test: Added gcxs to benchmarks to be tested with CodSpeed (#748)

Author: DeaMariaLeon

benchmarks/test_benchmark_coo.py

@@ -10,17 +10,20 @@
 DENSITY = 0.01
 
 
-def side_ids(side):
-    return f"{side=}"
+def format_id(format):
+    return f"{format=}"
 
 
-@pytest.mark.parametrize("side", [100, 500, 1000], ids=side_ids)
-def test_matmul(benchmark, side, seed, max_size):
-    if side**2 >= max_size:
+@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((side, side), density=DENSITY, random_state=rng)
-    y = sparse.random((side, side), density=DENSITY, random_state=rng)
+    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
 
@@ -29,20 +32,20 @@ def bench():
         x @ y
 
 
-def get_test_id(param):
-    side, rank = param
-    return f"{side=}-{rank=}"
+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]), ids=get_test_id)
+@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 = request.param
+    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, random_state=rng)
-    y = sparse.random(shape, density=DENSITY, random_state=rng)
+    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
 
 
@@ -56,14 +59,19 @@ def bench():
         f(x, y)
 
 
-@pytest.fixture(params=[100, 500, 1000], ids=side_ids)
+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 = request.param
+    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, random_state=rng)
-    y = sparse.random((side, side), density=DENSITY, random_state=rng)
+    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
 
 
@@ -77,15 +85,15 @@ def bench():
         f(x, y)
 
 
-@pytest.fixture(params=itertools.product([100, 500, 1000], [1, 2, 3]), ids=get_test_id)
+@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 = request.param
+    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, random_state=rng)
+    return sparse.random(shape, density=DENSITY, format=format, random_state=rng)
 
 
 def test_index_scalar(benchmark, indexing_args):
@@ -123,3 +131,43 @@ def test_index_fancy(benchmark, indexing_args, seed):
     @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