Good First Issue: Add MLX Op Handler for `aten.bitwise_or`

[metascroy]

🚀 The feature, motivation and pitch

Good First Issue: Add MLX Op Handler for aten.bitwise_or

Summary

Add support for aten.bitwise_or in the MLX delegate. This op performs element-wise bitwise OR and is needed for bit manipulation, flags/masks, and low-level data processing.

Background

The MLX delegate currently has no handler for bitwise_or. MLX has native support via mlx::core::bitwise_or which works for both boolean and integer tensors.

Approach: New schema node + runtime

Add a BitwiseOrNode to handle this via MLX's native bitwise_or.

Steps

1. Add node to backends/mlx/serialization/schema.fbs

   table BitwiseOrNode {
     a: Tid;
     b: Tid;
     out: Tid;
   }

   Add BitwiseOrNode to the OpNode union (append only, do not reorder).

2. Regenerate serialization code

   python backends/mlx/serialization/generate.py

3. Add C++ runtime exec function in backends/mlx/runtime/MLXInterpreter.h

   inline void exec_bitwise_or(
       const BitwiseOrNode& n, ExecutionState& st, StreamOrDevice s) {
     auto a = st.get_tensor(n.a());
     auto b = st.get_tensor(n.b());
     auto out = mlx::core::bitwise_or(a, b, s);
     st.set_tensor(n.out(), out);
   }

4. Add handler in backends/mlx/ops.py

   Add to the _BINARY_OPS table:

   # In _BINARY_OPS list, add:
   ([torch.ops.aten.bitwise_or.Tensor, torch.ops.aten.bitwise_or.Scalar], BitwiseOrNode, "aten.bitwise_or", True),

5. Add test in backends/mlx/test/test_ops.py

   Use the _BINARY_OP_TESTS table:

   # Add to _BINARY_OP_TESTS list:
   {"op_name": "bitwise_or_bool", "op_fn": torch.bitwise_or, 
    "shapes": _SHAPES_3, "dtypes": [torch.bool], 
    "input_fn_a": _bool_input_fn(), "input_fn_b": _bool_input_fn()},
   {"op_name": "bitwise_or_int", "op_fn": torch.bitwise_or, 
    "shapes": _SHAPES_3, "dtypes": [torch.int32, torch.int64], 
    "input_fn_a": _int_input_fn(0, 256), "input_fn_b": _int_input_fn(0, 256)},

Running tests

python -m executorch.backends.mlx.test.run_all_tests -k bitwise_or

References

• MLX C++: array bitwise_or(const array &a, const array &b, StreamOrDevice s = {})
• PyTorch signature: bitwise_or(Tensor self, Tensor other) -> Tensor
• Supported dtypes: int8, int16, int32, int64, uint8, bool

Alternatives

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Additional context

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RFC (Optional)

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