fix tests

fastNLP/modules/encoder/variational_rnn.py

@@ -6,6 +6,14 @@ from torch.nn.utils.rnn import PackedSequence
 
 from fastNLP.modules.utils import initial_parameter
 
+try:
+    from torch import flip
+except ImportError:
+   def flip(x, dims):
+        indices = [slice(None)] * x.dim()
+        for dim in dims:
+            indices[dim] = torch.arange(x.size(dim) - 1, -1, -1, dtype=torch.long, device=x.device)
+        return x[tuple(indices)]
 
 class VarRnnCellWrapper(nn.Module):
     """Wrapper for normal RNN Cells, make it support variational dropout
@@ -102,13 +110,13 @@ class VarRNNBase(nn.Module):
         for layer in range(self.num_layers):
             output_list = []
             for direction in range(self.num_directions):
-                input_x = input if direction == 0 else input.flip(0)
+                input_x = input if direction == 0 else flip(input, [0])
                 idx = self.num_directions * layer + direction
                 cell = self._all_cells[idx]
                 hi = (hx[0][idx], hx[1][idx]) if is_lstm else hx[idx]
                 mask_xi = mask_x if layer == 0 else mask_out
                 output_x, hidden_x = cell(input_x, hi, mask_xi, mask_h)
-                output_list.append(output_x if direction == 0 else output_x.flip(0))
+                output_list.append(output_x if direction == 0 else flip(output_x, [0]))
                 hidden_list.append(hidden_x)
             input = torch.cat(output_list, dim=-1)