Add Numba support for IfElse Op

aesara/link/numba/dispatch/basic.py

@@ -16,6 +16,7 @@
 from aesara.graph.basic import Apply
 from aesara.graph.fg import FunctionGraph
 from aesara.graph.type import Type
+from aesara.ifelse import IfElse
 from aesara.link.utils import (
     compile_function_src,
     fgraph_to_python,
@@ -682,3 +683,32 @@ def batched_dot(x, y):
 # NOTE: The remaining `aesara.tensor.blas` `Op`s appear unnecessary, because
 # they're only used to optimize basic `Dot` nodes, and those GEMV and GEMM
 # optimizations are apparently already performed by Numba
+
+
+@numba_funcify.register(IfElse)
+def numba_funcify_IfElse(op, **kwargs):
+    n_outs = op.n_outs
+
+    if n_outs > 1:
+
+        @numba.njit
+        def ifelse(cond, *args):
+            if cond:
+                res = args[:n_outs]
+            else:
+                res = args[n_outs:]
+
+            return res
+
+    else:
+
+        @numba.njit
+        def ifelse(cond, *args):
+            if cond:
+                res = args[:n_outs]
+            else:
+                res = args[n_outs:]
+
+            return res[0]
+
+    return ifelse

tests/link/test_numba.py

@@ -22,9 +22,10 @@
 from aesara.compile.sharedvalue import SharedVariable
 from aesara.graph.basic import Apply, Constant
 from aesara.graph.fg import FunctionGraph
-from aesara.graph.op import Op
+from aesara.graph.op import Op, get_test_value
 from aesara.graph.optdb import OptimizationQuery
 from aesara.graph.type import Type
+from aesara.ifelse import ifelse
 from aesara.link.numba.dispatch import basic as numba_basic
 from aesara.link.numba.linker import NumbaLinker
 from aesara.scalar.basic import Composite
@@ -3076,3 +3077,63 @@ def power_of_2(previous_power, max_value):
         np.array(45).astype(config.floatX),
     ]
     compare_numba_and_py(out_fg, test_input_vals)
+
+
+@pytest.mark.parametrize(
+    "inputs, cond_fn, true_vals, false_vals",
+    [
+        ([], lambda: np.array(True), np.r_[1, 2, 3], np.r_[-1, -2, -3]),
+        (
+            [set_test_value(aet.dscalar(), np.array(0.2, dtype=np.float64))],
+            lambda x: x < 0.5,
+            np.r_[1, 2, 3],
+            np.r_[-1, -2, -3],
+        ),
+        (
+            [
+                set_test_value(aet.dscalar(), np.array(0.3, dtype=np.float64)),
+                set_test_value(aet.dscalar(), np.array(0.5, dtype=np.float64)),
+            ],
+            lambda x, y: x > y,
+            x,
+            y,
+        ),
+        (
+            [
+                set_test_value(aet.dvector(), np.array([0.3, 0.1], dtype=np.float64)),
+                set_test_value(aet.dvector(), np.array([0.5, 0.9], dtype=np.float64)),
+            ],
+            lambda x, y: aet.all(x > y),
+            x,
+            y,
+        ),
+        (
+            [
+                set_test_value(aet.dvector(), np.array([0.3, 0.1], dtype=np.float64)),
+                set_test_value(aet.dvector(), np.array([0.5, 0.9], dtype=np.float64)),
+            ],
+            lambda x, y: aet.all(x > y),
+            [x, 2 * x],
+            [y, 3 * y],
+        ),
+        (
+            [
+                set_test_value(aet.dvector(), np.array([0.5, 0.9], dtype=np.float64)),
+                set_test_value(aet.dvector(), np.array([0.3, 0.1], dtype=np.float64)),
+            ],
+            lambda x, y: aet.all(x > y),
+            [x, 2 * x],
+            [y, 3 * y],
+        ),
+    ],
+)
+def test_numba_ifelse(inputs, cond_fn, true_vals, false_vals):
+
+    out = ifelse(cond_fn(*inputs), true_vals, false_vals)
+
+    if not isinstance(out, list):
+        out = [out]
+
+    out_fg = FunctionGraph(inputs, out)
+
+    compare_numba_and_py(out_fg, [get_test_value(i) for i in out_fg.inputs])