def test_raw_array_search(self):
d = 32
nb = 1024
nq = 128
k = 10
# make GT on Faiss CPU
xq = faiss.randn(nq * d, 1234).reshape(nq, d)
xb = faiss.randn(nb * d, 1235).reshape(nb, d)
index = faiss.IndexFlatL2(d)
index.add(xb)
gt_D, gt_I = index.search(xq, k)
# move to pytorch & GPU
xq_t = torch.from_numpy(xq).cuda()
xb_t = torch.from_numpy(xb).cuda()
# resource object, can be re-used over calls
res = faiss.StandardGpuResources()
D, I = search_raw_array_pytorch(res, xb_t, xq_t, k)
# back to CPU for verification
D = D.cpu().numpy()
I = I.cpu().numpy()
assert np.all(I == gt_I)
assert np.all(np.abs(D - gt_D).max() < 1e-4)
def test_interop(self):
d = 16
nq = 5
nb = 20
xq = faiss.randn(nq * d, 1234).reshape(nq, d)
xb = faiss.randn(nb * d, 1235).reshape(nb, d)
res = faiss.StandardGpuResources()
index = faiss.GpuIndexFlatIP(res, d)
index.add(xb)
# reference CPU result
Dref, Iref = index.search(xq, 5)
# query is pytorch tensor (CPU)
xq_torch = torch.FloatTensor(xq)
D2, I2 = search_index_pytorch(index, xq_torch, 5)
assert np.all(Iref == I2.numpy())
# query is pytorch tensor (GPU)
xq_torch = xq_torch.cuda()
# no need for a sync here
D3, I3 = search_index_pytorch(index, xq_torch, 5)
# D3 and I3 are on torch tensors on GPU as well.
# this does a sync, which is useful because faiss and
# pytorch use different Cuda streams.
res.syncDefaultStreamCurrentDevice()
assert np.all(Iref == I3.cpu().numpy())
def test_interop(self):
d = 128
nq = 100
nb = 1000
k = 10
xq = faiss.randn(nq * d, 1234).reshape(nq, d)
xb = faiss.randn(nb * d, 1235).reshape(nb, d)
res = faiss.StandardGpuResources()
# Let's run on a non-default stream
s = torch.cuda.Stream()
# Torch will run on this stream
with torch.cuda.stream(s):
# query is pytorch tensor (CPU and GPU)
xq_torch_cpu = torch.FloatTensor(xq)
xq_torch_gpu = xq_torch_cpu.cuda()
index = faiss.GpuIndexFlatIP(res, d)
index.add(xb)
# Query with GPU tensor (this will be done on the current pytorch stream)
D2, I2 = search_index_pytorch(res, index, xq_torch_gpu, k)
Dref, Iref = index.search(xq, k)
assert np.all(Iref == I2.cpu().numpy())
# Query with CPU tensor
D3, I3 = search_index_pytorch(res, index, xq_torch_cpu, k)
assert np.all(Iref == I3.numpy())
def test_interop(self):
d = 16
nq = 5
nb = 20
xq = faiss.randn(nq * d, 1234).reshape(nq, d)
xb = faiss.randn(nb * d, 1235).reshape(nb, d)
res = faiss.StandardGpuResources()
index = faiss.GpuIndexFlatIP(res, d)
index.add(xb)
# reference CPU result
Dref, Iref = index.search(xq, 5)
# query is pytorch tensor (CPU)
xq_torch = torch.FloatTensor(xq)
D2, I2 = search_index_pytorch(index, xq_torch, 5)
assert np.all(Iref == I2.numpy())
# query is pytorch tensor (GPU)
xq_torch = xq_torch.cuda()
# no need for a sync here
D3, I3 = search_index_pytorch(index, xq_torch, 5)
# D3 and I3 are on torch tensors on GPU as well.
# this does a sync, which is useful because faiss and
# pytorch use different Cuda streams.
res.syncDefaultStreamCurrentDevice()
assert np.all(Iref == I3.cpu().numpy())
def test_raw_array_search(self):
d = 32
nb = 1024
nq = 128
k = 10
# make GT on Faiss CPU
xq = faiss.randn(nq * d, 1234).reshape(nq, d)
xb = faiss.randn(nb * d, 1235).reshape(nb, d)
index = faiss.IndexFlatL2(d)
index.add(xb)
gt_D, gt_I = index.search(xq, k)
# resource object, can be re-used over calls
res = faiss.StandardGpuResources()
# put on same stream as pytorch to avoid synchronizing streams
res.setDefaultNullStreamAllDevices()
for xq_row_major in True, False:
for xb_row_major in True, False:
# move to pytorch & GPU
xq_t = torch.from_numpy(xq).cuda()
xb_t = torch.from_numpy(xb).cuda()
if not xq_row_major:
xq_t = xq_t.t().clone().t()
assert not xq_t.is_contiguous()
if not xb_row_major:
xb_t = xb_t.t().clone().t()
assert not xb_t.is_contiguous()
D, I = search_raw_array_pytorch(res, xb_t, xq_t, k)
# back to CPU for verification
D = D.cpu().numpy()
I = I.cpu().numpy()
assert np.all(I == gt_I)
assert np.all(np.abs(D - gt_D).max() < 1e-4)
# test on subset
try:
D, I = search_raw_array_pytorch(res, xb_t, xq_t[60:80], k)
except TypeError:
if not xq_row_major:
# then it is expected
continue
# otherwise it is an error
raise
# back to CPU for verification
D = D.cpu().numpy()
I = I.cpu().numpy()
assert np.all(I == gt_I[60:80])
assert np.all(np.abs(D - gt_D[60:80]).max() < 1e-4)
def do_test(self, index_key):
d = 32
index = faiss.index_factory(d, index_key)
index.train(faiss.randn((100, d), 123))
# reference reconstruction
index.add(faiss.randn((100, d), 345))
index.add(faiss.randn((100, d), 678))
ref_recons = index.reconstruct_n(0, 200)
# with lookup
index.reset()
rs = np.random.RandomState(123)
ids = rs.choice(10000, size=200, replace=False).astype(np.int64)
index.add_with_ids(faiss.randn((100, d), 345), ids[:100])
index.set_direct_map_type(faiss.DirectMap.Hashtable)
index.add_with_ids(faiss.randn((100, d), 678), ids[100:])
# compare
for i in range(0, 200, 13):
recons = index.reconstruct(int(ids[i]))
self.assertTrue(np.all(recons == ref_recons[i]))
# test I/O
buf = faiss.serialize_index(index)
index2 = faiss.deserialize_index(buf)
# compare
for i in range(0, 200, 13):
recons = index2.reconstruct(int(ids[i]))
self.assertTrue(np.all(recons == ref_recons[i]))
# remove
toremove = np.ascontiguousarray(ids[0:200:3])
sel = faiss.IDSelectorArray(50, faiss.swig_ptr(toremove[:50]))
# test both ways of removing elements
nremove = index2.remove_ids(sel)
nremove += index2.remove_ids(toremove[50:])
self.assertEqual(nremove, len(toremove))
for i in range(0, 200, 13):
if i % 3 == 0:
self.assertRaises(
RuntimeError,
index2.reconstruct, int(ids[i])
)
else:
recons = index2.reconstruct(int(ids[i]))
self.assertTrue(np.all(recons == ref_recons[i]))
# index error should raise
self.assertRaises(
RuntimeError,
index.reconstruct, 20000
)
def do_test(self, d, dsub, nbit=8, metric=None):
if metric is None:
self.do_test(d, dsub, nbit, faiss.METRIC_INNER_PRODUCT)
self.do_test(d, dsub, nbit, faiss.METRIC_L2)
return
# faiss.cvar.distance_compute_blas_threshold = 1000000
M = d // dsub
pq = faiss.ProductQuantizer(d, M, nbit)
xt = faiss.randn((max(1000, pq.ksub * 50), d), 123)
pq.cp.niter = 4 # to avoid timeouts in tests
pq.train(xt)
centroids = faiss.vector_to_array(pq.centroids)
centroids = centroids.reshape(pq.M, pq.ksub, pq.dsub)
nx = 100
x = faiss.randn((nx, d), 555)
ref_tab = np.zeros((nx, M, pq.ksub), "float32")
# computation of tables in numpy
for sq in range(M):
i0, i1 = sq * dsub, (sq + 1) * dsub
xsub = x[:, i0:i1]
centsq = centroids[sq, :, :]
if metric == faiss.METRIC_INNER_PRODUCT:
ref_tab[:, sq, :] = xsub @ centsq.T
elif metric == faiss.METRIC_L2:
xsub3 = xsub.reshape(nx, 1, dsub)
cent3 = centsq.reshape(1, pq.ksub, dsub)
ref_tab[:, sq, :] = ((xsub3 - cent3)**2).sum(2)
else:
assert False
sp = faiss.swig_ptr
new_tab = np.zeros((nx, M, pq.ksub), "float32")
if metric == faiss.METRIC_INNER_PRODUCT:
pq.compute_inner_prod_tables(nx, sp(x), sp(new_tab))
elif metric == faiss.METRIC_L2:
pq.compute_distance_tables(nx, sp(x), sp(new_tab))
else:
assert False
# compute sdc tables in numpy
cent1 = np.expand_dims(centroids, axis=2) # [M, ksub, 1, dsub]
cent2 = np.expand_dims(centroids, axis=1) # [M, 1, ksub, dsub]
ref_sdc_tab = ((cent1 - cent2)**2).sum(3)
pq.compute_sdc_table()
new_sdc_tab = faiss.vector_to_array(pq.sdc_table)
new_sdc_tab = new_sdc_tab.reshape(M, pq.ksub, pq.ksub)
np.testing.assert_array_almost_equal(ref_tab, new_tab, decimal=5)
np.testing.assert_array_almost_equal(ref_sdc_tab,
new_sdc_tab,
decimal=5)
def test_reconstuct_after_add(self):
index = faiss.index_factory(10, 'IVF5,SQfp16')
index.train(faiss.randn((100, 10), 123))
index.add(faiss.randn((100, 10), 345))
index.make_direct_map()
index.add(faiss.randn((100, 10), 678))
# should not raise an exception
index.reconstruct(5)
print(index.ntotal)
index.reconstruct(150)
def test_weighted(self):
d = 32
sigma = 0.1
# Data is naturally clustered in 10 clusters.
# 5 clusters have 100 points
# 5 clusters have 10 points
# run k-means with 5 clusters
ccent = faiss.randn((10, d), 123)
faiss.normalize_L2(ccent)
x = [
ccent[i] + sigma * faiss.randn((100, d), 1234 + i)
for i in range(5)
]
x += [
ccent[i] + sigma * faiss.randn((10, d), 1234 + i)
for i in range(5, 10)
]
x = np.vstack(x)
clus = faiss.Clustering(d, 5)
index = faiss.IndexFlatL2(d)
clus.train(x, index)
cdis1, perm1 = index.search(ccent, 1)
# distance^2 of ground-truth centroids to clusters
cdis1_first = cdis1[:5].sum()
cdis1_last = cdis1[5:].sum()
# now assign weight 0.1 to the 5 first clusters and weight 10
# to the 5 last ones and re-run k-means
weights = np.ones(100 * 5 + 10 * 5, dtype='float32')
weights[:100 * 5] = 0.1
weights[100 * 5:] = 10
clus = faiss.Clustering(d, 5)
index = faiss.IndexFlatL2(d)
clus.train(x, index, weights=weights)
cdis2, perm2 = index.search(ccent, 1)
# distance^2 of ground-truth centroids to clusters
cdis2_first = cdis2[:5].sum()
cdis2_last = cdis2[5:].sum()
print(cdis1_first, cdis1_last)
print(cdis2_first, cdis2_last)
# with the new clustering, the last should be much (*2) closer
# to their centroids
self.assertGreater(cdis1_last, cdis1_first * 2)
self.assertGreater(cdis2_first, cdis2_last * 2)
def test_white(self):
# generate data
d = 4
nt = 1000
nb = 200
nq = 200
# normal distribition
x = faiss.randn((nt + nb + nq) * d, 1234).reshape(nt + nb + nq, d)
index = faiss.index_factory(d, 'Flat')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
# NN search on normal distribution
index.add(xb)
Do, Io = index.search(xq, 5)
# make distribution very skewed
x *= [10, 4, 1, 0.5]
rr, _ = np.linalg.qr(faiss.randn(d * d).reshape(d, d))
x = np.dot(x, rr).astype('float32')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
# L2 search on skewed distribution
index = faiss.index_factory(d, 'Flat')
index.add(xb)
Dl2, Il2 = index.search(xq, 5)
# whiten + L2 search on L2 distribution
index = faiss.index_factory(d, 'PCAW%d,Flat' % d)
index.train(xt)
index.add(xb)
Dw, Iw = index.search(xq, 5)
# make sure correlation of whitened results with original
# results is much better than simple L2 distances
# should be 961 vs. 264
assert (faiss.eval_intersection(Io, Iw) >
2 * faiss.eval_intersection(Io, Il2))
def test_white(self):
# generate data
d = 4
nt = 1000
nb = 200
nq = 200
# normal distribition
x = faiss.randn((nt + nb + nq) * d, 1234).reshape(nt + nb + nq, d)
index = faiss.index_factory(d, 'Flat')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
# NN search on normal distribution
index.add(xb)
Do, Io = index.search(xq, 5)
# make distribution very skewed
x *= [10, 4, 1, 0.5]
rr, _ = np.linalg.qr(faiss.randn(d * d).reshape(d, d))
x = np.dot(x, rr).astype('float32')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
# L2 search on skewed distribution
index = faiss.index_factory(d, 'Flat')
index.add(xb)
Dl2, Il2 = index.search(xq, 5)
# whiten + L2 search on L2 distribution
index = faiss.index_factory(d, 'PCAW%d,Flat' % d)
index.train(xt)
index.add(xb)
Dw, Iw = index.search(xq, 5)
# make sure correlation of whitened results with original
# results is much better than simple L2 distances
# should be 961 vs. 264
assert (faiss.eval_intersection(Io, Iw) >
2 * faiss.eval_intersection(Io, Il2))
def test_indexflat(self):
index = faiss.IndexFlatL2(32)
x = faiss.randn((100, 32), 1234)
index.add(x)
subset = [4, 7, 45]
np.testing.assert_equal(x[subset], index.reconstruct_batch(subset))
def test_chain(self):
# generate data
d = 4
nt = 1000
nb = 200
nq = 200
# normal distribition
x = faiss.randn((nt + nb + nq) * d, 1234).reshape(nt + nb + nq, d)
# make distribution very skewed
x *= [10, 4, 1, 0.5]
rr, _ = np.linalg.qr(faiss.randn(d * d).reshape(d, d))
x = np.dot(x, rr).astype('float32')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
index = faiss.index_factory(d, "L2norm,PCA2,L2norm,Flat")
assert index.chain.size() == 3
l2_1 = faiss.downcast_VectorTransform(index.chain.at(0))
assert l2_1.norm == 2
pca = faiss.downcast_VectorTransform(index.chain.at(1))
assert not pca.is_trained
index.train(xt)
assert pca.is_trained
index.add(xb)
D, I = index.search(xq, 5)
# do the computation manually and check if we get the same result
def manual_trans(x):
x = x.copy()
faiss.normalize_L2(x)
x = pca.apply_py(x)
faiss.normalize_L2(x)
return x
index2 = faiss.IndexFlatL2(2)
index2.add(manual_trans(xb))
D2, I2 = index2.search(manual_trans(xq), 5)
assert np.all(I == I2)
def test_chain(self):
# generate data
d = 4
nt = 1000
nb = 200
nq = 200
# normal distribition
x = faiss.randn((nt + nb + nq) * d, 1234).reshape(nt + nb + nq, d)
# make distribution very skewed
x *= [10, 4, 1, 0.5]
rr, _ = np.linalg.qr(faiss.randn(d * d).reshape(d, d))
x = np.dot(x, rr).astype('float32')
xt = x[:nt]
xb = x[nt:-nq]
xq = x[-nq:]
index = faiss.index_factory(d, "L2norm,PCA2,L2norm,Flat")
assert index.chain.size() == 3
l2_1 = faiss.downcast_VectorTransform(index.chain.at(0))
assert l2_1.norm == 2
pca = faiss.downcast_VectorTransform(index.chain.at(1))
assert not pca.is_trained
index.train(xt)
assert pca.is_trained
index.add(xb)
D, I = index.search(xq, 5)
# do the computation manually and check if we get the same result
def manual_trans(x):
x = x.copy()
faiss.normalize_L2(x)
x = pca.apply_py(x)
faiss.normalize_L2(x)
return x
index2 = faiss.IndexFlatL2(2)
index2.add(manual_trans(xb))
D2, I2 = index2.search(manual_trans(xq), 5)
assert np.all(I == I2)
def dump_load_factory(self, fs):
xq = faiss.randn((25, 10), 123)
xb = faiss.randn((25, 10), 124)
index = faiss.index_factory(10, fs)
index.train(xb)
index.add(xb)
Dref, Iref = index.search(xq, 4)
buf = io.BytesIO()
pickle.dump(index, buf)
buf.seek(0)
index2 = pickle.load(buf)
Dnew, Inew = index2.search(xq, 4)
np.testing.assert_array_equal(Iref, Inew)
np.testing.assert_array_equal(Dref, Dnew)
def test_raw_array_search(self):
d = 32
nb = 1024
nq = 128
k = 10
# make GT on Faiss CPU
xq = faiss.randn(nq * d, 1234).reshape(nq, d)
xb = faiss.randn(nb * d, 1235).reshape(nb, d)
index = faiss.IndexFlatL2(d)
index.add(xb)
gt_D, gt_I = index.search(xq, k)
# move to pytorch & GPU
xq_t = torch.from_numpy(xq).cuda()
xb_t = torch.from_numpy(xb).cuda()
# resource object, can be re-used over calls
res = faiss.StandardGpuResources()
# put on same stream as pytorch to avoid synchronizing streams
res.setDefaultNullStreamAllDevices()
D, I = search_raw_array_pytorch(res, xb_t, xq_t, k)
# back to CPU for verification
D = D.cpu().numpy()
I = I.cpu().numpy()
assert np.all(I == gt_I)
assert np.all(np.abs(D - gt_D).max() < 1e-4)
# test on subset
D, I = search_raw_array_pytorch(res, xb_t, xq_t[60:80], k)
# back to CPU for verification
D = D.cpu().numpy()
I = I.cpu().numpy()
assert np.all(I == gt_I[60:80])
assert np.all(np.abs(D - gt_D[60:80]).max() < 1e-4)
def do_test(self, d, dsub, nbit=8, metric=None):
if metric is None:
self.do_test(d, dsub, nbit, faiss.METRIC_INNER_PRODUCT)
self.do_test(d, dsub, nbit, faiss.METRIC_L2)
return
M = d // dsub
pq = faiss.ProductQuantizer(d, M, nbit)
pq.train(faiss.randn((max(1000, pq.ksub * 50), d), 123))
centroids = faiss.vector_to_array(pq.centroids)
centroids = centroids.reshape(pq.M, pq.ksub, pq.dsub)
nx = 100
x = faiss.randn((nx, d), 555)
ref_tab = np.zeros((nx, M, pq.ksub), "float32")
# computation of tables in numpy
for sq in range(M):
i0, i1 = sq * dsub, (sq + 1) * dsub
xsub = x[:, i0:i1]
centsq = centroids[sq, :, :]
if metric == faiss.METRIC_INNER_PRODUCT:
ref_tab[:, sq, :] = xsub @ centsq.T
elif metric == faiss.METRIC_L2:
xsub3 = xsub.reshape(nx, 1, dsub)
cent3 = centsq.reshape(1, pq.ksub, dsub)
ref_tab[:, sq, :] = ((xsub3 - cent3)**2).sum(2)
else:
assert False
sp = faiss.swig_ptr
new_tab = np.zeros((nx, M, pq.ksub), "float32")
if metric == faiss.METRIC_INNER_PRODUCT:
pq.compute_inner_prod_tables(nx, sp(x), sp(new_tab))
elif metric == faiss.METRIC_L2:
pq.compute_distance_tables(nx, sp(x), sp(new_tab))
else:
assert False
np.testing.assert_array_almost_equal(ref_tab, new_tab, decimal=5)
def test_exception(self):
index = faiss.index_factory(32, "IVF2,Flat")
x = faiss.randn((100, 32), 1234)
index.train(x)
index.add(x)
# make sure it raises an exception even if it enters the openmp for
subset = np.zeros(1200, dtype=int)
self.assertRaises(
RuntimeError,
lambda : index.reconstruct_batch(subset),
)
def run_bench(d, dsub, nbit=8, metric=None):
M = d // dsub
pq = faiss.ProductQuantizer(d, M, nbit)
pq.train(faiss.randn((max(1000, pq.ksub * 50), d), 123))
sp = faiss.swig_ptr
times = []
nrun = 100
print(f"d={d} dsub={dsub} ksub={pq.ksub}", end="\t")
res = []
for nx in 1, 10, 100:
x = faiss.randn((nx, d), 555)
times = []
for run in range(nrun):
t0 = time.time()
new_tab = np.zeros((nx, M, pq.ksub), "float32")
if metric == faiss.METRIC_INNER_PRODUCT:
pq.compute_inner_prod_tables(nx, sp(x), sp(new_tab))
elif metric == faiss.METRIC_L2:
pq.compute_distance_tables(nx, sp(x), sp(new_tab))
else:
assert False
t1 = time.time()
if run >= nrun // 5: # the rest is considered warmup
times.append((t1 - t0))
times = np.array(times) * 1000
print(f"nx={nx}: {np.mean(times):.3f} ms (± {np.std(times):.4f})",
end="\t")
res.append(times.mean())
print()
return res
"""small test script to benchmark the SIMD implementation of the
distance computations for the additional metrics. Call eg. with L1 to
get L1 distance computations.
"""
import faiss
import sys
import time
d = 64
nq = 4096
nb = 16384
print("sample")
xq = faiss.randn((nq, d), 123)
xb = faiss.randn((nb, d), 123)
mt_name = "L2" if len(sys.argv) < 2 else sys.argv[1]
mt = getattr(faiss, "METRIC_" + mt_name)
print("distances")
t0 = time.time()
dis = faiss.pairwise_distances(xq, xb, mt)
t1 = time.time()
print("nq=%d nb=%d d=%d %s: %.3f s" % (nq, nb, d, mt_name, t1 - t0))
def random_unitary(n, d, seed):
x = faiss.randn(n * d, seed).reshape(n, d)
faiss.normalize_L2(x)
return x
def test_raw_array_search(self):
d = 32
nb = 1024
nq = 128
k = 10
# make GT on Faiss CPU
xq = faiss.randn(nq * d, 1234).reshape(nq, d)
xb = faiss.randn(nb * d, 1235).reshape(nb, d)
index = faiss.IndexFlatL2(d)
index.add(xb)
gt_D, gt_I = index.search(xq, k)
# resource object, can be re-used over calls
res = faiss.StandardGpuResources()
# Let's have pytorch use a non-default stream
s = torch.cuda.Stream()
with torch.cuda.stream(s):
for xq_row_major in True, False:
for xb_row_major in True, False:
# move to pytorch & GPU
xq_t = torch.from_numpy(xq).cuda()
xb_t = torch.from_numpy(xb).cuda()
if not xq_row_major:
xq_t = to_column_major(xq_t)
assert not xq_t.is_contiguous()
if not xb_row_major:
xb_t = to_column_major(xb_t)
assert not xb_t.is_contiguous()
D, I = search_raw_array_pytorch(res, xb_t, xq_t, k)
# back to CPU for verification
D = D.cpu().numpy()
I = I.cpu().numpy()
assert np.all(I == gt_I)
assert np.all(np.abs(D - gt_D).max() < 1e-4)
# test on subset
try:
# This internally uses the current pytorch stream
D, I = search_raw_array_pytorch(
res, xb_t, xq_t[60:80], k)
except TypeError:
if not xq_row_major:
# then it is expected
continue
# otherwise it is an error
raise
# back to CPU for verification
D = D.cpu().numpy()
I = I.cpu().numpy()
assert np.all(I == gt_I[60:80])
assert np.all(np.abs(D - gt_D[60:80]).max() < 1e-4)
