cuda_to_block.cu

/*!
 *  Copyright 2020-2021 Contributors
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 * \file graph/transform/cuda/cuda_to_block.cu
 * \brief Functions to convert a set of edges into a graph block with local
 * ids.
 */


#include <dgl/runtime/device_api.h>
#include <dgl/immutable_graph.h>
#include <cuda_runtime.h>
#include <utility>
#include <algorithm>
#include <memory>

#include "../../../runtime/cuda/cuda_common.h"
#include "../../heterograph.h"
#include "../to_bipartite.h"
#include "cuda_map_edges.cuh"

using namespace dgl::aten;
using namespace dgl::runtime::cuda;
using namespace dgl::transform::cuda;

namespace dgl {
namespace transform {

namespace {

template<typename IdType>
class DeviceNodeMapMaker {
 public:
  DeviceNodeMapMaker(
      const std::vector<int64_t>& maxNodesPerType) :
      max_num_nodes_(0) {
    max_num_nodes_ = *std::max_element(maxNodesPerType.begin(),
        maxNodesPerType.end());
  }

  /**
  * \brief This function builds node maps for each node type, preserving the
  * order of the input nodes. Here it is assumed the lhs_nodes are not unique,
  * and thus a unique list is generated.
  *
  * \param lhs_nodes The set of source input nodes.
  * \param rhs_nodes The set of destination input nodes.
  * \param node_maps The node maps to be constructed.
  * \param count_lhs_device The number of unique source nodes (on the GPU).
  * \param lhs_device The unique source nodes (on the GPU).
  * \param stream The stream to operate on.
  */
  void Make(
      const std::vector<IdArray>& lhs_nodes,
      const std::vector<IdArray>& rhs_nodes,
      DeviceNodeMap<IdType> * const node_maps,
      int64_t * const count_lhs_device,
      std::vector<IdArray>* const lhs_device,
      cudaStream_t stream) {
    const int64_t num_ntypes = lhs_nodes.size() + rhs_nodes.size();

    CUDA_CALL(cudaMemsetAsync(
      count_lhs_device,
      0,
      num_ntypes*sizeof(*count_lhs_device),
      stream));

    // possibly dublicate lhs nodes
    const int64_t lhs_num_ntypes = static_cast<int64_t>(lhs_nodes.size());
    for (int64_t ntype = 0; ntype < lhs_num_ntypes; ++ntype) {
      const IdArray& nodes = lhs_nodes[ntype];
      if (nodes->shape[0] > 0) {
        CHECK_EQ(nodes->ctx.device_type, kDLGPU);
        node_maps->LhsHashTable(ntype).FillWithDuplicates(
            nodes.Ptr<IdType>(),
            nodes->shape[0],
            (*lhs_device)[ntype].Ptr<IdType>(),
            count_lhs_device+ntype,
            stream);
      }
    }

    // unique rhs nodes
    const int64_t rhs_num_ntypes = static_cast<int64_t>(rhs_nodes.size());
    for (int64_t ntype = 0; ntype < rhs_num_ntypes; ++ntype) {
      const IdArray& nodes = rhs_nodes[ntype];
      if (nodes->shape[0] > 0) {
        node_maps->RhsHashTable(ntype).FillWithUnique(
            nodes.Ptr<IdType>(),
            nodes->shape[0],
            stream);
      }
    }
  }

  /**
  * \brief This function builds node maps for each node type, preserving the
  * order of the input nodes. Here it is assumed both lhs_nodes and rhs_nodes
  * are unique.
  *
  * \param lhs_nodes The set of source input nodes.
  * \param rhs_nodes The set of destination input nodes.
  * \param node_maps The node maps to be constructed.
  * \param stream The stream to operate on.
  */
  void Make(
      const std::vector<IdArray>& lhs_nodes,
      const std::vector<IdArray>& rhs_nodes,
      DeviceNodeMap<IdType> * const node_maps,
      cudaStream_t stream) {
    const int64_t num_ntypes = lhs_nodes.size() + rhs_nodes.size();

    // unique lhs nodes
    const int64_t lhs_num_ntypes = static_cast<int64_t>(lhs_nodes.size());
    for (int64_t ntype = 0; ntype < lhs_num_ntypes; ++ntype) {
      const IdArray& nodes = lhs_nodes[ntype];
      if (nodes->shape[0] > 0) {
        CHECK_EQ(nodes->ctx.device_type, kDLGPU);
        node_maps->LhsHashTable(ntype).FillWithUnique(
            nodes.Ptr<IdType>(),
            nodes->shape[0],
            stream);
      }
    }

    // unique rhs nodes
    const int64_t rhs_num_ntypes = static_cast<int64_t>(rhs_nodes.size());
    for (int64_t ntype = 0; ntype < rhs_num_ntypes; ++ntype) {
      const IdArray& nodes = rhs_nodes[ntype];
      if (nodes->shape[0] > 0) {
        node_maps->RhsHashTable(ntype).FillWithUnique(
            nodes.Ptr<IdType>(),
            nodes->shape[0],
            stream);
      }
    }
  }

 private:
  IdType max_num_nodes_;
};


// Since partial specialization is not allowed for functions, use this as an
// intermediate for ToBlock where XPU = kDLGPU.
template<typename IdType>
std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlockGPU(
    HeteroGraphPtr graph,
    const std::vector<IdArray> &rhs_nodes,
    const bool include_rhs_in_lhs,
    std::vector<IdArray>* const lhs_nodes_ptr) {
  std::vector<IdArray>& lhs_nodes = *lhs_nodes_ptr;
  const bool generate_lhs_nodes = lhs_nodes.empty();

  cudaStream_t stream = 0;
  const auto& ctx = graph->Context();
  auto device = runtime::DeviceAPI::Get(ctx);

  CHECK_EQ(ctx.device_type, kDLGPU);
  for (const auto& nodes : rhs_nodes) {
    CHECK_EQ(ctx.device_type, nodes->ctx.device_type);
  }

  // Since DST nodes are included in SRC nodes, a common requirement is to fetch
  // the DST node features from the SRC nodes features. To avoid expensive sparse lookup,
  // the function assures that the DST nodes in both SRC and DST sets have the same ids.
  // As a result, given the node feature tensor ``X`` of type ``utype``,
  // the following code finds the corresponding DST node features of type ``vtype``:

  const int64_t num_etypes = graph->NumEdgeTypes();
  const int64_t num_ntypes = graph->NumVertexTypes();

  CHECK(rhs_nodes.size() == static_cast<size_t>(num_ntypes))
    << "rhs_nodes not given for every node type";

  std::vector<EdgeArray> edge_arrays(num_etypes);
  for (int64_t etype = 0; etype < num_etypes; ++etype) {
    const auto src_dst_types = graph->GetEndpointTypes(etype);
    const dgl_type_t dsttype = src_dst_types.second;
    if (!aten::IsNullArray(rhs_nodes[dsttype])) {
      edge_arrays[etype] = graph->Edges(etype);
    }
  }

  // count lhs and rhs nodes
  std::vector<int64_t> maxNodesPerType(num_ntypes*2, 0);
  for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
    maxNodesPerType[ntype+num_ntypes] += rhs_nodes[ntype]->shape[0];

    if (generate_lhs_nodes) {
      if (include_rhs_in_lhs) {
        maxNodesPerType[ntype] += rhs_nodes[ntype]->shape[0];
      }
    } else {
      maxNodesPerType[ntype] += lhs_nodes[ntype]->shape[0];
    }
  }
  if (generate_lhs_nodes) {
    // we don't have lhs_nodes, see we need to count inbound edges to get an
    // upper bound
    for (int64_t etype = 0; etype < num_etypes; ++etype) {
      const auto src_dst_types = graph->GetEndpointTypes(etype);
      const dgl_type_t srctype = src_dst_types.first;
      if (edge_arrays[etype].src.defined()) {
        maxNodesPerType[srctype] += edge_arrays[etype].src->shape[0];
      }
    }
  }

  // gather lhs_nodes
  std::vector<IdArray> src_nodes(num_ntypes);
  if (generate_lhs_nodes) {
    std::vector<int64_t> src_node_offsets(num_ntypes, 0);
    for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
      src_nodes[ntype] = NewIdArray(maxNodesPerType[ntype], ctx,
          sizeof(IdType)*8);
      if (include_rhs_in_lhs) {
        // place rhs nodes first
        device->CopyDataFromTo(rhs_nodes[ntype].Ptr<IdType>(), 0,
            src_nodes[ntype].Ptr<IdType>(), src_node_offsets[ntype],
            sizeof(IdType)*rhs_nodes[ntype]->shape[0],
            rhs_nodes[ntype]->ctx, src_nodes[ntype]->ctx,
            rhs_nodes[ntype]->dtype,
            stream);
        src_node_offsets[ntype] += sizeof(IdType)*rhs_nodes[ntype]->shape[0];
      }
    }
    for (int64_t etype = 0; etype < num_etypes; ++etype) {
      const auto src_dst_types = graph->GetEndpointTypes(etype);
      const dgl_type_t srctype = src_dst_types.first;
      if (edge_arrays[etype].src.defined()) {
        device->CopyDataFromTo(
            edge_arrays[etype].src.Ptr<IdType>(), 0,
            src_nodes[srctype].Ptr<IdType>(),
            src_node_offsets[srctype],
            sizeof(IdType)*edge_arrays[etype].src->shape[0],
            rhs_nodes[srctype]->ctx,
            src_nodes[srctype]->ctx,
            rhs_nodes[srctype]->dtype,
            stream);

        src_node_offsets[srctype] += sizeof(IdType)*edge_arrays[etype].src->shape[0];
      }
    }
  } else {
    for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
      src_nodes[ntype] = lhs_nodes[ntype];
    }
  }

  // allocate space for map creation process
  DeviceNodeMapMaker<IdType> maker(maxNodesPerType);
  DeviceNodeMap<IdType> node_maps(maxNodesPerType, num_ntypes, ctx, stream);

  if (generate_lhs_nodes) {
    lhs_nodes.reserve(num_ntypes);
    for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
      lhs_nodes.emplace_back(NewIdArray(
          maxNodesPerType[ntype], ctx, sizeof(IdType)*8));
    }
  }

  std::vector<int64_t> num_nodes_per_type(num_ntypes*2);
  // populate RHS nodes from what we already know
  for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
    num_nodes_per_type[num_ntypes+ntype] = rhs_nodes[ntype]->shape[0];
  }

  // populate the mappings
  if (generate_lhs_nodes) {
    int64_t * count_lhs_device = static_cast<int64_t*>(
        device->AllocWorkspace(ctx, sizeof(int64_t)*num_ntypes*2));

    maker.Make(
        src_nodes,
        rhs_nodes,
        &node_maps,
        count_lhs_device,
        &lhs_nodes,
        stream);

    device->CopyDataFromTo(
        count_lhs_device, 0,
        num_nodes_per_type.data(), 0,
        sizeof(*num_nodes_per_type.data())*num_ntypes,
        ctx,
        DGLContext{kDLCPU, 0},
        DGLType{kDLInt, 64, 1},
        stream);
    device->StreamSync(ctx, stream);

    // wait for the node counts to finish transferring
    device->FreeWorkspace(ctx, count_lhs_device);
  } else {
    maker.Make(
        lhs_nodes,
        rhs_nodes,
        &node_maps,
        stream);

    for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
      num_nodes_per_type[ntype] = lhs_nodes[ntype]->shape[0];
    }
  }

  std::vector<IdArray> induced_edges;
  induced_edges.reserve(num_etypes);
  for (int64_t etype = 0; etype < num_etypes; ++etype) {
    if (edge_arrays[etype].id.defined()) {
      induced_edges.push_back(edge_arrays[etype].id);
    } else {
      induced_edges.push_back(
            aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx));
    }
  }

  // build metagraph -- small enough to be done on CPU
  const auto meta_graph = graph->meta_graph();
  const EdgeArray etypes = meta_graph->Edges("eid");
  const IdArray new_dst = Add(etypes.dst, num_ntypes);
  const auto new_meta_graph = ImmutableGraph::CreateFromCOO(
      num_ntypes * 2, etypes.src, new_dst);

  // allocate vector for graph relations while GPU is busy
  std::vector<HeteroGraphPtr> rel_graphs;
  rel_graphs.reserve(num_etypes);

  // map node numberings from global to local, and build pointer for CSR
  std::vector<IdArray> new_lhs;
  std::vector<IdArray> new_rhs;
  std::tie(new_lhs, new_rhs) = MapEdges(graph, edge_arrays, node_maps, stream);

  // resize lhs nodes
  if (generate_lhs_nodes) {
    for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
      lhs_nodes[ntype]->shape[0] = num_nodes_per_type[ntype];
    }
  }

  // build the heterograph
  for (int64_t etype = 0; etype < num_etypes; ++etype) {
    const auto src_dst_types = graph->GetEndpointTypes(etype);
    const dgl_type_t srctype = src_dst_types.first;
    const dgl_type_t dsttype = src_dst_types.second;

    if (rhs_nodes[dsttype]->shape[0] == 0) {
      // No rhs nodes are given for this edge type. Create an empty graph.
      rel_graphs.push_back(CreateFromCOO(
          2, lhs_nodes[srctype]->shape[0], rhs_nodes[dsttype]->shape[0],
          aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx),
          aten::NullArray(DLDataType{kDLInt, sizeof(IdType)*8, 1}, ctx)));
    } else {
      rel_graphs.push_back(CreateFromCOO(
          2,
          lhs_nodes[srctype]->shape[0],
          rhs_nodes[dsttype]->shape[0],
          new_lhs[etype],
          new_rhs[etype]));
    }
  }

  HeteroGraphPtr new_graph = CreateHeteroGraph(
      new_meta_graph, rel_graphs, num_nodes_per_type);

  // return the new graph, the new src nodes, and new edges
  return std::make_tuple(new_graph, induced_edges);
}

}  // namespace

template<>
std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlock<kDLGPU, int32_t>(
    HeteroGraphPtr graph,
    const std::vector<IdArray> &rhs_nodes,
    bool include_rhs_in_lhs,
    std::vector<IdArray>* const lhs_nodes) {
  return ToBlockGPU<int32_t>(graph, rhs_nodes, include_rhs_in_lhs, lhs_nodes);
}

template<>
std::tuple<HeteroGraphPtr, std::vector<IdArray>>
ToBlock<kDLGPU, int64_t>(
    HeteroGraphPtr graph,
    const std::vector<IdArray> &rhs_nodes,
    bool include_rhs_in_lhs,
    std::vector<IdArray>* const lhs_nodes) {
  return ToBlockGPU<int64_t>(graph, rhs_nodes, include_rhs_in_lhs, lhs_nodes);
}

}  // namespace transform
}  // namespace dgl