diff --git a/python/paddle/incubate/nn/functional/fused_transformer.py b/python/paddle/incubate/nn/functional/fused_transformer.py
index d8dfdb02425e751cd86cbb58d7b1240f2666fa29..01d2161b22342d9c1f2fd70c814f0982fce74b4a 100644
--- a/python/paddle/incubate/nn/functional/fused_transformer.py
+++ b/python/paddle/incubate/nn/functional/fused_transformer.py
@@ -526,7 +526,7 @@ def fused_multi_head_attention(
     Parameters:
         x (Tensor): The input tensor of fused_multi_head_attention. The shape is
             `[batch\_size, sequence\_len, embed\_dim]`.
-        qkv_weight (Tensor): The qkv weight tensor. The shape is `[3, num_head, dim_head, dim_embed]`.
+        qkv_weight (Tensor): The qkv weight tensor. If `transpose_qkv_wb` is False, the shape is `[3, num_head, dim_head, dim_embed]`. Otherwise, the shape is `[dim_embed, 3 * dim_embed]`.
         linear_weight (Tensor): The linear weight tensor. The shape is `[embed_dim, embed_dim]`.
         pre_layer_norm (bool, optional): whether it is pre_layer_norm (True) or post_layer_norm architecture
                                         (False). Default False.
@@ -536,7 +536,7 @@ def fused_multi_head_attention(
         ln_bias (Tensor, optional): The bias tensor of layernorm. Default None.
         pre_ln_epsilon (float, optional): Small float value added to denominator of the pre layer_norm
             to avoid dividing by zero. Default is 1e-5.
-        qkv_bias (Tensor, optional): The bias of qkv computation. The shape is `[3, num_head, dim_head]`.
+        qkv_bias (Tensor, optional): The bias of qkv computation. If `transpose_qkv_wb` is False, the shape is `[3, num_head, dim_head]`. Otherwise, the shape is `[3 * dim_embed]`.
             Default None.
         linear_bias (Tensor, optional): The bias of linear. The shape is `[embed_dim]`. Default None.
         cache_kv (Tensor, optional): For generation model, cache structure. The shape is `[2, bsz, num_head, seq_len, head_dim]`. Default None.
@@ -571,7 +571,7 @@ def fused_multi_head_attention(
         add_residual (bool, optional): Whether add residual at the end. Default is True.
         num_heads (int, optional): If enable transpose_qkv_wb, should provide the num_heads. Default is -1, means not transpose qkv wb.
         transpose_qkv_wb (bool, optional): Whether transpose the qkv_weight and qkv_bias in the op. Only support GPU for now. Default is false, means not transpose qkv wb.
-        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
 
     Returns:
         Tensor: The output Tensor, the data type and shape is same as `x`.
diff --git a/python/paddle/incubate/nn/layer/fused_transformer.py b/python/paddle/incubate/nn/layer/fused_transformer.py
index b49cc50086c324ee5ff1b6388da04a10e14a8146..d1305b674fd0eca37a509beedeb10ec0746288bd 100644
--- a/python/paddle/incubate/nn/layer/fused_transformer.py
+++ b/python/paddle/incubate/nn/layer/fused_transformer.py
@@ -246,12 +246,13 @@ class FusedMultiHeadAttention(Layer):
         epsilon (float, optional): The small value added to the variance to prevent
             division by zero. Default: 1e-05.
         nranks (int, optional): Distributed tensor model parallel nranks. Default is 1, means not using tensor parallel.
+        ring_id (int, optional): For distributed tensor model parallel. Default is -1, means not using tensor parallel.
         transpose_qkv_wb (bool, optional): Support input qkv matmul weight shape as
             [hidden_size, 3 * hidden_size] and qkv matmul bias shape as [3 * hidden_size].
             Will transpose the weight to [3, num_head, head_dim, hidden_size] and transpose bias to
             [3, num_head, hidden_size] in the fused_attention_op. Only support for GPU for now.
             The default value is False, which is not do transpose to qkv_w and qkv_b.
-        ring_id (int, optional): For distributed tensor model parallel. Default is -1, means not using tensor parallel.
+        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
 
     Examples: