v0.13.0

Highlights

• Block sparse matrix multiply speeds up MoEs by >2x
  • some numbers
• Improved quantization algorithm should work well for all networks
  • see evaluations
• Improved gpu command submission speeds up training and inference
  • some numbers

Core

• Bitwise ops added:
  • mx.bitwise_[or|and|xor], mx.[left|right]_shift, operator overloads
• Groups added to Conv1d
• Added mx.metal.device_info to get better informed memory limits
• Added resettable memory stats
• mlx.optimizers.clip_grad_norm and mlx.utils.tree_reduce added
• Add mx.arctan2
• Unary ops now accept array-like inputs ie one can do mx.sqrt(2)

Bugfixes

• Fixed shape for slice update
• Bugfix in quantize that used slightly wrong scales/biases
• Fixed memory leak for multi-output primitives encountered with gradient checkpointing
• Fixed conversion from other frameworks for all datatypes
• Fixed index overflow for matmul with large batch size
• Fixed initialization ordering that occasionally caused segfaults