Package index • Rmlx

Core Tensor API

as_mlx(): Create MLX array from R object

is.mlx(): Test if object is an MLX array

Rmlx-package Rmlx: Rmlx: R Interface to Apple's MLX Arrays

mlx-methods: Base R generics with mlx methods

mlx_dim(): Get dimensions helper

mlx_dtype(): Get data type helper

mlx_eval(): Force evaluation of lazy MLX operations

`[`(<mlx>) `[<-`(<mlx>): Subset MLX array

dim(<mlx>): Get dimensions of MLX array

`dim<-`(<mlx>): Set dimensions of MLX array

length(<mlx>): Get length of MLX array

print(<mlx>): Print MLX array

str(<mlx>): Object structure for MLX array

t(<mlx>): Transpose of MLX matrix

as.matrix(<mlx>): Convert MLX array to R matrix/array

as.array(<mlx>): Convert MLX array to R array

as.vector(<mlx>): Convert MLX array to R vector

Device & Execution

mlx_default_device(): Get or set default MLX device

with_default_device(): Temporarily set the default MLX device

mlx_new_stream() mlx_default_stream(): MLX streams for asynchronous execution

mlx_set_default_stream(): Set the default MLX stream

mlx_synchronize(): Synchronize MLX execution

mlx_forward(): Forward pass utility

mlx_grad() mlx_value_grad(): Automatic differentiation for MLX functions

mlx_stop_gradient(): Stop gradient propagation through an mlx array

mlx_compile(): Compile an MLX Function for Optimized Execution

mlx_disable_compile() mlx_enable_compile(): Control Global Compilation Behavior

Creation & Randomness

mlx_zeros(): Create arrays of zeros on MLX devices

mlx_ones(): Create arrays of ones on MLX devices

mlx_zeros_like(): Zeros shaped like an existing mlx array

mlx_ones_like(): Ones shaped like an existing mlx array

mlx_full(): Fill an mlx array with a constant value

mlx_eye(): Identity-like matrices on MLX devices

mlx_identity(): Identity matrices on MLX devices

mlx_arange(): Numerical ranges on MLX devices

mlx_linspace(): Evenly spaced ranges on MLX devices

mlx_rand_bernoulli(): Sample Bernoulli random variables on mlx arrays

mlx_rand_categorical(): Sample from a categorical distribution on mlx arrays

mlx_rand_gumbel(): Sample from the Gumbel distribution on mlx arrays

mlx_rand_laplace(): Sample from the Laplace distribution on mlx arrays

mlx_rand_multivariate_normal(): Sample from a multivariate normal distribution on mlx arrays

mlx_rand_normal(): Sample from a normal distribution on mlx arrays

mlx_rand_permutation(): Generate random permutations on mlx arrays

mlx_rand_randint(): Sample random integers on mlx arrays

mlx_rand_truncated_normal(): Sample from a truncated normal distribution on mlx arrays

mlx_rand_uniform(): Sample from a uniform distribution on mlx arrays

mlx_key() mlx_key_split(): Construct MLX random number generator keys

mlx_key_bits(): Generate raw random bits on MLX arrays

Shape & Indexing

mlx_reshape(): Reshape an mlx array

mlx_stack(): Stack mlx arrays along a new axis

mlx_squeeze(): Remove singleton dimensions

mlx_expand_dims(): Insert singleton dimensions

mlx_repeat(): Repeat array elements

mlx_tile(): Tile an array

mlx_pad() mlx_split(): Pad or split mlx arrays

mlx_roll(): Roll array elements

mlx_moveaxis() aperm(<mlx>): Reorder mlx array axes

mlx_contiguous(): Ensure contiguous memory layout

mlx_flatten(): Flatten axes of an mlx array

mlx_swapaxes(): Swap two axes of an mlx array

mlx_unflatten(): Unflatten an axis into multiple axes

mlx_meshgrid(): Construct coordinate arrays from input vectors

mlx_broadcast_to(): Broadcast an array to a new shape

mlx_broadcast_arrays(): Broadcast multiple arrays to a shared shape

mlx_where(): Elementwise conditional selection

mlx_tri() mlx_tril() mlx_triu(): Triangular helpers for MLX arrays

mlx_slice_update(): Update a slice of an mlx array

mlx_gather(): Gather elements from an mlx array

abind(): Bind mlx arrays along an axis

rbind(<mlx>): Row-bind mlx arrays

cbind(<mlx>): Column-bind mlx arrays

Ordering & Selection

mlx_sort() mlx_argsort(): Sort and argsort for mlx arrays

mlx_topk() mlx_partition() mlx_argpartition(): Top-k selection and partitioning on mlx arrays

mlx_argmax() mlx_argmin(): Argmax and argmin on mlx arrays

Math & Reductions

Math(<mlx>): Math operations for MLX arrays

Ops(<mlx>): Arithmetic and comparison operators for MLX arrays

mlx_sum() mlx_prod() mlx_all() mlx_any() mlx_mean() mlx_var() mlx_std(): Reduce mlx arrays

mean(<mlx>): Mean of MLX array elements

mlx_cumsum() mlx_cumprod(): Cumulative sum and product

mlx_clip(): Clip mlx array values into a range

mlx_maximum(): Elementwise maximum of two mlx arrays

mlx_minimum(): Elementwise minimum of two mlx arrays

mlx_hadamard_transform(): Hadamard transform for MLX arrays

mlx_softmax(): Softmax for mlx arrays

mlx_logsumexp(): Log-sum-exp reduction for mlx arrays

mlx_logcumsumexp(): Log cumulative sum exponential for mlx arrays

mlx_isnan() mlx_isinf() mlx_isfinite(): Elementwise NaN and infinity predicates

mlx_isposinf() mlx_isneginf(): Detect signed infinities in mlx arrays

mlx_nan_to_num(): Replace NaN and infinite values with finite numbers

mlx_real() mlx_imag() mlx_conjugate(): Complex-valued helpers for mlx arrays

mlx_degrees() mlx_radians(): Convert between radians and degrees

mlx_isclose(): Element-wise approximate equality

mlx_allclose(): Test if all elements of two arrays are close

all.equal(<mlx>): Test if two MLX arrays are (nearly) equal

colSums(): Column sums for mlx arrays

rowSums(): Row sums for mlx arrays

colMeans(): Column means for mlx arrays

rowMeans(): Row means for mlx arrays

fft(): Fast Fourier Transform

mlx_fft() mlx_fft2() mlx_fftn(): Fast Fourier transforms for MLX arrays

Linear Algebra

`%*%`(<mlx>): Matrix multiplication for MLX arrays

mlx_addmm(): Fused matrix multiply and add for MLX arrays

crossprod(<mlx>): Cross product

tcrossprod(<mlx>): Transposed cross product

outer(): Outer product of two vectors

diag(): Diagonal matrix extraction and construction

chol(<mlx>): Cholesky decomposition for mlx arrays

chol2inv(): Inverse from Cholesky decomposition

kronecker() kronecker.default(): Kronecker product dispatcher

qr(<mlx>): QR decomposition for mlx arrays

svd(): Singular value decomposition

svd(<mlx>): Singular value decomposition for mlx arrays

solve(<mlx>): Solve a system of linear equations

pinv(): Moore-Penrose pseudoinverse for MLX arrays

mlx_kron(): Kronecker product for mlx arrays

mlx_inv(): Compute matrix inverse

mlx_tri_inv(): Compute triangular matrix inverse

mlx_cholesky_inv(): Compute matrix inverse via Cholesky decomposition

mlx_lu(): LU factorization

mlx_norm(): Matrix and vector norms for mlx arrays

mlx_solve_triangular(): Solve triangular systems with mlx arrays

mlx_trace(): Matrix trace for mlx arrays

diag(<mlx>) mlx_diagonal(): Extract diagonal or construct diagonal matrix for mlx arrays

mlx_eig(): Eigen decomposition for mlx arrays

mlx_eigh(): Eigen decomposition of Hermitian mlx arrays

mlx_eigvals(): Eigenvalues of mlx arrays

mlx_eigvalsh(): Eigenvalues of Hermitian mlx arrays

mlx_cross(): Vector cross product with mlx arrays

Input & Output

mlx_save(): Save an MLX array to disk

mlx_load(): Load an MLX array from disk

mlx_save_safetensors(): Save MLX arrays to the safetensors format

mlx_load_safetensors(): Load MLX arrays from the safetensors format

mlx_save_gguf(): Save MLX arrays to the GGUF format

mlx_load_gguf(): Load MLX tensors from the GGUF format

Neural Network Layers

mlx_linear(): Create a learnable linear transformation

mlx_sequential(): Compose modules sequentially

mlx_set_training(): Toggle training mode for MLX modules

mlx_embedding(): Embedding layer

mlx_conv1d(): 1D Convolution

mlx_conv2d(): 2D Convolution

mlx_conv3d(): 3D Convolution

mlx_conv_transpose1d(): 1D Transposed Convolution

mlx_conv_transpose2d(): 2D Transposed Convolution

mlx_conv_transpose3d(): 3D Transposed Convolution

mlx_quantize(): Quantize a Matrix

mlx_dequantize(): Dequantize a Matrix

mlx_quantized_matmul(): Quantized Matrix Multiplication

mlx_gather_qmm(): Gather-based Quantized Matrix Multiplication

Activation Functions

mlx_relu(): Rectified linear activation module

mlx_gelu(): GELU activation

mlx_sigmoid(): Sigmoid activation

mlx_tanh(): Tanh activation

mlx_silu(): SiLU (Swish) activation

mlx_leaky_relu(): Leaky ReLU activation

mlx_softmax_layer(): Softmax activation

Regularization & Normalization

mlx_dropout(): Dropout layer

mlx_layer_norm(): Layer normalization

mlx_batch_norm(): Batch normalization

Loss Functions

mlx_mse_loss(): Mean squared error loss

mlx_l1_loss(): L1 loss (Mean Absolute Error)

mlx_binary_cross_entropy(): Binary cross-entropy loss

mlx_cross_entropy(): Cross-entropy loss

Training Utilities

mlx_parameters(): Collect parameters from modules

mlx_param_values(): Retrieve parameter arrays

mlx_param_set_values(): Assign arrays back to parameters

mlx_optimizer_sgd(): Stochastic gradient descent optimizer

mlx_train_step(): Single training step helper