Feature Request

Describe the Feature Request

Implementation of ONNX Operator

Describe Preferred Solution

Implementation following the same Operator interface as defined in ONNX. See Contribution Guidelines

Additional Context

ONNX Flatten Operator

Feature Request

Describe the Feature Request

Implementation of ONNX Operator

Describe Preferred Solution

Implementation following the same Operator interface as defined in ONNX. See Contribution Guidelines

Additional Context

ONNX QLinearMatMul Operator

Feature Request

Describe the Feature Request

Calculates the hyperbolic cosine of the given input tensor element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#CumSum

Feature Request

Describe the Feature Request

Computes the indices of the min elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equals 1. If keepdims equals 0, then the resulting tensor has the reduced dimension pruned. If select_last_index is True (default False), the index of the last occurrence of the min is selected if the min appears more than once in the input. Otherwise the index of the first occurrence is selected. The type of the output tensor is integer.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#ArgMin

Feature Request

Describe the Feature Request

Calculates the hyperbolic arcsine of the given input tensor element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Asinh

Feature Request

Describe the Feature Request

Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Asin

Feature Request

Describe the Feature Request

Tensor contraction over specified indices and outer product.

Here is a full description of Einsum in ONNX:
https://github.com/onnx/onnx/blob/main/docs/Operators.md#einsum

Additional Context

You can take inspiration to EZKL or Julia-lang implementation of Einsum:

• https://github.com/zkonduit/ezkl/blob/bc5abf50a23bc00b95e133d380c5b0926a1528fd/src/tensor/ops.rs#L367
• https://github.com/ahwillia/Einsum.jl

Bug Report

Orion version:

Broken Link at "Get Started Orion", the link https://orion.gizatech.xyz/apis/get-started

Current behavior:

Transfer to a page that is not found

Expected behavior:

Should transfer to the API page

Steps to reproduce:

Related code:

In docs https://github.com/gizatechxyz/orion/blob/main/docs/resources/tutorials/mnist-classification-with-feedforward-neural-network.md

at the bottom of page

Other information:

Feature Request

Describe the Feature Request

Implementation of ONNX Operator

Describe Preferred Solution

Implementation following the same Operator interface as defined in ONNX. See Contribution Guidelines

Additional Context

ONNX ConvInteger Operator

Feature Request

Describe the Feature Request

Calculates the cosine of the given input tensor, element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Cos

Feature Request

Describe the Feature Request

Applies a linear transformation to the incoming tensor.

It should take as parameter:

• the input Tensor
• the weights Tensor
• the bias Tensor

And return the result tensor after linear transformation.

It should panic if:

• input Tensor shape is not 1D.
• weights shape do not match input
• weight shape do not match bias

If the feature request is approved, would you be willing to submit a PR?
(Help can be provided if you need assistance submitting a PR)

• Yes
• No

Feature Request

Describe the Feature Request

Implementation of ONNX Operator

Describe Preferred Solution

Implementation following the same Operator interface as defined in ONNX. See Contribution Guidelines

Additional Context

ONNX Constant Operator

Feature Request

Describe the Feature Request

Ceil takes one input data (Tensor) and produces one output data (Tensor) where the ceil is, y = ceil(x), is applied to the tensor elementwise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Ceil

Feature Request

Describe the Feature Request

Implementation of ONNX Operator

Describe Preferred Solution

Implementation following the same Operator interface as defined in ONNX. See Contribution Guidelines

Additional Context

ONNX MaxPool Operator

Feature Request

Describe the Feature Request

Implementation of ONNX Operator

Describe Preferred Solution

Implementation following the same Operator interface as defined in ONNX. See Contribution Guidelines

Additional Context

ONNX OneHot Operator

Feature Request

Describe the Feature Request

Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#acos

Update the existing examples in the documentation to reflect the change of fixed points from Q5.26 to Q8.23 versions.

Feature Request

Describe the Feature Request

Or returns the tensor resulted from performing the or logical operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).

Full description here:
https://github.com/onnx/onnx/blob/main/docs/Operators.md#Or

If the feature request is approved, would you be willing to submit a PR?

• Yes
• No

Feature Request

Describe the Feature Request

Calculates the hyperbolic cosine of the given input tensor element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Cosh

Feature Request

Describe the Feature Request

Returns the tensor resulted from performing the xor logical operation elementwise on the input tensors A and B (with Numpy-style broadcasting support).

This operator supports multidirectional (i.e., Numpy-style) broadcasting.

Link to the full ONNX description of XOR: https://github.com/onnx/onnx/blob/main/docs/Operators.md#xor

If the feature request is approved, would you be willing to submit a PR?
(Help can be provided if you need assistance submitting a PR)

• Yes
• No

Feature Request

Describe the Feature Request

Add a threshold parameter to the relu_u32 function. Since u32 numbers range from 0 ... 2** 32 -1, without the threshold parameter, relu_u32([original_array]) will always equal [original_array], as if the function was never applied.
Describe Preferred Solution

Added a threshold parameter below which the original values in the array will be mapped to zero

Related Code

relu_u32([original_array],threshold)
Additional Context

If the feature request is approved, would you be willing to submit a PR?
(Help can be provided if you need assistance submitting a PR)

• Yes
• No

Feature Request

Describe the Feature Request

The convolution operator consumes an input tensor and a filter, and computes the output.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Conv

Currently, when creating a fixed point, there is no range checking. But we should check the range for security reasons.

However, when using neural networks, we need to perform wide-ranging intermediate calculations with fixed points. But checking will create an error if the result is out of range.

I propose three approaches, but feel free to share yours if you have a better idea:

Approach 1:

Add unsafe parameter as boolean to all FixedTrait ops functions. When unsafe is true, the range of the new fixed point is not checked.
Interesting, but couldn't benefit from arithmetic operators (+-*/), as there's no way to pass the unsafe parameter.

Approach 2:

We implement wide operations (wide_mul, wide_exp, etc.) that return a tuple of fixed types.

Approach 3:

We implement a check_range function in FixedTrait. By default, calculations don't check the range, but we can easily check the result as follows:

(a + b).check_range()

Feature Request

Describe the Feature Request

The operator computes the log of softmax values for the given input.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#logsoftmax

Feature Request

Describe the Feature Request

Selects slices from an input tensor along a given axis where condition evaluates to True for each axis index. In case axis is not provided, input is flattened before elements are selected. Compress behaves like numpy.compress: https://docs.scipy.org/doc/numpy/reference/generated/numpy.compress.html

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Compress

Feature Request

Describe the Feature Request

Calculates the hyperbolic arctangent of the given input tensor element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Atanh

Feature Request

Describe the Feature Request

Calculates the hyperbolic arccosine of the given input tensor element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#acosh

Bug Report

Orion version:

fa81f4a
Current behavior:

For all signed integers, x < y returns true where x==y
Expected behavior:

x < y should return false where x == y
Steps to reproduce:

Related code:

let x = IntegerTrait::<i32>::new(1,false);
let y = IntegerTrait::<i32>::new(1,false);
assert((x<y) == false);

Other information:

https://github.com/gizatechxyz/onnx-cairo/blob/7b72200dd118c820b2dde3974cc836cee7e10b26/src/numbers/fixed_point/types.cairo#L142

produces error in cairo 1.0, i don't think into is needed

error: Type annotations needed. Failed to infer ?2
 --> types.cairo:142:9
        self.mag.into().print();
        ^*************^

Feature Request

Describe the Feature Request

Implementation of ONNX Operator

Describe Preferred Solution

Implementation following the same Operator interface as defined in ONNX. See Contribution Guidelines

Additional Context

ONNX QLinearConv Operator

Bug Report

Current behavior:
Currently Argmin and Argmax panics with 1D tensor.

    let tensor = i32_tensor_1x3_helper();

    let result = tensor.argmin(0,Option::None(()),Option::None(()));

>>>> panicked with [('shape cannot be empty') ]

Bug Report

ONNX-Cairo version:
commit @7bf1e1127bc638a5af4f27834374568535adb3ce

Current behavior:

There is no range check on the magnitude value being passed to any function creating a fixed type
Example: Fixed::new(value,t|f)does not check whether value is within the range of 0 and 2**31 -1

Affected functions seem to be:

trait Fixed {
    fn new(mag: u128, sign: bool) -> FixedType;
    fn new_unscaled(mag: u128, sign: bool) -> FixedType;
    fn from_felt(val: felt252) -> FixedType;
    fn from_unscaled_felt(val: felt252) -> FixedType;
    ...
}

Expected behavior:

Fixed::new(value, t|f) and similar functions should panic where value>=2**31

Feature Request

Describe the Feature Request

Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.

Full description here: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Atan

Bug Report

Orion version:

e2608c1

Current behavior:
Linear tests not running because of improper import

Expected behavior:
All tests should run including tests in the linear_test.cairo file

Steps to reproduce:
scarb run test

Related code:

Other information: