runner.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

"""SuperBench Runner."""

import random
from pathlib import Path

from joblib import Parallel, delayed
from omegaconf import ListConfig, OmegaConf

from superbench.common.utils import SuperBenchLogger, logger
from superbench.runner.ansible import AnsibleClient


class SuperBenchRunner():
    """SuperBench runner class."""
    def __init__(self, sb_config, docker_config, ansible_config, sb_output_dir):
        """Initilize.

        Args:
            sb_config (DictConfig): SuperBench config object.
            docker_config (DictConfig): Docker config object.
            ansible_config (DictConfig): Ansible config object.
            sb_output_dir (str): SuperBench output directory.
        """
        self._sb_config = sb_config
        self._docker_config = docker_config
        self._ansible_config = ansible_config
        self._sb_output_dir = sb_output_dir
        self._output_path = Path(sb_output_dir).expanduser().resolve()
        self._ansible_client = AnsibleClient(ansible_config)

        self.__set_logger('sb-run.log')
        logger.info('Runner uses config: %s.', self._sb_config)
        logger.info('Runner writes to: %s.', str(self._output_path))

        self._sb_benchmarks = self._sb_config.superbench.benchmarks
        self.__validate_sb_config()
        self._sb_enabled_benchmarks = self.__get_enabled_benchmarks()
        logger.info('Runner will run: %s', self._sb_enabled_benchmarks)

    def __set_logger(self, filename):
        """Set logger and add file handler.

        Args:
            filename (str): Log file name.
        """
        SuperBenchLogger.add_handler(logger.logger, filename=str(self._output_path / filename))

    def __validate_sb_config(self):    # noqa: C901
        """Validate SuperBench config object.

        Raise:
            InvalidConfigError: If input config is invalid.
        """
        # TODO: add validation and defaulting
        if not self._sb_config.superbench.env:
            self._sb_config.superbench.env = {}
        for name in self._sb_benchmarks:
            if not self._sb_benchmarks[name].modes:
                self._sb_benchmarks[name].modes = []
            for idx, mode in enumerate(self._sb_benchmarks[name].modes):
                if mode.name == 'local':
                    if not mode.proc_num:
                        self._sb_benchmarks[name].modes[idx].proc_num = 1
                    if not mode.prefix:
                        self._sb_benchmarks[name].modes[idx].prefix = ''
                elif mode.name == 'torch.distributed':
                    if not mode.proc_num:
                        self._sb_benchmarks[name].modes[idx].proc_num = 8
                elif mode.name == 'mpi':
                    if not mode.mca:
                        self._sb_benchmarks[name].modes[idx].mca = {
                            'pml': 'ob1',
                            'btl': '^openib',
                            'btl_tcp_if_exclude': 'lo,docker0',
                            'coll_hcoll_enable': 0,
                        }
                    if not mode.env:
                        self._sb_benchmarks[name].modes[idx].env = {}
                    for key in ['PATH', 'LD_LIBRARY_PATH', 'SB_MICRO_PATH']:
                        self._sb_benchmarks[name].modes[idx].env.setdefault(key, None)

    def __get_enabled_benchmarks(self):
        """Get enabled benchmarks list.

        Return:
            list: List of benchmarks which will be executed.
        """
        if self._sb_config.superbench.enable:
            if isinstance(self._sb_config.superbench.enable, str):
                return [self._sb_config.superbench.enable]
            elif isinstance(self._sb_config.superbench.enable, (list, ListConfig)):
                return list(self._sb_config.superbench.enable)
        return [k for k, v in self._sb_benchmarks.items() if v.enable]

    def __get_mode_command(self, benchmark_name, mode):
        """Get runner command for given mode.

        Args:
            benchmark_name (str): Benchmark name.
            mode (DictConfig): Runner mode.

        Return:
            str: Runner command.
        """
        exec_command = ('sb exec --output-dir {output_dir} -c sb.config.yaml -C superbench.enable={name}').format(
            name=benchmark_name,
            output_dir=self._sb_output_dir,
        )
        mode_command = exec_command
        if mode.name == 'local':
            mode_command = '{prefix} {command}'.format(
                prefix=mode.prefix.format(proc_rank=mode.proc_rank, proc_num=mode.proc_num),
                command=exec_command,
            )
            mode_command = f'PROC_RANK={mode.proc_rank} {mode_command.strip()}'
        elif mode.name == 'torch.distributed':
            # TODO: replace with torch.distributed.run in v1.9
            # TODO: only supports node_num=1 and node_num=all currently
            mode_command = (
                'python3 -m torch.distributed.launch '
                '--use_env --no_python --nproc_per_node={proc_num} '
                '--nnodes={node_num} --node_rank=$NODE_RANK '
                '--master_addr=$MASTER_ADDR --master_port=$MASTER_PORT '
                '{command} {torch_distributed_suffix}'
            ).format(
                proc_num=mode.proc_num,
                node_num=1 if mode.node_num == 1 else '$NNODES',
                command=exec_command,
                torch_distributed_suffix=(
                    'superbench.benchmarks.{name}.parameters.distributed_impl=ddp '
                    'superbench.benchmarks.{name}.parameters.distributed_backend=nccl'
                ).format(name=benchmark_name),
            )
        elif mode.name == 'mpi':
            mode_command = (
                'mpirun '    # use default OpenMPI in image
                '-tag-output '    # tag mpi output with [jobid,rank]<stdout/stderr> prefix
                '-allow-run-as-root '    # allow mpirun to run when executed by root user
                '-hostfile hostfile '    # use prepared hostfile
                '-map-by ppr:{proc_num}:node '    # launch {proc_num} processes on each node
                '-bind-to numa '    # bind processes to numa
                '{mca_list} {env_list} {command}'
            ).format(
                proc_num=mode.proc_num,
                mca_list=' '.join(f'-mca {k} {v}' for k, v in mode.mca.items()),
                env_list=' '.join(f'-x {k}={v}' if v else f'-x {k}' for k, v in mode.env.items()),
                command=exec_command,
            )
        else:
            logger.warning('Unknown mode %s.', mode.name)
        return mode_command.strip()

    def deploy(self):    # pragma: no cover
        """Deploy SuperBench environment."""
        logger.info('Preparing SuperBench environment.')
        extravars = {
            'ssh_port': random.randint(1 << 14, (1 << 15) - 1),
            'output_dir': str(self._output_path),
            'docker_image': self._docker_config.image,
        }
        if bool(self._docker_config.username) and bool(self._docker_config.password):
            extravars.update(
                {
                    'docker_registry': self._docker_config.registry,
                    'docker_username': self._docker_config.username,
                    'docker_password': self._docker_config.password,
                }
            )
        self._ansible_client.run(self._ansible_client.get_playbook_config('deploy.yaml', extravars=extravars))

    def check_env(self):    # pragma: no cover
        """Check SuperBench environment."""
        logger.info('Checking SuperBench environment.')
        OmegaConf.save(config=self._sb_config, f=str(self._output_path / 'sb.config.yaml'))
        self._ansible_client.run(
            self._ansible_client.get_playbook_config(
                'check_env.yaml',
                extravars={
                    'output_dir': str(self._output_path),
                    'env': '\n'.join(f'{k}={v}' for k, v in self._sb_config.superbench.env.items()),
                }
            )
        )

    def fetch_results(self):    # pragma: no cover
        """Fetch benchmark results on all nodes."""
        try:
            (self._output_path / 'nodes').mkdir(mode=0o755, parents=True, exist_ok=True)
        except Exception:
            logger.exception('Failed to create directory %s.', str(self._output_path / 'nodes'))
            raise
        self._ansible_client.run(
            self._ansible_client.get_playbook_config(
                'fetch_results.yaml',
                extravars={
                    'sb_output_dir': self._sb_output_dir,
                    'absolute_output_dir': str(self._output_path),
                }
            )
        )

    def _run_proc(self, benchmark_name, mode, vars):
        """Run the process.

        Args:
            benchmark_name (str): Benchmark name.
            mode (DictConfig): Runner mode.
            vars (dict): Process variables.

        Returns:
            int: Process return code.
        """
        mode.update(vars)
        logger.info('Runner is going to run %s in %s mode, proc rank %d.', benchmark_name, mode.name, mode.proc_rank)
        ansible_runner_config = self._ansible_client.get_shell_config(
            (
                'docker exec sb-workspace bash -c '
                "'set -o allexport && source sb.env && set +o allexport && {command}'"
            ).format(command=self.__get_mode_command(benchmark_name, mode))
        )
        if mode.name == 'mpi':
            ansible_runner_config = self._ansible_client.update_mpi_config(ansible_runner_config)
        rc = self._ansible_client.run(ansible_runner_config, sudo=True)
        return rc

    def run(self):
        """Run the SuperBench benchmarks distributedly."""
        self.check_env()
        for benchmark_name in self._sb_benchmarks:
            if benchmark_name not in self._sb_enabled_benchmarks:
                continue
            benchmark_config = self._sb_benchmarks[benchmark_name]
            for mode in benchmark_config.modes:
                if mode.name == 'local':
                    Parallel(n_jobs=mode.proc_num if mode.parallel else 1)(
                        delayed(self._run_proc)(benchmark_name, mode, {
                            'proc_rank': proc_rank
                        }) for proc_rank in range(mode.proc_num)
                    )
                elif mode.name == 'torch.distributed' or mode.name == 'mpi':
                    self._run_proc(benchmark_name, mode, {'proc_rank': 0})
                else:
                    logger.warning('Unknown mode %s.', mode.name)
            self.fetch_results()