Spaces:
Runtime error
Runtime error
| from dataclasses import dataclass | |
| from typing import Dict, List, Optional, Tuple | |
| import numpy as np | |
| from hivemind import PeerID, get_logger | |
| from src.data_structures import RemoteModuleInfo, ServerState | |
| __all__ = ["choose_best_blocks", "should_choose_other_blocks"] | |
| logger = get_logger(__file__) | |
| class Span: | |
| start: int | |
| end: int | |
| throughput: float | |
| def length(self): | |
| return self.end - self.start | |
| def move_to(self, new_start: int) -> None: | |
| self.start, self.end = new_start, new_start + self.length | |
| def _compute_spans(module_infos: List[Optional[RemoteModuleInfo]]) -> Tuple[Dict[PeerID, Span], np.ndarray]: | |
| spans = {} | |
| throughputs = np.zeros(len(module_infos)) | |
| for block, module in enumerate(module_infos): | |
| if module is None: | |
| continue | |
| for peer_id, server in module.servers.items(): | |
| if server.state == ServerState.OFFLINE: | |
| continue | |
| if peer_id in spans: | |
| spans[peer_id].start = min(spans[peer_id].start, block) | |
| spans[peer_id].end = max(spans[peer_id].start, block + 1) | |
| else: | |
| spans[peer_id] = Span(start=block, end=block + 1, throughput=server.throughput) | |
| throughputs[block] += server.throughput | |
| return spans, throughputs | |
| def _choose_best_start(throughputs: np.ndarray, num_blocks: int, cur_start: Optional[int]) -> int: | |
| options = ( | |
| (sorted(throughputs[i : i + num_blocks]), i != cur_start, i) | |
| for i in range(0, len(throughputs) - num_blocks + 1) | |
| ) | |
| return min(options)[-1] | |
| def choose_best_blocks(num_blocks: int, module_infos: List[Optional[RemoteModuleInfo]]) -> List[int]: | |
| _, throughputs = _compute_spans(module_infos) | |
| start = _choose_best_start(throughputs, num_blocks, None) | |
| return list(range(start, start + num_blocks)) | |
| def should_choose_other_blocks( | |
| local_peer_id: PeerID, module_infos: List[Optional[RemoteModuleInfo]], balance_quality: float | |
| ) -> bool: | |
| if balance_quality > 1.0: | |
| return True # Forces rebalancing on each check (may be used for debugging purposes) | |
| spans, throughputs = _compute_spans(module_infos) | |
| initial_throughput = throughputs.min() | |
| assert local_peer_id in spans, "Span served by this server is not present in the DHT" | |
| local_span = spans[local_peer_id] | |
| throughputs[local_span.start : local_span.end] -= local_span.throughput | |
| new_start = _choose_best_start(throughputs, local_span.length, local_span.start) | |
| if local_span.start == new_start: | |
| return False # This server is on its best place already | |
| local_span.move_to(new_start) | |
| throughputs[local_span.start : local_span.end] += local_span.throughput | |
| moved = True | |
| while moved: | |
| servers = list(spans.keys()) | |
| np.random.shuffle(servers) | |
| moved = False | |
| for peer_id in servers: | |
| span = spans[peer_id] | |
| throughputs[span.start : span.end] -= span.throughput | |
| new_start = _choose_best_start(throughputs, span.length, span.start) | |
| if span.start != new_start: | |
| span.move_to(new_start) | |
| moved = True | |
| throughputs[span.start : span.end] += span.throughput | |
| new_throughput = throughputs.min() | |
| actual_quality = initial_throughput / new_throughput | |
| logger.info(f"Swarm balance quality: {actual_quality * 100:.1f}%") | |
| eps = 1e-6 | |
| return actual_quality < balance_quality - eps | |