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from dataclasses import dataclass
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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from hivemind import PeerID, get_logger
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from petals.data_structures import RemoteModuleInfo, ServerState
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__all__ = ["choose_best_blocks", "should_choose_other_blocks"]
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logger = get_logger(__name__)
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@dataclass
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class Span:
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start: int
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end: int
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throughput: float
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state: ServerState
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@property
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def length(self):
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return self.end - self.start
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def move_to(self, new_start: int) -> None:
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self.start, self.end = new_start, new_start + self.length
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def compute_spans(module_infos: List[Optional[RemoteModuleInfo]]) -> Tuple[Dict[PeerID, Span], np.ndarray]:
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spans = {}
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throughputs = np.zeros(len(module_infos))
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for block, module in enumerate(module_infos):
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if module is None:
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continue
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# We sort servers here to ensure that we get exactly the same throughputs for a given set of servers.
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# If the order were not defined, we would get slightly different values due to floating point errors,
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# which may cause excess block replacements.
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for peer_id, server in sorted(module.servers.items()):
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if server.state == ServerState.OFFLINE:
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continue
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if peer_id in spans:
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spans[peer_id].start = min(spans[peer_id].start, block)
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spans[peer_id].end = max(spans[peer_id].start, block + 1)
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else:
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spans[peer_id] = Span(start=block, end=block + 1, throughput=server.throughput, state=server.state)
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throughputs[block] += server.throughput
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return spans, throughputs
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def _choose_best_start(throughputs: np.ndarray, num_blocks: int) -> int:
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options = ((sorted(throughputs[i : i + num_blocks]), i) for i in range(0, len(throughputs) - num_blocks + 1))
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return min(options)[-1]
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def choose_best_blocks(num_blocks: int, module_infos: List[Optional[RemoteModuleInfo]]) -> List[int]:
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_, throughputs = compute_spans(module_infos)
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start = _choose_best_start(throughputs, num_blocks)
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return list(range(start, start + num_blocks))
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def should_choose_other_blocks(
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local_peer_id: PeerID, module_infos: List[Optional[RemoteModuleInfo]], balance_quality: float
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) -> bool:
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if balance_quality > 1.0:
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return True # Forces rebalancing on each check (may be used for debugging purposes)
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spans, throughputs = compute_spans(module_infos)
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initial_throughput = throughputs.min()
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eps = 1e-3
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assert local_peer_id in spans, "Span served by this server is not present in the DHT"
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local_span = spans[local_peer_id]
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throughputs[local_span.start : local_span.end] -= local_span.throughput * (1 + eps)
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# Without (1 + eps) here, we would sometimes subtract a value slightly less than local_span.throughput
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# due to the floating point error, which would cause excess block replacements.
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# Also, subtracting local_span.throughput * (1 + eps) makes _choose_best_start() prefer
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# the previous server position in case of other things being almost equal.
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if initial_throughput > eps and throughputs.min() <= 0:
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return False # Switching blocks would make the swarm disjoint
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new_start = _choose_best_start(throughputs, local_span.length)
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if local_span.start == new_start:
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return False # This server is on its best place already
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throughputs[local_span.start : local_span.end] += local_span.throughput * eps
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local_span.move_to(new_start)
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throughputs[local_span.start : local_span.end] += local_span.throughput
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moved = True
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while moved:
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servers = list(spans.keys())
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np.random.shuffle(servers)
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moved = False
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for peer_id in servers:
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span = spans[peer_id]
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throughputs[span.start : span.end] -= span.throughput * (1 + eps)
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new_start = _choose_best_start(throughputs, span.length)
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throughputs[span.start : span.end] += span.throughput * eps
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if span.start != new_start:
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span.move_to(new_start)
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moved = True
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throughputs[span.start : span.end] += span.throughput
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new_throughput = throughputs.min()
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if new_throughput < initial_throughput or new_throughput < eps:
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return False
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actual_quality = initial_throughput / new_throughput
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logger.info(f"Swarm balance quality: {actual_quality * 100:.1f}%")
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return actual_quality < balance_quality - eps
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