fix dygraph in gru (#4568)

* fix dygraph in gru

* fix dy gru4rec

PaddleRec/gru4rec/dy_graph/gru4rec_dy.py

@@ -247,31 +247,7 @@ def train_ptb_lm():
     model_type = args.model_type
 
     vocab_size = 37484
-    if model_type == "test":
-        num_layers = 1
-        batch_size = 2
-        hidden_size = 10
-        num_steps = 4
-        init_scale = 0.1
-        max_grad_norm = 5.0
-        epoch_start_decay = 1
-        max_epoch = 1
-        dropout = 0.0
-        lr_decay = 0.5
-        base_learning_rate = 1.0
-    elif model_type == "small":
-        num_layers = 2
-        batch_size = 20
-        hidden_size = 200
-        num_steps = 20
-        init_scale = 0.1
-        max_grad_norm = 5.0
-        epoch_start_decay = 4
-        max_epoch = 2
-        dropout = 0.0
-        lr_decay = 0.5
-        base_learning_rate = 1.0
-    elif model_type == "gru4rec":
+    if model_type == "gru4rec":
         num_layers = 1
         batch_size = 500
         hidden_size = 100
@@ -283,30 +259,6 @@ def train_ptb_lm():
         dropout = 0.0
         lr_decay = 0.5
         base_learning_rate = 0.05
-    elif model_type == "medium":
-        num_layers = 2
-        batch_size = 20
-        hidden_size = 650
-        num_steps = 35
-        init_scale = 0.05
-        max_grad_norm = 5.0
-        epoch_start_decay = 6
-        max_epoch = 39
-        dropout = 0.5
-        lr_decay = 0.8
-        base_learning_rate = 1.0
-    elif model_type == "large":
-        num_layers = 2
-        batch_size = 20
-        hidden_size = 1500
-        num_steps = 35
-        init_scale = 0.04
-        max_grad_norm = 10.0
-        epoch_start_decay = 14
-        max_epoch = 55
-        dropout = 0.65
-        lr_decay = 1.0 / 1.15
-        base_learning_rate = 1.0
     else:
         print("model type not support")
         return
@@ -433,7 +385,7 @@ def train_ptb_lm():
                 out_loss = dy_loss.numpy()
                 acc_ = acc.numpy()[0]
 
-                init_hidden = last_hidden
+                init_hidden = last_hidden.detach()
                 dy_loss.backward()
                 sgd.minimize(dy_loss)
                 ptb_model.clear_gradients()