Crate rten_tensor

rten_tensor provides multi-dimensional arrays, commonly referred to as tensors in a machine learning context.

Each tensor is a combination of data and a layout. The data can be owned, borrowed or mutably borrowed. This is analagous to Vec<T>, &[T] and &mut [T] for 1D arrays. The layout determines the number of dimensions (the rank), the size of each dimension, and the strides (gap between successive indices along a given dimension).

Key types and traits

The base type for all tensors is TensorBase. This is not normally used directly but instead via a type alias, depending on whether the number of dimensions (the rank) of the tensor is known at compile time or only at runtime, as well as whether the tensor owns, borrows or mutably borrows its data.

Rank	Owned (like Vec<T>)	Borrowed (like &[T])	Mutably borrowed
Static	NdTensor	NdTensorView	NdTensorViewMut
Dynamic	Tensor	TensorView	TensorViewMut

All tensors implement the Layout trait, which provide methods to query the shape, dimension count and strides of the tensor. Tensor views provide various methods for indexing, iterating, slicing and transforming them. The AsView trait provides access to these methods for owned and mutably borrowed tensors. Conceptually it is similar to how Deref allows accesing methods for &[T] on a Vec<T>. The preferred way to import the traits is via the prelude:

use rten_tensor::prelude::*;
use rten_tensor::NdTensor;

let tensor = NdTensor::from_data([2, 2], vec![1, 2, 3, 4]);

// Logs 1, 3, 2, 4.
for x in tensor.transposed().iter() {
  println!("{}", x);
}

Modules

• prelude
  This module provides a convenient way to import the most common traits from this library via a glob import.

Macros

• ndtensor
  Construct an NdTensor.
• tensor
  Construct a Tensor.

Structs

• AxisChunks
  Iterator over slices of a tensor along an axis. See TensorView::axis_chunks.
• AxisChunksMut
  Iterator over mutable slices of a tensor along an axis. See TensorViewMut::axis_chunks_mut.
• AxisIter
  Iterator over slices of a tensor along an axis. See TensorView::axis_iter.
• AxisIterMut
  Iterator over mutable slices of a tensor along an axis. See TensorViewMut::axis_iter_mut.
• BroadcastIter
  Iterator over elements of a tensor which broadcasts to a different shape.
• DynIndices
  Iterator over a range of N-dimensional indices, where N is not known at compile time.
• DynLayout
  Defines the valid indices for an N-dimensional array and how to map them to offsets in a linear buffer, where N can be varied at runtime.
• GlobalAlloc
  Implementation of Alloc which wraps the global allocator.
• Indices
  Iterator over a range of N-dimensional indices, where N may be known at compile time (see NdIndices) or only at runtime (DynIndices).
• InnerIter
  Iterator over views of the N innermost dimensions of a tensor with element type T and layout L.
• InnerIterMut
  Iterator over mutable views of the N innermost dimensions of a tensor.
• Iter
  Iterator over elements of a tensor, in their logical order.
• IterMut
  Mutable iterator over elements of a tensor.
• Lanes
  Iterator over 1D slices of a tensor along a target dimension of size N.
• LanesMut
  Mutable version of Lanes.
• NdIndices
  Iterator over a range of N-dimensional indices, where N is known at compile time.
• NdLayout
  Defines the valid indices for an N-dimensional array and how to map them to offsets in a linear buffer, where N is known at compile time.
• Offsets
  Iterator over element offsets of a tensor.
• SliceRange
  A range for slicing a Tensor or NdTensor.
• TensorBase
  The base type for multi-dimensional arrays. This consists of storage for elements, plus a layout which maps from a multi-dimensional array index to a storage offset. This base type is not normally used directly but instead through a type alias which selects the storage type and layout.
• WeaklyCheckedView
  A view of a tensor which does “weak” checking when indexing via view[<index>]. This means that it does not bounds-check individual dimensions, but does bounds-check the computed offset.

Enums

• OverlapPolicy
  Specifies whether a tensor or view may have an overlapping layout.
• SliceItem
  Specifies a subset of a dimension to include when slicing a tensor or view.

Traits

• Alloc
  Storage allocation trait.
• AsView
  Trait implemented by all variants of TensorBase, which provides a view method to get an immutable view of the tensor, plus methods which forward to such a view.
• IntoLayout
  Trait for shapes which can be used to create a contiguous layout.
• IntoSliceItems
  Used to convert sequences of indices and/or ranges into a uniform [SliceItem] array that can be used to slice a tensor.
• Layout
  Layouts describe the shape of a tensor, ie. the number of dimensions and size of each, and the mapping between indices and offsets in the data storage.
• MatrixLayout
  Provides convenience methods for querying the shape and strides of a matrix.
• MutLayout
  MutLayout extends Layout with methods for creating, modifying and transforming layouts.
• RandomSource
  Trait for sources of random data for tensors, for use with Tensor::rand.

Functions

• is_valid_permutation
  Return true if permutation is a valid permutation of dimensions for a tensor of rank ndim.
• to_slice_items
  Convert a slice of indices into SliceItems.

Type Aliases

• DynSliceItems
  Dynamically sized array of SliceItems, which avoids allocating in the common case where the length is small.
• Matrix
  View of a slice as a matrix.
• MatrixMut
  Mutable view of a slice as a matrix.
• NdTensor
  Tensor with a static dimension count.
• NdTensorView
  View of a slice of a tensor with a static dimension count.
• NdTensorViewMut
  Mutable view of a slice of a tensor with a static dimension count.
• Tensor
  Tensor with a dynamic dimension count.
• TensorView
  View of a slice of a tensor with a dynamic dimension count.
• TensorViewMut
  Mutable view of a slice of a tensor with a dynamic dimension count.