Numba Blockwise: Fix OpFromGraph as core_op

pytensor/compile/builders.py

@@ -4,6 +4,7 @@ import warnings
 from collections.abc import Callable, Sequence
 from copy import copy
 from functools import partial
+from itertools import chain
 from typing import Union, cast
 
 from pytensor.compile.function import function
@@ -47,11 +48,15 @@ def infer_shape(outs, inputs, input_shapes):
             assert len(inp_shp) == inp.type.ndim
 
     shape_feature = ShapeFeature()
-    shape_feature.on_attach(FunctionGraph([], []))
+    fgraph = FunctionGraph([], [], features=[shape_feature])
+    for v in chain.from_iterable(s for s in input_shapes if s is not None):
+        # Import input_shape nodes, as for some graphs ShapeFeature assumes these were seen before
+        if (node := v.owner) is not None:
+            fgraph.import_node(node, import_missing=True)
 
     # Initialize shape_of with the input shapes
     for inp, inp_shp in zip(inputs, input_shapes, strict=True):
-        shape_feature.set_shape(inp, inp_shp)
+        shape_feature.set_shape(inp, inp_shp, override=True)
 
     def local_traverse(out):
         """

pytensor/link/numba/dispatch/blockwise.py

@@ -36,7 +36,6 @@ def numba_funcify_Blockwise(op: BlockwiseWithCoreShape, node, **kwargs):
     core_op_fn, core_op_key = numba_funcify_and_cache_key(
         core_op,
         node=core_node,
-        parent_node=node,
         **kwargs,
     )
     core_op_fn = store_core_outputs(core_op_fn, nin=nin, nout=nout)

pytensor/link/numba/dispatch/elemwise.py

@@ -274,7 +274,6 @@ def numba_funcify_Elemwise(op, node, **kwargs):
     scalar_op_fn, scalar_cache_key = numba_funcify_and_cache_key(
         op.scalar_op,
         node=scalar_node,
-        parent_node=node,
         **kwargs,
     )
 

tests/link/numba/test_compile_ops.py

@@ -4,9 +4,10 @@ import pytest
 from pytensor import OpFromGraph, config, function, ifelse
 from pytensor import tensor as pt
 from pytensor.compile import ViewOp
+from pytensor.graph import vectorize_graph
 from pytensor.raise_op import assert_op
 from pytensor.scalar import Add
-from pytensor.tensor import matrix
+from pytensor.tensor import dmatrix, dtensor3, matrix
 from pytensor.tensor.elemwise import Elemwise
 from tests.link.numba.test_basic import compare_numba_and_py
 
@@ -171,6 +172,24 @@ def test_ofg_aliased_outputs():
         np.testing.assert_allclose(res, np.ones((2, 2)))
 
 
+def test_ofg_elemwise_regression():
+    # Regression bug for https://github.com/pymc-devs/pytensor/issues/1507
+    x = dmatrix("x", shape=(None, None))
+    z = OpFromGraph(
+        inputs=[x],
+        outputs=[x + 1],
+    )(x)
+
+    x_batched = dtensor3("X_batched", shape=(None, None, None))
+    z_batched = vectorize_graph(z, {x: x_batched})
+    compare_numba_and_py(
+        [x_batched],
+        [z_batched],
+        [np.random.normal(size=(3, 2, 4))],
+        eval_obj_mode=False,
+    )
+
+
 def test_check_and_raise():
     x = pt.vector()
     x_test_value = np.array([1.0, 2.0], dtype=config.floatX)