Normalize negative axes

pytensor/link/numba/dispatch/elemwise.py

@@ -164,6 +164,18 @@ 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],
@@ -218,6 +230,8 @@ def create_axis_reducer(
 
     """
 
+    axis = normalize_axis(axis, ndim)
+
     reduce_elemwise_fn_name = "careduce_axis"
 
     identity = str(identity)
@@ -340,6 +354,8 @@ 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]
+
     careduce_fn_name = f"careduce_{scalar_op}"
     global_env = {}
     to_reduce = reversed(sorted(axes))
@@ -409,6 +425,8 @@ def jit_compile_reducer(node, fn, **kwds):
 
 
 def create_axis_apply_fn(fn, axis, ndim, dtype):
+    axis = normalize_axis(axis, ndim)
+
     reaxis_first = tuple(i for i in range(ndim) if i != axis) + (axis,)
 
     @numba_basic.numba_njit(boundscheck=False)
@@ -609,6 +627,8 @@ 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:
         reduce_max_py = create_axis_reducer(
             scalar_maximum, -np.inf, axis, x_at.ndim, x_dtype, keepdims=True
@@ -646,6 +666,7 @@ 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:
         reduce_sum_py = create_axis_reducer(
             add_as, 0.0, axis, sm_at.ndim, sm_dtype, keepdims=True
@@ -676,6 +697,7 @@ 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:
         reduce_max_py = create_axis_reducer(