graph_send_recv

paddle.incubate. graph_send_recv ( x: Tensor, src_index: Tensor, dst_index: Tensor, pool_type: Literal['sum', 'mean', 'max', 'min'] = 'sum', out_size: int | Tensor | None = None, name: str | None = None ) → Tensor [source]

Warning

API “paddle.incubate.operators.graph_send_recv.graph_send_recv” is deprecated since 2.4.0, and will be removed in future versions. Please use “paddle.geometric.send_u_recv” instead. Reason: graph_send_recv in paddle.incubate will be removed in future

Graph Learning Send_Recv combine operator.

This operator is mainly used in Graph Learning domain, and the main purpose is to reduce intermediate memory consumption in the process of message passing. Take x as the input tensor, we first use src_index to gather the corresponding data, and then use dst_index to update the corresponding position of output tensor in different pooling types, like sum, mean, max, or min. Besides, we can set out_size to get necessary output shape.

           Given:

X = [[0, 2, 3],
     [1, 4, 5],
     [2, 6, 7]]

src_index = [0, 1, 2, 0]

dst_index = [1, 2, 1, 0]

pool_type = "sum"

out_size = None

Then:

Out = [[0, 2, 3],
       [2, 8, 10],
       [1, 4, 5]]

Parameters

x (Tensor) – The input tensor, and the available data type is float32, float64, int32, int64.
src_index (Tensor) – An 1-D tensor, and the available data type is int32, int64.
dst_index (Tensor) – An 1-D tensor, and should have the same shape as src_index. The available data type is int32, int64.
pool_type (str) – The pooling types of graph_send_recv, including sum, mean, max, min. Default value is sum.
out_size (int|Tensor|None) – We can set out_size to get necessary output shape. If not set or out_size is smaller or equal to 0, then this input will not be used. Otherwise, out_size should be equal with or larger than max(dst_index) + 1.
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.

Returns

The output tensor, should have the same shape and same dtype as input tensor x.: If out_size is set correctly, then it should have the same shape as x except the 0th dimension.

Return type

out (Tensor)

Examples

           >>> 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") # type: ignore[operator]
>>> 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) # type: ignore[operator]
>>> 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") # type: ignore[operator]
>>> print(out)
Tensor(shape=[3, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , 2. , 3. ],
 [2. , 8. , 10.],
 [0. , 0. , 0. ]])

          