Refactor vectorize literal encoding helper

pytensor/link/numba/dispatch/elemwise.py

-import base64
-import pickle
 from collections.abc import Callable
 from functools import singledispatch
 from numbers import Number
@@ -22,7 +20,11 @@ from pytensor.link.numba.dispatch.basic import (
     numba_njit,
     use_optimized_cheap_pass,
 )
-from pytensor.link.numba.dispatch.vectorize_codegen import _jit_options, _vectorized
+from pytensor.link.numba.dispatch.vectorize_codegen import (
+    _jit_options,
+    _vectorized,
+    encode_literals,
+)
 from pytensor.link.utils import compile_function_src, get_name_for_object
 from pytensor.scalar.basic import (
     AND,
@@ -478,19 +480,16 @@ def numba_funcify_Elemwise(op, node, **kwargs):
         **kwargs,
     )
 
-    ndim = node.outputs[0].ndim
-    output_bc_patterns = tuple([(False,) * ndim for _ in node.outputs])
-    input_bc_patterns = tuple([input_var.broadcastable for input_var in node.inputs])
-    output_dtypes = tuple(variable.dtype for variable in node.outputs)
+    input_bc_patterns = tuple([inp.type.broadcastable for inp in node.inputs])
+    output_bc_patterns = tuple([out.type.broadcastable for out in node.outputs])
+    output_dtypes = tuple(out.type.dtype for out in node.outputs)
     inplace_pattern = tuple(op.inplace_pattern.items())
 
     # numba doesn't support nested literals right now...
-    input_bc_patterns_enc = base64.encodebytes(pickle.dumps(input_bc_patterns)).decode()
-    output_bc_patterns_enc = base64.encodebytes(
-        pickle.dumps(output_bc_patterns)
-    ).decode()
-    output_dtypes_enc = base64.encodebytes(pickle.dumps(output_dtypes)).decode()
-    inplace_pattern_enc = base64.encodebytes(pickle.dumps(inplace_pattern)).decode()
+    input_bc_patterns_enc = encode_literals(input_bc_patterns)
+    output_bc_patterns_enc = encode_literals(output_bc_patterns)
+    output_dtypes_enc = encode_literals(output_dtypes)
+    inplace_pattern_enc = encode_literals(inplace_pattern)
 
     def elemwise_wrapper(*inputs):
         return _vectorized(

pytensor/link/numba/dispatch/vectorize_codegen.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import base64
 import pickle
+from collections.abc import Sequence
 from typing import Any
 
 import numba
@@ -13,6 +14,10 @@ from numba.core.base import BaseContext
 from numba.np import arrayobj
 
 
+def encode_literals(literals: Sequence) -> str:
+    return base64.encodebytes(pickle.dumps(literals)).decode()
+
+
 _jit_options = {
     "fastmath": {
         "arcp",  # Allow Reciprocal