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import asyncio
import copy
import os
import tempfile
from typing import Any
import pandas as pd
from datasets import load_dataset
from evaluation.benchmarks.aider_bench.helper import (
FAKE_RESPONSES,
INST_SUFFIXES,
INSTRUCTIONS_ADDENDUM,
)
from evaluation.utils.shared import (
EvalMetadata,
EvalOutput,
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,
load_from_toml,
parse_arguments,
)
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
# Configure visibility of unit tests to the Agent.
USE_UNIT_TESTS = os.environ.get('USE_UNIT_TESTS', 'false').lower() == 'true'
SKIP_NUM = os.environ.get('SKIP_NUM')
SKIP_NUM = (
int(SKIP_NUM) if SKIP_NUM and SKIP_NUM.isdigit() and int(SKIP_NUM) >= 0 else None
)
def get_config(
metadata: EvalMetadata,
) -> AppConfig:
config = AppConfig(
default_agent=metadata.agent_class,
run_as_openhands=False,
runtime=os.environ.get('RUNTIME', 'docker'),
max_iterations=metadata.max_iterations,
sandbox=SandboxConfig(
base_container_image='python:3.11-bookworm',
enable_auto_lint=True,
use_host_network=False,
timeout=100,
api_key=os.environ.get('ALLHANDS_API_KEY', None),
remote_runtime_api_url=os.environ.get('SANDBOX_REMOTE_RUNTIME_API_URL'),
keep_runtime_alive=False,
remote_runtime_init_timeout=1800,
),
# 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
# copy 'draft_editor' config if exists
config_copy = copy.deepcopy(config)
load_from_toml(config_copy)
if 'draft_editor' in config_copy.llms:
config.set_llm_config(config_copy.llms['draft_editor'], 'draft_editor')
return config
def initialize_runtime(
runtime: Runtime,
instance: pd.Series,
):
"""Initialize the runtime for the agent.
This function is called before the runtime is used to run the agent.
"""
logger.info(f"\n{'-' * 50} BEGIN Runtime Initialization Fn {'-' * 50}\n")
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
action = CmdRunAction(command='cd /workspace')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
assert obs.exit_code == 0
with tempfile.TemporaryDirectory() as tmpdir:
file_path = os.path.join(tmpdir, f'{instance.instance_name}.py')
with open(file_path, 'w') as f:
f.write(instance.signature)
runtime.copy_to(
file_path,
'/workspace',
)
if USE_UNIT_TESTS:
file_path = os.path.join(tmpdir, f'{instance.instance_name}_test.py')
with open(file_path, 'w') as f:
f.write(instance.test)
runtime.copy_to(
file_path,
'/workspace',
)
logger.info(f"\n{'-' * 50} END Runtime Initialization Fn {'-' * 50}\n")
def complete_runtime(
runtime: Runtime,
instance: pd.Series,
) -> 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"\n{'-' * 50} BEGIN Runtime Completion Fn {'-' * 50}\n")
obs: CmdOutputObservation
# Rewriting the test file to ignore any changes Agent may have made.
script_name = f'{instance.instance_name}_test.py'
with tempfile.TemporaryDirectory() as tmpdir:
file_path = os.path.join(tmpdir, script_name)
with open(file_path, 'w') as f:
f.write(instance.test)
runtime.copy_to(
file_path,
'/workspace',
)
logger.info(f'Running test file: {script_name}')
action = CmdRunAction(command=f'python3 -m unittest {script_name}')
logger.info(action, extra={'msg_type': 'ACTION'})
obs = runtime.run_action(action)
logger.info(obs, extra={'msg_type': 'OBSERVATION'})
exit_code = 1
if isinstance(obs, CmdOutputObservation):
exit_code = obs.exit_code
logger.info(f"\n{'-' * 50} END Runtime Completion Fn {'-' * 50}\n")
runtime.close()
return {
'test_output': obs.content,
'exit_code': exit_code,
}
def process_instance(
instance: pd.Series,
metadata: EvalMetadata,
reset_logger: bool = True,
) -> EvalOutput:
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, str(instance.instance_id), log_dir)
else:
logger.info(
f'\nStarting evaluation for instance {str(instance.instance_id)}.\n'
)
# =============================================
# build instruction
# =============================================
# Prepare instruction
logger.info(instance)
instruction = instance.instruction
instruction += INSTRUCTIONS_ADDENDUM.format(
signature_file=f'{instance.instance_name}.py',
)
if USE_UNIT_TESTS:
logger.info(
f'\nInstruction to run test_file: {instance.instance_name}_test.py\n'
)
instruction += (
f'Use `python -m unittest {instance.instance_name}_test.py` to run the test_file '
'and verify the correctness of your solution. DO NOT EDIT the test file.\n\n'
)
instruction += (
'IMPORTANT: You should ONLY interact with the environment provided '
'to you AND NEVER ASK FOR HUMAN HELP.\n'
)
# NOTE: You can actually set slightly different instruction for different agents
instruction += INST_SUFFIXES[metadata.agent_class]
# =============================================
# create sandbox and run the agent
# =============================================
runtime: Runtime = create_runtime(config)
call_async_from_sync(runtime.connect)
initialize_runtime(runtime, instance=instance)
# Here's how you can run the agent (similar to the `main` function) and get the final task state
state: State | None = asyncio.run(
run_controller(
config=config,
initial_user_action=MessageAction(content=instruction),
runtime=runtime,
fake_user_response_fn=FAKE_RESPONSES[metadata.agent_class],
)
)
if state is None:
raise ValueError('State should not be None.')
# # =============================================
# # result evaluation
# # =============================================
return_val = complete_runtime(runtime, instance)
exit_code = return_val['exit_code']
test_output = return_val['test_output']
errors = []
test_cases = None
if test_output.find('SyntaxError') != -1:
errors += 'SyntaxError'
elif test_output.find('IndentationError') != -1:
errors += 'IndentationError'
else:
test_cases = test_output[: test_output.find('\r')]
test_result = {
'exit_code': exit_code,
'test_cases': test_cases,
'errors': errors,
}
# 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)
metrics = state.metrics.get() if state.metrics else None
# Save the output
output = EvalOutput(
instance_id=str(instance.instance_id),
instance=instance.to_dict(),
instruction=instruction,
metadata=metadata,
history=histories,
metrics=metrics,
error=state.last_error if state and state.last_error else None,
test_result=test_result,
)
return output
if __name__ == '__main__':
args = parse_arguments()
dataset = load_dataset('RajMaheshwari/Exercism-Python')
aider_bench_tests = dataset['train'].to_pandas()
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,
'AiderBench',
args.agent_cls,
args.max_iterations,
args.eval_note,
args.eval_output_dir,
)
output_file = os.path.join(metadata.eval_output_dir, 'output.jsonl')
# Parse dataset IDs if provided
eval_ids = None
if args.eval_ids:
eval_ids = str(args.eval_ids).split(',')
logger.info(f'\nUsing specific dataset IDs: {eval_ids}\n')
instances = prepare_dataset(
aider_bench_tests,
output_file,
args.eval_n_limit,
eval_ids=eval_ids,
skip_num=SKIP_NUM,
)
run_evaluation(
instances,
metadata,
output_file,
args.eval_num_workers,
process_instance,
)
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