Orion

Tensor

A Tensor represents a multi-dimensional array of elements.

A Tensor represents a multi-dimensional array of elements and is depicted as a struct containing both the tensor's shape and a flattened array of its data. The generic Tensor is defined as follows:

struct Tensor<T> {
    shape: Span<usize>,
    data: Span<T>
}

Data types

Orion supports currently these tensor types.

TensorTrait

use orion::operators::tensor::TensorTrait;

TensorTrait defines the operations that can be performed on a Tensor.

Arithmetic Operations

Tensor implements arithmetic traits. This allows you to perform basic arithmetic operations using the associated operators. (+, -, *, / ). Tensors arithmetic operations supports broadcasting.

Two tensors are “broadcastable” if the following rules hold:

  • Each tensor has at least one dimension.

  • When iterating over the dimension sizes, starting at the trailing dimension, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.

Examples

Element-wise add.

use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor, U32TensorAdd};

fn element_wise_add_example() -> Tensor<u32> {
    // We instantiate two 3D Tensors here.
    let tensor_1 = TensorTrait::new(
        shape: array![2, 2, 2].span(), data: array![0, 1, 2, 3, 4, 5, 6, 7].span(),
    );
    let tensor_2 = TensorTrait::new(
        shape: array![2, 2, 2].span(), data: array![0, 1, 2, 3, 4, 5, 6, 7].span(),
    );

    // We can add two tensors as follows.
    return tensor_1 + tensor_2;
}
>>> [[[0,2],[4,6]],[[8,10],[12,14]]]

Add two tensors of different shapes but compatible in broadcasting.

use core::array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{TensorTrait, Tensor, U32Tensor, U32TensorAdd};

fn broadcasting_add_example() -> Tensor<u32> {
    // We instantiate two 3D Tensors here.
    let tensor_1 = TensorTrait::new(
        shape: array![2, 2, 2].span(),
        data: array![0, 1, 2, 3, 4, 5, 6, 7].span(),
    );
    let tensor_2 = TensorTrait::new(
        shape: array![1, 2, 1].span(),
        data: array![10, 100].span(),
    );

    // We can add two tensors as follows.
    return tensor_1 + tensor_2;
}
>>> [[[10,11],[102,103]],[[14,15],[106,107]]]
PreviousOperatorsNexttensor.new

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