Support numba compiled sort and argsort functions

pytensor/link/numba/dispatch/basic.py

@@ -37,6 +37,7 @@
 from pytensor.tensor.math import Dot
 from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
 from pytensor.tensor.slinalg import Solve
+from pytensor.tensor.sort import ArgSortOp, SortOp
 from pytensor.tensor.type import TensorType
 from pytensor.tensor.type_other import MakeSlice, NoneConst
 
@@ -432,6 +433,68 @@ def shape_i(x):
     return shape_i
 
 
+@numba_funcify.register(SortOp)
+def numba_funcify_SortOp(op, node, **kwargs):
+    @numba_njit
+    def sort_f(a, axis):
+        axis = axis.item()
+
+        a_swapped = np.swapaxes(a, axis, -1)
+        a_sorted = np.sort(a_swapped)
+        a_sorted_swapped = np.swapaxes(a_sorted, -1, axis)
+
+        return a_sorted_swapped
+
+    if op.kind != "quicksort":
+        warnings.warn(
+            (
+                f'Numba function sort doesn\'t support kind="{op.kind}"'
+                " switching to `quicksort`."
+            ),
+            UserWarning,
+        )
+
+    return sort_f
+
+
+@numba_funcify.register(ArgSortOp)
+def numba_funcify_ArgSortOp(op, node, **kwargs):
+    def argsort_f_kind(kind):
+        @numba_njit
+        def argort_vec(X, axis):
+            axis = axis.item()
+
+            Y = np.swapaxes(X, axis, 0)
+            result = np.empty_like(Y)
+
+            indices = list(np.ndindex(Y.shape[1:]))
+
+            for idx in indices:
+                result[(slice(None), *idx)] = np.argsort(
+                    Y[(slice(None), *idx)], kind=kind
+                )
+
+            result = np.swapaxes(result, 0, axis)
+
+            return result
+
+        return argort_vec
+
+    kind = op.kind
+
+    if kind not in ["quicksort", "mergesort"]:
+        kind = "quicksort"
+        warnings.warn(
+            (
+                f'Numba function argsort doesn\'t support kind="{op.kind}"'
+                " switching to `quicksort`."
+            ),
+            UserWarning,
+        )
+
+    return argsort_f_kind(kind)
+
+
 @numba.extending.intrinsic
 def direct_cast(typingctx, val, typ):
     if isinstance(typ, numba.types.TypeRef):

tests/link/numba/test_basic.py

@@ -33,6 +33,7 @@
 from pytensor.tensor import blas
 from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
+from pytensor.tensor.sort import ArgSortOp, SortOp
 
 
 if TYPE_CHECKING:
@@ -378,6 +379,70 @@ def test_Shape(x, i):
     compare_numba_and_py([], [g], [])
 
 
+@pytest.mark.parametrize(
+    "x",
+    [
+        [],  # Empty list
+        [3, 2, 1],  # Simple list
+        np.random.randint(0, 10, (3, 2, 3, 4, 4)),  # Multi-dimensional array
+    ],
+)
+@pytest.mark.parametrize("axis", [0, -1, None])
+@pytest.mark.parametrize(
+    ("kind", "exc"),
+    [
+        ["quicksort", None],
+        ["mergesort", UserWarning],
+        ["heapsort", UserWarning],
+        ["stable", UserWarning],
+    ],
+)
+def test_Sort(x, axis, kind, exc):
+    if axis:
+        g = SortOp(kind)(pt.as_tensor_variable(x), axis)
+    else:
+        g = SortOp(kind)(pt.as_tensor_variable(x))
+
+    cm = contextlib.suppress() if not exc else pytest.warns(exc)
+
+    with cm:
+        compare_numba_and_py([], [g], [])
+
+
+@pytest.mark.parametrize(
+    "x",
+    [
+        [],  # Empty list
+        [3, 2, 1],  # Simple list
+        None,  # Multi-dimensional array (see below)
+    ],
+)
+@pytest.mark.parametrize("axis", [0, -1, None])
+@pytest.mark.parametrize(
+    ("kind", "exc"),
+    [
+        ["quicksort", None],
+        ["heapsort", None],
+        ["stable", UserWarning],
+    ],
+)
+def test_ArgSort(x, axis, kind, exc):
+    if x is None:
+        x = np.arange(5 * 5 * 5 * 5)
+        np.random.shuffle(x)
+        x = np.reshape(x, (5, 5, 5, 5))
+
+    if axis:
+        g = ArgSortOp(kind)(pt.as_tensor_variable(x), axis)
+    else:
+        g = ArgSortOp(kind)(pt.as_tensor_variable(x))
+
+    cm = contextlib.suppress() if not exc else pytest.warns(exc)
+
+    with cm:
+        compare_numba_and_py([], [g], [])
+
+
 @pytest.mark.parametrize(
     "v, shape, ndim",
     [