v0.20.0

Highlights

• Even faster GEMMs
  • Peaking at 23.89 TFlops on M2 Ultra benchmarks
• BFS graph optimizations
  • Over 120tks with Mistral 7B!
• Fast batched QMV/QVM for KV quantized attention benchmarks

Core

• New Features
  • mx.linalg.eigh and mx.linalg.eigvalsh
  • mx.nn.init.sparse
  • 64bit type support for mx.cumprod, mx.cumsum
• Performance
  • Faster long column reductions
  • Wired buffer support for large models
  • Better Winograd dispatch condition for convs
  • Faster scatter/gather
  • Faster mx.random.uniform and mx.random.bernoulli
  • Better threadgroup sizes for large arrays
• Misc
  • Added Python 3.13 to CI
  • C++20 compatibility

Bugfixes

• Fix command encoder synchronization
• Fix mx.vmap with gather and constant outputs
• Fix fused sdpa with differing key and value strides
• Support mx.array.__format__ with spec
• Fix multi output array leak
• Fix RMSNorm weight mismatch error