diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 57b8f84be12..f9db8917f94 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -6519,9 +6519,9 @@ class DynamicGRUV2(PrimitiveWithInfer):
           Tensor of shape :math:`(\text{hidden_size}, 3 \times \text{hidden_size})`.
           The data type must be float16.
         - **bias_input** (Tensor) - Input-hidden bias. Tensor of shape :math:`(3 \times \text{hidden_size})`, or None.
-          The data type must be float16 or float32.
+          Has the same data type with input `init_h`.
         - **bias_hidden** (Tensor) - Hidden-hidden bias. Tensor of shape :math:`(3 \times \text{hidden_size})`, or None.
-          The data type must be float16 or float32.
+          Has the same data type with input `init_h`.
         - **seq_length** (Tensor) - The length of each batch. Tensor of shape :math:`(\text{batch_size})`.
           Only `None` is currently supported.
         - **init_h** (Tensor) - Hidden state of initial time.
@@ -6563,15 +6563,6 @@ class DynamicGRUV2(PrimitiveWithInfer):
         >>> print(output[0].shape)
         (2, 8, 16)
     """
-    __mindspore_signature__ = (
-        sig.make_sig('x', dtype=sig.sig_dtype.T1),
-        sig.make_sig('weight_input', dtype=sig.sig_dtype.T2),
-        sig.make_sig('weight_hidden', dtype=sig.sig_dtype.T3),
-        sig.make_sig('bias_input', dtype=sig.sig_dtype.T),
-        sig.make_sig('bias_hidden', dtype=sig.sig_dtype.T),
-        sig.make_sig('seq_length', dtype=sig.sig_dtype.T4),
-        sig.make_sig('init_h', dtype=sig.sig_dtype.T),
-    )
 
     @prim_attr_register
     def __init__(self,
@@ -6639,15 +6630,16 @@ class DynamicGRUV2(PrimitiveWithInfer):
         validator.check_tensor_dtype_valid("x dtype", x_dtype, [mstype.float16], self.name)
         validator.check_tensor_dtype_valid("weight input dtype", winput_dtype, [mstype.float16], self.name)
         validator.check_tensor_dtype_valid("weight hidden dtype", whidden_dtype, [mstype.float16], self.name)
-        validator.check_tensor_dtype_valid("init_h dtype", h_dtype, (mstype.float16, mstype.float32), self.name)
+        valid_dtypes = [mstype.float16, mstype.float32]
+        validator.check_tensor_dtype_valid("init_h dtype", h_dtype, valid_dtypes, self.name)
         b_dtype = h_dtype
         if binput_dtype is not None:
-            validator.check_tensor_dtype_valid("bias input dtype", binput_dtype,
-                                               (mstype.float16, mstype.float32), self.name)
+            args = {'init_h': h_dtype, 'bias_input': binput_dtype}
+            validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name)
             b_dtype = binput_dtype
         elif bhidden_dtype is not None:
-            validator.check_tensor_dtype_valid("bias hidden dtype", bhidden_dtype,
-                                               (mstype.float16, mstype.float32), self.name)
+            args = {'init_h': h_dtype, 'bias_hidden': bhidden_dtype}
+            validator.check_tensors_dtypes_same_and_valid(args, valid_dtypes, self.name)
             b_dtype = bhidden_dtype
 
         return b_dtype, b_dtype, b_dtype, b_dtype, b_dtype, b_dtype