Merge branch 'develop' into pr/578

.all-contributorsrc

@@ -277,15 +277,43 @@
       "profile": "https://github.com/tekkac",
       "contributions": [
         "doc"
-      ],
+      ]
     },
+    {
       "login": "canacechan",
       "name": "canacechan",
       "avatar_url": "https://avatars.githubusercontent.com/u/127183619?v=4",
       "profile": "https://github.com/canacechan",
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "TAdev0",
+      "name": "Tristan",
+      "avatar_url": "https://avatars.githubusercontent.com/u/122918260?v=4",
+      "profile": "https://nodeguardians.io/character/98995858fd55 ",
+      "contributions": [
+        "code"
+      ]
+    },
+    {
+      "login": "Gakunt",
+      "name": "Kugo",
+      "avatar_url": "https://avatars.githubusercontent.com/u/153402253?v=4",
+      "profile": "https://github.com/Gakunt",
+      "contributions": [
+        "doc"
+      ]
+    },
+      {
+      "login": "FriendlyLifeguard",
+      "name": "Beeyoung",
+      "avatar_url": "https://avatars.githubusercontent.com/u/55970530?v=4",
+      "profile": "http://alankang.xyz",
+      "contributions": [
+        "code"
+      ]
     }
   ],
   "contributorsPerLine": 7,

README.md

@@ -23,7 +23,7 @@
 
 # Orion: An Open-source Framework for Validity and ZK ML ✨
 <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
-[![All Contributors](https://img.shields.io/badge/all_contributors-30-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-32-orange.svg?style=flat-square)](#contributors-)
 <!-- ALL-CONTRIBUTORS-BADGE:END -->
 
 Orion is an open-source, community-driven framework dedicated to Provable Machine Learning. It provides essential components and a new ONNX runtime for building verifiable Machine Learning models using [STARKs](https://starkware.co/stark/).
@@ -57,7 +57,7 @@ For a full list of all authors and contributors, see [the contributors page](htt
 
 This project is licensed under the **MIT license**.
 
-See [LICENSE](https://github.com/franalgaba/onnx-cairo/blob/main/LICENSE/README.md) for more information.
+See [LICENSE](https://github.com/franalgaba/onnx-cairo/blob/main/LICENSE) for more information.
 
 ## Contributors ✨
 
@@ -108,6 +108,9 @@ Thanks goes to these wonderful people:
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/Vid201"><img src="https://avatars.githubusercontent.com/u/38610409?v=4?s=100" width="100px;" alt="Vid Kersic"/><br /><sub><b>Vid Kersic</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=Vid201" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/tekkac"><img src="https://avatars.githubusercontent.com/u/98529704?v=4?s=100" width="100px;" alt="Trunks @ Carbonable"/><br /><sub><b>Trunks @ Carbonable</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=tekkac" title="Documentation">📖</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/canacechan"><img src="https://avatars.githubusercontent.com/u/127183619?v=4?s=100" width="100px;" alt="canacechan"/><br /><sub><b>canacechan</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=canacechan" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://nodeguardians.io/character/98995858fd55 "><img src="https://avatars.githubusercontent.com/u/122918260?v=4?s=100" width="100px;" alt="Tristan"/><br /><sub><b>Tristan</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=TAdev0" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Gakunt"><img src="https://avatars.githubusercontent.com/u/153402253?v=4?s=100" width="100px;" alt="Kugo"/><br /><sub><b>Kugo</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=Gakunt" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="http://alankang.xyz"><img src="https://avatars.githubusercontent.com/u/55970530?v=4?s=100" width="100px;" alt="Beeyoung"/><br /><sub><b>Beeyoung</b></sub></a><br /><a href="https://github.com/gizatechxyz/orion/commits?author=FriendlyLifeguard" title="Code">💻</a></td>
     </tr>
   </tbody>
 </table>

docs/SUMMARY.md

@@ -144,6 +144,7 @@
     * [tensor.not](framework/operators/tensor/tensor.not.md)
     * [tensor.erf](framework/operators/tensor/tensor.erf.md)
     * [tensor.reduce\_log\_sum](framework/operators/tensor/tensor.reduce\_log\_sum.md)
+    * [tensor.reduce\_log\_sum\_exp](framework/operators/tensor/tensor.reduce\_log\_sum\_exp.md)
     * [tensor.unique](framework/operators/tensor/tensor.unique.md)
     * [tensor.compress](framework/operators/tensor/tensor.compress.md)
     * [tensor.layer_normalization](framework/operators/tensor/tensor.layer_normalization.md)

docs/framework/compatibility.md

@@ -111,6 +111,7 @@ You can see below the list of current supported ONNX Operators:
 |          [ReduceLogSum](operators/tensor/tensor.reduce\_log\_sum.md)          | :white\_check\_mark: |
 |                     [Erf](operators/tensor/tensor.erf.md)                     | :white\_check\_mark: |
 |                [Compress](operators/tensor/tensor.compress.md)                | :white\_check\_mark: |
+|      [ReduceLogSumExp](operators/tensor/tensor.reduce\_log\_sum\_exp.md)      | :white\_check\_mark: |
 |     [Layer_normalization](operators/tensor/tensor.layer_normalization.md)     | :white\_check\_mark: |
 |              [ScatterND](operators/tensor/tensor.scatter/_nd.md)              | :white\_check\_mark: |
 |       [DequantizeLinear](operators/tensor/tensor.dequantize_linear.md)        | :white\_check\_mark: |

docs/framework/numbers/fixed-point/README.md

@@ -69,6 +69,7 @@ use orion::numbers::fixed_point::core::FixedTrait;
 | [`fp.sinh`](fp.sinh.md) | Returns the value of the hyperbolic sine of the fixed point number. |
 | [`fp.tanh`](fp.tanh.md) | Returns the value of the hyperbolic tangent of the fixed point number. |
 | [`fp.sign`](fp.sign.md) | Returns the element-wise indication of the sign of the input fixed point number. |
+| [`fp.erf`](fp.erf.md) | Returns the error function of the input fixed point number computed element-wise. |
 
 ### Arithmetic & Comparison operators
 

docs/framework/operators/machine-learning/linear-classifier/linear_classifier.predict.md

@@ -1,7 +1,7 @@
 # LinearClassifierTrait::predict
 
 ```rust 
-   fn predict(ref self: LinearClassifier<T>, X: Tensor<T>) -> Tensor<T>;
+   fn predict(classifier: LinearClassifier<T>, X: Tensor<T>) -> Tensor<T>;
 ```
 
 Linear Classifier. Performs the linear classification.
@@ -85,7 +85,7 @@ fn linear_classifier_helper(
 fn linear_classifier_multi_softmax() -> (Span<usize>, Tensor<FP16x16>) {
     let (mut classifier, X) = linear_classifier_helper(POST_TRANSFORM::SOFTMAX);
 
-    let (labels, mut scores) = LinearClassifierTrait::predict(ref classifier, X);
+    let (labels, mut scores) = LinearClassifierTrait::predict(classifier, X);
 
     (labels, scores)
 }

docs/framework/operators/machine-learning/linear-regressor/linear_regressor.predict.md

@@ -1,14 +1,14 @@
 # LinearRegressorTrait::predict
 
 ```rust 
-   fn predict(ref self: LinearRegressor<T>, X: Tensor<T>) -> Tensor<T>;
+   fn predict(regressor: LinearRegressor<T>, X: Tensor<T>) -> Tensor<T>;
 ```
 
 Linear Regressor. Performs the generalized linear regression evaluation.
 
 ## Args
 
-* `self`: LinearRegressor<T> - A LinearRegressor object.
+* `regressor`: LinearRegressor<T> - A LinearRegressor object.
 * `X`:  Input 2D tensor.
 
 ## Returns
@@ -68,7 +68,7 @@ fn example_linear_regressor() -> Tensor<FP16x16> {
         post_transform
     };
 
-    let scores = LinearRegressorTrait::predict(ref regressor, X);
+    let scores = LinearRegressorTrait::predict(regressor, X);
 
     scores
 }
@@ -120,7 +120,7 @@ fn example_linear_regressor_2() -> Tensor<FP16x16> {
         post_transform
     };
 
-    let scores = LinearRegressorTrait::predict(ref regressor, X);
+    let scores = LinearRegressorTrait::predict(regressor, X);
 
     scores
 }

docs/framework/operators/machine-learning/tree-ensemble-classifier/tree_ensemble_classifier.predict.md

@@ -1,7 +1,7 @@
 # TreeEnsembleClassifier::predict
 
 ```rust 
-   fn predict(ref self: TreeEnsembleClassifier<T>, X: Tensor<T>) -> (Span<usize>, MutMatrix::<T>);
+   fn predict(classifier: TreeEnsembleClassifier<T>, X: Tensor<T>) -> (Span<usize>, MutMatrix::<T>);
 ```
 
 Tree Ensemble classifier. Returns the top class for each of N inputs.
@@ -185,7 +185,7 @@ fn tree_ensemble_classifier_helper(
 fn test_tree_ensemble_classifier_multi_pt_softmax() -> (Span<usize>, MutMatrix::<FP16x16>) {
     let (mut classifier, X) = tree_ensemble_classifier_helper(POST_TRANSFORM::SOFTMAX);
 
-    let (labels, scores) = TreeEnsembleClassifierTrait::predict(ref classifier, X);
+    let (labels, scores) = TreeEnsembleClassifierTrait::predict(classifier, X);
     (labels, scores)
 }   
 

docs/framework/operators/machine-learning/tree-ensemble-regressor/tree_ensemble_regressor.predict.md

@@ -1,7 +1,7 @@
 # TreeEnsembleRegressor::predict
 
 ```rust 
-   fn predict(ref self: TreeEnsembleRegressor<T>, X: Tensor<T>) -> (Span<usize>, MutMatrix::<T>);
+   fn predict(regressor: TreeEnsembleRegressor<T>, X: Tensor<T>) -> (Span<usize>, MutMatrix::<T>);
 ```
 
 Tree Ensemble regressor. Returns the regressed values for each input in N.
@@ -160,7 +160,7 @@ fn tree_ensemble_regressor_helper(
 
 fn test_tree_ensemble_regressor_SUM() -> MutMatrix::<FP16x16> {
     let (mut regressor, X) = tree_ensemble_regressor_helper(AGGREGATE_FUNCTION::SUM);
-    let mut res = TreeEnsembleRegressorTrait::predict(ref regressor, X);
+    let mut res = TreeEnsembleRegressorTrait::predict(regressor, X);
     res
 }
 >>> 

docs/framework/operators/neural-network/README.md

@@ -37,6 +37,6 @@ Orion supports currently these `NN` types.
 | [`nn.gemm`](nn.gemm.md) | Performs General Matrix multiplication. |
 | [`nn.grid_sample`](nn.grid\_sample.md) | Computes the grid sample of the input tensor and input grid. |
 | [`nn.col2im`](nn.col2im.md) | Rearranges column blocks back into a multidimensional image |
-| [`nn.conv_transpose`](nn.conv\_transpose.md) | Performs the convolution transpose of the input data tensor and weigth tensor. |
-| [`nn.conv`](nn.conv.md) | Performs the convolution of the input data tensor and weigth tensor. |
+| [`nn.conv_transpose`](nn.conv\_transpose.md) | Performs the convolution transpose of the input data tensor and weight tensor. |
+| [`nn.conv`](nn.conv.md) | Performs the convolution of the input data tensor and weight tensor. |
 

docs/framework/operators/neural-network/nn.conv.md

@@ -15,7 +15,7 @@
     ) -> Tensor<T>
 ```
 
-The convolution operator consumes an input tensor and a filter (input weigth tensor), and computes the output.
+The convolution operator consumes an input tensor and a filter (input weight tensor), and computes the output.
 
 ## Args
 

docs/framework/operators/neural-network/nn.conv_transpose.md

@@ -16,7 +16,7 @@
 ) -> Tensor<T>
 ```
 
-The convolution transpose operator consumes an input tensor and a input weigth tensor, and computes the output.
+The convolution transpose operator consumes an input tensor and a input weight tensor, and computes the output.
 
 ## Args
 

docs/framework/operators/tensor/README.md

@@ -118,6 +118,7 @@ use orion::operators::tensor::TensorTrait;
 | [`tensor.gather_nd`](tensor.gather\_nd.md) | 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`](tensor.reduce\_log\_sum.md) | Computes the log sum of the input tensor's elements along the provided axes.  |
 | [`tensor.erf`](tensor.erf.md) | Computes the error function of the given input tensor element-wise. |
+| [`tensor.reduce_log_sum_exp`](tensor.reduce\_log\_sum\_exp.md) | Computes the log sum of the exponentials of the input tensor's elements along the provided axes. |
 | [`tensor.layer_normalization`](tensor.layer\_normalization.md) | computes the layer normalization of the input tensor. |
 | [`tensor.split`](tensor.split.md) | Split a tensor into a list of tensors, along the specified ‘axis’.  |
 | [`tensor.random_uniform_like`](tensor.random\_uniform\_like.md) | RandomUniformLike generates a tensor with random values using a uniform distribution, matching the shape of the input tensor. |

docs/framework/operators/tensor/tensor.min.md

@@ -4,7 +4,7 @@
    fn min(tensors: Span<Tensor<T>>) -> Tensor<T>;
 ```
 
-Returns the element-wise minumum values from a list of input tensors
+Returns the element-wise minimum values from a list of input tensors
 The input tensors must have either:
 * Exactly the same shape
 * The same number of dimensions and the length of each dimension is either a common length or 1.

docs/framework/operators/tensor/tensor.qlinear_add.md

@@ -8,7 +8,7 @@ Performs the sum of quantized Tensors
 
 It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. 
 The quantization formula is y = saturate((x / y_scale) + y_zero_point).
-It perfoms the addition of the two vectors once dequantized, then return the quantization of the result of the addition.
+It performs the addition of the two vectors once dequantized, then return the quantization of the result of the addition.
 The broadcasting is supported
 Scale and zero point must have same shape and the same type. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). 
 Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization.

docs/framework/operators/tensor/tensor.qlinear_matmul.md

@@ -8,7 +8,7 @@ Multiplies quantized Tensors
 
 It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. 
 The quantization formula is y = saturate((x / y_scale) + y_zero_point).
-It perfoms the multiplication of the two vectors once dequantized. If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes.
+It performs the multiplication of the two vectors once dequantized. If either argument is N-D, N > 2, it is treated as a stack of matrices residing in the last two indexes.
 Then return the quantization of the result of the multiplication.
 Scale and zero point must have same shape and the same type. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). 
 Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization.

docs/framework/operators/tensor/tensor.qlinear_mul.md

@@ -8,7 +8,7 @@ Performs the element-wise multiplication of quantized Tensors
 
 It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. 
 The quantization formula is y = saturate((x / y_scale) + y_zero_point).
-It perfoms the element-wise multiplication of the two vectors once dequantized, then return the quantization of the result of the multiplication.
+It performs the element-wise multiplication of the two vectors once dequantized, then return the quantization of the result of the multiplication.
 The broadcasting is supported
 Scale and zero point must have same shape and the same type. They must be either scalar (per tensor) or N-D tensor (per row for 'a' and per column for 'b'). 
 Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization.

docs/framework/operators/tensor/tensor.reduce_log_sum_exp.md

@@ -0,0 +1,60 @@
+## tensor.reduce_log_sum_exp 
+
+```rust 
+   fn reduce_log_sum_exp(self: @Tensor<T>, axis: usize, keepdims: bool) -> Tensor<T>; 
+```
+
+Computes the log sum of the exponentials of the input tensor's elements along the provided axes. 
+
+## Args 
+* 'self'(`@Tensor<T>`) - The input tensor.
+* 'axis'(`usize`) - The dimension to reduce.
+* 'keepdims'(`bool`) - If true, retains reduced dimensions with length 1.
+
+## Panics 
+
+* Panics if axis is not in the range of the input tensor's dimensions.
+
+## Returns 
+
+Returns a new `Tensor<T>` instance with the specified axis reduced by summing its elements.
+
+
+## Example
+
+```rust
+use core::array::{ArrayTrait, SpanTrait};
+use orion::operators::tensor::{TensorTrait, Tensor};
+use orion::operators::tensor::FP32x32Tensor;
+use orion::numbers::{FixedTrait, FP32x32};
+
+fn reduce_log_sum_exp() -> Tensor<FP32x32> {
+    let mut shape = ArrayTrait::<usize>::new();
+    shape.append(3);
+    shape.append(2);
+    shape.append(2);
+
+    let mut data = ArrayTrait::new();
+    data.append(FP32x32 { mag: 4294967296, sign: false });
+    data.append(FP32x32 { mag: 8589934592, sign: false });
+    data.append(FP32x32 { mag: 12884901888, sign: false });
+    data.append(FP32x32 { mag: 17179869184, sign: false });
+    data.append(FP32x32 { mag: 21474836480, sign: false });
+    data.append(FP32x32 { mag: 25769803776, sign: false });
+    data.append(FP32x32 { mag: 30064771072, sign: false });
+    data.append(FP32x32 { mag: 34359738368, sign: false });
+    data.append(FP32x32 { mag: 38654705664, sign: false });
+    data.append(FP32x32 { mag: 42949672960, sign: false });
+    data.append(FP32x32 { mag: 47244640256, sign: false });
+    data.append(FP32x32 { mag: 51539607552, sign: false });
+    TensorTrait::new(shape.span(), data.span())
+
+    let tensor = TensorTrait::<FP32x32>::new(shape.span(), data.span());
+
+    return tensor.reduce_log_sum_exp(axis: 2, keepdims: false);
+
+ }   
+
+
+>>> [[9215828, 16323477, 20115004], [22716772, 24699744, 26302432]]
+``` 

nodegen/helpers.py

@@ -10,7 +10,7 @@
 class FixedImpl(Enum):
     FP8x23 = 'FP8x23'
     FP16x16 = 'FP16x16'
-    FP64x64 = 'FP64x64'
+    FP32x32 = 'FP32x32'
 
 
 
@@ -20,14 +20,15 @@ def to_fp(x: np.ndarray, fp_impl: FixedImpl):
             return (x * 2**23).astype(np.int64)
         case FixedImpl.FP16x16:
             return (x * 2**16).astype(np.int64)
-        case FixedImpl.FP64x64:
-            return (x * 2**64)
+        case FixedImpl.FP32x32:
+            return (x * 2**32).astype(np.int64)
+
 
 
 class Dtype(Enum):
     FP8x23 = 'FP8x23'
     FP16x16 = 'FP16x16'
-    FP64x64 = 'FP64x64'
+    FP32x32 = 'FP32x32'
     I8 = 'i8'
     I32 = 'i32'
     U32 = 'u32'
@@ -173,8 +174,8 @@ def get_data_statement(data: np.ndarray, dtype: Dtype) -> list[str]:
             return ["FP8x23 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
         case Dtype.FP16x16:
             return ["FP16x16 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
-        case Dtype.FP64x64:
-            return ["FP64x64 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
+        case Dtype.FP32x32:
+            return ["FP32x32 { "+f"mag: {abs(int(x))}, sign: {str(x < 0).lower()} "+"}" for x in data.flatten()]
         case Dtype.BOOL:
             return [str(x).lower() for x in data.flatten()]
         case Dtype.COMPLEX64:
@@ -253,6 +254,7 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
     Dtype.FP16x16: ["orion::operators::tensor::{FP16x16Tensor, FP16x16TensorAdd}",],
     Dtype.BOOL: ["orion::operators::tensor::BoolTensor",],
     Dtype.COMPLEX64: ["orion::operators::tensor::Complex64Tensor",],
+    Dtype.FP32x32: ["orion::operators::tensor::FP32x32Tensor",],
 }
 
 
@@ -280,6 +282,7 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
     Dtype.I8: ["orion::operators::tensor::I8TensorPartialEq",],
     Dtype.FP8x23: ["orion::operators::tensor::FP8x23TensorPartialEq",],
     Dtype.FP16x16: ["orion::operators::tensor::FP16x16TensorPartialEq",],
+    Dtype.FP32x32: ["orion::operators::tensor::FP32x32TensorPartialEq",],
     Dtype.BOOL: ["orion::operators::tensor::BoolTensorPartialEq",],
     Dtype.COMPLEX64: ["orion::operators::tensor::Complex64TensorPartialEq",],
 }
@@ -291,6 +294,7 @@ def find_all_types(tensors: list[Tensor | Sequence]) -> list[Dtype]:
     Dtype.I8: ["orion::numbers::NumberTrait"],
     Dtype.FP8x23: ["orion::numbers::{FixedTrait, FP8x23}",],
     Dtype.FP16x16: ["orion::numbers::{FixedTrait, FP16x16}",],
+    Dtype.FP32x32: ["orion::numbers::{FixedTrait, FP32x32}",],
     Dtype.BOOL: [],
     Dtype.COMPLEX64: ["orion::numbers::{NumberTrait, complex64}",],
 }

nodegen/node/reduce_log_sum.py

@@ -1,7 +1,7 @@
 import numpy as np
 from nodegen.node import RunAll
 from ..helpers import  make_test, to_fp, Tensor, Dtype, FixedImpl
-import numpy as np
+
 
 
 class Reduce_log_sum(RunAll):