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import os
import time
import faiss
import random
import torch
from multiprocessing import Pool
from colbert.modeling.inference import ModelInference
from colbert.utils.utils import print_message, flatten, batch
from colbert.indexing.loaders import load_doclens
class FaissIndex():
def __init__(self, index_path, faiss_index_path, nprobe, part_range=None):
print_message("#> Loading the FAISS index from", faiss_index_path, "..")
faiss_part_range = os.path.basename(faiss_index_path).split('.')[-2].split('-')
if len(faiss_part_range) == 2:
faiss_part_range = range(*map(int, faiss_part_range))
assert part_range[0] in faiss_part_range, (part_range, faiss_part_range)
assert part_range[-1] in faiss_part_range, (part_range, faiss_part_range)
else:
faiss_part_range = None
self.part_range = part_range
self.faiss_part_range = faiss_part_range
self.faiss_index = faiss.read_index(faiss_index_path)
self.faiss_index.nprobe = nprobe
print_message("#> Building the emb2pid mapping..")
all_doclens = load_doclens(index_path, flatten=False)
pid_offset = 0
if faiss_part_range is not None:
print(f"#> Restricting all_doclens to the range {faiss_part_range}.")
pid_offset = len(flatten(all_doclens[:faiss_part_range.start]))
all_doclens = all_doclens[faiss_part_range.start:faiss_part_range.stop]
self.relative_range = None
if self.part_range is not None:
start = self.faiss_part_range.start if self.faiss_part_range is not None else 0
a = len(flatten(all_doclens[:self.part_range.start - start]))
b = len(flatten(all_doclens[:self.part_range.stop - start]))
self.relative_range = range(a, b)
print(f"self.relative_range = {self.relative_range}")
all_doclens = flatten(all_doclens)
total_num_embeddings = sum(all_doclens)
self.emb2pid = torch.zeros(total_num_embeddings, dtype=torch.int)
offset_doclens = 0
for pid, dlength in enumerate(all_doclens):
self.emb2pid[offset_doclens: offset_doclens + dlength] = pid_offset + pid
offset_doclens += dlength
print_message("len(self.emb2pid) =", len(self.emb2pid))
self.parallel_pool = Pool(16)
def retrieve(self, faiss_depth, Q, verbose=False):
embedding_ids = self.queries_to_embedding_ids(faiss_depth, Q, verbose=verbose)
pids = self.embedding_ids_to_pids(embedding_ids, verbose=verbose)
if self.relative_range is not None:
pids = [[pid for pid in pids_ if pid in self.relative_range] for pids_ in pids]
return pids
def queries_to_embedding_ids(self, faiss_depth, Q, verbose=True):
# Flatten into a matrix for the faiss search.
num_queries, embeddings_per_query, dim = Q.size()
Q_faiss = Q.view(num_queries * embeddings_per_query, dim).cpu().contiguous()
# Search in large batches with faiss.
print_message("#> Search in batches with faiss. \t\t",
f"Q.size() = {Q.size()}, Q_faiss.size() = {Q_faiss.size()}",
condition=verbose)
embeddings_ids = []
faiss_bsize = embeddings_per_query * 5000
for offset in range(0, Q_faiss.size(0), faiss_bsize):
endpos = min(offset + faiss_bsize, Q_faiss.size(0))
print_message("#> Searching from {} to {}...".format(offset, endpos), condition=verbose)
some_Q_faiss = Q_faiss[offset:endpos].float().numpy()
_, some_embedding_ids = self.faiss_index.search(some_Q_faiss, faiss_depth)
embeddings_ids.append(torch.from_numpy(some_embedding_ids))
embedding_ids = torch.cat(embeddings_ids)
# Reshape to (number of queries, non-unique embedding IDs per query)
embedding_ids = embedding_ids.view(num_queries, embeddings_per_query * embedding_ids.size(1))
return embedding_ids
def embedding_ids_to_pids(self, embedding_ids, verbose=True):
# Find unique PIDs per query.
print_message("#> Lookup the PIDs..", condition=verbose)
all_pids = self.emb2pid[embedding_ids]
print_message(f"#> Converting to a list [shape = {all_pids.size()}]..", condition=verbose)
all_pids = all_pids.tolist()
print_message("#> Removing duplicates (in parallel if large enough)..", condition=verbose)
if len(all_pids) > 5000:
all_pids = list(self.parallel_pool.map(uniq, all_pids))
else:
all_pids = list(map(uniq, all_pids))
print_message("#> Done with embedding_ids_to_pids().", condition=verbose)
return all_pids
def uniq(l):
return list(set(l))
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