Add numba overload for Nonzero

pytensor/link/numba/dispatch/basic.py

@@ -33,6 +33,7 @@
 from pytensor.scalar.basic import ScalarType
 from pytensor.scalar.math import Softplus
 from pytensor.sparse import SparseTensorType
+from pytensor.tensor.basic import Nonzero
 from pytensor.tensor.blas import BatchedDot
 from pytensor.tensor.math import Dot
 from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
@@ -744,3 +745,15 @@ def ifelse(cond, *args):
             return res[0]
 
     return ifelse
+
+
+@numba_funcify.register(Nonzero)
+def numba_funcify_Nonzero(op, node, **kwargs):
+    @numba_njit
+    def nonzero(a):
+        result_tuple = np.nonzero(a)
+        if a.ndim == 1:
+            return result_tuple[0]
+        return list(result_tuple)
+
+    return nonzero

tests/link/numba/test_basic.py

@@ -287,7 +287,6 @@ def assert_fn(x, y):
     )
     test_inputs_copy = (inp.copy() for inp in test_inputs) if inplace else test_inputs
     numba_res = pytensor_numba_fn(*test_inputs_copy)
-
     if isinstance(graph_outputs, tuple | list):
         for j, p in zip(numba_res, py_res, strict=True):
             assert_fn(j, p)
@@ -893,3 +892,17 @@ def test_function_overhead(mode, benchmark):
     assert np.sum(fn(test_x)) == 1000
 
     benchmark(fn, test_x)
+
+
+@pytest.mark.parametrize(
+    "input_data",
+    [np.array([1, 0, 3]), np.array([[0, 1], [2, 0]]), np.array([[0, 0], [0, 0]])],
+)
+def test_Nonzero(input_data):
+    a = pt.tensor("a", shape=(None,) * input_data.ndim)
+
+    graph_outputs = pt.nonzero(a)
+
+    compare_numba_and_py(
+        graph_inputs=[a], graph_outputs=graph_outputs, test_inputs=[input_data]
+    )