v0.9.0

Highlights:

• Fast partial RoPE (used by Phi-2)
• Fast gradients for RoPE, RMSNorm, and LayerNorm
  • Up to 7x faster, benchmarks

Core

• More overhead reductions
• Partial fast RoPE (fast Phi-2)
• Better buffer donation for copy
• Type hierarchy and issubdtype
• Fast VJPs for RoPE, RMSNorm, and LayerNorm

NN

• Module.set_dtype
• Chaining in nn.Module (model.freeze().update(…))

Bugfixes

• Fix set item bugs
• Fix scatter vjp
• Check shape integer overlow on array construction
• Fix bug with module attributes
• Fix two bugs for odd shaped QMV
• Fix GPU sort for large sizes
• Fix bug in negative padding for convolutions
• Fix bug in multi-stream race condition for graph evaluation
• Fix random normal generation for half precision

v0.8.0

Highlights

• More perf!
• mx.fast.rms_norm and mx.fast.layer_norm
• Switch to Nanobind substantially reduces overhead
• Up to 4x faster __setitem__ (e.g. a[...] = b)

Core

• mx.inverse, CPU only
• vmap over mx.matmul and mx.addmm
• Switch to nanobind from pybind11
• Faster setitem indexing
  • Benchmarks
• mx.fast.rms_norm, token generation benchmark
• mx.fast.layer_norm, token generation benchmark
• vmap for inverse and svd
• Faster non-overlapping pooling

Optimizers

• Set minimum value in cosine decay scheduler

Bugfixes

• Fix bug in multi-dimensional reduction

v0.7.0

Highlights

• Perf improvements for attention ops:
  • No copy broadcast matmul (benchmarks)
  • Fewer copies in reshape

Core

• Faster broadcast + gemm
  • benchmarks
• mx.linalg.svd (CPU only)
• Fewer copies in reshape
• Faster small reductions
  • benchmarks

NN

• nn.RNN, nn.LSTM, nn.GRU

Bugfixes

• Fix bug in depth traversal ordering
• Fix two edge case bugs in compilation
• Fix bug with modules with dictionaries of weights
• Fix bug with scatter which broke MOE training
• Fix bug with compilation kernel collision

v0.6.0

Highlights:

• Faster quantized matrix-vector multiplies
  • Benchmarks
• mx.fast.scaled_dot_product_attention fused op

Core

• Memory allocation API improvements
• Faster GPU reductions for smaller sizes (between 2 and 7x)
  • Benchmarks
• mx.fast.scaled_dot_product_attention fused op
• Faster quantized matrix-vector multiplications
• Pickle support for mx.array

NN

• Dilation on convolution layers

Bugfixes

• Fix mx.topk
• Fix reshape for zero sizes

v0.5.0

Highlights:

• Faster convolutions.
  • Up to 14x faster for some common sizes.
  • See benchmarks

Core

• mx.where properly handles inf
• Faster and more general convolutions
  • Input and kernel dilation
  • Asymmetric padding
  • Support for cross-correlation and convolution
• atleast_{1,2,3}d accept any number of arrays

NN

• nn.Upsample layer
  • Supports nearest neighbor and linear interpolation
  • Any number of dimensions

Optimizers

• Linear schedule and schedule joiner:
  • Use for e.g. linear warmup + cosine decay

Bugfixes

• arange throws on inf inputs
• Fix Cmake build with MLX
• Fix logsumexp inf edge case
• Fix grad of power w.r.t. to exponent edge case
• Fix compile with inf constants
• Bug temporary bug in convolution

v0.4.0

Highlights:

• Partial shapeless compilation
  • Default shapeless compilation for all activations
  • Can be more than 5x faster than uncompiled versions
• CPU kernel fusion
  • Some functions can be up to 10x faster

Core

• CPU compilation
• Shapeless compilation for some cases
  • mx.compile(function, shapeless=True)
• Up to 10x faster scatter: benchmarks
• mx.atleast_1d, mx.atleast_2d, mx.atleast_3d

Bugfixes

• Bug with tolist with bfloat16 and float16
• Bug with argmax on M3

v0.3.0

Highlights:

• mx.fast subpackage
• Custom mx.fast.rope up to 20x faster

Core

• Support metadata with safetensors
• Up to 5x faster scatter and 30% faster gather
• 40% faster bfloat16 quantizated matrix-vector multiplies
• mx.fast subpackage with a fast RoPE
• Context manager mx.stream to set the default device

NN

• Average and Max pooling layers for 1D and 2D inputs

Optimizers

• Support schedulers for e.g. learning rates
• A few basic schedulers:
  • optimizers.step_decay
  • optimizers.cosine_decay
  • opimtizers.exponential_decay

Bugfixes

• Fix bug in remainder with negative numerators and integers
• Fix bug with slicing into softmax
• Fix quantized matmuls with non 32 multiples

v0.2.0

Highlights:

• mx.compile makes stuff go fast
  • Some functions are up to 10x faster (benchmarks)
  • Training models anywhere from 10% to twice as fast (benchmarks)
  • Simple syntax for compiling full training steps

Core

• mx.compile function transformation
• Find devices properly for iOS
• Up to 10x faster GPU gather
• __abs__ overload for abs on arrays
• loc and scale in parameter for mx.random.normal

NN

• Margin ranking loss
• BCE loss with weights

Bugfixes

• Fix for broken eval during function transformations
• Fix mx.var to give inf with doff >= nelem
• Fix loading empty modules in nn.Sequential

v0.1.0

Highlights

• Memory use improvements:
  • Gradient checkpointing for training with mx.checkpoint
  • Better graph execution order
  • Buffer donation

Core

• Gradient checkpointing with mx.checkpoint
• CPU only QR factorization mx.linalg.qr
• Release Python GIL during mx.eval
• Depth-based graph execution order
• Lazy loading arrays from files
• Buffer donation for reduced memory use
• mx.diag, mx.diagonal
• Breaking: array.shape is a Python tuple
• GPU support for int64 and uint64 reductions
• vmap over reductions and arg reduction:
  • sum, prod, max, min, all, any
  • argmax, argmin

NN

• Softshrink activation

Bugfixes

• Comparisons with inf work, and fix mx.isinf
• Bug fix with RoPE cache
• Handle empty Matmul on the CPU
• Negative shape checking for mx.full
• Correctly propagate NaN in some binary ops
  • mx.logaddexp, mx.maximum, mx.minimum
• Fix > 4D non-contiguous binary ops
• Fix mx.log1p with inf input
• Fix SGD to apply weight decay even with 0 momentum

v0.0.11

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