LSTM Ascend parameter type fp16 change to fp32

model_zoo/official/nlp/lstm/src/lstm.py

@@ -198,15 +198,15 @@ class StackLSTMAscend(nn.Cell):
             # forward weight init
             w_np_fw = np.random.uniform(-stdv,
                                         stdv,
-                                        (input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float16)
+                                        (input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float32)
             w_fw = Parameter(initializer(Tensor(w_np_fw), w_np_fw.shape), name="w_fw_layer" + str(i))
             weights_fw.append(w_fw)
             # forward bias init
             if has_bias:
-                b_fw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float16)
+                b_fw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float32)
                 b_fw = Parameter(initializer(Tensor(b_fw), b_fw.shape), name="b_fw_layer" + str(i))
             else:
-                b_fw = np.zeros((hidden_size * 4)).astype(np.float16)
+                b_fw = np.zeros((hidden_size * 4)).astype(np.float32)
                 b_fw = Parameter(initializer(Tensor(b_fw), b_fw.shape), name="b_fw_layer" + str(i))
             bias_fw.append(b_fw)
 
@@ -214,21 +214,21 @@ class StackLSTMAscend(nn.Cell):
                 # backward weight init
                 w_np_bw = np.random.uniform(-stdv,
                                             stdv,
-                                            (input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float16)
+                                            (input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float32)
                 w_bw = Parameter(initializer(Tensor(w_np_bw), w_np_bw.shape), name="w_bw_layer" + str(i))
                 weights_bw.append(w_bw)
 
                 # backward bias init
                 if has_bias:
-                    b_bw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float16)
+                    b_bw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float32)
                     b_bw = Parameter(initializer(Tensor(b_bw), b_bw.shape), name="b_bw_layer" + str(i))
                 else:
-                    b_bw = np.zeros((hidden_size * 4)).astype(np.float16)
+                    b_bw = np.zeros((hidden_size * 4)).astype(np.float32)
                     b_bw = Parameter(initializer(Tensor(b_bw), b_bw.shape), name="b_bw_layer" + str(i))
                 bias_bw.append(b_bw)
 
         # layer init
-        self.lstm = LSTM_Ascend(bidirectional=bidirectional)
+        self.lstm = LSTM_Ascend(bidirectional=bidirectional).to_float(mstype.float16)
 
         self.weight_fw = ParameterTuple(tuple(weights_fw))
         self.weight_bw = ParameterTuple(tuple(weights_bw))