small fixes in comments

python/tvm/relay/frontend/pytorch.py

@@ -2345,7 +2345,7 @@ def bidir_gru_cell(
 
     def gru_layers(self, input_data, layer_weights_dicts, bidirectional, dropout_p=0.0):
         """
-        Methods iterates layers for Stacked LSTM
+        Methods iterates layers for Stacked GRU
         """
         layers_num = len(layer_weights_dicts)
         # split input sequence to samples set
@@ -2368,7 +2368,7 @@ def gru_layers(self, input_data, layer_weights_dicts, bidirectional, dropout_p=0
             if dropout_p != 0 and i < layers_num - 1:
                 # for input in input_seqs:
                 #     input = _op.dropout(input, dropout_p)
-                raise NotImplementedError("Dropout for LSTM has not been supported yet!")
+                raise NotImplementedError("Dropout for GRU has not been supported yet!")
 
         return _op.stack(input_seqs, 0), _op.stack(output_hiddens, 0)
 
@@ -2447,7 +2447,7 @@ def gru(self, inputs, input_types):
             names = ["hidden_state", "w_inp", "w_hid", "b_inp", "b_hid"]
             if bidirectional:
                 rsd = len(_weights) % (2 * weights_num)
-                assert rsd == 0, "got an incorrect number of LSTM weights"
+                assert rsd == 0, "got an incorrect number of GRU weights"
                 for i in range(0, len(_weights), 2 * weights_num):
                     fw_tensors = [layers_h[2 * k], *_weights[i : i + 4]]
                     fw_weights_dict = dict(zip(names, fw_tensors))
@@ -2457,7 +2457,7 @@ def gru(self, inputs, input_types):
                     layer_weights_dicts.append([fw_weights_dict, rev_weights_dict])
                     k += 1
             else:
-                assert len(_weights) % weights_num == 0, "got an incorrect number of LSTM weights"
+                assert len(_weights) % weights_num == 0, "got an incorrect number of GRU weights"
                 for i in range(0, len(_weights), weights_num):
                     fw_tensors = [layers_h[k], *_weights[i : i + 4]]
                     fw_weights_dict = dict(zip(names, fw_tensors))
@@ -2467,7 +2467,7 @@ def gru(self, inputs, input_types):
             names = ["hidden_state", "w_inp", "w_hid"]
             if bidirectional:
                 rsd = len(_weights) % (2 * weights_num)
-                assert rsd == 0, "got an incorrect number of LSTM weights"
+                assert rsd == 0, "got an incorrect number of GRU weights"
                 for i in range(0, len(_weights), 2 * weights_num):
                     fw_tensors = [layers_h[2 * k], *_weights[i : i + 2]]
                     fw_weights_dict = dict(zip(names, fw_tensors))
@@ -2477,7 +2477,7 @@ def gru(self, inputs, input_types):
                     layer_weights_dicts.append([fw_weights_dict, rev_weights_dict])
                     k += 1
             else:
-                assert len(_weights) % weights_num == 0, "got an incorrect number of LSTM weights"
+                assert len(_weights) % weights_num == 0, "got an incorrect number of GRU weights"
                 for i in range(0, len(_weights), weights_num):
                     fw_tensors = [layers_h[k], *_weights[i : i + 2]]
                     fw_weights_dict = dict(zip(names, fw_tensors))