LSTM network adapt to cpu target.

mindspore/model_zoo/lstm.py

@@ -17,7 +17,7 @@ import math
 
 import numpy as np
 
-from mindspore import Parameter, Tensor, nn
+from mindspore import Parameter, Tensor, nn, context, ParameterTuple
 from mindspore.common.initializer import initializer
 from mindspore.ops import operations as P
 
@@ -57,6 +57,24 @@ def lstm_default_state(batch_size, hidden_size, num_layers, bidirectional):
     if bidirectional:
         num_directions = 2
 
+    if context.get_context("device_target") == "CPU":
+        h_list = []
+        c_list = []
+        for i in range(num_layers):
+            hi = Parameter(initializer(
+                Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32)),
+                [num_directions, batch_size, hidden_size]
+            ), name='h' + str(i))
+            h_list.append(hi)
+            ci = Parameter(initializer(
+                Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32)),
+                [num_directions, batch_size, hidden_size]
+            ), name='c' + str(i))
+            c_list.append(ci)
+        h = ParameterTuple(tuple(h_list))
+        c = ParameterTuple(tuple(c_list))
+        return h, c
+
     h = Tensor(
         np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
     c = Tensor(