from __future__ import annotations import multiprocessing as mp import threading from typing import Dict, Optional, Sequence, Union import torch from hivemind import DHT, MAX_DHT_TIME_DISCREPANCY_SECONDS, BatchTensorDescriptor, get_dht_time from hivemind.moe.server.layers import add_custom_models_from_file from hivemind.moe.server.runtime import Runtime from hivemind.proto.runtime_pb2 import CompressionType from hivemind.utils.logging import get_logger, use_hivemind_log_handler from src import declare_active_modules, BloomConfig from src.bloom.from_pretrained import DTYPE_MAP, load_pretrained_block from src.data_structures import CHAIN_DELIMITER, UID_DELIMITER from src.server.backend import TransformerBackend from src.server.cache import MemoryCache from src.server.handler import TransformerConnectionHandler use_hivemind_log_handler("in_root_logger") logger = get_logger(__file__) class Server(threading.Thread): """Serves one or more bloom layers for inference, forward and backward; announces oneself to the DHT""" def __init__( self, dht: DHT, module_backends: Dict[str, TransformerBackend], *, device: torch.device, num_connection_handlers: int = 8, update_period: float = 30, expiration: Optional[float] = None, start: bool, **kwargs, ): threading.Thread.__init__(self) self.dht, self.module_backends, self.update_period = dht, module_backends, update_period self.conn_handlers = [ TransformerConnectionHandler(dht, self.module_backends) for _ in range(num_connection_handlers) ] self.runtime = Runtime(self.module_backends, device=device, **kwargs) self.dht_handler_thread = ModuleAnnouncerThread( self.module_backends, dht, update_period, expiration, daemon=True ) self.checkpoint_saver = None # no need to save checkpoints since we do not change model state if start: self.run_in_background(await_ready=True) def run(self): """ Starts Server in the current thread. Initializes dht if necessary, starts connection handlers, runs Runtime (self.runtime) to process incoming requests. """ logger.info(f"Serving {len(self.module_backends)} blocks:") for expert_name, backend in self.module_backends.items(): num_parameters = sum(p.numel() for p in backend.module.parameters() if p.requires_grad) logger.info(f"{expert_name}: {backend.module.__class__.__name__}, {num_parameters} parameters") if not self.dht.is_alive(): self.dht.run_in_background(await_ready=True) if self.module_backends: self.dht_handler_thread.start() if self.checkpoint_saver is not None: self.checkpoint_saver.start() for process in self.conn_handlers: if not process.is_alive(): process.start() process.ready.result() try: self.runtime.run() finally: self.shutdown() # noinspection PyMethodOverriding @classmethod def create( cls, prefix: Optional[str], converted_model_name_or_path: str, num_blocks: Optional[int] = None, block_indices: Optional[str] = None, num_handlers: Optional[int] = None, min_batch_size: int = 1, max_batch_size: int = 4096, torch_dtype: str = "auto", cache_size_bytes: Optional[int] = None, device: Union[str, torch.device] = None, initial_peers: Sequence[str] = (), compression=CompressionType.NONE, stats_report_interval: Optional[int] = None, custom_module_path=None, update_period: float = 30, expiration: Optional[float] = None, use_auth_token: Optional[str] = None, *, start: bool, **kwargs, ) -> Server: """Create a server with one or more bloom blocks. See run_server.py for documentation.""" if custom_module_path is not None: add_custom_models_from_file(custom_module_path) if prefix is None: prefix = converted_model_name_or_path assert UID_DELIMITER not in prefix and CHAIN_DELIMITER not in prefix, ( f"Cannot use model name as prefix (contains '{UID_DELIMITER}' or '{CHAIN_DELIMITER}'); " f"Please specify --prefix manually when starting a server" ) logger.info(f"Automatic dht prefix: {prefix}") assert (block_indices is None) != (num_blocks is None), "please specify num_blocks or block_indices, not both" dht = DHT(initial_peers=initial_peers, start=True, **kwargs) visible_maddrs_str = [str(a) for a in dht.get_visible_maddrs()] logger.info(f"Running DHT node on {visible_maddrs_str}, initial peers = {initial_peers}") device = device or ("cuda" if torch.cuda.is_available() else "cpu") memory_cache = MemoryCache(device, cache_size_bytes) if isinstance(torch_dtype, str): torch_dtype = DTYPE_MAP[torch_dtype] assert torch_dtype in DTYPE_MAP.values(), f"torch_dtype must be one of {list(DTYPE_MAP.values())}" if block_indices is not None: try: first_block_index, last_block_index = block_indices.split(":") first_block_index, last_block_index = map(int, map(str.strip, (first_block_index, last_block_index))) except Exception as e: logger.error(f"Failed to parse --block_indices ({e}), must be start:end (e.g. 0:18)") raise block_indices = range(first_block_index, last_block_index) else: assert num_blocks is not None block_indices = range(num_blocks) # TODO replace with proper load balancing block_config = BloomConfig.from_pretrained( converted_model_name_or_path, use_auth_token=use_auth_token ) # initialize modules blocks = {} for block_index in block_indices: module_uid = f"{prefix}.{block_index}" block = load_pretrained_block( converted_model_name_or_path, block_index, block_config, torch_dtype=torch_dtype, use_auth_token=use_auth_token, ) for param in block.parameters(): param.requires_grad = False blocks[module_uid] = TransformerBackend( module_uid, block, memory_cache=memory_cache, args_schema=(BatchTensorDescriptor(1, 2048, block_config.hidden_size, compression=compression),), kwargs_schema={}, outputs_schema=(BatchTensorDescriptor(1, 2048, block_config.hidden_size, compression=compression),), min_batch_size=min_batch_size, max_batch_size=max_batch_size, ) num_handlers = num_handlers if num_handlers is not None else len(blocks) * 4 return cls( dht, blocks, num_connection_handlers=num_handlers, device=device, stats_report_interval=stats_report_interval, update_period=update_period, expiration=expiration, start=start, ) def run_in_background(self, await_ready=True, timeout=None): """ Starts Server in a background thread. if await_ready, this method will wait until background server is ready to process incoming requests or for :timeout: seconds max. """ self.start() if await_ready and not self.ready.wait(timeout=timeout): raise TimeoutError("Server didn't notify .ready in {timeout} seconds") @property def ready(self) -> mp.synchronize.Event: """ An event (multiprocessing.Event) that is set when the server is ready to process requests. Example ======= >>> server.start() >>> server.ready.wait(timeout=10) >>> print("Server ready" if server.ready.is_set() else "Server didn't start in 10 seconds") """ return self.runtime.ready # mp.Event that is true if self is ready to process batches def shutdown(self): """ Gracefully terminate the server, process-safe. Please note that terminating server otherwise (e.g. by killing processes) may result in zombie processes. If you did already cause a zombie outbreak, your only option is to kill them with -9 (SIGKILL). """ self.ready.clear() for process in self.conn_handlers: process.terminate() process.join() logger.debug("Connection handlers terminated") if self.module_backends: self.dht_handler_thread.stop.set() self.dht_handler_thread.join() if self.checkpoint_saver is not None: self.checkpoint_saver.stop.set() self.checkpoint_saver.join() self.dht.shutdown() self.dht.join() logger.debug(f"Shutting down runtime") self.runtime.shutdown() logger.info("Server shutdown succesfully") class ModuleAnnouncerThread(threading.Thread): """Periodically announces that this server hosts the specified modules, visible to all DHT peers""" def __init__( self, module_backends, dht: DHT, update_period: float = 30, expiration: Optional[int] = None, **kwargs ): super().__init__(**kwargs) if expiration is None: expiration = max(2 * update_period, MAX_DHT_TIME_DISCREPANCY_SECONDS) self.module_backends = module_backends self.dht = dht self.update_period = update_period self.expiration = expiration self.stop = threading.Event() def run(self) -> None: declare_active_modules(self.dht, self.module_backends.keys(), get_dht_time() + self.expiration) while not self.stop.wait(self.update_period): declare_active_modules(self.dht, self.module_backends.keys(), get_dht_time() + self.expiration)