Refactor `lower_aligned` helper

pytensor/xtensor/rewriting/shape.py

@@ -9,6 +9,7 @@ from pytensor.tensor import (
 )
 from pytensor.xtensor.basic import tensor_from_xtensor, xtensor_from_tensor
 from pytensor.xtensor.rewriting.basic import register_lower_xtensor
+from pytensor.xtensor.rewriting.utils import lower_aligned
 from pytensor.xtensor.shape import (
     Concat,
     ExpandDims,
@@ -70,15 +71,7 @@ def lower_concat(fgraph, node):
     concat_axis = out_dims.index(concat_dim)
 
     # Convert input XTensors to Tensors and align batch dimensions
-    tensor_inputs = []
-    for inp in node.inputs:
-        inp_dims = inp.type.dims
-        order = [
-            inp_dims.index(out_dim) if out_dim in inp_dims else "x"
-            for out_dim in out_dims
-        ]
-        tensor_inp = tensor_from_xtensor(inp).dimshuffle(order)
-        tensor_inputs.append(tensor_inp)
+    tensor_inputs = [lower_aligned(inp, out_dims) for inp in node.inputs]
 
     # Broadcast non-concatenated dimensions of each input
     non_concat_shape = [None] * len(out_dims)

pytensor/xtensor/rewriting/utils.py

+import typing
+from collections.abc import Sequence
+
 from pytensor.compile import optdb
 from pytensor.graph.rewriting.basic import NodeRewriter, in2out
 from pytensor.graph.rewriting.db import EquilibriumDB, RewriteDatabase
 from pytensor.tensor.rewriting.ofg import inline_ofg_expansion
+from pytensor.tensor.variable import TensorVariable
+from pytensor.xtensor.type import XTensorVariable
 
 
 lower_xtensor_db = EquilibriumDB(ignore_newtrees=False)
@@ -49,3 +54,10 @@ def register_lower_xtensor(
             **kwargs,
         )
         return node_rewriter
+
+
+def lower_aligned(x: XTensorVariable, out_dims: Sequence[str]) -> TensorVariable:
+    """Lower an XTensorVariable to a TensorVariable so that it's dimensions are aligned with "out_dims"."""
+    inp_dims = {d: i for i, d in enumerate(x.type.dims)}
+    ds_order = tuple(inp_dims.get(dim, "x") for dim in out_dims)
+    return typing.cast(TensorVariable, x.values.dimshuffle(ds_order))

pytensor/xtensor/rewriting/vectorization.py

@@ -2,8 +2,8 @@ from pytensor.graph import node_rewriter
 from pytensor.tensor.blockwise import Blockwise
 from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.random.utils import compute_batch_shape
-from pytensor.xtensor.basic import tensor_from_xtensor, xtensor_from_tensor
-from pytensor.xtensor.rewriting.utils import register_lower_xtensor
+from pytensor.xtensor.basic import xtensor_from_tensor
+from pytensor.xtensor.rewriting.utils import lower_aligned, register_lower_xtensor
 from pytensor.xtensor.vectorization import XRV, XBlockwise, XElemwise
 
 
@@ -13,15 +13,7 @@ def lower_elemwise(fgraph, node):
     out_dims = node.outputs[0].type.dims
 
     # Convert input XTensors to Tensors and align batch dimensions
-    tensor_inputs = []
-    for inp in node.inputs:
-        inp_dims = inp.type.dims
-        order = [
-            inp_dims.index(out_dim) if out_dim in inp_dims else "x"
-            for out_dim in out_dims
-        ]
-        tensor_inp = tensor_from_xtensor(inp).dimshuffle(order)
-        tensor_inputs.append(tensor_inp)
+    tensor_inputs = [lower_aligned(inp, out_dims) for inp in node.inputs]
 
     tensor_outs = Elemwise(scalar_op=node.op.scalar_op)(
         *tensor_inputs, return_list=True
@@ -42,17 +34,10 @@ def lower_blockwise(fgraph, node):
     batch_dims = node.outputs[0].type.dims[:batch_ndim]
 
     # Convert input Tensors to XTensors, align batch dimensions and place core dimension at the end
-    tensor_inputs = []
-    for inp, core_dims in zip(node.inputs, op.core_dims[0]):
-        inp_dims = inp.type.dims
-        # Align the batch dims of the input, and place the core dims on the right
-        batch_order = [
-            inp_dims.index(batch_dim) if batch_dim in inp_dims else "x"
-            for batch_dim in batch_dims
+    tensor_inputs = [
+        lower_aligned(inp, batch_dims + core_dims)
+        for inp, core_dims in zip(node.inputs, op.core_dims[0], strict=True)
     ]
-        core_order = [inp_dims.index(core_dim) for core_dim in core_dims]
-        tensor_inp = tensor_from_xtensor(inp).dimshuffle(batch_order + core_order)
-        tensor_inputs.append(tensor_inp)
 
     signature = op.signature or getattr(op.core_op, "gufunc_signature", None)
     if signature is None:
@@ -92,17 +77,10 @@ def lower_rv(fgraph, node):
     param_batch_dims = old_out.type.dims[len(op.extra_dims) : batch_ndim]
 
     # Convert params Tensors to XTensors, align batch dimensions and place core dimension at the end
-    tensor_params = []
-    for inp, core_dims in zip(params, op.core_dims[0]):
-        inp_dims = inp.type.dims
-        # Align the batch dims of the input, and place the core dims on the right
-        batch_order = [
-            inp_dims.index(batch_dim) if batch_dim in inp_dims else "x"
-            for batch_dim in param_batch_dims
+    tensor_params = [
+        lower_aligned(inp, param_batch_dims + core_dims)
+        for inp, core_dims in zip(params, op.core_dims[0], strict=True)
     ]
-        core_order = [inp_dims.index(core_dim) for core_dim in core_dims]
-        tensor_inp = tensor_from_xtensor(inp).dimshuffle(batch_order + core_order)
-        tensor_params.append(tensor_inp)
 
     size = None
     if op.extra_dims: