Function _mm_handler

backends/mlx/ops.py:866–891

Handle mm/bmm/matmul: matrix multiplication without bias. All three ops compute matrix products with different dimension expectations: - mm: 2D x 2D - bmm: 3D x 3D (batched) - matmul: arbitrary dimensions (NumPy semantics) MLX's matmul handles all cases, so we emit AddmmNode wi

(P: MLXProgramBuilder, n: Node)

    ]
)
def _mm_handler(P: MLXProgramBuilder, n: Node) -> Slot:
    """Handle mm/bmm/matmul: matrix multiplication without bias.

    All three ops compute matrix products with different dimension expectations:
    - mm: 2D x 2D
    - bmm: 3D x 3D (batched)
    - matmul: arbitrary dimensions (NumPy semantics)

    MLX's matmul handles all cases, so we emit AddmmNode with bias=None.
    """
    args = P.args(n)
    require_args(args, 2, 2, "aten.mm/bmm/matmul")
    require_kwargs(P.kwargs(n), set(), "aten.mm/bmm/matmul")
    mat1, mat2 = args[0], args[1]

    out = P.make_or_get_slot(n)

    P.emit(
        AddmmNode(
            mat1=P.slot_to_tid(mat1),
            mat2=P.slot_to_tid(mat2),
            out=P.slot_to_tid(out),
            bias=None,
        )
    )
    return out


@REGISTRY.register(