!12933 Fix lstm output data type of Ascend.

From: @liu_xiao_93
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/ccsrc/backend/optimizer/ascend/ir_fission/pack_fission.cc

@@ -60,7 +60,7 @@ AnfNodePtr CreateNewPack(const FuncGraphPtr &func_graph, const CNodePtr &origin_
     }
   }
   new_shape.erase(new_shape.begin() + axis + 1);
-  AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_pack_cnode, 0)}, {output_shape},
+  AnfAlgo::SetOutputInferTypeAndShape({AnfAlgo::GetOutputInferDataType(origin_pack_cnode, 0)}, {new_shape},
                                       new_pack.get());
   return new_pack;
 }

mindspore/nn/layer/lstm.py

@@ -253,11 +253,14 @@ class LSTM(Cell):
             x = self.transpose(x, (1, 0, 2))
         h, c = hx
         if self.is_ascend:
+            x_dtype = F.dtype(x)
+            h_dtype = F.dtype(h)
+            c_dtype = F.dtype(c)
             _check_input_3d(F.shape(h), "h of hx", self.cls_name)
             _check_input_3d(F.shape(c), "c of hx", self.cls_name)
-            _check_input_dtype(F.dtype(x), "x", [mstype.float32, mstype.float16], self.cls_name)
-            _check_input_dtype(F.dtype(h), "h", [mstype.float32, mstype.float16], self.cls_name)
-            _check_input_dtype(F.dtype(c), "c", [mstype.float32, mstype.float16], self.cls_name)
+            _check_input_dtype(x_dtype, "x", [mstype.float32, mstype.float16], self.cls_name)
+            _check_input_dtype(h_dtype, "h", [mstype.float32, mstype.float16], self.cls_name)
+            _check_input_dtype(c_dtype, "c", [mstype.float32, mstype.float16], self.cls_name)
             x = self.cast(x, mstype.float16)
             h = self.cast(h, mstype.float16)
             c = self.cast(c, mstype.float16)
@@ -265,6 +268,9 @@ class LSTM(Cell):
                 x, h, c = self._stacked_bi_dynamic_rnn(x, h, c, self.w_list, self.b_list)
             else:
                 x, h, c = self._stacked_dynamic_rnn(x, h, c, self.w_list, self.b_list)
+            x = self.cast(x, x_dtype)
+            h = self.cast(h, h_dtype)
+            c = self.cast(c, c_dtype)
         else:
             x, h, c, _, _ = self.lstm(x, h, c, self.weight)
         if self.batch_first: