Merge pull request #569 from gizatechxyz/develop

Merge Develop into Main
gizatechxyz · Feb 11, 2024 · 185c1a8 · 185c1a8
2 parents 8ff7389 + 4d4be27
commit 185c1a8
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diff --git a/.all-contributorsrc b/.all-contributorsrc
@@ -269,6 +269,23 @@
       "contributions": [
         "code"
       ]
+    },
+    {
+      "login": "tekkac",
+      "name": "Trunks @ Carbonable",
+      "avatar_url": "https://avatars.githubusercontent.com/u/98529704?v=4",
+      "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"
+      ]
     }
   ],
   "contributorsPerLine": 7,

diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml
@@ -9,5 +9,5 @@ jobs:
       - uses: actions/checkout@v3
       - uses: software-mansion/setup-scarb@v1
         with:
-          scarb-version: "2.4.0"
+          scarb-version: "2.5.3"
       - run: scarb test --workspace && scarb fmt --workspace
diff --git a/.tool-versions b/.tool-versions
@@ -1 +1 @@
-scarb 2.4.0
+scarb 2.5.3
diff --git a/README.md b/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-29-orange.svg?style=flat-square)](#contributors-)
+[![All Contributors](https://img.shields.io/badge/all_contributors-30-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/).
@@ -47,7 +47,7 @@ Join the community and help build a safer and transparent AI in our [Discord](ht
 
 ## 🚀 Orion Usage
 - For an insightful overview of impressive proof of concepts, models, and tutorials created by our community, please visit [Orion Usage](https://github.com/gizatechxyz/orion/blob/main/orion-usage.md).
-- Discover a currated list of tutorials and models developed using Orion in [Orion-Hub](https://github.com/gizatechxyz/Orion-Hub).
+- Discover a curated list of tutorials and models developed using Orion in [Orion-Hub](https://github.com/gizatechxyz/Orion-Hub).
 
 ## ✍️ Authors & contributors
 
@@ -106,6 +106,8 @@ Thanks goes to these wonderful people:
     </tr>
     <tr>
       <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>
     </tr>
   </tbody>
 </table>

diff --git a/Scarb.toml b/Scarb.toml
@@ -1,15 +1,15 @@
 [package]
 name = "orion"
-version = "0.2.1"
-cairo-version = "2.4.0"
+version = "0.2.3"
+cairo-version = "2.5.3"
 edition = "2023_10"
 description = "ONNX Runtime in Cairo for verifiable ML inference using STARK"
 homepage = "https://github.com/gizatechxyz/orion"
 
 [dependencies]
-alexandria_merkle_tree = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "01a7690" }
-alexandria_data_structures = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "01a7690" }
-alexandria_sorting = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "01a7690" }
+alexandria_merkle_tree = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "800f5ad" }
+alexandria_data_structures = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "800f5ad" }
+alexandria_sorting = { git = "https://github.com/keep-starknet-strange/alexandria.git", rev = "800f5ad" }
 cubit = { git = "https://github.com/influenceth/cubit.git", rev = "6275608" }
 
 [scripts]

diff --git a/docgen/src/main.rs b/docgen/src/main.rs
@@ -82,6 +82,14 @@ fn main() {
     let trait_name: &str = "SVMRegressorTrait";
     doc_trait(trait_path, doc_path, label);
     doc_functions(trait_path, doc_path, trait_name, label);
+
+    // SVM CLASSIFIER DOC
+    let trait_path = "src/operators/ml/svm/svm_classifier.cairo";
+    let doc_path = "docs/framework/operators/machine-learning/svm-classifier";
+    let label = "svm_classifier";
+    let trait_name: &str = "SVMClassifierTrait";
+    doc_trait(trait_path, doc_path, label);
+    doc_functions(trait_path, doc_path, trait_name, label);
 }
 
 fn doc_trait(trait_path: &str, doc_path: &str, label: &str) {

diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
@@ -3,7 +3,11 @@
 All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+## [Unreleased] - 2024-01-17
 
+## Added
+- Scatter Nd Operator.
+- 
 ## [Unreleased] - 2023-12-25
 
 ## Added

diff --git a/docs/SUMMARY.md b/docs/SUMMARY.md
@@ -146,7 +146,17 @@
     * [tensor.reduce\_log\_sum](framework/operators/tensor/tensor.reduce\_log\_sum.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)
+    * [tensor.layer_normalization](framework/operators/tensor/tensor.layer_normalization.md)
+    * [tensor.scatter\_nd](framework/operators/tensor/tensor.scatter\_nd.md)
+    * [tensor.dynamic_quantize_linear](framework/operators/tensor/tensor.dequantize_linear.md)
+    * [tensor.optional](framework/operators/tensor/tensor.optional.md)
+    * [tensor.reverse_sequence](framework/operators/tensor/tensor.reverse_sequence.md)
+    * [tensor.split_to_sequence](framework/operators/tensor/tensor.split_to_sequence.md)
+    * [tensor.range](framework/operators/tensor/tensor.range.md)
+    * [tensor.hann_window](framework/operators/tensor/tensor.hann_window.md)
+    * [tensor.hamming_window](framework/operators/tensor/tensor.hamming_window.md)
+    * [tensor.blackman_window](framework/operators/tensor/tensor.blackman_window.md)
+    * [tensor.random_uniform_like](framework/operators/tensor/tensor.random_uniform_like.md)
   * [Neural Network](framework/operators/neural-network/README.md)
     * [nn.relu](framework/operators/neural-network/nn.relu.md)
     * [nn.leaky\_relu](framework/operators/neural-network/nn.leaky\_relu.md)
@@ -160,6 +170,12 @@
     * [nn.hard\_sigmoid](framework/operators/neural-network/nn.hard\_sigmoid.md)
     * [nn.thresholded\_relu](framework/operators/neural-network/nn.thresholded\_relu.md)
     * [nn.gemm](framework/operators/neural-network/nn.gemm.md)
+    * [nn.grid\_sample](framework/operators/neural-network/nn.grid\_sample.md)
+    * [nn.col2im](framework/operators/neural-network/nn.col2im.md)
+    * [nn.conv_transpose](framework/operators/neural-network/nn.conv\_transpose.md)
+    * [nn.conv](framework/operators/neural-network/nn.conv.md)
+    * [nn.depth_to_space](framework/operators/neural-network/nn.depth_to_space.md)
+    * [nn.space_to_depth](framework/operators/neural-network/nn.space_to_depth.md)
   * [Machine Learning](framework/operators/machine-learning/README.md)
     * [Tree Ensemble Classifier](framework/operators/machine-learning/tree-ensemble-classifier/README.md)
       * [tree\_ensemble\_classifier.predict](framework/operators/machine-learning/tree-ensemble-classifier/tree\_ensemble\_classifier.predict.md)
@@ -171,6 +187,8 @@
       * [linear\_regressor.predict](framework/operators/machine-learning/linear-regressor/linear\_regressor.predict.md)
     * [SVM Regressor](framework/operators/machine-learning/svm-regressor/README.md)
       * [svm\_regressor.predict](framework/operators/machine-learning/svm-regressor/svm\_regressor.predict.md)
+    * [SVM Classifier](framework/operators/machine-learning/svm-classifier/README.md)
+      * [svm\_classifier.predict](framework/operators/machine-learning/svm-classifier/svm\_classifier.predict.md)
     * [Sequence](framework/operators/sequence/README.md)
       * [sequence.sequence\_construct](framework/operators/sequence/sequence.sequence\_construct.md)
       * [sequence.sequence\_empty](framework/operators/sequence/sequence.sequence\_empty.md)

diff --git a/docs/academy/tutorials/provable-mlr-forecasting-aaves-lifetime-repayments.md b/docs/academy/tutorials/provable-mlr-forecasting-aaves-lifetime-repayments.md
@@ -481,7 +481,7 @@ fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
                 normalized_array.append(diff / range);
                 i += 1;
             },
-            Option::None(_) => { break; }
+            Option::None => { break; }
         };
     };
     // convert normalized array values to tensor format
@@ -619,7 +619,7 @@ fn add_bias_term(x_feature: Tensor<FP16x16>, axis: u32) -> Tensor<FP16x16> {
                     result.append(*x_val);
                     j += 1;
                 },
-                Option::None(_) => { break; }
+                Option::None => { break; }
             };
         };
         result.append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly  
@@ -855,7 +855,7 @@ fn calculate_r_score(Y_values: Tensor<FP16x16>, Y_pred_values: Tensor<FP16x16>)
                 squared_mean_diff_vals.append(squared_mean_diff);
                 i += 1;
             },
-            Option::None(_) => { break; }
+            Option::None => { break; }
         }
     };
 

diff --git a/docs/academy/tutorials/verifiable-linear-regression-model-in-orion.md b/docs/academy/tutorials/verifiable-linear-regression-model-in-orion.md
@@ -24,7 +24,7 @@ $$
 
 #### Generating the dataset
 
-In the following [notebook](https://github.com/gizatechxyz/orion\_tutorials/tree/main/verifiable\_linear\_regression\_model), we will create a synthetic dataset that will serve as the backbone throughout our tutorial.
+In the following [notebook](https://github.com/gizatechxyz/orion\_tutorials/tree/main/basic/verifiable\_linear\_regression\_model), we will create a synthetic dataset that will serve as the backbone throughout our tutorial.
 
 ```python
 import numpy as np
@@ -152,7 +152,7 @@ test = "scarb cairo-test -f linear_regression_test"
 
 ```
 
-#### Gerating the dataset in Cairo
+#### Generating the dataset in Cairo
 
 Now let’s generate the files required to begin our transition to Cairo. In our Jupyter Notebook, we will execute the code required to turn our synthetic dataset to fixed point values and represent our X and y values as Fixedpoint Tensors in Orion.
 

diff --git a/docs/academy/tutorials/verifiable-principal-components-analysis.md b/docs/academy/tutorials/verifiable-principal-components-analysis.md
@@ -302,7 +302,7 @@ version = "0.1.0"
 orion = { git = "https://github.com/gizatechxyz/orion.git", rev = "v0.1.7" }
 ```
 
-#### Gerating the dataset in Cairo
+#### Generating the dataset in Cairo
 
 Now let's generate the necessary files to begin our transition to Cairo. In our Jupyter Notebook, we will run the necessary code to convert our iris dataset obtained from sklearn.datasets into fixed point values and represent our X, and y values as fixed point tensors in Orion. For the purposes of the tutorial, we will work directly with the Xstd data obtained from python, so we will also convert these to fixed point values.
 
@@ -443,7 +443,7 @@ fn div_by_scalar(self: @Tensor<FP16x16>, divisor: u32) -> Tensor<FP16x16> {
             Option::Some(elem) => {
                 data_array.append(FixedTrait::new(*elem.mag / divisor, *elem.sign));
             },
-            Option::None(_) => {
+            Option::None => {
                 break TensorTrait::<FP16x16>::new((*self).shape, data_array.span());
             }
         };
@@ -457,7 +457,7 @@ fn div_by_fp(self: @Tensor<FP16x16>, divisor: FP16x16) -> Tensor<FP16x16> {
     loop {
         match data.pop_front() {
             Option::Some(elem) => { data_array.append(FP16x16Div::div(*elem, divisor)); },
-            Option::None(_) => {
+            Option::None => {
                 break TensorTrait::<FP16x16>::new((*self).shape, data_array.span());
             }
         };
@@ -618,7 +618,7 @@ fn extract_diagonal(self: @Tensor<FP16x16>) -> Tensor<FP16x16> {
                 Option::Some(elem) => { if x == y {
                     data_array.append(*elem);
                 }; },
-                Option::None(_) => { break; }
+                Option::None => { break; }
             };
             y += 1;
         };
@@ -672,7 +672,7 @@ fn update_eigen_values(
                 y += 1;
                 index += 1;
             },
-            Option::None(_) => { break; }
+            Option::None => { break; }
         };
     };
 

diff --git a/docs/academy/tutorials/verifiable-support-vector-machine.md b/docs/academy/tutorials/verifiable-support-vector-machine.md
@@ -3,7 +3,7 @@
 <figure><img src="../../.gitbook/assets/orion_banner_tutorial_#4.png" alt=""><figcaption></figcaption></figure>
 
 {% hint style="info" %}
-Repository and Notebooks can be found [here](https://github.com/gizatechxyz/orion\_tutorials/tree/main/verifiable\_support\_vector\_machine).
+Repository and Notebooks can be found [here](https://github.com/gizatechxyz/orion_tutorials/tree/main/basic/verifiable_support_vector_machine).
 {% endhint %}
 
 The Support Vector Machines (SVM) model is a supervised learning technique used for classification and regression. It is employed to solve binary classification problems where it identifies the hyperplane that best divides a data set into classes. This hyperplane results from maximizing the margin between the two classes. By determining this optimal hyperplane, predictions can be made for new data points and understand how the input attributes influence classification.
@@ -177,7 +177,7 @@ version = "0.1.0"
 orion = { git = "https://github.com/gizatechxyz/orion.git", rev = "v0.1.0" }
 ```
 
-### Gerating the dataset in Cairo
+### Generating the dataset in Cairo
 
 Now let's generate the necessary files to begin our transition to Cairo. In our Jupyter Notebook, we'll run the necessary code to convert our dataset obtained with make\_blobs from Scikit-learn into fixed-point values and represent our X\_train, y\_train, X\_test, and y\_test values as fixed-point tensors in Orion.
 
@@ -363,7 +363,7 @@ fn accuracy(y: @Tensor<FP16x16>, z: @Tensor<FP16x16>) -> FP16x16 {
                     counter += 1;
                 };
             },
-            Option::None(_) => {
+            Option::None => {
                 break;
             }
         };
@@ -407,7 +407,7 @@ fn less(y: @Tensor<FP16x16>, z: @Tensor<FP16x16>) -> Tensor<FP16x16> {
 
                 smaller_index = (1 + smaller_index) % smaller_data.len();
             },
-            Option::None(_) => {
+            Option::None => {
                 break;
             }
         };
@@ -431,7 +431,7 @@ fn sign(z: @Tensor<FP16x16>) -> Tensor<FP16x16> {
                 };
                 data_result.append(result);
             },
-            Option::None(_) => {
+            Option::None => {
                 break;
             }
         };

diff --git a/docs/framework/compatibility.md b/docs/framework/compatibility.md
@@ -37,12 +37,16 @@ You can see below the list of current supported ONNX Operators:
 |      [ThresholdedRelu](operators/neural-network/nn.thresholded\_relu.md)      | :white\_check\_mark: |
 |               [Sigmoid](operators/neural-network/nn.sigmoid.md)               | :white\_check\_mark: |
 |               [Softmax](operators/neural-network/nn.softmax.md)               | :white\_check\_mark: |
-|           [Softmax_zero](operators/neural-network/nn.softmax_zero.md)         | :white\_check\_mark: |
+|          [Softmax_zero](operators/neural-network/nn.softmax_zero.md)          | :white\_check\_mark: |
 |            [LogSoftmax](operators/neural-network/nn.logsoftmax.md)            | :white\_check\_mark: |
 |              [Softsign](operators/neural-network/nn.softsign.md)              | :white\_check\_mark: |
 |              [Softplus](operators/neural-network/nn.softplus.md)              | :white\_check\_mark: |
 |                [Linear](operators/neural-network/nn.linear.md)                | :white\_check\_mark: |
 |          [HardSigmoid](operators/neural-network/nn.hard\_sigmoid.md)          | :white\_check\_mark: |
+|           [GridSample](operators/neural-network/nn.grid\_sample_.md)          | :white\_check\_mark: |
+|            [Col2im](operators/neural-network/nn.col2im\_sigmoid.md)           | :white\_check\_mark: |
+|       [ConvTranspose](operators/neural-network/nn.conv\_transpose_.md)        | :white\_check\_mark: |
+|                  [Conv](operators/neural-network/nn.conv.md)                  | :white\_check\_mark: |
 |                    [Sinh](operators/tensor/tensor.sinh.md)                    | :white\_check\_mark: |
 |                   [Asinh](operators/tensor/tensor.asinh.md)                   | :white\_check\_mark: |
 |                   [Atanh](operators/tensor/tensor.atanh.md)                   | :white\_check\_mark: |
@@ -103,10 +107,22 @@ You can see below the list of current supported ONNX Operators:
 |                  [IsNaN](operators/tensor/tensor.is\_nan.md)                  | :white\_check\_mark: |
 |                  [IsInf](operators/tensor/tensor.is\_inf.md)                  | :white\_check\_mark: |
 |                     [Not](operators/tensor/tensor.not.md)                     | :white\_check\_mark: |
-|              [GatherND](operators/tensor/tensor.gather/_nd.md)                | :white\_check\_mark: |
-|       [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: |
+|               [GatherND](operators/tensor/tensor.gather/_nd.md)               | :white\_check\_mark: |
+|          [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: |
 |     [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: |
+|         [SpaceToDepth](operators/neural-network/nn.space_to_depth.md)         | :white\_check\_mark: |
+|         [DepthToSpace](operators/neural-network/nn.depth_to_space.md)         | :white\_check\_mark: |
+|                [Optional](operators/tensor/tensor.optional.md)                | :white\_check\_mark: |
+|        [ReverseSequence](operators/tensor/tensor.reverse_sequence.md)         | :white\_check\_mark: |
+|        [SplitToSequence](operators/tensor/tensor.split_to_sequence.md)        | :white\_check\_mark: |
+|                   [Range](operators/tensor/tensor.range.md)                   | :white\_check\_mark: |
+|          [HannWindow](operators/tensor/tensor.tensor.hann_window.md)          | :white\_check\_mark: |
+|       [HammingWindow](operators/tensor/tensor.tensor.hamming_window.md)       | :white\_check\_mark: |
+|      [BlackmanWindow](operators/tensor/tensor.tensor.blackman_window.md)      | :white\_check\_mark: |
+|      [RandomUniformLike](operators/tensor/tensor.tensor.random_uniform_like.md)      | :white\_check\_mark: |
 
-Current Operators support: **97/156 (62%)**
+Current Operators support: **98/156 (62%)**
diff --git a/docs/framework/operators/machine-learning/svm-classifier/README.md b/docs/framework/operators/machine-learning/svm-classifier/README.md
@@ -0,0 +1,23 @@
+# SVM Classifier
+
+`SVMClassifierTrait` provides a trait definition for svm classification problem.
+
+```rust
+use orion::operators::ml::SVMClassifierTrait;
+```
+
+### Data types
+
+Orion supports currently only fixed point data types for `SVMClassifierTrait`.
+
+| Data type            | dtype                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Fixed point (signed) | `SVMClassifierTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
+
+
+***
+
+| function | description |
+| --- | --- |
+| [`svm_classifier.predict`](svm_classifier.predict.md) | Returns the top class for each of N inputs. |
+