Refactor spectral scale_obs to use existing normalization function

cpp/include/raft/linalg/normalize.cuh

@@ -18,9 +18,11 @@
 
 #include "detail/normalize.cuh"
 
+#include <raft/core/device_mdspan.hpp>
 #include <raft/core/operators.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/linalg/norm_types.hpp>
+#include <raft/util/input_validation.hpp>
 
 namespace raft {
 namespace linalg {

cpp/include/raft/spectral/detail/modularity_maximization.hpp

@@ -19,6 +19,7 @@
 #include <raft/core/resource/cublas_handle.hpp>
 #include <raft/core/resource/cuda_stream.hpp>
 #include <raft/linalg/detail/cublas_wrappers.hpp>
+#include <raft/linalg/normalize.cuh>
 #include <raft/spectral/cluster_solvers.cuh>
 #include <raft/spectral/detail/spectral_util.cuh>
 #include <raft/spectral/eigen_solvers.cuh>
@@ -101,8 +102,9 @@ std::tuple<vertex_t, weight_t, vertex_t> modularity_maximization(
 
   // notice that at this point the matrix has already been transposed, so we are scaling
   // columns
-  scale_obs(nEigVecs, n, eigVecs);
-  RAFT_CHECK_CUDA(stream);
+  auto dataset_view = raft::make_device_matrix_view(eigVecs, nEigVecs, n);
+  raft::linalg::row_normalize(
+    handle, raft::make_const_mdspan(dataset_view), dataset_view, raft::linalg::L2Norm);
 
   // Find partition clustering
   auto pair_cluster = cluster_solver.solve(handle, n, nEigVecs, eigVecs, clusters);

cpp/include/raft/spectral/detail/spectral_util.cuh

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -39,85 +39,6 @@
 namespace raft {
 namespace spectral {
 
-template <typename index_type_t, typename value_type_t>
-RAFT_KERNEL scale_obs_kernel(index_type_t m, index_type_t n, value_type_t* obs)
-{
-  index_type_t i, j, k, index, mm;
-  value_type_t alpha, v, last;
-  bool valid;
-  // ASSUMPTION: kernel is launched with either 2, 4, 8, 16 or 32 threads in x-dimension
-
-  // compute alpha
-  mm    = (((m + blockDim.x - 1) / blockDim.x) * blockDim.x);  // m in multiple of blockDim.x
-  alpha = 0.0;
-
-  for (j = threadIdx.y + blockIdx.y * blockDim.y; j < n; j += blockDim.y * gridDim.y) {
-    for (i = threadIdx.x; i < mm; i += blockDim.x) {
-      // check if the thread is valid
-      valid = i < m;
-
-      // get the value of the last thread
-      last = __shfl_sync(warp_full_mask(), alpha, blockDim.x - 1, blockDim.x);
-
-      // if you are valid read the value from memory, otherwise set your value to 0
-      alpha = (valid) ? obs[i + j * m] : 0.0;
-      alpha = alpha * alpha;
-
-      // do prefix sum (of size warpSize=blockDim.x =< 32)
-      for (k = 1; k < blockDim.x; k *= 2) {
-        v = __shfl_up_sync(warp_full_mask(), alpha, k, blockDim.x);
-        if (threadIdx.x >= k) alpha += v;
-      }
-      // shift by last
-      alpha += last;
-    }
-  }
-
-  // scale by alpha
-  alpha = __shfl_sync(warp_full_mask(), alpha, blockDim.x - 1, blockDim.x);
-  alpha = raft::sqrt(alpha);
-  for (j = threadIdx.y + blockIdx.y * blockDim.y; j < n; j += blockDim.y * gridDim.y) {
-    for (i = threadIdx.x; i < m; i += blockDim.x) {  // blockDim.x=32
-      index      = i + j * m;
-      obs[index] = obs[index] / alpha;
-    }
-  }
-}
-
-template <typename index_type_t>
-index_type_t next_pow2(index_type_t n)
-{
-  index_type_t v;
-  // Reference:
-  // http://graphics.stanford.edu/~seander/bithacks.html#RoundUpPowerOf2Float
-  v = n - 1;
-  v |= v >> 1;
-  v |= v >> 2;
-  v |= v >> 4;
-  v |= v >> 8;
-  v |= v >> 16;
-  return v + 1;
-}
-
-template <typename index_type_t, typename value_type_t>
-cudaError_t scale_obs(index_type_t m, index_type_t n, value_type_t* obs)
-{
-  index_type_t p2m;
-
-  // find next power of 2
-  p2m = next_pow2<index_type_t>(m);
-  // setup launch configuration
-  unsigned int xsize = std::max(2, std::min(p2m, 32));
-  dim3 nthreads{xsize, 256 / xsize, 1};
-
-  dim3 nblocks{1, (n + nthreads.y - 1) / nthreads.y, 1};
-
-  // launch scaling kernel (scale each column of obs by its norm)
-  scale_obs_kernel<index_type_t, value_type_t><<<nblocks, nthreads>>>(m, n, obs);
-
-  return cudaSuccess;
-}
-
 template <typename vertex_t, typename edge_t, typename weight_t>
 void transform_eigen_matrix(raft::resources const& handle,
                             edge_t n,