File size: 11,126 Bytes
246d201 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 |
"""Implements evaluation of agents on ML-Bench, a benchmark for assessing the effectiveness of
Large Language Models (LLMs) in leveraging existing functions in open-source libraries for
machine learning tasks. The benchmark is introduced in the paper "ML-Bench: Evaluating Large
Language Models for Code Generation in Repository-Level Machine Learning Tasks"
(https://arxiv.org/abs/2311.09835).
Please see https://ghcr.io/super-dainiu/ml_bench and https://huggingface.co/datasets/super-dainiu/ml-bench
for more details on the dataset and docker image used in this evaluation script.
TODOs:
- Support additional evaluation settings, such as providing raw README content or using a
retriever to extract relevant segments.
- Clean up the code and docker image used for evaluation.
"""
import asyncio
import os
from typing import Any
import pandas as pd
from datasets import load_dataset
from evaluation.utils.shared import (
EvalMetadata,
EvalOutput,
codeact_user_response,
compatibility_for_eval_history_pairs,
make_metadata,
prepare_dataset,
reset_logger_for_multiprocessing,
run_evaluation,
)
from openhands.controller.state.state import State
from openhands.core.config import (
AppConfig,
SandboxConfig,
get_llm_config_arg,
get_parser,
load_app_config,
)
from openhands.core.logger import openhands_logger as logger
from openhands.core.main import create_runtime, run_controller
from openhands.events.action import CmdRunAction, MessageAction
from openhands.events.observation import CmdOutputObservation
from openhands.runtime.base import Runtime
from openhands.utils.async_utils import call_async_from_sync
config = load_app_config()
AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = {
'CodeActAgent': codeact_user_response,
}
AGENT_CLS_TO_INST_SUFFIX = {
'CodeActAgent': 'When you think you have completed the task, please finish the interaction using the "finish" tool.\n'
}
ID2CONDA = {
1: 'dgl_DS',
2: 'bert_DS',
3: 'lavis_DS',
4: 'if_DS',
5: 'V2V_DS',
6: 'esm_DS',
7: 'OP_DS',
8: 'TSL_DS',
9: 'EAP_DS',
10: 'PG_DS',
11: 'PIM_DS',
12: 'AD2_DS',
13: 'L3_DS',
14: 'MZ2_DS',
15: 'GSA2_DS',
}
def get_config(
metadata: EvalMetadata,
) -> AppConfig:
config = AppConfig(
default_agent=metadata.agent_class,
run_as_openhands=False,
runtime='docker',
max_iterations=metadata.max_iterations,
sandbox=SandboxConfig(
base_container_image='public.ecr.aws/i5g0m1f6/ml-bench',
enable_auto_lint=True,
use_host_network=False,
),
# do not mount workspace
workspace_base=None,
workspace_mount_path=None,
)
config.set_llm_config(metadata.llm_config)
agent_config = config.get_agent_config(metadata.agent_class)
agent_config.enable_prompt_extensions = False
return config
def initialize_runtime(
runtime: Runtime,
instance: pd.Series, # this argument is not required
):
"""Initialize the runtime for the agent.
This function is called before the runtime is used to run the agent.
"""
logger.info(f"{'-' * 50} BEGIN Runtime Initialization Fn {'-' * 50}")
obs: CmdOutputObservation
# Set instance id
action = CmdRunAction(command='mkdir -p /workspace')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
# Set up the task environment
action = CmdRunAction(command=f'conda activate {ID2CONDA[instance["github_id"]]}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
repo_url = instance['github']
repo_name = repo_url.split('/')[-1]
action = CmdRunAction(command=f'git clone {repo_url} /workspace/{repo_name}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
action = CmdRunAction(command=f'chmod -R 777 /workspace/{repo_name}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
# Navigate to the task's code path
task_path = os.path.join('/workspace', repo_name, instance['path'][2:])
action = CmdRunAction(command=f'cd {task_path}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
logger.info(f"{'-' * 50} END Runtime Initialization Fn {'-' * 50}")
def complete_runtime(
runtime: Runtime,
instance: pd.Series, # this argument is not required, but it is used to get the workspace_dir_name
) -> dict[str, Any]:
"""Complete the runtime for the agent.
This function is called before the runtime is used to run the agent.
If you need to do something in the sandbox to get the correctness metric after
the agent has run, modify this function.
"""
logger.info(f"{'-' * 50} BEGIN Runtime Completion Fn {'-' * 50}")
obs: CmdOutputObservation
repo_url = instance['github']
repo_name = repo_url.split('/')[-1]
task_path = os.path.join('/workspace', repo_name, instance['path'][2:])
# Evaluate the agent's script
eval_script = os.path.join(task_path, 'run.sh')
logger.info(f'Running evaluation script: {eval_script}')
action = CmdRunAction(command=f'cat {eval_script}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
if obs.exit_code == 0:
eval_script_content = obs.content
else:
logger.error(f'Error reading evaluation script: {obs.content}')
eval_script_content = ''
action = CmdRunAction(
command=f'timeout 120s conda run -n {ID2CONDA[instance["github_id"]]} bash {eval_script}',
timeout=600,
)
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
if obs.exit_code == 0:
eval_output = obs.content
else:
logger.error(f'Error running evaluation script: {obs.content}')
eval_output = ''
outputs = {
'eval_script_content': eval_script_content,
'eval_output': eval_output,
}
if obs.exit_code != 0 and obs.exit_code != 124:
logger.warning(f'Evaluation script failed with exit code {obs.exit_code}')
logger.warning(f'Output: {eval_output}')
outputs['success'] = int(
'KeyboardInterrupt' in eval_output
) # super-dainiu: assume ``KeyboardInterrupt`` is a success as is done in ML-Bench
else:
logger.info(f'Evaluation script succeeded with exit code {obs.exit_code}')
logger.info(f'Output: {eval_output}')
outputs['success'] = 1
outputs['eval_exit_code'] = obs.exit_code
logger.info(f"{'-' * 50} END Runtime Completion Fn {'-' * 50}")
return outputs
def process_instance(instance: Any, metadata: EvalMetadata, reset_logger: bool = True):
config = get_config(metadata)
# Setup the logger properly, so you can run multi-processing to parallelize the evaluation
if reset_logger:
log_dir = os.path.join(metadata.eval_output_dir, 'infer_logs')
reset_logger_for_multiprocessing(logger, instance['instance_id'], log_dir)
else:
logger.info(f'Starting evaluation for instance {instance["instance_id"]}.')
repo_url = instance['github']
repo_name = repo_url.split('/')[-1]
task_path = os.path.join('/workspace', repo_name, instance['path'][2:])
# Prepare the task instruction
instruction = (
f'Please complete the Machine Learning task in the following repository: {repo_name}\n\n'
f'{instance["instruction"]}\n\n'
'You should create a script named `run.sh` under the specified path in the repo to run the task.\n\n'
f'You can find the task repo at: {task_path}\n\n'
+ (
'Here is the prefix code for the task:\n'
'```bash\n'
f'{instance["prefix_code"]}\n'
'```\n\n'
if instance['prefix_code']
else ''
)
+ 'You should terminate the subprocess after running the task (e.g., call subprocess.Popen(args).wait()).'
)
instruction += AGENT_CLS_TO_INST_SUFFIX[metadata.agent_class]
runtime = create_runtime(config)
call_async_from_sync(runtime.connect)
initialize_runtime(runtime, instance)
# Run the agent
state: State | None = asyncio.run(
run_controller(
config=config,
initial_user_action=MessageAction(content=instruction),
runtime=runtime,
fake_user_response_fn=AGENT_CLS_TO_FAKE_USER_RESPONSE_FN.get(
metadata.agent_class
),
)
)
assert state is not None
metrics = state.metrics.get() if state.metrics else {}
test_result = complete_runtime(runtime)
# history is now available as a stream of events, rather than list of pairs of (Action, Observation)
# for compatibility with the existing output format, we can remake the pairs here
# remove when it becomes unnecessary
histories = compatibility_for_eval_history_pairs(state.history)
# Save the output
output = EvalOutput(
instance_id=instance['instance_id'],
instance=instance.to_dict(),
instruction=instruction,
metadata=metadata,
history=histories,
test_result=test_result,
metrics=metrics,
)
return output
if __name__ == '__main__':
parser = get_parser()
parser.add_argument(
'-s',
'--eval-split',
type=str,
default='quarter',
choices=['full', 'quarter'],
help='data split to evaluate on, either full or quarter',
)
args, _ = parser.parse_known_args()
data_split = args.eval_split
ml_bench = load_dataset('super-dainiu/ml-bench', split=data_split).to_pandas()
ml_bench.rename(columns={'id': 'instance_id'}, inplace=True)
llm_config = None
if args.llm_config:
llm_config = get_llm_config_arg(args.llm_config)
# modify_params must be False for evaluation purpose, for reproducibility and accurancy of results
llm_config.modify_params = False
if llm_config is None:
raise ValueError(f'Could not find LLM config: --llm_config {args.llm_config}')
metadata = make_metadata(
llm_config,
f'ml-bench-{data_split}',
args.agent_cls,
args.max_iterations,
args.eval_note,
args.eval_output_dir,
)
output_file = os.path.join(metadata.eval_output_dir, 'output.jsonl')
instances = prepare_dataset(ml_bench, output_file, args.eval_n_limit)
run_evaluation(
instances, metadata, output_file, args.eval_num_workers, process_instance
)
|