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import itertools
import operator
import sparse
import pytest
import numpy as np
DENSITY = 0.01
def format_id(format):
return f"{format=}"
@pytest.mark.parametrize("format", ["coo", "gcxs"])
def test_matmul(benchmark, sides, format, seed, max_size, ids=format_id):
m, n, p = sides
if m * n >= max_size or n * p >= max_size:
pytest.skip()
rng = np.random.default_rng(seed=seed)
x = sparse.random((m, n), density=DENSITY, format=format, random_state=rng)
y = sparse.random((n, p), density=DENSITY, format=format, random_state=rng)
x @ y # Numba compilation
@benchmark
def bench():
x @ y
def get_test_id(params):
side, rank, format = params
return f"{side=}-{rank=}-{format=}"
@pytest.fixture(params=itertools.product([100, 500, 1000], [1, 2, 3, 4], ["coo", "gcxs"]), ids=get_test_id)
def elemwise_args(request, seed, max_size):
side, rank, format = request.param
if side**rank >= max_size:
pytest.skip()
rng = np.random.default_rng(seed=seed)
shape = (side,) * rank
x = sparse.random(shape, density=DENSITY, format=format, random_state=rng)
y = sparse.random(shape, density=DENSITY, format=format, random_state=rng)
return x, y
@pytest.mark.parametrize("f", [operator.add, operator.mul])
def test_elemwise(benchmark, f, elemwise_args):
x, y = elemwise_args
f(x, y)
@benchmark
def bench():
f(x, y)
def get_elemwise_ids(params):
side, format = params
return f"{side=}-{format=}"
@pytest.fixture(params=itertools.product([100, 500, 1000], ["coo", "gcxs"]), ids=get_elemwise_ids)
def elemwise_broadcast_args(request, seed, max_size):
side, format = request.param
if side**2 >= max_size:
pytest.skip()
rng = np.random.default_rng(seed=seed)
x = sparse.random((side, 1, side), density=DENSITY, format=format, random_state=rng)
y = sparse.random((side, side), density=DENSITY, format=format, random_state=rng)
return x, y
@pytest.mark.parametrize("f", [operator.add, operator.mul])
def test_elemwise_broadcast(benchmark, f, elemwise_broadcast_args):
x, y = elemwise_broadcast_args
f(x, y)
@benchmark
def bench():
f(x, y)
@pytest.fixture(params=itertools.product([100, 500, 1000], [1, 2, 3], ["coo", "gcxs"]), ids=get_test_id)
def indexing_args(request, seed, max_size):
side, rank, format = request.param
if side**rank >= max_size:
pytest.skip()
rng = np.random.default_rng(seed=seed)
shape = (side,) * rank
return sparse.random(shape, density=DENSITY, format=format, random_state=rng)
def test_index_scalar(benchmark, indexing_args):
x = indexing_args
side = x.shape[0]
rank = x.ndim
x[(side // 2,) * rank] # Numba compilation
@benchmark
def bench():
x[(side // 2,) * rank]
def test_index_slice(benchmark, indexing_args):
x = indexing_args
side = x.shape[0]
rank = x.ndim
x[(slice(side // 2),) * rank] # Numba compilation
@benchmark
def bench():
x[(slice(side // 2),) * rank]
def test_index_fancy(benchmark, indexing_args, seed):
x = indexing_args
side = x.shape[0]
rng = np.random.default_rng(seed=seed)
index = rng.integers(0, side, size=(side // 2,))
x[index] # Numba compilation
@benchmark
def bench():
x[index]
def get_sides_ids(param):
m, n, p = param
return f"{m=}-{n=}-{p=}"
@pytest.fixture(params=itertools.product([200, 500, 1000], [200, 500, 1000], [200, 500, 1000]), ids=get_sides_ids)
def sides(request):
m, n, p = request.param
return m, n, p
@pytest.fixture(params=([(0, "coo"), (0, "gcxs"), (1, "gcxs")]), ids=["coo", "gcxs-0-axis", "gcxs-1-axis"])
def densemul_args(request, sides, seed, max_size):
compressed_axis, format = request.param
m, n, p = sides
if m * n >= max_size or n * p >= max_size:
pytest.skip()
rng = np.random.default_rng(seed=seed)
if format == "coo":
x = sparse.random((m, n), density=DENSITY / 10, format=format, random_state=rng)
else:
x = sparse.random((m, n), density=DENSITY / 10, format=format, random_state=rng).change_compressed_axes(
(compressed_axis,)
)
t = rng.random((n, p))
return x, t
def test_gcxs_dot_ndarray(benchmark, densemul_args):
x, t = densemul_args
# Numba compilation
x @ t
@benchmark
def bench():
x @ t
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