feat(mlx): add handler for aten.roll (#19038)

## Summary

Adds an MLX delegate handler for `aten.roll`, mapping `torch.roll` onto
`mlx::core::roll` via a new `RollNode` in the schema. Replaces the
default decomposition (`index_select + arange + cat`) with a single
native kernel — needed by Swin Transformer's shift-window attention.

Flat roll (`dims=[]`) raises `NotImplementedError` for now; no known
consumer needs it yet.

Generated files (`MLXLoader.*`, `schema_generated.h`,
`mlx_graph_schema.py`, `_generated_serializers.py`,
`_generated_inspector.py`, `_generated/`) are regenerated from
`schema.fbs` by `backends/mlx/CMakeLists.txt` at build time and are
deliberately not committed.

Fixes #18919.

## Test plan

- `python backends/mlx/serialization/generate.py` — regenerates cleanly
with `RollNode` in all expected outputs.
- `lintrunner --skip MYPY --paths-cmd 'git diff --name-only
upstream/main'` — no issues.
- End-to-end `run_all_tests -k roll` not run locally (no executorch
build on this machine); relying on CI. Happy to push fixes if it finds
anything.

cc @metascroy

Co-authored-by: Ishan Godawatta <ishan.godawatta@arm.com>

Author: IshanG97

backends/mlx/ops.py

@@ -117,6 +117,7 @@
     RepeatNode,
     ReshapeNode,
     RMSNormNode,
+    RollNode,
     RopeNode,
     RoundNode,
     RsqrtNode,
@@ -1678,6 +1679,45 @@ def _repeat_handler(P: MLXProgramBuilder, n: Node) -> Slot:
     return out
 
 
+@REGISTRY.register(target=[torch.ops.aten.roll.default])
+def _roll_handler(P: MLXProgramBuilder, n: Node) -> Slot:
+    args = P.args(n)
+    require_args(args, 2, 3, "aten.roll")
+    require_kwargs(P.kwargs(n), set(), "aten.roll")
+    x = args[0]
+    shifts_arg = args[1]
+    dims_arg = args[2] if len(args) > 2 else []
+
+    shifts = [shifts_arg] if isinstance(shifts_arg, int) else list(shifts_arg)
+    dims: List[int] = [dims_arg] if isinstance(dims_arg, int) else list(dims_arg)
+
+    # Flat roll (torch.roll with dims=[]) would require reshape + roll +
+    # reshape at the graph level. Not yet supported; Swin-style usage always
+    # passes explicit dims.
+    if not dims:
+        raise NotImplementedError(
+            "aten.roll without dims (flat roll) is not supported by the MLX "
+            "delegate yet."
+        )
+    if len(shifts) != len(dims):
+        raise ValueError(
+            f"aten.roll: shifts and dims must have the same length, got "
+            f"shifts={shifts} (len={len(shifts)}) dims={dims} (len={len(dims)})"
+        )
+    require_static_ints(dims, "dims", "aten.roll")
+
+    out = P.make_or_get_slot(n)
+    P.emit(
+        RollNode(
+            x=P.slot_to_tid(x),
+            out=P.slot_to_tid(out),
+            shift=[P.to_int_or_vid(s) for s in shifts],
+            axes=dims,
+        )
+    )
+    return out
+
+
 @REGISTRY.register(target=[torch.ops.aten.index.Tensor])
 def _index_handler(P: MLXProgramBuilder, n: Node) -> Slot:
     args = P.args(n)

backends/mlx/runtime/MLXInterpreter.h

@@ -1733,6 +1733,13 @@ inline void exec_all(const AllNode& n, ExecutionState& st, StreamOrDevice s) {
   }
 }
 
+inline void exec_roll(const RollNode& n, ExecutionState& st, StreamOrDevice s) {
+  const auto& x = st.const_tensor_ref(n.x);
+  auto shifts = to_shape(n.shift, st);
+  std::vector<int> axes(n.axes.begin(), n.axes.end());
+  st.set_tensor(n.out, roll(x, shifts, axes, s));
+}
+
 inline void
 exec_repeat(const RepeatNode& n, ExecutionState& st, StreamOrDevice s) {
   const auto& x = st.const_tensor_ref(n.x);
@@ -2210,6 +2217,9 @@ class Interpreter {
       case OpCode::REPEAT:
         ops::exec_repeat(std::get<RepeatNode>(instr.node), st, s);
         break;
+      case OpCode::ROLL:
+        ops::exec_roll(std::get<RollNode>(instr.node), st, s);
+        break;
       case OpCode::SORT:
         ops::exec_sort(std::get<SortNode>(instr.node), st, s);
         break;

backends/mlx/serialization/schema.fbs

@@ -673,6 +673,16 @@ table ArgPartitionNode {
     axis: int32;
 }
 
+// Shift tensor elements along specified axes with wrap-around.
+// Maps to mlx::core::roll(a, shifts, axes).
+// Flat roll (torch.roll with dims=None) is not yet supported.
+table RollNode {
+    x: Tid (required);
+    out: Tid (required);
+    shift: [IntOrVid] (required);  // Shift amount per axis (can be dynamic)
+    axes: [int32] (required);      // Axes to roll along; len(shift) == len(axes)
+}
+
 
 // =============================================================================
 // Math ops - Unary element-wise
@@ -1119,7 +1129,8 @@ union OpNode {
     GatherQmmNode,
     ScanNode,
     MetalKernelNode,
-    BitwiseInvertNode
+    BitwiseInvertNode,
+    RollNode
     // BC: Add new op nodes here (append only)
 }
 

backends/mlx/test/test_ops.py

@@ -855,6 +855,59 @@ def create_inputs(self) -> Tuple[torch.Tensor, ...]:
         return (x,)
 
 
+class RollModel(nn.Module):
+    """Model that rolls a tensor along specified dimensions."""
+
+    def __init__(self, shifts: Tuple[int, ...], dims: Tuple[int, ...]):
+        super().__init__()
+        self.shifts = shifts
+        self.dims = dims
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.roll(x, shifts=self.shifts, dims=self.dims)
+
+
+@register_test
+class RollTest(OpTestCase):
+    """Test case for torch.roll()."""
+
+    name = "roll"
+    rtol = 1e-5
+    atol = 1e-5
+
+    def __init__(
+        self,
+        input_shape: Tuple[int, ...] = (4, 5),
+        shifts: Tuple[int, ...] = (1,),
+        dims: Tuple[int, ...] = (0,),
+    ):
+        self.input_shape = input_shape
+        self.shifts = shifts
+        self.dims = dims
+        shift_str = ",".join(str(s) for s in shifts)
+        dim_str = ",".join(str(d) for d in dims)
+        self.name = f"roll_shift({shift_str})_dim({dim_str})"
+
+    @classmethod
+    def get_test_configs(cls) -> List["RollTest"]:
+        return [
+            cls(input_shape=(8,), shifts=(2,), dims=(0,)),
+            cls(input_shape=(4, 5), shifts=(1,), dims=(0,)),
+            cls(input_shape=(4, 5), shifts=(-2,), dims=(1,)),
+            cls(input_shape=(3, 4, 5), shifts=(3,), dims=(2,)),
+            cls(input_shape=(3, 4, 5), shifts=(1, 2), dims=(0, 2)),
+            cls(input_shape=(3, 4, 5), shifts=(-1, -2, -3), dims=(0, 1, 2)),
+            cls(input_shape=(3, 4, 5), shifts=(2,), dims=(-1,)),
+        ]
+
+    def create_model(self) -> nn.Module:
+        return RollModel(self.shifts, self.dims)
+
+    def create_inputs(self) -> Tuple[torch.Tensor, ...]:
+        x = torch.randn(self.input_shape)
+        return (x,)
+
+
 class CatNModel(nn.Module):
     """Model that concatenates N tensors along a dimension."""