v0.26.0

Highlights

• 5 bit quantization
• Significant progress on CUDA back-end by @zcbenz

Core

Features

• 5bit quants
• Allow per-target Metal debug flags
• Add complex eigh
• reduce vjp for mx.all and mx.any
• real and imag properties
• Non-symmetric mx.linalg.eig and mx.linalg.eigh
• convolution vmap
• Add more complex unary ops (sqrt, square, ...)
• Complex scan
• Add mx.broadcast_shapes
• Added output_padding parameters in conv_transpose
• Add random normal distribution for complex numbers
• Add mx.fft.fftshift and mx.fft.ifftshift` helpers
• Enable vjp for quantized scale and bias

Performance

• Optimizing Complex Matrix Multiplication using Karatsuba’s Algorithm
• Much faster 1D conv

Cuda

• Generalize gpu backend
• Use fallbacks in fast primitives when eval_gpu is not implemented
• Add memory cache to CUDA backend
• Do not check event.is_signaled() in eval_impl
• Build for compute capability 70 instead of 75 in CUDA backend
• CUDA backend: backbone

Bug Fixes

• Fix out-of-bounds default value in logsumexp/softmax
• include mlx::core::version() symbols in the mlx static library
• Fix Nearest upsample
• Fix large arg reduce
• fix conv grad
• Fix some complex vjps
• Fix typo in row_reduce_small
• Fix put_along_axis for empty arrays
• Close a couple edge case bugs: hadamard and addmm on empty inputs
• Fix fft for integer overflow with large batches
• fix: conv_general differences between gpu, cpu
• Fix batched vector sdpa
• GPU Hadamard for large N
• Improve bandwidth for elementwise ops
• Fix compile merging
• Fix shapeless export to throw on dim mismatch
• Fix mx.linalg.pinv for singular matrices
• Fixed shift operations
• Fix integer overflow in qmm

Contributors

Thanks to some awesome contributors!

@ivanfioravanti, @awni, @angeloskath, @zcbenz, @Jckwind, @iExalt, @thesuryash, @ParamThakkar123, @djphoenix, @ita9naiwa, @hdeng-apple, @Redempt1onzzZZ, @charan-003, @skyzh, @wisefool769, @barronalex @aturker1