hgt_sample_cpu.cpp

#include "hgt_sample_cpu.h"

#include <random>

edge_t split(const rel_t &rel_type) {
  std::vector<std::string> result(3);
  int start = 0, end = 0;
  for (int i = 0; i < 3; i++) {
    end = rel_type.find(delim, start);
    result[i] = rel_type.substr(start, end - start);
    start = end + 2;
  }
  return std::make_tuple(result[0], result[1], result[2]);
}

torch::Tensor vec_to_tensor(const std::vector<int64_t> &v) {
  return torch::from_blob((int64_t *)v.data(), {(int64_t)v.size()}, at::kLong)
      .clone();
}

template <typename Container>
void update_budget(
    std::unordered_map<node_t, std::unordered_map<int64_t, float>> *budget_dict,
    const node_t &node_type, //
    const Container &sampled_nodes,
    const std::unordered_map<node_t, std::unordered_map<int64_t, int64_t>>
        &global_to_local_node_dict,
    const std::unordered_map<rel_t, edge_t> &rel_to_edge_type,
    const c10::Dict<rel_t, torch::Tensor> &colptr_dict,
    const c10::Dict<rel_t, torch::Tensor> &row_dict) {

  for (const auto &kv : colptr_dict) {
    const auto &rel_type = kv.key();
    const auto &edge_type = rel_to_edge_type.at(rel_type);
    const auto &src_node_type = std::get<0>(edge_type);
    const auto &dst_node_type = std::get<2>(edge_type);

    if (node_type != dst_node_type)
      continue;

    const auto &global_to_local_node =
        global_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 &budget = (*budget_dict)[src_node_type];

    for (const auto &v : sampled_nodes) {
      const int64_t col_start = colptr_data[v], col_end = colptr_data[v + 1];
      if (col_end != col_start) {
        const auto inv_deg = 1.f / float(col_end - col_start);
        for (int64_t j = col_start; j < col_end; j++) {
          const auto w = row_data[j];
          // Only add the neighbor in case we have not yet seen it before:
          if (global_to_local_node.find(w) == global_to_local_node.end())
            budget[row_data[j]] += inv_deg;
        }
      }
    }
  }

  auto &budget = (*budget_dict)[node_type];
  for (const auto &v : sampled_nodes)
    budget.erase(v);
}

std::unordered_set<int64_t>
sample_from(const std::unordered_map<int64_t, float> &budget,
            const int64_t num_samples) {

  std::unordered_set<int64_t> output;

  // Compute the squared L2 norm:
  auto norm = 0.f;
  for (const auto &kv : budget)
    norm += kv.second * kv.second;

  if (norm == 0.) // No need to sample if there are no nodes in the budget:
    return output;

  // Generate `num_samples` sorted random values between `[0., norm)`:
  std::default_random_engine gen{std::random_device{}()};
  std::uniform_real_distribution<float> dis(0.f, norm);
  std::vector<float> samples(num_samples);
  for (int64_t i = 0; i < num_samples; i++)
    samples[i] = dis(gen);
  std::sort(samples.begin(), samples.end());

  // Iterate through the budget to compute the cumulative probability
  // `cum_prob[i]` for node `i`. The j-th sample is assigned to node `i` iff
  // `cum_prob[i-1] < samples[j] < cum_prob[i]`.
  // The implementation assigns two iterators on budget and samples,
  // respectively, and then computes the node samples in linear time by
  // alternatingly incrementing the two iterators based on their values.
  output.reserve(num_samples);

  auto j = samples.begin();
  auto cum_prob = 0.f;
  for (const auto &kv : budget) {
    cum_prob += kv.second * kv.second;

    // Increment iterator `j` until its value is greater than `cum_prob`:
    while (*j < cum_prob && j != samples.end()) {
      output.insert(kv.first);
      j++;
    }

    // Terminate early in case we have completed the sampling:
    if (j == samples.end())
      break;
  }

  return output;
}

std::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, std::vector<int64_t>> &num_samples_dict,
               int64_t num_hops) {

  // Create mapping to convert single string relations to edge type triplets:
  std::unordered_map<rel_t, edge_t> rel_to_edge_type;
  for (const auto &kv : colptr_dict) {
    const auto &rel_type = kv.key();
    rel_to_edge_type[rel_type] = split(rel_type);
  }

  // Initialize various data structures for the sampling process:
  std::unordered_map<node_t, std::vector<int64_t>> sampled_nodes_dict;
  std::unordered_map<node_t, std::unordered_map<int64_t, int64_t>>
      global_to_local_node_dict;
  std::unordered_map<node_t, std::unordered_map<int64_t, float>> budget_dict;
  for (const auto &kv : num_samples_dict) {
    const auto &node_type = kv.key();
    sampled_nodes_dict[node_type];
    global_to_local_node_dict[node_type];
    budget_dict[node_type];
  }

  // Add all input nodes of every node type to the sampled output set, and
  // compute initial budget (line 1-5):
  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 &sampled_nodes = sampled_nodes_dict.at(node_type);
    auto &global_to_local_node = global_to_local_node_dict.at(node_type);

    // Add each origin node to the sampled output nodes (line 1):
    for (int64_t i = 0; i < input_node.numel(); i++) {
      const auto v = input_node_data[i];
      sampled_nodes.push_back(v);
      global_to_local_node[v] = i;
    }

    // Update budget after input nodes have been added to the sampled output set
    // (line 2-5):
    update_budget<std::vector<int64_t>>(
        &budget_dict, node_type, sampled_nodes, global_to_local_node_dict,
        rel_to_edge_type, colptr_dict, row_dict);
  }

  // Sample nodes for each node type in each layer (line 6 - 18):
  for (int64_t ell = 0; ell < num_hops; ell++) {
    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 of `node_type` according to the budget
      // (line 9-11):
      const auto samples = sample_from(budget, num_samples);

      if (samples.size() > 0) {
        // Add sampled nodes to the sampled output set (line 13):
        auto &sampled_nodes = sampled_nodes_dict.at(node_type);
        auto &global_to_local_node = global_to_local_node_dict.at(node_type);
        for (const auto &v : samples) {
          sampled_nodes.push_back(v);
          global_to_local_node[v] = sampled_nodes.size();
        }

        // Add neighbors of newly sampled nodes to the bucket (line 14-15):
        update_budget<std::unordered_set<int64_t>>(
            &budget_dict, node_type, samples, global_to_local_node_dict,
            rel_to_edge_type, colptr_dict, row_dict);
      }
    }
  }

  // Reconstruct the sampled adjacency matrix among the sampled nodes (line 19):
  c10::Dict<rel_t, torch::Tensor> output_row_dict;
  c10::Dict<rel_t, torch::Tensor> output_col_dict;
  c10::Dict<rel_t, torch::Tensor> output_edge_dict;
  for (const auto &kv : colptr_dict) {
    const auto &rel_type = kv.key();
    const auto &edge_type = rel_to_edge_type.at(rel_type);
    const auto &src_node_type = std::get<0>(edge_type);
    const auto &dst_node_type = std::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 &sampled_dst_nodes = sampled_nodes_dict[dst_node_type];
    const auto &global_to_local_src = global_to_local_node_dict[src_node_type];

    std::vector<int64_t> rows, cols, edges;
    for (int64_t i = 0; i < (int64_t)sampled_dst_nodes.size(); i++) {
      const auto v = sampled_dst_nodes[i];
      const int64_t col_start = colptr_data[v], col_end = colptr_data[v + 1];
      for (int64_t j = col_start; j < col_end; j++) {
        const auto w = row_data[j];
        if (global_to_local_src.find(w) != global_to_local_src.end()) {
          rows.push_back(global_to_local_src.at(w));
          cols.push_back(i);
          edges.push_back(j);
        }
      }
    }

    if (rows.size() > 0) {
      output_row_dict.insert(rel_type, vec_to_tensor(rows));
      output_col_dict.insert(rel_type, vec_to_tensor(cols));
      output_edge_dict.insert(rel_type, vec_to_tensor(edges));
    }
  }

  // Generate tensor-valued output node dict (line 20):
  c10::Dict<node_t, torch::Tensor> output_node_dict;
  for (const auto &kv : sampled_nodes_dict) {
    if (kv.second.size() > 0)
      output_node_dict.insert(kv.first, vec_to_tensor(kv.second));
  }

  return std::make_tuple(output_node_dict, output_row_dict, output_col_dict,
                         output_edge_dict);
}