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add benchmarking for hnsw flat based on efSearch #3858

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add benchmarking for hnsw flat based on efSearch #3858

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91 changes: 33 additions & 58 deletions faiss/perf_tests/bench_hnsw.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,10 +52,12 @@ def accumulate_perf_counter(
def run_on_dataset(
ds: Dataset,
M: int,
num_threads:
int,
num_threads: int,
num_add_iterations: int,
num_search_iterations: int,
efSearch: int = 16,
efConstruction: int = 40
efConstruction: int = 40,
search_bounded_queue: bool = True,
) -> Dict[str, int]:
xq = ds.get_queries()
xb = ds.get_database()
Expand All @@ -67,22 +69,27 @@ def run_on_dataset(
# pyre-ignore[16]: Module `faiss` has no attribute `omp_set_num_threads`.
faiss.omp_set_num_threads(num_threads)
index = faiss.IndexHNSWFlat(d, M)
index.hnsw.efConstruction = 40 # default
index.hnsw.efConstruction = efConstruction # default
with timed_execution() as t:
index.add(xb)
for _ in range(num_add_iterations):
index.add(xb)
counters = {}
accumulate_perf_counter("add", t, counters)
counters["nb"] = nb
counters["num_add_iterations"] = num_add_iterations

index.hnsw.efSearch = efSearch
index.hnsw.search_bounded_queue = search_bounded_queue
with timed_execution() as t:
D, I = index.search(xq, k)
for _ in range(num_search_iterations):
D, I = index.search(xq, k)
accumulate_perf_counter("search", t, counters)
counters["nq"] = nq
counters["efSearch"] = efSearch
counters["efConstruction"] = efConstruction
counters["M"] = M
counters["d"] = d
counters["num_search_iterations"] = num_search_iterations

return counters

Expand All @@ -93,61 +100,25 @@ def run(
nq: int,
M: int,
num_threads: int,
num_add_iterations: int = 1,
num_search_iterations: int = 1,
efSearch: int = 16,
efConstruction: int = 40,
search_bounded_queue: bool = True,
) -> Dict[str, int]:
ds = SyntheticDataset(d=d, nb=nb, nt=0, nq=nq, metric="L2", seed=1338)
return run_on_dataset(
ds,
M=M,
num_add_iterations=num_add_iterations,
num_search_iterations=num_search_iterations,
num_threads=num_threads,
efSearch=efSearch,
efConstruction=efConstruction,
search_bounded_queue=search_bounded_queue,
)


def _merge_counters(
element: Dict[str, int], accu: Optional[Dict[str, int]] = None
) -> Dict[str, int]:
if accu is None:
return dict(element)
else:
assert accu.keys() <= element.keys(), (
"Accu keys must be a subset of element keys: "
f"{accu.keys()} not a subset of {element.keys()}"
)
for key in accu.keys():
if is_perf_counter(key):
accu[key] += element[key]
return accu


def run_with_iterations(
iterations: int,
d: int,
nb: int,
nq: int,
M: int,
num_threads: int,
efSearch: int = 16,
efConstruction: int = 40,
) -> Dict[str, int]:
result = None
for _ in range(iterations):
counters = run(
d=d,
nb=nb,
nq=nq,
M=M,
num_threads=num_threads,
efSearch=efSearch,
efConstruction=efConstruction,
)
result = _merge_counters(counters, result)
assert result is not None
return result


def _accumulate_counters(
element: Dict[str, int], accu: Optional[Dict[str, List[int]]] = None
) -> Dict[str, List[int]]:
Expand All @@ -169,10 +140,13 @@ def main():
parser.add_argument("-M", "--M", type=int, required=True)
parser.add_argument("-t", "--num-threads", type=int, required=True)
parser.add_argument("-w", "--warm-up-iterations", type=int, default=0)
parser.add_argument("-i", "--num-iterations", type=int, default=20)
parser.add_argument("-i", "--num-search-iterations", type=int, default=20)
parser.add_argument("-i", "--num-add-iterations", type=int, default=20)
parser.add_argument("-r", "--num-repetitions", type=int, default=20)
parser.add_argument("-s", "--ef-search", type=int, default=16)
parser.add_argument("-c", "--ef-construction", type=int, default=40)
parser.add_argument("-b", "--search-bounded-queue", action="store_true")

parser.add_argument("-n", "--nb", type=int, default=5000)
parser.add_argument("-q", "--nq", type=int, default=500)
parser.add_argument("-d", "--d", type=int, default=128)
Expand All @@ -181,15 +155,17 @@ def main():
if args.warm_up_iterations > 0:
print(f"Warming up for {args.warm_up_iterations} iterations...")
# warm-up
run_with_iterations(
iterations=args.warm_up_iterations,
run(
num_search_iterations=args.warm_up_iterations,
num_add_iterations=args.warm_up_iterations,
d=args.d,
nb=args.nb,
nq=args.nq,
M=args.M,
num_threads=args.num_threads,
efSearch=args.ef_search,
efConstruction=args.ef_construction,
search_bounded_queue=args.search_bounded_queue,
)
print(
f"Running benchmark with dataset(nb={args.nb}, nq={args.nq}, "
Expand All @@ -198,24 +174,23 @@ def main():
)
result = None
for _ in range(args.num_repetitions):
counters = run_with_iterations(
iterations=args.num_iterations,
counters = run(
num_search_iterations=args.num_search_iterations,
num_add_iterations=args.num_add_iterations,
d=args.d,
nb=args.nb,
nq=args.nq,
M=args.M,
num_threads=args.num_threads,
efSearch=args.ef_search,
efConstruction=args.ef_construction,
search_bounded_queue=args.search_bounded_queue,
)
result = _accumulate_counters(counters, result)
assert result is not None
for counter, values in result.items():
if is_perf_counter(counter):
print(
"%s t=%.3f us (± %.4f)" % (
counter,
np.mean(values),
np.std(values)
)
"%s t=%.3f us (± %.4f)" %
(counter, np.mean(values), np.std(values))
)
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