remove random_seed parameter

cpp/bench/ann/src/raft/raft_ann_bench_param_parser.h

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2023, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -55,7 +55,6 @@ void parse_build_param(const nlohmann::json& conf,
   param.n_lists = conf.at("nlist");
   if (conf.contains("niter")) { param.kmeans_n_iters = conf.at("niter"); }
   if (conf.contains("ratio")) { param.kmeans_trainset_fraction = 1.0 / (double)conf.at("ratio"); }
-  if (conf.contains("random_seed")) { param.random_seed = conf.at("random_seed"); }
 }
 
 template <typename T, typename IdxT>
@@ -88,7 +87,6 @@ void parse_build_param(const nlohmann::json& conf,
                                "', should be either 'cluster' or 'subspace'");
     }
   }
-  if (conf.contains("random_seed")) { param.random_seed = conf.at("random_seed"); }
 }
 
 template <typename T, typename IdxT>

cpp/include/raft/neighbors/detail/ivf_flat_build.cuh

@@ -361,12 +361,13 @@ inline auto build(raft::resources const& handle,
 
   // Train the kmeans clustering
   {
+    int random_seed     = 137;
     auto trainset_ratio = std::max<size_t>(
       1, n_rows / std::max<size_t>(params.kmeans_trainset_fraction * n_rows, index.n_lists()));
     auto n_rows_train = n_rows / trainset_ratio;
     auto trainset     = make_device_matrix<T, IdxT>(handle, n_rows_train, index.dim());
     raft::spatial::knn::detail::utils::subsample(
-      handle, dataset, n_rows, trainset.view(), params.random_seed);
+      handle, dataset, n_rows, trainset.view(), random_seed);
     auto centers_view = raft::make_device_matrix_view<float, IdxT>(
       index.centers().data_handle(), index.n_lists(), index.dim());
     raft::cluster::kmeans_balanced_params kmeans_params;

cpp/include/raft/neighbors/detail/ivf_pq_build.cuh

@@ -1751,6 +1751,7 @@ auto build(raft::resources const& handle,
   utils::memzero(index.inds_ptrs().data_handle(), index.inds_ptrs().size(), stream);
 
   {
+    int random_seed     = 137;
     auto trainset_ratio = std::max<size_t>(
       1,
       size_t(n_rows) / std::max<size_t>(params.kmeans_trainset_fraction * n_rows, index.n_lists()));
@@ -1766,14 +1767,14 @@ auto build(raft::resources const& handle,
 
     if constexpr (std::is_same_v<T, float>) {
       raft::spatial::knn::detail::utils::subsample(
-        handle, dataset, n_rows, trainset.view(), params.random_seed);
+        handle, dataset, n_rows, trainset.view(), random_seed);
     } else {
       // TODO(tfeher): Enable codebook generation with any type T, and then remove
       // trainset tmp.
       auto trainset_tmp =
         make_device_mdarray<T>(handle, device_mr, make_extents<IdxT>(n_rows_train, dim));
       raft::spatial::knn::detail::utils::subsample(
-        handle, dataset, n_rows, trainset_tmp.view(), params.random_seed);
+        handle, dataset, n_rows, trainset_tmp.view(), random_seed);
       cudaDeviceSynchronize();
       RAFT_LOG_INFO("Subsampling done, converting to float");
       raft::linalg::unaryOp(trainset.data_handle(),

cpp/include/raft/neighbors/ivf_flat_types.hpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -76,12 +76,6 @@ struct index_params : ann::index_params {
    * flag to `true` if you prefer to use as little GPU memory for the database as possible.
    */
   bool conservative_memory_allocation = false;
-  /**
-   * Seed used for random sampling if kmeans_trainset_fraction < 1.
-   *
-   * Value -1 disables random sampling, and results in sampling with a fixed stride.
-   */
-  int random_seed = 0;
 };
 
 struct search_params : ann::search_params {

cpp/include/raft/neighbors/ivf_pq_types.hpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -105,13 +105,6 @@ struct index_params : ann::index_params {
    * flag to `true` if you prefer to use as little GPU memory for the database as possible.
    */
   bool conservative_memory_allocation = false;
-
-  /**
-   * Seed used for random sampling if kmeans_trainset_fraction < 1.
-   *
-   * Value -1 disables random sampling, and results in sampling with a fixed stride.
-   */
-  int random_seed = 0;
 };
 
 struct search_params : ann::search_params {

cpp/test/neighbors/ann_ivf_flat.cuh

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022-2024, NVIDIA CORPORATION.
+ * Copyright (c) 2022-2023, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -73,15 +73,14 @@ struct AnnIvfFlatInputs {
   raft::distance::DistanceType metric;
   bool adaptive_centers;
   bool host_dataset;
-  int seed;
 };
 
 template <typename IdxT>
 ::std::ostream& operator<<(::std::ostream& os, const AnnIvfFlatInputs<IdxT>& p)
 {
   os << "{ " << p.num_queries << ", " << p.num_db_vecs << ", " << p.dim << ", " << p.k << ", "
      << p.nprobe << ", " << p.nlist << ", " << static_cast<int>(p.metric) << ", "
-     << p.adaptive_centers << ", " << p.host_dataset << "," << p.seed << '}' << std::endl;
+     << p.adaptive_centers << ", " << p.host_dataset << '}' << std::endl;
   return os;
 }
 
@@ -179,7 +178,6 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
         index_params.add_data_on_build        = false;
         index_params.kmeans_trainset_fraction = 0.5;
         index_params.metric_arg               = 0;
-        index_params.random_seed              = ps.seed;
 
         ivf_flat::index<DataT, IdxT> idx(handle_, index_params, ps.dim);
         ivf_flat::index<DataT, IdxT> index_2(handle_, index_params, ps.dim);
@@ -329,7 +327,6 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
     index_params.add_data_on_build        = false;
     index_params.kmeans_trainset_fraction = 1.0;
     index_params.metric_arg               = 0;
-    index_params.random_seed              = ps.seed;
 
     auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
       (const DataT*)database.data(), ps.num_db_vecs, ps.dim);
@@ -500,7 +497,6 @@ class AnnIVFFlatTest : public ::testing::TestWithParam<AnnIvfFlatInputs<IdxT>> {
         index_params.add_data_on_build        = true;
         index_params.kmeans_trainset_fraction = 0.5;
         index_params.metric_arg               = 0;
-        index_params.random_seed              = ps.seed;
 
         // Create IVF Flat index
         auto database_view = raft::make_device_matrix_view<const DataT, IdxT>(
@@ -611,8 +607,6 @@ const std::vector<AnnIvfFlatInputs<int64_t>> inputs = {
   {20, 100000, 16, 10, 20, 1024, raft::distance::DistanceType::L2Expanded, true},
   {1000, 100000, 16, 10, 20, 1024, raft::distance::DistanceType::L2Expanded, true},
   {10000, 131072, 8, 10, 20, 1024, raft::distance::DistanceType::L2Expanded, false},
-  {10000, 1000000, 96, 10, 20, 1024, raft::distance::DistanceType::L2Expanded, false, true, -1},
-  {10000, 1000000, 96, 10, 20, 1024, raft::distance::DistanceType::L2Expanded, false, false, -1},
 
   // host input data
   {1000, 10000, 16, 10, 40, 1024, raft::distance::DistanceType::L2Expanded, false, true},

python/pylibraft/pylibraft/neighbors/ivf_flat/cpp/c_ivf_flat.pxd

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -56,7 +56,6 @@ cdef extern from "raft/neighbors/ivf_flat_types.hpp" \
         double kmeans_trainset_fraction
         bool adaptive_centers
         bool conservative_memory_allocation
-        int random_seed
 
     cdef cppclass index[T, IdxT](ann_index):
         index(const device_resources& handle,

python/pylibraft/pylibraft/neighbors/ivf_flat/ivf_flat.pyx

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2023-2024, NVIDIA CORPORATION.
+# Copyright (c) 2023, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -113,11 +113,6 @@ cdef class IndexParams:
         adding new data (through the classification of the added data);
         that is, `index.centers()` "drift" together with the changing
         distribution of the newly added data.
-    random_seed : int, default = 0
-        Seed used for random sampling if kmeans_trainset_fraction < 1.
-        Value -1 disables random sampling, and results in sampling with a
-        fixed stride.
-
     """
     cdef c_ivf_flat.index_params params
 
@@ -127,16 +122,14 @@ cdef class IndexParams:
                  kmeans_n_iters=20,
                  kmeans_trainset_fraction=0.5,
                  add_data_on_build=True,
-                 bool adaptive_centers=False,
-                 random_seed=0):
+                 bool adaptive_centers=False):
         self.params.n_lists = n_lists
         self.params.metric = _get_metric(metric)
         self.params.metric_arg = 0
         self.params.kmeans_n_iters = kmeans_n_iters
         self.params.kmeans_trainset_fraction = kmeans_trainset_fraction
         self.params.add_data_on_build = add_data_on_build
         self.params.adaptive_centers = adaptive_centers
-        self.params.random_seed = random_seed
 
     @property
     def n_lists(self):
@@ -162,10 +155,6 @@ cdef class IndexParams:
     def adaptive_centers(self):
         return self.params.adaptive_centers
 
-    @property
-    def random_seed(self):
-        return self.params.random_seed
-
 
 cdef class Index:
     cdef readonly bool trained

python/pylibraft/pylibraft/neighbors/ivf_pq/cpp/c_ivf_pq.pxd

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -78,7 +78,6 @@ cdef extern from "raft/neighbors/ivf_pq_types.hpp" \
         codebook_gen codebook_kind
         bool force_random_rotation
         bool conservative_memory_allocation
-        int random_seed
 
     cdef cppclass index[IdxT](ann_index):
         index(const device_resources& handle,

python/pylibraft/pylibraft/neighbors/ivf_pq/ivf_pq.pyx

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -156,10 +156,6 @@ cdef class IndexParams:
         repeated calls to `extend` (extending the database).
         To disable this behavior and use as little GPU memory for the
         database as possible, set this flat to `True`.
-    random_seed : int, default = 0
-        Seed used for random sampling if kmeans_trainset_fraction < 1.
-        Value -1 disables random sampling, and results in sampling with a
-        fixed stride.
     """
     def __init__(self, *,
                  n_lists=1024,
@@ -171,8 +167,7 @@ cdef class IndexParams:
                  codebook_kind="subspace",
                  force_random_rotation=False,
                  add_data_on_build=True,
-                 conservative_memory_allocation=False,
-                 random_seed=0):
+                 conservative_memory_allocation=False):
         self.params.n_lists = n_lists
         self.params.metric = _get_metric(metric)
         self.params.metric_arg = 0
@@ -190,7 +185,6 @@ cdef class IndexParams:
         self.params.add_data_on_build = add_data_on_build
         self.params.conservative_memory_allocation = \
             conservative_memory_allocation
-        self.params.random_seed = random_seed
 
     @property
     def n_lists(self):
@@ -232,10 +226,6 @@ cdef class IndexParams:
     def conservative_memory_allocation(self):
         return self.params.conservative_memory_allocation
 
-    @property
-    def random_seed(self):
-        return self.params.random_seed
-
 
 cdef class Index:
     # We store a pointer to the index because it dose not have a trivial