Added performance testing for Numba

tests/link/test_numba_performance.py

+import timeit
+
+import numpy as np
+import pytest
+
+import aesara.tensor as aet
+from aesara import config
+from aesara.compile.function import function
+from aesara.compile.mode import Mode
+from aesara.graph.optdb import OptimizationQuery
+from aesara.link.numba.linker import NumbaLinker
+from aesara.tensor.math import Max
+
+
+opts = OptimizationQuery(include=[None], exclude=["cxx_only", "BlasOpt"])
+numba_mode = Mode(NumbaLinker(), opts)
+py_mode = Mode("py", opts)
+
+
+@pytest.mark.parametrize(
+    "careduce_fn, numpy_fn, axis, inputs, input_vals",
+    [
+        pytest.param(
+            lambda x, axis=None: Max(axis)(x),
+            np.max,
+            (0, 1),
+            [
+                aet.matrix(),
+            ],
+            [np.arange(3000 * 2000, dtype=config.floatX).reshape((3000, 2000))],
+        )
+    ],
+)
+def test_careduce_performance(careduce_fn, numpy_fn, axis, inputs, input_vals):
+    g = careduce_fn(*inputs, axis=axis)
+
+    aesara_numba_fn = function(
+        inputs,
+        g,
+        mode=numba_mode,
+    )
+
+    # aesara_c_fn = function(
+    #     inputs,
+    #     g,
+    #     mode=Mode("cvm")
+    # )
+
+    numpy_res = numpy_fn(*input_vals)
+    numba_res = aesara_numba_fn(*input_vals)
+    # c_res = aesara_c_fn(*input_vals)
+
+    assert np.array_equal(numba_res, numpy_res)
+
+    # FYI: To test the Numba JITed function directly, use `aesara_numba_fn.fn.jit_fn`
+
+    numpy_timer = timeit.Timer("numpy_fn(*input_vals)", "pass", globals=locals())
+    numba_timer = timeit.Timer(
+        "aesara_numba_fn.fn.jit_fn(*input_vals)", "pass", globals=locals()
+    )
+    # c_timer = timeit.Timer("aesara_c_fn(*input_vals)", "pass", globals=locals())
+
+    n_loops, _ = numpy_timer.autorange()
+
+    numpy_times = numpy_timer.repeat(5, n_loops)
+    numba_times = numba_timer.repeat(5, n_loops)
+    # c_times = c_timer.repeat(5, n_loops)
+
+    mean_numba_time = np.mean(numba_times)
+    mean_numpy_time = np.mean(numpy_times)
+    # mean_c_time = np.mean(c_times)
+
+    assert mean_numba_time / mean_numpy_time >= 0.75