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Changelog

Source: NEWS.md

Rmlx (development version)

  • Converting R data to integer and boolean arrays now uses less temporary memory. Suitable integer and logical inputs retain their R storage type, and conversion copies directly into MLX storage.

  • Embedding lookup now uses one native MLX take operation, preserving weight gradients and dtype and avoiding per-token transfers through R.

  • Axis validation and shape-based operations now recognize the single dimension of MLX vectors, including explicit axes, stacking, splitting, and neural layers.

  • Multi-axis reductions now use one native MLX operation, fixing variance and standard deviation over multiple axes and explicit reductions to scalars.

  • Converting R numeric vectors and arrays to float32 with as_mlx() is faster and uses less temporary memory.

  • mlx_cross_entropy() is faster and uses less memory, especially with many classes. It also improves numerical stability and gives clearer errors for missing target indices.

  • Updated the bundled and minimum supported MLX version to 0.32.3 and compilation to C++20. Fixed mlx_gather_qmm() for its new argument layout while keeping the R interface unchanged.

Rmlx 0.4.0

  • Bundled .mlxfn examples now include both pre-metadata and current MLX serialization formats, preserving import tests across MLX 0.31.1–0.32.1.
  • scale() now preserves MLX-supplied center and scale vectors as MLX attributes, avoiding a round trip through base R.
  • mlx objects can now have row, column and dimnames, and vectors can have names. These work much like base R. Names for any dimension may be NULL. Subsetting with a non-existent name is an error. Many functions, e.g. solve(), %*%, etc. preserve dimnames like their R analogues.

Rmlx 0.3.0

  • We now allow float64 arrays. (Note that mlx doesn’t yet do operations on the gpu for these, however.)
  • Breaking change: arrays no longer have an associated device. Instead, use mlx_device(), with_device() or local_device() to choose a device to work on. This also makes irrelevant a bug in which many/most operations were silently ignoring their operands’ device.
  • Operations which are currently cpu-only used to switch silently to cpu. They now take a device argument which must be explicitly set to "cpu". This is a teaching tool to help the user understand what operations can take place on which device.
  • rbind() and cbind() now accept 1D vectors.
  • Bugfix: mlx_expand_dims() works with 1D vectors.

Rmlx 0.2.3

  • Speeded up single-axis [ subsetting by routing it through MLX take() instead of the generic meshgrid gather path.
  • Added mlx_take_along_axis(), mlx_put_along_axis(), and mlx_scatter_add_axis() wrappers for MLX’s axis-aligned indexed update ops.
  • Added mlx_metal_kernel() to build custom Metal kernels from R and keep inputs and outputs as MLX arrays.
  • Updated the bundled MLX source tarball and minimum supported MLX version to 0.31.1.
  • Fixed the Linux bundled-build LAPACK shim for complex eigendecomposition and SVD entry points used by MLX 0.31.1.
  • Fixed compatibility with MLX 0.31.1.
  • Force clean recompilation when the detected MLX install or build flags change, avoiding stale object files after MLX upgrades.

Rmlx 0.2.2

Rmlx 0.2.1

Rmlx 0.2.0

Rmlx 0.1.0.9000 (development)

  • Speeded up subset assignment. We also now fail on NAs in indices.
  • Exposed mlx_cast() for casting arrays between dtypes/devices (previously internal .mlx_cast).
  • %*% now requires both its arguments to be matrices (unlike base R).
  • as_mlx(x) no longer returns scalars if x is a length-one vector.
  • mlx_arange(start, stop, step) now matches seq() behavior (stop included if reachable).
  • New mlx_device() to return device associated with x.
  • Renamed mlx_get_device() to mlx_best_device().
  • mlx_slice_update() now accepts 1-based (inclusive) start/stop indices to match the rest of the R API; internal callers were updated accordingly.
  • Added mlx_shape() to expose MLX-native shapes (never NULL) and aligned dim.mlx() with base R semantics (returns NULL for 1-D vectors/scalars).
  • mlx_rand_categorical(), mlx_rand_permutation(), mlx_cross_entropy(), mlx_gather_qmm(), and mlx_embedding() now accept 1-based indices for inputs/outputs, consistently with R.
  • Added negative numeric indexing support for [/[<- on mlx arrays and documented subsetting semantics.
  • Added mlx_import_function() to import MLX functions from (e.g.) Python.
  • Added mlx_array(), mlx_matrix(), mlx_vector(), and mlx_scalar() for fast construction of MLX objects when data and dimensions are already known.
  • mlx_array()/mlx_matrix() now recycle shorter payloads when they evenly divide the target shape.
  • Added mlx_fft(), mlx_fft2(), and mlx_fftn() wrappers around MLX FFT kernels.
  • Added distribution functions mlx_d/p/qnorm(), mlx_d/p/qunif() etc.
  • Added mlx_quantile().
  • Added mlx_coordinate_descent(), a coordinate descent algorithm.
  • Fixed several [/[<- bugs affecting non-contiguous, unsorted, and duplicate subsetting patterns on mlx arrays.
  • as_mlx() now takes a much faster path for large numeric matrices by letting MLX handle column-major inputs directly.
  • Base reducers all() and any() applied to mlx arrays now return plain R logical scalars; mlx_all()/mlx_any() continue to yield mlx booleans.
  • Added mlx-aware wrappers for row(), col(), asplit(), and backsolve().
  • Added scale.mlx() to center/scale matrices entirely on the MLX backend (with MLX arrays stored in the scaled:center / scaled:scale attributes).
  • scale.mlx() now always records its scaled:center / scaled:scale attributes as 1 x p MLX arrays, keeping them lazily evaluated even after coercion.
  • as.matrix.mlx() now preserves any user-set attributes (including the MLX scaling metadata) when copying arrays back to base R.
  • Created a new benchmarks vignette.
  • Added pre-commit hooks to run, commit and print benchmark.

Rmlx 0.1.0

  • Initial release on r-universe.