from __future__ import annotations import gc import math import multiprocessing as mp import random import threading import time from typing import Dict, List, Optional, Sequence, Union import hivemind 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 from transformers import PretrainedConfig import petals from petals.constants import DTYPE_MAP, PUBLIC_INITIAL_PEERS from petals.data_structures import CHAIN_DELIMITER, UID_DELIMITER, ServerInfo, ServerState from petals.server import block_selection from petals.server.backend import TransformerBackend, merge_inference_pools_inplace from petals.server.block_utils import get_block_size, resolve_block_dtype from petals.server.from_pretrained import load_pretrained_block from petals.server.handler import TransformerConnectionHandler from petals.server.memory_cache import MemoryCache from petals.server.reachability import ReachabilityProtocol, check_direct_reachability, validate_reachability from petals.server.throughput import get_dtype_name, get_server_throughput from petals.utils.auto_config import AutoDistributedConfig from petals.utils.convert_block import QuantType, check_device_balance, convert_block from petals.utils.dht import declare_active_modules, get_remote_module_infos from petals.utils.ping import PingAggregator from petals.utils.random import sample_up_to from petals.utils.version import get_compatible_model_repo logger = get_logger(__name__) class Server: """ Runs ModuleContainer, periodically checks that the network is balanced, restarts the ModuleContainer with other layers if the imbalance is significant """ def __init__( self, *, initial_peers: List[str], dht_prefix: Optional[str], converted_model_name_or_path: str, public_name: Optional[str] = None, throughput: Union[float, str], num_blocks: Optional[int] = None, block_indices: Optional[str] = None, num_handlers: int = 8, inference_max_length: Optional[int] = None, min_batch_size: int = 1, max_batch_size: Optional[int] = None, max_chunk_size_bytes: int = 256 * 1024 * 1024, attn_cache_tokens: Optional[int] = None, torch_dtype: str = "auto", revision: Optional[str] = None, cache_dir: Optional[str] = None, max_disk_space: Optional[int] = None, alloc_timeout: float = 5, device: Optional[Union[str, torch.device]] = None, compression=CompressionType.NONE, stats_report_interval: Optional[int] = None, custom_module_path=None, update_period: float = 60, expiration: Optional[float] = None, request_timeout: float = 3 * 60, session_timeout: float = 30 * 60, step_timeout: float = 5 * 60, prefetch_batches: int = 1, sender_threads: int = 1, balance_quality: float = 0.75, mean_balance_check_period: float = 120, mean_block_selection_delay: float = 5, token: Optional[Union[str, bool]] = None, quant_type: Optional[QuantType] = None, tensor_parallel_devices: Optional[Sequence[torch.device]] = None, skip_reachability_check: bool = False, reachable_via_relay: Optional[bool] = None, use_relay: bool = True, use_auto_relay: bool = True, adapters: Sequence[str] = (), **kwargs, ): """Create a server with one or more bloom blocks. See run_server.py for documentation.""" converted_model_name_or_path = get_compatible_model_repo(converted_model_name_or_path) self.converted_model_name_or_path = converted_model_name_or_path self.num_handlers = num_handlers self.compression = compression self.stats_report_interval, self.update_period = stats_report_interval, update_period self.prefetch_batches, self.sender_threads = prefetch_batches, sender_threads self.revision, self.token = revision, token if custom_module_path is not None: add_custom_models_from_file(custom_module_path) self.block_config = AutoDistributedConfig.from_pretrained( converted_model_name_or_path, use_auth_token=token, revision=revision, ) if dht_prefix is None: dht_prefix = self.block_config.dht_prefix assert UID_DELIMITER not in dht_prefix and CHAIN_DELIMITER not in dht_prefix, ( f"DHT prefix should not contain '{UID_DELIMITER}' or '{CHAIN_DELIMITER}'. " f"Please specify another --dht_prefix manually when starting a server" ) self.dht_prefix = dht_prefix if expiration is None: expiration = max(2 * update_period, MAX_DHT_TIME_DISCREPANCY_SECONDS) self.expiration = expiration self.request_timeout = request_timeout self.session_timeout, self.step_timeout = session_timeout, step_timeout self.module_uids = [ f"{self.dht_prefix}{UID_DELIMITER}{block_index}" for block_index in range(self.block_config.num_hidden_layers) ] if reachable_via_relay is None: is_reachable = check_direct_reachability(initial_peers=initial_peers, use_relay=False, **kwargs) reachable_via_relay = is_reachable is False # if can't check reachability (returns None), run a full peer logger.info(f"This server is accessible {'via relays' if reachable_via_relay else 'directly'}") self.dht = DHT( initial_peers=initial_peers, start=True, num_workers=self.block_config.num_hidden_layers, use_relay=use_relay, use_auto_relay=use_auto_relay, client_mode=reachable_via_relay, **kwargs, ) self.reachability_protocol = ReachabilityProtocol.attach_to_dht(self.dht) if not reachable_via_relay else None visible_maddrs_str = [str(a) for a in self.dht.get_visible_maddrs()] if initial_peers == PUBLIC_INITIAL_PEERS: logger.info("Connecting to the public swarm") else: logger.info(f"Connecting to a private swarm, initial peers: {initial_peers}") logger.info(f"Running a server on {visible_maddrs_str}") self.should_validate_reachability = not skip_reachability_check and initial_peers == PUBLIC_INITIAL_PEERS if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" device = torch.device(device) if device.type == "cuda" and device.index is None: device = torch.device(device.type, index=0) self.device = device torch_dtype = resolve_block_dtype(self.block_config, DTYPE_MAP[torch_dtype]) self.torch_dtype = torch_dtype if tensor_parallel_devices is None: tensor_parallel_devices = (device,) self.tensor_parallel_devices = tuple(map(torch.device, tensor_parallel_devices)) if len(self.tensor_parallel_devices) > 1: logger.info(f"Model weights will be split between {', '.join(tensor_parallel_devices)}") check_device_balance(self.tensor_parallel_devices) if quant_type is None: if device.type == "cuda": quant_type = QuantType.NF4 if self.block_config.model_type == "llama" else QuantType.INT8 else: quant_type = QuantType.NONE self.quant_type = quant_type logger.info(f"Model weights are loaded in {get_dtype_name(torch_dtype, quant_type)} format") is_multiquery_attn = self.block_config.num_key_value_groups > 1 if max_batch_size is None: max_batch_size = 8192 if is_multiquery_attn else 2048 if inference_max_length is None: inference_max_length = 8192 if is_multiquery_attn else 2048 self.min_batch_size, self.max_batch_size = min_batch_size, max_batch_size self.inference_max_length = inference_max_length self.max_chunk_size_bytes = max_chunk_size_bytes # For attention cache in GPU or RAM if attn_cache_tokens is None: attn_cache_tokens = 32768 if is_multiquery_attn else 8192 cache_values_per_block = 2 * self.block_config.hidden_size * attn_cache_tokens cache_values_per_block //= self.block_config.num_key_value_groups self._cache_bytes_per_block = cache_values_per_block * torch.finfo(self.torch_dtype).bits // 8 # For disk cache self.cache_dir = cache_dir self.max_disk_space = max_disk_space self.adapters = adapters assert num_blocks is None or block_indices is None, "Please specify num_blocks or block_indices, not both" if num_blocks is None and block_indices is None: num_blocks = self._choose_num_blocks() if num_blocks is not None: num_blocks = min(num_blocks, self.block_config.num_hidden_layers) 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: raise ValueError(f"Failed to parse `--block_indices {block_indices}`, must be start:end (e.g. 0:18)") block_indices = range(first_block_index, last_block_index) num_blocks = len(block_indices) self.strict_block_indices, self.num_blocks = block_indices, num_blocks gib = 1024**3 self.attn_cache_bytes = self._cache_bytes_per_block * num_blocks logger.info(f"Attention cache for all blocks will consume up to {self.attn_cache_bytes / gib:.2f} GiB") self.alloc_timeout = alloc_timeout assert isinstance(throughput, float) or throughput in ["auto", "eval"] if throughput in ["auto", "eval"]: throughput_info = get_server_throughput( converted_model_name_or_path, self.block_config, device, torch_dtype, num_blocks=num_blocks, quant_type=quant_type, tensor_parallel_devices=self.tensor_parallel_devices, reachable_via_relay=reachable_via_relay, force_eval=(throughput == "eval"), cache_dir=cache_dir, ) else: throughput_info = {"throughput": throughput} self.server_info = ServerInfo( state=ServerState.JOINING, public_name=public_name, version=petals.__version__, adapters=tuple(adapters), torch_dtype=str(torch_dtype).replace("torch.", ""), quant_type=quant_type.name.lower(), using_relay=reachable_via_relay, **throughput_info, ) self.balance_quality = balance_quality self.mean_balance_check_period = mean_balance_check_period self.mean_block_selection_delay = mean_block_selection_delay self.stop = threading.Event() def _choose_num_blocks(self) -> int: assert self.device.type == "cuda", ( "GPU is not available. If you want to run a CPU-only server, please specify --num_blocks. " "CPU-only servers in the public swarm are discouraged since they are much slower" ) num_devices = len(self.tensor_parallel_devices) if self.tensor_parallel_devices else 1 if num_devices > 1: memory_per_device = tuple( torch.cuda.get_device_properties(device).total_memory for device in self.tensor_parallel_devices ) total_memory = min(memory_per_device) * num_devices if max(memory_per_device) / min(memory_per_device) > 1.5: raise ValueError( "GPU devices have highly uneven memory, which makes tensor parallelism inefficient. " "Please launch individual servers on each GPU or set --num_blocks manually to " "override this exception." ) else: total_memory = torch.cuda.get_device_properties(self.device).total_memory gib = 1024**3 # Estimate of GPU memory used in rpc_backward (2 GiB for BLOOM, proportional for other models) autograd_memory = 2 * gib * num_devices / 14336 * self.block_config.hidden_size block_size = get_block_size(self.block_config, "memory", dtype=self.torch_dtype, quant_type=self.quant_type) total_memory_per_block = block_size + self._cache_bytes_per_block if self.adapters: # Delay import of petals.utils.peft to avoid unnecessary import of bitsandbytes from petals.utils.peft import estimate_adapter_memory_per_block total_memory_per_block += estimate_adapter_memory_per_block( self.block_config, self.torch_dtype, self.adapters, token=self.token, cache_dir=self.cache_dir, max_disk_space=self.max_disk_space, ) num_blocks = math.floor((total_memory - autograd_memory) / total_memory_per_block) assert num_blocks >= 1, "Your GPU does not have enough memory to serve at least one block" num_blocks = min(num_blocks, self.block_config.num_hidden_layers) logger.info( f"Server will fill your GPU memory with {num_blocks} transformer blocks. " f"If you want to leave some free GPU memory, please specify a lesser --num_blocks manually" ) return num_blocks def run(self): while True: block_indices = self._choose_blocks() self.module_container = ModuleContainer.create( dht=self.dht, dht_prefix=self.dht_prefix, converted_model_name_or_path=self.converted_model_name_or_path, block_config=self.block_config, attn_cache_bytes=self.attn_cache_bytes, alloc_timeout=self.alloc_timeout, server_info=self.server_info, block_indices=block_indices, num_handlers=self.num_handlers, min_batch_size=self.min_batch_size, max_batch_size=self.max_batch_size, max_chunk_size_bytes=self.max_chunk_size_bytes, inference_max_length=self.inference_max_length, torch_dtype=self.torch_dtype, cache_dir=self.cache_dir, max_disk_space=self.max_disk_space, device=self.device, compression=self.compression, stats_report_interval=self.stats_report_interval, update_period=self.update_period, expiration=self.expiration, request_timeout=self.request_timeout, session_timeout=self.session_timeout, step_timeout=self.step_timeout, prefetch_batches=self.prefetch_batches, sender_threads=self.sender_threads, revision=self.revision, token=self.token, quant_type=self.quant_type, tensor_parallel_devices=self.tensor_parallel_devices, should_validate_reachability=self.should_validate_reachability, start=True, ) try: self.module_container.ready.wait() while True: timeout = random.random() * 2 * self.mean_balance_check_period if self.stop.wait(timeout): return if not self.module_container.is_healthy(): logger.warning("One of subprocesses crashed, restarting the server") break if self._should_choose_other_blocks(): logger.info("Swarm is imbalanced, server will load other blocks") break # Stop serving this set of modules finally: self.module_container.shutdown() self._clean_memory_and_fds() def _clean_memory_and_fds(self): del self.module_container gc.collect() # In particular, this closes unused file descriptors if self.device.type == "cuda": torch.cuda.empty_cache() allocated_vram = torch.cuda.memory_allocated(self.device) reserved_vram = torch.cuda.memory_reserved(self.device) gib = 1024**3 logger.info( f"Cleaning up, left {allocated_vram / gib:.1f} GiB allocated memory, " f"{reserved_vram / gib:.1f} GiB reserved memory" ) def _choose_blocks(self) -> List[int]: if self.strict_block_indices is not None: return self.strict_block_indices # If multiple servers (e.g., launched on the same machine by a script) get to this line at the same time, # this delay decreases the probability of a race condition while choosing the best blocks to serve. time.sleep(random.random() * 2 * self.mean_block_selection_delay) module_infos = get_remote_module_infos(self.dht, self.module_uids, latest=True) return block_selection.choose_best_blocks(self.num_blocks, module_infos) def _should_choose_other_blocks(self) -> bool: if self.strict_block_indices is not None: return False module_infos = get_remote_module_infos(self.dht, self.module_uids, latest=True) return block_selection.should_choose_other_blocks(self.dht.peer_id, module_infos, self.balance_quality) def shutdown(self): self.stop.set() if self.reachability_protocol is not None: self.reachability_protocol.shutdown() self.dht.shutdown() self.dht.join() class ModuleContainer(threading.Thread): """Serves a set of specific Bloom layers for inference, forward, and backward. Announces itself over the DHT.""" # noinspection PyMethodOverriding @classmethod def create( cls, *, dht: DHT, dht_prefix: str, converted_model_name_or_path: str, block_config: PretrainedConfig, attn_cache_bytes: int, alloc_timeout: float, server_info: ServerInfo, block_indices: List[int], min_batch_size: int, max_batch_size: int, max_chunk_size_bytes: int, torch_dtype: torch.dtype, cache_dir: str, max_disk_space: int, device: Union[str, torch.device], compression: CompressionType, update_period: float, expiration: Optional[float], revision: Optional[str], token: Optional[Union[str, bool]], quant_type: QuantType, tensor_parallel_devices: Sequence[torch.device], should_validate_reachability: bool, **kwargs, ) -> ModuleContainer: module_uids = [f"{dht_prefix}{UID_DELIMITER}{block_index}" for block_index in block_indices] memory_cache = MemoryCache(attn_cache_bytes, alloc_timeout) server_info.state = ServerState.JOINING dht_announcer = ModuleAnnouncerThread( module_uids, dht, server_info, block_config=block_config, memory_cache=memory_cache, update_period=update_period, expiration=expiration, daemon=True, ) dht_announcer.start() logger.info(f"Announced that blocks {block_indices} are joining") assert len(tensor_parallel_devices) >= 1 and all(isinstance(d, torch.device) for d in tensor_parallel_devices) blocks = {} try: for module_uid, block_index in zip(module_uids, block_indices): block = load_pretrained_block( converted_model_name_or_path, block_index, config=block_config, torch_dtype=torch_dtype, revision=revision, token=token, cache_dir=cache_dir, max_disk_space=max_disk_space, ) block = convert_block( block, block_index, block_config, tensor_parallel_devices, device, quant_type, adapters=server_info.adapters, freeze=True, token=token, cache_dir=cache_dir, max_disk_space=max_disk_space, ) blocks[module_uid] = TransformerBackend( module_uid, block, config=block_config, memory_cache=memory_cache, backend_dtype=torch_dtype, max_chunk_size_bytes=max_chunk_size_bytes, args_schema=( BatchTensorDescriptor( 1, 2048, block_config.hidden_size, dtype=torch_dtype, compression=compression ), ), kwargs_schema={}, outputs_schema=( BatchTensorDescriptor( 1, 2048, block_config.hidden_size, dtype=torch_dtype, compression=compression ), ), min_batch_size=min_batch_size, max_batch_size=max_batch_size, ) merge_inference_pools_inplace(blocks) if should_validate_reachability: validate_reachability(dht.peer_id) except: logger.debug("Shutting down backends") for backend in blocks.values(): backend.shutdown() dht_announcer.announce(ServerState.OFFLINE) logger.info(f"Announced that blocks {module_uids} are offline") raise return cls( dht, dht_prefix, blocks, dht_announcer=dht_announcer, server_info=server_info, update_period=update_period, expiration=expiration, **kwargs, ) def __init__( self, dht: DHT, dht_prefix: str, module_backends: Dict[str, TransformerBackend], *, inference_max_length: int, num_handlers: int, dht_announcer: ModuleAnnouncerThread, server_info: ServerInfo, update_period: float, expiration: Optional[float] = None, request_timeout: float, session_timeout: float, step_timeout: float, start: bool, **kwargs, ): super().__init__() self.dht, self.module_backends = dht, module_backends self.server_info, self.update_period, self.expiration = server_info, update_period, expiration handler_event_queues = [mp.Queue() for _ in range(num_handlers)] self.conn_handlers = [ TransformerConnectionHandler( dht, self.module_backends, adapters=server_info.adapters, dht_prefix=dht_prefix, handler_event_queues=handler_event_queues, handler_index=i, inference_max_length=inference_max_length, request_timeout=request_timeout, session_timeout=session_timeout, step_timeout=step_timeout, quant_type=QuantType[server_info.quant_type.upper()], ) for i in range(num_handlers) ] self.runtime = RuntimeWithDeduplicatedPools(self.module_backends, device=None, **kwargs) # note: We set device=None in runtime to avoid moving all modules to device 0 in runtime.run(). tensor_parallel has already moved it as needed. dht_announcer.announce(ServerState.ONLINE) self.dht_announcer = dht_announcer if start: self.run_in_background(await_ready=True) def run(self): """ Runs ModuleContainer in the current thread. Initializes dht if necessary, starts connection handlers, runs Runtime (self.runtime) to process incoming requests. """ for handler in self.conn_handlers: handler.run_in_background() self.runtime.run() def run_in_background(self, await_ready=True, timeout=None): """ Starts ModuleContainer in a background thread. if await_ready, this method will wait until the container 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("ModuleContainer didn't notify .ready in {timeout} seconds") @property def ready(self) -> mp.synchronize.Event: """ An event (multiprocessing.Event) that is set when the container is ready to process requests. Example ======= >>> container.start() >>> container.ready.wait(timeout=10) >>> print("Container ready" if container.ready.is_set() else "Container didn't start in 10 seconds") """ return self.runtime.ready # mp.Event that is true if self is ready to process batches def is_healthy(self) -> bool: return all(handler.is_alive() for handler in self.conn_handlers) and all( pool.is_alive() for pool in self.runtime.pools ) def shutdown(self): """ Gracefully terminate the container, process-safe. Please note that terminating container 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.dht_announcer.announce(ServerState.OFFLINE) logger.info(f"Announced that blocks {list(self.module_backends.keys())} are offline") self.ready.clear() logger.debug("Shutting down connection handlers") for handler in self.conn_handlers: handler.shutdown() logger.debug(f"Shutting down pools") for pool in self.runtime.pools: if pool.is_alive(): pool.shutdown() logger.debug(f"Shutting down runtime") self.runtime.shutdown() logger.debug("Shutting down backends") for backend in self.module_backends.values(): backend.shutdown() logger.info("Module container shut down successfully") class ModuleAnnouncerThread(threading.Thread): """Periodically announces that this container hosts the specified modules, visible to all DHT peers""" def __init__( self, module_uids: List[str], dht: DHT, server_info: ServerInfo, *, block_config: PretrainedConfig, memory_cache: MemoryCache, update_period: float, expiration: float, max_pinged: int = 5, **kwargs, ): super().__init__(**kwargs) self.module_uids = module_uids self.dht = dht self.server_info = server_info self.memory_cache = memory_cache self.bytes_per_token = block_config.hidden_size * torch.finfo(DTYPE_MAP[server_info.torch_dtype]).bits // 8 self.bytes_per_token //= block_config.num_key_value_groups self.update_period = update_period self.expiration = expiration self.trigger = threading.Event() self.max_pinged = max_pinged dht_prefix = module_uids[0].split(UID_DELIMITER)[0] block_indices = [int(uid.split(UID_DELIMITER)[-1]) for uid in module_uids] start_block, end_block = min(block_indices), max(block_indices) + 1 self.next_uids = [f"{dht_prefix}{UID_DELIMITER}{i}" for i in range(start_block + 1, end_block + 1)] self.ping_aggregator = PingAggregator(self.dht) def run(self) -> None: while True: start_time = time.perf_counter() self.server_info.cache_tokens_left = self.memory_cache.bytes_left // self.bytes_per_token if self.server_info.state != ServerState.OFFLINE: self._ping_next_servers() self.server_info.next_pings = { peer_id.to_base58(): rtt for peer_id, rtt in self.ping_aggregator.to_dict().items() } else: self.server_info.next_pings = None # No need to ping if we're disconnecting declare_active_modules( self.dht, self.module_uids, self.server_info, expiration_time=get_dht_time() + self.expiration, ) if self.server_info.state == ServerState.OFFLINE: break delay = self.update_period - (time.perf_counter() - start_time) if delay < 0: logger.warning( f"Declaring blocks to DHT takes more than --update_period, consider increasing it (currently {self.update_period})" ) self.trigger.wait(max(delay, 0)) self.trigger.clear() def announce(self, state: ServerState) -> None: self.server_info.state = state self.trigger.set() if state == ServerState.OFFLINE: self.join() def _ping_next_servers(self) -> Dict[hivemind.PeerID, float]: module_infos = get_remote_module_infos(self.dht, self.next_uids, latest=True) middle_servers = {peer_id for info in module_infos[:-1] if info is not None for peer_id in info.servers} pinged_servers = set(sample_up_to(middle_servers, self.max_pinged)) pinged_servers.discard(self.dht.peer_id) if module_infos[-1] is not None: # Sample servers hosting the block after the last one (most likely continuations) separately pinged_servers |= set(sample_up_to(module_infos[-1].servers, self.max_pinged)) self.ping_aggregator.ping(list(pinged_servers)) class RuntimeWithDeduplicatedPools(Runtime): """A version of hivemind.moe.server.runtime.Runtime that allows multiple backends to reuse a task pool""" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.pools = tuple(set(self.pools))