zj0ddlmZddlmZddlZddlmZddlmZddlm Z ddl m Z ddl m Z er dd lmZdd lmZgZ dddZ dddZdS)) annotations) TYPE_CHECKINGN)_C_ops)check_variable_and_dtype)Variable) LayerHelper)in_dynamic_or_pir_mode)Sequence)Tensorxr neighborscount value_buffer Tensor | None index_buffername str | Nonereturntuple[Tensor, Tensor, Tensor]c X||dnd}tr!tj|||||\}}} ||| fSt|dddt|dddt|dd d|r$t|d d dt|d d dt dit } | |j }| |j }| |j } | d||||r|nd|r|ndd||| d||| fS)a Reindex Graph API. This API is mainly used in Graph Learning domain, which should be used in conjunction with `paddle.geometric.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. 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]. Then after graph_reindex, we will have 3 different outputs: reindex_src: [3, 4, 0, 5, 6, 7, 6], reindex_dst: [0, 0, 1, 1, 1, 2, 2] and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]. We can see that the numbers in `reindex_src` and `reindex_dst` is the corresponding index of nodes in `out_nodes`. Note: The number in x should be unique, otherwise it would cause potential errors. We will reindex all the nodes from 0. 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. Only useful for gpu version. Default is None. index_buffer (Tensor, optional): Index buffer for hashtable. The data type should be int32, and should be filled with -1. Only useful for gpu version. `value_buffer` and `index_buffer` should be both not None if you want to speed up by using hashtable buffer. Default is None. 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 = [8, 9, 0, 4, 7, 6, 7] >>> count = [2, 3, 2] >>> x = paddle.to_tensor(x, dtype="int64") >>> neighbors = paddle.to_tensor(neighbors, dtype="int64") >>> count = paddle.to_tensor(count, dtype="int32") >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_graph(x, neighbors, count) >>> print(reindex_src.numpy()) [3 4 0 5 6 7 6] >>> print(reindex_dst.numpy()) [0 0 1 1 1 2 2] >>> print(out_nodes.numpy()) [0 1 2 8 9 4 7 6] NTFXint32int64 graph_reindex NeighborsCountrHashTable_ValueHashTable_Index reindex_graphdtyperrrrr Reindex_Src Reindex_Dst Out_Nodestypeinputsoutputs)r ) r rr rrlocals"create_variable_for_type_inferencer" append_op r r rrrruse_buffer_hashtable reindex_src reindex_dst out_nodeshelpers h/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/geometric/reindex.pyr r "sL(\-E53.4.B     / / + [)K22Q%7III; 2OUGgHHH  +g    ! +g    5 5FHH 5 5F;;!';JJK;;!';JJK999HHI  "/CM||/CM||   '&"    Y ..Sequence[Tensor]c ||dnd}trMtj|d}tj|d}tj|||||\}}} ||| fSt |t r|g}t |t r|g}tj|d}tj|d}t|dddt|d dd t|d d d |r$t|d d d t|dd d tdit} | |j }| |j }| |j } tj|d}tj|d}| d ||||r|nd|r|ndd||| d||| fS)a5 Reindex HeterGraph API. This API is mainly used in Graph Learning domain, which should be used in conjunction with `paddle.geometric.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. Take input nodes x = [0, 1, 2] as an example. For graph A, suppose 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]. For graph B, suppose we have neighbors = [0, 2, 3, 5, 1], and count = [1, 3, 1], then we know that the neighbors of 0 is [0], the neighbors of 1 is [2, 3, 5], and the neighbors of 3 is [1]. We will get following outputs: 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] and out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5]. Note: The number in x should be unique, otherwise it would cause potential errors. We support multi-edge-types neighbors reindexing in reindex_heter_graph api. We will reindex all the nodes from 0. Args: x (Tensor): The input nodes which we sample neighbors for. The available data type is int32, int64. neighbors (list|tuple): The neighbors of the input nodes `x` from different graphs. The data type should be the same with `x`. count (list|tuple): The neighbor counts of the input nodes `x` from different graphs. 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. Only useful for gpu version. Default is None. index_buffer (Tensor, optional): Index buffer for hashtable. The data type should be int32, and should be filled with -1. Only useful for gpu version. `value_buffer` and `index_buffer` should be both not None if you want to speed up by using hashtable buffer. Default is None. 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_a = [8, 9, 0, 4, 7, 6, 7] >>> count_a = [2, 3, 2] >>> x = paddle.to_tensor(x, dtype="int64") >>> neighbors_a = paddle.to_tensor(neighbors_a, dtype="int64") >>> count_a = paddle.to_tensor(count_a, dtype="int32") >>> neighbors_b = [0, 2, 3, 5, 1] >>> count_b = [1, 3, 1] >>> neighbors_b = paddle.to_tensor(neighbors_b, dtype="int64") >>> count_b = paddle.to_tensor(count_b, dtype="int32") >>> neighbors = [neighbors_a, neighbors_b] >>> count = [count_a, count_b] >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_heter_graph(x, neighbors, count) >>> print(reindex_src.numpy()) [3 4 0 5 6 7 6 0 2 8 9 1] >>> print(reindex_dst.numpy()) [0 0 1 1 1 2 2 0 1 1 1 2] >>> print(out_nodes.numpy()) [0 1 2 8 9 4 7 6 3 5] NTFr)axisrrheter_graph_reindexrrrrrrreindex_heter_graphr!r#r$r()r;) r paddleconcatrr isinstancerrrr,r-r"r.r/s r5r;r;sf\(\-E5 3M)!444  e!,,,.4.B     / / + [)K22)X&& K %"" ia000I M%a ( ( (EQ%79NOOO; 2OUGgHHH  +g    ! +g    ; ;&(( ; ;F;;!';JJK;;!';JJK999HHI ia000I M%a ( ( (E  "/CM||/CM||   '&"    Y ..r6)NNN)r r r r rr rrrrrrrr)r r r r7rr7rrrrrrrr) __future__rtypingrr<rpaddle.base.data_feederrpaddle.base.frameworkrpaddle.base.layer_helperrpaddle.frameworkr collections.abcr r __all__r r;r6r5rHs#""""" <<<<<<******000000333333(((((( #'"& t/t/t/t/t/v#'"& H/H/H/H/H/H/H/r6