mirror of
https://github.com/facebookresearch/faiss.git
synced 2026-10-12 02:13:36 +00:00
Tia Lim
0f0f728166
Summary: Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5349 # this is a no-op change Ran `black -l 80` over all tracked Python files under `fbcode/faiss/`, using line length 80 to match faiss's documented coding style (`CONTRIBUTING.md`: "80 character line length (both for C++ and Python)"). 172 files were reformatted; 28 were already compliant. This is a pure-formatting change. `black` runs its AST-equivalence safety check on every file, so no identifiers, string/numeric values, operators, or control flow were altered — only whitespace, indentation, line wrapping, blank lines, trailing commas, and string-quote normalization (single to double quotes). `fbcode/faiss/` is intentionally excluded from Meta's standard Python autoformatter (`arc f` / `arc lint -a` report no formatter for these paths) because it mirrors to public GitHub, so the Python had drifted out of any consistent style. The C++ is already clang-format-clean (0 of 807 files change), so this effort is Python-only. This diff brings the Python to canonical `black` formatting. Excluded `demos/index_pq_flat_separate_codes_from_codebook.py`: it contains a Jupyter `!rm` shell-magic line and is not valid Python, so `black` cannot parse it. This is the base of a 3-diff stack: (1) this black reformat; (2) `.flake8` alignment (max-line-length 80 + black-compatible ignores E203/W503); (3) genuine lint fixes (F401/F403/F841/B011). Reviewed By: mnorris11 Differential Revision: D109736099 fbshipit-source-id: 9a054635766ed11aa3760a788395babd3d198abf
788 lines
24 KiB
Python
788 lines
24 KiB
Python
# 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 unittest
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import time
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import numpy as np
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import faiss
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from faiss.contrib import datasets
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from common_faiss_tests import (
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compare_binary_result_lists,
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for_all_simd_levels,
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NoneSIMDLevel,
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)
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# the tests tend to timeout in stress modes + dev otherwise
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faiss.omp_set_num_threads(4)
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@for_all_simd_levels
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class TestSearch(unittest.TestCase):
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def test_PQ4_accuracy(self):
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ds = datasets.SyntheticDataset(32, 2000, 5000, 1000)
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index_gt = faiss.IndexFlatL2(32)
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index_gt.add(ds.get_database())
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Dref, Iref = index_gt.search(ds.get_queries(), 10)
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index = faiss.index_factory(32, "PQ16x4fs")
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index.train(ds.get_train())
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index.add(ds.get_database())
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Da, Ia = index.search(ds.get_queries(), 10)
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nq = Iref.shape[0]
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recall_at_1 = (Iref[:, 0] == Ia[:, 0]).sum() / nq
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assert recall_at_1 > 0.6
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def test_PQ4_search_matches_none(self):
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"""Fast-scan PQ search dispatches via the 256/512-bit QBS path.
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The integer QBS kernel is deterministic across SIMD levels *only if*
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the upstream float PQ distance table is bit-identical AND the
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handler's end() method (int-to-float conversion) compiles to
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identical FP code. Two cases break this:
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1. aarch64 NEON/SVE: float distance-table computation reduces
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in a different order, producing non-bit-identical LUT.
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2. DD mode: per-SIMD TUs compile with different flags (-mfma
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vs no -mfma), so handler end() may contract mul+add into
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fma, producing 1-ULP float distance differences.
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In both cases, fall back to a recall@1 check.
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"""
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ds = datasets.SyntheticDataset(32, 2000, 5000, 200)
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index = faiss.index_factory(32, "PQ16x4fs")
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index.train(ds.get_train())
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index.add(ds.get_database())
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xq = ds.get_queries()
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nq = xq.shape[0]
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M, ksub = index.pq.M, index.pq.ksub
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tab = np.zeros((nq, M, ksub), dtype="float32")
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index.pq.compute_distance_tables(
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nq, faiss.swig_ptr(xq), faiss.swig_ptr(tab)
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)
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D, I = index.search(xq, 10)
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with NoneSIMDLevel():
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tab_none = np.zeros((nq, M, ksub), dtype="float32")
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index.pq.compute_distance_tables(
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nq, faiss.swig_ptr(xq), faiss.swig_ptr(tab_none)
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)
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D_none, I_none = index.search(xq, 10)
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lut_diffs = int((tab != tab_none).sum())
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dd_mode = "DD" in faiss.get_compile_options()
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if lut_diffs == 0 and not dd_mode:
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compare_binary_result_lists(D, I, D_none, I_none)
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else:
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recall_at_1 = (I[:, 0] == I_none[:, 0]).mean()
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self.assertGreater(
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recall_at_1,
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0.95,
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f"LUT diverges ({lut_diffs} entries differ, DD={dd_mode}); "
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f"recall@1 vs NONE = {recall_at_1:.3f}",
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)
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class TestFastScanDDSpeed(unittest.TestCase):
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"""Verify DD-mode FastScan kernels use real SIMD."""
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def _build_index(self):
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d, n, nq, k = 128, 100_000, 1000, 10
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np.random.seed(123)
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xb = np.random.randn(n, d).astype("float32")
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xq = np.random.randn(nq, d).astype("float32")
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index = faiss.index_factory(d, "PQ16x4fs")
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index.train(xb)
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index.add(xb)
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index.search(xq, k)
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return index, xq, k
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def _bench(self, index, xq, k, level, iters=50):
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faiss.SIMDConfig.set_level(level)
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t0 = time.time()
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for _ in range(iters):
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index.search(xq, k)
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return time.time() - t0
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def _check_speedup(self, none_time, simd_time, label):
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speedup = none_time / simd_time
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print(
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f"FastScan DD: NONE={none_time:.3f}s, "
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f"{label}={simd_time:.3f}s, {speedup:.1f}x"
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)
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# 2x is conservative: scalar-to-AVX2 theoretical max is ~32x,
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# observed 5-27x in isolation. 2x leaves margin for CI noise.
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self.assertGreater(
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speedup,
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2.0,
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f"{label} should be >2x faster than NONE, " f"got {speedup:.1f}x",
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)
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@unittest.skipUnless(
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"DD" in faiss.get_compile_options()
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and "OPTIMIZE" in faiss.get_compile_options()
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and faiss.SIMDConfig.is_simd_level_available(faiss.SIMDLevel_AVX2),
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"DD-only, needs opt mode and AVX2",
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)
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def test_dd_fastscan_avx2_faster_than_none(self):
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index, xq, k = self._build_index()
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none_t = self._bench(index, xq, k, faiss.SIMDLevel_NONE)
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avx2_t = self._bench(index, xq, k, faiss.SIMDLevel_AVX2)
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faiss.SIMDConfig.set_level(faiss.SIMDConfig.get_dispatched_level())
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self._check_speedup(none_t, avx2_t, "AVX2")
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@unittest.skipUnless(
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"DD" in faiss.get_compile_options()
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and "OPTIMIZE" in faiss.get_compile_options()
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and faiss.SIMDConfig.is_simd_level_available(faiss.SIMDLevel_AVX512),
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"DD-only, needs opt mode and AVX512",
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)
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def test_dd_fastscan_avx512_faster_than_none(self):
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index, xq, k = self._build_index()
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none_t = self._bench(index, xq, k, faiss.SIMDLevel_NONE)
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avx512_t = self._bench(index, xq, k, faiss.SIMDLevel_AVX512)
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faiss.SIMDConfig.set_level(faiss.SIMDConfig.get_dispatched_level())
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self._check_speedup(none_t, avx512_t, "AVX512")
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@for_all_simd_levels
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class TestRounding(unittest.TestCase):
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def do_test_rounding(self, implem=4, metric=faiss.METRIC_L2):
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ds = datasets.SyntheticDataset(32, 2000, 5000, 200)
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index = faiss.index_factory(32, "PQ16x4", metric)
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index.train(ds.get_train())
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index.add(ds.get_database())
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Dref, Iref = index.search(ds.get_queries(), 10)
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nq = Iref.shape[0]
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index2 = faiss.IndexPQFastScan(index)
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# simply repro normal search
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index2.implem = 2
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D2, I2 = index2.search(ds.get_queries(), 10)
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np.testing.assert_array_equal(I2, Iref)
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np.testing.assert_array_equal(D2, Dref)
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# rounded LUT with correction
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index2.implem = implem
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D4, I4 = index2.search(ds.get_queries(), 10)
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# check accuracy of indexes
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recalls = {}
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for rank in 1, 10:
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recalls[rank] = (Iref[:, :1] == I4[:, :rank]).sum() / nq
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min_r1 = 0.98 if metric == faiss.METRIC_INNER_PRODUCT else 0.99
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self.assertGreaterEqual(recalls[1], min_r1)
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self.assertGreater(recalls[10], 0.995)
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# check accuracy of distances
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# err3 = ((D3 - D2) ** 2).sum()
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err4 = ((D4 - D2) ** 2).sum()
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nf = (D2**2).sum()
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self.assertLess(err4, nf * 1e-4)
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def test_implem_4(self):
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self.do_test_rounding(4)
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def test_implem_4_ip(self):
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self.do_test_rounding(4, faiss.METRIC_INNER_PRODUCT)
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def test_implem_12(self):
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self.do_test_rounding(12)
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def test_implem_12_ip(self):
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self.do_test_rounding(12, faiss.METRIC_INNER_PRODUCT)
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def test_implem_14(self):
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self.do_test_rounding(14)
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def test_implem_14_ip(self):
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self.do_test_rounding(12, faiss.METRIC_INNER_PRODUCT)
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@for_all_simd_levels
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class TestReconstruct(unittest.TestCase):
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def test_pqfastscan(self):
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ds = datasets.SyntheticDataset(20, 1000, 1000, 0)
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index = faiss.index_factory(20, "PQ5x4")
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index.train(ds.get_train())
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index.add(ds.get_database())
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recons = index.reconstruct_n(0, index.ntotal)
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index2 = faiss.IndexPQFastScan(index)
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recons2 = index2.reconstruct_n(0, index.ntotal)
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np.testing.assert_array_equal(recons, recons2)
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def test_aqfastscan(self):
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ds = datasets.SyntheticDataset(20, 1000, 1000, 0)
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index = faiss.index_factory(20, "RQ5x4_Nrq2x4")
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index.train(ds.get_train())
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index.add(ds.get_database())
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recons = index.reconstruct_n(0, index.ntotal)
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index2 = faiss.IndexAdditiveQuantizerFastScan(index)
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recons2 = index2.reconstruct_n(0, index.ntotal)
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np.testing.assert_array_equal(recons, recons2)
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#########################################################
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# Kernel unit test
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#########################################################
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def reference_accu(codes, LUT):
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nq, nsp, is_16 = LUT.shape
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nb, nsp_2 = codes.shape
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assert is_16 == 16
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assert nsp_2 == nsp // 2
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accu = np.zeros((nq, nb), "uint16")
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for i in range(nq):
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for j in range(nb):
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a = np.uint16(0)
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for sp in range(0, nsp, 2):
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c = codes[j, sp // 2]
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a += LUT[i, sp, c & 15].astype("uint16")
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a += LUT[i, sp + 1, c >> 4].astype("uint16")
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accu[i, j] = a
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return accu
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# disabled because the function to write to mem is not implemented currently
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class ThisIsNotATestLoop5: # (unittest.TestCase):
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def do_loop5_kernel(self, nq, bb):
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"""unit test for the accumulation kernel"""
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nb = bb * 32 # databse size
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nsp = 24 # number of sub-quantizers
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rs = np.random.RandomState(123)
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codes = rs.randint(256, size=(nb, nsp // 2)).astype("uint8")
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LUT = rs.randint(256, size=(nq, nsp, 16)).astype("uint8")
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accu_ref = reference_accu(codes, LUT)
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def to_A(x):
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return faiss.array_to_AlignedTable(x.ravel())
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sp = faiss.swig_ptr
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LUT_a = faiss.AlignedTableUint8(LUT.size)
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faiss.pq4_pack_LUT(nq, nsp, sp(LUT), LUT_a.get())
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codes_a = faiss.AlignedTableUint8(codes.size)
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faiss.pq4_pack_codes(sp(codes), nb, nsp, nb, nb, nsp, codes_a.get())
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accu_a = faiss.AlignedTableUint16(nq * nb)
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accu_a.clear()
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faiss.loop5_kernel_accumulate_1_block_to_mem(
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nq, nb, nsp, codes_a.get(), LUT_a.get(), accu_a.get()
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)
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accu = faiss.AlignedTable_to_array(accu_a).reshape(nq, nb)
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np.testing.assert_array_equal(accu_ref, accu)
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def test_11(self):
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self.do_loop5_kernel(1, 1)
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def test_21(self):
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self.do_loop5_kernel(2, 1)
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def test_12(self):
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self.do_loop5_kernel(1, 2)
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def test_22(self):
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self.do_loop5_kernel(2, 2)
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##########################################################
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# Tests for various IndexPQFastScan implementations
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##########################################################
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def verify_with_draws(testcase, Dref, Iref, Dnew, Inew):
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"""Verify a list of results where there are draws in the distances
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(because they are integer)."""
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np.testing.assert_array_almost_equal(Dref, Dnew, decimal=5)
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# here we have to be careful because of draws
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for i in range(len(Iref)):
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if np.all(Iref[i] == Inew[i]):
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continue
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# we can deduce nothing about the latest line
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skip_dis = Dref[i, -1]
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for dis in np.unique(Dref):
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if dis == skip_dis:
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continue
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mask = Dref[i, :] == dis
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testcase.assertEqual(set(Iref[i, mask]), set(Inew[i, mask]))
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class TestImplems(unittest.TestCase):
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def __init__(self, *args):
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unittest.TestCase.__init__(self, *args)
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self.cache = {}
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self.k = 10
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def get_index(self, d, metric):
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if (d, metric) not in self.cache:
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ds = datasets.SyntheticDataset(d, 1000, 2000, 200)
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target_size = d // 2
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index = faiss.index_factory(d, "PQ%dx4" % target_size, metric)
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index.train(ds.get_train())
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index.add(ds.get_database())
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index2 = faiss.IndexPQFastScan(index)
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# uint8 LUT but no SIMD
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index2.implem = 4
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Dref, Iref = index2.search(ds.get_queries(), 10)
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# check CodePacker
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codes_ref = faiss.vector_to_array(index.codes)
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codes_ref = codes_ref.reshape(-1, index.code_size)
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index2codes = faiss.vector_to_array(index2.codes)
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code_packer = index2.get_CodePacker()
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index2codes = index2codes.reshape(-1, code_packer.block_size)
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for i in range(0, len(codes_ref), 13):
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code_new = code_packer.unpack_1(index2codes, i)
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np.testing.assert_array_equal(codes_ref[i], code_new)
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self.cache[(d, metric)] = (ds, index, Dref, Iref)
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return self.cache[(d, metric)]
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def do_with_params(self, d, params, metric=faiss.METRIC_L2):
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ds, index, Dref, Iref = self.get_index(d, metric)
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index2 = self.build_fast_scan_index(index, params)
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Dnew, Inew = index2.search(ds.get_queries(), self.k)
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Dref = Dref[:, : self.k]
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Iref = Iref[:, : self.k]
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verify_with_draws(self, Dref, Iref, Dnew, Inew)
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def build_fast_scan_index(self, index, params):
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index2 = faiss.IndexPQFastScan(index)
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index2.implem = 5
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return index2
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@for_all_simd_levels
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class TestImplem12(TestImplems):
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def build_fast_scan_index(self, index, qbs):
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index2 = faiss.IndexPQFastScan(index)
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index2.qbs = qbs
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index2.implem = 12
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return index2
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def test_qbs7(self):
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self.do_with_params(32, 0x223)
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def test_qbs7b(self):
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self.do_with_params(32, 0x133)
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def test_qbs6(self):
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self.do_with_params(32, 0x33)
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def test_qbs6_ip(self):
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self.do_with_params(32, 0x33, faiss.METRIC_INNER_PRODUCT)
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def test_qbs6b(self):
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# test codepath where qbs is not known at compile time
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self.do_with_params(32, 0x1113)
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def test_qbs6_odd_dim(self):
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self.do_with_params(30, 0x33)
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@for_all_simd_levels
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class TestImplem13(TestImplems):
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def build_fast_scan_index(self, index, qbs):
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index2 = faiss.IndexPQFastScan(index)
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index2.qbs = qbs
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index2.implem = 13
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return index2
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def test_qbs7(self):
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self.do_with_params(32, 0x223)
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def test_qbs7_k1(self):
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self.k = 1
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self.do_with_params(32, 0x223)
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@for_all_simd_levels
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class TestImplem14(TestImplems):
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def build_fast_scan_index(self, index, params):
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qbs, bbs = params
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index2 = faiss.IndexPQFastScan(index, bbs)
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index2.qbs = qbs
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index2.implem = 14
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return index2
|
|
|
|
def test_1_32(self):
|
|
self.do_with_params(32, (1, 32))
|
|
|
|
def test_1_64(self):
|
|
self.do_with_params(32, (1, 64))
|
|
|
|
def test_2_32(self):
|
|
self.do_with_params(32, (2, 32))
|
|
|
|
def test_2_64(self):
|
|
self.do_with_params(32, (2, 64))
|
|
|
|
def test_qbs_1_32_k1(self):
|
|
self.k = 1
|
|
self.do_with_params(32, (1, 32))
|
|
|
|
def test_qbs_1_64_k1(self):
|
|
self.k = 1
|
|
self.do_with_params(32, (1, 64))
|
|
|
|
def test_1_32_odd_dim(self):
|
|
self.do_with_params(30, (1, 32))
|
|
|
|
def test_1_64_odd_dim(self):
|
|
self.do_with_params(30, (1, 64))
|
|
|
|
|
|
@for_all_simd_levels
|
|
class TestImplem15(TestImplems):
|
|
|
|
def build_fast_scan_index(self, index, params):
|
|
qbs, bbs = params
|
|
index2 = faiss.IndexPQFastScan(index, bbs)
|
|
index2.qbs = qbs
|
|
index2.implem = 15
|
|
return index2
|
|
|
|
def test_1_32(self):
|
|
self.do_with_params(32, (1, 32))
|
|
|
|
def test_2_64(self):
|
|
self.do_with_params(32, (2, 64))
|
|
|
|
|
|
@for_all_simd_levels
|
|
class TestAdd(unittest.TestCase):
|
|
|
|
def do_test_add(self, d, bbs):
|
|
|
|
ds = datasets.SyntheticDataset(d, 2000, 5000, 200)
|
|
|
|
index = faiss.index_factory(d, f"PQ{d // 2}x4np")
|
|
index.train(ds.get_train())
|
|
|
|
xb = ds.get_database()
|
|
index.add(xb[:1235])
|
|
|
|
index2 = faiss.IndexPQFastScan(index, bbs)
|
|
index2.add(xb[1235:])
|
|
new_codes = faiss.AlignedTable_to_array(index2.codes)
|
|
|
|
index.add(xb[1235:])
|
|
index3 = faiss.IndexPQFastScan(index, bbs)
|
|
ref_codes = faiss.AlignedTable_to_array(index3.codes)
|
|
self.assertEqual(index3.ntotal, index2.ntotal)
|
|
|
|
np.testing.assert_array_equal(ref_codes, new_codes)
|
|
|
|
def test_add(self):
|
|
self.do_test_add(32, 32)
|
|
|
|
def test_add_bbs64(self):
|
|
self.do_test_add(32, 64)
|
|
|
|
def test_add_odd_d(self):
|
|
self.do_test_add(30, 64)
|
|
|
|
def test_constructor(self):
|
|
d = 32
|
|
ds = datasets.SyntheticDataset(d, 2000, 5000, 200)
|
|
|
|
index = faiss.index_factory(d, f"PQ{d // 2}x4np")
|
|
index.train(ds.get_train())
|
|
index.add(ds.get_database())
|
|
Dref, Iref = index.search(ds.get_queries(), 10)
|
|
nq = Iref.shape[0]
|
|
|
|
index2 = faiss.IndexPQFastScan(d, d // 2, 4)
|
|
index2.train(ds.get_train())
|
|
index2.add(ds.get_database())
|
|
Dnew, Inew = index2.search(ds.get_queries(), 10)
|
|
|
|
recall_at_1 = (Iref[:, 0] == Inew[:, 0]).sum() / nq
|
|
|
|
self.assertGreaterEqual(recall_at_1, 0.99)
|
|
|
|
data = faiss.serialize_index(index2)
|
|
index3 = faiss.deserialize_index(data)
|
|
|
|
self.assertEqual(index2.implem, index3.implem)
|
|
|
|
D3, I3 = index3.search(ds.get_queries(), 10)
|
|
np.testing.assert_array_equal(D3, Dnew)
|
|
np.testing.assert_array_equal(I3, Inew)
|
|
|
|
|
|
class TestAQFastScan(unittest.TestCase):
|
|
|
|
def subtest_accuracy(self, aq, st, implem, metric_type="L2"):
|
|
"""
|
|
Compare IndexAdditiveQuantizerFastScan with IndexAQ (qint8)
|
|
"""
|
|
d = 16
|
|
ds = datasets.SyntheticDataset(d, 1000, 1000, 500, metric_type)
|
|
gt = ds.get_groundtruth(k=1)
|
|
|
|
if metric_type == "L2":
|
|
metric = faiss.METRIC_L2
|
|
postfix1 = "_Nqint8"
|
|
postfix2 = f"_N{st}2x4"
|
|
else:
|
|
metric = faiss.METRIC_INNER_PRODUCT
|
|
postfix1 = postfix2 = ""
|
|
|
|
index = faiss.index_factory(d, f"{aq}3x4{postfix1}", metric)
|
|
index.train(ds.get_train())
|
|
index.add(ds.get_database())
|
|
Dref, Iref = index.search(ds.get_queries(), 1)
|
|
|
|
indexfs = faiss.index_factory(d, f"{aq}3x4fs_32{postfix2}", metric)
|
|
indexfs.train(ds.get_train())
|
|
indexfs.add(ds.get_database())
|
|
indexfs.implem = implem
|
|
Da, Ia = indexfs.search(ds.get_queries(), 1)
|
|
|
|
nq = Iref.shape[0]
|
|
recall_ref = (Iref == gt).sum() / nq
|
|
recall = (Ia == gt).sum() / nq
|
|
|
|
assert abs(recall_ref - recall) < 0.05
|
|
|
|
def xx_test_accuracy(self):
|
|
for metric in "L2", "IP":
|
|
for implem in 0, 12, 13, 14, 15:
|
|
self.subtest_accuracy("RQ", "rq", implem, metric)
|
|
self.subtest_accuracy("LSQ", "lsq", implem, metric)
|
|
|
|
def subtest_from_idxaq(self, implem, metric):
|
|
if metric == "L2":
|
|
metric_type = faiss.METRIC_L2
|
|
st = "_Nrq2x4"
|
|
else:
|
|
metric_type = faiss.METRIC_INNER_PRODUCT
|
|
st = ""
|
|
|
|
d = 16
|
|
ds = datasets.SyntheticDataset(d, 1000, 2000, 1000, metric=metric)
|
|
gt = ds.get_groundtruth(k=1)
|
|
index = faiss.index_factory(d, "RQ8x4" + st, metric_type)
|
|
index.train(ds.get_train())
|
|
index.add(ds.get_database())
|
|
Dref, Iref = index.search(ds.get_queries(), 1)
|
|
|
|
indexfs = faiss.IndexAdditiveQuantizerFastScan(index)
|
|
indexfs.implem = implem
|
|
D1, I1 = indexfs.search(ds.get_queries(), 1)
|
|
|
|
nq = Iref.shape[0]
|
|
recall_ref = (Iref == gt).sum() / nq
|
|
recall1 = (I1 == gt).sum() / nq
|
|
assert abs(recall_ref - recall1) < 0.05
|
|
|
|
def xx_test_from_idxaq(self):
|
|
for implem in 2, 3, 4:
|
|
self.subtest_from_idxaq(implem, "L2")
|
|
self.subtest_from_idxaq(implem, "IP")
|
|
|
|
def subtest_factory(self, aq, M, bbs, st):
|
|
"""
|
|
Format: {AQ}{M}x4fs_{bbs}_N{st}
|
|
|
|
AQ (str): `LSQ` or `RQ`
|
|
M (int): number of subquantizers
|
|
bbs (int): build block size
|
|
st (str): search type, `lsq2x4` or `rq2x4`
|
|
"""
|
|
AQ = faiss.AdditiveQuantizer
|
|
d = 16
|
|
|
|
if bbs > 0:
|
|
index = faiss.index_factory(d, f"{aq}{M}x4fs_{bbs}_N{st}2x4")
|
|
else:
|
|
index = faiss.index_factory(d, f"{aq}{M}x4fs_N{st}2x4")
|
|
bbs = 32
|
|
|
|
assert index.bbs == bbs
|
|
aq = faiss.downcast_AdditiveQuantizer(index.aq)
|
|
assert aq.M == M
|
|
|
|
if aq == "LSQ":
|
|
assert isinstance(aq, faiss.LocalSearchQuantizer)
|
|
if aq == "RQ":
|
|
assert isinstance(aq, faiss.ResidualQuantizer)
|
|
|
|
if st == "lsq":
|
|
assert aq.search_type == AQ.ST_norm_lsq2x4
|
|
if st == "rq":
|
|
assert aq.search_type == AQ.ST_norm_rq2x4
|
|
|
|
def test_factory(self):
|
|
self.subtest_factory("LSQ", 16, 64, "lsq")
|
|
self.subtest_factory("LSQ", 16, 64, "rq")
|
|
self.subtest_factory("RQ", 16, 64, "rq")
|
|
self.subtest_factory("RQ", 16, 64, "lsq")
|
|
self.subtest_factory("LSQ", 64, 0, "lsq")
|
|
|
|
def subtest_io(self, factory_str):
|
|
d = 8
|
|
ds = datasets.SyntheticDataset(d, 1000, 500, 100)
|
|
|
|
index = faiss.index_factory(d, factory_str)
|
|
index.train(ds.get_train())
|
|
index.add(ds.get_database())
|
|
D1, I1 = index.search(ds.get_queries(), 1)
|
|
|
|
index2 = faiss.deserialize_index(faiss.serialize_index(index))
|
|
D2, I2 = index2.search(ds.get_queries(), 1)
|
|
np.testing.assert_array_equal(I1, I2)
|
|
|
|
def test_io(self):
|
|
self.subtest_io("LSQ4x4fs_Nlsq2x4")
|
|
self.subtest_io("LSQ4x4fs_Nrq2x4")
|
|
self.subtest_io("RQ4x4fs_Nrq2x4")
|
|
self.subtest_io("RQ4x4fs_Nlsq2x4")
|
|
|
|
|
|
# programatically generate tests to get finer test granularity.
|
|
|
|
|
|
def add_TestAQFastScan_subset_accuracy(aq, st, implem, metric):
|
|
setattr(
|
|
TestAQFastScan,
|
|
f"test_accuracy_{metric}_{aq}_implem{implem}",
|
|
lambda self: self.subtest_accuracy(aq, st, implem, metric),
|
|
)
|
|
|
|
|
|
for metric in "L2", "IP":
|
|
for implem in 0, 12, 13, 14, 15:
|
|
add_TestAQFastScan_subset_accuracy("LSQ", "lsq", implem, metric)
|
|
add_TestAQFastScan_subset_accuracy("RQ", "rq", implem, metric)
|
|
|
|
|
|
def add_TestAQFastScan_subtest_from_idxaq(implem, metric):
|
|
setattr(
|
|
TestAQFastScan,
|
|
f"test_from_idxaq_{metric}_implem{implem}",
|
|
lambda self: self.subtest_from_idxaq(implem, metric),
|
|
)
|
|
|
|
|
|
for implem in 2, 3, 4:
|
|
add_TestAQFastScan_subtest_from_idxaq(implem, "L2")
|
|
add_TestAQFastScan_subtest_from_idxaq(implem, "IP")
|
|
|
|
# Apply decorator after dynamic method generation.
|
|
TestAQFastScan = for_all_simd_levels(TestAQFastScan)
|
|
|
|
|
|
@for_all_simd_levels
|
|
class TestPAQFastScan(unittest.TestCase):
|
|
|
|
def subtest_accuracy(self, paq):
|
|
"""
|
|
Compare IndexPAQFastScan with IndexPAQ (qint8)
|
|
"""
|
|
d = 16
|
|
ds = datasets.SyntheticDataset(d, 1000, 1000, 500)
|
|
gt = ds.get_groundtruth(k=1)
|
|
|
|
index = faiss.index_factory(d, f"{paq}2x3x4_Nqint8")
|
|
index.train(ds.get_train())
|
|
index.add(ds.get_database())
|
|
Dref, Iref = index.search(ds.get_queries(), 1)
|
|
|
|
indexfs = faiss.index_factory(d, f"{paq}2x3x4fs_Nlsq2x4")
|
|
indexfs.train(ds.get_train())
|
|
indexfs.add(ds.get_database())
|
|
Da, Ia = indexfs.search(ds.get_queries(), 1)
|
|
|
|
nq = Iref.shape[0]
|
|
recall_ref = (Iref == gt).sum() / nq
|
|
recall = (Ia == gt).sum() / nq
|
|
|
|
assert abs(recall_ref - recall) < 0.05
|
|
|
|
def test_accuracy_PLSQ(self):
|
|
self.subtest_accuracy("PLSQ")
|
|
|
|
def test_accuracy_PRQ(self):
|
|
self.subtest_accuracy("PRQ")
|
|
|
|
def subtest_factory(self, paq):
|
|
index = faiss.index_factory(16, f"{paq}2x3x4fs_Nlsq2x4")
|
|
q = faiss.downcast_Quantizer(index.aq)
|
|
self.assertEqual(q.nsplits, 2)
|
|
self.assertEqual(q.subquantizer(0).M, 3)
|
|
|
|
def test_factory(self):
|
|
self.subtest_factory("PRQ")
|
|
self.subtest_factory("PLSQ")
|
|
|
|
def subtest_io(self, factory_str):
|
|
d = 8
|
|
ds = datasets.SyntheticDataset(d, 1000, 500, 100)
|
|
|
|
index = faiss.index_factory(d, factory_str)
|
|
index.train(ds.get_train())
|
|
index.add(ds.get_database())
|
|
D1, I1 = index.search(ds.get_queries(), 1)
|
|
|
|
index2 = faiss.deserialize_index(faiss.serialize_index(index))
|
|
D2, I2 = index2.search(ds.get_queries(), 1)
|
|
np.testing.assert_array_equal(I1, I2)
|
|
|
|
def test_io(self):
|
|
self.subtest_io("PLSQ2x3x4fs_Nlsq2x4")
|
|
self.subtest_io("PRQ2x3x4fs_Nrq2x4")
|
|
|
|
|
|
@for_all_simd_levels
|
|
class TestBlockDecode(unittest.TestCase):
|
|
|
|
def test_issue_2739(self):
|
|
ds = datasets.SyntheticDataset(960, 200, 1, 0)
|
|
M = 32
|
|
index = faiss.index_factory(ds.d, f"PQ{M}x4fs")
|
|
index.train(ds.get_train())
|
|
index.add(ds.get_database())
|
|
|
|
decoded = index.pq.decode(index.pq.compute_codes(ds.get_database()))[
|
|
0, ::100
|
|
]
|
|
np.testing.assert_array_equal(decoded, index.reconstruct(0)[::100])
|