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#include "ego_sample_cpu.h"
#include <ATen/Parallel.h>
#include "utils.h"
#ifdef _WIN32
#include <process.h>
#endif
inline torch::Tensor vec2tensor(std::vector<int64_t> vec) {
return torch::from_blob(vec.data(), {(int64_t)vec.size()}, at::kLong).clone();
}
// Returns `rowptr`, `col`, `n_id`, `e_id`, `ptr`, `root_n_id`
std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor,
torch::Tensor, torch::Tensor>
ego_k_hop_sample_adj_cpu(torch::Tensor rowptr, torch::Tensor col,
torch::Tensor idx, int64_t depth,
int64_t num_neighbors, bool replace) {
std::vector<torch::Tensor> out_rowptrs(idx.numel() + 1);
std::vector<torch::Tensor> out_cols(idx.numel());
std::vector<torch::Tensor> out_n_ids(idx.numel());
std::vector<torch::Tensor> out_e_ids(idx.numel());
auto out_root_n_id = torch::empty({idx.numel()}, at::kLong);
out_rowptrs[0] = torch::zeros({1}, at::kLong);
auto rowptr_data = rowptr.data_ptr<int64_t>();
auto col_data = col.data_ptr<int64_t>();
auto idx_data = idx.data_ptr<int64_t>();
auto out_root_n_id_data = out_root_n_id.data_ptr<int64_t>();
at::parallel_for(0, idx.numel(), 1, [&](int64_t begin, int64_t end) {
int64_t row_start, row_end, row_count, vec_start, vec_end, v, w;
for (int64_t g = begin; g < end; g++) {
std::set<int64_t> n_id_set;
n_id_set.insert(idx_data[g]);
std::vector<int64_t> n_ids;
n_ids.push_back(idx_data[g]);
vec_start = 0, vec_end = n_ids.size();
for (int64_t d = 0; d < depth; d++) {
for (int64_t i = vec_start; i < vec_end; i++) {
v = n_ids[i];
row_start = rowptr_data[v], row_end = rowptr_data[v + 1];
row_count = row_end - row_start;
if (row_count <= num_neighbors) {
for (int64_t e = row_start; e < row_end; e++) {
w = col_data[e];
n_id_set.insert(w);
n_ids.push_back(w);
}
} else if (replace) {
for (int64_t j = 0; j < num_neighbors; j++) {
w = col_data[row_start + uniform_randint(row_count)];
n_id_set.insert(w);
n_ids.push_back(w);
}
} else {
std::unordered_set<int64_t> perm;
for (int64_t j = row_count - num_neighbors; j < row_count; j++) {
if (!perm.insert(uniform_randint(j)).second) {
perm.insert(j);
}
}
for (int64_t j : perm) {
w = col_data[row_start + j];
n_id_set.insert(w);
n_ids.push_back(w);
}
}
}
vec_start = vec_end;
vec_end = n_ids.size();
}
n_ids.clear();
std::map<int64_t, int64_t> n_id_map;
std::map<int64_t, int64_t>::iterator iter;
int64_t i = 0;
for (int64_t v : n_id_set) {
n_ids.push_back(v);
n_id_map[v] = i;
i++;
}
out_root_n_id_data[g] = n_id_map[idx_data[g]];
std::vector<int64_t> rowptrs, cols, e_ids;
for (int64_t v : n_ids) {
row_start = rowptr_data[v], row_end = rowptr_data[v + 1];
for (int64_t e = row_start; e < row_end; e++) {
w = col_data[e];
iter = n_id_map.find(w);
if (iter != n_id_map.end()) {
cols.push_back(iter->second);
e_ids.push_back(e);
}
}
rowptrs.push_back(cols.size());
}
out_rowptrs[g + 1] = vec2tensor(rowptrs);
out_cols[g] = vec2tensor(cols);
out_n_ids[g] = vec2tensor(n_ids);
out_e_ids[g] = vec2tensor(e_ids);
}
});
auto out_ptr = torch::empty({idx.numel() + 1}, at::kLong);
auto out_ptr_data = out_ptr.data_ptr<int64_t>();
out_ptr_data[0] = 0;
int64_t node_cumsum = 0, edge_cumsum = 0;
for (int64_t g = 1; g < idx.numel(); g++) {
node_cumsum += out_n_ids[g - 1].numel();
edge_cumsum += out_cols[g - 1].numel();
out_rowptrs[g + 1].add_(edge_cumsum);
out_cols[g].add_(node_cumsum);
out_ptr_data[g] = node_cumsum;
out_root_n_id_data[g] += node_cumsum;
}
node_cumsum += out_n_ids[idx.numel() - 1].numel();
out_ptr_data[idx.numel()] = node_cumsum;
return std::make_tuple(torch::cat(out_rowptrs, 0), torch::cat(out_cols, 0),
torch::cat(out_n_ids, 0), torch::cat(out_e_ids, 0),
out_ptr, out_root_n_id);
}
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