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#include "hgt_sample_cpu.h"
#include "utils.h"
#ifdef _WIN32
#include <process.h>
#endif
#define MAX_NEIGHBORS 50
using namespace std;
edge_t split(const rel_t &rel_type) {
vector<string> result(3);
int start = 0, end;
for (int i = 0; i < 3; i++) {
end = rel_type.find("__", start);
result[i] = rel_type.substr(start, end - start);
start = end + 2;
}
return make_tuple(result[0], result[1], result[2]);
}
void update_budget_(
unordered_map<node_t, unordered_map<int64_t, float>> *budget_dict,
const node_t &node_type, const vector<int64_t> &samples,
const unordered_map<node_t, unordered_map<int64_t, int64_t>>
&to_local_node_dict,
const unordered_map<rel_t, edge_t> &to_edge_type,
const c10::Dict<rel_t, torch::Tensor> &colptr_dict,
const c10::Dict<rel_t, torch::Tensor> &row_dict) {
if (samples.empty())
return;
for (const auto &kv : colptr_dict) {
const auto &rel_type = kv.key();
const auto &edge_type = to_edge_type.at(rel_type);
const auto &src_node_type = get<0>(edge_type);
const auto &dst_node_type = get<2>(edge_type);
if (node_type != dst_node_type)
continue;
const auto &to_local_src_node = to_local_node_dict.at(src_node_type);
const auto *colptr_data = kv.value().data_ptr<int64_t>();
const auto *row_data = row_dict.at(rel_type).data_ptr<int64_t>();
auto &src_budget = budget_dict->at(src_node_type);
for (const auto &w : samples) {
const auto &col_start = colptr_data[w], &col_end = colptr_data[w + 1];
if (col_end - col_start > MAX_NEIGHBORS) {
// There might be same neighbors with large neighborhood sizes.
// In order to prevent that we fill our budget with many values of low
// probability, we instead sample a fixed amount without replacement:
auto indices = choice(col_end - col_start, MAX_NEIGHBORS, false);
auto *indices_data = indices.data_ptr<int64_t>();
for (int64_t i = 0; i < indices.numel(); i++) {
const auto &v = row_data[col_start + indices_data[i]];
// Only add the neighbor in case we have not yet seen it before:
if (to_local_src_node.find(v) == to_local_src_node.end())
src_budget[v] += 1.f / float(MAX_NEIGHBORS);
}
} else if (col_end != col_start) {
const auto inv_deg = 1.f / float(col_end - col_start);
for (int64_t i = col_start; i < col_end; i++) {
const auto &v = row_data[i];
// Only add the neighbor in case we have not yet seen it before:
if (to_local_src_node.find(v) == to_local_src_node.end())
src_budget[v] += inv_deg;
}
}
}
}
}
vector<int64_t> sample_from(const unordered_map<int64_t, float> &budget,
const int64_t num_samples) {
vector<int64_t> indices;
vector<float> weights;
indices.reserve(budget.size());
weights.reserve(budget.size());
for (const auto &kv : budget) {
indices.push_back(kv.first);
weights.push_back(kv.second * kv.second);
}
const auto weight = from_vector(weights, true);
const auto sample = choice(budget.size(), num_samples, false, weight);
const auto *sample_data = sample.data_ptr<int64_t>();
vector<int64_t> out(sample.numel());
for (int64_t i = 0; i < sample.numel(); i++) {
out[i] = indices[sample_data[i]];
}
return out;
}
tuple<c10::Dict<node_t, torch::Tensor>, c10::Dict<rel_t, torch::Tensor>,
c10::Dict<rel_t, torch::Tensor>, c10::Dict<rel_t, torch::Tensor>>
hgt_sample_cpu(const c10::Dict<rel_t, torch::Tensor> &colptr_dict,
const c10::Dict<rel_t, torch::Tensor> &row_dict,
const c10::Dict<node_t, torch::Tensor> &input_node_dict,
const c10::Dict<node_t, vector<int64_t>> &num_samples_dict,
const int64_t num_hops) {
// Create a mapping to convert single string relations to edge type triplets:
unordered_map<rel_t, edge_t> to_edge_type;
for (const auto &kv : colptr_dict) {
const auto &rel_type = kv.key();
to_edge_type[rel_type] = split(rel_type);
}
// Initialize some necessary data structures for the sampling process:
unordered_map<node_t, vector<int64_t>> nodes_dict;
unordered_map<node_t, unordered_map<int64_t, int64_t>> to_local_node_dict;
unordered_map<node_t, unordered_map<int64_t, float>> budget_dict;
for (const auto &kv : num_samples_dict) {
const auto &node_type = kv.key();
nodes_dict[node_type];
to_local_node_dict[node_type];
budget_dict[node_type];
}
// Add the input nodes to the sampled output nodes (line 1):
for (const auto &kv : input_node_dict) {
const auto &node_type = kv.key();
const auto &input_node = kv.value();
const auto *input_node_data = input_node.data_ptr<int64_t>();
auto &nodes = nodes_dict.at(node_type);
auto &to_local_node = to_local_node_dict.at(node_type);
for (int64_t i = 0; i < input_node.numel(); i++) {
const auto &v = input_node_data[i];
nodes.push_back(v);
to_local_node[v] = i;
}
}
// Update the budget based on the initial input set (line 3-5):
for (const auto &kv : nodes_dict) {
const auto &node_type = kv.first;
const auto &last_samples = kv.second;
update_budget_(&budget_dict, node_type, last_samples, to_local_node_dict,
to_edge_type, colptr_dict, row_dict);
}
for (int64_t ell = 0; ell < num_hops; ell++) {
unordered_map<node_t, vector<int64_t>> samples_dict;
for (auto &kv : budget_dict) {
const auto &node_type = kv.first;
auto &budget = kv.second;
const auto num_samples = num_samples_dict.at(node_type)[ell];
// Sample `num_samples` nodes, according to the budget (line 9-11):
const auto samples = sample_from(budget, num_samples);
samples_dict[node_type] = samples;
// Add samples to the sampled output nodes, and erase them from the budget
// (line 13/15):
auto &nodes = nodes_dict.at(node_type);
auto &to_local_node = to_local_node_dict.at(node_type);
for (const auto &v : samples) {
to_local_node[v] = nodes.size();
nodes.push_back(v);
budget.erase(v);
}
}
if (ell < num_hops - 1) {
// Add neighbors of newly sampled nodes to the budget (line 14):
// Note that we do not need to update the budget in the last iteration.
for (const auto &kv : samples_dict) {
const auto &node_type = kv.first;
const auto &last_samples = kv.second;
update_budget_(&budget_dict, node_type, last_samples,
to_local_node_dict, to_edge_type, colptr_dict, row_dict);
}
}
}
c10::Dict<node_t, torch::Tensor> out_node_dict;
c10::Dict<rel_t, torch::Tensor> out_row_dict;
c10::Dict<rel_t, torch::Tensor> out_col_dict;
c10::Dict<rel_t, torch::Tensor> out_edge_dict;
// Reconstruct the sampled adjacency matrix among the sampled nodes (line 19):
for (const auto &kv : colptr_dict) {
const auto &rel_type = kv.key();
const auto &edge_type = to_edge_type.at(rel_type);
const auto &src_node_type = get<0>(edge_type);
const auto &dst_node_type = get<2>(edge_type);
const auto *colptr_data = kv.value().data_ptr<int64_t>();
const auto *row_data = row_dict.at(rel_type).data_ptr<int64_t>();
const auto &dst_nodes = nodes_dict.at(dst_node_type);
const auto &to_local_src_node = to_local_node_dict.at(src_node_type);
vector<int64_t> rows, cols, edges;
for (int64_t i = 0; i < (int64_t)dst_nodes.size(); i++) {
const auto &w = dst_nodes[i];
const auto &col_start = colptr_data[w], &col_end = colptr_data[w + 1];
if (col_end - col_start > MAX_NEIGHBORS) {
auto indices = choice(col_end - col_start, MAX_NEIGHBORS, false);
auto *indices_data = indices.data_ptr<int64_t>();
for (int64_t j = 0; j < indices.numel(); j++) {
const auto &v = row_data[col_start + indices_data[j]];
if (to_local_src_node.find(v) != to_local_src_node.end()) {
rows.push_back(to_local_src_node.at(v));
cols.push_back(i);
edges.push_back(col_start + j);
}
}
} else {
for (int64_t j = col_start; j < col_end; j++) {
const auto &v = row_data[j];
if (to_local_src_node.find(v) != to_local_src_node.end()) {
rows.push_back(to_local_src_node.at(v));
cols.push_back(i);
edges.push_back(j);
}
}
}
}
out_row_dict.insert(rel_type, from_vector<int64_t>(rows));
out_col_dict.insert(rel_type, from_vector<int64_t>(cols));
out_edge_dict.insert(rel_type, from_vector<int64_t>(edges));
}
// Generate tensor-valued output node dictionary (line 20):
for (const auto &kv : nodes_dict) {
const auto &node_type = kv.first;
const auto &nodes = kv.second;
if (!nodes.empty())
out_node_dict.insert(node_type, from_vector<int64_t>(nodes));
}
return make_tuple(out_node_dict, out_row_dict, out_col_dict, out_edge_dict);
}
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