Implementing other backends and tests

jatinchowdhury18 · Nov 13, 2024 · bf557dd · bf557dd
1 parent 395a9cc
commit bf557dd
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Showing 14 changed files with 522 additions and 398 deletions.
diff --git a/RTNeural/ModelT.h b/RTNeural/ModelT.h
@@ -77,8 +77,8 @@ namespace modelt_detail
         json_parser::debug_print("Loading a no-op layer!", debug);
     }
 
-    template <typename T, int in_size, int out_size>
-    void loadLayer(DenseT<T, in_size, out_size>& dense, int& json_stream_idx, const nlohmann::json& l,
+    template <typename T, int in_size, int out_size, bool has_bias>
+    void loadLayer(DenseT<T, in_size, out_size, has_bias>& dense, int& json_stream_idx, const nlohmann::json& l,
         const std::string& type, int layerDims, bool debug)
     {
         using namespace json_parser;

diff --git a/RTNeural/config.h b/RTNeural/config.h
@@ -13,6 +13,19 @@
 #define RTNEURAL_DEFAULT_ALIGNMENT 16
 #endif
 
+#if defined(_MSVC_LANG)
+#define RTNEURAL_CPLUSPLUS _MSVC_LANG
+#elif defined(__cplusplus)
+#define RTNEURAL_CPLUSPLUS __cplusplus
+#endif
+
+#if defined(RTNEURAL_CPLUSPLUS) && RTNEURAL_CPLUSPLUS >= 201703L
+#define RTNEURAL_HAS_CPP17 1
+#define RTNEURAL_IF_CONSTEXPR if constexpr
+#else
+#define RTNEURAL_IF_CONSTEXPR if
+#endif
+
 /**
     Facilitate testing real-time safety with RealtimeSanitizer (RADSan)
 

diff --git a/RTNeural/dense/dense.h b/RTNeural/dense/dense.h
@@ -63,6 +63,8 @@ template <typename T>
 class Dense final : public Layer<T>
 {
 public:
+    static constexpr bool dense_has_bias = true;
+
     /** Constructs a dense layer for a given input and output size. */
     Dense(int in_size, int out_size)
         : Layer<T>(in_size, out_size)
@@ -157,14 +159,15 @@ class Dense final : public Layer<T>
  * Static implementation of a fully-connected (dense) layer,
  * with no activation.
  */
-template <typename T, int in_sizet, int out_sizet>
+template <typename T, int in_sizet, int out_sizet, bool has_bias = true>
 class DenseT
 {
     static constexpr auto weights_size = in_sizet * out_sizet;
 
 public:
     static constexpr auto in_size = in_sizet;
     static constexpr auto out_size = out_sizet;
+    static constexpr bool dense_has_bias = has_bias;
 
     DenseT()
     {
@@ -188,10 +191,19 @@ class DenseT
     RTNEURAL_REALTIME void reset() { }
 
     /** Performs forward propagation for this layer. */
-    RTNEURAL_REALTIME inline void forward(const T (&ins)[in_size]) noexcept
+    template <bool b = has_bias>
+    RTNEURAL_REALTIME inline typename std::enable_if<b>::type forward(const T (&ins)[in_size]) noexcept
+    {
+        for(int i = 0; i < out_size; ++i)
+            outs[i] = std::inner_product(ins, ins + in_size, &weights[i * in_size], bias[i]);
+    }
+
+    /** Performs forward propagation for this layer (no bias). */
+    template <bool b = has_bias>
+    RTNEURAL_REALTIME inline typename std::enable_if<! b>::type forward(const T (&ins)[in_size]) noexcept
     {
         for(int i = 0; i < out_size; ++i)
-            outs[i] = std::inner_product(ins, ins + in_size, &weights[i * in_size], (T)0) + bias[i];
+            outs[i] = std::inner_product(ins, ins + in_size, &weights[i * in_size], (T)0);
     }
 
     /**

diff --git a/RTNeural/dense/dense_eigen.h b/RTNeural/dense/dense_eigen.h
@@ -2,8 +2,8 @@
 #define DENSEEIGEN_H_INCLUDED
 
 #include "../Layer.h"
-#include "../config.h"
 #include "../common.h"
+#include "../config.h"
 #include <Eigen/Dense>
 
 namespace RTNEURAL_NAMESPACE
@@ -17,6 +17,8 @@ template <typename T>
 class Dense : public Layer<T>
 {
 public:
+    static constexpr bool dense_has_bias = true;
+
     /** Constructs a dense layer for a given input and output size. */
     Dense(int in_size, int out_size)
         : Layer<T>(in_size, out_size)
@@ -123,18 +125,23 @@ template <typename T, int in_sizet, int out_sizet, bool has_bias = true>
 class DenseT
 {
     using out_vec_type = Eigen::Matrix<T, out_sizet, 1>;
+#if RTNEURAL_HAS_CPP17
     using in_vec_type = typename std::conditional<has_bias, Eigen::Matrix<T, in_sizet + 1, 1>, Empty>::type;
+#else
+    using in_vec_type = Eigen::Matrix<T, in_sizet + 1, 1>;
+#endif
     using mat_type = Eigen::Matrix<T, out_sizet, in_sizet + (has_bias ? 1 : 0)>;
 
 public:
     static constexpr auto in_size = in_sizet;
     static constexpr auto out_size = out_sizet;
+    static constexpr bool dense_has_bias = has_bias;
 
     DenseT()
         : outs(outs_internal)
     {
         weights = mat_type::Zero();
-        if constexpr (has_bias)
+        RTNEURAL_IF_CONSTEXPR(has_bias)
         {
             ins_internal = in_vec_type::Zero();
             ins_internal(in_size, 0) = (T)1;
@@ -167,7 +174,7 @@ class DenseT
 
     /** Performs forward propagation for this layer (no bias). */
     template <bool b = has_bias>
-    RTNEURAL_REALTIME inline typename std::enable_if<! b>::type forward(const Eigen::Matrix<T, in_size, 1>& ins) noexcept
+    RTNEURAL_REALTIME inline typename std::enable_if<!b>::type forward(const Eigen::Matrix<T, in_size, 1>& ins) noexcept
     {
         outs.noalias() = weights * ins;
     }
@@ -202,7 +209,12 @@ class DenseT
      * Sets the layer bias from a given array of size
      * bias[out_size]
      */
-    RTNEURAL_REALTIME void setBias(const T* b)
+#if RTNEURAL_HAS_CPP17
+    template <bool b = has_bias>
+    RTNEURAL_REALTIME inline typename std::enable_if<b>::type setBias(const T* b)
+#else
+    RTNEURAL_REALTIME inline void setBias(const T* b)
+#endif
     {
         for(int i = 0; i < out_size; ++i)
             weights(i, in_size) = b[i];