Fix scalar Blockwise in numba backend

pytensor/link/numba/dispatch/blockwise.py

@@ -86,6 +86,7 @@ def impl(*inputs_and_core_shapes):
                 output_bc_patterns,
                 output_dtypes,
                 inplace_pattern,
+                False,  # allow_core_scalar
                 (),  # constant_inputs
                 inputs,
                 tuple_core_shapes,
@@ -98,6 +99,7 @@ def impl(*inputs_and_core_shapes):
         # If the core op cannot be cached, the Blockwise wrapper cannot be cached either
         blockwise_key = None
     else:
+        blockwise_cache_version = 1
         blockwise_key = "_".join(
             map(
                 str,
@@ -108,6 +110,7 @@ def impl(*inputs_and_core_shapes):
                     blockwise_op.signature,
                     input_bc_patterns,
                     core_op_key,
+                    blockwise_cache_version,
                 ),
             )
         )

pytensor/link/numba/dispatch/compile_ops.py

@@ -68,7 +68,7 @@ def numba_funcify_OpFromGraph(op, node=None, **kwargs):
     output_specs = [Out(o, borrow=False) for o in fgraph.outputs]
     insert_deepcopy(fgraph, wrapped_inputs=input_specs, wrapped_outputs=output_specs)
     fgraph_fn, fgraph_cache_key = numba_funcify_and_cache_key(
-        fgraph, squeeze_output=True, **kwargs
+        fgraph, squeeze_output=True, fgraph_name="numba_ofg", **kwargs
     )
 
     if fgraph_cache_key is None:

pytensor/link/numba/dispatch/elemwise.py

@@ -365,6 +365,7 @@ def impl(*inputs):
                 output_bc_patterns_enc,
                 output_dtypes_enc,
                 inplace_pattern_enc,
+                True,  # allow_core_scalar
                 (),  # constant_inputs
                 inputs,
                 core_output_shapes,  # core_shapes

pytensor/link/numba/dispatch/random.py

@@ -470,6 +470,7 @@ def impl(core_shape, rng, size, *dist_params):
                 output_bc_patterns,
                 output_dtypes,
                 inplace_pattern,
+                True,  # allow_core_scalar
                 (rng,),
                 dist_params,
                 (numba_ndarray.to_fixed_tuple(core_shape, core_shape_len),),

pytensor/link/numba/dispatch/scalar.py

@@ -236,7 +236,7 @@ def numba_funcify_Composite(op, node, **kwargs):
     _ = kwargs.pop("storage_map", None)
 
     composite_fn, fgraph_key = numba_funcify_and_cache_key(
-        op.fgraph, squeeze_output=True, **kwargs
+        op.fgraph, squeeze_output=True, fgraph_name="numba_composite", **kwargs
     )
     if fgraph_key is None:
         composite_key = None

pytensor/link/numba/dispatch/scan.py

@@ -98,7 +98,9 @@ def numba_funcify_Scan(op: Scan, node, **kwargs):
     output_specs = [Out(x, borrow=False) for x in fgraph.outputs]
     insert_deepcopy(fgraph, wrapped_inputs=input_specs, wrapped_outputs=output_specs)
 
-    scan_inner_func, inner_func_cache_key = numba_funcify_and_cache_key(op.fgraph)
+    scan_inner_func, inner_func_cache_key = numba_funcify_and_cache_key(
+        op.fgraph, fgraph_name="numba_scan"
+    )
 
     outer_in_names_to_vars = {
         (f"outer_in_{i}" if i > 0 else "n_steps"): v for i, v in enumerate(node.inputs)

pytensor/link/numba/dispatch/vectorize_codegen.py

@@ -82,6 +82,7 @@ def _vectorized(
     output_bc_patterns,
     output_dtypes,
     inplace_pattern,
+    allow_core_scalar,
     constant_inputs_types,
     input_types,
     output_core_shape_types,
@@ -93,6 +94,7 @@ def _vectorized(
         output_bc_patterns,
         output_dtypes,
         inplace_pattern,
+        allow_core_scalar,
         constant_inputs_types,
         input_types,
         output_core_shape_types,
@@ -119,6 +121,10 @@ def _vectorized(
     inplace_pattern = inplace_pattern.literal_value
     inplace_pattern = pickle.loads(base64.decodebytes(inplace_pattern.encode()))
 
+    if not isinstance(allow_core_scalar, types.Literal):
+        raise TypeError("allow_core_scalar must be literal.")
+    allow_core_scalar = allow_core_scalar.literal_value
+
     batch_ndim = len(input_bc_patterns[0])
     nin = len(constant_inputs_types) + len(input_types)
     nout = len(output_bc_patterns)
@@ -142,8 +148,7 @@ def _vectorized(
     core_input_types = []
     for input_type, bc_pattern in zip(input_types, input_bc_patterns, strict=True):
         core_ndim = input_type.ndim - len(bc_pattern)
-        # TODO: Reconsider this
-        if core_ndim == 0:
+        if allow_core_scalar and core_ndim == 0:
             core_input_type = input_type.dtype
         else:
             core_input_type = types.Array(
@@ -196,7 +201,7 @@ def codegen(
         sig,
         args,
     ):
-        [_, _, _, _, _, constant_inputs, inputs, output_core_shapes, size] = args
+        [_, _, _, _, _, _, constant_inputs, inputs, output_core_shapes, size] = args
 
         constant_inputs = cgutils.unpack_tuple(builder, constant_inputs)
         inputs = cgutils.unpack_tuple(builder, inputs)
@@ -256,6 +261,7 @@ def codegen(
             output_bc_patterns_val,
             input_types,
             output_types,
+            core_scalar=allow_core_scalar,
         )
 
         if len(outputs) == 1:
@@ -429,6 +435,7 @@ def make_loop_call(
     output_bc: tuple[tuple[bool, ...], ...],
     input_types: tuple[Any, ...],
     output_types: tuple[Any, ...],
+    core_scalar: bool = True,
 ):
     safe = (False, False)
 
@@ -486,7 +493,7 @@ def make_loop_call(
             idxs_bc,
             *safe,
         )
-        if core_ndim == 0:
+        if core_scalar and core_ndim == 0:
             # Retrive scalar item at index
             val = builder.load(ptr)
             # val.set_metadata("alias.scope", input_scope_set)
@@ -499,15 +506,19 @@ def make_loop_call(
                 dtype=input_type.dtype, ndim=core_ndim, layout=input_type.layout
             )
             core_array = context.make_array(core_arry_type)(context, builder)
-            core_shape = cgutils.unpack_tuple(builder, input.shape)[-core_ndim:]
-            core_strides = cgutils.unpack_tuple(builder, input.strides)[-core_ndim:]
+            core_shape = cgutils.unpack_tuple(builder, input.shape)[
+                input_type.ndim - core_ndim :
+            ]
+            core_strides = cgutils.unpack_tuple(builder, input.strides)[
+                input_type.ndim - core_ndim :
+            ]
             itemsize = context.get_abi_sizeof(context.get_data_type(input_type.dtype))
             context.populate_array(
                 core_array,
                 # TODO whey do we need to bitcast?
                 data=builder.bitcast(ptr, core_array.data.type),
-                shape=cgutils.pack_array(builder, core_shape),
-                strides=cgutils.pack_array(builder, core_strides),
+                shape=core_shape,
+                strides=core_strides,
                 itemsize=context.get_constant(types.intp, itemsize),
                 # TODO what is meminfo about?
                 meminfo=None,

tests/link/numba/test_blockwise.py

@@ -2,10 +2,12 @@
 import pytest
 
 from pytensor import function
-from pytensor.tensor import lvector, tensor, tensor3
+from pytensor.graph import Apply
+from pytensor.scalar import ScalarOp
+from pytensor.tensor import TensorVariable, lvector, tensor, tensor3, vector
 from pytensor.tensor.basic import Alloc, ARange, constant
 from pytensor.tensor.blockwise import Blockwise, BlockwiseWithCoreShape
-from pytensor.tensor.elemwise import DimShuffle
+from pytensor.tensor.elemwise import DimShuffle, Elemwise
 from pytensor.tensor.nlinalg import SVD, Det
 from pytensor.tensor.slinalg import Cholesky, cholesky
 from tests.link.numba.test_basic import compare_numba_and_py, numba_mode
@@ -90,3 +92,39 @@ def test_blockwise_scalar_dimshuffle():
     )
     out = blockwise_scalar_ds(x)
     compare_numba_and_py([x], [out], [np.arange(9)], eval_obj_mode=False)
+
+
+def test_blockwise_vs_elemwise_scalar_op():
+    # Regression test for https://github.com/pymc-devs/pytensor/issues/1760
+
+    class TestScalarOp(ScalarOp):
+        def make_node(self, x):
+            return Apply(self, [x], [x.type()])
+
+        def perform(self, node, inputs, outputs):
+            [x] = inputs
+            if isinstance(node.inputs[0], TensorVariable):
+                assert isinstance(x, np.ndarray)
+            else:
+                assert isinstance(x, np.number | float)
+            out = x + 1
+            if isinstance(node.outputs[0], TensorVariable):
+                out = np.asarray(out)
+            outputs[0][0] = out
+
+    x = vector("x")
+    y = Elemwise(TestScalarOp())(x)
+    with pytest.warns(
+        UserWarning,
+        match="Numba will use object mode to run TestScalarOp's perform method",
+    ):
+        fn = function([x], y, mode="NUMBA")
+    np.testing.assert_allclose(fn(np.zeros((3,))), [1, 1, 1])
+
+    z = Blockwise(TestScalarOp(), signature="()->()")(x)
+    with pytest.warns(
+        UserWarning,
+        match="Numba will use object mode to run TestScalarOp's perform method",
+    ):
+        fn = function([x], z, mode="NUMBA")
+    np.testing.assert_allclose(fn(np.zeros((3,))), [1, 1, 1])