Benchmark scripts and pyg::sampler::Mapper (#45)

* added benchmark * update * update * update * update * changelog * update * todo Co-authored-by: Zeyuan Tan <[email protected]>
pyg-team · May 11, 2022 · afcc419 · afcc419
1 parent 54e2907
commit afcc419
Showing 5 changed files with 154 additions and 13 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -5,6 +5,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [Unreleased]
 ### Added
+- Added `pyg::sampler::Mapper` utility for mapping global to local node indices ([#45](https://github.com/pyg-team/pyg-lib/pull/45)
+- Added benchmark script ([#45](https://github.com/pyg-team/pyg-lib/pull/45)
 - Added download script for benchmark data ([#44](https://github.com/pyg-team/pyg-lib/pull/44)
 - Added `biased sampling` utils ([#38](https://github.com/pyg-team/pyg-lib/pull/38))
 - Added `CHANGELOG.md` ([#39](https://github.com/pyg-team/pyg-lib/pull/39))

diff --git a/benchmark/main.py b/benchmark/main.py
@@ -0,0 +1,31 @@
+import time
+
+import torch
+
+import pyg_lib
+from pyg_lib.testing import to_edge_index, withDataset, withSeed
+
+
+@withSeed
+@withDataset('DIMACS10', 'citationCiteseer')
+def test_subgraph(dataset, **kwargs):
+    (rowptr, col), num_nodes = dataset, dataset[0].size(0) - 1
+    perm = torch.randperm(num_nodes, dtype=rowptr.dtype, device=rowptr.device)
+    nodes = perm[:num_nodes // 100]
+
+    t = time.perf_counter()
+    for _ in range(10):
+        pyg_lib.sampler.subgraph(rowptr, col, nodes)
+    print(time.perf_counter() - t)
+
+    edge_index = to_edge_index(rowptr, col)
+    from torch_geometric.utils import subgraph
+
+    t = time.perf_counter()
+    for _ in range(10):
+        subgraph(nodes, edge_index, num_nodes=num_nodes, relabel_nodes=True)
+    print(time.perf_counter() - t)
+
+
+if __name__ == '__main__':
+    test_subgraph()
diff --git a/pyg_lib/csrc/sampler/cpu/mapper.h b/pyg_lib/csrc/sampler/cpu/mapper.h
@@ -0,0 +1,63 @@
+#pragma once
+
+#include <ATen/ATen.h>
+
+namespace pyg {
+namespace sampler {
+
+// TODO Implement `Mapper` as an interface/abstract class to allow for other
+// implementations as well.
+template <typename scalar_t>
+class Mapper {
+ public:
+  Mapper(scalar_t num_nodes, scalar_t num_entries)
+      : num_nodes(num_nodes), num_entries(num_entries) {
+    // Use a some simple heuristic to determine whether we can use a std::vector
+    // to perform the mapping instead of relying on the more memory-friendly,
+    // but slower std::unordered_map implementation:
+    use_vec = (num_nodes < 1000000) || (num_entries > num_nodes / 10);
+
+    if (use_vec)
+      to_local_vec = std::vector<scalar_t>(num_nodes, -1);
+  }
+
+  void fill(const scalar_t* nodes_data, const scalar_t size) {
+    if (use_vec) {
+      for (scalar_t i = 0; i < size; ++i)
+        to_local_vec[nodes_data[i]] = i;
+    } else {
+      for (scalar_t i = 0; i < size; ++i)
+        to_local_map.insert({nodes_data[i], i});
+    }
+  }
+
+  void fill(const at::Tensor& nodes) {
+    fill(nodes.data_ptr<scalar_t>(), nodes.numel());
+  }
+
+  bool exists(const scalar_t& node) {
+    if (use_vec)
+      return to_local_vec[node] >= 0;
+    else
+      return to_local_map.count(node) > 0;
+  }
+
+  scalar_t map(const scalar_t& node) {
+    if (use_vec)
+      return to_local_vec[node];
+    else {
+      const auto search = to_local_map.find(node);
+      return search != to_local_map.end() ? search->second : -1;
+    }
+  }
+
+ private:
+  scalar_t num_nodes, num_entries;
+
+  bool use_vec;
+  std::vector<scalar_t> to_local_vec;
+  std::unordered_map<scalar_t, scalar_t> to_local_map;
+};
+
+}  // namespace sampler
+}  // namespace pyg
diff --git a/pyg_lib/csrc/sampler/cpu/subgraph_kernel.cpp b/pyg_lib/csrc/sampler/cpu/subgraph_kernel.cpp
@@ -2,6 +2,7 @@
 #include <ATen/Parallel.h>
 #include <torch/library.h>
 
+#include "pyg_lib/csrc/sampler/cpu/mapper.h"
 #include "pyg_lib/csrc/utils/cpu/convert.h"
 
 namespace pyg {
@@ -18,31 +19,31 @@ std::tuple<at::Tensor, at::Tensor, c10::optional<at::Tensor>> subgraph_kernel(
   TORCH_CHECK(col.is_cpu(), "'col' must be a CPU tensor");
   TORCH_CHECK(nodes.is_cpu(), "'nodes' must be a CPU tensor");
 
-  const auto deg = rowptr.new_empty({nodes.size(0)});
+  const auto num_nodes = rowptr.size(0) - 1;
   const auto out_rowptr = rowptr.new_empty({nodes.size(0) + 1});
   at::Tensor out_col;
   c10::optional<at::Tensor> out_edge_id = c10::nullopt;
 
   AT_DISPATCH_INTEGRAL_TYPES(nodes.scalar_type(), "subgraph_kernel", [&] {
+    auto mapper = pyg::sampler::Mapper<scalar_t>(num_nodes, nodes.size(0));
+    mapper.fill(nodes);
+
     const auto rowptr_data = rowptr.data_ptr<scalar_t>();
     const auto col_data = col.data_ptr<scalar_t>();
     const auto nodes_data = nodes.data_ptr<scalar_t>();
 
-    std::unordered_map<scalar_t, scalar_t> to_local_node;
-    for (scalar_t i = 0; i < nodes.size(0); ++i)  // TODO parallelize
-      to_local_node.insert({nodes_data[i], i});
-
     // We first iterate over all nodes and collect information about the number
     // of edges in the induced subgraph.
+    const auto deg = rowptr.new_empty({nodes.size(0)});
     auto deg_data = deg.data_ptr<scalar_t>();
     auto grain_size = at::internal::GRAIN_SIZE;
     at::parallel_for(0, nodes.size(0), grain_size, [&](int64_t _s, int64_t _e) {
-      for (scalar_t i = _s; i < _e; ++i) {
+      for (size_t i = _s; i < _e; ++i) {
         const auto v = nodes_data[i];
         // Iterate over all neighbors and check if they are part of `nodes`:
         scalar_t d = 0;
-        for (scalar_t j = rowptr_data[v]; j < rowptr_data[v + 1]; ++j) {
-          if (to_local_node.count(col_data[j]) > 0)
+        for (size_t j = rowptr_data[v]; j < rowptr_data[v + 1]; ++j) {
+          if (mapper.exists(col_data[j]))
             d++;
         }
         deg_data[i] = d;
@@ -73,10 +74,9 @@ std::tuple<at::Tensor, at::Tensor, c10::optional<at::Tensor>> subgraph_kernel(
         // Iterate over all neighbors and check if they are part of `nodes`:
         scalar_t offset = out_rowptr_data[i];
         for (scalar_t j = rowptr_data[v]; j < rowptr_data[v + 1]; ++j) {
-          const auto w = col_data[j];
-          const auto search = to_local_node.find(w);
-          if (search != to_local_node.end()) {
-            out_col_data[offset] = search->second;
+          const auto w = mapper.map(col_data[j]);
+          if (w >= 0) {
+            out_col_data[offset] = w;
             if (return_edge_id)
               out_edge_id_data[offset] = j;
             offset++;

diff --git a/pyg_lib/testing.py b/pyg_lib/testing.py
@@ -1,12 +1,51 @@
 import os
 import os.path as osp
-from typing import Optional, Tuple
+from typing import Callable, Optional, Tuple
 
 import torch
 from torch import Tensor
 
 from pyg_lib import get_home_dir
 
+# Decorators ##################################################################
+
+
+def withSeed(func: Callable) -> Callable:
+    def wrapper(*args, **kwargs):
+        torch.manual_seed(12345)
+        func(*args, **kwargs)
+
+    return wrapper
+
+
+def withCUDA(func: Callable) -> Callable:
+    def wrapper(*args, **kwargs):
+        func(*args, device=torch.device('cpu'), **kwargs)
+        if torch.cuda.is_available():
+            func(*args, device=torch.device('cuda:0'), **kwargs)
+
+    return wrapper
+
+
+def withDataset(group: str, name: str) -> Callable:
+    def decorator(func: Callable) -> Callable:
+        def wrapper(*args, **kwargs):
+            dataset = get_sparse_matrix(
+                group,
+                name,
+                dtype=kwargs.get('dtype', torch.long),
+                device=kwargs.get('device', None),
+            )
+
+            func(*args, dataset=dataset, **kwargs)
+
+        return wrapper
+
+    return decorator
+
+
+# Helper functions ############################################################
+
 
 def get_sparse_matrix(
     group: str,
@@ -48,3 +87,9 @@ def get_sparse_matrix(
     col = torch.from_numpy(mat.indices).to(device, dtype)
 
     return rowptr, col
+
+
+def to_edge_index(rowptr: Tensor, col: Tensor) -> Tensor:
+    row = torch.arange(rowptr.size(0) - 1, dtype=col.dtype, device=col.device)
+    row = row.repeat_interleave(rowptr[1:] - rowptr[:-1])
+    return torch.stack([row, col], dim=0)