update

csrc/cpu/hgt_sample_cpu.cpp

 #include "hgt_sample_cpu.h"
 
+#include <chrono> // TODO
 #include <random>
 
+#include <ATen/Parallel.h>
+
 edge_t split(const rel_t &rel_type) {
   std::vector<std::string> result(3);
   int start = 0, end = 0;
@@ -14,8 +17,9 @@ edge_t split(const rel_t &rel_type) {
 }
 
 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();
+  auto *data = (int64_t *)v.data();
+  auto size = (int64_t)v.size();
+  return torch::from_blob(data, {size}, at::kLong).clone();
 }
 
 template <typename Container>
@@ -46,13 +50,30 @@ void update_budget(
 
     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 col_count = col_end - col_start;
+      if (col_count > 520) { // TODO
+        // There might be same neighbors with large neighborhood sizes.
+        // In order to prevent that we fill our budget stare with many values
+        // of low probability, we simply sample a subset without replacement.
+        std::unordered_set<int64_t> perm;
+        for (int64_t j = col_count - 520; j < col_count; j++) {
+          if (!perm.insert(rand() % j).second)
+            perm.insert(j);
+        }
+        const auto inv_deg = 1.f / 520.f;
+        for (const auto &p : perm) {
+          const auto w = row_data[col_start + p];
+          // 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[w] += inv_deg;
+        }
+      } else if (col_count > 0) {
         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;
+            budget[w] += inv_deg;
         }
       }
     }
@@ -91,7 +112,14 @@ sample_from(const std::unordered_map<int64_t, float> &budget,
   // 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.
+  // TODO
   output.reserve(num_samples);
+  for (const auto &kv : budget) {
+    output.insert(kv.first);
+    if (output.size() == num_samples)
+      break;
+  }
+  return output;
 
   auto j = samples.begin();
   auto cum_prob = 0.f;
@@ -120,6 +148,7 @@ hgt_sample_cpu(const c10::Dict<rel_t, torch::Tensor> &colptr_dict,
                const c10::Dict<node_t, std::vector<int64_t>> &num_samples_dict,
                int64_t num_hops) {
 
+  std::chrono::steady_clock::time_point a = std::chrono::steady_clock::now();
   // 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) {
@@ -155,46 +184,79 @@ hgt_sample_cpu(const c10::Dict<rel_t, torch::Tensor> &colptr_dict,
       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):
+  for (const auto &kv : input_node_dict) {
+    const auto &node_type = kv.key();
+    const auto &sampled_nodes = sampled_nodes_dict.at(node_type);
 
-    // 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);
   }
 
+  std::chrono::steady_clock::time_point b = std::chrono::steady_clock::now();
+  std::cout
+      << "[1] = "
+      << std::chrono::duration_cast<std::chrono::microseconds>(b - a).count()
+      << "[µs]" << std::endl;
+
+  a = std::chrono::steady_clock::now();
   // 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];
+    std::vector<node_t> node_types; // Only iterate over non-empty budgets.
+    for (const auto &kv : budget_dict) {
+      if (kv.second.size() > 0)
+        node_types.push_back(kv.first);
+    }
 
-      // Sample `num_samples` nodes of `node_type` according to the budget
-      // (line 9-11):
-      const auto samples = sample_from(budget, num_samples);
+    std::unordered_map<node_t, std::unordered_set<int64_t>>
+        tmp_sampled_nodes_dict;
+    at::parallel_for(0, node_types.size(), 1, [&](int64_t begin, int64_t end) {
+      for (int64_t i = begin; i < end; i++) {
+        const auto &node_type = node_types[i];
+        const auto &budget = budget_dict.at(node_type);
+        const auto num_samples = num_samples_dict.at(node_type)[ell];
 
-      if (samples.size() > 0) {
-        // Add sampled nodes to the sampled output set (line 13):
+        // Sample `num_samples` nodes of `node_type` according to the budget
+        // (line 9-11):
+        const auto tmp_sampled_nodes = sample_from(budget, num_samples);
+        tmp_sampled_nodes_dict[node_type] = tmp_sampled_nodes;
+
+        // Add intermediate samples 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) {
+        for (const auto &v : tmp_sampled_nodes) {
           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);
       }
+    });
+
+    for (const auto &kv : tmp_sampled_nodes_dict) {
+      // Add neighbors of newly sampled nodes to the bucket (line 14-15):
+      update_budget<std::unordered_set<int64_t>>(
+          &budget_dict, kv.first, kv.second, global_to_local_node_dict,
+          rel_to_edge_type, colptr_dict, row_dict);
     }
   }
+  b = std::chrono::steady_clock::now();
+  std::cout
+      << "[2] = "
+      << std::chrono::duration_cast<std::chrono::microseconds>(b - a).count()
+      << "[µs]" << std::endl;
 
+  a = std::chrono::steady_clock::now();
   // 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;
+
+  // TODO: Parallelize across edge types?
+  //
+  // at::parallel_for(0, edge_types.size(), 1, [&](int64_t begin, int64_t end) {
   for (const auto &kv : colptr_dict) {
     const auto &rel_type = kv.key();
     const auto &edge_type = rel_to_edge_type.at(rel_type);
@@ -234,6 +296,11 @@ hgt_sample_cpu(const c10::Dict<rel_t, torch::Tensor> &colptr_dict,
     if (kv.second.size() > 0)
       output_node_dict.insert(kv.first, vec_to_tensor(kv.second));
   }
+  b = std::chrono::steady_clock::now();
+  std::cout
+      << "[3] = "
+      << std::chrono::duration_cast<std::chrono::microseconds>(b - a).count()
+      << "[µs]" << std::endl;
 
   return std::make_tuple(output_node_dict, output_row_dict, output_col_dict,
                          output_edge_dict);

test/test_hgt_sample.py

+# from typing import Dict, List
+
+import torch
+# from torch import Tensor
+# from torch_sparse import SparseTensor
+
+
+def test_hgt_sample():
+    rowptr = torch.tensor([0, 1, 3, 4])
+    # row = torch.tensor([0, 1, 1, 2])
+    col = torch.tensor([1, 0, 2, 1])
+    # _ = SparseTensor(row=row, col=col, sparse_sizes=(6, 6))
+
+    rowptr_dict = {'paper__to__paper': rowptr}
+    col_dict = {'paper__to__paper': col}
+    node_idx_dict = {'paper': torch.arange(rowptr.numel() - 1)}
+    num_neighbors_dict = {'paper__to__paper': [5, 5]}
+    num_hops = 2
+
+    # nid = torch.tensor([0, 1])
+    fn = torch.ops.torch_sparse.hgt_sample
+    # print(fn)
+    # fn(rowptr_dict, col_dict, node_idx_dict, num_neighbors_dict, num_hops)
+
+    fn(rowptr_dict, col_dict, node_idx_dict, num_neighbors_dict, num_hops)
+
+    # out = fn(rowptr, col, nid, 1, 3, False)
+    # rowptr, col, nid, eid, ptr, root_n_id = out
+
+    # assert nid.tolist() == [0, 1, 2, 3, 0, 1, 2]
+    # assert rowptr.tolist() == [0, 3, 5, 7, 8, 10, 12, 14]
+    # #      row             [0, 0, 0, 1, 1, 2, 2, 3, 4, 4, 5, 5, 6, 6]
+    # assert col.tolist() == [1, 2, 3, 0, 2, 0, 1, 0, 5, 6, 4, 6, 4, 5]
+    # assert eid.tolist() == [0, 1, 2, 3, 4, 5, 6, 9, 0, 1, 3, 4, 5, 6]
+    # assert ptr.tolist() == [0, 4, 7]
+    # assert root_n_id.tolist() == [0, 5]
+
+
+# import timeit
+# print('Timeit', timeit.timeit(test_hgt_sample))