Add reflect and symmetric padding modes to mx.pad

[katlun-lgtm]

Summary

Adds numpy.pad-compatible "reflect" and "symmetric" modes to mx.pad. These join the existing "constant" and "edge" modes.

Both modes match numpy.pad semantics for arbitrary pad sizes — when the pad width exceeds the axis length the reflection repeats, exactly as NumPy does. (Earlier attempts at these modes were limited to pad < dim; this implementation removes that restriction.)

• reflect — mirror padding that does not repeat the edge value (period 2(n-1)).
• symmetric — mirror padding that does repeat the edge value (period 2n).

Implementation

reflect_pad (in mlx/ops.cpp) builds a per-axis index map with a triangle-wave reflection function and gathers with take — one take per padded axis. A degenerate axis of length 1 maps every coordinate to 0. No new primitive or kernel is introduced; it composes existing ops, so it works on every backend and is differentiable for free.

Files changed

• mlx/ops.cpp — reflect_pad helper + reflect / symmetric dispatch branches in pad.
• python/src/ops.cpp — extend the mode Literal and docstring.
• python/tests/test_ops.py — test_pad_reflect_symmetric.
• tests/ops_tests.cpp — reflect/symmetric CHECK cases incl. multi-reflect.

Testing

All run locally on an M3 Max:

• Python test_pad_reflect_symmetric — 13 shape/pad-width cases × 2 modes compared element-for-element against numpy.pad (in-bounds, multi-reflect where pad ≫ axis, asymmetric per-axis, zero-width sides, and degenerate axes n==1, n==2). Exact match.
• C++ tests/ops_tests.cpp "test pad" — 9 assertions pass.
• Full C++ suite — 251 cases / 251 passed, 3442 assertions / 0 failed.

>>> import mlx.core as mx
>>> a = mx.array([1, 2, 3])
>>> mx.pad(a, 2, mode="reflect")
array([3, 2, 1, 2, 3, 2, 1], dtype=int32)
>>> mx.pad(a, 2, mode="symmetric")
array([2, 1, 1, 2, 3, 3, 2], dtype=int32)