Using profiler I see that this kernel is in the top important kernels affecting gpu time.
void at::native::elementwise_kernel<512, 1, at::native::gpu_kernel_impl<at::native::add_kernel_cuda(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(float, float)#1}>(at::TensorIterator&, at::native::add_kernel_cuda(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(float, float)#1} const&)::{lambda(int)#2}>(int, at::native::gpu_kernel_impl<at::native::add_kernel_cuda(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(float, float)#1}>(at::TensorIterator&, at::native::add_kernel_cuda(at::TensorIterator&, c10::Scalar)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(float, float)#1} const&)::{lambda(int)#2})

Looking at the code I see,
template<int nt, int vt, typename func_t>
C10_LAUNCH_BOUNDS_2(nt, launch_bound2)
__global__ void elementwise_kernel(int N, func_t f) {
  int tid = threadIdx.x;
  int nv = nt * vt;
  int idx = nv * blockIdx.x + tid;
  #pragma unroll
  for (int i = 0; i < vt; i++) {
    if (idx < N) {
      f(idx);
      idx += nt;
    }
  }
}

So what is important there is the f(). I think it is add_kernel_cuda. Am I right?