add hnsw flat benchmark (facebookresearch#3857)

Summary:
Pull Request resolved: facebookresearch#3857

add benchmarking for hnsw flat. ServiceLab requires us to register our python benchmarks with their custom python function in order to export the metrics correctly. I decided to split servicelab custom code inside `faiss/perf_tests/servicelab` folder as to not expose it to open source and the actual benchmarking logic for `hnsw` lives in `faiss/perf_tests/bench_hnsw.py` which will be exposed to open source

Reviewed By: kuarora

Differential Revision: D62316706

fbshipit-source-id: 6f88ed70ae78fa309a347371645fb012e25b55da

faiss/perf_tests/bench_hnsw.py

@@ -0,0 +1,221 @@
+import argparse
+import resource
+import time
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import Dict, Generator, List, Optional
+
+import faiss  # @manual=//faiss/python:pyfaiss
+import numpy as np
+from faiss.contrib.datasets import (  # @manual=//faiss/contrib:faiss_contrib
+    Dataset,
+    SyntheticDataset,
+)
+
+US_IN_S = 1_000_000
+
+
+@dataclass
+class PerfCounters:
+    wall_time_s: float = 0.0
+    user_time_s: float = 0.0
+    system_time_s: float = 0.0
+
+
+@contextmanager
+def timed_execution() -> Generator[PerfCounters, None, None]:
+    pcounters = PerfCounters()
+    wall_time_start = time.perf_counter()
+    rusage_start = resource.getrusage(resource.RUSAGE_SELF)
+    yield pcounters
+    wall_time_end = time.perf_counter()
+    rusage_end = resource.getrusage(resource.RUSAGE_SELF)
+    pcounters.wall_time_s = wall_time_end - wall_time_start
+    pcounters.user_time_s = rusage_end.ru_utime - rusage_start.ru_utime
+    pcounters.system_time_s = rusage_end.ru_stime - rusage_start.ru_stime
+
+
+def is_perf_counter(key: str) -> bool:
+    return key.endswith("_time_us")
+
+
+def accumulate_perf_counter(
+    phase: str,
+    t: PerfCounters,
+    counters: Dict[str, int]
+):
+    counters[f"{phase}_wall_time_us"] = int(t.wall_time_s * US_IN_S)
+    counters[f"{phase}_user_time_us"] = int(t.user_time_s * US_IN_S)
+    counters[f"{phase}_system_time_us"] = int(t.system_time_s * US_IN_S)
+
+
+def run_on_dataset(
+    ds: Dataset,
+    M: int,
+    num_threads:
+    int,
+    efSearch: int = 16,
+    efConstruction: int = 40
+) -> Dict[str, int]:
+    xq = ds.get_queries()
+    xb = ds.get_database()
+
+    nb, d = xb.shape
+    nq, d = xq.shape
+
+    k = 10
+    # 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
+    with timed_execution() as t:
+        index.add(xb)
+    counters = {}
+    accumulate_perf_counter("add", t, counters)
+    counters["nb"] = nb
+
+    index.hnsw.efSearch = efSearch
+    with timed_execution() as t:
+        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
+
+    return counters
+
+
+def run(
+    d: int,
+    nb: int,
+    nq: int,
+    M: int,
+    num_threads: int,
+    efSearch: int = 16,
+    efConstruction: int = 40,
+) -> 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_threads=num_threads,
+        efSearch=efSearch,
+        efConstruction=efConstruction,
+    )
+
+
+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]]:
+    if accu is None:
+        accu = {key: [value] for key, value in element.items()}
+        return accu
+    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():
+            accu[key].append(element[key])
+        return accu
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Benchmark HNSW")
+    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("-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("-n", "--nb", type=int, default=5000)
+    parser.add_argument("-q", "--nq", type=int, default=500)
+    parser.add_argument("-d", "--d", type=int, default=128)
+    args = parser.parse_args()
+
+    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,
+            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,
+        )
+    print(
+        f"Running benchmark with dataset(nb={args.nb}, nq={args.nq}, "
+        f"d={args.d}), M={args.M}, num_threads={args.num_threads}, "
+        f"efSearch={args.ef_search}, efConstruction={args.ef_construction}"
+    )
+    result = None
+    for _ in range(args.num_repetitions):
+        counters = run_with_iterations(
+            iterations=args.num_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,
+        )
+        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)
+                )
+            )