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| import os | |
| import ujson | |
| from functools import partial | |
| from colbert.utils.utils import print_message | |
| from colbert.modeling.tokenization import QueryTokenizer, DocTokenizer, tensorize_triples | |
| from colbert.utils.runs import Run | |
| class EagerBatcher(): | |
| def __init__(self, args, rank=0, nranks=1): | |
| self.rank, self.nranks = rank, nranks | |
| self.bsize, self.accumsteps = args.bsize, args.accumsteps | |
| self.query_tokenizer = QueryTokenizer(args.query_maxlen) | |
| self.doc_tokenizer = DocTokenizer(args.doc_maxlen) | |
| self.tensorize_triples = partial(tensorize_triples, self.query_tokenizer, self.doc_tokenizer) | |
| self.triples_path = args.triples | |
| self._reset_triples() | |
| def _reset_triples(self): | |
| self.reader = open(self.triples_path, mode='r', encoding="utf-8") | |
| self.position = 0 | |
| def __iter__(self): | |
| return self | |
| def __next__(self): | |
| queries, positives, negatives = [], [], [] | |
| for line_idx, line in zip(range(self.bsize * self.nranks), self.reader): | |
| if (self.position + line_idx) % self.nranks != self.rank: | |
| continue | |
| query, pos, neg = line.strip().split('\t') | |
| queries.append(query) | |
| positives.append(pos) | |
| negatives.append(neg) | |
| self.position += line_idx + 1 | |
| if len(queries) < self.bsize: | |
| raise StopIteration | |
| return self.collate(queries, positives, negatives) | |
| def collate(self, queries, positives, negatives): | |
| assert len(queries) == len(positives) == len(negatives) == self.bsize | |
| return self.tensorize_triples(queries, positives, negatives, self.bsize // self.accumsteps) | |
| def skip_to_batch(self, batch_idx, intended_batch_size): | |
| self._reset_triples() | |
| Run.warn(f'Skipping to batch #{batch_idx} (with intended_batch_size = {intended_batch_size}) for training.') | |
| _ = [self.reader.readline() for _ in range(batch_idx * intended_batch_size)] | |
| return None | |