utils.h

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/*
 * SPDX-FileCopyrightText: Copyright (c) 2021-2025, NVIDIA CORPORATION.
 * SPDX-License-Identifier: Apache-2.0
 */
 
#pragma once
 
#include "../condensed_hierarchy.cu"
 
#include <common/fast_int_div.cuh>
 
#include <cuml/cluster/hdbscan.hpp>
 
#include <raft/core/device_mdspan.hpp>
#include <raft/label/classlabels.cuh>
#include <raft/linalg/matrix_vector_op.cuh>
#include <raft/linalg/norm.cuh>
#include <raft/sparse/convert/csr.cuh>
#include <raft/sparse/op/sort.cuh>
#include <raft/util/cudart_utils.hpp>
 
#include <rmm/device_uvector.hpp>
#include <rmm/exec_policy.hpp>
 
#include <cub/cub.cuh>
#include <cuda/functional>
#include <thrust/copy.h>
#include <thrust/execution_policy.h>
#include <thrust/for_each.h>
#include <thrust/functional.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/reduce.h>
#include <thrust/sort.h>
#include <thrust/transform.h>
#include <thrust/transform_reduce.h>
#include <thrust/tuple.h>
 
#include <algorithm>
 
namespace ML {
namespace HDBSCAN {
namespace detail {
namespace Utils {
 
template <typename value_idx, typename value_t, typename CUBReduceFunc>
void cub_segmented_reduce(const value_t* in,
                          value_t* out,
                          int n_segments,
                          const value_idx* offsets,
                          cudaStream_t stream,
                          CUBReduceFunc cub_reduce_func)
{
  rmm::device_uvector<char> d_temp_storage(0, stream);
  size_t temp_storage_bytes = 0;
  cub_reduce_func(nullptr, temp_storage_bytes, in, out, n_segments, offsets, offsets + 1, stream);
  d_temp_storage.resize(temp_storage_bytes, stream);
 
  cub_reduce_func(
    d_temp_storage.data(), temp_storage_bytes, in, out, n_segments, offsets, offsets + 1, stream);
}
 
template <typename value_idx, typename value_t>
Common::CondensedHierarchy<value_idx, value_t> make_cluster_tree(
  const raft::handle_t& handle, Common::CondensedHierarchy<value_idx, value_t>& condensed_tree)
{
  auto stream        = handle.get_stream();
  auto thrust_policy = handle.get_thrust_policy();
  auto parents       = condensed_tree.get_parents();
  auto children      = condensed_tree.get_children();
  auto lambdas       = condensed_tree.get_lambdas();
  auto sizes         = condensed_tree.get_sizes();
 
  value_idx cluster_tree_edges = thrust::transform_reduce(
    thrust_policy,
    sizes,
    sizes + condensed_tree.get_n_edges(),
    cuda::proclaim_return_type<value_idx>(
      [=] __device__(value_idx a) -> value_idx { return static_cast<value_idx>(a > 1); }),
    static_cast<value_idx>(0),
    cuda::std::plus<value_idx>());
 
  // remove leaves from condensed tree
  rmm::device_uvector<value_idx> cluster_parents(cluster_tree_edges, stream);
  rmm::device_uvector<value_idx> cluster_children(cluster_tree_edges, stream);
  rmm::device_uvector<value_t> cluster_lambdas(cluster_tree_edges, stream);
  rmm::device_uvector<value_idx> cluster_sizes(cluster_tree_edges, stream);
 
  auto in = thrust::make_zip_iterator(thrust::make_tuple(parents, children, lambdas, sizes));
 
  auto out = thrust::make_zip_iterator(thrust::make_tuple(
    cluster_parents.data(), cluster_children.data(), cluster_lambdas.data(), cluster_sizes.data()));
 
  thrust::copy_if(thrust_policy,
                  in,
                  in + (condensed_tree.get_n_edges()),
                  sizes,
                  out,
                  [=] __device__(value_idx a) { return a > 1; });
 
  auto n_leaves = condensed_tree.get_n_leaves();
  thrust::transform(thrust_policy,
                    cluster_parents.begin(),
                    cluster_parents.end(),
                    cluster_parents.begin(),
                    [n_leaves] __device__(value_idx a) { return a - n_leaves; });
  thrust::transform(thrust_policy,
                    cluster_children.begin(),
                    cluster_children.end(),
                    cluster_children.begin(),
                    [n_leaves] __device__(value_idx a) { return a - n_leaves; });
 
  return Common::CondensedHierarchy<value_idx, value_t>(handle,
                                                        condensed_tree.get_n_leaves(),
                                                        cluster_tree_edges,
                                                        condensed_tree.get_n_clusters(),
                                                        std::move(cluster_parents),
                                                        std::move(cluster_children),
                                                        std::move(cluster_lambdas),
                                                        std::move(cluster_sizes));
}
 
template <typename value_idx, typename value_t>
void parent_csr(const raft::handle_t& handle,
                Common::CondensedHierarchy<value_idx, value_t>& condensed_tree,
                value_idx* sorted_parents,
                value_idx* indptr)
{
  auto stream        = handle.get_stream();
  auto thrust_policy = handle.get_thrust_policy();
 
  auto children   = condensed_tree.get_children();
  auto sizes      = condensed_tree.get_sizes();
  auto n_edges    = condensed_tree.get_n_edges();
  auto n_leaves   = condensed_tree.get_n_leaves();
  auto n_clusters = condensed_tree.get_n_clusters();
 
  // 0-index sorted parents by subtracting n_leaves for offsets and birth/stability indexing
  auto index_op = [n_leaves] __device__(const auto& x) { return x - n_leaves; };
  thrust::transform(
    thrust_policy, sorted_parents, sorted_parents + n_edges, sorted_parents, index_op);
 
  raft::sparse::convert::sorted_coo_to_csr(sorted_parents, n_edges, indptr, n_clusters + 1, stream);
}
 
template <typename value_idx, typename value_t>
void normalize(value_t* data, value_idx n, size_t m, cudaStream_t stream)
{
  rmm::device_uvector<value_t> sums(m, stream);
 
  // Compute row sums
  raft::linalg::rowNorm<raft::linalg::NormType::L1Norm, true, value_t, size_t>(
    sums.data(), data, (size_t)n, m, stream);
 
  // Divide vector by row sums (modify in place)
  raft::linalg::matrixVectorOp<true, false>(
    data,
    const_cast<value_t*>(data),
    sums.data(),
    n,
    (value_idx)m,
    [] __device__(value_t mat_in, value_t vec_in) { return mat_in / vec_in; },
    stream);
}
 
template <typename value_idx, typename value_t>
void softmax(const raft::handle_t& handle, value_t* data, value_idx n, size_t m)
{
  rmm::device_uvector<value_t> linf_norm(m, handle.get_stream());
 
  auto data_const_view =
    raft::make_device_matrix_view<const value_t, value_idx, raft::row_major>(data, (int)m, n);
  auto data_view =
    raft::make_device_matrix_view<value_t, value_idx, raft::row_major>(data, (int)m, n);
  auto linf_norm_const_view =
    raft::make_device_vector_view<const value_t, value_idx>(linf_norm.data(), (int)m);
  auto linf_norm_view = raft::make_device_vector_view<value_t, value_idx>(linf_norm.data(), (int)m);
 
  raft::linalg::norm<raft::linalg::NormType::LinfNorm, raft::Apply::ALONG_ROWS>(
    handle, data_const_view, linf_norm_view);
 
  raft::linalg::matrix_vector_op<raft::Apply::ALONG_COLUMNS>(
    handle,
    data_const_view,
    linf_norm_const_view,
    data_view,
    [] __device__(value_t mat_in, value_t vec_in) { return exp(mat_in - vec_in); });
}
 
};  // namespace Utils
};  // namespace detail
};  // namespace HDBSCAN
};  // namespace ML