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reorganize c++ sampler

parent 1fa9b9fa
Showing with 1040 additions and 1017 deletions
csrc/sampler/export.cpp
#include <iostream>
#include <omp.h>
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <pybind11/stl.h>
#include <torch/extension.h>
#include <time.h>
#include <random>
#include <parallel_hashmap/phmap.h>
// #include <boost/thread/mutex.hpp>
// #define MTX boost::mutex
#include<head.h>
#include <sampler.h>
#include <output.h>
#include <neighbors.h>
#define MTX std::mutex
using namespace std;
namespace py = pybind11;
namespace th = torch;
typedef int64_t NodeIDType;
typedef int64_t EdgeIDType;
typedef float WeightType;
typedef float TimeStampType;
#define EXTRAARGS , phmap::priv::hash_default_hash<K>, \
phmap::priv::hash_default_eq<K>, \
std::allocator<K>, 4, MTX
template <class K>
using HashT = phmap::parallel_flat_hash_set<K EXTRAARGS>;
template <class K, class V>
using HashM = phmap::parallel_flat_hash_map<K, V EXTRAARGS>;
class TemporalNeighborBlock;
class TemporalGraphBlock;
TemporalNeighborBlock& get_neighbors(string graph_name, th::Tensor row, th::Tensor col, int64_t num_nodes, int is_distinct, optional<th::Tensor> eid, optional<th::Tensor> edge_weight, optional<th::Tensor> time);
th::Tensor heads_unique(th::Tensor array, th::Tensor heads, int threads);
int nodeIdToInOut(NodeIDType nid, int pid, const vector<NodeIDType>& part_ptr);
int nodeIdToPartId(NodeIDType nid, const vector<NodeIDType>& part_ptr);
vector<th::Tensor> divide_nodes_to_part(th::Tensor nodes, const vector<NodeIDType>& part_ptr, int threads);
// vector<int64_t> sample_multinomial(vector<WeightType> weights, int num_samples, bool replacement, default_random_engine e);
NodeIDType sample_multinomial(const vector<WeightType>& weights, default_random_engine& e);
template<typename T>
inline py::array vec2npy(const std::vector<T> &vec)
{
// need to let python garbage collector handle C++ vector memory
// see https://github.com/pybind/pybind11/issues/1042
// non-copy value transfer
auto v = new std::vector<T>(vec);
auto capsule = py::capsule(v, [](void *v)
{ delete reinterpret_cast<std::vector<T> *>(v); });
return py::array(v->size(), v->data(), capsule);
// return py::array(vec.size(), vec.data());
}
/*
* NeighborSampler Utils
*/
class TemporalNeighborBlock
{
public:
vector<vector<NodeIDType>> neighbors;
vector<vector<TimeStampType>> timestamp;
vector<vector<EdgeIDType>> eid;
vector<vector<WeightType>> edge_weight;
vector<phmap::parallel_flat_hash_map<NodeIDType, int64_t>> inverted_index;
vector<int64_t> deg;
vector<phmap::parallel_flat_hash_set<NodeIDType>> neighbors_set;
bool with_eid = false;
bool weighted = false;
bool with_timestamp = false;
TemporalNeighborBlock(){}
// TemporalNeighborBlock(const TemporalNeighborBlock &tnb);
TemporalNeighborBlock(vector<vector<NodeIDType>>& neighbors,
vector<int64_t> &deg):
neighbors(neighbors), deg(deg){}
TemporalNeighborBlock(vector<vector<NodeIDType>>& neighbors,
vector<vector<WeightType>>& edge_weight,
vector<vector<EdgeIDType>>& eid,
vector<int64_t> &deg):
neighbors(neighbors), edge_weight(edge_weight),eid(eid), deg(deg)
{ this->with_eid=true;
this->weighted=true; }
TemporalNeighborBlock(vector<vector<NodeIDType>>& neighbors,
vector<vector<WeightType>>& edge_weight,
vector<vector<TimeStampType>>& timestamp,
vector<vector<EdgeIDType>>& eid,
vector<int64_t> &deg):
neighbors(neighbors), edge_weight(edge_weight), timestamp(timestamp),eid(eid), deg(deg)
{ this->with_eid=true;
this->weighted=true;
this->with_timestamp=true;}
py::array get_node_neighbor(NodeIDType node_id){
return vec2npy(neighbors[node_id]);
}
py::array get_node_neighbor_timestamp(NodeIDType node_id){
return vec2npy(timestamp[node_id]);
}
int64_t get_node_deg(NodeIDType node_id){
return deg[node_id];
}
bool empty(){
return this->deg.empty();
}
void update_edge_weight(th::Tensor row_or_eid, th::Tensor col, th::Tensor edge_weight);
void update_node_weight(th::Tensor nid, th::Tensor node_weight);
void update_all_node_weight(th::Tensor node_weight);
int64_t update_neighbors_with_time(th::Tensor row, th::Tensor col, th::Tensor time, th::Tensor eid, int is_distinct, std::optional<th::Tensor> edge_weight);
std::string serialize() const {
std::ostringstream oss;
// 序列化基本类型成员
oss << with_eid << " " << weighted << " " << with_timestamp << " ";
// 序列化 vector<vector<T>> 类型成员
auto serializeVecVec = [&oss](const auto& vecVec) {
for (const auto& vec : vecVec) {
oss << vec.size() << " ";
for (const auto& elem : vec) {
oss << elem << " ";
}
}
oss << "|"; // 添加一个分隔符以区分不同的 vector
};
serializeVecVec(neighbors);
serializeVecVec(timestamp);
serializeVecVec(eid);
serializeVecVec(edge_weight);
// 序列化 vector<int64_t> 类型成员
oss << deg.size() << " ";
for (const auto& d : deg) {
oss << d << " ";
}
oss << "|";
// 序列化 inverted_index
for (const auto& map : inverted_index) {
oss << map.size() << " ";
for (const auto& [key, value] : map) {
oss << key << " " << value << " ";
}
}
oss << "|";
// 序列化 neighbors_set
for (const auto& set : neighbors_set) {
oss << set.size() << " ";
for (const auto& elem : set) {
oss << elem << " ";
}
}
oss << "|";
return oss.str();
}
static TemporalNeighborBlock deserialize(const std::string& s) {
std::istringstream iss(s);
TemporalNeighborBlock tnb;
// 反序列化基本类型成员
iss >> tnb.with_eid >> tnb.weighted >> tnb.with_timestamp;
// 反序列化 vector<vector<T>> 类型成员
auto deserializeVecLong = [&iss](vector<vector<int64_t>>& vecVec) {
std::string segment;
std::getline(iss, segment, '|');
std::istringstream vec_iss(segment);
while (!vec_iss.eof()) {
size_t vec_size;
vec_iss >> vec_size;
if (vec_iss.eof()) break; // 防止多余的空白
vector<int64_t> vec(vec_size);
for (size_t i = 0; i < vec_size; ++i) {
vec_iss >> vec[i];
}
vecVec.push_back(vec);
}
};
auto deserializeVecFloat = [&iss](vector<vector<float>>& vecVec) {
std::string segment;
std::getline(iss, segment, '|');
std::istringstream vec_iss(segment);
while (!vec_iss.eof()) {
size_t vec_size;
vec_iss >> vec_size;
if (vec_iss.eof()) break; // 防止多余的空白
vector<float> vec(vec_size);
for (size_t i = 0; i < vec_size; ++i) {
vec_iss >> vec[i];
}
vecVec.push_back(vec);
}
};
deserializeVecLong(tnb.neighbors);
deserializeVecFloat(tnb.timestamp);
deserializeVecLong(tnb.eid);
deserializeVecFloat(tnb.edge_weight);
std::string segment;
// 反序列化 vector<int64_t> 类型成员
segment="";
std::getline(iss, segment, '|');
std::istringstream vec_iss(segment);
size_t vec_size;
vec_iss >> vec_size;
tnb.deg.resize(vec_size);
for (size_t i = 0; i < vec_size; ++i) {
vec_iss >> tnb.deg[i];
}
// 反序列化 inverted_index
segment="";
std::getline(iss, segment, '|');
std::istringstream map_iss(segment);
while (!map_iss.eof()) {
size_t map_size;
map_iss >> map_size;
if (map_iss.eof()) break;
phmap::parallel_flat_hash_map<NodeIDType, int64_t> map;
for (size_t i = 0; i < map_size; ++i) {
NodeIDType key;
int64_t value;
map_iss >> key >> value;
map[key] = value;
}
tnb.inverted_index.push_back(map);
}
// 反序列化 neighbors_set
std::getline(iss, segment, '|');
std::istringstream set_iss(segment);
while (!set_iss.eof()) {
size_t set_size;
set_iss >> set_size;
if (set_iss.eof()) break;
phmap::parallel_flat_hash_set<NodeIDType> set;
for (size_t i = 0; i < set_size; ++i) {
NodeIDType elem;
set_iss >> elem;
set.insert(elem);
}
tnb.neighbors_set.push_back(set);
}
return tnb;
}
};
class TemporalGraphBlock
{
public:
vector<NodeIDType> row;
vector<NodeIDType> col;
vector<EdgeIDType> eid;
vector<TimeStampType> delta_ts;
vector<int64_t> src_index;
vector<NodeIDType> sample_nodes;
vector<TimeStampType> sample_nodes_ts;
double sample_time = 0;
double tot_time = 0;
int64_t sample_edge_num = 0;
TemporalGraphBlock(){}
// TemporalGraphBlock(const TemporalGraphBlock &tgb);
TemporalGraphBlock(vector<NodeIDType> &_row, vector<NodeIDType> &_col,
vector<NodeIDType> &_sample_nodes):
row(_row), col(_col), sample_nodes(_sample_nodes){}
TemporalGraphBlock(vector<NodeIDType> &_row, vector<NodeIDType> &_col,
vector<NodeIDType> &_sample_nodes,
vector<TimeStampType> &_sample_nodes_ts):
row(_row), col(_col), sample_nodes(_sample_nodes),
sample_nodes_ts(_sample_nodes_ts){}
};
class T_TemporalGraphBlock
{
public:
th::Tensor row;
th::Tensor col;
th::Tensor eid;
th::Tensor delta_ts;
th::Tensor src_index;
th::Tensor sample_nodes;
th::Tensor sample_nodes_ts;
double sample_time = 0;
double tot_time = 0;
int64_t sample_edge_num = 0;
T_TemporalGraphBlock(){}
T_TemporalGraphBlock(th::Tensor &_row, th::Tensor &_col,
th::Tensor &_sample_nodes):
row(_row), col(_col), sample_nodes(_sample_nodes){}
};
// 辅助函数
template <typename T>
T* get_data_ptr(const th::Tensor& tensor) {
AT_ASSERTM(tensor.is_contiguous(), "Offset tensor must be contiguous");
AT_ASSERTM(tensor.dim() == 1, "Offset tensor must be one-dimensional");
return tensor.data_ptr<T>();
}
template <typename T>
torch::Tensor vecToTensor(const std::vector<T>& vec) {
// 确定数据类型
torch::ScalarType dtype;
if (std::is_same<T, int64_t>::value) {
dtype = torch::kInt64;
} else if (std::is_same<T, float>::value) {
dtype = torch::kFloat32;
} else {
throw std::runtime_error("Unsupported data type");
}
// 创建Tensor
torch::Tensor tensor = torch::from_blob(
const_cast<T*>(vec.data()), /* 数据指针 */
{static_cast<long>(vec.size())}, /* 尺寸 */
dtype /* 数据类型 */
);
return tensor;//.clone(); // 克隆Tensor以拷贝数据
}
TemporalNeighborBlock& get_neighbors(
string graph_name, th::Tensor row, th::Tensor col, int64_t num_nodes, int is_distinct, optional<th::Tensor> eid, optional<th::Tensor> edge_weight, optional<th::Tensor> time)
{ //row、col、time按time升序排列，由时间早的到时间晚的
auto src = get_data_ptr<NodeIDType>(row);
auto dst = get_data_ptr<NodeIDType>(col);
EdgeIDType* eid_ptr = eid ? get_data_ptr<EdgeIDType>(eid.value()) : nullptr;
WeightType* ew = edge_weight ? get_data_ptr<WeightType>(edge_weight.value()) : nullptr;
TimeStampType* t = time ? get_data_ptr<TimeStampType>(time.value()) : nullptr;
int64_t edge_num = row.size(0);
static phmap::parallel_flat_hash_map<string, TemporalNeighborBlock> tnb_map;
if(tnb_map.count(graph_name)==1)
return tnb_map[graph_name];
tnb_map[graph_name] = TemporalNeighborBlock();
TemporalNeighborBlock& tnb = tnb_map[graph_name];
double start_time = omp_get_wtime();
//初始化
tnb.neighbors.resize(num_nodes);
tnb.deg.resize(num_nodes, 0);
//初始化optional相关
tnb.with_eid = eid.has_value();
tnb.weighted = edge_weight.has_value();
tnb.with_timestamp = time.has_value();
if (tnb.with_eid) tnb.eid.resize(num_nodes);
if (tnb.weighted) {
tnb.edge_weight.resize(num_nodes);
tnb.inverted_index.resize(num_nodes);
}
if (tnb.with_timestamp) tnb.timestamp.resize(num_nodes);
//计算, 条件判断移出循环优化执行效率
for(int64_t i=0; i<edge_num; i++){
//计算节点邻居
tnb.neighbors[dst[i]].emplace_back(src[i]);
}
//如果有eid，插入
if(tnb.with_eid)
for(int64_t i=0; i<edge_num; i++){
tnb.eid[dst[i]].emplace_back(eid_ptr[i]);
}
//如果有权重信息，插入节点与邻居边的权重和反向索引
if(tnb.weighted)
for(int64_t i=0; i<edge_num; i++){
tnb.edge_weight[dst[i]].emplace_back(ew[i]);
if(tnb.with_eid) tnb.inverted_index[dst[i]][eid_ptr[i]]=tnb.neighbors[dst[i]].size()-1;
else tnb.inverted_index[dst[i]][src[i]]=tnb.neighbors[dst[i]].size()-1;
}
//如果有时序信息，插入节点与邻居边的时间
if(tnb.with_timestamp)
for(int64_t i=0; i<edge_num; i++){
tnb.timestamp[dst[i]].emplace_back(t[i]);
}
if(is_distinct){
for(int64_t i=0; i<num_nodes; i++){
//收集单边去重节点度
phmap::parallel_flat_hash_set<NodeIDType> temp_s;
temp_s.insert(tnb.neighbors[i].begin(), tnb.neighbors[i].end());
tnb.neighbors_set.emplace_back(temp_s);
tnb.deg[i] = tnb.neighbors_set[i].size();
}
}
else{
for(int64_t i=0; i<num_nodes; i++){
//收集单边节点度
tnb.deg[i] = tnb.neighbors[i].size();
}
}
double end_time = omp_get_wtime();
cout<<"get_neighbors consume: "<<end_time-start_time<<"s"<<endl;
return tnb;
}
void TemporalNeighborBlock::update_edge_weight(
th::Tensor row_or_eid, th::Tensor col, th::Tensor edge_weight){
AT_ASSERTM(this->weighted, "This Graph has no edge weight infomation");
auto dst = get_data_ptr<NodeIDType>(col);
WeightType* ew = get_data_ptr<WeightType>(edge_weight);
NodeIDType* src;
EdgeIDType* eid_ptr;
if(this->with_eid) eid_ptr = get_data_ptr<EdgeIDType>(row_or_eid);
else src = get_data_ptr<NodeIDType>(row_or_eid);
int64_t edge_num = col.size(0);
for(int64_t i=0; i<edge_num; i++){
//修改节点与邻居边的权重
AT_ASSERTM(this->inverted_index[dst[i]].count(src[i])==1, "Unexist Edge Index: "+to_string(src[i])+", "+to_string(dst[i]));
int index;
if(this->with_eid) index = this->inverted_index[dst[i]][eid_ptr[i]];
else index = this->inverted_index[dst[i]][src[i]];
this->edge_weight[dst[i]][index] = ew[i];
}
}
void TemporalNeighborBlock:: update_node_weight(th::Tensor nid, th::Tensor node_weight){
AT_ASSERTM(this->weighted, "This Graph has no edge weight infomation");
auto dst = get_data_ptr<NodeIDType>(nid);
WeightType* nw = get_data_ptr<WeightType>(node_weight);
int64_t node_num = nid.size(0);
for(int64_t i=0; i<node_num; i++){
//修改节点与邻居边的权重
AT_ASSERTM(dst[i]<this->deg.size(), "Unexist Node Index: "+to_string(dst[i]));
if(this->inverted_index[dst[i]].empty())
return;
for(auto index : this->inverted_index[dst[i]]){
this->edge_weight[dst[i]][index.second] = nw[i];
}
}
}
void TemporalNeighborBlock:: update_all_node_weight(th::Tensor node_weight){
AT_ASSERTM(this->weighted, "This Graph has no edge weight infomation");
WeightType* nw = get_data_ptr<WeightType>(node_weight);
int64_t node_num = node_weight.size(0);
AT_ASSERTM(node_num==this->neighbors.size(), "The tensor node_weight size is not suitable node number.");
for(int64_t i=0; i<node_num; i++){
//修改节点与邻居边的权重
for(int j=0; j<this->neighbors[i].size();j++){
this->edge_weight[i][j] = nw[this->neighbors[i][j]];
}
}
}
int64_t TemporalNeighborBlock::update_neighbors_with_time(
th::Tensor row, th::Tensor col, th::Tensor time,th::Tensor eid, int is_distinct, std::optional<th::Tensor> edge_weight){
//row、col、time按time升序排列，由时间早的到时间晚的
AT_ASSERTM(this->empty(), "Empty TemporalNeighborBlock, please use get_neighbors_with_time");
AT_ASSERTM(this->with_timestamp == true, "This Graph has no time infomation!");
auto src = get_data_ptr<NodeIDType>(row);
auto dst = get_data_ptr<NodeIDType>(col);
auto eid_ptr = get_data_ptr<EdgeIDType>(eid);
auto t = get_data_ptr<TimeStampType>(time);
WeightType* ew = edge_weight ? get_data_ptr<WeightType>(edge_weight.value()) : nullptr;
int64_t edge_num = row.size(0);
int64_t num_nodes = this->neighbors.size();
//处理optional的值
if(edge_weight.has_value()){
AT_ASSERTM(this->weighted == true, "This Graph has no edge weight");
}
if(this->weighted){
AT_ASSERTM(edge_weight.has_value(), "This Graph need edge weight");
}
// double start_time = omp_get_wtime();
if(is_distinct){
for(int64_t i=0; i<edge_num; i++){
//如果有新节点
if(dst[i]>=num_nodes){
num_nodes = dst[i]+1;
this->neighbors.resize(num_nodes);
this->deg.resize(num_nodes, 0);
this->eid.resize(num_nodes);
this->timestamp.resize(num_nodes);
//初始化optional相关
if (this->weighted) {
this->edge_weight.resize(num_nodes);
this->inverted_index.resize(num_nodes);
}
}
//更新节点邻居
this->neighbors[dst[i]].emplace_back(src[i]);
//插入eid
this->eid[dst[i]].emplace_back(eid_ptr[i]);
//插入节点与邻居边的时间
this->timestamp[dst[i]].emplace_back(t[i]);
//如果有权重信息，插入节点与邻居边的权重和反向索引
if(this->weighted){
this->edge_weight[dst[i]].emplace_back(ew[i]);
if(this->with_eid) this->inverted_index[dst[i]][eid_ptr[i]]=this->neighbors[dst[i]].size()-1;
else this->inverted_index[dst[i]][src[i]]=this->neighbors[dst[i]].size()-1;
}
this->neighbors_set[dst[i]].insert(src[i]);
this->deg[dst[i]]=this->neighbors_set[dst[i]].size();
}
}
else{
for(int64_t i=0; i<edge_num; i++){
//更新节点邻居
this->neighbors[dst[i]].emplace_back(src[i]);
//插入eid
this->eid[dst[i]].emplace_back(eid_ptr[i]);
//插入节点与邻居边的时间
this->timestamp[dst[i]].emplace_back(t[i]);
//如果有权重信息，插入节点与邻居边的权重和反向索引
if(this->weighted){
this->edge_weight[dst[i]].emplace_back(ew[i]);
this->inverted_index[dst[i]][src[i]]=this->neighbors[dst[i]].size()-1;
}
this->deg[dst[i]]=this->neighbors[dst[i]].size();
}
}
// double end_time = omp_get_wtime();
// cout<<"update_neighbors consume: "<<end_time-start_time<<"s"<<endl;
return num_nodes;
}
class ParallelSampler
{
public:
TemporalNeighborBlock& tnb;
NodeIDType num_nodes;
EdgeIDType num_edges;
int threads;
vector<int> fanouts;
// vector<NodeIDType> part_ptr;
// int pid;
int num_layers;
string policy;
std::vector<TemporalGraphBlock> ret;
ParallelSampler(TemporalNeighborBlock& _tnb, NodeIDType _num_nodes, EdgeIDType _num_edges, int _threads,
vector<int>& _fanouts, int _num_layers, string _policy) :
tnb(_tnb), num_nodes(_num_nodes), num_edges(_num_edges), threads(_threads),
fanouts(_fanouts), num_layers(_num_layers), policy(_policy)
{
omp_set_num_threads(_threads);
ret.clear();
ret.resize(_num_layers);
}
void reset()
{
ret.clear();
ret.resize(num_layers);
}
void neighbor_sample_from_nodes(th::Tensor nodes, optional<th::Tensor> root_ts);
void neighbor_sample_from_nodes_static(th::Tensor nodes);
void neighbor_sample_from_nodes_static_layer(th::Tensor nodes, int cur_layer);
void neighbor_sample_from_nodes_with_before(th::Tensor nodes, th::Tensor root_ts);
void neighbor_sample_from_nodes_with_before_layer(th::Tensor nodes, th::Tensor root_ts, int cur_layer);
};
void ParallelSampler :: neighbor_sample_from_nodes(th::Tensor nodes, optional<th::Tensor> root_ts)
{
omp_set_num_threads(threads);
if(policy == "weighted")
AT_ASSERTM(tnb.weighted, "Tnb has no weight infomation!");
else if(policy == "recent")
AT_ASSERTM(tnb.with_timestamp, "Tnb has no timestamp infomation!");
else if(policy == "uniform")
;
else{
throw runtime_error("The policy \"" + policy + "\" is not exit!");
}
if(tnb.with_timestamp){
AT_ASSERTM(tnb.with_timestamp, "Tnb has no timestamp infomation!");
AT_ASSERTM(root_ts.has_value(), "Parameter mismatch!");
neighbor_sample_from_nodes_with_before(nodes, root_ts.value());
}
else{
neighbor_sample_from_nodes_static(nodes);
}
}
void ParallelSampler :: neighbor_sample_from_nodes_static_layer(th::Tensor nodes, int cur_layer){
py::gil_scoped_release release;
double tot_start_time = omp_get_wtime();
TemporalGraphBlock tgb = TemporalGraphBlock();
int fanout = fanouts[cur_layer];
ret[cur_layer] = TemporalGraphBlock();
auto nodes_data = get_data_ptr<NodeIDType>(nodes);
vector<phmap::parallel_flat_hash_set<NodeIDType>> node_s_threads(threads);
phmap::parallel_flat_hash_set<NodeIDType> node_s;
vector<vector<NodeIDType>> eid_threads(threads);//row_threads(threads),col_threads(threads);
AT_ASSERTM(tnb.with_eid, "Tnb has no eid infomation! We need eid!");
// double start_time = omp_get_wtime();
int reserve_capacity = int(ceil(nodes.size(0) / threads)) * fanout;
#pragma omp parallel
{
int tid = omp_get_thread_num();
unsigned int loc_seed = tid;
// row_threads[tid].reserve(reserve_capacity);
// col_threads[tid].reserve(reserve_capacity);
eid_threads[tid].reserve(reserve_capacity);
#pragma omp for schedule(static, int(ceil(static_cast<float>((nodes.size(0)) / threads))))
for(int64_t i=0; i<nodes.size(0); i++){
// int tid = omp_get_thread_num();
NodeIDType node = nodes_data[i];
vector<NodeIDType> nei(tnb.neighbors[node]);
vector<EdgeIDType> edge;
edge = tnb.eid[node];
double s_start_time = omp_get_wtime();
if(tnb.deg[node]>fanout){
phmap::flat_hash_set<NodeIDType> temp_s;
default_random_engine e(8);//(time(0));
uniform_int_distribution<> u(0, tnb.deg[node]-1);
while(temp_s.size()!=fanout){
//循环选择fanout个邻居
NodeIDType indice;
if(policy == "weighted"){//考虑边权重信息
const vector<WeightType>& ew = tnb.edge_weight[node];
indice = sample_multinomial(ew, e);
}
else if(policy == "uniform"){//均匀采样
indice = u(e);
}
auto chosen_n_iter = nei.begin() + indice;
auto rst = temp_s.insert(*chosen_n_iter);
if(rst.second){ //不重复
auto chosen_e_iter = edge.begin() + indice;
eid_threads[tid].emplace_back(*chosen_e_iter);
node_s_threads[tid].insert(*chosen_n_iter);
}
}
// row_threads[tid].insert(row_threads[tid].end(),temp_s.begin(),temp_s.end());
// col_threads[tid].insert(col_threads[tid].end(), fanout, node);
}
else{
node_s_threads[tid].insert(nei.begin(), nei.end());
// row_threads[tid].insert(row_threads[tid].end(),nei.begin(),nei.end());
// col_threads[tid].insert(col_threads[tid].end(), tnb.deg[node], node);
eid_threads[tid].insert(eid_threads[tid].end(),edge.begin(), edge.end());
}
if(tid==0)
ret[0].sample_time += omp_get_wtime() - s_start_time;
}
}
// double end_time = omp_get_wtime();
// cout<<"neighbor_sample_from_nodes parallel part consume: "<<end_time-start_time<<"s"<<endl;
int size = 0;
vector<int> each_begin(threads);
for(int i = 0; i<threads; i++){
int s = eid_threads[i].size();
each_begin[i]=size;
size += s;
}
// ret[cur_layer].row.resize(size);
// ret[cur_layer].col.resize(size);
ret[cur_layer].eid.resize(size);
#pragma omp parallel for schedule(static, 1)
for(int i = 0; i<threads; i++){
// copy(row_threads[i].begin(), row_threads[i].end(), ret[cur_layer].row.begin()+each_begin[i]);
// copy(col_threads[i].begin(), col_threads[i].end(), ret[cur_layer].col.begin()+each_begin[i]);
copy(eid_threads[i].begin(), eid_threads[i].end(), ret[cur_layer].eid.begin()+each_begin[i]);
}
for(int i = 0; i<threads; i++)
node_s.insert(node_s_threads[i].begin(), node_s_threads[i].end());
ret[cur_layer].sample_nodes.assign(node_s.begin(), node_s.end());
ret[0].tot_time += omp_get_wtime() - tot_start_time;
ret[0].sample_edge_num += ret[cur_layer].eid.size();
py::gil_scoped_acquire acquire;
}
void ParallelSampler :: neighbor_sample_from_nodes_static(th::Tensor nodes){
for(int i=0;i<num_layers;i++){
if(i==0) neighbor_sample_from_nodes_static_layer(nodes, i);
else neighbor_sample_from_nodes_static_layer(vecToTensor<NodeIDType>(ret[i-1].sample_nodes), i);
}
}
void ParallelSampler :: neighbor_sample_from_nodes_with_before_layer(
th::Tensor nodes, th::Tensor root_ts, int cur_layer){
py::gil_scoped_release release;
double tot_start_time = omp_get_wtime();
ret[cur_layer] = TemporalGraphBlock();
auto nodes_data = get_data_ptr<NodeIDType>(nodes);
auto ts_data = get_data_ptr<TimeStampType>(root_ts);
int fanout = fanouts[cur_layer];
// HashT<pair<NodeIDType,TimeStampType> > node_s;
vector<TemporalGraphBlock> tgb_i(threads);
default_random_engine e(8);//(time(0));
// double start_time = omp_get_wtime();
int reserve_capacity = int(ceil(nodes.size(0) / threads)) * fanout;
#pragma omp parallel
{
int tid = omp_get_thread_num();
unsigned int loc_seed = tid;
tgb_i[tid].sample_nodes.reserve(reserve_capacity);
tgb_i[tid].sample_nodes_ts.reserve(reserve_capacity);
tgb_i[tid].delta_ts.reserve(reserve_capacity);
tgb_i[tid].eid.reserve(reserve_capacity);
tgb_i[tid].src_index.reserve(reserve_capacity);
#pragma omp for schedule(static, int(ceil(static_cast<float>((nodes.size(0)) / threads))))
for(int64_t i=0; i<nodes.size(0); i++){
// int tid = omp_get_thread_num();
NodeIDType node = nodes_data[i];
TimeStampType rtts = ts_data[i];
int end_index = lower_bound(tnb.timestamp[node].begin(), tnb.timestamp[node].end(), rtts)-tnb.timestamp[node].begin();
// cout<<node<<" "<<end_index<<" "<<tnb.deg[node]<<endl;
double s_start_time = omp_get_wtime();
if ((policy == "recent") || (end_index <= fanout)){
int start_index = max(0, end_index-fanout);
tgb_i[tid].src_index.insert(tgb_i[tid].src_index.end(), end_index-start_index, i);
tgb_i[tid].sample_nodes.insert(tgb_i[tid].sample_nodes.end(), tnb.neighbors[node].begin()+start_index, tnb.neighbors[node].begin()+end_index);
tgb_i[tid].sample_nodes_ts.insert(tgb_i[tid].sample_nodes_ts.end(), tnb.timestamp[node].begin()+start_index, tnb.timestamp[node].begin()+end_index);
tgb_i[tid].eid.insert(tgb_i[tid].eid.end(), tnb.eid[node].begin()+start_index, tnb.eid[node].begin()+end_index);
for(int cid = start_index; cid < end_index;cid++){
tgb_i[tid].delta_ts.emplace_back(rtts-tnb.timestamp[node][cid]);
}
}
else{
//可选邻居边大于扇出的话需要随机选择fanout个邻居
tgb_i[tid].src_index.insert(tgb_i[tid].src_index.end(), fanout, i);
uniform_int_distribution<> u(0, end_index-1);
//cout<<end_index<<endl;
// cout<<"start:"<<start_index<<" end:"<<end_index<<endl;
for(int i=0; i<fanout;i++){
int cid;
if(policy == "uniform")
cid = u(e);
// cid = rand_r(&loc_seed) % (end_index);
else if(policy == "weighted"){
const vector<WeightType>& ew = tnb.edge_weight[node];
cid = sample_multinomial(ew, e);
}
tgb_i[tid].sample_nodes.emplace_back(tnb.neighbors[node][cid]);
tgb_i[tid].sample_nodes_ts.emplace_back(tnb.timestamp[node][cid]);
tgb_i[tid].delta_ts.emplace_back(rtts-tnb.timestamp[node][cid]);
tgb_i[tid].eid.emplace_back(tnb.eid[node][cid]);
}
}
if(tid==0)
ret[0].sample_time += omp_get_wtime() - s_start_time;
}
}
// double end_time = omp_get_wtime();
// cout<<"neighbor_sample_from_nodes parallel part consume: "<<end_time-start_time<<"s"<<endl;
// start_time = omp_get_wtime();
int size = 0;
vector<int> each_begin(threads);
for(int i = 0; i<threads; i++){
int s = tgb_i[i].eid.size();
each_begin[i]=size;
size += s;
}
ret[cur_layer].eid.resize(size);
ret[cur_layer].src_index.resize(size);
ret[cur_layer].delta_ts.resize(size);
ret[cur_layer].sample_nodes.resize(size);
ret[cur_layer].sample_nodes_ts.resize(size);
#pragma omp parallel for schedule(static, 1)
for(int i = 0; i<threads; i++){
copy(tgb_i[i].eid.begin(), tgb_i[i].eid.end(), ret[cur_layer].eid.begin()+each_begin[i]);
copy(tgb_i[i].src_index.begin(), tgb_i[i].src_index.end(), ret[cur_layer].src_index.begin()+each_begin[i]);
copy(tgb_i[i].delta_ts.begin(), tgb_i[i].delta_ts.end(), ret[cur_layer].delta_ts.begin()+each_begin[i]);
copy(tgb_i[i].sample_nodes.begin(), tgb_i[i].sample_nodes.end(), ret[cur_layer].sample_nodes.begin()+each_begin[i]);
copy(tgb_i[i].sample_nodes_ts.begin(), tgb_i[i].sample_nodes_ts.end(), ret[cur_layer].sample_nodes_ts.begin()+each_begin[i]);
}
// end_time = omp_get_wtime();
// cout<<"end union consume: "<<end_time-start_time<<"s"<<endl;
ret[0].tot_time += omp_get_wtime() - tot_start_time;
ret[0].sample_edge_num += ret[cur_layer].eid.size();
py::gil_scoped_acquire acquire;
}
void ParallelSampler :: neighbor_sample_from_nodes_with_before(th::Tensor nodes, th::Tensor root_ts){
for(int i=0;i<num_layers;i++){
if(i==0) neighbor_sample_from_nodes_with_before_layer(nodes, root_ts, i);
else neighbor_sample_from_nodes_with_before_layer(vecToTensor<NodeIDType>(ret[i-1].sample_nodes),
vecToTensor<TimeStampType>(ret[i-1].sample_nodes_ts), i);
}
}
/*-------------------------------------------------------------------------------------**
**------------Utils--------------------------------------------------------------------**
**-------------------------------------------------------------------------------------*/
th::Tensor heads_unique(th::Tensor array, th::Tensor heads, int threads){
auto array_ptr = array.data_ptr<NodeIDType>();
phmap::parallel_flat_hash_set<NodeIDType> s(array_ptr, array_ptr+array.numel());
if(heads.numel()==0) return th::tensor(vector<NodeIDType>(s.begin(), s.end()));
AT_ASSERTM(heads.is_contiguous(), "Offset tensor must be contiguous");
AT_ASSERTM(heads.dim() == 1, "0ffset tensor must be one-dimensional");
auto heads_ptr = heads.data_ptr<NodeIDType>();
#pragma omp parallel for num_threads(threads)
for(int64_t i=0; i<heads.size(0); i++){
if(s.count(heads_ptr[i])==1){
#pragma omp critical(erase)
s.erase(heads_ptr[i]);
}
}
vector<NodeIDType> ret;
ret.reserve(s.size()+heads.numel());
ret.assign(heads_ptr, heads_ptr+heads.numel());
ret.insert(ret.end(), s.begin(), s.end());
// cout<<"s: "<<s.size()<<" array: "<<array.size()<<endl;
return th::tensor(ret);
}
int nodeIdToPartId(NodeIDType nid, const vector<NodeIDType>& part_ptr){
int partitionId = -1;
for(int i=0;i<part_ptr.size()-1;i++){
if(nid>=part_ptr[i]&&nid<part_ptr[i+1]){
partitionId = i;
break;
}
}
if(partitionId<0) throw "nid 不存在对应的分区";
return partitionId;
}
//0:inner; 1:outer
int nodeIdToInOut(NodeIDType nid, int pid, const vector<NodeIDType>& part_ptr){
if(nid>=part_ptr[pid]&&nid<part_ptr[pid+1]){
return 0;
}
return 1;
}
vector<th::Tensor> divide_nodes_to_part(
th::Tensor nodes, const vector<NodeIDType>& part_ptr, int threads){
double start_time = omp_get_wtime();
AT_ASSERTM(nodes.is_contiguous(), "Offset tensor must be contiguous");
AT_ASSERTM(nodes.dim() == 1, "0ffset tensor must be one-dimensional");
auto nodes_id = nodes.data_ptr<NodeIDType>();
vector<vector<vector<NodeIDType>>> node_part_threads;
vector<th::Tensor> result(part_ptr.size()-1);
//初始化点的分区，每个分区按线程划分避免冲突
for(int i = 0; i<threads; i++){
vector<vector<NodeIDType>> node_parts;
for(int j=0;j<part_ptr.size()-1;j++){
node_parts.push_back(vector<NodeIDType>());
}
node_part_threads.push_back(node_parts);
}
#pragma omp parallel for num_threads(threads) default(shared)
for(int64_t i=0; i<nodes.size(0); i++){
int tid = omp_get_thread_num();
int pid = nodeIdToPartId(nodes_id[i], part_ptr);
node_part_threads[tid][pid].emplace_back(nodes_id[i]);
}
#pragma omp parallel for num_threads(part_ptr.size()-1) default(shared)
for(int i = 0; i<part_ptr.size()-1; i++){
vector<NodeIDType> temp;
for(int j=0;j<threads;j++){
temp.insert(temp.end(), node_part_threads[j][i].begin(), node_part_threads[j][i].end());
}
result[i]=th::tensor(temp);
}
double end_time = omp_get_wtime();
// cout<<"end divide consume: "<<end_time-start_time<<"s"<<endl;
return result;
}
NodeIDType sample_multinomial(const vector<WeightType>& weights, default_random_engine& e){
NodeIDType sample_indice;
vector<WeightType> cumulative_weights;
partial_sum(weights.begin(), weights.end(), back_inserter(cumulative_weights));
AT_ASSERTM(cumulative_weights.back() > 0, "Edge weight sum should be greater than 0.");
uniform_real_distribution<WeightType> distribution(0.0, cumulative_weights.back());
WeightType random_value = distribution(e);
auto it = lower_bound(cumulative_weights.begin(), cumulative_weights.end(), random_value);
sample_indice = distance(cumulative_weights.begin(), it);
return sample_indice;
}
/*------------Python Bind--------------------------------------------------------------*/
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
......
csrc/sampler/include/head.h 0 → 100644
#pragma once
#include <iostream>
#include <torch/extension.h>
#include <omp.h>
#include <time.h>
#include <random>
#include <parallel_hashmap/phmap.h>
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <pybind11/stl.h>
using namespace std;
namespace py = pybind11;
namespace th = torch;
typedef int64_t NodeIDType;
typedef int64_t EdgeIDType;
typedef float WeightType;
typedef float TimeStampType;
class TemporalNeighborBlock;
class TemporalGraphBlock;
class ParallelSampler;
TemporalNeighborBlock& get_neighbors(string graph_name, th::Tensor row, th::Tensor col, int64_t num_nodes, int is_distinct, optional<th::Tensor> eid, optional<th::Tensor> edge_weight, optional<th::Tensor> time);
th::Tensor heads_unique(th::Tensor array, th::Tensor heads, int threads);
int nodeIdToInOut(NodeIDType nid, int pid, const vector<NodeIDType>& part_ptr);
int nodeIdToPartId(NodeIDType nid, const vector<NodeIDType>& part_ptr);
vector<th::Tensor> divide_nodes_to_part(th::Tensor nodes, const vector<NodeIDType>& part_ptr, int threads);
NodeIDType sample_multinomial(const vector<WeightType>& weights, default_random_engine& e);
// 辅助函数
template<typename T>
inline py::array vec2npy(const std::vector<T> &vec)
{
// need to let python garbage collector handle C++ vector memory
// see https://github.com/pybind/pybind11/issues/1042
// non-copy value transfer
auto v = new std::vector<T>(vec);
auto capsule = py::capsule(v, [](void *v)
{ delete reinterpret_cast<std::vector<T> *>(v); });
return py::array(v->size(), v->data(), capsule);
// return py::array(vec.size(), vec.data());
}
template <typename T>
T* get_data_ptr(const th::Tensor& tensor) {
AT_ASSERTM(tensor.is_contiguous(), "Offset tensor must be contiguous");
AT_ASSERTM(tensor.dim() == 1, "Offset tensor must be one-dimensional");
return tensor.data_ptr<T>();
}
template <typename T>
torch::Tensor vecToTensor(const std::vector<T>& vec) {
// 确定数据类型
torch::ScalarType dtype;
if (std::is_same<T, int64_t>::value) {
dtype = torch::kInt64;
} else if (std::is_same<T, float>::value) {
dtype = torch::kFloat32;
} else {
throw std::runtime_error("Unsupported data type");
}
// 创建Tensor
torch::Tensor tensor = torch::from_blob(
const_cast<T*>(vec.data()), /* 数据指针 */
{static_cast<long>(vec.size())}, /* 尺寸 */
dtype /* 数据类型 */
);
return tensor;//.clone(); // 克隆Tensor以拷贝数据
}
/*-------------------------------------------------------------------------------------**
**------------Utils--------------------------------------------------------------------**
**-------------------------------------------------------------------------------------*/
th::Tensor heads_unique(th::Tensor array, th::Tensor heads, int threads){
auto array_ptr = array.data_ptr<NodeIDType>();
phmap::parallel_flat_hash_set<NodeIDType> s(array_ptr, array_ptr+array.numel());
if(heads.numel()==0) return th::tensor(vector<NodeIDType>(s.begin(), s.end()));
AT_ASSERTM(heads.is_contiguous(), "Offset tensor must be contiguous");
AT_ASSERTM(heads.dim() == 1, "0ffset tensor must be one-dimensional");
auto heads_ptr = heads.data_ptr<NodeIDType>();
#pragma omp parallel for num_threads(threads)
for(int64_t i=0; i<heads.size(0); i++){
if(s.count(heads_ptr[i])==1){
#pragma omp critical(erase)
s.erase(heads_ptr[i]);
}
}
vector<NodeIDType> ret;
ret.reserve(s.size()+heads.numel());
ret.assign(heads_ptr, heads_ptr+heads.numel());
ret.insert(ret.end(), s.begin(), s.end());
// cout<<"s: "<<s.size()<<" array: "<<array.size()<<endl;
return th::tensor(ret);
}
int nodeIdToPartId(NodeIDType nid, const vector<NodeIDType>& part_ptr){
int partitionId = -1;
for(int i=0;i<part_ptr.size()-1;i++){
if(nid>=part_ptr[i]&&nid<part_ptr[i+1]){
partitionId = i;
break;
}
}
if(partitionId<0) throw "nid 不存在对应的分区";
return partitionId;
}
//0:inner; 1:outer
int nodeIdToInOut(NodeIDType nid, int pid, const vector<NodeIDType>& part_ptr){
if(nid>=part_ptr[pid]&&nid<part_ptr[pid+1]){
return 0;
}
return 1;
}
vector<th::Tensor> divide_nodes_to_part(
th::Tensor nodes, const vector<NodeIDType>& part_ptr, int threads){
double start_time = omp_get_wtime();
AT_ASSERTM(nodes.is_contiguous(), "Offset tensor must be contiguous");
AT_ASSERTM(nodes.dim() == 1, "0ffset tensor must be one-dimensional");
auto nodes_id = nodes.data_ptr<NodeIDType>();
vector<vector<vector<NodeIDType>>> node_part_threads;
vector<th::Tensor> result(part_ptr.size()-1);
//初始化点的分区，每个分区按线程划分避免冲突
for(int i = 0; i<threads; i++){
vector<vector<NodeIDType>> node_parts;
for(int j=0;j<part_ptr.size()-1;j++){
node_parts.push_back(vector<NodeIDType>());
}
node_part_threads.push_back(node_parts);
}
#pragma omp parallel for num_threads(threads) default(shared)
for(int64_t i=0; i<nodes.size(0); i++){
int tid = omp_get_thread_num();
int pid = nodeIdToPartId(nodes_id[i], part_ptr);
node_part_threads[tid][pid].emplace_back(nodes_id[i]);
}
#pragma omp parallel for num_threads(part_ptr.size()-1) default(shared)
for(int i = 0; i<part_ptr.size()-1; i++){
vector<NodeIDType> temp;
for(int j=0;j<threads;j++){
temp.insert(temp.end(), node_part_threads[j][i].begin(), node_part_threads[j][i].end());
}
result[i]=th::tensor(temp);
}
double end_time = omp_get_wtime();
// cout<<"end divide consume: "<<end_time-start_time<<"s"<<endl;
return result;
}
float getRandomFloat(unsigned int *seed, float min, float max) {
float scale = rand_r(seed) / (float) RAND_MAX; // 转换为0到1之间的浮点数
return min + scale * (max - min); // 调整到min到max之间
}
NodeIDType sample_multinomial(const vector<WeightType>& weights, default_random_engine& e){
NodeIDType sample_indice;
vector<WeightType> cumulative_weights;
partial_sum(weights.begin(), weights.end(), back_inserter(cumulative_weights));
AT_ASSERTM(cumulative_weights.back() > 0, "Edge weight sum should be greater than 0.");
// uniform_real_distribution<WeightType> distribution(0.0, cumulative_weights.back());
// WeightType random_value = distribution(e);
unsigned int loc_seed = omp_get_thread_num();
WeightType random_value = getRandomFloat(&loc_seed, 0.0, cumulative_weights.back());
auto it = lower_bound(cumulative_weights.begin(), cumulative_weights.end(), random_value);
sample_indice = distance(cumulative_weights.begin(), it);
return sample_indice;
}
csrc/sampler/include/neighbors.h 0 → 100644
#pragma once
#include<head.h>
/*
* NeighborSampler Utils
*/
class TemporalNeighborBlock
{
public:
vector<vector<NodeIDType>> neighbors;
vector<vector<TimeStampType>> timestamp;
vector<vector<EdgeIDType>> eid;
vector<vector<WeightType>> edge_weight;
vector<phmap::parallel_flat_hash_map<NodeIDType, int64_t>> inverted_index;
vector<int64_t> deg;
vector<phmap::parallel_flat_hash_set<NodeIDType>> neighbors_set;
bool with_eid = false;
bool weighted = false;
bool with_timestamp = false;
TemporalNeighborBlock(){}
// TemporalNeighborBlock(const TemporalNeighborBlock &tnb);
TemporalNeighborBlock(vector<vector<NodeIDType>>& neighbors,
vector<int64_t> &deg):
neighbors(neighbors), deg(deg){}
TemporalNeighborBlock(vector<vector<NodeIDType>>& neighbors,
vector<vector<WeightType>>& edge_weight,
vector<vector<EdgeIDType>>& eid,
vector<int64_t> &deg):
neighbors(neighbors), edge_weight(edge_weight),eid(eid), deg(deg)
{ this->with_eid=true;
this->weighted=true; }
TemporalNeighborBlock(vector<vector<NodeIDType>>& neighbors,
vector<vector<WeightType>>& edge_weight,
vector<vector<TimeStampType>>& timestamp,
vector<vector<EdgeIDType>>& eid,
vector<int64_t> &deg):
neighbors(neighbors), edge_weight(edge_weight), timestamp(timestamp),eid(eid), deg(deg)
{ this->with_eid=true;
this->weighted=true;
this->with_timestamp=true;}
py::array get_node_neighbor(NodeIDType node_id){
return vec2npy(neighbors[node_id]);
}
py::array get_node_neighbor_timestamp(NodeIDType node_id){
return vec2npy(timestamp[node_id]);
}
int64_t get_node_deg(NodeIDType node_id){
return deg[node_id];
}
bool empty(){
return this->deg.empty();
}
void update_edge_weight(th::Tensor row_or_eid, th::Tensor col, th::Tensor edge_weight);
void update_node_weight(th::Tensor nid, th::Tensor node_weight);
void update_all_node_weight(th::Tensor node_weight);
int64_t update_neighbors_with_time(th::Tensor row, th::Tensor col, th::Tensor time, th::Tensor eid, int is_distinct, std::optional<th::Tensor> edge_weight);
std::string serialize() const {
std::ostringstream oss;
// 序列化基本类型成员
oss << with_eid << " " << weighted << " " << with_timestamp << " ";
// 序列化 vector<vector<T>> 类型成员
auto serializeVecVec = [&oss](const auto& vecVec) {
for (const auto& vec : vecVec) {
oss << vec.size() << " ";
for (const auto& elem : vec) {
oss << elem << " ";
}
}
oss << "|"; // 添加一个分隔符以区分不同的 vector
};
serializeVecVec(neighbors);
serializeVecVec(timestamp);
serializeVecVec(eid);
serializeVecVec(edge_weight);
// 序列化 vector<int64_t> 类型成员
oss << deg.size() << " ";
for (const auto& d : deg) {
oss << d << " ";
}
oss << "|";
// 序列化 inverted_index
for (const auto& map : inverted_index) {
oss << map.size() << " ";
for (const auto& [key, value] : map) {
oss << key << " " << value << " ";
}
}
oss << "|";
// 序列化 neighbors_set
for (const auto& set : neighbors_set) {
oss << set.size() << " ";
for (const auto& elem : set) {
oss << elem << " ";
}
}
oss << "|";
return oss.str();
}
static TemporalNeighborBlock deserialize(const std::string& s) {
std::istringstream iss(s);
TemporalNeighborBlock tnb;
// 反序列化基本类型成员
iss >> tnb.with_eid >> tnb.weighted >> tnb.with_timestamp;
// 反序列化 vector<vector<T>> 类型成员
auto deserializeVecLong = [&iss](vector<vector<int64_t>>& vecVec) {
std::string segment;
std::getline(iss, segment, '|');
std::istringstream vec_iss(segment);
while (!vec_iss.eof()) {
size_t vec_size;
vec_iss >> vec_size;
if (vec_iss.eof()) break; // 防止多余的空白
vector<int64_t> vec(vec_size);
for (size_t i = 0; i < vec_size; ++i) {
vec_iss >> vec[i];
}
vecVec.push_back(vec);
}
};
auto deserializeVecFloat = [&iss](vector<vector<float>>& vecVec) {
std::string segment;
std::getline(iss, segment, '|');
std::istringstream vec_iss(segment);
while (!vec_iss.eof()) {
size_t vec_size;
vec_iss >> vec_size;
if (vec_iss.eof()) break; // 防止多余的空白
vector<float> vec(vec_size);
for (size_t i = 0; i < vec_size; ++i) {
vec_iss >> vec[i];
}
vecVec.push_back(vec);
}
};
deserializeVecLong(tnb.neighbors);
deserializeVecFloat(tnb.timestamp);
deserializeVecLong(tnb.eid);
deserializeVecFloat(tnb.edge_weight);
std::string segment;
// 反序列化 vector<int64_t> 类型成员
segment="";
std::getline(iss, segment, '|');
std::istringstream vec_iss(segment);
size_t vec_size;
vec_iss >> vec_size;
tnb.deg.resize(vec_size);
for (size_t i = 0; i < vec_size; ++i) {
vec_iss >> tnb.deg[i];
}
// 反序列化 inverted_index
segment="";
std::getline(iss, segment, '|');
std::istringstream map_iss(segment);
while (!map_iss.eof()) {
size_t map_size;
map_iss >> map_size;
if (map_iss.eof()) break;
phmap::parallel_flat_hash_map<NodeIDType, int64_t> map;
for (size_t i = 0; i < map_size; ++i) {
NodeIDType key;
int64_t value;
map_iss >> key >> value;
map[key] = value;
}
tnb.inverted_index.push_back(map);
}
// 反序列化 neighbors_set
std::getline(iss, segment, '|');
std::istringstream set_iss(segment);
while (!set_iss.eof()) {
size_t set_size;
set_iss >> set_size;
if (set_iss.eof()) break;
phmap::parallel_flat_hash_set<NodeIDType> set;
for (size_t i = 0; i < set_size; ++i) {
NodeIDType elem;
set_iss >> elem;
set.insert(elem);
}
tnb.neighbors_set.push_back(set);
}
return tnb;
}
};
TemporalNeighborBlock& get_neighbors(
string graph_name, th::Tensor row, th::Tensor col, int64_t num_nodes, int is_distinct, optional<th::Tensor> eid, optional<th::Tensor> edge_weight, optional<th::Tensor> time)
{ //row、col、time按time升序排列，由时间早的到时间晚的
auto src = get_data_ptr<NodeIDType>(row);
auto dst = get_data_ptr<NodeIDType>(col);
EdgeIDType* eid_ptr = eid ? get_data_ptr<EdgeIDType>(eid.value()) : nullptr;
WeightType* ew = edge_weight ? get_data_ptr<WeightType>(edge_weight.value()) : nullptr;
TimeStampType* t = time ? get_data_ptr<TimeStampType>(time.value()) : nullptr;
int64_t edge_num = row.size(0);
static phmap::parallel_flat_hash_map<string, TemporalNeighborBlock> tnb_map;
if(tnb_map.count(graph_name)==1)
return tnb_map[graph_name];
tnb_map[graph_name] = TemporalNeighborBlock();
TemporalNeighborBlock& tnb = tnb_map[graph_name];
double start_time = omp_get_wtime();
//初始化
tnb.neighbors.resize(num_nodes);
tnb.deg.resize(num_nodes, 0);
//初始化optional相关
tnb.with_eid = eid.has_value();
tnb.weighted = edge_weight.has_value();
tnb.with_timestamp = time.has_value();
if (tnb.with_eid) tnb.eid.resize(num_nodes);
if (tnb.weighted) {
tnb.edge_weight.resize(num_nodes);
tnb.inverted_index.resize(num_nodes);
}
if (tnb.with_timestamp) tnb.timestamp.resize(num_nodes);
//计算, 条件判断移出循环优化执行效率
for(int64_t i=0; i<edge_num; i++){
//计算节点邻居
tnb.neighbors[dst[i]].emplace_back(src[i]);
}
//如果有eid，插入
if(tnb.with_eid)
for(int64_t i=0; i<edge_num; i++){
tnb.eid[dst[i]].emplace_back(eid_ptr[i]);
}
//如果有权重信息，插入节点与邻居边的权重和反向索引
if(tnb.weighted)
for(int64_t i=0; i<edge_num; i++){
tnb.edge_weight[dst[i]].emplace_back(ew[i]);
if(tnb.with_eid) tnb.inverted_index[dst[i]][eid_ptr[i]]=tnb.neighbors[dst[i]].size()-1;
else tnb.inverted_index[dst[i]][src[i]]=tnb.neighbors[dst[i]].size()-1;
}
//如果有时序信息，插入节点与邻居边的时间
if(tnb.with_timestamp)
for(int64_t i=0; i<edge_num; i++){
tnb.timestamp[dst[i]].emplace_back(t[i]);
}
if(is_distinct){
for(int64_t i=0; i<num_nodes; i++){
//收集单边去重节点度
phmap::parallel_flat_hash_set<NodeIDType> temp_s;
temp_s.insert(tnb.neighbors[i].begin(), tnb.neighbors[i].end());
tnb.neighbors_set.emplace_back(temp_s);
tnb.deg[i] = tnb.neighbors_set[i].size();
}
}
else{
for(int64_t i=0; i<num_nodes; i++){
//收集单边节点度
tnb.deg[i] = tnb.neighbors[i].size();
}
}
double end_time = omp_get_wtime();
cout<<"get_neighbors consume: "<<end_time-start_time<<"s"<<endl;
return tnb;
}
void TemporalNeighborBlock::update_edge_weight(
th::Tensor row_or_eid, th::Tensor col, th::Tensor edge_weight){
AT_ASSERTM(this->weighted, "This Graph has no edge weight infomation");
auto dst = get_data_ptr<NodeIDType>(col);
WeightType* ew = get_data_ptr<WeightType>(edge_weight);
NodeIDType* src;
EdgeIDType* eid_ptr;
if(this->with_eid) eid_ptr = get_data_ptr<EdgeIDType>(row_or_eid);
else src = get_data_ptr<NodeIDType>(row_or_eid);
int64_t edge_num = col.size(0);
for(int64_t i=0; i<edge_num; i++){
//修改节点与邻居边的权重
AT_ASSERTM(this->inverted_index[dst[i]].count(src[i])==1, "Unexist Edge Index: "+to_string(src[i])+", "+to_string(dst[i]));
int index;
if(this->with_eid) index = this->inverted_index[dst[i]][eid_ptr[i]];
else index = this->inverted_index[dst[i]][src[i]];
this->edge_weight[dst[i]][index] = ew[i];
}
}
void TemporalNeighborBlock:: update_node_weight(th::Tensor nid, th::Tensor node_weight){
AT_ASSERTM(this->weighted, "This Graph has no edge weight infomation");
auto dst = get_data_ptr<NodeIDType>(nid);
WeightType* nw = get_data_ptr<WeightType>(node_weight);
int64_t node_num = nid.size(0);
for(int64_t i=0; i<node_num; i++){
//修改节点与邻居边的权重
AT_ASSERTM(dst[i]<this->deg.size(), "Unexist Node Index: "+to_string(dst[i]));
if(this->inverted_index[dst[i]].empty())
return;
for(auto index : this->inverted_index[dst[i]]){
this->edge_weight[dst[i]][index.second] = nw[i];
}
}
}
void TemporalNeighborBlock:: update_all_node_weight(th::Tensor node_weight){
AT_ASSERTM(this->weighted, "This Graph has no edge weight infomation");
WeightType* nw = get_data_ptr<WeightType>(node_weight);
int64_t node_num = node_weight.size(0);
AT_ASSERTM(node_num==this->neighbors.size(), "The tensor node_weight size is not suitable node number.");
for(int64_t i=0; i<node_num; i++){
//修改节点与邻居边的权重
for(int j=0; j<this->neighbors[i].size();j++){
this->edge_weight[i][j] = nw[this->neighbors[i][j]];
}
}
}
int64_t TemporalNeighborBlock::update_neighbors_with_time(
th::Tensor row, th::Tensor col, th::Tensor time,th::Tensor eid, int is_distinct, std::optional<th::Tensor> edge_weight){
//row、col、time按time升序排列，由时间早的到时间晚的
AT_ASSERTM(this->empty(), "Empty TemporalNeighborBlock, please use get_neighbors_with_time");
AT_ASSERTM(this->with_timestamp == true, "This Graph has no time infomation!");
auto src = get_data_ptr<NodeIDType>(row);
auto dst = get_data_ptr<NodeIDType>(col);
auto eid_ptr = get_data_ptr<EdgeIDType>(eid);
auto t = get_data_ptr<TimeStampType>(time);
WeightType* ew = edge_weight ? get_data_ptr<WeightType>(edge_weight.value()) : nullptr;
int64_t edge_num = row.size(0);
int64_t num_nodes = this->neighbors.size();
//处理optional的值
if(edge_weight.has_value()){
AT_ASSERTM(this->weighted == true, "This Graph has no edge weight");
}
if(this->weighted){
AT_ASSERTM(edge_weight.has_value(), "This Graph need edge weight");
}
// double start_time = omp_get_wtime();
if(is_distinct){
for(int64_t i=0; i<edge_num; i++){
//如果有新节点
if(dst[i]>=num_nodes){
num_nodes = dst[i]+1;
this->neighbors.resize(num_nodes);
this->deg.resize(num_nodes, 0);
this->eid.resize(num_nodes);
this->timestamp.resize(num_nodes);
//初始化optional相关
if (this->weighted) {
this->edge_weight.resize(num_nodes);
this->inverted_index.resize(num_nodes);
}
}
//更新节点邻居
this->neighbors[dst[i]].emplace_back(src[i]);
//插入eid
this->eid[dst[i]].emplace_back(eid_ptr[i]);
//插入节点与邻居边的时间
this->timestamp[dst[i]].emplace_back(t[i]);
//如果有权重信息，插入节点与邻居边的权重和反向索引
if(this->weighted){
this->edge_weight[dst[i]].emplace_back(ew[i]);
if(this->with_eid) this->inverted_index[dst[i]][eid_ptr[i]]=this->neighbors[dst[i]].size()-1;
else this->inverted_index[dst[i]][src[i]]=this->neighbors[dst[i]].size()-1;
}
this->neighbors_set[dst[i]].insert(src[i]);
this->deg[dst[i]]=this->neighbors_set[dst[i]].size();
}
}
else{
for(int64_t i=0; i<edge_num; i++){
//更新节点邻居
this->neighbors[dst[i]].emplace_back(src[i]);
//插入eid
this->eid[dst[i]].emplace_back(eid_ptr[i]);
//插入节点与邻居边的时间
this->timestamp[dst[i]].emplace_back(t[i]);
//如果有权重信息，插入节点与邻居边的权重和反向索引
if(this->weighted){
this->edge_weight[dst[i]].emplace_back(ew[i]);
this->inverted_index[dst[i]][src[i]]=this->neighbors[dst[i]].size()-1;
}
this->deg[dst[i]]=this->neighbors[dst[i]].size();
}
}
// double end_time = omp_get_wtime();
// cout<<"update_neighbors consume: "<<end_time-start_time<<"s"<<endl;
return num_nodes;
}
\ No newline at end of file
csrc/sampler/include/output.h 0 → 100644
#pragma once
#include <head.h>
class TemporalGraphBlock
{
public:
vector<NodeIDType> row;
vector<NodeIDType> col;
vector<EdgeIDType> eid;
vector<TimeStampType> delta_ts;
vector<int64_t> src_index;
vector<NodeIDType> sample_nodes;
vector<TimeStampType> sample_nodes_ts;
double sample_time = 0;
double tot_time = 0;
int64_t sample_edge_num = 0;
TemporalGraphBlock(){}
// TemporalGraphBlock(const TemporalGraphBlock &tgb);
TemporalGraphBlock(vector<NodeIDType> &_row, vector<NodeIDType> &_col,
vector<NodeIDType> &_sample_nodes):
row(_row), col(_col), sample_nodes(_sample_nodes){}
TemporalGraphBlock(vector<NodeIDType> &_row, vector<NodeIDType> &_col,
vector<NodeIDType> &_sample_nodes,
vector<TimeStampType> &_sample_nodes_ts):
row(_row), col(_col), sample_nodes(_sample_nodes),
sample_nodes_ts(_sample_nodes_ts){}
};
class T_TemporalGraphBlock
{
public:
th::Tensor row;
th::Tensor col;
th::Tensor eid;
th::Tensor delta_ts;
th::Tensor src_index;
th::Tensor sample_nodes;
th::Tensor sample_nodes_ts;
double sample_time = 0;
double tot_time = 0;
int64_t sample_edge_num = 0;
T_TemporalGraphBlock(){}
T_TemporalGraphBlock(th::Tensor &_row, th::Tensor &_col,
th::Tensor &_sample_nodes):
row(_row), col(_col), sample_nodes(_sample_nodes){}
};
\ No newline at end of file
csrc/sampler/include/sampler.h 0 → 100644
#pragma once
#include <head.h>
#include <neighbors.h>
# include <output.h>
class ParallelSampler
{
public:
TemporalNeighborBlock& tnb;
NodeIDType num_nodes;
EdgeIDType num_edges;
int threads;
vector<int> fanouts;
// vector<NodeIDType> part_ptr;
// int pid;
int num_layers;
string policy;
std::vector<TemporalGraphBlock> ret;
ParallelSampler(TemporalNeighborBlock& _tnb, NodeIDType _num_nodes, EdgeIDType _num_edges, int _threads,
vector<int>& _fanouts, int _num_layers, string _policy) :
tnb(_tnb), num_nodes(_num_nodes), num_edges(_num_edges), threads(_threads),
fanouts(_fanouts), num_layers(_num_layers), policy(_policy)
{
omp_set_num_threads(_threads);
ret.clear();
ret.resize(_num_layers);
}
void reset()
{
ret.clear();
ret.resize(num_layers);
}
void neighbor_sample_from_nodes(th::Tensor nodes, optional<th::Tensor> root_ts);
void neighbor_sample_from_nodes_static(th::Tensor nodes);
void neighbor_sample_from_nodes_static_layer(th::Tensor nodes, int cur_layer);
void neighbor_sample_from_nodes_with_before(th::Tensor nodes, th::Tensor root_ts);
void neighbor_sample_from_nodes_with_before_layer(th::Tensor nodes, th::Tensor root_ts, int cur_layer);
};
void ParallelSampler :: neighbor_sample_from_nodes(th::Tensor nodes, optional<th::Tensor> root_ts)
{
omp_set_num_threads(threads);
if(policy == "weighted")
AT_ASSERTM(tnb.weighted, "Tnb has no weight infomation!");
else if(policy == "recent")
AT_ASSERTM(tnb.with_timestamp, "Tnb has no timestamp infomation!");
else if(policy == "uniform")
;
else{
throw runtime_error("The policy \"" + policy + "\" is not exit!");
}
if(tnb.with_timestamp){
AT_ASSERTM(tnb.with_timestamp, "Tnb has no timestamp infomation!");
AT_ASSERTM(root_ts.has_value(), "Parameter mismatch!");
neighbor_sample_from_nodes_with_before(nodes, root_ts.value());
}
else{
neighbor_sample_from_nodes_static(nodes);
}
}
void ParallelSampler :: neighbor_sample_from_nodes_static_layer(th::Tensor nodes, int cur_layer){
py::gil_scoped_release release;
double tot_start_time = omp_get_wtime();
TemporalGraphBlock tgb = TemporalGraphBlock();
int fanout = fanouts[cur_layer];
ret[cur_layer] = TemporalGraphBlock();
auto nodes_data = get_data_ptr<NodeIDType>(nodes);
vector<phmap::parallel_flat_hash_set<NodeIDType>> node_s_threads(threads);
// vector<vector<NodeIDType>> node_threads(threads);
phmap::parallel_flat_hash_set<NodeIDType> node_s;
vector<vector<NodeIDType>> eid_threads(threads);//row_threads(threads),col_threads(threads);
vector<vector<NodeIDType>> src_index_threads(threads);
AT_ASSERTM(tnb.with_eid, "Tnb has no eid infomation! We need eid!");
// double start_time = omp_get_wtime();
int reserve_capacity = int(ceil(nodes.size(0) / threads)) * fanout;
#pragma omp parallel
{
int tid = omp_get_thread_num();
unsigned int loc_seed = tid;
eid_threads[tid].reserve(reserve_capacity);
src_index_threads[tid].reserve(reserve_capacity);
// node_threads[tid].reserve(reserve_capacity);
#pragma omp for schedule(static, int(ceil(static_cast<float>((nodes.size(0)) / threads))))
for(int64_t i=0; i<nodes.size(0); i++){
// int tid = omp_get_thread_num();
NodeIDType node = nodes_data[i];
vector<NodeIDType>& nei = tnb.neighbors[node];
vector<EdgeIDType> edge;
edge = tnb.eid[node];
double s_start_time = omp_get_wtime();
if(tnb.deg[node]>fanout){
src_index_threads[tid].insert(src_index_threads[tid].end(), fanout, i);
phmap::flat_hash_set<NodeIDType> temp_s;
default_random_engine e(8);//(time(0));
uniform_int_distribution<> u(0, tnb.deg[node]-1);
while(temp_s.size()!=fanout){
// for(int i=0;i<fanout;i++){
//循环选择fanout个邻居
NodeIDType indice;
if(policy == "weighted"){//考虑边权重信息
const vector<WeightType>& ew = tnb.edge_weight[node];
indice = sample_multinomial(ew, e);
}
else if(policy == "uniform"){//均匀采样
// indice = u(e);
indice = rand_r(&loc_seed) % (tnb.deg[node]);
}
auto chosen_n_iter = nei.begin() + indice;
// auto chosen_e_iter = edge.begin() + indice;
// eid_threads[tid].emplace_back(*chosen_e_iter);
// node_threads[tid].emplace_back(*chosen_n_iter);
auto rst = temp_s.insert(*chosen_n_iter);
if(rst.second){ //不重复
auto chosen_e_iter = edge.begin() + indice;
eid_threads[tid].emplace_back(*chosen_e_iter);
node_s_threads[tid].insert(*chosen_n_iter);
}
}
}
else{
src_index_threads[tid].insert(src_index_threads[tid].end(), tnb.deg[node], i);
// node_threads[tid].insert(node_threads[tid].end(), nei.begin(), nei.end());
node_s_threads[tid].insert(nei.begin(), nei.end());
eid_threads[tid].insert(eid_threads[tid].end(),edge.begin(), edge.end());
}
if(tid==0)
ret[0].sample_time += omp_get_wtime() - s_start_time;
}
}
// double end_time = omp_get_wtime();
// cout<<"neighbor_sample_from_nodes parallel part consume: "<<end_time-start_time<<"s"<<endl;
int size = 0;
vector<int> each_begin(threads);
for(int i = 0; i<threads; i++){
int s = eid_threads[i].size();
each_begin[i]=size;
size += s;
}
ret[cur_layer].eid.resize(size);
ret[cur_layer].sample_nodes.resize(size);
ret[cur_layer].src_index.resize(size);
#pragma omp parallel for schedule(static, 1)
for(int i = 0; i<threads; i++){
copy(eid_threads[i].begin(), eid_threads[i].end(), ret[cur_layer].eid.begin()+each_begin[i]);
// copy(node_threads[i].begin(), node_threads[i].end(), ret[cur_layer].sample_nodes.begin()+each_begin[i]);
copy(src_index_threads[i].begin(), src_index_threads[i].end(), ret[cur_layer].src_index.begin()+each_begin[i]);
}
for(int i = 0; i<threads; i++)
node_s.insert(node_s_threads[i].begin(), node_s_threads[i].end());
ret[cur_layer].sample_nodes.assign(node_s.begin(), node_s.end());
ret[0].tot_time += omp_get_wtime() - tot_start_time;
ret[0].sample_edge_num += ret[cur_layer].eid.size();
py::gil_scoped_acquire acquire;
}
void ParallelSampler :: neighbor_sample_from_nodes_static(th::Tensor nodes){
for(int i=0;i<num_layers;i++){
if(i==0) neighbor_sample_from_nodes_static_layer(nodes, i);
else neighbor_sample_from_nodes_static_layer(vecToTensor<NodeIDType>(ret[i-1].sample_nodes), i);
}
}
void ParallelSampler :: neighbor_sample_from_nodes_with_before_layer(
th::Tensor nodes, th::Tensor root_ts, int cur_layer){
py::gil_scoped_release release;
double tot_start_time = omp_get_wtime();
ret[cur_layer] = TemporalGraphBlock();
auto nodes_data = get_data_ptr<NodeIDType>(nodes);
auto ts_data = get_data_ptr<TimeStampType>(root_ts);
int fanout = fanouts[cur_layer];
// HashT<pair<NodeIDType,TimeStampType> > node_s;
vector<TemporalGraphBlock> tgb_i(threads);
default_random_engine e(8);//(time(0));
// double start_time = omp_get_wtime();
int reserve_capacity = int(ceil(nodes.size(0) / threads)) * fanout;
#pragma omp parallel
{
int tid = omp_get_thread_num();
unsigned int loc_seed = tid;
tgb_i[tid].sample_nodes.reserve(reserve_capacity);
tgb_i[tid].sample_nodes_ts.reserve(reserve_capacity);
tgb_i[tid].delta_ts.reserve(reserve_capacity);
tgb_i[tid].eid.reserve(reserve_capacity);
tgb_i[tid].src_index.reserve(reserve_capacity);
#pragma omp for schedule(static, int(ceil(static_cast<float>((nodes.size(0)) / threads))))
for(int64_t i=0; i<nodes.size(0); i++){
// int tid = omp_get_thread_num();
NodeIDType node = nodes_data[i];
TimeStampType rtts = ts_data[i];
int end_index = lower_bound(tnb.timestamp[node].begin(), tnb.timestamp[node].end(), rtts)-tnb.timestamp[node].begin();
// cout<<node<<" "<<end_index<<" "<<tnb.deg[node]<<endl;
double s_start_time = omp_get_wtime();
if ((policy == "recent") || (end_index <= fanout)){
int start_index = max(0, end_index-fanout);
tgb_i[tid].src_index.insert(tgb_i[tid].src_index.end(), end_index-start_index, i);
tgb_i[tid].sample_nodes.insert(tgb_i[tid].sample_nodes.end(), tnb.neighbors[node].begin()+start_index, tnb.neighbors[node].begin()+end_index);
tgb_i[tid].sample_nodes_ts.insert(tgb_i[tid].sample_nodes_ts.end(), tnb.timestamp[node].begin()+start_index, tnb.timestamp[node].begin()+end_index);
tgb_i[tid].eid.insert(tgb_i[tid].eid.end(), tnb.eid[node].begin()+start_index, tnb.eid[node].begin()+end_index);
for(int cid = start_index; cid < end_index;cid++){
tgb_i[tid].delta_ts.emplace_back(rtts-tnb.timestamp[node][cid]);
}
}
else{
//可选邻居边大于扇出的话需要随机选择fanout个邻居
tgb_i[tid].src_index.insert(tgb_i[tid].src_index.end(), fanout, i);
uniform_int_distribution<> u(0, end_index-1);
//cout<<end_index<<endl;
// cout<<"start:"<<start_index<<" end:"<<end_index<<endl;
for(int i=0; i<fanout;i++){
int cid;
if(policy == "uniform")
// cid = u(e);
cid = rand_r(&loc_seed) % (end_index);
else if(policy == "weighted"){
const vector<WeightType>& ew = tnb.edge_weight[node];
cid = sample_multinomial(ew, e);
}
tgb_i[tid].sample_nodes.emplace_back(tnb.neighbors[node][cid]);
tgb_i[tid].sample_nodes_ts.emplace_back(tnb.timestamp[node][cid]);
tgb_i[tid].delta_ts.emplace_back(rtts-tnb.timestamp[node][cid]);
tgb_i[tid].eid.emplace_back(tnb.eid[node][cid]);
}
}
if(tid==0)
ret[0].sample_time += omp_get_wtime() - s_start_time;
}
}
// double end_time = omp_get_wtime();
// cout<<"neighbor_sample_from_nodes parallel part consume: "<<end_time-start_time<<"s"<<endl;
// start_time = omp_get_wtime();
int size = 0;
vector<int> each_begin(threads);
for(int i = 0; i<threads; i++){
int s = tgb_i[i].eid.size();
each_begin[i]=size;
size += s;
}
ret[cur_layer].eid.resize(size);
ret[cur_layer].src_index.resize(size);
ret[cur_layer].delta_ts.resize(size);
ret[cur_layer].sample_nodes.resize(size);
ret[cur_layer].sample_nodes_ts.resize(size);
#pragma omp parallel for schedule(static, 1)
for(int i = 0; i<threads; i++){
copy(tgb_i[i].eid.begin(), tgb_i[i].eid.end(), ret[cur_layer].eid.begin()+each_begin[i]);
copy(tgb_i[i].src_index.begin(), tgb_i[i].src_index.end(), ret[cur_layer].src_index.begin()+each_begin[i]);
copy(tgb_i[i].delta_ts.begin(), tgb_i[i].delta_ts.end(), ret[cur_layer].delta_ts.begin()+each_begin[i]);
copy(tgb_i[i].sample_nodes.begin(), tgb_i[i].sample_nodes.end(), ret[cur_layer].sample_nodes.begin()+each_begin[i]);
copy(tgb_i[i].sample_nodes_ts.begin(), tgb_i[i].sample_nodes_ts.end(), ret[cur_layer].sample_nodes_ts.begin()+each_begin[i]);
}
// end_time = omp_get_wtime();
// cout<<"end union consume: "<<end_time-start_time<<"s"<<endl;
ret[0].tot_time += omp_get_wtime() - tot_start_time;
ret[0].sample_edge_num += ret[cur_layer].eid.size();
py::gil_scoped_acquire acquire;
}
void ParallelSampler :: neighbor_sample_from_nodes_with_before(th::Tensor nodes, th::Tensor root_ts){
for(int i=0;i<num_layers;i++){
if(i==0) neighbor_sample_from_nodes_with_before_layer(nodes, root_ts, i);
else neighbor_sample_from_nodes_with_before_layer(vecToTensor<NodeIDType>(ret[i-1].sample_nodes),
vecToTensor<TimeStampType>(ret[i-1].sample_nodes_ts), i);
}
}
\ No newline at end of file
starrygl/sample/sample_core/neighbor_sampler.py
......@@ -10,7 +10,8 @@ from typing import Optional, Tuple
import graph_store
from distparser import SampleType, NUM_SAMPLER
from base import BaseSampler, NegativeSampling, SampleOutput
from sample_cores import ParallelSampler, get_neighbors, heads_unique
# from sample_cores import ParallelSampler, get_neighbors, heads_unique
from starrygl.lib.libstarrygl_ops_sampler import ParallelSampler, get_neighbors
from torch.distributed.rpc import rpc_async
def outer_sample(graph_name, nodes, ts, fanout_index, with_outer_sample = SampleType.Outer):# 默认此时继续向外采样
......
starrygl/sample/sample_core/reddit_demo_before.py
......@@ -8,7 +8,7 @@ from torch_geometric.datasets import Reddit
import time
from tqdm import tqdm
from Utils import GraphData
from .Utils import GraphData
class NegLinkSampler:
......@@ -26,7 +26,7 @@ class NegLinkInductiveSampler:
return np.random.choice(self.nodes, size=n)
def load_reddit_dataset(data_path):
df = pd.read_csv('/home/zlj/hzq/project/code/TGL/DATA/{}/edges.csv'.format("REDDIT"))
df = pd.read_csv('/mnt/data/hzq/DATA/{}/edges.csv'.format("REDDIT"))
num_nodes = max(int(df['src'].max()), int(df['dst'].max())) + 1
src = torch.tensor(df['src'].to_numpy(dtype=int))
dst = torch.tensor(df['dst'].to_numpy(dtype=int))
......@@ -36,115 +36,119 @@ def load_reddit_dataset(data_path):
return g, df
parser=argparse.ArgumentParser()
parser.add_argument('--data', type=str, help='dataset name',default="REDDIT")
parser.add_argument('--config', type=str, help='path to config file',default="/home/zlj/hzq/project/code/TGL/config/TGN.yml")
parser.add_argument('--batch_size', type=int, default=600, help='path to config file')
parser.add_argument('--num_thread', type=int, default=64, help='number of thread')
args=parser.parse_args()
seed=10
torch.manual_seed(seed) # 为CPU设置随机种子
torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU，为所有GPU设置随机种子
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
g_data, df = load_reddit_dataset("/home/zlj/hzq/code/dataset/reddit")
print(g_data)
# for worker in [1,2,3,4,5,6,7,8,9,10,20,30]:
# import random
# timestamp = [random.randint(1, 5) for i in range(0, g.num_edges)]
# timestamp = torch.FloatTensor(timestamp)
# print('begin load')
# pre = time.time()
# # timestamp = torch.load('/home/zlj/hzq/code/gnn/my_sampler/TemporalSample/timestamp.my')
# tnb = torch.load("tnb_reddit_before.my")
# end = time.time()
# print("load time:", end-pre)
# row, col = g.edge_index
edge_weight=None
# g_data = GraphData(id=1, edge_index=g.edge_index, timestamp=timestamp, data=g, partptr=torch.tensor([0, g.num_nodes//4, g.num_nodes//4*2, g.num_nodes//4*3, g.num_nodes]))
from neighbor_sampler import NeighborSampler, SampleType, get_neighbors
print('begin tnb')
row, col = g_data.edge_index
row = torch.cat([row, col])
col = torch.cat([col, row])
eid = torch.cat([g_data.eid, g_data.eid])
timestamp = torch.cat([g_data.edge_ts, g_data.edge_ts])
timestamp,ind = timestamp.sort()
timestamp = timestamp.float().contiguous()
eid = eid[ind].contiguous()
row = row[ind]
col = col[ind]
print(row, col)
pre = time.time()
tnb = get_neighbors("reddit", row.contiguous(), col.contiguous(), g_data.num_nodes, 0, eid, edge_weight, timestamp)
end = time.time()
print("init tnb time:", end-pre)
torch.save(tnb, "tnb_reddit_before.my")
pre = time.time()
sampler = NeighborSampler(g_data.num_nodes,
tnb=tnb,
num_layers=1,
fanout=[10],
graph_data=g_data,
workers=10,
policy="recent",
graph_name='a')
end = time.time()
print("init time:", end-pre)
# neg_link_sampler = NegLinkSampler(g_data.num_nodes)
from base import NegativeSampling, NegativeSamplingMode
neg_link_sampler = NegativeSampling(NegativeSamplingMode.triplet)
# from torch_geometric.sampler import NeighborSampler, NumNeighbors, NodeSamplerInput, SamplerOutput
# pre = time.time()
# num_nei = NumNeighbors([100, 100])
# node_idx = NodeSamplerInput(input_id=None, node=torch.tensor(range(g.num_nodes//4, g.num_nodes//4+600000)))# (input_id=None, node=torch.masked_select(torch.arange(g.num_nodes),node_data['train_mask']))
# sampler = NeighborSampler(g, num_nei)
# end = time.time()
# print("init time:", end-pre)
out = []
tot_time = 0
sam_time = 0
sam_edge = 0
pre = time.time()
for _, rows in tqdm(df.groupby(df.index // args.batch_size), total=len(df) // args.batch_size):
# root_nodes = torch.tensor(np.concatenate([rows.src.values, rows.dst.values, neg_link_sampler.sample(len(rows))])).long()
# ts = torch.tensor(np.concatenate([rows.time.values, rows.time.values, rows.time.values]).astype(np.float32))
# outi = sampler.sample_from_nodes(root_nodes, ts=ts)
edges = torch.tensor(np.stack([rows.src.values, rows.dst.values])).long()
outi, meta = sampler.sample_from_edges(edges=edges, ets=torch.tensor(rows.time.values).float(), neg_sampling=neg_link_sampler)
tot_time += outi[0].tot_time
sam_time += outi[0].sample_time
# print(outi[0].sample_edge_num)
sam_edge += outi[0].sample_edge_num
out.append(outi)
end = time.time()
print("sample time", end-pre)
print("tot_time", tot_time)
print("sam_time", sam_time)
print("sam_edge", sam_edge)
print('eid_list:', out[23][0].eid())
# print('delta_ts_list:', out[10][0].delta_ts)
print('node:', out[23][0].sample_nodes())
# print('node_ts:', out[23][0].sample_nodes_ts)
# print('eid_list:', out[23][1].eid)
# print('node:', out[23][1].sample_nodes)
# print('node_ts:', out[23][1].sample_nodes_ts)
# print('edge_index_list:', out[0][0].edge_index)
\ No newline at end of file
def test():
parser=argparse.ArgumentParser()
parser.add_argument('--data', type=str, help='dataset name',default="REDDIT")
parser.add_argument('--config', type=str, help='path to config file',default="/home/zlj/hzq/project/code/TGL/config/TGN.yml")
parser.add_argument('--batch_size', type=int, default=600, help='path to config file')
parser.add_argument('--num_thread', type=int, default=64, help='number of thread')
args=parser.parse_args()
seed=10
torch.manual_seed(seed) # 为CPU设置随机种子
torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU，为所有GPU设置随机种子
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
g_data, df = load_reddit_dataset("/mnt/data/hzq/DATA/REDDIT")
print(g_data)
# for worker in [1,2,3,4,5,6,7,8,9,10,20,30]:
# import random
# timestamp = [random.randint(1, 5) for i in range(0, g.num_edges)]
# timestamp = torch.FloatTensor(timestamp)
# print('begin load')
# pre = time.time()
# # timestamp = torch.load('/home/zlj/hzq/code/gnn/my_sampler/TemporalSample/timestamp.my')
# tnb = torch.load("tnb_reddit_before.my")
# end = time.time()
# print("load time:", end-pre)
# row, col = g.edge_index
edge_weight=None
# g_data = GraphData(id=1, edge_index=g.edge_index, timestamp=timestamp, data=g, partptr=torch.tensor([0, g.num_nodes//4, g.num_nodes//4*2, g.num_nodes//4*3, g.num_nodes]))
from .neighbor_sampler import NeighborSampler, SampleType, get_neighbors
print('begin tnb')
row, col = g_data.edge_index
row = torch.cat([row, col])
col = torch.cat([col, row])
eid = torch.cat([g_data.eid, g_data.eid])
timestamp = torch.cat([g_data.edge_ts, g_data.edge_ts])
timestamp,ind = timestamp.sort()
timestamp = timestamp.float().contiguous()
eid = eid[ind].contiguous()
row = row[ind]
col = col[ind]
print(row, col)
pre = time.time()
tnb = get_neighbors("reddit", row.contiguous(), col.contiguous(), g_data.num_nodes, 0, eid, edge_weight, timestamp)
end = time.time()
print("init tnb time:", end-pre)
torch.save(tnb, "tnb_reddit_before.my")
pre = time.time()
sampler = NeighborSampler(g_data.num_nodes,
tnb=tnb,
num_layers=1,
fanout=[10],
graph_data=g_data,
workers=32,
policy="recent",
graph_name='a')
end = time.time()
print("init time:", end-pre)
# neg_link_sampler = NegLinkSampler(g_data.num_nodes)
from .base import NegativeSampling, NegativeSamplingMode
neg_link_sampler = NegativeSampling(NegativeSamplingMode.triplet)
# from torch_geometric.sampler import NeighborSampler, NumNeighbors, NodeSamplerInput, SamplerOutput
# pre = time.time()
# num_nei = NumNeighbors([100, 100])
# node_idx = NodeSamplerInput(input_id=None, node=torch.tensor(range(g.num_nodes//4, g.num_nodes//4+600000)))# (input_id=None, node=torch.masked_select(torch.arange(g.num_nodes),node_data['train_mask']))
# sampler = NeighborSampler(g, num_nei)
# end = time.time()
# print("init time:", end-pre)
out = []
tot_time = 0
sam_time = 0
sam_edge = 0
pre = time.time()
for _, rows in tqdm(df.groupby(df.index // args.batch_size), total=len(df) // args.batch_size):
# root_nodes = torch.tensor(np.concatenate([rows.src.values, rows.dst.values, neg_link_sampler.sample(len(rows))])).long()
# ts = torch.tensor(np.concatenate([rows.time.values, rows.time.values, rows.time.values]).astype(np.float32))
# outi = sampler.sample_from_nodes(root_nodes, ts=ts)
edges = torch.tensor(np.stack([rows.src.values, rows.dst.values])).long()
outi, meta = sampler.sample_from_edges(edges=edges, ets=torch.tensor(rows.time.values).float(), neg_sampling=neg_link_sampler)
tot_time += outi[0].tot_time
sam_time += outi[0].sample_time
# print(outi[0].sample_edge_num)
sam_edge += outi[0].sample_edge_num
out.append(outi)
end = time.time()
print("row", out[23][0].row())
print("sample time", end-pre)
print("tot_time", tot_time)
print("sam_time", sam_time)
print("sam_edge", sam_edge)
print('eid_list:', out[23][0].eid())
# print('delta_ts_list:', out[10][0].delta_ts)
print('node:', out[23][0].sample_nodes())
# print('node_ts:', out[23][0].sample_nodes_ts)
# print('eid_list:', out[23][1].eid)
# print('node:', out[23][1].sample_nodes)
# print('node_ts:', out[23][1].sample_nodes_ts)
# print('edge_index_list:', out[0][0].edge_index)
if __name__ == "__main__":
test()
\ No newline at end of file
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