Merge pull request #891 from prady0t/making-benchmarking-deterministic

Making benchmarking tests deterministic

Author: prady0t

benchmarks/conftest.py

@@ -1,9 +1,11 @@
 import pytest
 
+import numpy as np
 
-@pytest.fixture
-def seed(scope="session"):
-    return 42
+
+@pytest.fixture(scope="function")
+def rng():
+    return np.random.default_rng(seed=42)
 
 
 @pytest.fixture

benchmarks/test_benchmark_coo.py

@@ -5,8 +5,6 @@
 
 import pytest
 
-import numpy as np
-
 DENSITY = 0.01
 
 
@@ -15,13 +13,12 @@ def format_id(format):
 
 
 @pytest.mark.parametrize("format", ["coo", "gcxs"])
-def test_matmul(benchmark, sides, format, seed, max_size, ids=format_id):
+def test_matmul(benchmark, sides, format, rng, 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)
 
@@ -38,11 +35,10 @@ def get_test_id(params):
 
 
 @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):
+def elemwise_args(request, rng, 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)
@@ -65,11 +61,10 @@ def get_elemwise_ids(params):
 
 
 @pytest.fixture(params=itertools.product([100, 500, 1000], ["coo", "gcxs"]), ids=get_elemwise_ids)
-def elemwise_broadcast_args(request, seed, max_size):
+def elemwise_broadcast_args(request, rng, 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
@@ -86,11 +81,10 @@ def bench():
 
 
 @pytest.fixture(params=itertools.product([100, 500, 1000], [1, 2, 3], ["coo", "gcxs"]), ids=get_test_id)
-def indexing_args(request, seed, max_size):
+def indexing_args(request, rng, 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)
@@ -120,10 +114,9 @@ def bench():
         x[(slice(side // 2),) * rank]
 
 
-def test_index_fancy(benchmark, indexing_args, seed):
+def test_index_fancy(benchmark, indexing_args, rng):
     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
@@ -145,12 +138,11 @@ def sides(request):
 
 
 @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):
+def densemul_args(request, sides, rng, 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:

benchmarks/test_elemwise.py

@@ -7,7 +7,6 @@
 
 import pytest
 
-import numpy as np
 import scipy.sparse as sps
 
 DENSITY = 0.001
@@ -18,11 +17,10 @@ def get_test_id(side):
 
 
 @pytest.fixture(params=[100, 500, 1000], ids=get_test_id)
-def elemwise_args(request, seed, max_size):
+def elemwise_args(request, rng, max_size):
     side = request.param
     if side**2 >= max_size:
         pytest.skip()
-    rng = np.random.default_rng(seed=seed)
     s1_sps = sps.random(side, side, format="csr", density=DENSITY, random_state=rng) * 10
     s1_sps.sum_duplicates()
     s2_sps = sps.random(side, side, format="csr", density=DENSITY, random_state=rng) * 10

benchmarks/test_tensordot.py

@@ -34,12 +34,11 @@ def get_tensor_ids(param):
     ids=get_tensor_ids,
     scope="function",
 )
-def tensordot_args(request, sides, seed, max_size):
+def tensordot_args(request, sides, rng, max_size):
     m, n, p, q = sides
     if m * n * p * q >= max_size:
         pytest.skip()
     left_index, right_index, left_format, right_format = request.param
-    rng = np.random.default_rng(seed=seed)
 
     t = rng.random((m, n))