OpenHands/evaluation/benchmarks/swe_bench/run_infer.py

import asyncio
import json
import os
import tempfile
from typing import Any

import pandas as pd
import toml
from datasets import load_dataset

import openhands.agenthub

from evaluation.utils.shared import (
    EvalException,
    EvalMetadata,
    EvalOutput,
    assert_and_raise,
    codeact_user_response,
    make_metadata,
    prepare_dataset,
    reset_logger_for_multiprocessing,
    run_evaluation,
    update_llm_config_for_completions_logging,
)
from openhands.controller.state.state import State
from openhands.core.config import (
    AgentConfig,
    AppConfig,
    SandboxConfig,
    get_llm_config_arg,
    get_parser,
)
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, ErrorObservation
from openhands.events.serialization.event import event_to_dict
from openhands.runtime.base import Runtime
from openhands.utils.async_utils import call_async_from_sync
from openhands.utils.shutdown_listener import sleep_if_should_continue

USE_HINT_TEXT = os.environ.get('USE_HINT_TEXT', 'false').lower() == 'true'
USE_INSTANCE_IMAGE = os.environ.get('USE_INSTANCE_IMAGE', 'false').lower() == 'true'
RUN_WITH_BROWSING = os.environ.get('RUN_WITH_BROWSING', 'false').lower() == 'true'

AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = {
    'CodeActAgent': codeact_user_response,
}


def _get_swebench_workspace_dir_name(instance: pd.Series) -> str:
    return f'{instance.repo}__{instance.version}'.replace('/', '__')


def get_instruction(instance: pd.Series, metadata: EvalMetadata):
    workspace_dir_name = _get_swebench_workspace_dir_name(instance)
    # Prepare instruction

    # Instruction based on Anthropic's official trajectory
    # https://github.com/eschluntz/swe-bench-experiments/tree/main/evaluation/verified/20241022_tools_claude-3-5-sonnet-updated/trajs
    instruction = (
        '<uploaded_files>\n'
        f'/workspace/{workspace_dir_name}\n'
        '</uploaded_files>\n'
        f"I've uploaded a python code repository in the directory {workspace_dir_name}. Consider the following PR description:\n\n"
        f'<pr_description>\n'
        f'{instance.problem_statement}\n'
        '</pr_description>\n\n'
        'Can you help me implement the necessary changes to the repository so that the requirements specified in the <pr_description> are met?\n'
        "I've already taken care of all changes to any of the test files described in the <pr_description>. This means you DON'T have to modify the testing logic or any of the tests in any way!\n"
        'Your task is to make the minimal changes to non-tests files in the /workspace directory to ensure the <pr_description> is satisfied.\n'
        'Follow these steps to resolve the issue:\n'
        '1. As a first step, it might be a good idea to explore the repo to familiarize yourself with its structure.\n'
        '2. Create a script to reproduce the error and execute it with `python <filename.py>` using the BashTool, to confirm the error\n'
        '3. Edit the sourcecode of the repo to resolve the issue\n'
        '4. Rerun your reproduce script and confirm that the error is fixed!\n'
        '5. Think about edgecases and make sure your fix handles them as well\n'
        "Your thinking should be thorough and so it's fine if it's very long.\n"
        )

    if RUN_WITH_BROWSING:
        instruction += (
            '<IMPORTANT!>\n'
            'You SHOULD NEVER attempt to browse the web. '
            '</IMPORTANT!>\n'
        )
    return instruction


# TODO: migrate all swe-bench docker to ghcr.io/openhands
DOCKER_IMAGE_PREFIX = os.environ.get('EVAL_DOCKER_IMAGE_PREFIX', 'docker.io/xingyaoww/')
logger.info(f'Using docker image prefix: {DOCKER_IMAGE_PREFIX}')


def get_instance_docker_image(instance_id: str) -> str:
    image_name = 'sweb.eval.x86_64.' + instance_id
    image_name = image_name.replace(
        '__', '_s_'
    )  # to comply with docker image naming convention
    return (DOCKER_IMAGE_PREFIX.rstrip('/') + '/' + image_name).lower()


def get_config(
    instance: pd.Series,
    metadata: EvalMetadata,
) -> AppConfig:
    SWE_BENCH_CONTAINER_IMAGE = 'ghcr.io/opendevin/eval-swe-bench:full-v1.2.1'
    if USE_INSTANCE_IMAGE:
        # We use a different instance image for the each instance of swe-bench eval
        base_container_image = get_instance_docker_image(instance['instance_id'])
        logger.info(
            f'Using instance container image: {base_container_image}. '
            f'Please make sure this image exists. '
            f'Submit an issue on https://github.com/All-Hands-AI/OpenHands if you run into any issues.'
        )
    else:
        base_container_image = SWE_BENCH_CONTAINER_IMAGE
        logger.info(f'Using swe-bench container image: {base_container_image}')

    config = AppConfig(
        default_agent=metadata.agent_class,
        run_as_openhands=False,
        max_iterations=metadata.max_iterations,
        runtime=os.environ.get('RUNTIME', 'eventstream'),
        sandbox=SandboxConfig(
            base_container_image=base_container_image,
            enable_auto_lint=True,
            use_host_network=False,
            # large enough timeout, since some testcases take very long to run
            timeout=300,
            # Add platform to the sandbox config to solve issue 4401
            platform='linux/amd64',
            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=3600,
        ),
        # do not mount workspace
        workspace_base=None,
        workspace_mount_path=None,
    )
    config.set_llm_config(
        update_llm_config_for_completions_logging(
            metadata.llm_config, metadata.eval_output_dir, instance['instance_id']
        )
    )
    agent_config = AgentConfig(
        codeact_enable_jupyter=False,
        codeact_enable_browsing=RUN_WITH_BROWSING,
        codeact_enable_llm_editor=False,
    )
    config.set_agent_config(agent_config)
    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('-' * 30)
    logger.info('BEGIN Runtime Initialization Fn')
    logger.info('-' * 30)
    workspace_dir_name = _get_swebench_workspace_dir_name(instance)
    obs: CmdOutputObservation

    # Set instance id
    action = CmdRunAction(
        command=f"""echo 'export SWE_INSTANCE_ID={instance['instance_id']}' >> ~/.bashrc && echo 'export PIP_CACHE_DIR=~/.cache/pip' >> ~/.bashrc && echo "alias git='git --no-pager'" >> ~/.bashrc"""
    )
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(
        obs.exit_code == 0, f'Failed to export SWE_INSTANCE_ID: {str(obs)}'
    )

    action = CmdRunAction(command="""export USER=$(whoami); echo USER=${USER} """)
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(obs.exit_code == 0, f'Failed to export USER: {str(obs)}')

    if USE_INSTANCE_IMAGE:
        # inject the init script
        script_dir = os.path.dirname(__file__)

        # inject the instance info
        action = CmdRunAction(command='mkdir -p /swe_util/eval_data/instances')
        action.timeout = 600
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        assert_and_raise(
            obs.exit_code == 0,
            f'Failed to create /swe_util/eval_data/instances: {str(obs)}',
        )

        swe_instance_json_name = 'swe-bench-instance.json'
        with tempfile.TemporaryDirectory() as temp_dir:
            # Construct the full path for the desired file name within the temporary directory
            temp_file_path = os.path.join(temp_dir, swe_instance_json_name)
            # Write to the file with the desired name within the temporary directory
            with open(temp_file_path, 'w') as f:
                if not isinstance(instance, dict):
                    json.dump([instance.to_dict()], f)
                else:
                    json.dump([instance], f)

            # Copy the file to the desired location
            runtime.copy_to(temp_file_path, '/swe_util/eval_data/instances/')

        # inject the instance swe entry
        runtime.copy_to(
            str(os.path.join(script_dir, 'scripts/setup/instance_swe_entry.sh')),
            '/swe_util/',
        )
        action = CmdRunAction(command='cat ~/.bashrc')
        action.timeout = 600
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        assert_and_raise(obs.exit_code == 0, f'Failed to cat ~/.bashrc: {str(obs)}')

        action = CmdRunAction(command='source ~/.bashrc')
        action.timeout = 600
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        if isinstance(obs, ErrorObservation):
            logger.error(f'Failed to source ~/.bashrc: {str(obs)}')
        assert_and_raise(obs.exit_code == 0, f'Failed to source ~/.bashrc: {str(obs)}')

        action = CmdRunAction(command='source /swe_util/instance_swe_entry.sh')
        action.timeout = 3600
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        assert_and_raise(
            obs.exit_code == 0,
            f'Failed to source /swe_util/instance_swe_entry.sh: {str(obs)}',
        )
    else:
        action = CmdRunAction(command='source /swe_util/swe_entry.sh')
        action.timeout = 1800
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        assert_and_raise(
            obs.exit_code == 0,
            f'Failed to source /swe_util/swe_entry.sh: {str(obs)}',
        )

    action = CmdRunAction(command=f'cd /workspace/{workspace_dir_name}')
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(
        obs.exit_code == 0,
        f'Failed to cd to /workspace/{workspace_dir_name}: {str(obs)}',
    )

    action = CmdRunAction(command='git reset --hard')
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(obs.exit_code == 0, f'Failed to git reset --hard: {str(obs)}')

    action = CmdRunAction(
        command='for remote_name in $(git remote); do git remote remove "${remote_name}"; done'
    )
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(obs.exit_code == 0, f'Failed to remove git remotes: {str(obs)}')

    logger.info('-' * 30)
    logger.info('END Runtime Initialization Fn')
    logger.info('-' * 30)


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('-' * 30)
    logger.info('BEGIN Runtime Completion Fn')
    logger.info('-' * 30)
    obs: CmdOutputObservation
    workspace_dir_name = _get_swebench_workspace_dir_name(instance)

    action = CmdRunAction(command=f'cd /workspace/{workspace_dir_name}')
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(
        isinstance(obs, CmdOutputObservation) and obs.exit_code == 0,
        f'Failed to cd to /workspace/{workspace_dir_name}: {str(obs)}',
    )

    action = CmdRunAction(command='git config --global core.pager ""')
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(
        isinstance(obs, CmdOutputObservation) and obs.exit_code == 0,
        f'Failed to git config --global core.pager "": {str(obs)}',
    )

    action = CmdRunAction(command='git add -A')
    action.timeout = 600
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert_and_raise(
        isinstance(obs, CmdOutputObservation) and obs.exit_code == 0,
        f'Failed to git add -A: {str(obs)}',
    )

    n_retries = 0
    git_patch = None
    while n_retries < 5:
        action = CmdRunAction(
            command=f'git diff --no-color --cached {instance["base_commit"]}',
            keep_prompt=False,
        )
        action.timeout = 600 + 100 * n_retries
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        n_retries += 1
        if isinstance(obs, CmdOutputObservation):
            if obs.exit_code == 0:
                git_patch = obs.content.strip()
                break
            else:
                logger.info('Failed to get git diff, retrying...')
                sleep_if_should_continue(10)
        elif isinstance(obs, ErrorObservation):
            logger.error(f'Error occurred: {obs.content}. Retrying...')
            sleep_if_should_continue(10)
        else:
            assert_and_raise(False, f'Unexpected observation type: {str(obs)}')

    assert_and_raise(git_patch is not None, 'Failed to get git diff (None)')

    logger.info('-' * 30)
    logger.info('END Runtime Completion Fn')
    logger.info('-' * 30)
    return {'git_patch': git_patch}


def process_instance(
    instance: pd.Series,
    metadata: EvalMetadata,
    reset_logger: bool = True,
) -> EvalOutput:
    config = get_config(instance, 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}.')

    runtime = create_runtime(config)
    call_async_from_sync(runtime.connect)

    try:
        initialize_runtime(runtime, instance)

        instruction = get_instruction(instance, metadata)

        # 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=AGENT_CLS_TO_FAKE_USER_RESPONSE_FN[
                    metadata.agent_class
                ],
            )
        )

        # if fatal error, throw EvalError to trigger re-run
        if (
            state.last_error
            and 'fatal error during agent execution' in state.last_error
            and 'stuck in a loop' not in state.last_error
        ):
            raise EvalException('Fatal error detected: ' + state.last_error)

        # ======= THIS IS SWE-Bench specific =======
        # Get git patch
        return_val = complete_runtime(runtime, instance)
        git_patch = return_val['git_patch']
        logger.info(
            f'Got git diff for instance {instance.instance_id}:\n--------\n{git_patch}\n--------'
        )
    finally:
        runtime.close()
    # ==========================================

    # ======= Attempt to evaluate the agent's edits =======
    # we use eval_infer.sh to evaluate the agent's edits, not here
    # because the agent may alter the environment / testcases
    test_result = {
        'git_patch': git_patch,
    }

    # If you are working on some simpler benchmark that only evaluates the final model output (e.g., in a MessageAction)
    # You can simply get the LAST `MessageAction` from the returned `state.history` and parse it for evaluation.
    if state is None:
        raise ValueError('State should not be None.')

    # NOTE: this is NO LONGER the event stream, but an agent history that includes delegate agent's events
    histories = [event_to_dict(event) for event in state.history]
    metrics = state.metrics.get() if state.metrics else None

    # Save the output
    output = EvalOutput(
        instance_id=instance.instance_id,
        instruction=instruction,
        instance=instance.to_dict(),  # SWE Bench specific
        test_result=test_result,
        metadata=metadata,
        history=histories,
        metrics=metrics,
        error=state.last_error if state and state.last_error else None,
    )
    return output


def filter_dataset(dataset: pd.DataFrame, filter_column: str) -> pd.DataFrame:
    file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.toml')
    if os.path.exists(file_path):
        with open(file_path, 'r') as file:
            data = toml.load(file)
            if 'selected_ids' in data:
                selected_ids = data['selected_ids']
                logger.info(
                    f'Filtering {len(selected_ids)} tasks from "selected_ids"...'
                )
                subset = dataset[dataset[filter_column].isin(selected_ids)]
                logger.info(f'Retained {subset.shape[0]} tasks after filtering')
                return subset
    return dataset


if __name__ == '__main__':
    parser = get_parser()
    parser.add_argument(
        '--dataset',
        type=str,
        default='princeton-nlp/SWE-bench',
        help='data set to evaluate on, either full-test or lite-test',
    )
    parser.add_argument(
        '--split',
        type=str,
        default='test',
        help='split to evaluate on',
    )
    args, _ = parser.parse_known_args()

    # NOTE: It is preferable to load datasets from huggingface datasets and perform post-processing
    # so we don't need to manage file uploading to OpenHands's repo
    dataset = load_dataset(args.dataset, split=args.split)
    logger.info(f'Loaded dataset {args.dataset} with split {args.split}')
    swe_bench_tests = filter_dataset(dataset.to_pandas(), 'instance_id')

    llm_config = None
    if args.llm_config:
        llm_config = get_llm_config_arg(args.llm_config)
        llm_config.log_completions = True

    if llm_config is None:
        raise ValueError(f'Could not find LLM config: --llm_config {args.llm_config}')

    details = {}
    _agent_cls = openhands.agenthub.Agent.get_cls(args.agent_cls)

    dataset_descrption = (
        args.dataset.replace('/', '__') + '-' + args.split.replace('/', '__')
    )
    metadata = make_metadata(
        llm_config,
        dataset_descrption,
        args.agent_cls,
        args.max_iterations,
        args.eval_note,
        args.eval_output_dir,
        details=details,
    )

    output_file = os.path.join(metadata.eval_output_dir, 'output.jsonl')
    instances = prepare_dataset(swe_bench_tests, output_file, args.eval_n_limit)

    if len(instances) > 0 and not isinstance(
        instances['PASS_TO_PASS'][instances['PASS_TO_PASS'].index[0]], str
    ):
        for col in ['PASS_TO_PASS', 'FAIL_TO_PASS']:
            instances[col] = instances[col].apply(lambda x: str(x))

    run_evaluation(
        instances,
        metadata,
        output_file,
        args.eval_num_workers,
        process_instance,
        timeout_seconds=120 * 60,  # 2 hour PER instance should be more than enough
    )