Mlx

Highlights:
- GGUF improvements:
- Native quantizations `Q4_0`, `Q4_1`, and `Q8_0`
- Metadata

Core
- Support for reading and writing GGUF metadata
- Native GGUF quantization (`Q4_0`, `Q4_1`, and `Q8_0`)
- Quantize with group size of 32 (2x32, 4x32, and 8x32)

NN
- `Module.save_weights` supports safetensors
- `nn.init` package with several commonly used neural network initializers
- Binary cross entropy and cross entropy losses can take probabilities as targets
- `Adafactor` in `nn.optimizers`

Bugfixes

- Fix `isinf` and friends for integer types
- Fix array creation from list Python ints to `int64`, `uint`, and `float32`
- Fix power VJP for `0` inputs
- Fix out of bounds `inf` reads in `gemv`
- `mx.arange` crashes on NaN inputs

Highlights:

- Faster matmul: up to 2.5x faster for certain sizes, [benchmarks](https://github.com/ml-explore/mlx/pull/424#issuecomment-1898815724)
- Fused matmul + addition (for faster linear layers)

Core

- Quantization supports sizes other than multiples of 32
- Faster GEMM (matmul)
- ADMM primitive (fused addition and matmul)
- `mx.isnan`, `mx.isinf`, `isposinf`, `isneginf`
- `mx.tile`
- VJPs for `scatter_min` and `scatter_max`
- Multi output split primitive

NN
- Losses: Gaussian negative log-likelihood

Misc

- Performance enhancements for graph evaluation with lots of outputs
- Default PRNG seed is based on current time instead of 0
- Primitive VJP takes output as input. Reduces redundant work without need for simplification
- PRNGs default seed based on system time rather than fixed to 0
- Format boolean printing in Python style when in Python

Bugfixes

- Scatter < 32 bit precision and integer overflow fix
- Overflow with `mx.eye`
- Report Metal out of memory issues instead of silent failure
- Change `mx.round` to follow NumPy which rounds to even

Highlights:

- Initial (and experimental) GGUF support
- Support Python buffer protocol (easy interoperability with NumPy, Jax, Tensorflow, PyTorch, etc)
- `at[]` syntax for scatter style operations: `x.at[idx].add(y)`, (`min`, `max`, `prod`, etc)

Core

- Array creation from other mx.array’s (`mx.array([x, y])`)
- Complete support for Python buffer protocol
- `mx.inner`, `mx.outer`
- mx.logical_and, mx.logical_or, and operator overloads
- Array at syntax for scatter ops
- Better support for in-place operations (`+=`, `*=`, `-=`, ...)
- VJP for scatter and scatter add
- Constants (`mx.pi`, `mx.inf`, `mx.newaxis`, …)

NN

- GLU activation
- `cosine_similarity` loss
- Cache for `RoPE` and `ALiBi`

Bugfixes / Misc

- Fix data type with `tri`
- Fix saving non-contiguous arrays
- Fix graph retention for inlace state, and remove `retain_graph`
- Multi-output primitives
- Better support for loading devices

Core

- Support for loading and saving HuggingFace's safetensor format
- Transposed quantization matmul kernels
- `mlx.core.linalg` sub-package with `mx.linalg.norm` (Frobenius, infininty, p-norms)
- `tensordot` and `repeat`

NN
- Layers
- `Bilinear`,`Identity`, `InstanceNorm`
- `Dropout2D`, `Dropout3D`
- more customizable `Transformer` (pre/post norm, dropout)
- More activations: `SoftSign`, `Softmax`, `HardSwish`, `LogSoftmax`
- Configurable scale in `RoPE` positional encodings
- Losses: `hinge`, `huber`, `log_cosh`

Misc
- Faster GPU reductions for certain cases
- Change to memory allocation to allow swapping

Core

- quantize, dequantize, quantized_matmul
- moveaxis, swapaxes, flatten
- stack
- floor, ceil, clip
- tril, triu, tri
- linspace

Optimizers
- RMSProp, Adamax, Adadelta, Lion

NN

- Layers: `QuantizedLinear`, `ALiBi` positional encodings
- Losses: Label smoothing, Smooth L1 loss, Triplet loss

Misc
- Bug fixes

- Core ops `remainder`, `eye`, `identity`
- Additional functionality in `mlx.nn`
- Losses: binary cross entropy, kl divergence, mse, l1
- Activations: PRELU, Mish, and several others
- More optimizers: AdamW, Nesterov momentum, Adagrad
- Bug fixes