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paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table.h
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| // 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. | ||
|
|
||
| #pragma once | ||
| #include "heter_comm.h" | ||
| #include "paddle/fluid/platform/enforce.h" | ||
| #ifdef PADDLE_WITH_HETERPS | ||
| namespace paddle { | ||
| namespace framework { | ||
| struct GpuPsGraphNode { | ||
| int64_t node_id; | ||
| int neighbor_size, neighbor_offset; | ||
| // this node's neighbor is stored on [neighbor_offset,neighbor_offset + | ||
| // neighbor_size) of int64_t *neighbor_list; | ||
| }; | ||
|
|
||
| struct GpuPsCommGraph { | ||
| int64_t *neighbor_list; | ||
| GpuPsGraphNode *node_list; | ||
| int neighbor_size, node_size; | ||
| // the size of neighbor array and graph_node_list array | ||
| GpuPsCommGraph() | ||
| : neighbor_list(NULL), node_list(NULL), neighbor_size(0), node_size(0) {} | ||
| GpuPsCommGraph(int64_t *neighbor_list_, GpuPsGraphNode *node_list_, | ||
| int neighbor_size_, int node_size_) | ||
| : neighbor_list(neighbor_list_), | ||
| node_list(node_list_), | ||
| neighbor_size(neighbor_size_), | ||
| node_size(node_size_) {} | ||
| }; | ||
|
|
||
| /* | ||
| suppose we have a graph like this | ||
| 0----3-----5----7 | ||
| \ |\ |\ | ||
| 17 8 9 1 2 | ||
| we save the nodes in arbitrary order, | ||
| in this example,the order is | ||
| [0,5,1,2,7,3,8,9,17] | ||
| let us name this array u_id; | ||
| we record each node's neighbors: | ||
| 0:3,17 | ||
| 5:3,7 | ||
| 1:7 | ||
| 2:7 | ||
| 7:1,2,5 | ||
| 3:0,5,8,9 | ||
| 8:3 | ||
| 9:3 | ||
| 17:0 | ||
| by concatenating each node's neighbor_list in the order we save the node id. | ||
| we get [3,17,3,7,7,7,1,2,5,0,5,8,9,3,3,0] | ||
| this is the neighbor_list of GpuPsCommGraph | ||
| given this neighbor_list and the order to save node id, | ||
| we know, | ||
| node 0's neighbors are in the range [0,1] of neighbor_list | ||
| node 5's neighbors are in the range [2,3] of neighbor_list | ||
| node 1's neighbors are in the range [4,4] of neighbor_list | ||
| node 2:[5,5] | ||
| node 7:[6,6] | ||
| node 3:[9,12] | ||
| node 8:[13,13] | ||
| node 9:[14,14] | ||
| node 17:[15,15] | ||
| ... | ||
| by the above information, | ||
| we generate a node_list:GpuPsGraphNode *graph_node_list in GpuPsCommGraph | ||
| of size 9, | ||
| where node_list[i].id = u_id[i] | ||
| then we have: | ||
| node_list[0]-> node_id:0, neighbor_size:2, neighbor_offset:0 | ||
| node_list[1]-> node_id:5, neighbor_size:2, neighbor_offset:2 | ||
| node_list[2]-> node_id:1, neighbor_size:1, neighbor_offset:4 | ||
| node_list[3]-> node_id:2, neighbor_size:1, neighbor_offset:5 | ||
| node_list[4]-> node_id:7, neighbor_size:3, neighbor_offset:6 | ||
| node_list[5]-> node_id:3, neighbor_size:4, neighbor_offset:9 | ||
| node_list[6]-> node_id:8, neighbor_size:1, neighbor_offset:13 | ||
| node_list[7]-> node_id:9, neighbor_size:1, neighbor_offset:14 | ||
| node_list[8]-> node_id:17, neighbor_size:1, neighbor_offset:15 | ||
| */ | ||
| struct NeighborSampleResult { | ||
| int64_t *val; | ||
| int *actual_sample_size, sample_size, key_size; | ||
| NeighborSampleResult(int _sample_size, int _key_size) | ||
| : sample_size(_sample_size), key_size(_key_size) { | ||
| actual_sample_size = NULL; | ||
| val = NULL; | ||
| }; | ||
| ~NeighborSampleResult() { | ||
| if (val != NULL) cudaFree(val); | ||
| if (actual_sample_size != NULL) cudaFree(actual_sample_size); | ||
| } | ||
| }; | ||
|
|
||
| struct NodeQueryResult { | ||
| int64_t *val; | ||
| int actual_sample_size; | ||
| NodeQueryResult() { | ||
| val = NULL; | ||
| actual_sample_size = 0; | ||
| }; | ||
| ~NodeQueryResult() { | ||
| if (val != NULL) cudaFree(val); | ||
| } | ||
| }; | ||
| class GpuPsGraphTable : public HeterComm<int64_t, int, int> { | ||
| public: | ||
| GpuPsGraphTable(std::shared_ptr<HeterPsResource> resource) | ||
| : HeterComm<int64_t, int, int>(1, resource) { | ||
| load_factor_ = 0.25; | ||
| } | ||
| void build_graph_from_cpu(std::vector<GpuPsCommGraph> &cpu_node_list); | ||
| NodeQueryResult *graph_node_sample(int gpu_id, int sample_size); | ||
| NeighborSampleResult *graph_neighbor_sample(int gpu_id, int64_t *key, | ||
| int sample_size, int len); | ||
| NodeQueryResult *query_node_list(int gpu_id, int start, int query_size); | ||
| void clear_graph_info(); | ||
| void move_neighbor_sample_result_to_source_gpu(int gpu_id, int gpu_num, | ||
| int sample_size, int *h_left, | ||
| int *h_right, | ||
| int64_t *src_sample_res, | ||
| int *actual_sample_size); | ||
|
|
||
| private: | ||
| std::vector<GpuPsCommGraph> gpu_graph_list; | ||
| }; | ||
| } | ||
| }; | ||
| #include "paddle/fluid/framework/fleet/heter_ps/graph_gpu_ps_table_inl.h" | ||
| #endif |
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