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Extend test suite to int8 and uint8 #596

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34 changes: 24 additions & 10 deletions python/scripts/test_index.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@
ScalarKind.BF16,
ScalarKind.I8,
]
dtypes = [np.float32, np.float64, np.float16]
dtypes = [np.float32, np.float64, np.float16, np.int8, np.uint8]
threads = 2

connectivity_options = [3, 13, 50, DEFAULT_CONNECTIVITY]
Expand All @@ -49,8 +49,10 @@ def reset_randomness():
@pytest.mark.parametrize("metric", [MetricKind.Cos, MetricKind.L2sq])
@pytest.mark.parametrize("batch_size", [1, 7, 1024])
@pytest.mark.parametrize("quantization", [ScalarKind.F32, ScalarKind.I8])
@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
def test_index_initialization_and_addition(ndim, metric, quantization, dtype, batch_size):
@pytest.mark.parametrize("dtype", dtypes)
def test_index_initialization_and_addition(
ndim, metric, quantization, dtype, batch_size
):
reset_randomness()

index = Index(ndim=ndim, metric=metric, dtype=quantization, multi=False)
Expand All @@ -63,7 +65,9 @@ def test_index_initialization_and_addition(ndim, metric, quantization, dtype, ba
@pytest.mark.parametrize("ndim", [3, 97, 256])
@pytest.mark.parametrize("metric", [MetricKind.Cos, MetricKind.L2sq])
@pytest.mark.parametrize("batch_size", [1, 7, 1024])
@pytest.mark.parametrize("quantization", [ScalarKind.F32, ScalarKind.F16, ScalarKind.I8])
@pytest.mark.parametrize(
"quantization", [ScalarKind.F32, ScalarKind.F16, ScalarKind.I8]
)
@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.float16])
def test_index_retrieval(ndim, metric, quantization, dtype, batch_size):
reset_randomness()
Expand Down Expand Up @@ -103,7 +107,9 @@ def test_index_retrieval(ndim, metric, quantization, dtype, batch_size):
# Try a transposed version of the same vectors, that is not C-contiguous
# and should raise an exception!
index = Index(ndim=ndim, metric=metric, dtype=quantization, multi=False)
vectors = random_vectors(count=ndim, ndim=batch_size, dtype=dtype) #! reversed dims
vectors = random_vectors(
count=ndim, ndim=batch_size, dtype=dtype
) #! reversed dims
assert vectors.strides == (batch_size * dtype().itemsize, dtype().itemsize)
assert vectors.T.strides == (dtype().itemsize, batch_size * dtype().itemsize)
with pytest.raises(Exception):
Expand Down Expand Up @@ -220,7 +226,9 @@ def test_index_save_load_restore_copy(ndim, quantization, batch_size):
copied_index = index.copy()
assert len(copied_index) == len(index)
if batch_size > 0:
assert np.allclose(np.vstack(copied_index.get(keys)), np.vstack(index.get(keys)))
assert np.allclose(
np.vstack(copied_index.get(keys)), np.vstack(index.get(keys))
)

index.save("tmp.usearch")
index.clear()
Expand All @@ -244,7 +252,9 @@ def test_index_save_load_restore_copy(ndim, quantization, batch_size):
copied_index = index.copy()
assert len(copied_index) == len(index)
if batch_size > 0:
assert np.allclose(np.vstack(copied_index.get(keys)), np.vstack(index.get(keys)))
assert np.allclose(
np.vstack(copied_index.get(keys)), np.vstack(index.get(keys))
)

# Perform the same operations in RAM, without touching the filesystem
serialized_index = index.save()
Expand All @@ -255,7 +265,9 @@ def test_index_save_load_restore_copy(ndim, quantization, batch_size):
assert len(deserialized_index) == len(index)
assert set(np.array(deserialized_index.keys)) == set(np.array(index.keys))
if batch_size > 0:
assert np.allclose(np.vstack(deserialized_index.get(keys)), np.vstack(index.get(keys)))
assert np.allclose(
np.vstack(deserialized_index.get(keys)), np.vstack(index.get(keys))
)

deserialized_index.reset()
index.reset()
Expand All @@ -280,7 +292,7 @@ def test_index_contains_remove_rename(batch_size):
removed_keys = keys[: batch_size // 2]
remaining_keys = keys[batch_size // 2 :]
index.remove(removed_keys)
del index[removed_keys] # ! This will trigger the `__delitem__` dunder method
del index[removed_keys] # ! This will trigger the `__delitem__` dunder method
assert len(index) == (len(keys) - len(removed_keys))
assert np.sum(index.contains(keys)) == len(remaining_keys)
assert np.sum(index.count(keys)) == len(remaining_keys)
Expand Down Expand Up @@ -313,7 +325,9 @@ def test_index_oversubscribed_search(batch_size: int, threads: int):
assert np.all(index.contains(keys))
assert np.all(index.count(keys) == np.ones(batch_size))

batch_matches: BatchMatches = index.search(vectors, batch_size * 10, threads=threads)
batch_matches: BatchMatches = index.search(
vectors, batch_size * 10, threads=threads
)
for i, match in enumerate(batch_matches):
assert i == match.keys[0]
assert len(match.keys) == batch_size
Expand Down