mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-12 02:13:36 +00:00
Tia Lim
22220d2c4f
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5351 Fixes 45 genuine (non-style) flake8 issues across 20 faiss Python files, on top of the black-80 base in this stack. These are real code issues, not formatting. - `B011` (19): replaced `assert False` / `assert False, "msg"` with `raise AssertionError(...)`. `assert` statements are stripped under `python -O`, so an `assert False` guard silently vanishes in optimized builds; `raise AssertionError` always fires. - `F401` (10) / `F403` (7): the intentional star-import re-exports in `python/loader.py` and `python/loader_fb.py` (which expose the SWIG symbols as `faiss.X`) are annotated with `# noqa: F401,F403`; genuinely unused imports in test/bench scripts are removed. - `F841` (9): unused local variables removed, or replaced with `_` in tuple unpacking, preserving behavior. No runtime behavior changes; formatting stays at black width 80. Reviewed By: mnorris11 Differential Revision: D109739445 fbshipit-source-id: 7d140d86cab9b2df691003bd043fc880a31410cd
80 lines
2.2 KiB
Python
80 lines
2.2 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import time
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import os
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import numpy as np
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import faiss
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os.system("grep -m1 'model name' < /proc/cpuinfo")
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def format_tab(x):
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return "\n".join("\t".join("%g" % xi for xi in row) for row in x)
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def run_bench(d, dsub, nbit=8, metric=None):
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M = d // dsub
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pq = faiss.ProductQuantizer(d, M, nbit)
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pq.train(faiss.randn((max(1000, pq.ksub * 50), d), 123))
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sp = faiss.swig_ptr
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times = []
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nrun = 100
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print(f"d={d} dsub={dsub} ksub={pq.ksub}", end="\t")
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res = []
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for nx in 1, 10, 100:
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x = faiss.randn((nx, d), 555)
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times = []
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for run in range(nrun):
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t0 = time.time()
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new_tab = np.zeros((nx, M, pq.ksub), "float32")
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if metric == faiss.METRIC_INNER_PRODUCT:
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pq.compute_inner_prod_tables(nx, sp(x), sp(new_tab))
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elif metric == faiss.METRIC_L2:
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pq.compute_distance_tables(nx, sp(x), sp(new_tab))
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else:
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raise AssertionError()
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t1 = time.time()
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if run >= nrun // 5: # the rest is considered warmup
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times.append((t1 - t0))
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times = np.array(times) * 1000
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print(
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f"nx={nx}: {np.mean(times):.3f} ms (± {np.std(times):.4f})",
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end="\t",
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)
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res.append(times.mean())
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print()
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return res
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# for have_threads in True, False:
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for have_threads in False, True:
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if have_threads:
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# good config for Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
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nthread = 32
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else:
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nthread = 1
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faiss.omp_set_num_threads(nthread)
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for metric in faiss.METRIC_INNER_PRODUCT, faiss.METRIC_L2:
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print("============= nthread=", nthread, "metric=", metric)
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allres = []
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for dsub in 2, 4, 8:
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for nbit in 4, 8:
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for M in 8, 20:
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res = run_bench(M * dsub, dsub, nbit, metric)
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allres.append(res)
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allres = np.array(allres)
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print("formatted result:")
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print(format_tab(allres))
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