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Optimize vector-db-benchmark #15

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Optimize vector-db-benchmark #15

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16 changes: 12 additions & 4 deletions engine/base_client/search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,13 @@
DEFAULT_TOP = 10
MAX_QUERIES = int(os.getenv("MAX_QUERIES", -1))

def chunkify(lst, n):
"""Split list into n approximately equal chunks."""
return [lst[i::n] for i in range(n)]

def process_chunk(chunk, search_one):
"""Process a chunk of queries."""
return [search_one(q) for q in chunk]

class BaseSearcher:
MP_CONTEXT = None
Expand Down Expand Up @@ -58,6 +64,7 @@ def _search_one(cls, query, top: Optional[int] = None):
precision = len(ids.intersection(query.expected_result[:top])) / top
return precision, end - start


def search_all(
self,
distance,
Expand All @@ -72,7 +79,7 @@ def search_all(
self.setup_search()

search_one = functools.partial(self.__class__._search_one, top=top)
used_queries = queries
used_queries = list(queries)


if MAX_QUERIES > 0:
Expand All @@ -86,6 +93,7 @@ def search_all(
)
else:
ctx = get_context(self.get_mp_start_method())
query_chunks = chunkify(used_queries, parallel)

with ctx.Pool(
processes=parallel,
Expand All @@ -100,9 +108,9 @@ def search_all(
if parallel > 10:
time.sleep(15) # Wait for all processes to start
start = time.perf_counter()
precisions, latencies = list(
zip(*pool.imap_unordered(search_one, iterable=tqdm.tqdm(used_queries)))
)
results = pool.starmap(process_chunk, [(chunk, search_one) for chunk in query_chunks])

precisions, latencies = zip(*[item for sublist in results for item in sublist])

total_time = time.perf_counter() - start

Expand Down