OpenHands/evaluation/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 agenthub
from evaluation.swe_bench.prompt import CODEACT_SWE_PROMPT
from evaluation.utils.shared import (
    EvalMetadata,
    EvalOutput,
    codeact_user_response,
    make_metadata,
    prepare_dataset,
    reset_logger_for_multiprocessing,
    run_evaluation,
)
from opendevin.controller.state.state import State
from opendevin.core.config import (
    AppConfig,
    SandboxConfig,
    get_llm_config_arg,
    parse_arguments,
)
from opendevin.core.logger import opendevin_logger as logger
from opendevin.core.main import create_runtime, run_controller
from opendevin.events.action import CmdRunAction
from opendevin.events.observation import CmdOutputObservation, ErrorObservation
from opendevin.runtime.runtime import Runtime

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

AGENT_CLS_TO_FAKE_USER_RESPONSE_FN = {
    'CodeActAgent': codeact_user_response,
    'CodeActSWEAgent': codeact_user_response,
}

AGENT_CLS_TO_INST_SUFFIX = {
    'CodeActAgent': 'When you think you have fixed the issue through code changes, please run the following command: <execute_bash> exit </execute_bash>.\n',
    'CodeActSWEAgent': 'When you think you have fixed the issue through code changes, please run the following command: <execute_bash> exit </execute_bash>.\n',
}


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
    if metadata.agent_class == 'CodeActSWEAgent':
        instruction = (
            'We are currently solving the following issue within our repository. Here is the issue text:\n'
            '--- BEGIN ISSUE ---\n'
            f'{instance.problem_statement}\n'
            '--- END ISSUE ---\n\n'
        )
        if USE_HINT_TEXT and instance.hints_text:
            instruction += (
                f'--- BEGIN HINTS ---\n{instance.hints_text}\n--- END HINTS ---\n'
            )
        instruction += CODEACT_SWE_PROMPT.format(workspace_dir_name=workspace_dir_name)
    else:
        # Testing general agents
        instruction = (
            f'Please fix the following issue for the repository in /workspace/{workspace_dir_name}.\n'
            'Environment has been set up for you to start working. You may assume all necessary tools are installed.\n\n'
            '# Problem Statement\n'
            f'{instance.problem_statement}\n\n'
        )
        if USE_HINT_TEXT and instance.hints_text:
            instruction += f'# Hints\n{instance.hints_text}\n\n'
        instruction += (
            'IMPORTANT: You should ONLY interact with the environment provided to you AND NEVER ASK FOR HUMAN HELP.\n'
            'You should NOT modify any existing test case files. If needed, you can add new test cases in a NEW file to reproduce the issue.\n'
            'You SHOULD INCLUDE PROPER INDENTATION in your edit commands.\n'
        )

    # NOTE: You can actually set slightly different instruction for different agents
    instruction += AGENT_CLS_TO_INST_SUFFIX[metadata.agent_class]
    return instruction


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
        container_image = 'sweb.eval.x86_64.' + instance['instance_id']
    else:
        container_image = SWE_BENCH_CONTAINER_IMAGE

    config = AppConfig(
        default_agent=metadata.agent_class,
        run_as_devin=False,
        runtime='eventstream',
        max_budget_per_task=4,
        max_iterations=metadata.max_iterations,
        sandbox=SandboxConfig(
            container_image=container_image,
            enable_auto_lint=True,
            use_host_network=False,
            # always make sure we are using the latest source code
            update_source_code=True,
            # large enough timeout, since some testcases take very long to run
            timeout=300,
        ),
        # 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,  # 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"""
    )
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0

    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')
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = await runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        assert (
            obs.exit_code == 0
        ), f'Failed to create /swe_util/eval_data/instances: {obs.content}'

        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
            await runtime.copy_to(temp_file_path, '/swe_util/eval_data/instances/')

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

        action = CmdRunAction(command='source ~/.bashrc')
        logger.info(action, extra={'msg_type': 'ACTION'})
        obs = await runtime.run_action(action)
        logger.info(obs, extra={'msg_type': 'OBSERVATION'})
        assert obs.exit_code == 0

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

    action = CmdRunAction(command=f'cd /workspace/{workspace_dir_name}')
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0

    action = CmdRunAction(command='git reset --hard')
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0

    action = CmdRunAction(
        command='for remote_name in $(git remote); do git remote remove "${remote_name}"; done'
    )
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0

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


async 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}')
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0

    action = CmdRunAction(command='git config --global core.pager ""')
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0

    action = CmdRunAction(command='git add -A')
    logger.info(action, extra={'msg_type': 'ACTION'})
    obs = await runtime.run_action(action)
    logger.info(obs, extra={'msg_type': 'OBSERVATION'})
    assert obs.exit_code == 0

    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 = await 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...')
                await asyncio.sleep(10)
        elif isinstance(obs, ErrorObservation):
            logger.error(f'Error occurred: {obs.content}. Retrying...')
            await asyncio.sleep(10)
        else:
            raise ValueError(f'Unexpected observation type: {type(obs)}')

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


async 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 = await create_runtime(config, sid=instance.instance_id)
    await 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 = await run_controller(
        config=config,
        task_str=instruction,
        runtime=runtime,
        fake_user_response_fn=AGENT_CLS_TO_FAKE_USER_RESPONSE_FN[metadata.agent_class],
    )

    # ======= THIS IS SWE-Bench specific =======
    # Get git patch
    return_val = await 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--------'
    )
    # ==========================================

    # ======= 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.')

    # 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=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__':
    args = parse_arguments()

    # NOTE: It is preferable to load datasets from huggingface datasets and perform post-processing
    # so we don't need to manage file uploading to OpenDevin's repo
    dataset = load_dataset('princeton-nlp/SWE-bench_Lite')
    swe_bench_tests = filter_dataset(dataset['test'].to_pandas(), 'instance_id')

    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}')

    details = {}
    _agent_cls = agenthub.Agent.get_cls(args.agent_cls)
    if hasattr(_agent_cls, 'system_message'):
        details['system_message'] = _agent_cls.system_message
    if hasattr(_agent_cls, 'in_context_example'):
        details['in_context_example'] = _agent_cls.in_context_example

    metadata = make_metadata(
        llm_config,
        'swe-bench-lite',
        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)

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