[xdoctest][task 313] reformat example code with google style in pytho…

…n/paddle/sparse/nn/functional/transformer.py (PaddlePaddle#57131)

* [Doctest]fix No.313, test=docs_preview

* cast to float32

---------

Co-authored-by: SigureMo <[email protected]>

python/paddle/sparse/nn/functional/transformer.py

@@ -46,50 +46,50 @@ def attention(
     ``d`` represents ``head_dim`` .
 
     Args:
-        query(DenseTensor): `query` in the Attention module. 4D Tensor with float32 or float64.
-        key(DenseTensor): `key` in the Attention module. 4D Tensor with float32 or float64.
-        value(DenseTensor): `value` in the Attention module. 4D Tensor with float32 or float64.
-        sparse_mask(SparseCsrTensor): The sparse layout in the Attention module. Its dense shape
-            is `[batch_size*num_heads, seq_len, seq_len]` .  `nnz` of each batch must be the same.
+        query (DenseTensor): `query` in the Attention module. 4D Tensor with float32 or float64.
+        key (DenseTensor): `key` in the Attention module. 4D Tensor with float32 or float64.
+        value (DenseTensor): `value` in the Attention module. 4D Tensor with float32 or float64.
+        sparse_mask (SparseCsrTensor): The sparse layout in the Attention module. Its dense shape
+            is `[batch_size*num_heads, seq_len, seq_len]`. `nnz` of each batch must be the same.
             dtype of `crows` and `cols` must be int64, dtype of `values` can be float32 or float64.
-        key_padding_mask(DenseTensor, optional): The key padding mask tensor in the Attention module.
+        key_padding_mask (DenseTensor, optional): The key padding mask tensor in the Attention module.
             2D tensor with shape: [batch_size, seq_len]. dtype can be float32 or float64. Default: None.
-        attn_mask(DenseTensor, optional): The attention mask tensor in the Attention module.
+        attn_mask (DenseTensor, optional): The attention mask tensor in the Attention module.
             2D tensor with shape: [seq_len, seq_len]. dtype can be float32 or float64. Default: None.
-        name(str, optional): The default value is None. Normally there is no need for user
-                        to set this property. For more information, please refer to
-                        :ref:`api_guide_Name`.
+        name (str, optional): The default value is None. Normally there is no need for user
+            to set this property. For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
         4D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. dtype is same with input.
 
     Examples:
         .. code-block:: python
 
-            # required: gpu
-            import paddle
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> import paddle
+            >>> paddle.device.set_device('gpu')
 
-            batch_size = 16
-            num_heads = 16
-            seq_len = 512
-            head_dim = 32
+            >>> batch_size = 16
+            >>> num_heads = 16
+            >>> seq_len = 512
+            >>> head_dim = 32
 
-            query = paddle.rand([batch_size, num_heads, seq_len, head_dim])
-            key = paddle.rand([batch_size, num_heads, seq_len, head_dim])
-            value = paddle.rand([batch_size, num_heads, seq_len, head_dim])
+            >>> query = paddle.rand([batch_size, num_heads, seq_len, head_dim])
+            >>> key = paddle.rand([batch_size, num_heads, seq_len, head_dim])
+            >>> value = paddle.rand([batch_size, num_heads, seq_len, head_dim])
 
-            query.stop_gradient = False
-            key.stop_gradient = False
-            value.stop_gradient = False
+            >>> query.stop_gradient = False
+            >>> key.stop_gradient = False
+            >>> value.stop_gradient = False
 
-            mask = paddle.nn.functional.dropout(paddle.ones([seq_len, seq_len])).expand([batch_size, num_heads, seq_len, seq_len])
-            sp_mask = mask.reshape([-1, seq_len, seq_len]).to_sparse_csr()
+            >>> mask = paddle.nn.functional.dropout(paddle.ones([seq_len, seq_len])).expand([batch_size, num_heads, seq_len, seq_len])
+            >>> sp_mask = mask.reshape([-1, seq_len, seq_len]).to_sparse_csr()
 
-            kp_mask = paddle.randint(0, 2, [batch_size, seq_len])
-            attn_mask = paddle.randint(0, 2, [seq_len, seq_len])
+            >>> kp_mask = paddle.randint(0, 2, [batch_size, seq_len]).astype(paddle.float32)
+            >>> attn_mask = paddle.randint(0, 2, [seq_len, seq_len]).astype(paddle.float32)
 
-            output = paddle.sparse.nn.functional.attention(query, key, value, sp_mask, kp_mask, attn_mask)
-            output.backward()
+            >>> output = paddle.sparse.nn.functional.attention(query, key, value, sp_mask, kp_mask, attn_mask)
+            >>> output.backward()
     """
     return _C_ops.sparse_fused_attention(
         query, key, value, sparse_mask, key_padding_mask, attn_mask