petals/cli/run_server.py

import configargparse

from hivemind.proto.runtime_pb2 import CompressionType
from hivemind.utils.limits import increase_file_limit
from hivemind.utils.logging import get_logger, use_hivemind_log_handler

from src.server.server import Server

use_hivemind_log_handler("in_root_logger")
logger = get_logger(__name__)


def main():
    # fmt:off
    parser = configargparse.ArgParser(default_config_files=["config.yml"])
    parser.add('-c', '--config', required=False, is_config_file=True, help='config file path')

    parser.add_argument('--block_config', type=str, default='bigscience/bloom', help="name or path of model config")
    parser.add_argument('--num_blocks', type=int, default=1, help="The number of blocks to serve")
    parser.add_argument('--host_maddrs', type=list, nargs='+', default=['/ip4/0.0.0.0/tcp/0'], required=False,
                        help='Multiaddrs to listen for external connections from other p2p instances; default: all IPv4 and TCP: /ip4/0.0.0.0/tcp/0')
    parser.add_argument('--announce_maddrs', type=list, nargs='+', default=None, required=False,
                        help='Visible multiaddrs the host announces for external connections from other p2p instances')

    parser.add_argument('--compression', type=str, default='NONE', required=False, help='Tensor compression communication')

    parser.add_argument('--num_handlers', type=int, default=None, required=False,
                        help='server will use this many processes to handle incoming requests')
    parser.add_argument('--min_batch_size', type=int, default=1,
                        help='Minimum required batch size for all expert operations')
    parser.add_argument('--max_batch_size', type=int, default=16384,
                        help='The total number of examples in the same batch will not exceed this value')
    parser.add_argument('--cache_size_bytes', type=int, default=None,
                        help='The size of memory cache for storing past attention keys/values between inference steps')
    parser.add_argument('--device', type=str, default=None, required=False,
                        help='all experts will use this device in torch notation; default: cuda if available else cpu')

    parser.add_argument('--update_period', type=float, required=False, default=30,
                        help='Server will report experts to DHT once in this many seconds')
    parser.add_argument('--expiration', type=float, required=False, default=None,
                        help='DHT entries will expire after this many seconds')
    parser.add_argument('--initial_peers', type=str, nargs='*', required=False, default=[],
                        help='multiaddrs of one or more active DHT peers (if you want to join an existing DHT)')
    parser.add_argument('--increase_file_limit', action='store_true',
                        help='On *nix, this will increase the max number of processes '
                             'a server can spawn before hitting "Too many open files"; Use at your own risk.')
    parser.add_argument('--stats_report_interval', type=int, required=False,
                        help='Interval between two reports of batch processing performance statistics')

    parser.add_argument('--custom_module_path', type=str, required=False,
                        help='Path of a file with custom nn.modules, wrapped into special decorator')

    # fmt:on
    args = vars(parser.parse_args())
    args.pop("config", None)

    if args.pop("increase_file_limit"):
        increase_file_limit()

    compression_type = args.pop("compression")
    compression = getattr(CompressionType, compression_type)

    server = Server.create(**args, start=True, compression=compression)

    try:
        server.join()
    except KeyboardInterrupt:
        logger.info("Caught KeyboardInterrupt, shutting down")
    finally:
        server.shutdown()


if __name__ == "__main__":
    main()