[xdoctest] reformat example code with google style in No.286-290 (#56797)

* 8.30下午

# doctest: +REQUIRES(env:GPU)这个不清楚怎么改，还请帮忙看一下

* Update softmax_mask_fuse.py

修改了相关内容

* Update graph_reindex.py

* Update graph_sample_neighbors.py

全都测了一遍，全都是successful

* fix cases on cpu

* fix gpu cases

---------
Co-authored-by: NSigureMo <sigure.qaq@gmail.com>

python/paddle/incubate/operators/graph_reindex.py

@@ -80,33 +80,47 @@ def graph_reindex(
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            x = [0, 1, 2]
-            neighbors_e1 = [8, 9, 0, 4, 7, 6, 7]
-            count_e1 = [2, 3, 2]
-            x = paddle.to_tensor(x, dtype="int64")
-            neighbors_e1 = paddle.to_tensor(neighbors_e1, dtype="int64")
-            count_e1 = paddle.to_tensor(count_e1, dtype="int32")
-
-            reindex_src, reindex_dst, out_nodes = \
-                paddle.incubate.graph_reindex(x, neighbors_e1, count_e1)
-            # reindex_src: [3, 4, 0, 5, 6, 7, 6]
-            # reindex_dst: [0, 0, 1, 1, 1, 2, 2]
-            # out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]
-
-            neighbors_e2 = [0, 2, 3, 5, 1]
-            count_e2 = [1, 3, 1]
-            neighbors_e2 = paddle.to_tensor(neighbors_e2, dtype="int64")
-            count_e2 = paddle.to_tensor(count_e2, dtype="int32")
-
-            neighbors = paddle.concat([neighbors_e1, neighbors_e2])
-            count = paddle.concat([count_e1, count_e2])
-            reindex_src, reindex_dst, out_nodes = \
-                paddle.incubate.graph_reindex(x, neighbors, count)
-            # reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1]
-            # reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2]
-            # out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5]
+            >>> import paddle
+
+            >>> x = [0, 1, 2]
+            >>> neighbors_e1 = [8, 9, 0, 4, 7, 6, 7]
+            >>> count_e1 = [2, 3, 2]
+            >>> x = paddle.to_tensor(x, dtype="int64")
+            >>> neighbors_e1 = paddle.to_tensor(neighbors_e1, dtype="int64")
+            >>> count_e1 = paddle.to_tensor(count_e1, dtype="int32")
+
+            >>> reindex_src, reindex_dst, out_nodes = paddle.incubate.graph_reindex(
+            ...     x,
+            ...     neighbors_e1,
+            ...     count_e1,
+            ... )
+            >>> print(reindex_src)
+            Tensor(shape=[7], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [3, 4, 0, 5, 6, 7, 6])
+            >>> print(reindex_dst)
+            Tensor(shape=[7], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [0, 0, 1, 1, 1, 2, 2])
+            >>> print(out_nodes)
+            Tensor(shape=[8], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [0, 1, 2, 8, 9, 4, 7, 6])
+
+            >>> neighbors_e2 = [0, 2, 3, 5, 1]
+            >>> count_e2 = [1, 3, 1]
+            >>> neighbors_e2 = paddle.to_tensor(neighbors_e2, dtype="int64")
+            >>> count_e2 = paddle.to_tensor(count_e2, dtype="int32")
+
+            >>> neighbors = paddle.concat([neighbors_e1, neighbors_e2])
+            >>> count = paddle.concat([count_e1, count_e2])
+            >>> reindex_src, reindex_dst, out_nodes = paddle.incubate.graph_reindex(x, neighbors, count)
+            >>> print(reindex_src)
+            Tensor(shape=[12], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1])
+            >>> print(reindex_dst)
+            Tensor(shape=[12], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2])
+            >>> print(out_nodes)
+            Tensor(shape=[10], dtype=int64, place=Place(cpu), stop_gradient=True,
+            [0, 1, 2, 8, 9, 4, 7, 6, 3, 5])
 
     """
     if flag_buffer_hashtable:

python/paddle/incubate/operators/graph_sample_neighbors.py

@@ -80,19 +80,22 @@ def graph_sample_neighbors(
     Examples:
         .. code-block:: python
 
-            import paddle
-            # edges: (3, 0), (7, 0), (0, 1), (9, 1), (1, 2), (4, 3), (2, 4),
-            #        (9, 5), (3, 5), (9, 6), (1, 6), (9, 8), (7, 8)
-            row = [3, 7, 0, 9, 1, 4, 2, 9, 3, 9, 1, 9, 7]
-            colptr = [0, 2, 4, 5, 6, 7, 9, 11, 11, 13, 13]
-            nodes = [0, 8, 1, 2]
-            sample_size = 2
-            row = paddle.to_tensor(row, dtype="int64")
-            colptr = paddle.to_tensor(colptr, dtype="int64")
-            nodes = paddle.to_tensor(nodes, dtype="int64")
-            out_neighbors, out_count = \
-                paddle.incubate.graph_sample_neighbors(row, colptr, nodes,
-                                                    sample_size=sample_size)
+            >>> import paddle
+            >>> # edges: (3, 0), (7, 0), (0, 1), (9, 1), (1, 2), (4, 3), (2, 4),
+            >>> #        (9, 5), (3, 5), (9, 6), (1, 6), (9, 8), (7, 8)
+            >>> row = [3, 7, 0, 9, 1, 4, 2, 9, 3, 9, 1, 9, 7]
+            >>> colptr = [0, 2, 4, 5, 6, 7, 9, 11, 11, 13, 13]
+            >>> nodes = [0, 8, 1, 2]
+            >>> sample_size = 2
+            >>> row = paddle.to_tensor(row, dtype="int64")
+            >>> colptr = paddle.to_tensor(colptr, dtype="int64")
+            >>> nodes = paddle.to_tensor(nodes, dtype="int64")
+            >>> out_neighbors, out_count = paddle.incubate.graph_sample_neighbors(
+            ...     row,
+            ...     colptr,
+            ...     nodes,
+            ...     sample_size=sample_size
+            ... )
 
     """
 

python/paddle/incubate/operators/graph_send_recv.py

@@ -91,30 +91,40 @@ def graph_send_recv(
 
         .. code-block:: python
 
-            import paddle
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
-            src_index = indexes[:, 0]
-            dst_index = indexes[:, 1]
-            out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum")
-            # Outputs: [[0., 2., 3.], [2., 8., 10.], [1., 4., 5.]]
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
-            src_index = indexes[:, 0]
-            dst_index = indexes[:, 1]
-            out_size = paddle.max(dst_index) + 1
-            out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum", out_size=out_size)
-            # Outputs: [[0., 2., 3.], [[2., 8., 10.]]]
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
-            src_index = indexes[:, 0]
-            dst_index = indexes[:, 1]
-            out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum")
-            # Outputs: [[0., 2., 3.], [2., 8., 10.], [0., 0., 0.]]
-
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index = indexes[:, 0]
+            >>> dst_index = indexes[:, 1]
+            >>> out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum")
+            >>> print(out)
+            Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[0. , 2. , 3. ],
+             [2. , 8. , 10.],
+             [1. , 4. , 5. ]])
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index = indexes[:, 0]
+            >>> dst_index = indexes[:, 1]
+            >>> out_size = paddle.max(dst_index) + 1
+            >>> out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum", out_size=out_size)
+            >>> print(out)
+            Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[0. , 2. , 3. ],
+             [2. , 8. , 10.]])
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index = indexes[:, 0]
+            >>> dst_index = indexes[:, 1]
+            >>> out = paddle.incubate.graph_send_recv(x, src_index, dst_index, pool_type="sum")
+            >>> print(out)
+            Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[0. , 2. , 3. ],
+             [2. , 8. , 10.],
+             [0. , 0. , 0. ]])
     """
 
     if pool_type not in ["sum", "mean", "max", "min"]:

python/paddle/incubate/operators/softmax_mask_fuse.py

@@ -40,20 +40,21 @@ def softmax_mask_fuse(x, mask, name=None):
                               For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
-        4-D Tensor. A location into which the result is stored. It’s dimension is 4D. Has same shape with x.
+        4-D Tensor. A location into which the result is stored. It's dimension is 4D. Has same shape with x.
 
     Examples:
         .. code-block:: python
 
-            # required: gpu
-            import paddle
-            import paddle.incubate as incubate
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> import paddle
+            >>> import paddle.incubate as incubate
 
-            x = paddle.rand([2, 8, 8, 32])
-            mask = paddle.rand([2, 1, 8, 32])
+            >>> x = paddle.rand([2, 8, 8, 32])
+            >>> mask = paddle.rand([2, 1, 8, 32])
 
-            rst = incubate.softmax_mask_fuse(x, mask)
-            # [[[[0.02404429, 0.04658398, 0.02746007, ..., 0.01489375, 0.02397441, 0.02851614] ... ]]]
+            >>> rst = incubate.softmax_mask_fuse(x, mask)
+            >>> rst.shape
+            [2, 8, 8, 32]
     """
     if in_dynamic_mode():
         out = _legacy_C_ops.fused_softmax_mask(x, mask)

python/paddle/incubate/operators/softmax_mask_fuse_upper_triangle.py

@@ -38,22 +38,35 @@ def softmax_mask_fuse_upper_triangle(x):
                         The third dimension of x must be same with the fourth dimension of x.
 
     Returns:
-        4-D Tensor. A location into which the result is stored. It’s dimension is 4D. Has same dimension with x.
+        4-D Tensor. A location into which the result is stored. It's dimension is 4D. Has same dimension with x.
 
     Examples:
         .. code-block:: python
 
-            # required: gpu
-            import paddle
-            import paddle.incubate as incubate
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> import paddle
+            >>> import paddle.incubate as incubate
 
-            x = paddle.rand((1, 1, 32, 32))
+            >>> paddle.seed(1)
+            >>> paddle.set_device("gpu")
+            >>> x = paddle.rand((1, 1, 32, 32))
 
-            rst = incubate.softmax_mask_fuse_upper_triangle(x)
-            # [[[[1.        , 0.        , 0.        , ..., 0., 0., 0.],
-            #    [0.45324376, 0.54675621, 0.        , ..., 0., 0., 0.],
-            #    [0.32674268, 0.28156221, 0.39169508, ..., 0., 0., 0.]
-            #     ... ]]]
+            >>> rst = incubate.softmax_mask_fuse_upper_triangle(x)
+            >>> print(rst)
+            Tensor(shape=[1, 1, 32, 32], dtype=float32, place=Place(gpu:0), stop_gradient=True,
+            [[[[1.        , 0.        , 0.        , ..., 0.        ,
+                0.        , 0.        ],
+               [0.49575609, 0.50424391, 0.        , ..., 0.        ,
+                0.        , 0.        ],
+               [0.26035303, 0.25114325, 0.48850375, ..., 0.        ,
+                0.        , 0.        ],
+                ...,
+               [0.04379999, 0.04194880, 0.05150032, ..., 0.02721255,
+                0.        , 0.        ],
+               [0.02348574, 0.01959674, 0.02609110, ..., 0.04046615,
+                0.02248267, 0.        ],
+               [0.02280738, 0.03144657, 0.02892209, ..., 0.03885521,
+                0.03342311, 0.02842640]]]])
     """
     if in_dynamic_mode():
         out = _legacy_C_ops.fused_softmax_mask_upper_triangle(x)