# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from paddle import _C_ops from paddle.fluid.data_feeder import check_variable_and_dtype from paddle.fluid.layer_helper import LayerHelper from paddle.framework import in_dynamic_mode from paddle.utils import deprecated @deprecated( since="2.4.0", update_to="paddle.geometric.reindex_graph", level=1, reason="paddle.incubate.graph_reindex will be removed in future", ) def graph_reindex( x, neighbors, count, value_buffer=None, index_buffer=None, flag_buffer_hashtable=False, name=None, ): """ Graph Reindex API. This API is mainly used in Graph Learning domain, which should be used in conjunction with `graph_sample_neighbors` API. And the main purpose is to reindex the ids information of the input nodes, and return the corresponding graph edges after reindex. Notes: The number in x should be unique, otherwise it would cause potential errors. Besides, we also support multi-edge-types neighbors reindexing. If we have different edge_type neighbors for x, we should concatenate all the neighbors and count of x. We will reindex all the nodes from 0. Take input nodes x = [0, 1, 2] as an example. If we have neighbors = [8, 9, 0, 4, 7, 6, 7], and count = [2, 3, 2], then we know that the neighbors of 0 is [8, 9], the neighbors of 1 is [0, 4, 7], and the neighbors of 2 is [6, 7]. Args: x (Tensor): The input nodes which we sample neighbors for. The available data type is int32, int64. neighbors (Tensor): The neighbors of the input nodes `x`. The data type should be the same with `x`. count (Tensor): The neighbor count of the input nodes `x`. And the data type should be int32. value_buffer (Tensor, optional): Value buffer for hashtable. The data type should be int32, and should be filled with -1. Default is None. index_buffer (Tensor, optional): Index buffer for hashtable. The data type should be int32, and should be filled with -1. Default is None. flag_buffer_hashtable (bool, optional): Whether to use buffer for hashtable to speed up. Default is False. Only useful for gpu version currently. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: - reindex_src (Tensor), The source node index of graph edges after reindex. - reindex_dst (Tensor), The destination node index of graph edges after reindex. - out_nodes (Tensor), The index of unique input nodes and neighbors before reindex, where we put the input nodes `x` in the front, and put neighbor nodes in the back. 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, ... ) >>> 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: if value_buffer is None or index_buffer is None: raise ValueError( "`value_buffer` and `index_buffer` should not" "be None if `flag_buffer_hashtable` is True." ) if in_dynamic_mode(): reindex_src, reindex_dst, out_nodes = _C_ops.reindex_graph( x, neighbors, count, value_buffer, index_buffer, ) return reindex_src, reindex_dst, out_nodes check_variable_and_dtype(x, "X", ("int32", "int64"), "graph_reindex") check_variable_and_dtype( neighbors, "Neighbors", ("int32", "int64"), "graph_reindex" ) check_variable_and_dtype(count, "Count", ("int32"), "graph_reindex") if flag_buffer_hashtable: check_variable_and_dtype( value_buffer, "HashTable_Value", ("int32"), "graph_reindex" ) check_variable_and_dtype( index_buffer, "HashTable_Index", ("int32"), "graph_reindex" ) helper = LayerHelper("graph_reindex", **locals()) reindex_src = helper.create_variable_for_type_inference(dtype=x.dtype) reindex_dst = helper.create_variable_for_type_inference(dtype=x.dtype) out_nodes = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="graph_reindex", inputs={ "X": x, "Neighbors": neighbors, "Count": count, "HashTable_Value": value_buffer if flag_buffer_hashtable else None, "HashTable_Index": index_buffer if flag_buffer_hashtable else None, }, outputs={ "Reindex_Src": reindex_src, "Reindex_Dst": reindex_dst, "Out_Nodes": out_nodes, }, ) return reindex_src, reindex_dst, out_nodes