = {
|
||
'model_id': args.judge_model,
|
||
'api_url': args.judge_api_url,
|
||
'api_key': args.judge_api_key,
|
||
'eval_type': 'openai_api',
|
||
'generation_config': {
|
||
'temperature': 0.0,
|
||
'max_tokens': args.judge_max_tokens,
|
||
},
|
||
}
|
||
|
||
truncation_tokens = args.truncation_tokens
|
||
_patch_adapters_for_truncation(args.tokenizer_path, truncation_tokens)
|
||
|
||
dataset_configs = load_dataset_configs(args.config)
|
||
|
||
print('=' * 60)
|
||
print(f'Config: {args.config}')
|
||
print(f'Model: {args.model}')
|
||
print(f'API URL: {args.api_url}')
|
||
print(f'Dataset Dir: {args.dataset_dir}')
|
||
print(f'Output Dir: {args.output_dir}')
|
||
print(f'Suite: {args.suite}')
|
||
print(f'Limit: {limit if limit is not None else "ALL"}')
|
||
print(f'Thinking: {enable_thinking}')
|
||
print(f'Seed: {args.seed}')
|
||
print(f'Batch Size: {args.batch_size}')
|
||
print(f'Tokenizer Path: {args.tokenizer_path}')
|
||
print(f'Truncation Tokens: {truncation_tokens}')
|
||
print(f'Multi-run datasets: {multi_run}')
|
||
print(f'Single-run datasets: {single_run}')
|
||
print(f'Agent datasets: {agent}')
|
||
print('=' * 60)
|
||
|
||
for dataset_name in multi_run:
|
||
if dataset_name not in dataset_configs:
|
||
print(f'WARNING: {dataset_name} not in YAML config, skipping')
|
||
continue
|
||
ds_cfg = dataset_configs[dataset_name]
|
||
num_runs = MULTI_RUN_CONFIG.get(dataset_name, 1)
|
||
for run_idx in range(num_runs):
|
||
print(f"\n{'='*60}")
|
||
print(f'Running: {dataset_name} (run {run_idx + 1}/{num_runs}, seed={args.seed})')
|
||
print(f"{'='*60}")
|
||
task_cfg = build_task_config(
|
||
dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
|
||
args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
|
||
run_idx=run_idx,
|
||
)
|
||
try:
|
||
run_task(task_cfg)
|
||
except Exception as e:
|
||
print(f'ERROR in {dataset_name} (run {run_idx + 1}): {e}')
|
||
continue
|
||
|
||
for dataset_name in single_run:
|
||
if dataset_name not in dataset_configs:
|
||
print(f'WARNING: {dataset_name} not in YAML config, skipping')
|
||
continue
|
||
ds_cfg = dataset_configs[dataset_name]
|
||
print(f"\n{'='*60}")
|
||
print(f'Running: {dataset_name} (seed={args.seed})')
|
||
print(f"{'='*60}")
|
||
task_cfg = build_task_config(
|
||
dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
|
||
args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
|
||
)
|
||
try:
|
||
run_task(task_cfg)
|
||
except Exception as e:
|
||
print(f'ERROR in {dataset_name}: {e}')
|
||
continue
|
||
|
||
for dataset_name in agent:
|
||
if dataset_name not in dataset_configs:
|
||
print(f'WARNING: {dataset_name} not in YAML config, skipping')
|
||
continue
|
||
ds_cfg = dataset_configs[dataset_name]
|
||
print(f"\n{'='*60}")
|
||
print(f'Running: {dataset_name} (seed={args.seed})')
|
||
print(f"{'='*60}")
|
||
task_cfg = build_task_config(
|
||
dataset_name, ds_cfg, args.batch_size, enable_thinking, args.seed, limit,
|
||
args.output_dir, args.model, args.api_url, args.dataset_dir, judge_model_args,
|
||
)
|
||
try:
|
||
run_task(task_cfg)
|
||
except Exception as e:
|
||
print(f'ERROR in {dataset_name}: {e}')
|
||
continue
|
||
|
||
print('\nAll benchmarks done!')
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|