Introduce RealtimeSanitizer (RADSan) real-time safety checking (#121)

* Introduce configurable RADSan checks

* Fix test fail output and comment RADSan config

* Re-enable workflow restrictions

* Add RADSan status badge to README

* Add realtime attribute to Layer methods

---------

Co-authored-by: jatinchowdhury18 <[email protected]>

.github/workflows/radsan.yml

@@ -0,0 +1,34 @@
+name: RADSan Real-Time Safety
+
+on:
+  push:
+    branches:
+    - main
+    - develop
+  pull_request:
+    branches:
+    - main
+    - develop
+
+jobs:
+  build_and_test:
+    name: Check real-time safety with RADSan
+    runs-on: ubuntu-latest
+    container: realtimesanitizer/radsan-clang:latest
+    steps:
+    - name: Install CMake and Git
+      run: apt-get update && apt-get install -y cmake git
+
+    - name: Checkout code
+      uses: actions/checkout@v2
+      with:
+        submodules: true
+
+    - name: Configure
+      run: cmake -Bbuild -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DRTNEURAL_ENABLE_RADSAN=ON
+
+    - name: Build
+      run: cmake --build build --config Release --parallel
+
+    - name: Test
+      run: cd build && ctest -C Release --output-on-failure

CMakeLists.txt

@@ -1,6 +1,7 @@
 cmake_minimum_required(VERSION 3.5)
 project(RTNeural VERSION 1.0.0)
 include(cmake/CXXStandard.cmake)
+include(cmake/Sanitizers.cmake)
 
 add_subdirectory(RTNeural)
 

README.md

@@ -9,6 +9,7 @@
 [![Tests](https://github.com/jatinchowdhury18/RTNeural/workflows/Tests/badge.svg)](https://github.com/jatinchowdhury18/RTNeural/actions/workflows/tests.yml)
 [![Bench](https://github.com/jatinchowdhury18/RTNeural/workflows/Bench/badge.svg)](https://github.com/jatinchowdhury18/RTNeural/actions/workflows/bench.yml)
 [![Examples](https://github.com/jatinchowdhury18/RTNeural/actions/workflows/examples.yml/badge.svg)](https://github.com/jatinchowdhury18/RTNeural/actions/workflows/examples.yml)
+[![RADSan](https://github.com/jatinchowdhury18/RTNeural/actions/workflows/radsan.yml/badge.svg)](https://github.com/jatinchowdhury18/RTNeural/actions/workflows/radsan.yml)
 [![codecov](https://codecov.io/gh/jatinchowdhury18/RTNeural/branch/main/graph/badge.svg?token=QBEBVSCQTW)](https://codecov.io/gh/jatinchowdhury18/RTNeural)
 [![arXiv](https://img.shields.io/badge/arXiv-2106.03037-b31b1b.svg)](https://arxiv.org/abs/2106.03037)
 [![License](https://img.shields.io/badge/License-BSD-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

RTNeural/CMakeLists.txt

@@ -51,3 +51,7 @@ target_compile_definitions(RTNeural
     PUBLIC
         RTNEURAL_NAMESPACE=${RTNEURAL_NAMESPACE}
 )
+
+if(RTNEURAL_ENABLE_RADSAN)
+    rtneural_radsan_configure(RTNeural)
+endif()

RTNeural/Model.h

@@ -9,6 +9,7 @@
 #include "batchnorm/batchnorm.tpp"
 #include "batchnorm/batchnorm2d.h"
 #include "batchnorm/batchnorm2d.tpp"
+#include "config.h"
 #include "conv1d/conv1d.h"
 #include "conv1d/conv1d.tpp"
 #include "conv2d/conv2d.h"
@@ -71,14 +72,14 @@ class Model
     }
 
     /** Resets the state of the network layers. */
-    void reset()
+    RTNEURAL_REALTIME void reset()
     {
         for(auto* l : layers)
             l->reset();
     }
 
     /** Performs forward propagation for this model. */
-    inline T forward(const T* input)
+    RTNEURAL_REALTIME inline T forward(const T* input)
     {
         layers[0]->forward(input, outs[0].data());
 
@@ -91,7 +92,7 @@ class Model
     }
 
     /** Returns a pointer to the output of the final layer in the network. */
-    inline const T* getOutputs() const noexcept
+    RTNEURAL_REALTIME inline const T* getOutputs() const noexcept
     {
         return outs.back().data();
     }

RTNeural/ModelT.h

@@ -351,20 +351,20 @@ class ModelT
 
     /** Get a reference to the layer at index `Index`. */
     template <int Index>
-    auto& get() noexcept
+    RTNEURAL_REALTIME auto& get() noexcept
     {
         return std::get<Index>(layers);
     }
 
     /** Get a reference to the layer at index `Index`. */
     template <int Index>
-    const auto& get() const noexcept
+    RTNEURAL_REALTIME const auto& get() const noexcept
     {
         return std::get<Index>(layers);
     }
 
     /** Resets the state of the network layers. */
-    void reset()
+    RTNEURAL_REALTIME void reset()
     {
         modelt_detail::forEachInTuple([&](auto& layer, size_t)
             { layer.reset(); },
@@ -373,7 +373,7 @@ class ModelT
 
     /** Performs forward propagation for this model. */
     template <int N = in_size>
-    inline typename std::enable_if<(N > 1), T>::type
+    RTNEURAL_REALTIME inline typename std::enable_if<(N > 1), T>::type
     forward(const T* input)
     {
 #if RTNEURAL_USE_XSIMD
@@ -400,7 +400,7 @@ class ModelT
 
     /** Performs forward propagation for this model. */
     template <int N = in_size>
-    inline typename std::enable_if<N == 1, T>::type
+    RTNEURAL_REALTIME inline typename std::enable_if<N == 1, T>::type
     forward(const T* input)
     {
 #if RTNEURAL_USE_XSIMD
@@ -426,7 +426,7 @@ class ModelT
     }
 
     /** Returns a pointer to the output of the final layer in the network. */
-    inline const T* getOutputs() const noexcept
+    RTNEURAL_REALTIME inline const T* getOutputs() const noexcept
     {
         return outs;
     }

RTNeural/RTNeural.h

@@ -5,21 +5,8 @@
 // C++ STL includes
 #include <limits>
 
-#ifndef RTNEURAL_NAMESPACE
-#define RTNEURAL_NAMESPACE RTNeural
-#endif
-
-// Handle default RTNeural defines
-#ifndef RTNEURAL_DEFAULT_ALIGNMENT
-#if _MSC_VER
-#pragma message("RTNEURAL_DEFAULT_ALIGNMENT was not defined! Using default alignment = 16.")
-#else
-#warning "RTNEURAL_DEFAULT_ALIGNMENT was not defined! Using default alignment = 16."
-#endif
-#define RTNEURAL_DEFAULT_ALIGNMENT 16
-#endif
-
 // RTNeural includes:
+#include "config.h"
 #include "Model.h"
 #include "ModelT.h"
 #include "model_loader.h"

RTNeural/activation/activation.h

@@ -2,6 +2,7 @@
 #define ACTIVATION_H_INCLUDED
 
 #include "../Layer.h"
+#include "../config.h"
 #include <functional>
 
 namespace RTNEURAL_NAMESPACE
@@ -24,7 +25,7 @@ class Activation : public Layer<T>
     std::string getName() const noexcept override { return name; }
 
     /** Implements the forward propagation step for this layer. */
-    inline void forward(const T* input, T* out) noexcept override
+    RTNEURAL_REALTIME inline void forward(const T* input, T* out) noexcept override
     {
         for(int i = 0; i < Layer<T>::out_size; ++i)
             out[i] = func(input[i]);
@@ -71,7 +72,7 @@ class TanhActivation final : public Activation<T>
     }
 
     /** Performs forward propagation for tanh activation. */
-    inline void forward(const T* input, T* out) noexcept override
+    RTNEURAL_REALTIME inline void forward(const T* input, T* out) noexcept override
     {
         for(int i = 0; i < Layer<T>::out_size; ++i)
             out[i] = MathsProvider::tanh(input[i]);
@@ -94,10 +95,10 @@ class TanhActivationT
     /** Returns true since this layer is an activation layer. */
     constexpr bool isActivation() const noexcept { return true; }
 
-    void reset() { }
+    RTNEURAL_REALTIME void reset() { }
 
     /** Performs forward propagation for tanh activation. */
-    inline void forward(const T (&ins)[size]) noexcept
+    RTNEURAL_REALTIME inline void forward(const T (&ins)[size]) noexcept
     {
         for(int i = 0; i < size; ++i)
             outs[i] = MathsProvider::tanh(ins[i]);
@@ -142,10 +143,10 @@ class ReLuActivationT
     /** Returns true since this layer is an activation layer. */
     constexpr bool isActivation() const noexcept { return true; }
 
-    void reset() { }
+    RTNEURAL_REALTIME void reset() { }
 
     /** Performs forward propagation for ReLU activation. */
-    inline void forward(const T (&ins)[size]) noexcept
+    RTNEURAL_REALTIME inline void forward(const T (&ins)[size]) noexcept
     {
         for(int i = 0; i < size; ++i)
             outs[i] = std::max((T)0, ins[i]);
@@ -190,10 +191,10 @@ class SigmoidActivationT
     /** Returns true since this layer is an activation layer. */
     constexpr bool isActivation() const noexcept { return true; }
 
-    void reset() { }
+    RTNEURAL_REALTIME void reset() { }
 
     /** Performs forward propagation for sigmoid activation. */
-    inline void forward(const T (&ins)[size]) noexcept
+    RTNEURAL_REALTIME inline void forward(const T (&ins)[size]) noexcept
     {
         for(int i = 0; i < size; ++i)
             outs[i] = MathsProvider::sigmoid(ins[i]);
@@ -222,7 +223,7 @@ class SoftmaxActivation final : public Activation<T>
     }
 
     /** Performs forward propagation for softmax activation. */
-    inline void forward(const T* input, T* out) noexcept override
+    RTNEURAL_REALTIME inline void forward(const T* input, T* out) noexcept override
     {
         T exp_sum = 0;
         for(int i = 0; i < Layer<T>::out_size; ++i)
@@ -255,10 +256,10 @@ class SoftmaxActivationT
     /** Returns true since this layer is an activation layer. */
     constexpr bool isActivation() const noexcept { return true; }
 
-    void reset() { }
+    RTNEURAL_REALTIME void reset() { }
 
     /** Performs forward propagation for softmax activation. */
-    inline void forward(const T (&ins)[size]) noexcept
+    RTNEURAL_REALTIME inline void forward(const T (&ins)[size]) noexcept
     {
         T exp_sum = 0;
         for(int i = 0; i < size; ++i)
@@ -297,7 +298,7 @@ class ELuActivation final : public Activation<T>
     }
 
     /** Sets a custom value for the layer's "alpha" parameter. */
-    void set_alpha(T newAlpha) { alpha = newAlpha; }
+    RTNEURAL_REALTIME void set_alpha(T newAlpha) { alpha = newAlpha; }
 
 private:
     T alpha = (T)1;
@@ -319,11 +320,11 @@ class ELuActivationT
     /** Returns true since this layer is an activation layer. */
     constexpr bool isActivation() const noexcept { return true; }
 
-    void reset() { }
+    RTNEURAL_REALTIME void reset() { }
 
     /** Performs forward propagation for elu activation. */
     template <int A_N = AlphaNumerator, int A_D = AlphaDenominator>
-    inline typename std::enable_if<A_N == 1 && A_D == 1, void>::type
+    RTNEURAL_REALTIME inline typename std::enable_if<A_N == 1 && A_D == 1, void>::type
     forward(const T (&ins)[size]) noexcept
     {
         for(int i = 0; i < size; ++i)
@@ -332,7 +333,7 @@ class ELuActivationT
 
     /** Performs forward propagation for elu activation (with custom alpha parameter). */
     template <int A_N = AlphaNumerator, int A_D = AlphaDenominator>
-    inline typename std::enable_if<A_N != 1 || A_D != 1, void>::type
+    RTNEURAL_REALTIME inline typename std::enable_if<A_N != 1 || A_D != 1, void>::type
     forward(const T (&ins)[size]) noexcept
     {
         static constexpr T alpha = (T)AlphaNumerator / (T)AlphaDenominator;
@@ -355,13 +356,13 @@ class PReLUActivation final : public Activation<T>
     }
 
     /** Performs forward propagation for prelu activation. */
-    inline void forward(const T* input, T* out) noexcept override
+    RTNEURAL_REALTIME inline void forward(const T* input, T* out) noexcept override
     {
         for(auto i = 0; i < Layer<T>::in_size; ++i)
             out[i] = input[i] >= (T)0 ? input[i] : (input[i] * alpha[i]);
     }
 
-    void setAlphaVals(const std::vector<T>& alphaVals)
+    RTNEURAL_REALTIME void setAlphaVals(const std::vector<T>& alphaVals)
     {
         if(alphaVals.size() == 1)
         {
@@ -399,16 +400,16 @@ class PReLUActivationT
     /** Returns false since this layer has weights even though it is an activation layer. */
     constexpr bool isActivation() const noexcept { return false; }
 
-    void reset() { }
+    RTNEURAL_REALTIME void reset() { }
 
     /** Performs forward propagation for prelu activation. */
-    inline void forward(const T (&ins)[size]) noexcept
+    RTNEURAL_REALTIME inline void forward(const T (&ins)[size]) noexcept
     {
         for(auto i = 0; i < size; ++i)
             outs[i] = ins[i] >= (T)0 ? ins[i] : (ins[i] * alpha[i]);
     }
 
-    void setAlphaVals(const std::vector<T>& alphaVals)
+    RTNEURAL_REALTIME void setAlphaVals(const std::vector<T>& alphaVals)
     {
         if(alphaVals.size() == 1)
         {