Adapt Numba vectorize iterator for RandomVariables

pytensor/link/numba/dispatch/basic.py

@@ -62,10 +62,16 @@ def numba_njit(*args, **kwargs):
     kwargs.setdefault("no_cpython_wrapper", True)
     kwargs.setdefault("no_cfunc_wrapper", True)
 
-    # Supress caching warnings
+    # Suppress cache warning for internal functions
+    # We have to add an ansi escape code for optional bold text by numba
     warnings.filterwarnings(
         "ignore",
-        message='Cannot cache compiled function "numba_funcified_fgraph" as it uses dynamic globals',
+        message=(
+            "(\x1b\\[1m)*"  # ansi escape code for bold text
+            "Cannot cache compiled function "
+            '"(numba_funcified_fgraph|store_core_outputs)" '
+            "as it uses dynamic globals"
+        ),
         category=NumbaWarning,
     )
 

pytensor/link/numba/dispatch/elemwise.py

@@ -24,6 +24,7 @@ from pytensor.link.numba.dispatch.vectorize_codegen import (
     _jit_options,
     _vectorized,
     encode_literals,
+    store_core_outputs,
 )
 from pytensor.link.utils import compile_function_src, get_name_for_object
 from pytensor.scalar.basic import (
@@ -480,10 +481,15 @@ def numba_funcify_Elemwise(op, node, **kwargs):
         **kwargs,
     )
 
+    nin = len(node.inputs)
+    nout = len(node.outputs)
+    core_op_fn = store_core_outputs(scalar_op_fn, nin=nin, nout=nout)
+
     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())
+    core_output_shapes = tuple(() for _ in range(nout))
 
     # numba doesn't support nested literals right now...
     input_bc_patterns_enc = encode_literals(input_bc_patterns)
@@ -493,12 +499,15 @@ def numba_funcify_Elemwise(op, node, **kwargs):
 
     def elemwise_wrapper(*inputs):
         return _vectorized(
-            scalar_op_fn,
+            core_op_fn,
             input_bc_patterns_enc,
             output_bc_patterns_enc,
             output_dtypes_enc,
             inplace_pattern_enc,
+            (),  # constant_inputs
             inputs,
+            core_output_shapes,  # core_shapes
+            None,  # size
         )
 
     # Pure python implementation, that will be used in tests

pytensor/link/numba/dispatch/vectorize_codegen.py

@@ -2,8 +2,9 @@ from __future__ import annotations
 
 import base64
 import pickle
-from collections.abc import Sequence
-from typing import Any
+from collections.abc import Callable, Sequence
+from textwrap import indent
+from typing import Any, cast
 
 import numba
 import numpy as np
@@ -11,13 +12,54 @@ from llvmlite import ir
 from numba import TypingError, types
 from numba.core import cgutils
 from numba.core.base import BaseContext
+from numba.core.types.misc import NoneType
 from numba.np import arrayobj
 
+from pytensor.link.numba.dispatch import basic as numba_basic
+from pytensor.link.utils import compile_function_src
+
 
 def encode_literals(literals: Sequence) -> str:
     return base64.encodebytes(pickle.dumps(literals)).decode()
 
 
+def store_core_outputs(core_op_fn: Callable, nin: int, nout: int) -> Callable:
+    """Create a Numba function that wraps a core function and stores its vectorized outputs.
+
+    @njit
+    def store_core_outputs(i0, i1, ..., in, o0, o1, ..., on):
+        to0, to1, ..., ton = core_op_fn(i0, i1, ..., in)
+        o0[...] = to0
+        o1[...] = to1
+        ...
+        on[...] = ton
+
+    """
+    inputs = [f"i{i}" for i in range(nin)]
+    outputs = [f"o{i}" for i in range(nout)]
+    inner_outputs = [f"t{output}" for output in outputs]
+
+    inp_signature = ", ".join(inputs)
+    out_signature = ", ".join(outputs)
+    inner_out_signature = ", ".join(inner_outputs)
+    store_outputs = "\n".join(
+        [
+            f"{output}[...] = {inner_output}"
+            for output, inner_output in zip(outputs, inner_outputs)
+        ]
+    )
+    func_src = f"""
+def store_core_outputs({inp_signature}, {out_signature}):
+    {inner_out_signature} = core_op_fn({inp_signature})
+{indent(store_outputs, " " * 4)}
+"""
+    global_env = {"core_op_fn": core_op_fn}
+    func = compile_function_src(
+        func_src, "store_core_outputs", {**globals(), **global_env}
+    )
+    return cast(Callable, numba_basic.numba_njit(func))
+
+
 _jit_options = {
     "fastmath": {
         "arcp",  # Allow Reciprocal
@@ -39,7 +81,10 @@ def _vectorized(
     output_bc_patterns,
     output_dtypes,
     inplace_pattern,
-    inputs,
+    constant_inputs_types,
+    input_types,
+    output_core_shape_types,
+    size_type,
 ):
     arg_types = [
         scalar_func,
@@ -47,7 +92,10 @@ def _vectorized(
         output_bc_patterns,
         output_dtypes,
         inplace_pattern,
-        inputs,
+        constant_inputs_types,
+        input_types,
+        output_core_shape_types,
+        size_type,
     ]
 
     if not isinstance(input_bc_patterns, types.Literal):
@@ -70,34 +118,82 @@ def _vectorized(
     inplace_pattern = inplace_pattern.literal_value
     inplace_pattern = pickle.loads(base64.decodebytes(inplace_pattern.encode()))
 
-    n_outputs = len(output_bc_patterns)
+    batch_ndim = len(input_bc_patterns[0])
+    nin = len(constant_inputs_types) + len(input_types)
+    nout = len(output_bc_patterns)
+
+    if nin == 0:
+        raise TypingError("Empty argument list to vectorized op.")
+
+    if nout == 0:
+        raise TypingError("Empty list of outputs for vectorized op.")
 
-    if not len(inputs) > 0:
-        raise TypingError("Empty argument list to elemwise op.")
+    if not all(isinstance(input, types.Array) for input in input_types):
+        raise TypingError("Vectorized inputs must be arrays.")
 
-    if not n_outputs > 0:
-        raise TypingError("Empty list of outputs for elemwise op.")
+    if not all(
+        len(pattern) == batch_ndim for pattern in input_bc_patterns + output_bc_patterns
+    ):
+        raise TypingError(
+            "Vectorized broadcastable patterns must have the same length."
+        )
+
+    core_input_types = []
+    for input_type, bc_pattern in zip(input_types, input_bc_patterns):
+        core_ndim = input_type.ndim - len(bc_pattern)
+        # TODO: Reconsider this
+        if core_ndim == 0:
+            core_input_type = input_type.dtype
+        else:
+            core_input_type = types.Array(
+                dtype=input_type.dtype, ndim=core_ndim, layout=input_type.layout
+            )
+        core_input_types.append(core_input_type)
 
-    if not all(isinstance(input, types.Array) for input in inputs):
-        raise TypingError("Inputs to elemwise must be arrays.")
-    ndim = inputs[0].ndim
+    core_out_types = [
+        types.Array(numba.from_dtype(np.dtype(dtype)), len(output_core_shape), "C")
+        for dtype, output_core_shape in zip(output_dtypes, output_core_shape_types)
+    ]
 
-    if not all(input.ndim == ndim for input in inputs):
-        raise TypingError("Inputs to elemwise must have the same rank.")
+    out_types = [
+        types.Array(
+            numba.from_dtype(np.dtype(dtype)), batch_ndim + len(output_core_shape), "C"
+        )
+        for dtype, output_core_shape in zip(output_dtypes, output_core_shape_types)
+    ]
 
-    if not all(len(pattern) == ndim for pattern in output_bc_patterns):
-        raise TypingError("Invalid output broadcasting pattern.")
+    for output_idx, input_idx in inplace_pattern:
+        output_type = input_types[input_idx]
+        core_out_types[output_idx] = types.Array(
+            dtype=output_type.dtype,
+            ndim=output_type.ndim - batch_ndim,
+            layout=input_type.layout,
+        )
+        out_types[output_idx] = output_type
 
-    scalar_signature = typingctx.resolve_function_type(
-        scalar_func, [in_type.dtype for in_type in inputs], {}
+    core_signature = typingctx.resolve_function_type(
+        scalar_func,
+        [
+            *constant_inputs_types,
+            *core_input_types,
+            *core_out_types,
+        ],
+        {},
     )
 
+    ret_type = types.Tuple(out_types)
+
+    if len(output_dtypes) == 1:
+        ret_type = ret_type.types[0]
+    sig = ret_type(*arg_types)
+
     # So we can access the constant values in codegen...
     input_bc_patterns_val = input_bc_patterns
     output_bc_patterns_val = output_bc_patterns
     output_dtypes_val = output_dtypes
     inplace_pattern_val = inplace_pattern
-    input_types = inputs
+    input_types = input_types
+    size_is_none = isinstance(size_type, NoneType)
 
     def codegen(
         ctx,
@@ -105,8 +201,16 @@ def _vectorized(
         sig,
         args,
     ):
-        [_, _, _, _, _, inputs] = args
+        [_, _, _, _, _, constant_inputs, inputs, output_core_shapes, size] = args
+
+        constant_inputs = cgutils.unpack_tuple(builder, constant_inputs)
         inputs = cgutils.unpack_tuple(builder, inputs)
+        output_core_shapes = [
+            cgutils.unpack_tuple(builder, shape)
+            for shape in cgutils.unpack_tuple(builder, output_core_shapes)
+        ]
+        size = None if size_is_none else cgutils.unpack_tuple(builder, size)
+
         inputs = [
             arrayobj.make_array(ty)(ctx, builder, val)
             for ty, val in zip(input_types, inputs)
@@ -118,6 +222,7 @@ def _vectorized(
             builder,
             in_shapes,
             input_bc_patterns_val,
+            size,
         )
 
         outputs, output_types = make_outputs(
@@ -129,6 +234,7 @@ def _vectorized(
             inplace_pattern_val,
             inputs,
             input_types,
+            output_core_shapes,
         )
 
         make_loop_call(
@@ -136,8 +242,9 @@ def _vectorized(
             ctx,
             builder,
             scalar_func,
-            scalar_signature,
+            core_signature,
             iter_shape,
+            constant_inputs,
             inputs,
             outputs,
             input_bc_patterns_val,
@@ -162,67 +269,92 @@ def _vectorized(
             builder, sig.return_type, [out._getvalue() for out in outputs]
         )
 
-    ret_types = [
-        types.Array(numba.from_dtype(np.dtype(dtype)), ndim, "C")
-        for dtype in output_dtypes
-    ]
-
-    for output_idx, input_idx in inplace_pattern:
-        ret_types[output_idx] = input_types[input_idx]
-
-    ret_type = types.Tuple(ret_types)
-
-    if len(output_dtypes) == 1:
-        ret_type = ret_type.types[0]
-    sig = ret_type(*arg_types)
-
     return sig, codegen
 
 
 def compute_itershape(
     ctx: BaseContext,
     builder: ir.IRBuilder,
-    in_shapes: tuple[ir.Instruction, ...],
+    in_shapes: list[list[ir.Instruction]],
     broadcast_pattern: tuple[tuple[bool, ...], ...],
+    size: list[ir.Instruction] | None,
 ):
     one = ir.IntType(64)(1)
-    ndim = len(in_shapes[0])
-    shape = [None] * ndim
-    for i in range(ndim):
+    batch_ndim = len(broadcast_pattern[0])
+    shape = [None] * batch_ndim
+    if size is not None:
+        shape = size
+        for i in range(batch_ndim):
             for j, (bc, in_shape) in enumerate(zip(broadcast_pattern, in_shapes)):
                 length = in_shape[i]
                 if bc[i]:
                     with builder.if_then(
                         builder.icmp_unsigned("!=", length, one), likely=False
                     ):
+                        msg = f"Vectorized input {j} is expected to have shape 1 in axis {i}"
+                        ctx.call_conv.return_user_exc(builder, ValueError, (msg,))
+                else:
+                    with builder.if_then(
+                        builder.icmp_unsigned("!=", length, shape[i]), likely=False
+                    ):
+                        with builder.if_else(
+                            builder.icmp_unsigned("==", length, one)
+                        ) as (
+                            then,
+                            otherwise,
+                        ):
+                            with then:
                                 msg = (
-                        f"Input {j} to elemwise is expected to have shape 1 in axis {i}"
+                                    f"Incompatible vectorized shapes for input {j} and axis {i}. "
+                                    f"Input {j} has shape 1, but is not statically "
+                                    "known to have shape 1, and thus not broadcastable."
+                                )
+                                ctx.call_conv.return_user_exc(
+                                    builder, ValueError, (msg,)
+                                )
+                            with otherwise:
+                                msg = f"Vectorized input {j} has an incompatible shape in axis {i}."
+                                ctx.call_conv.return_user_exc(
+                                    builder, ValueError, (msg,)
                                 )
+    else:
+        # Size is implied by the broadcast pattern
+        for i in range(batch_ndim):
+            for j, (bc, in_shape) in enumerate(zip(broadcast_pattern, in_shapes)):
+                length = in_shape[i]
+                if bc[i]:
+                    with builder.if_then(
+                        builder.icmp_unsigned("!=", length, one), likely=False
+                    ):
+                        msg = f"Vectorized input {j} is expected to have shape 1 in axis {i}"
                         ctx.call_conv.return_user_exc(builder, ValueError, (msg,))
                 elif shape[i] is not None:
                     with builder.if_then(
                         builder.icmp_unsigned("!=", length, shape[i]), likely=False
                     ):
-                    with builder.if_else(builder.icmp_unsigned("==", length, one)) as (
+                        with builder.if_else(
+                            builder.icmp_unsigned("==", length, one)
+                        ) as (
                             then,
                             otherwise,
                         ):
                             with then:
                                 msg = (
-                                f"Incompatible shapes for input {j} and axis {i} of "
-                                f"elemwise. Input {j} has shape 1, but is not statically "
+                                    f"Incompatible vectorized shapes for input {j} and axis {i}. "
+                                    f"Input {j} has shape 1, but is not statically "
                                     "known to have shape 1, and thus not broadcastable."
                                 )
-                            ctx.call_conv.return_user_exc(builder, ValueError, (msg,))
+                                ctx.call_conv.return_user_exc(
+                                    builder, ValueError, (msg,)
+                                )
                             with otherwise:
-                            msg = (
-                                f"Input {j} to elemwise has an incompatible "
-                                f"shape in axis {i}."
+                                msg = f"Vectorized input {j} has an incompatible shape in axis {i}."
+                                ctx.call_conv.return_user_exc(
+                                    builder, ValueError, (msg,)
                                 )
-                            ctx.call_conv.return_user_exc(builder, ValueError, (msg,))
                 else:
                     shape[i] = length
-    for i in range(ndim):
+        for i in range(batch_ndim):
             if shape[i] is None:
                 shape[i] = one
     return shape
@@ -237,27 +369,32 @@ def make_outputs(
     inplace: tuple[tuple[int, int], ...],
     inputs: tuple[Any, ...],
     input_types: tuple[Any, ...],
-):
-    arrays = []
-    ar_types: list[types.Array] = []
+    output_core_shapes: tuple,
+) -> tuple[list[ir.Value], list[types.Array]]:
+    output_arrays = []
+    output_arry_types = []
     one = ir.IntType(64)(1)
     inplace_dict = dict(inplace)
-    for i, (bc, dtype) in enumerate(zip(out_bc, dtypes)):
+    for i, (core_shape, bc, dtype) in enumerate(
+        zip(output_core_shapes, out_bc, dtypes)
+    ):
         if i in inplace_dict:
-            arrays.append(inputs[inplace_dict[i]])
-            ar_types.append(input_types[inplace_dict[i]])
+            output_arrays.append(inputs[inplace_dict[i]])
+            output_arry_types.append(input_types[inplace_dict[i]])
             # We need to incref once we return the inplace objects
             continue
         dtype = numba.from_dtype(np.dtype(dtype))
-        arrtype = types.Array(dtype, len(iter_shape), "C")
-        ar_types.append(arrtype)
+        output_ndim = len(iter_shape) + len(core_shape)
+        arrtype = types.Array(dtype, output_ndim, "C")
+        output_arry_types.append(arrtype)
         # This is actually an internal numba function, I guess we could
         # call `numba.nd.unsafe.ndarray` instead?
-        shape = [
+        batch_shape = [
             length if not bc_dim else one for length, bc_dim in zip(iter_shape, bc)
         ]
+        shape = batch_shape + core_shape
         array = arrayobj._empty_nd_impl(ctx, builder, arrtype, shape)
-        arrays.append(array)
+        output_arrays.append(array)
 
     # If there is no inplace operation, we know that all output arrays
     # don't alias. Informing llvm can make it easier to vectorize.
@@ -265,7 +402,7 @@ def make_outputs(
         # The first argument is the output pointer
         arg = builder.function.args[0]
         arg.add_attribute("noalias")
-    return arrays, ar_types
+    return output_arrays, output_arry_types
 
 
 def make_loop_call(
@@ -275,6 +412,7 @@ def make_loop_call(
     scalar_func: Any,
     scalar_signature: types.FunctionType,
     iter_shape: tuple[ir.Instruction, ...],
+    constant_inputs: tuple[ir.Instruction, ...],
     inputs: tuple[ir.Instruction, ...],
     outputs: tuple[ir.Instruction, ...],
     input_bc: tuple[tuple[bool, ...], ...],
@@ -283,18 +421,8 @@ def make_loop_call(
     output_types: tuple[Any, ...],
 ):
     safe = (False, False)
-    n_outputs = len(outputs)
-
-    # context.printf(builder, "iter shape: " + ', '.join(["%i"] * len(iter_shape)) + "\n", *iter_shape)
 
-    # Extract shape and stride information from the array.
-    # For later use in the loop body to do the indexing
-    def extract_array(aryty, obj):
-        shape = cgutils.unpack_tuple(builder, obj.shape)
-        strides = cgutils.unpack_tuple(builder, obj.strides)
-        data = obj.data
-        layout = aryty.layout
-        return (data, shape, strides, layout)
+    n_outputs = len(outputs)
 
     # TODO I think this is better than the noalias attribute
     # for the input, but self_ref isn't supported in a released
@@ -306,12 +434,6 @@ def make_loop_call(
     # input_scope_set = mod.add_metadata([input_scope, output_scope])
     # output_scope_set = mod.add_metadata([input_scope, output_scope])
 
-    inputs = tuple(extract_array(aryty, ary) for aryty, ary in zip(input_types, inputs))
-
-    outputs = tuple(
-        extract_array(aryty, ary) for aryty, ary in zip(output_types, outputs)
-    )
-
     zero = ir.Constant(ir.IntType(64), 0)
 
     # Setup loops and initialize accumulators for outputs
@@ -338,69 +460,105 @@ def make_loop_call(
 
     # Load values from input arrays
     input_vals = []
-    for array_info, bc in zip(inputs, input_bc):
-        idxs_bc = [zero if bc else idx for idx, bc in zip(idxs, bc)]
-        ptr = cgutils.get_item_pointer2(context, builder, *array_info, idxs_bc, *safe)
+    for input, input_type, bc in zip(inputs, input_types, input_bc):
+        core_ndim = input_type.ndim - len(bc)
+
+        idxs_bc = [zero if bc else idx for idx, bc in zip(idxs, bc)] + [
+            zero
+        ] * core_ndim
+        ptr = cgutils.get_item_pointer2(
+            context,
+            builder,
+            input.data,
+            cgutils.unpack_tuple(builder, input.shape),
+            cgutils.unpack_tuple(builder, input.strides),
+            input_type.layout,
+            idxs_bc,
+            *safe,
+        )
+        if core_ndim == 0:
+            # Retrive scalar item at index
             val = builder.load(ptr)
             # val.set_metadata("alias.scope", input_scope_set)
             # val.set_metadata("noalias", output_scope_set)
+        else:
+            # Retrieve array item at index
+            # This is a streamlined version of Numba's `GUArrayArg.load`
+            # TODO check layout arg!
+            core_arry_type = types.Array(
+                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:]
+            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),
+                itemsize=context.get_constant(types.intp, itemsize),
+                # TODO what is meminfo about?
+                meminfo=None,
+            )
+            val = core_array._getvalue()
+
         input_vals.append(val)
 
+    # Create output slices to pass to inner func
+    output_slices = []
+    for output, output_type, bc in zip(outputs, output_types, output_bc):
+        core_ndim = output_type.ndim - len(bc)
+        size_type = output.shape.type.element  # type: ignore
+        output_shape = cgutils.unpack_tuple(builder, output.shape)  # type: ignore
+        output_strides = cgutils.unpack_tuple(builder, output.strides)  # type: ignore
+
+        idxs_bc = [zero if bc else idx for idx, bc in zip(idxs, bc)] + [
+            zero
+        ] * core_ndim
+        ptr = cgutils.get_item_pointer2(
+            context,
+            builder,
+            output.data,  # type:ignore
+            output_shape,
+            output_strides,
+            output_type.layout,
+            idxs_bc,
+            *safe,
+        )
+
+        # Retrieve array item at index
+        # This is a streamlined version of Numba's `GUArrayArg.load`
+        core_arry_type = types.Array(
+            dtype=output_type.dtype, ndim=core_ndim, layout=output_type.layout
+        )
+        core_array = context.make_array(core_arry_type)(context, builder)
+        core_shape = output_shape[-core_ndim:] if core_ndim > 0 else []
+        core_strides = output_strides[-core_ndim:] if core_ndim > 0 else []
+        itemsize = context.get_abi_sizeof(context.get_data_type(output_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, ty=size_type),
+            strides=cgutils.pack_array(builder, core_strides, ty=size_type),
+            itemsize=context.get_constant(types.intp, itemsize),
+            # TODO what is meminfo about?
+            meminfo=None,
+        )
+        val = core_array._getvalue()
+        output_slices.append(val)
+
     inner_codegen = context.get_function(scalar_func, scalar_signature)
 
     if isinstance(scalar_signature.args[0], types.StarArgTuple | types.StarArgUniTuple):
         input_vals = [context.make_tuple(builder, scalar_signature.args[0], input_vals)]
-    output_values = inner_codegen(builder, input_vals)
 
-    if isinstance(scalar_signature.return_type, types.Tuple | types.UniTuple):
-        output_values = cgutils.unpack_tuple(builder, output_values)
-        func_output_types = scalar_signature.return_type.types
-    else:
-        output_values = [output_values]
-        func_output_types = [scalar_signature.return_type]
-
-    # Update output value or accumulators respectively
-    for i, ((accu, _), value) in enumerate(zip(output_accumulator, output_values)):
-        if accu is not None:
-            load = builder.load(accu)
-            # load.set_metadata("alias.scope", output_scope_set)
-            # load.set_metadata("noalias", input_scope_set)
-            new_value = builder.fadd(load, value)
-            builder.store(new_value, accu)
-            # TODO belongs to noalias scope
-            # store.set_metadata("alias.scope", output_scope_set)
-            # store.set_metadata("noalias", input_scope_set)
-        else:
-            idxs_bc = [zero if bc else idx for idx, bc in zip(idxs, output_bc[i])]
-            ptr = cgutils.get_item_pointer2(context, builder, *outputs[i], idxs_bc)
-            # store = builder.store(value, ptr)
-            value = context.cast(
-                builder, value, func_output_types[i], output_types[i].dtype
-            )
-            arrayobj.store_item(context, builder, output_types[i], value, ptr)
-            # store.set_metadata("alias.scope", output_scope_set)
-            # store.set_metadata("noalias", input_scope_set)
+    inner_codegen(builder, [*constant_inputs, *input_vals, *output_slices])
 
-    # Close the loops and write accumulator values to the output arrays
+    # Close the loops
     for depth, loop in enumerate(loop_stack[::-1]):
-        for output, (accu, accu_depth) in enumerate(output_accumulator):
-            if accu_depth == depth:
-                idxs_bc = [
-                    zero if bc else idx for idx, bc in zip(idxs, output_bc[output])
-                ]
-                ptr = cgutils.get_item_pointer2(
-                    context, builder, *outputs[output], idxs_bc
-                )
-                load = builder.load(accu)
-                # load.set_metadata("alias.scope", output_scope_set)
-                # load.set_metadata("noalias", input_scope_set)
-                # store = builder.store(load, ptr)
-                load = context.cast(
-                    builder, load, func_output_types[output], output_types[output].dtype
-                )
-                arrayobj.store_item(context, builder, output_types[output], load, ptr)
-                # store.set_metadata("alias.scope", output_scope_set)
-                # store.set_metadata("noalias", input_scope_set)
         loop.__exit__(None, None, None)
 
     return