Add fast tanh approximation

NeuralAmpModeler/dsp/dsp.cpp

@@ -12,6 +12,10 @@
 #include "numpy_util.h"
 #include "util.h"
 
+#define tanh_impl_ std::tanh
+//#define tanh_impl_ fast_tanh_
+
+
 constexpr auto _INPUT_BUFFER_SAFETY_FACTOR = 32;
 
 DSP::DSP() { this->_stale_params = true; }
@@ -191,25 +195,38 @@ void sigmoid_(Eigen::MatrixXf &x, const long i_start, const long i_end,
       x(i, j) = 1.0 / (1.0 + expf(-x(i, j)));
 }
 
+inline float fast_tanh_(const float x)
+{
+    const float ax = fabs(x);
+    const float x2 = x * x;
+
+    return(x * (2.45550750702956f + 2.45550750702956f * ax +
+        (0.893229853513558f + 0.821226666969744f * ax) * x2) /
+        (2.44506634652299f + (2.44506634652299f + x2) *
+            fabs(x + 0.814642734961073f * x * ax)));
+}
+
+
 void tanh_(Eigen::MatrixXf &x, const long i_start, const long i_end,
            const long j_start, const long j_end) {
   for (long j = j_start; j < j_end; j++)
     for (long i = i_start; i < i_end; i++)
-      x(i, j) = tanh(x(i, j));
+      x(i, j) = tanh_impl_(x(i, j));
 }
 
 void tanh_(Eigen::MatrixXf &x, const long j_start, const long j_end) {
   tanh_(x, 0, x.rows(), j_start, j_end);
 }
 
 void tanh_(Eigen::MatrixXf& x) {
+
     float* ptr = x.data();
 
     long size = x.rows() * x.cols();
 
     for (long pos = 0; pos < size; pos++)
     {
-        ptr[pos] = tanh(ptr[pos]);
+        ptr[pos] = tanh_impl_(ptr[pos]);
     }
 }