fastgrnncuda: fix gradient return

Author: MJ10

pytorch/edgeml_pytorch/cuda/fastgrnn_cuda_kernel.cu

@@ -397,6 +397,10 @@ std::vector<torch::Tensor> fastgrnn_unroll_cuda_backward(
   auto d_nu = torch::zeros_like(initial_h);
   auto d_bias_z = torch::zeros_like(initial_h);
   auto d_bias_h_prime = torch::zeros_like(initial_h);
+  auto d_w1 = torch::empty(0);
+  auto d_w2 = torch::empty(0);
+  auto d_u1 = torch::empty(0);
+  auto d_u2 = torch::empty(0);
 
   bool w_low_rank = w1.size(0) != 0;
   bool u_low_rank = u1.size(0) != 0;
@@ -501,20 +505,14 @@ std::vector<torch::Tensor> fastgrnn_unroll_cuda_backward(
   d_zeta = (d_zeta.sum(0, true)).sum(1, true);
   d_nu = (d_nu.sum(0, true)).sum(1, true);
   if (w_low_rank) {
-    auto d_w1 = torch::mm(w2.transpose(0, 1), d_w);
-    auto d_w2 = torch::mm(d_w, w1.transpose(0, 1));
+    d_w1 = torch::mm(w2.transpose(0, 1), d_w);
+    d_w2 = torch::mm(d_w, w1.transpose(0, 1));
     d_w = torch::empty(0);
-  } else {
-    auto d_w1 = torch::empty(0);
-    auto d_w2 = torch::empty(0);
   }
   if(u_low_rank) {
-    auto d_u1 = torch::mm(u2.transpose(0, 1), d_u);
-    auto d_u2 = torch::mm(d_u, u1.transpose(0, 1));
+    d_u1 = torch::mm(u2.transpose(0, 1), d_u);
+    d_u2 = torch::mm(d_u, u1.transpose(0, 1));
     d_u = torch::empty(0);
-  } else {
-    auto d_u1 = torch::empty(0);
-    auto d_u2 = torch::empty(0);
   }
   return {d_input, d_bias_z, d_bias_h_prime, d_zeta, d_nu, d_old_h, d_w, d_u, d_w1, d_w2, d_u1, d_u2};
 }