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@@ -1,54 +1,23 @@
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-from dataclasses import dataclass
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-from typing import Dict, List, Optional, Tuple
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+from typing import Dict, List
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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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-
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-__all__ = ["choose_best_blocks", "should_choose_other_blocks"]
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+from petals.data_structures import RemoteModuleInfo, RemoteSpanInfo, ServerState
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+from petals.utils.dht import compute_spans
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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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-
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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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-
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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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-
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-
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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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-
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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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+def compute_throughputs(spans: Dict[PeerID, RemoteSpanInfo], *, total_blocks: int) -> np.ndarray:
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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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- 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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-
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- throughputs[block] += server.throughput
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-
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- return spans, throughputs
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+ throughputs = np.zeros(total_blocks)
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+ for span in sorted(spans.values(), key=lambda span: span.peer_id):
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+ throughputs[span.start : span.end] += span.throughput
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+ return throughputs
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def _choose_best_start(throughputs: np.ndarray, num_blocks: int) -> int:
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@@ -56,19 +25,26 @@ def _choose_best_start(throughputs: np.ndarray, num_blocks: int) -> int:
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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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+def choose_best_blocks(num_blocks: int, module_infos: List[RemoteModuleInfo]) -> List[int]:
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+ spans = compute_spans(module_infos, min_state=ServerState.JOINING)
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+ throughputs = compute_throughputs(spans, total_blocks=len(module_infos))
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+
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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 _move_span(span: RemoteSpanInfo, new_start: int):
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+ span.start, span.end = new_start, new_start + span.length
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+
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+
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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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+ local_peer_id: PeerID, module_infos: List[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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+ spans = compute_spans(module_infos, min_state=ServerState.JOINING)
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+ throughputs = compute_throughputs(spans, total_blocks=len(module_infos))
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initial_throughput = throughputs.min()
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eps = 1e-3
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@@ -88,7 +64,7 @@ def should_choose_other_blocks(
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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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+ _move_span(local_span, 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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@@ -105,7 +81,7 @@ def should_choose_other_blocks(
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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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+ _move_span(span, new_start)
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moved = True
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throughputs[span.start : span.end] += span.throughput
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