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.

Data type	dtype
32-bit integer (signed)	Tensor<i32>
8-bit integer (signed)	Tensor<i8>
32-bit integer (unsigned)	Tensor<u32>
Fixed point (signed)	Tensor<FP8x23 | FP16x16 | FP64x64 | FP32x32>

---

TensorTrait

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

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

function	description
tensor.new	Returns a new tensor with the given shape and data.
tensor.reshape	Returns a new tensor with the specified target shape and the same data as the input tensor.
tensor.flatten	Flattens the input tensor into a 2D tensor.
tensor.constant_of_shape	Generate a tensor with given value and shape.
tensor.transpose	Returns a new tensor with the axes rearranged according to the given permutation.
tensor.at	Retrieves the value at the specified indices of a Tensor.
tensor.ravel_index	Converts a multi-dimensional index to a one-dimensional index.
tensor.unravel_index	Converts a one-dimensional index to a multi-dimensional index.
tensor.equal	Check if two tensors are equal element-wise.
tensor.greater	Check if each element of the first tensor is greater than the corresponding element of the second tensor.
tensor.greater_equal	Check if each element of the first tensor is greater than or equal to the corresponding element of the second tensor.
tensor.less	Check if each element of the first tensor is less than the corresponding element of the second tensor.
tensor.less_equal	Check if each element of the first tensor is less than or equal to the corresponding element of the second tensor.
tensor.or	Computes the logical OR of two tensors element-wise.
tensor.xor	Computes the logical XOR of two tensors element-wise.
tensor.stride	Computes the stride of each dimension in the tensor.
tensor.onehot	Produces one-hot tensor based on input.
tensor.max_in_tensor	Returns the maximum value in the tensor.
tensor.min_in_tensor	Returns the minimum value in the tensor.
tensor.min	Returns the minimum value in the tensor.
tensor.max	Returns the maximum value in the tensor.
tensor.reduce_sum	Reduces a tensor by summing its elements along a specified axis.
tensor.reduce_prod	Reduces a tensor to its products along specified axis.
tensor.argmax	Returns the index of the maximum value along the specified axis.
tensor.argmin	Returns the index of the minimum value along the specified axis.
tensor.cumsum	Performs cumulative sum of the input elements along the given axis.
tensor.matmul	Performs matrix product of two tensors.
tensor.exp	Computes the exponential of all elements of the input tensor.
tensor.log	Computes the natural log of all elements of the input tensor.
tensor.abs	Computes the absolute value of all elements in the input tensor.
tensor.neg	Computes the negation of all elements in the input tensor.
tensor.ceil	Rounds up the value of each element in the input tensor.
tensor.sqrt	Computes the square root of all elements of the input tensor.
tensor.sin	Computes the sine of all elements of the input tensor.
tensor.cos	Computes the cosine of all elements of the input tensor.
tensor.atan	Computes the arctangent (inverse of tangent) of all elements of the input tensor.
tensor.asin	Computes the arcsine (inverse of sine) of all elements of the input tensor.
tensor.acos	Computes the arccosine (inverse of cosine) of all elements of the input tensor.
tensor.sinh	Computes the hyperbolic sine of all elements of the input tensor.
tensor.tanh	Computes the hyperbolic tangent of all elements of the input tensor.
tensor.cosh	Computes the hyperbolic cosine of all elements of the input tensor.
tensor.asinh	Computes the inverse hyperbolic sine of all elements of the input tensor.
tensor.acosh	Computes the inverse hyperbolic cosine of all elements of the input tensor.
tensor.slice	Produces a slice of the input tensor along multiple axes.
tensor.concat	Concatenate a list of tensors into a single tensor.
tensor.quantize_linear	Quantizes a Tensor to i8 using linear quantization.
tensor.dequantize_linear	Dequantizes an i8 Tensor using linear dequantization.
tensor.qlinear_add	Performs the sum of two quantized i8 Tensors.
tensor.qlinear_mul	Performs the element-wise multiplication of quantized Tensors.
tensor.qlinear_matmul	Performs the product of two quantized i8 Tensors.
tensor.qlinear_concat	Concatenate a list of tensors after dequantizing them with their respective scales and zero_points and returns the quantized result.
tensor.qlinear_leakyrelu	Applies the Leaky Relu operator to a quantized Tensor
tensor.gather	Gather entries of the axis dimension of data.
tensor.nonzero	Produces indices of the elements that are non-zero (in row-major order - by dimension).
tensor.squeeze	Removes dimensions of size 1 from the shape of a tensor.
tensor.unsqueeze	Inserts single-dimensional entries to the shape of an input tensor.
tensor.sign	Calculates the sign of the given input tensor element-wise.
tensor.clip	Clip operator limits the given input within an interval.
tensor.and	Computes the logical AND of two tensors element-wise.
tensor.identity	Return a Tensor with the same shape and contents as input.
tensor.where	Return elements chosen from x or y depending on condition.
tensor.bitwise_and	Computes the bitwise AND of two tensors element-wise.
tensor.bitwise_xor	Computes the bitwise XOR of two tensors element-wise.
tensor.bitwise_or	Computes the bitwise OR of two tensors element-wise.
tensor.resize	Resizes the input tensor.
tensor.round	Computes the round value of all elements in the input tensor.
tensor.reduce_l1	Computes the L1 norm of the input tensor's elements along the provided axes.
tensor.trilu	Returns the upper or lower triangular part of a tensor or a batch of 2D matrices.
tensor.scatter	Produces a copy of input data, and updates value to values specified by updates at specific index positions specified by indices.
tensor.reduce_sum_square	Computes the sum square of the input tensor's elements along the provided axes.
tensor.reduce_l2	Computes the L2 norm of the input tensor's elements along the provided axes.
tensor.gather_elements	GatherElements is an indexing operation that produces its output by indexing into the input data tensor at index positions determined by elements of the indices tensor.
tensor.reduce_min	Computes the min of the input tensor's elements along the provided axes.
tensor.reduce_mean	Computes the mean of the input tensor's elements along the provided axes.
tensor.pow	Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function f(x) = x^exponent, is applied to the data tensor elementwise.
tensor.binarizer	Maps the values of a tensor element-wise to 0 or 1 based on the comparison against a threshold value.
tensor.array_feature_extractor	Selects elements of the input tensor based on the indices passed applied to the last tensor axis.
tensor.reduce_min	Computes the min of the input tensor's elements along the provided axes.
tensor.is_nan	Returns which elements of the input are NaN.
tensor.is_inf	Maps infinity to true and other values to false.
tensor.not	Computes the logical negation of all elements in the input tensor.
tensor.gather_nd	Given data tensor of rank r >= 1, indices tensor of rank q >= 1, and batch_dims integer b, this operator gathers slices of data into an output tensor of rank q + r - indices_shape[-1] - 1 - b.
tensor.reduce_log_sum	Computes the log sum of the input tensor's elements along the provided axes.
tensor.erf	Computes the error function of the given input tensor element-wise.
tensor.reduce_log_sum_exp	Computes the log sum of the exponentials of the input tensor's elements along the provided axes.
tensor.layer_normalization	computes the layer normalization of the input tensor.
tensor.split	Split a tensor into a list of tensors, along the specified ‘axis’.
tensor.random_uniform_like	RandomUniformLike generates a tensor with random values using a uniform distribution, matching the shape of the input tensor.
tensor.split_to_sequence	Split a tensor into a sequence of tensors, along the specified ‘axis’.
tensor.range	Generate a tensor containing a sequence of numbers that begin at start and extends by increments of delta up to limit (exclusive).
tensor.hann_window	Generates a Hann window as described in the paper https://ieeexplore.ieee.org/document/1455106.
tensor.hamming_window	Generates a Hamming window as described in the paper https://ieeexplore.ieee.org/document/1455106.
tensor.blackman_window	Generates a Blackman window as described in the paper https://ieeexplore.ieee.org/document/1455106.
tensor.reverse_sequence	Reverse batch of sequences having different lengths specified by sequence_lens.
tensor.optional	Constructs an optional-type value containing either an empty optional of a certain type specified by the attribute, or a non-empty value containing the input element.
tensor.dynamic_quantize_linear	Computes the Scale, Zero Point and FP32->8Bit conversion of FP32 Input data.
tensor.scatter_nd	The output of the operation is produced by creating a copy of the input data, and then updating its value to values specified by updates at specific index positions specified by indices. Its output shape is the same as the shape of data
tensor.label_encoder	Maps each element in the input tensor to another value.

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]]]