/*!
 *  Copyright 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_compact_graph.cu
 * \brief Functions to find and eliminate the common isolated nodes across
 * all given graphs with the same set of nodes.
 */


#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 "../compact.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 {

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

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

  // possibly duplicated nodes
  for (int64_t ntype = 0; ntype < num_ntypes; ++ntype) {
    const IdArray& nodes = input_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],
          (*unique_nodes_device)[ntype].Ptr<IdType>(),
          count_unique_device+ntype,
          stream);
    }
  }
}


template<typename IdType>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphsGPU(
    const std::vector<HeteroGraphPtr> &graphs,
    const std::vector<IdArray> &always_preserve) {


  const auto& ctx = graphs[0]->Context();
  auto device = runtime::DeviceAPI::Get(ctx);
  cudaStream_t stream = runtime::CUDAThreadEntry::ThreadLocal()->stream;

  CHECK_EQ(ctx.device_type, kDLGPU);

  // Step 1: Collect the nodes that has connections for each type.
  const uint64_t num_ntypes = graphs[0]->NumVertexTypes();
  std::vector<std::vector<EdgeArray>> all_edges(graphs.size());   // all_edges[i][etype]

  // count the number of nodes per type
  std::vector<int64_t> max_vertex_cnt(num_ntypes, 0);
  for (size_t i = 0; i < graphs.size(); ++i) {
    const HeteroGraphPtr curr_graph = graphs[i];
    const int64_t num_etypes = curr_graph->NumEdgeTypes();

    for (IdType etype = 0; etype < num_etypes; ++etype) {
      IdType srctype, dsttype;
      std::tie(srctype, dsttype) = curr_graph->GetEndpointTypes(etype);

      const int64_t n_edges = curr_graph->NumEdges(etype);
      max_vertex_cnt[srctype] += n_edges;
      max_vertex_cnt[dsttype] += n_edges;
    }
  }

  for (size_t i = 0; i < always_preserve.size(); ++i) {
    max_vertex_cnt[i] += always_preserve[i]->shape[0];
  }

  // gather all nodes
  std::vector<IdArray> all_nodes(num_ntypes);
  std::vector<int64_t> node_offsets(num_ntypes, 0);

  for (uint64_t ntype = 0; ntype < num_ntypes; ++ntype) {
    all_nodes[ntype] = NewIdArray(max_vertex_cnt[ntype], ctx,
      sizeof(IdType)*8);
    // copy the nodes in always_preserve
    if (ntype < always_preserve.size() && always_preserve[ntype]->shape[0] > 0) {
      device->CopyDataFromTo(
          always_preserve[ntype].Ptr<IdType>(), 0,
          all_nodes[ntype].Ptr<IdType>(),
          node_offsets[ntype],
          sizeof(IdType)*always_preserve[ntype]->shape[0],
          always_preserve[ntype]->ctx,
          all_nodes[ntype]->ctx,
          always_preserve[ntype]->dtype);
      node_offsets[ntype] += sizeof(IdType)*always_preserve[ntype]->shape[0];
    }
  }

  for (size_t i = 0; i < graphs.size(); ++i) {
    const HeteroGraphPtr curr_graph = graphs[i];
    const int64_t num_etypes = curr_graph->NumEdgeTypes();

    all_edges[i].reserve(num_etypes);
    for (int64_t etype = 0; etype < num_etypes; ++etype) {
      dgl_type_t srctype, dsttype;
      std::tie(srctype, dsttype) = curr_graph->GetEndpointTypes(etype);

      const EdgeArray edges = curr_graph->Edges(etype, "eid");

      if (edges.src.defined()) {
        device->CopyDataFromTo(
            edges.src.Ptr<IdType>(), 0,
            all_nodes[srctype].Ptr<IdType>(),
            node_offsets[srctype],
            sizeof(IdType)*edges.src->shape[0],
            edges.src->ctx,
            all_nodes[srctype]->ctx,
            edges.src->dtype);
        node_offsets[srctype] += sizeof(IdType)*edges.src->shape[0];
      }
      if (edges.dst.defined()) {
        device->CopyDataFromTo(
            edges.dst.Ptr<IdType>(), 0,
            all_nodes[dsttype].Ptr<IdType>(),
            node_offsets[dsttype],
            sizeof(IdType)*edges.dst->shape[0],
            edges.dst->ctx,
            all_nodes[dsttype]->ctx,
            edges.dst->dtype);
        node_offsets[dsttype] += sizeof(IdType)*edges.dst->shape[0];
      }
      all_edges[i].push_back(edges);
    }
  }

  // Step 2: Relabel the nodes for each type to a smaller ID space
  //         using BuildNodeMaps

  // allocate space for map creation
  // the hashmap on GPU
  DeviceNodeMap<IdType> node_maps(max_vertex_cnt, 0, ctx, stream);
  // number of unique nodes per type on CPU
  std::vector<int64_t> num_induced_nodes(num_ntypes);
  // number of unique nodes per type on GPU
  int64_t * count_unique_device = static_cast<int64_t*>(
      device->AllocWorkspace(ctx, sizeof(int64_t)*num_ntypes));
  // the set of unique nodes per type
  std::vector<IdArray> induced_nodes(num_ntypes);
  for (uint64_t ntype = 0; ntype < num_ntypes; ++ntype) {
    induced_nodes[ntype] = NewIdArray(max_vertex_cnt[ntype], ctx,
      sizeof(IdType)*8);
  }

  BuildNodeMaps(
    all_nodes,
    &node_maps,
    count_unique_device,
    &induced_nodes,
    stream);

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

  // wait for the node counts to finish transferring
  device->FreeWorkspace(ctx, count_unique_device);

  // resize induced nodes
  for (uint64_t ntype = 0; ntype < num_ntypes; ++ntype) {
    induced_nodes[ntype]->shape[0] = num_induced_nodes[ntype];
  }

  // Step 3: Remap the edges of each graph using MapEdges
  std::vector<HeteroGraphPtr> new_graphs;
  for (size_t i = 0; i < graphs.size(); ++i) {
    const HeteroGraphPtr curr_graph = graphs[i];
    const auto meta_graph = curr_graph->meta_graph();
    const int64_t num_etypes = curr_graph->NumEdgeTypes();

    std::vector<HeteroGraphPtr> rel_graphs;
    rel_graphs.reserve(num_etypes);

    std::vector<IdArray> new_src;
    std::vector<IdArray> new_dst;
    std::tie(new_src, new_dst) = MapEdges(
      curr_graph, all_edges[i], node_maps, stream);

    for (IdType etype = 0; etype < num_etypes; ++etype) {
      IdType srctype, dsttype;
      std::tie(srctype, dsttype) = curr_graph->GetEndpointTypes(etype);

      rel_graphs.push_back(UnitGraph::CreateFromCOO(
          srctype == dsttype ? 1 : 2,
          induced_nodes[srctype]->shape[0],
          induced_nodes[dsttype]->shape[0],
          new_src[etype],
          new_dst[etype]));
    }

    new_graphs.push_back(CreateHeteroGraph(meta_graph, rel_graphs, num_induced_nodes));
  }

  return std::make_pair(new_graphs, induced_nodes);
}

}  // namespace

template<>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphs<kDLGPU, int32_t>(
    const std::vector<HeteroGraphPtr> &graphs,
    const std::vector<IdArray> &always_preserve) {
  return CompactGraphsGPU<int32_t>(graphs, always_preserve);
}

template<>
std::pair<std::vector<HeteroGraphPtr>, std::vector<IdArray>>
CompactGraphs<kDLGPU, int64_t>(
    const std::vector<HeteroGraphPtr> &graphs,
    const std::vector<IdArray> &always_preserve) {
  return CompactGraphsGPU<int64_t>(graphs, always_preserve);
}

}  // namespace transform
}  // namespace dgl
