Merge branch 'master' into dist-sampler-relabel-1

CHANGELOG.md

@@ -5,7 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [0.3.0] - 2023-MM-DD
 ### Added
-- Added low-level support for distributed neighborhood sampling ([#246](https://github.com/pyg-team/pyg-lib/pull/246), [#253](https://github.com/pyg-team/pyg-lib/pull/253), [#254](https://github.com/pyg-team/pyg-lib/pull/254))
+- Added low-level support for distributed neighborhood sampling ([#246](https://github.com/pyg-team/pyg-lib/pull/246), [#252](https://github.com/pyg-team/pyg-lib/pull/252), [#253](https://github.com/pyg-team/pyg-lib/pull/253), [#254](https://github.com/pyg-team/pyg-lib/pull/254))
 - Added support for homogeneous and heterogeneous biased neighborhood sampling ([#247](https://github.com/pyg-team/pyg-lib/pull/247), [#251](https://github.com/pyg-team/pyg-lib/pull/251))
 - Added dispatch for XPU device in `index_sort` ([#243](https://github.com/pyg-team/pyg-lib/pull/243))
 - Added `metis` partitioning ([#229](https://github.com/pyg-team/pyg-lib/pull/229))

pyg_lib/csrc/sampler/cpu/dist_merge_outputs_kernel.cpp

@@ -0,0 +1,170 @@
+#include <ATen/ATen.h>
+#include <ATen/Parallel.h>
+#include <torch/library.h>
+
+#include "parallel_hashmap/phmap.h"
+
+#include "pyg_lib/csrc/sampler/cpu/mapper.h"
+#include "pyg_lib/csrc/utils/cpu/convert.h"
+#include "pyg_lib/csrc/utils/types.h"
+
+namespace pyg {
+namespace sampler {
+
+namespace {
+
+template <bool disjoint>
+std::tuple<at::Tensor,
+           at::Tensor,
+           c10::optional<at::Tensor>,
+           std::vector<int64_t>>
+merge_outputs(
+    const std::vector<at::Tensor>& node_ids,
+    const std::vector<at::Tensor>& edge_ids,
+    const std::vector<std::vector<int64_t>>& cumsum_neighbors_per_node,
+    const std::vector<int64_t>& partition_ids,
+    const std::vector<int64_t>& partition_orders,
+    const int64_t num_partitions,
+    const int64_t num_neighbors,
+    const c10::optional<at::Tensor>& batch) {
+  at::Tensor out_node_id;
+  at::Tensor out_edge_id;
+  c10::optional<at::Tensor> out_batch = c10::nullopt;
+
+  auto offset = num_neighbors;
+
+  if (num_neighbors < 0) {
+    // find maximum population
+    std::vector<std::vector<int64_t>> population(num_partitions);
+    std::vector<int64_t> max_populations(num_partitions);
+
+    at::parallel_for(0, num_partitions, 1, [&](size_t _s, size_t _e) {
+      for (auto p_id = _s; p_id < _e; p_id++) {
+        auto cummsum1 =
+            std::vector<int64_t>(cumsum_neighbors_per_node[p_id].begin() + 1,
+                                 cumsum_neighbors_per_node[p_id].end());
+        auto cummsum2 =
+            std::vector<int64_t>(cumsum_neighbors_per_node[p_id].begin(),
+                                 cumsum_neighbors_per_node[p_id].end() - 1);
+        std::transform(cummsum1.begin(), cummsum1.end(), cummsum2.begin(),
+                       std::back_inserter(population[p_id]),
+                       [](int64_t a, int64_t b) { return std::abs(a - b); });
+        auto max =
+            *max_element(population[p_id].begin(), population[p_id].end());
+        max_populations[p_id] = max;
+      }
+    });
+    offset = *max_element(max_populations.begin(), max_populations.end());
+  }
+
+  const auto p_size = partition_ids.size();
+  std::vector<int64_t> sampled_neighbors_per_node(p_size);
+
+  const auto scalar_type = node_ids[0].scalar_type();
+  AT_DISPATCH_INTEGRAL_TYPES(scalar_type, "merge_outputs_kernel", [&] {
+    std::vector<scalar_t> sampled_node_ids(p_size * offset, -1);
+    std::vector<scalar_t> sampled_edge_ids(p_size * offset, -1);
+    std::vector<std::vector<scalar_t>> sampled_node_ids_vec(p_size);
+    std::vector<std::vector<scalar_t>> sampled_edge_ids_vec(p_size);
+
+    std::vector<scalar_t> sampled_batch;
+    if constexpr (disjoint) {
+      sampled_batch = std::vector<scalar_t>(p_size * offset, -1);
+    }
+    const auto batch_data =
+        disjoint ? batch.value().data_ptr<scalar_t>() : nullptr;
+
+    for (auto p_id = 0; p_id < num_partitions; p_id++) {
+      sampled_node_ids_vec[p_id] =
+          pyg::utils::to_vector<scalar_t>(node_ids[p_id]);
+      sampled_edge_ids_vec[p_id] =
+          pyg::utils::to_vector<scalar_t>(edge_ids[p_id]);
+    }
+    at::parallel_for(0, p_size, 1, [&](size_t _s, size_t _e) {
+      for (auto j = _s; j < _e; j++) {
+        auto p_id = partition_ids[j];
+        auto p_order = partition_orders[j];
+
+        // When it comes to node and batch, we omit seed nodes.
+        // In the case of edges, we take into account all sampled edge ids.
+        auto begin_node = cumsum_neighbors_per_node[p_id][p_order];
+        auto begin_edge = begin_node - cumsum_neighbors_per_node[p_id][0];
+
+        auto end_node = cumsum_neighbors_per_node[p_id][p_order + 1];
+        auto end_edge = end_node - cumsum_neighbors_per_node[p_id][0];
+
+        std::copy(sampled_node_ids_vec[p_id].begin() + begin_node,
+                  sampled_node_ids_vec[p_id].begin() + end_node,
+                  sampled_node_ids.begin() + j * offset);
+        std::copy(sampled_edge_ids_vec[p_id].begin() + begin_edge,
+                  sampled_edge_ids_vec[p_id].begin() + end_edge,
+                  sampled_edge_ids.begin() + j * offset);
+
+        if constexpr (disjoint) {
+          std::fill(sampled_batch.begin() + j * offset,
+                    sampled_batch.begin() + j * offset + end_node - begin_node,
+                    batch_data[j]);
+        }
+
+        sampled_neighbors_per_node[j] = end_node - begin_node;
+      }
+    });
+
+    // Remove auxilary -1 numbers:
+    auto neg =
+        std::remove(sampled_node_ids.begin(), sampled_node_ids.end(), -1);
+    sampled_node_ids.erase(neg, sampled_node_ids.end());
+    out_node_id = pyg::utils::from_vector(sampled_node_ids);
+
+    neg = std::remove(sampled_edge_ids.begin(), sampled_edge_ids.end(), -1);
+    sampled_edge_ids.erase(neg, sampled_edge_ids.end());
+    out_edge_id = pyg::utils::from_vector(sampled_edge_ids);
+
+    if constexpr (disjoint) {
+      neg = std::remove(sampled_batch.begin(), sampled_batch.end(), -1);
+      sampled_batch.erase(neg, sampled_batch.end());
+      out_batch = pyg::utils::from_vector(sampled_batch);
+    }
+  });
+
+  return std::make_tuple(out_node_id, out_edge_id, out_batch,
+                         sampled_neighbors_per_node);
+}
+
+#define DISPATCH_MERGE_OUTPUTS(disjoint, ...) \
+  if (disjoint)                               \
+    return merge_outputs<true>(__VA_ARGS__);  \
+  if (!disjoint)                              \
+    return merge_outputs<false>(__VA_ARGS__);
+
+}  // namespace
+
+std::tuple<at::Tensor,
+           at::Tensor,
+           c10::optional<at::Tensor>,
+           std::vector<int64_t>>
+merge_sampler_outputs_kernel(
+    const std::vector<at::Tensor>& node_ids,
+    const std::vector<at::Tensor>& edge_ids,
+    const std::vector<std::vector<int64_t>>& cumsum_neighbors_per_node,
+    const std::vector<int64_t>& partition_ids,
+    const std::vector<int64_t>& partition_orders,
+    const int64_t num_partitions,
+    const int64_t num_neighbors,
+    const c10::optional<at::Tensor>& batch,
+    bool disjoint) {
+  DISPATCH_MERGE_OUTPUTS(
+      disjoint, node_ids, edge_ids, cumsum_neighbors_per_node, partition_ids,
+      partition_orders, num_partitions, num_neighbors, batch);
+}
+
+// We use `BackendSelect` as a fallback to the dispatcher logic as automatic
+// dispatching of std::vector<at::Tensor> is not yet supported by PyTorch.
+// See: pytorch/aten/src/ATen/templates/RegisterBackendSelect.cpp.
+TORCH_LIBRARY_IMPL(pyg, BackendSelect, m) {
+  m.impl(TORCH_SELECTIVE_NAME("pyg::merge_sampler_outputs"),
+         TORCH_FN(merge_sampler_outputs_kernel));
+}
+
+}  // namespace sampler
+}  // namespace pyg

pyg_lib/csrc/sampler/dist_merge_outputs.cpp

@@ -0,0 +1,59 @@
+#include "dist_merge_outputs.h"
+
+#include <ATen/core/dispatch/Dispatcher.h>
+#include <torch/library.h>
+
+#include "pyg_lib/csrc/utils/check.h"
+
+namespace pyg {
+namespace sampler {
+
+std::tuple<at::Tensor,
+           at::Tensor,
+           c10::optional<at::Tensor>,
+           std::vector<int64_t>>
+merge_sampler_outputs(
+    const std::vector<at::Tensor>& node_ids,
+    const std::vector<at::Tensor>& edge_ids,
+    const std::vector<std::vector<int64_t>>& cumsum_neighbors_per_node,
+    const std::vector<int64_t>& partition_ids,
+    const std::vector<int64_t>& partition_orders,
+    const int64_t num_partitions,
+    const int64_t num_neighbors,
+    const c10::optional<at::Tensor>& batch,
+    bool disjoint) {
+  std::vector<at::TensorArg> node_ids_args;
+  std::vector<at::TensorArg> edge_ids_args;
+  pyg::utils::fill_tensor_args(node_ids_args, node_ids, "node_ids", 0);
+  pyg::utils::fill_tensor_args(edge_ids_args, edge_ids, "edge_ids", 0);
+
+  at::CheckedFrom c{"merge_sampler_outputs"};
+  at::checkAllDefined(c, {node_ids_args});
+  at::checkAllDefined(c, {edge_ids_args});
+
+  TORCH_CHECK(partition_ids.size() == partition_orders.size(),
+              "Every partition ID must be assigned a sampling order");
+
+  if (disjoint) {
+    TORCH_CHECK(batch.has_value(),
+                "Disjoint sampling requires 'batch' to be specified");
+  }
+
+  static auto op = c10::Dispatcher::singleton()
+                       .findSchemaOrThrow("pyg::merge_sampler_outputs", "")
+                       .typed<decltype(merge_sampler_outputs)>();
+  return op.call(node_ids, edge_ids, cumsum_neighbors_per_node, partition_ids,
+                 partition_orders, num_partitions, num_neighbors, batch,
+                 disjoint);
+}
+
+TORCH_LIBRARY_FRAGMENT(pyg, m) {
+  m.def(TORCH_SELECTIVE_SCHEMA(
+      "pyg::merge_sampler_outputs(Tensor[] node_ids, Tensor[] edge_ids, "
+      "int[][] cumsum_neighbors_per_node, int[] partition_ids, int[] "
+      "partition_orders, int num_partitions, int num_neighbors, Tensor? "
+      "batch, bool disjoint) -> (Tensor, Tensor, Tensor?, int[])"));
+}
+
+}  // namespace sampler
+}  // namespace pyg

pyg_lib/csrc/sampler/dist_merge_outputs.h

@@ -0,0 +1,34 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include "pyg_lib/csrc/macros.h"
+#include "pyg_lib/csrc/utils/types.h"
+
+namespace pyg {
+namespace sampler {
+
+// For distributed training purposes. Merges sampler outputs from different
+// partitions, so that they are sorted according to the sampling order.
+// Removes seed nodes from sampled nodes and calculates how many neighbors
+// were sampled by each source node based on the cummulative sum of sampled
+// neighbors for each input node.
+// Returns the unified node, edge and batch indices as well as the merged
+// cummulative sum of sampled neighbors.
+PYG_API
+std::tuple<at::Tensor,
+           at::Tensor,
+           c10::optional<at::Tensor>,
+           std::vector<int64_t>>
+merge_sampler_outputs(
+    const std::vector<at::Tensor>& node_ids,
+    const std::vector<at::Tensor>& edge_ids,
+    const std::vector<std::vector<int64_t>>& cumsum_neighbors_per_node,
+    const std::vector<int64_t>& partition_ids,
+    const std::vector<int64_t>& partition_orders,
+    const int64_t num_partitions,
+    const int64_t num_neighbors,
+    const c10::optional<at::Tensor>& batch = c10::nullopt,
+    bool disjoint = false);
+
+}  // namespace sampler
+}  // namespace pyg

pyg_lib/csrc/sampler/neighbor.h

@@ -61,6 +61,11 @@ hetero_neighbor_sample(
     std::string strategy = "uniform",
     bool return_edge_id = true);
 
+// For distributed sampling purposes. Leverages the `neighbor_sample` function
+// internally. Samples one-hop neighborhoods with duplicates from all node
+// indices in `seed` in the graph given by `(rowptr, col)`.
+// Returns the original node and edge indices for all sampled nodes and edges.
+// Lastly, returns the cummulative sum of sampled neighbors for each input node.
 PYG_API
 std::tuple<at::Tensor, at::Tensor, std::vector<int64_t>> dist_neighbor_sample(
     const at::Tensor& rowptr,

pyg_lib/sampler/__init__.py

@@ -165,50 +165,6 @@ def hetero_neighbor_sample(
             num_nodes_per_hop_dict, num_edges_per_hop_dict)
 
 
-def dist_neighbor_sample(
-    rowptr: Tensor,
-    col: Tensor,
-    seed: Tensor,
-    num_neighbors: int,
-    time: Optional[Tensor] = None,
-    seed_time: Optional[Tensor] = None,
-    edge_weight: Optional[Tensor] = None,
-    csc: bool = False,
-    replace: bool = False,
-    directed: bool = True,
-    disjoint: bool = False,
-    temporal_strategy: str = 'uniform',
-) -> Tuple[Tensor, Tensor, List[int]]:
-    r"""For distributed sampling purpose. Leverages the
-    :meth:`neighbor_sample`. Samples one hop neighborhood with duplicates from
-    all node indices in :obj:`seed` in the graph given by :obj:`(rowptr, col)`.
-
-    Args:
-        num_neighbors (int): Maximum number of neighbors to sample in the
-            current layer.
-        kwargs: Arguments of :meth:`neighbor_sample`.
-
-    Returns:
-        (torch.Tensor, torch.Tensor, List[int]): Returns the original node and
-        edge indices for all sampled nodes and edges. Lastly, returns the
-        cummulative sum of the amount of sampled neighbors for each input node.
-    """
-    return torch.ops.pyg.dist_neighbor_sample(
-        rowptr,
-        col,
-        seed,
-        num_neighbors,
-        time,
-        seed_time,
-        edge_weight,
-        csc,
-        replace,
-        directed,
-        disjoint,
-        temporal_strategy,
-    )
-
-
 def subgraph(
     rowptr: Tensor,
     col: Tensor,
@@ -351,7 +307,6 @@ def hetero_relabel_neighborhood(
 __all__ = [
     'neighbor_sample',
     'hetero_neighbor_sample',
-    'dist_neighbor_sample',
     'subgraph',
     'random_walk',
     'relabel_neighborhood',