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644 lines
26 KiB
Python
644 lines
26 KiB
Python
from __future__ import annotations
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import gc
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import math
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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 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
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from transformers import BloomConfig
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from petals.bloom.from_pretrained import DTYPE_MAP, load_pretrained_block
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from petals.constants import PUBLIC_INITIAL_PEERS
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from petals.data_structures import CHAIN_DELIMITER, UID_DELIMITER, ServerState
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from petals.dht_utils import declare_active_modules, get_remote_module_infos
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from petals.server import block_selection
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from petals.server.backend import TransformerBackend, merge_inference_pools_inplace
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from petals.server.block_utils import get_block_size
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from petals.server.handler import TransformerConnectionHandler
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from petals.server.memory_cache import MemoryCache
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from petals.server.reachability import ReachabilityProtocol, check_direct_reachability, validate_reachability
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from petals.server.throughput import get_dtype_name, get_host_throughput
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from petals.utils.convert_block import check_device_balance, convert_block
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from petals.utils.disk_cache import DEFAULT_CACHE_DIR
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logger = get_logger(__file__)
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class Server:
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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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*,
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initial_peers: List[str],
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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 = 2048,
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inference_max_length: int = 2048,
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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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max_disk_space: Optional[int] = None,
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attn_cache_size: Optional[int] = None,
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alloc_timeout: float = 60,
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device: Optional[Union[str, torch.device]] = None,
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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 = 150,
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expiration: Optional[float] = None,
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request_timeout: float = 3 * 60,
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session_timeout: float = 30 * 60,
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step_timeout: float = 5 * 60,
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prefetch_batches: int = 1,
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sender_threads: int = 1,
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balance_quality: float = 0.75,
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mean_balance_check_period: float = 120,
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mean_block_selection_delay: float = 2.5,
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use_auth_token: Optional[str] = None,
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load_in_8bit: Optional[bool] = None,
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tensor_parallel_devices: Optional[Sequence[torch.device]] = None,
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skip_reachability_check: bool = False,
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dht_client_mode: Optional[bool] = None,
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use_relay: bool = True,
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use_auto_relay: bool = True,
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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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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.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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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.debug(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.request_timeout = request_timeout
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self.session_timeout, self.step_timeout = session_timeout, step_timeout
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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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if dht_client_mode is None:
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is_reachable = check_direct_reachability(initial_peers=initial_peers, use_relay=False, **kwargs)
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dht_client_mode = is_reachable is False # if could not check reachability (returns None), run a full peer
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logger.info(f"This server will run DHT in {'client' if dht_client_mode else 'full peer'} mode")
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self.dht = DHT(
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initial_peers=initial_peers,
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start=True,
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num_workers=self.block_config.n_layer,
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use_relay=use_relay,
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use_auto_relay=use_auto_relay,
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client_mode=dht_client_mode,
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**kwargs,
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)
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self.reachability_protocol = ReachabilityProtocol.attach_to_dht(self.dht) if not dht_client_mode else None
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visible_maddrs_str = [str(a) for a in self.dht.get_visible_maddrs()]
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if initial_peers == PUBLIC_INITIAL_PEERS:
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logger.info(f"Connecting to the public swarm, peer_id = {self.dht.peer_id}")
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else:
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logger.info(f"Running DHT node on {visible_maddrs_str}, initial peers = {initial_peers}")
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self.should_validate_reachability = not skip_reachability_check and initial_peers == PUBLIC_INITIAL_PEERS
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if device is None:
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device = "cuda" if torch.cuda.is_available() else "cpu"
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device = torch.device(device)
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if device.type == "cuda" and device.index is None:
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device = torch.device(device.type, index=0)
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self.device = device
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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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if tensor_parallel_devices is None:
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tensor_parallel_devices = (device,)
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self.tensor_parallel_devices = tuple(map(torch.device, tensor_parallel_devices))
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if len(self.tensor_parallel_devices) > 1:
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logger.info(f"Model weights will be split between {', '.join(tensor_parallel_devices)}")
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check_device_balance(self.tensor_parallel_devices)
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if load_in_8bit is None:
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load_in_8bit = device.type == "cuda"
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if load_in_8bit and len(self.tensor_parallel_devices) > 1:
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load_in_8bit = False
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logger.warning(
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"Tensor parallelism doesn't work properly with 8-bit weights yet, loading weights in 16-bit. "
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"You can explicitly set `--load_in_8bit True` to override this"
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)
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self.load_in_8bit = load_in_8bit
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logger.info(f"Model weights will be loaded in {get_dtype_name(torch_dtype, load_in_8bit)} format")
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assert num_blocks is None or block_indices is None, "Please specify num_blocks or block_indices, not both"
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if num_blocks is None and block_indices is None:
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num_blocks = self._choose_num_blocks()
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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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raise ValueError(f"Failed to parse `--block_indices {block_indices}`, must be start:end (e.g. 0:18)")
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block_indices = range(first_block_index, last_block_index)
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num_blocks = len(block_indices)
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self.strict_block_indices, self.num_blocks = block_indices, num_blocks
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gib = 1024**3
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if attn_cache_size is None:
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# Hidden size is 14336 for the bigscience/bloom-petals model. For other models, scale accordingly
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attn_cache_size = 0.5 * gib * num_blocks * self.block_config.hidden_size / 14336
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self.attn_cache_size, self.alloc_timeout = attn_cache_size, alloc_timeout
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logger.info(f"Attention cache for all blocks will consume up to {attn_cache_size / gib:.2f} GiB")
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if cache_dir is None:
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cache_dir = DEFAULT_CACHE_DIR
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self.cache_dir = cache_dir
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self.max_disk_space = max_disk_space
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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(
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self.block_config,
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device,
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torch_dtype,
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load_in_8bit=load_in_8bit,
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tensor_parallel_devices=self.tensor_parallel_devices,
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force_eval=(throughput == "eval"),
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cache_dir=cache_dir,
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)
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self.throughput = throughput
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self.balance_quality = 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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def _choose_num_blocks(self) -> int:
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assert self.device.type == "cuda", (
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"GPU is not available. If you want to run a CPU-only server, please specify --num_blocks. "
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"CPU-only servers in the public swarm are discouraged since they are much slower"
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)
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num_devices = len(self.tensor_parallel_devices) if self.tensor_parallel_devices else 1
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if num_devices > 1:
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memory_per_device = tuple(
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torch.cuda.get_device_properties(device).total_memory for device in self.tensor_parallel_devices
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)
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total_memory = min(memory_per_device) * num_devices
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if max(memory_per_device) / min(memory_per_device) > 1.5:
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raise ValueError(
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"GPU devices have highly uneven memory, which makes tensor parallelism inefficient. "
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"Please launch individual servers on each GPU or set --num_blocks manually to "
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"override this exception."
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)
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else:
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total_memory = torch.cuda.get_device_properties(self.device).total_memory
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block_size = get_block_size(self.block_config, "memory", dtype=self.torch_dtype, load_in_8bit=self.load_in_8bit)
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# The estimates below are for bigscience/bloom-petals, serving as an upper bound for other models
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gib = 1024**3
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attn_cache_per_block = 0.5 * gib * num_devices # TODO: This does not account for manually set --attn_cache_size
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autograd_memory = 2 * gib * num_devices # GPU memory used for intermediate tensors in rpc_backward
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num_blocks = math.floor((total_memory - autograd_memory) / (block_size + attn_cache_per_block))
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assert num_blocks >= 1, "Your GPU does not have enough memory to serve at least one block"
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logger.info(
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f"Server will fill all your GPU memory with {num_blocks} transformer blocks. "
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f"If you want to leave some free GPU memory, please specify a lesser --num_blocks manually"
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)
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return min(num_blocks, self.block_config.n_layer)
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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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attn_cache_size=self.attn_cache_size,
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alloc_timeout=self.alloc_timeout,
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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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max_disk_space=self.max_disk_space,
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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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request_timeout=self.request_timeout,
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session_timeout=self.session_timeout,
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step_timeout=self.step_timeout,
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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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tensor_parallel_devices=self.tensor_parallel_devices,
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should_validate_reachability=self.should_validate_reachability,
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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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if self.stop.wait(timeout):
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return
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if not self.module_container.is_healthy():
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logger.warning("One of subprocesses crashed, restarting the server")
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break
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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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if self.device.type == "cuda":
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torch.cuda.empty_cache()
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allocated_vram = torch.cuda.memory_allocated(self.device)
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reserved_vram = torch.cuda.memory_reserved(self.device)
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gib = 1024**3
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logger.info(
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f"Cleaning up, left {allocated_vram / gib:.1f} GiB allocated memory, "
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f"{reserved_vram / gib:.1f} GiB reserved memory"
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)
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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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# 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.balance_quality)
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def shutdown(self):
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self.stop.set()
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if self.reachability_protocol is not None:
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self.reachability_protocol.shutdown()
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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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# 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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attn_cache_size: int,
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alloc_timeout: float,
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throughput: float,
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block_indices: List[int],
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min_batch_size: int,
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max_batch_size: int,
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torch_dtype: torch.dtype,
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cache_dir: str,
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max_disk_space: int,
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device: Union[str, torch.device],
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compression: CompressionType,
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update_period: float,
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expiration: Optional[float],
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use_auth_token: Optional[str],
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load_in_8bit: bool,
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tensor_parallel_devices: Sequence[torch.device],
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should_validate_reachability: bool,
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**kwargs,
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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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joining_announcer = ModuleAnnouncerThread(
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module_uids,
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dht,
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ServerState.JOINING,
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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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joining_announcer.start()
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logger.info(f"Announced that blocks {block_indices} are joining")
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assert len(tensor_parallel_devices) >= 1 and all(isinstance(d, torch.device) for d in tensor_parallel_devices)
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memory_cache = MemoryCache(attn_cache_size, alloc_timeout)
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blocks = {}
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try:
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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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max_disk_space=max_disk_space,
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)
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block = convert_block(block, block_config, tensor_parallel_devices, device, load_in_8bit, freeze=True)
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backend_dtype = next(block.parameters()).dtype if torch_dtype == "auto" else torch_dtype
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blocks[module_uid] = TransformerBackend(
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module_uid,
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block,
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config=block_config,
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memory_cache=memory_cache,
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backend_dtype=backend_dtype,
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args_schema=(
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BatchTensorDescriptor(
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1, 2048, block_config.hidden_size, dtype=backend_dtype, 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=backend_dtype, 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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if should_validate_reachability:
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validate_reachability(dht.peer_id)
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except:
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logger.debug("Shutting down backends")
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for backend in blocks.values():
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backend.shutdown()
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joining_announcer.stop.set()
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|
joining_announcer.join()
|
|
declare_active_modules(
|
|
dht,
|
|
module_uids,
|
|
expiration_time=get_dht_time() + expiration,
|
|
state=ServerState.OFFLINE,
|
|
throughput=throughput,
|
|
)
|
|
logger.info(f"Announced that blocks {module_uids} are offline")
|
|
raise
|
|
else:
|
|
joining_announcer.stop.set()
|
|
joining_announcer.join()
|
|
|
|
merge_inference_pools_inplace(blocks)
|
|
|
|
return cls(
|
|
dht,
|
|
blocks,
|
|
throughput=throughput,
|
|
update_period=update_period,
|
|
expiration=expiration,
|
|
**kwargs,
|
|
)
|
|
|
|
def __init__(
|
|
self,
|
|
dht: DHT,
|
|
module_backends: Dict[str, TransformerBackend],
|
|
*,
|
|
inference_max_length: int,
|
|
num_handlers: int,
|
|
throughput: float,
|
|
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.throughput, self.update_period, self.expiration = throughput, update_period, expiration
|
|
self.conn_handlers = [
|
|
TransformerConnectionHandler(
|
|
dht,
|
|
self.module_backends,
|
|
inference_max_length=inference_max_length,
|
|
request_timeout=request_timeout,
|
|
session_timeout=session_timeout,
|
|
step_timeout=step_timeout,
|
|
)
|
|
for _ 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.
|
|
self.online_announcer = ModuleAnnouncerThread(
|
|
list(self.module_backends.keys()),
|
|
dht,
|
|
ServerState.ONLINE,
|
|
throughput=throughput,
|
|
update_period=update_period,
|
|
expiration=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):
|
|
"""
|
|
Runs ModuleContainer in the current thread. Initializes dht if necessary, starts connection handlers,
|
|
runs Runtime (self.runtime) to process incoming requests.
|
|
"""
|
|
if not self.dht.is_alive():
|
|
self.dht.run_in_background(await_ready=True)
|
|
|
|
self.online_announcer.start()
|
|
|
|
if self.checkpoint_saver is not None:
|
|
self.checkpoint_saver.start()
|
|
|
|
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.online_announcer.stop.set()
|
|
self.online_announcer.join()
|
|
|
|
declare_active_modules(
|
|
self.dht,
|
|
self.module_backends.keys(),
|
|
expiration_time=get_dht_time() + self.expiration,
|
|
state=ServerState.OFFLINE,
|
|
throughput=self.throughput,
|
|
)
|
|
logger.info(f"Announced that blocks {list(self.module_backends.keys())} are offline")
|
|
|
|
self.ready.clear()
|
|
|
|
for handler in self.conn_handlers:
|
|
handler.shutdown()
|
|
logger.debug("Connection handlers terminated")
|
|
|
|
if self.checkpoint_saver is not None:
|
|
self.checkpoint_saver.stop.set()
|
|
self.checkpoint_saver.join()
|
|
|
|
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,
|
|
state: ServerState,
|
|
*,
|
|
throughput: float,
|
|
update_period: float = 30,
|
|
expiration: float,
|
|
**kwargs,
|
|
):
|
|
super().__init__(**kwargs)
|
|
self.module_uids = module_uids
|
|
self.dht = dht
|
|
self.state = state
|
|
self.throughput = throughput
|
|
self.update_period = update_period
|
|
self.expiration = expiration
|
|
self.stop = threading.Event()
|
|
|
|
def run(self) -> None:
|
|
while True:
|
|
declare_active_modules(
|
|
self.dht,
|
|
self.module_uids,
|
|
expiration_time=get_dht_time() + self.expiration,
|
|
state=self.state,
|
|
throughput=self.throughput,
|
|
)
|
|
if self.stop.wait(self.update_period):
|
|
break
|
|
|
|
|
|
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))
|