Vectorize CumOp and simplify infer_shape for vector case

pytensor/tensor/extra_ops.py

@@ -13,6 +13,7 @@ from pytensor.gradient import (
 )
 from pytensor.graph.basic import Apply, Constant, Variable
 from pytensor.graph.op import Op
+from pytensor.graph.replace import _vectorize_node
 from pytensor.link.c.op import COp
 from pytensor.link.c.params_type import ParamsType
 from pytensor.link.c.type import EnumList, Generic
@@ -360,7 +361,7 @@ class CumOp(COp):
             )
 
     def infer_shape(self, fgraph, node, shapes):
-        if self.axis is None:
+        if self.axis is None and len(shapes[0]) > 1:
             return [(prod(shapes[0]),)]  # Flatten
 
         return shapes
@@ -473,6 +474,25 @@ def cumprod(x, axis=None):
     return CumOp(axis=axis, mode="mul")(x)
 
 
+@_vectorize_node.register(CumOp)
+def vectorize_cum_op(op: CumOp, node: Apply, batch_x):
+    """Vectorize the CumOp to work on a batch of inputs."""
+    [original_x] = node.inputs
+    batch_ndim = batch_x.ndim - original_x.ndim
+    axis = op.axis
+    if axis is None and original_x.ndim == 1:
+        axis = 0
+    elif axis is not None:
+        axis = normalize_axis_index(op.axis, original_x.ndim)
+
+    if axis is None:
+        # Ravel all unbatched dimensions and perform CumOp on the last axis
+        batch_x_raveled = [batch_x.flatten(ndim=batch_ndim + 1) for x in batch_x]
+        return type(op)(axis=-1, mode=op.mode).make_node(batch_x_raveled)
+    else:
+        return type(op)(axis=axis + batch_ndim, mode=op.mode).make_node(batch_x)
+
+
 def diff(x, n=1, axis=-1):
     """Calculate the `n`-th order discrete difference along the given `axis`.