OpenHands/evaluation/aider_bench/run_infer.py

import asyncio
import os
import tempfile
from typing import Any

import pandas as pd
from datasets import load_dataset

from evaluation.aider_bench.helper import (
    FAKE_RESPONSES,
    INST_SUFFIXES,
    INSTRUCTIONS_ADDENDUM,
)
from evaluation.utils.shared import (
    EvalMetadata,
    EvalOutput,
    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,
    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
from openhands.events.observation import CmdOutputObservation
from openhands.runtime.runtime import Runtime


def get_config(
    metadata: EvalMetadata,
) -> AppConfig:
    config = AppConfig(
        default_agent=metadata.agent_class,
        run_as_openhands=False,
        runtime='eventstream',
        max_iterations=metadata.max_iterations,
        sandbox=SandboxConfig(
            base_container_image='python:3.11-bookworm',
            enable_auto_lint=True,
            use_host_network=False,
            timeout=100,
        ),
        # do not mount workspace
        workspace_base=None,
        workspace_mount_path=None,
    )
    config.set_llm_config(metadata.llm_config)
    return config


async 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"{'-' * 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 = await runtime.run_action(action)
    assert obs.exit_code == 0

    action = CmdRunAction(command='cd /workspace')
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await 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)
        await runtime.copy_to(
            file_path,
            '/workspace',
        )
    logger.info(f"{'-' * 50} END Runtime Initialization Fn {'-' * 50}")


async 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"{'-' * 50} BEGIN Runtime Completion Fn {'-' * 50}")
    obs: CmdOutputObservation

    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)
        await runtime.copy_to(
            file_path,
            '/workspace',
        )
        logger.info(f'Running test file: {script_name}')

    action = CmdRunAction(
        command=f'python -m unittest {script_name}',
        keep_prompt=False,
    )
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await 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"{'-' * 50} END Runtime Completion Fn {'-' * 50}")

    return {
        'test_output': obs.content,
        'exit_code': exit_code,
    }


async 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'Starting evaluation for instance {str(instance.instance_id)}.')

    # =============================================
    # build instruction
    # =============================================

    # Prepare instruction
    logger.info(instance)
    instruction = instance.instruction
    instruction += INSTRUCTIONS_ADDENDUM.format(
        signature_file=f'{instance.instance_name}.py',
    )
    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 = await create_runtime(config, sid=str(instance.instance_id))

    await 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 = await run_controller(
        config=config,
        task_str=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 = await 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 = state.history.compatibility_for_eval_history_pairs()
    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)

    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')
    instances = prepare_dataset(aider_bench_tests, output_file, args.eval_n_limit)

    asyncio.run(
        run_evaluation(
            instances,
            metadata,
            output_file,
            args.eval_num_workers,
            process_instance,
        )
    )