Mlx

https://github.com/ml-explore/mlx/compare/v0.15.2...v0.16.0
https://github.com/ml-explore/mlx-c/pull/31/files

🚀

Highlights

- Fast Metal GPU FFTs
- On average ~30x faster than CPU
- [More benchmarks](https://github.com/ml-explore/mlx/pull/1102)
- `mx.distributed` with `all_sum` and `all_gather`

Core
- Added dlpack device `__dlpack_device__`
- Fast GPU FFTs [benchmarks](https://github.com/ml-explore/mlx/pull/1102)
- Add docs for the `mx.distributed`
- Add `mx.view` op

NN
- ``softmin``, ``hardshrink``, and ``hardtanh`` activations

Bugfixes
- Fix broadcast bug in bitwise ops
- Allow more buffers for JIT compilation
- Fix matvec vector stride bug
- Fix multi-block sort stride management
- Stable cumprod grad at 0
- Buf fix with race condition in scan

🚀

Highlights

* Small-size build that JIT compiles kernels and omits the CPU backend which results in a binary <4MB
- Series of PRs [1](https://github.com/ml-explore/mlx/pull/1105), [2](https://github.com/ml-explore/mlx/pull/1123), [3](https://github.com/ml-explore/mlx/pull/1091), [4](https://github.com/ml-explore/mlx/pull/1132), [5](https://github.com/ml-explore/mlx/pull/1139)
* `mx.gather_qmm` quantized equivalent for `mx.gather_mm` which speeds up MoE inference by ~2x
- [Some numbers](https://github.com/ml-explore/mlx-examples/pull/782)
* Grouped 2D convolutions
- [Some numbers](https://github.com/ml-explore/mlx/pull/1129)


Core

* `mx.conjugate`
* `mx.conv3d` and `nn.Conv3d`
* List based indexing
* Started `mx.distributed` which uses MPI (if installed) for communication across machines
- `mx.distributed.init`
- `mx.distributed.all_gather`
- `mx.distributed.all_reduce_sum`
* Support conversion to and from dlpack
* `mx.linalg.cholesky` on CPU
* `mx.quantized_matmul` sped up for vector-matrix products
* `mx.trace`
* `mx.block_masked_mm` now supports floating point masks!

Fixes

* Error messaging in eval
* Add some missing docs
* Scatter index bug
* The extensions example now compiles and runs
* CPU copy bug with many dimensions