Crate rstsr_core

API document of rstsr-core

rstsr-core is the core of RSTSR series:

• It defines data structure and some traits (interface) of tensor, storage, device.
• It realizes two basic devices: DeviceCpuSerial and DeviceFaer, so rstsr-core alone is a functional tensor toolkit library.

If you are more aware of matmul efficiency, or by other considerations (we will try to implement BLAS and Lapack features in future), you may find DeviceOpenBLAS in rstsr-openblas helpful. We hope to implement more devices in future.

User document

User document refers to readthedocs. This document is still in construction.

API specifications

API specifications is provided for summary of important identifiers (structs, traits and functions) in rstsr-core.

Currently, more detailed documentation for each identifiers are still constructing.

We also provide fullfillment for Array API standard. This fullfillment check shows how much functionalities we have achieved to be a basic tensor (array) library.

For NumPy users, in this meantime, fullfillment for Array API standard shows the similar parts of RSTSR and NumPy; and these parts in API specifications shows the difference between RSTSR and NumPy:

• Tensor Structure and Ownership
• RSTSR Specific Identifiers
• Storage-dependent manuplication
• Developer Area

Variable naming convention

Abbreviations of type annotations

Abbreviation	Meaning
TR	Tensor type (struct TensorBase)
T	Data type (can be f64, Complex<f32>, etc)
B	Backend (device) type (applies DeviceAPI)
D	Dimensional (applies DimAPI)
S	Storage (struct storage::Storage)
R	Data with lifetime and mutability (applies DataCloneAPI)
C	Content (usually Vec<T> for CPU devices)

Variable or Names

Rule	Convention
suffix API	Traits. To avoid naming collision with structs or other crates. Example: DeviceAPI, AsArrayAPI
suffix _f	Fallible functions. Those functions pairs with a function that panics when error. Example: zeros_f, sin_f, reshape_f
prefix to_ or no prefix (manuplication functions)	Ensures that input is reference. Example: reshape, transpose, to_dim
prefix into_ (some manuplication functions)	Only changes layout, and does not change data with its lifetime and mutability. Example: into_broadcast, into_transpose, into_dyn
prefix into_ output is always Tensor (some manuplication functions)	Give owned tensor. This special case will occur when there is possiblity to generate a new tensor by manuplication function. For these cases, prefix to_ and change_ will give output TensorCow, prefix into_ will give Tensor. Example: into_shape, into_layout
prefix change_ output is always TensorCow (some manuplication functions)	Give copy-on-write tensor by consuming input. This special case will occur when there is possiblity to generate a new tensor by manuplication function. Example: change_shape, change_layout
type annotation Args	Input tuple type as overloadable parameters. Example: zeros, asarray
type annotation Inp	Additional annotation that rust requires for type deduction. Example: zeros, asarray

Re-exports

pub use tensorbase::Tensor;

pub use tensorbase::TensorAny;

pub use tensorbase::TensorArc;

pub use tensorbase::TensorBase;

pub use tensorbase::TensorCow;

pub use tensorbase::TensorMut;

pub use tensorbase::TensorRef;

pub use tensorbase::TensorReference;

pub use tensorbase::TensorView;

pub use tensorbase::TensorViewMut;

Modules

api_specification

API Specification of RSTSR

array_api_standard

Array API standard Fullfillment

device_cpu_serial

Backend for CPU, serial only.

device_faer

Backend for CPU, some using rayon for parallel, but matmul should be implemented elsewhere.

feature_rayon

This module contains some features for rayon (parallel).

format

prelude

prelude_dev

storage

tensor

tensorbase

Type Aliases

DeviceCpu