fix some bug for DynamicGRUV2.

mindspore/ops/operations/nn_ops.py

@@ -6491,7 +6491,7 @@ class DynamicRNN(PrimitiveWithInfer):
 
 class DynamicGRUV2(PrimitiveWithInfer):
     r"""
-    DynamicGRUV2 Operator.
+    Applies a single-layer gated recurrent unit (GRU) to an input sequence.
 
     Args:
         direction (str): A string identifying the direction in the op. Default: 'UNIDIRECTIONAL'.
@@ -6532,19 +6532,19 @@ class DynamicGRUV2(PrimitiveWithInfer):
         - **y** (Tensor) - A Tensor of shape :math:
           if num_proj > 0 `(num_step, batch_size, min(hidden_size, num_proj)`,
           if num_proj == 0 `(num_step, batch_size, hidden_size)`.
-          Has the same data type with input `bais_type`.
+          Has the same data type with input `bias_type`.
         - **output_h** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
-          Has the same data type with input `bais_type`.
+          Has the same data type with input `bias_type`.
         - **update** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
-          Has the same data type with input `bais_type`.
+          Has the same data type with input `bias_type`.
         - **reset** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
-          Has the same data type with input `bais_type`.
+          Has the same data type with input `bias_type`.
         - **new** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
-          Has the same data type with input `bais_type`.
+          Has the same data type with input `bias_type`.
         - **hidden_new** (Tensor) - A Tensor of shape :math:`(\text{num_step}, \text{batch_size}, \text{hidden_size})`.
-          Has the same data type with input `bais_type`.
+          Has the same data type with input `bias_type`.
 
-        - If `bias_input` and `bias_hidden` both are `None`, `bias_type` is float32.
+        - If `bias_input` and `bias_hidden` both are `None`, `bias_type` is date type of `init_h`.
         - If `bias_input` is not `None`, `bias_type` is the date type of `bias_input`.
         - If `bias_input` is `None` and `bias_hidden` is not `None, `bias_type` is the date type of `bias_hidden`.
 
@@ -6563,6 +6563,15 @@ 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,
@@ -6631,7 +6640,7 @@ class DynamicGRUV2(PrimitiveWithInfer):
         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)
-        b_dtype = mstype.float32
+        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)