LSTM output shape and actvations input format were fixed in onnx fron…

…tend

python/tvm/relay/frontend/onnx.py

@@ -2174,9 +2174,7 @@ def bidir_lstm_cell(
         cls,
         input_seqs,
         weight_dicts,
-        f_act,
-        g_act,
-        h_act,
+        acts,
     ):
         """
         Bidirectional LSTM cell
@@ -2185,17 +2183,17 @@ def bidir_lstm_cell(
         forward_outputs, fw_H_t, fw_C_t = _op.lstm_cell(
             input_seqs,
             **weight_dicts[0],
-            f_act=f_act,
-            g_act=g_act,
-            h_act=h_act,
+            f_act=acts[0],
+            g_act=acts[1],
+            h_act=acts[2],
         )
 
         reverse_outputs, rev_H_t, rev_C_t = _op.lstm_cell(
             input_seqs,
             **weight_dicts[1],
-            f_act=f_act,
-            g_act=g_act,
-            h_act=h_act,
+            f_act=acts[3],
+            g_act=acts[4],
+            h_act=acts[5],
             backwards=True,
         )
 
@@ -2267,11 +2265,8 @@ def _impl_v7(cls, inputs, attr, params):
                     beta = betas[beta_loc]
                     beta_loc += 1
                 acts.append(cls._activation_helper(activation, alpha, beta))
-            f_act, g_act, h_act = acts
         else:
-            f_act = _op.sigmoid
-            g_act = _op.tanh
-            h_act = _op.tanh
+            acts = [_op.sigmoid, _op.tanh, _op.tanh] * num_directions
 
         # TODO (vvchernov): It can be replaced by _op.split if issue #8412 is resolved
         X_steps = LSTM.unbind(X, axis=0)
@@ -2318,24 +2313,22 @@ def _impl_v7(cls, inputs, attr, params):
             output, H, C = LSTM.bidir_lstm_cell(
                 input_seqs=X_steps,
                 weight_dicts=weights_dicts,
-                f_act=f_act,
-                g_act=g_act,
-                h_act=h_act,
+                acts=acts,
             )
         else:
             # outputs shape = [seqs_num, (batch_size, hidden_size)]
             outputs, H, C = _op.lstm_cell(
                 input_seqs=X_steps,
                 **weights_dicts[0],
-                f_act=f_act,
-                g_act=g_act,
-                h_act=h_act,
+                f_act=acts[0],
+                g_act=acts[1],
+                h_act=acts[2],
             )
 
             # output shape = (seqs_num, num_directions, batch_size, hidden_size)
             output = _op.expand_dims(_op.stack(outputs, axis=0), axis=1)
-            H = _op.expand_dims(H, axis=1)
-            C = _op.expand_dims(C, axis=1)
+            H = _op.expand_dims(H, axis=0)
+            C = _op.expand_dims(C, axis=0)
 
         return _expr.TupleWrapper(_expr.Tuple((output, H, C)), 3)