Fixed mish gradient

src/activation_kernels.cu

@@ -40,7 +40,8 @@ __device__ float gelu_activate_kernel(float x){return (0.5*x*(1 + tanhf(0.797885
 __device__ float softplus_kernel(float x, float threshold = 20) {
     if (x > threshold) return x;                // too large
     else if (x < -threshold) return expf(x);    // too small
-    return logf(expf(x) + 1);
+    return log1pf(expf(x));
+    //return logf(expf(x) + 1);
 }
 __device__ float plse_activate_kernel(float x)
 {
@@ -257,8 +258,8 @@ __global__ void activate_array_mish_kernel(float *x, int n, float *activation_in
         // Pytorch: https://github.com/thomasbrandon/mish-cuda/blob/master/csrc/mish.h#L17-L20
         // TF: https://github.com/tensorflow/addons/blob/093cdfa85d334cbe19a37624c33198f3140109ed/tensorflow_addons/custom_ops/activations/cc/kernels/mish_op.h#L40-L49
         // log1p(x) == log(x + 1)
-        output_gpu[i] = x_val * tanh_activate_kernel( softplus_kernel(x_val, MISH_THRESHOLD) );
-        //output_gpu[i] = mish_yashas(x_val);
+        //output_gpu[i] = x_val * tanh_activate_kernel( softplus_kernel(x_val, MISH_THRESHOLD) );
+        output_gpu[i] = mish_yashas(x_val);
         //output_gpu[i] = mish_njuffa(x_val);
     }
 }
@@ -355,7 +356,8 @@ __global__ void gradient_array_mish_kernel(int n, float *activation_input_gpu, f
         // log1p(x) == log(x + 1)
         const float inp = activation_input_gpu[i];
         const float sp = softplus_kernel(inp, MISH_THRESHOLD);
-        const float grad_sp = 1 - expf(-sp);
+        const float grad_sp = -expm1f(-sp);
+        //const float grad_sp = 1 - expf(-sp);
         const float tsp = tanh(sp);
         const float grad_tsp = (1 - tsp*tsp) * grad_sp;
         const float grad = inp * grad_tsp + tsp;