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476 lines
18 KiB
Python
476 lines
18 KiB
Python
from __future__ import annotations
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import gc
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import multiprocessing as mp
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import random
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import threading
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import time
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from typing import Dict, List, Optional, Sequence, Union
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import numpy as np
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import psutil
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import torch
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from hivemind import DHT, MAX_DHT_TIME_DISCREPANCY_SECONDS, BatchTensorDescriptor, get_dht_time
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from hivemind.moe.server.layers import add_custom_models_from_file
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from hivemind.moe.server.runtime import Runtime
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from hivemind.proto.runtime_pb2 import CompressionType
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from hivemind.utils.logging import get_logger, use_hivemind_log_handler
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from src import BloomConfig, declare_active_modules
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from src.bloom.from_pretrained import DTYPE_MAP, load_pretrained_block
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from src.data_structures import CHAIN_DELIMITER, UID_DELIMITER, ServerState
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from src.dht_utils import get_remote_module_infos
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from src.server import block_selection
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from src.server.backend import TransformerBackend
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from src.server.cache import MemoryCache
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from src.server.handler import TransformerConnectionHandler
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from src.server.throughput import get_host_throughput
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from src.utils.convert_8bit import replace_8bit_linear
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use_hivemind_log_handler("in_root_logger")
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logger = get_logger(__file__)
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class Server(threading.Thread):
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"""
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Runs ModuleContainer, periodically checks that the network is balanced,
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restarts the ModuleContainer with other layers if the imbalance is significant
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"""
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def __init__(
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self,
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prefix: Optional[str],
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converted_model_name_or_path: str,
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throughput: Union[float, str],
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num_blocks: Optional[int] = None,
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block_indices: Optional[str] = None,
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num_handlers: int = 8,
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min_batch_size: int = 1,
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max_batch_size: int = 4096,
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inference_max_length: int = 4096,
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torch_dtype: str = "auto",
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revision: str = "main",
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cache_dir: Optional[str] = None,
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attn_cache_size: Optional[int] = None,
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device: Optional[Union[str, torch.device]] = None,
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initial_peers: Sequence[str] = (),
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compression=CompressionType.NONE,
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stats_report_interval: Optional[int] = None,
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custom_module_path=None,
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update_period: float = 30,
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expiration: Optional[float] = None,
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prefetch_batches: int = 1,
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sender_threads: int = 1,
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min_balance_quality: float = 0.0,
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mean_balance_check_period: float = 150,
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mean_block_selection_delay: float = 0.5,
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use_auth_token: Optional[str] = None,
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load_in_8bit: bool = False,
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*,
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start: bool,
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**kwargs,
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):
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"""Create a server with one or more bloom blocks. See run_server.py for documentation."""
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super().__init__()
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self.converted_model_name_or_path = converted_model_name_or_path
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self.num_handlers = num_handlers
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self.min_batch_size, self.max_batch_size = min_batch_size, max_batch_size
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self.inference_max_length = inference_max_length
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self.cache_dir = cache_dir
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self.attn_cache_size = attn_cache_size
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self.compression = compression
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self.stats_report_interval, self.update_period = stats_report_interval, update_period
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self.prefetch_batches, self.sender_threads = prefetch_batches, sender_threads
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self.use_auth_token = use_auth_token
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self.load_in_8bit = load_in_8bit
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if custom_module_path is not None:
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add_custom_models_from_file(custom_module_path)
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if prefix is None:
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prefix = converted_model_name_or_path
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assert UID_DELIMITER not in prefix and CHAIN_DELIMITER not in prefix, (
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f"Cannot use model name as prefix (contains '{UID_DELIMITER}' or '{CHAIN_DELIMITER}'); "
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f"Please specify --prefix manually when starting a server"
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)
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logger.info(f"Automatic dht prefix: {prefix}")
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self.prefix = prefix
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if expiration is None:
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expiration = max(2 * update_period, MAX_DHT_TIME_DISCREPANCY_SECONDS)
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self.expiration = expiration
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self.dht = DHT(initial_peers=initial_peers, start=True, **kwargs)
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visible_maddrs_str = [str(a) for a in self.dht.get_visible_maddrs()]
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logger.info(f"Running DHT node on {visible_maddrs_str}, initial peers = {initial_peers}")
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device = device or ("cuda" if torch.cuda.is_available() else "cpu")
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self.device = device
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self.memory_cache = MemoryCache(device, attn_cache_size)
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assert isinstance(throughput, float) or throughput in ["auto", "eval"]
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if throughput in ["auto", "eval"]:
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throughput = get_host_throughput(device, force_eval=(throughput == "eval"))
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self.throughput = throughput
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if isinstance(torch_dtype, str):
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torch_dtype = DTYPE_MAP[torch_dtype]
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assert torch_dtype in DTYPE_MAP.values(), f"torch_dtype must be one of {list(DTYPE_MAP.values())}"
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self.torch_dtype = torch_dtype
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self.block_config = BloomConfig.from_pretrained(
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converted_model_name_or_path,
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use_auth_token=use_auth_token,
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revision=revision,
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)
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self.module_uids = [f"{self.prefix}.{block_index}" for block_index in range(self.block_config.n_layer)]
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assert (block_indices is None) != (num_blocks is None), "please specify num_blocks or block_indices, not both"
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if block_indices is not None:
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try:
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first_block_index, last_block_index = block_indices.split(":")
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first_block_index, last_block_index = map(int, map(str.strip, (first_block_index, last_block_index)))
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except Exception as e:
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logger.error(f"Failed to parse --block_indices ({e}), must be start:end (e.g. 0:18)")
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raise
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block_indices = range(first_block_index, last_block_index)
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self.strict_block_indices, self.num_blocks = block_indices, num_blocks
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self.min_balance_quality = min_balance_quality
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self.mean_balance_check_period = mean_balance_check_period
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self.mean_block_selection_delay = mean_block_selection_delay
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self.stop = threading.Event()
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if start:
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self.start()
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def run(self):
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while True:
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block_indices = self._choose_blocks()
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self.module_container = ModuleContainer.create(
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dht=self.dht,
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prefix=self.prefix,
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converted_model_name_or_path=self.converted_model_name_or_path,
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block_config=self.block_config,
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memory_cache=self.memory_cache,
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throughput=self.throughput,
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block_indices=block_indices,
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num_handlers=self.num_handlers,
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min_batch_size=self.min_batch_size,
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max_batch_size=self.max_batch_size,
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inference_max_length=self.inference_max_length,
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torch_dtype=self.torch_dtype,
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cache_dir=self.cache_dir,
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device=self.device,
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compression=self.compression,
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stats_report_interval=self.stats_report_interval,
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update_period=self.update_period,
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expiration=self.expiration,
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prefetch_batches=self.prefetch_batches,
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sender_threads=self.sender_threads,
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use_auth_token=self.use_auth_token,
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load_in_8bit=self.load_in_8bit,
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start=True,
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)
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try:
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self.module_container.ready.wait()
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while True:
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timeout = random.random() * 2 * self.mean_balance_check_period
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# TODO: Follow ModuleContainer status (to restart/stop if it crashes)
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if self.stop.wait(timeout):
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return
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if self._should_choose_other_blocks():
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logger.info("Swarm is imbalanced, server will load other blocks")
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break # Stop serving this set of modules
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finally:
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self.module_container.shutdown()
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self._clean_memory_and_fds()
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def _clean_memory_and_fds(self):
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del self.module_container
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gc.collect() # In particular, this closes unused file descriptors
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cur_proc = psutil.Process()
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num_fds = [proc.num_fds() for proc in [cur_proc] + psutil.Process().children(recursive=True)]
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logger.info(f"Cleanup complete, {sum(num_fds)} open file descriptors left")
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def _choose_blocks(self) -> List[int]:
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if self.strict_block_indices is not None:
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return self.strict_block_indices
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assert self.num_blocks is not None
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# If multiple servers (e.g., launched on the same machine by a script) get to this line at the same time,
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# this delay decreases the probability of a race condition while choosing the best blocks to serve.
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time.sleep(random.random() * 2 * self.mean_block_selection_delay)
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module_infos = get_remote_module_infos(self.dht, self.module_uids, expiration_time=np.inf)
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return block_selection.choose_best_blocks(self.num_blocks, module_infos)
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def _should_choose_other_blocks(self) -> bool:
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if self.strict_block_indices is not None:
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return False
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module_infos = get_remote_module_infos(self.dht, self.module_uids, expiration_time=np.inf)
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return block_selection.should_choose_other_blocks(self.dht.peer_id, module_infos, self.min_balance_quality)
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def shutdown(self):
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self.stop.set()
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self.dht.shutdown()
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self.dht.join()
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class ModuleContainer(threading.Thread):
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"""Serves a set of specific Bloom layers for inference, forward, and backward. Announces itself over the DHT."""
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def __init__(
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self,
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dht: DHT,
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module_backends: Dict[str, TransformerBackend],
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*,
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inference_max_length: int,
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num_connection_handlers: int,
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throughput: float,
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update_period: float,
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expiration: Optional[float] = None,
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start: bool,
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**kwargs,
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):
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super().__init__()
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self.dht, self.module_backends = dht, module_backends
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self.throughput, self.update_period, self.expiration = throughput, update_period, expiration
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self.conn_handlers = [
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TransformerConnectionHandler(dht, self.module_backends, inference_max_length)
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for _ in range(num_connection_handlers)
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]
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self.runtime = Runtime(self.module_backends, **kwargs)
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self.dht_handler_thread = ModuleAnnouncerThread(
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self.module_backends,
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dht,
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throughput=throughput,
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update_period=update_period,
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expiration=expiration,
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daemon=True,
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)
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self.checkpoint_saver = None # no need to save checkpoints since we do not change model state
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if start:
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self.run_in_background(await_ready=True)
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def run(self):
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"""
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Runs ModuleContainer in the current thread. Initializes dht if necessary, starts connection handlers,
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runs Runtime (self.runtime) to process incoming requests.
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"""
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logger.info(f"Serving {len(self.module_backends)} blocks:")
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for expert_name, backend in self.module_backends.items():
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num_parameters = sum(p.numel() for p in backend.module.parameters() if p.requires_grad)
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logger.info(f"{expert_name}: {backend.module.__class__.__name__}, {num_parameters} parameters")
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if not self.dht.is_alive():
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self.dht.run_in_background(await_ready=True)
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if self.module_backends:
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self.dht_handler_thread.start()
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if self.checkpoint_saver is not None:
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self.checkpoint_saver.start()
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for handler in self.conn_handlers:
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handler.run_in_background()
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self.runtime.run()
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# noinspection PyMethodOverriding
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@classmethod
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def create(
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cls,
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*,
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dht: DHT,
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prefix: str,
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converted_model_name_or_path: str,
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block_config: BloomConfig,
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memory_cache: MemoryCache,
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throughput: float,
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block_indices: List[int],
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num_handlers: Optional[int],
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min_batch_size: int,
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max_batch_size: int,
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inference_max_length: int,
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torch_dtype: torch.dtype,
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cache_dir: Optional[str],
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device: Union[str, torch.device],
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compression: CompressionType,
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stats_report_interval: Optional[int],
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update_period: float,
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expiration: Optional[float],
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prefetch_batches: int,
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sender_threads: int,
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use_auth_token: Optional[str],
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load_in_8bit: bool,
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start: bool,
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) -> ModuleContainer:
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module_uids = [f"{prefix}.{block_index}" for block_index in block_indices]
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declare_active_modules(
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dht,
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module_uids,
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expiration_time=get_dht_time() + expiration,
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state=ServerState.JOINING,
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throughput=throughput,
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)
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logger.info(f"Announced that blocks {block_indices} are joining")
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blocks = {}
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for module_uid, block_index in zip(module_uids, block_indices):
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block = load_pretrained_block(
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converted_model_name_or_path,
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block_index,
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block_config,
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torch_dtype=torch_dtype,
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use_auth_token=use_auth_token,
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cache_dir=cache_dir,
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)
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if load_in_8bit:
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dtype = block.input_layernorm.weight.dtype
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block = replace_8bit_linear(block)
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block = block.to(device)
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for param in block.parameters():
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param.requires_grad = False
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blocks[module_uid] = TransformerBackend(
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module_uid,
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block,
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memory_cache=memory_cache,
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backend_dtype=None if torch_dtype == "auto" else torch_dtype,
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args_schema=(
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BatchTensorDescriptor(
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1, 2048, block_config.hidden_size, dtype=torch.float32, compression=compression
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),
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),
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kwargs_schema={},
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outputs_schema=(
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BatchTensorDescriptor(
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1, 2048, block_config.hidden_size, dtype=torch.float32, compression=compression
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),
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),
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min_batch_size=min_batch_size,
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max_batch_size=max_batch_size,
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)
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return cls(
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dht,
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blocks,
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throughput=throughput,
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num_connection_handlers=num_handlers,
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inference_max_length=inference_max_length,
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device=device,
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stats_report_interval=stats_report_interval,
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update_period=update_period,
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expiration=expiration,
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prefetch_batches=prefetch_batches,
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sender_threads=sender_threads,
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start=start,
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)
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def run_in_background(self, await_ready=True, timeout=None):
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"""
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Starts ModuleContainer in a background thread. if await_ready, this method will wait until the container
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is ready to process incoming requests or for :timeout: seconds max.
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"""
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self.start()
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if await_ready and not self.ready.wait(timeout=timeout):
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raise TimeoutError("ModuleContainer didn't notify .ready in {timeout} seconds")
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@property
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def ready(self) -> mp.synchronize.Event:
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"""
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An event (multiprocessing.Event) that is set when the container is ready to process requests.
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Example
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=======
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>>> container.start()
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>>> container.ready.wait(timeout=10)
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>>> print("Container ready" if container.ready.is_set() else "Container didn't start in 10 seconds")
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"""
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return self.runtime.ready # mp.Event that is true if self is ready to process batches
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def shutdown(self):
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"""
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Gracefully terminate the container, process-safe.
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Please note that terminating container otherwise (e.g. by killing processes) may result in zombie processes.
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If you did already cause a zombie outbreak, your only option is to kill them with -9 (SIGKILL).
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"""
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if self.module_backends:
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self.dht_handler_thread.stop.set()
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self.dht_handler_thread.join()
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declare_active_modules(
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self.dht,
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self.module_backends.keys(),
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expiration_time=get_dht_time() + self.expiration,
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state=ServerState.OFFLINE,
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throughput=self.throughput,
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)
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logger.info(f"Announced that blocks {list(self.module_backends.keys())} are offline")
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self.ready.clear()
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for handler in self.conn_handlers:
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handler.shutdown()
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logger.debug("Connection handlers terminated")
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if self.checkpoint_saver is not None:
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self.checkpoint_saver.stop.set()
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self.checkpoint_saver.join()
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logger.debug(f"Shutting down pools")
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for pool in self.runtime.pools:
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if pool.is_alive():
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pool.shutdown()
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logger.debug(f"Shutting down runtime")
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self.runtime.shutdown()
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logger.info("Module container shut down succesfully")
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class ModuleAnnouncerThread(threading.Thread):
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"""Periodically announces that this container hosts the specified modules, visible to all DHT peers"""
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def __init__(
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self,
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module_backends: Dict[str, TransformerBackend],
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dht: DHT,
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*,
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throughput: float,
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update_period: float = 30,
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expiration: float,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.module_backends = module_backends
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self.dht = dht
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self.throughput = throughput
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self.update_period = update_period
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self.expiration = expiration
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self.stop = threading.Event()
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def run(self) -> None:
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while True:
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declare_active_modules(
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self.dht,
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self.module_backends.keys(),
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expiration_time=get_dht_time() + self.expiration,
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state=ServerState.ONLINE,
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throughput=self.throughput,
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)
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if self.stop.wait(self.update_period):
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break
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