Fix alstm models and the corresponding yaml documents

examples/benchmarks/ALSTM/workflow_config_alstm_Alpha158.yaml

@@ -69,7 +69,7 @@ task:
             loss: mse
             n_jobs: 20
             GPU: 0
-            rnn_type: GRU
+            rnn_type: LSTM
     dataset:
         class: TSDatasetH
         module_path: qlib.data.dataset

examples/benchmarks/ALSTM/workflow_config_alstm_Alpha360.yaml

@@ -60,7 +60,7 @@ task:
             metric: loss
             loss: mse
             GPU: 0
-            rnn_type: GRU
+            rnn_type: LSTM
     dataset:
         class: DatasetH
         module_path: qlib.data.dataset

qlib/contrib/model/pytorch_alstm.py

@@ -52,6 +52,7 @@ def __init__(
         optimizer="adam",
         GPU=0,
         seed=None,
+        rnn_type="GRU",
         **kwargs
     ):
         # Set logger.
@@ -103,6 +104,7 @@ def __init__(
                 self.device,
                 self.use_gpu,
                 seed,
+                self.rnn_type = rnn_type 
             )
         )
 
@@ -115,6 +117,7 @@ def __init__(
             hidden_size=self.hidden_size,
             num_layers=self.num_layers,
             dropout=self.dropout,
+            rnn_type=self.rnn_type,
         )
         self.logger.info("model:\n{:}".format(self.ALSTM_model))
         self.logger.info("model size: {:.4f} MB".format(count_parameters(self.ALSTM_model)))

qlib/contrib/model/pytorch_alstm_ts.py

@@ -56,6 +56,7 @@ def __init__(
         n_jobs=10,
         GPU=0,
         seed=None,
+        rnn_type="GRU",
         **kwargs
     ):
         # Set logger.
@@ -77,6 +78,7 @@ def __init__(
         self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
         self.n_jobs = n_jobs
         self.seed = seed
+        self.rnn_type = rnn_type
 
         self.logger.info(
             "ALSTM parameters setting:"
@@ -122,6 +124,7 @@ def __init__(
             hidden_size=self.hidden_size,
             num_layers=self.num_layers,
             dropout=self.dropout,
+            rnn_type=self.rnn_type,
         )
         self.logger.info("model:\n{:}".format(self.ALSTM_model))
         self.logger.info("model size: {:.4f} MB".format(count_parameters(self.ALSTM_model)))