Use numpy function to normalize axis argument

pytensor/link/numba/dispatch/elemwise.py

@@ -6,6 +6,7 @@ from typing import Any, Callable, Optional, Union
 
 import numba
 import numpy as np
+from numpy.core.numeric import normalize_axis_index, normalize_axis_tuple
 
 from pytensor import config
 from pytensor.graph.basic import Apply
@@ -164,18 +165,6 @@ def create_vectorize_func(
     return elemwise_fn
 
 
-def normalize_axis(axis, ndim):
-    if axis is None:
-        return axis
-
-    if axis < 0:
-        axis = ndim + axis
-
-    if axis < 0 or axis >= ndim:
-        raise np.AxisError(ndim=ndim, axis=axis)
-    return axis
-
-
 def create_axis_reducer(
     scalar_op: Op,
     identity: Union[np.ndarray, Number],
@@ -230,7 +219,7 @@ def create_axis_reducer(
 
     """
 
-    axis = normalize_axis(axis, ndim)
+    axis = normalize_axis_index(axis, ndim)
 
     reduce_elemwise_fn_name = "careduce_axis"
 
@@ -354,7 +343,7 @@ def create_multiaxis_reducer(
     if len(axes) == 1:
         return create_axis_reducer(scalar_op, identity, axes[0], ndim, dtype)
 
-    axes = [normalize_axis(axis, ndim) for axis in axes]
+    axes = normalize_axis_tuple(axes, ndim)
 
     careduce_fn_name = f"careduce_{scalar_op}"
     global_env = {}
@@ -425,7 +414,7 @@ def jit_compile_reducer(node, fn, **kwds):
 
 
 def create_axis_apply_fn(fn, axis, ndim, dtype):
-    axis = normalize_axis(axis, ndim)
+    axis = normalize_axis_index(axis, ndim)
 
     reaxis_first = tuple(i for i in range(ndim) if i != axis) + (axis,)
 
@@ -627,9 +616,8 @@ def numba_funcify_Softmax(op, node, **kwargs):
     x_dtype = numba.np.numpy_support.from_dtype(x_dtype)
     axis = op.axis
 
-    axis = normalize_axis(axis, x_at.ndim)
-
     if axis is not None:
+        axis = normalize_axis_index(axis, x_at.ndim)
         reduce_max_py = create_axis_reducer(
             scalar_maximum, -np.inf, axis, x_at.ndim, x_dtype, keepdims=True
         )
@@ -666,8 +654,8 @@ def numba_funcify_SoftmaxGrad(op, node, **kwargs):
     sm_dtype = numba.np.numpy_support.from_dtype(sm_dtype)
 
     axis = op.axis
-    axis = normalize_axis(axis, sm_at.ndim)
     if axis is not None:
+        axis = normalize_axis_index(axis, sm_at.ndim)
         reduce_sum_py = create_axis_reducer(
             add_as, 0.0, axis, sm_at.ndim, sm_dtype, keepdims=True
         )
@@ -697,9 +685,9 @@ def numba_funcify_LogSoftmax(op, node, **kwargs):
     x_dtype = x_at.type.numpy_dtype
     x_dtype = numba.np.numpy_support.from_dtype(x_dtype)
     axis = op.axis
-    axis = normalize_axis(axis, x_at.ndim)
 
     if axis is not None:
+        axis = normalize_axis_index(axis, x_at.ndim)
         reduce_max_py = create_axis_reducer(
             scalar_maximum, -np.inf, axis, x_at.ndim, x_dtype, keepdims=True
         )