test: Converting tensordot benchmark to run with CodSpeed (#761)

Author: DeaMariaLeon

benchmarks/test_tensordot.py

@@ -0,0 +1,69 @@
+import itertools
+
+import sparse
+
+import pytest
+
+import numpy as np
+
+DENSITY = 0.01
+
+
+def get_sides_ids(param):
+    m, n, p, q = param
+    return f"{m=}-{n=}-{p=}-{q=}"
+
+
+@pytest.fixture(
+    params=itertools.product([10, 50], [10, 20], [20, 50], [10, 50]),
+    ids=get_sides_ids,
+    scope="function",
+)
+def sides(request):
+    m, n, p, q = request.param
+    return m, n, p, q
+
+
+def get_tensor_ids(param):
+    left_index, right_index, left_format, right_format = param
+    return f"{left_index=}-{right_index=}-{left_format=}-{right_format=}"
+
+
+@pytest.fixture(
+    params=([(1, 2, "dense", "coo"), (1, 2, "coo", "coo"), (1, 1, "coo", "dense")]),
+    ids=get_tensor_ids,
+    scope="function",
+)
+def tensordot_args(request, sides, seed, 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))
+
+    if left_format == "dense" and right_format == "coo":
+        left_tensor = t
+        right_tensor = sparse.random((m, p, n, q), density=DENSITY, format=right_format, random_state=rng)
+
+    if left_format == "coo" and right_format == "coo":
+        left_tensor = sparse.random((m, p), density=DENSITY, format=left_format, random_state=rng)
+        right_tensor = sparse.random((m, n, p, q), density=DENSITY, format=right_format, random_state=rng)
+
+    if left_format == "coo" and right_format == "dense":
+        left_tensor = sparse.random((m, n, p, q), density=DENSITY, format=left_format, random_state=rng)
+        right_tensor = t
+
+    return left_index, right_index, left_tensor, right_tensor
+
+
+@pytest.mark.parametrize("return_type", [np.ndarray, sparse.COO])
+def test_tensordot(benchmark, return_type, tensordot_args):
+    left_index, right_index, left_tensor, right_tensor = tensordot_args
+
+    sparse.tensordot(left_tensor, right_tensor, axes=([0, left_index], [0, right_index]), return_type=return_type)
+
+    @benchmark
+    def bench():
+        sparse.tensordot(left_tensor, right_tensor, axes=([0, left_index], [0, right_index]), return_type=return_type)