| import os |
| import ujson |
|
|
| from functools import partial |
| from colbert.infra.config.config import ColBERTConfig |
| from colbert.utils.utils import flatten, print_message, zipstar |
| from colbert.modeling.reranker.tokenizer import RerankerTokenizer |
|
|
| from colbert.data.collection import Collection |
| from colbert.data.queries import Queries |
| from colbert.data.examples import Examples |
|
|
| |
|
|
|
|
| class RerankBatcher(): |
| def __init__(self, config: ColBERTConfig, triples, queries, collection, rank=0, nranks=1): |
| self.bsize, self.accumsteps = config.bsize, config.accumsteps |
| self.nway = config.nway |
| |
| assert self.accumsteps == 1, "The tensorizer doesn't support larger accumsteps yet --- but it's easy to add." |
|
|
| self.tokenizer = RerankerTokenizer(total_maxlen=config.doc_maxlen, base=config.checkpoint) |
| self.position = 0 |
|
|
| self.triples = Examples.cast(triples, nway=self.nway).tolist(rank, nranks) |
| self.queries = Queries.cast(queries) |
| self.collection = Collection.cast(collection) |
|
|
| def __iter__(self): |
| return self |
|
|
| def __len__(self): |
| return len(self.triples) |
|
|
| def __next__(self): |
| offset, endpos = self.position, min(self.position + self.bsize, len(self.triples)) |
| self.position = endpos |
|
|
| if offset + self.bsize > len(self.triples): |
| raise StopIteration |
|
|
| all_queries, all_passages, all_scores = [], [], [] |
|
|
| for position in range(offset, endpos): |
| query, *pids = self.triples[position] |
| pids = pids[:self.nway] |
|
|
| query = self.queries[query] |
|
|
| try: |
| pids, scores = zipstar(pids) |
| except: |
| scores = [] |
|
|
| passages = [self.collection[pid] for pid in pids] |
|
|
| all_queries.append(query) |
| all_passages.extend(passages) |
| all_scores.extend(scores) |
| |
| assert len(all_scores) in [0, len(all_passages)], len(all_scores) |
|
|
| return self.collate(all_queries, all_passages, all_scores) |
|
|
| def collate(self, queries, passages, scores): |
| assert len(queries) == self.bsize |
| assert len(passages) == self.nway * self.bsize |
|
|
| queries = flatten([[query] * self.nway for query in queries]) |
| return [(self.tokenizer.tensorize(queries, passages), scores)] |
|
|
| |
| |
| |
|
|
