v0.17.3

🚀

v0.17.1

🐛

v0.17.0

Highlights

• mx.einsum: PR
• Big speedups in reductions: benchmarks
• 2x faster model loading: PR
• mx.fast.metal_kernel for custom GPU kernels: docs

Core

• Faster program exits
• Laplace sampling
• mx.nan_to_num
• nn.tanh gelu approximation
• Fused GPU quantization ops
• Faster group norm
• bf16 winograd conv
• vmap support for mx.scatter
• mx.pad "edge" padding
• More numerically stable mx.var
• mx.linalg.cholesky_inv/mx.linalg.tri_inv
• mx.isfinite
• Complex mx.sign now mirrors NumPy 2.0 behaviour
• More flexible mx.fast.rope
• Update to nanobind 2.1

Bug Fixes

• gguf zero initialization
• expm1f overflow handling
• bfloat16 hadamard
• large arrays for various ops
• rope fix
• bf16 array creation
• preserve dtype in nn.Dropout
• nn.TransformerEncoder with norm_first=False
• excess copies from contiguity bug

v0.16.3

🚀

v0.16.2

🚀🚀

0.16.1

🚀

v0.16.0

Highlights

• @mx.custom_function for custom vjp/jvp/vmap transforms
• Up to 2x faster Metal GEMV and fast masked GEMV
  • benchmarks
• Fast hadamard_transform
  • benchmarks

Core

• Metal 3.2 support
• Reduced CPU binary size
• Added quantized GPU ops to JIT
• Faster GPU compilation
• Added grads for bitwise ops + indexing

Bug Fixes

• 1D scatter bug
• Strided sort bug
• Reshape copy bug
• Seg fault in mx.compile
• Donation condition in compilation
• Compilation of accelerate on iOS

v0.15.2

🚀

v0.15.1

🚀

v0.15.0

Highlights

• Fast Metal GPU FFTs
  • On average ~30x faster than CPU
  • More benchmarks
• mx.distributed with all_sum and all_gather

Core

• Added dlpack device __dlpack_device__
• Fast GPU FFTs benchmarks
• 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