Minor fixes

Author: ibhati

include/svs/index/ivf/common.h

@@ -250,19 +250,16 @@ void compute_matmul(
             m_int,         // const int M
             n_int,         // const int N
             k_int,         // const int K
-            1.0f,          // float alpha (explicitly float)
+            1.0f,          // float alpha
             data,          // const float* A
             k_int,         // const int lda
             centroids,     // const float* B
             k_int,         // const int ldb
-            0.0f,          // const float beta (explicitly float)
+            0.0f,          // const float beta
             results,       // float* c
             n_int          // const int ldc
         );
     } else if constexpr (std::is_same_v<T, BFloat16>) {
-        // Intel MKL BFloat16 GEMM requires careful parameter casting to avoid parameter
-        // errors Ensure all integer parameters are properly cast to int (MKL expects int,
-        // not size_t)
         int m_int = static_cast<int>(m);
         int n_int = static_cast<int>(n);
         int k_int = static_cast<int>(k);
@@ -274,19 +271,16 @@ void compute_matmul(
             m_int,                      // const int M
             n_int,                      // const int N
             k_int,                      // const int K
-            1.0f,                       // float alpha (explicitly float)
+            1.0f,                       // float alpha
             (const uint16_t*)data,      // const *uint16_t A
             k_int,                      // const int lda
             (const uint16_t*)centroids, // const uint16_t* B
             k_int,                      // const int ldb
-            0.0f,                       // const float beta (explicitly float)
+            0.0f,                       // const float beta
             results,                    // float* c
             n_int                       // const int ldc
         );
     } else if constexpr (std::is_same_v<T, Float16>) {
-        // Intel MKL Float16 GEMM requires careful parameter casting to avoid parameter
-        // errors Ensure all integer parameters are properly cast to int (MKL expects int,
-        // not size_t)
         int m_int = static_cast<int>(m);
         int n_int = static_cast<int>(n);
         int k_int = static_cast<int>(k);
@@ -298,12 +292,12 @@ void compute_matmul(
             m_int,                      // const int M
             n_int,                      // const int N
             k_int,                      // const int K
-            1.0f,                       // float alpha (explicitly float)
+            1.0f,                       // float alpha
             (const uint16_t*)data,      // const *uint16_t A
             k_int,                      // const int lda
             (const uint16_t*)centroids, // const uint16_t* B
             k_int,                      // const int ldb
-            0.0f,                       // const float beta (explicitly float)
+            0.0f,                       // const float beta
             results,                    // float* c
             n_int                       // const int ldc
         );

include/svs/index/ivf/hierarchical_kmeans.h

@@ -160,14 +160,12 @@ auto hierarchical_kmeans_clustering_impl(
 
     std::vector<std::vector<I>> clusters_level1_all;
 
-    // Declare timer outside if block to avoid scope issues
+    // Declare timer outside of block to avoid scope issues
     auto all_assignments_time = timer.push_back("level1 all assignments");
 
     if (!train_only) {
         // Step 5: Assign all data to clusters
-        auto all_assignments_alloc = timer.push_back("level1 all assignments alloc");
         auto assignments_level1_all = std::vector<size_t>(data.size());
-        all_assignments_alloc.finish();
 
         batchsize = parameters.minibatch_size_;
         num_batches = lib::div_round_up(data.size(), batchsize);
@@ -178,10 +176,7 @@ auto hierarchical_kmeans_clustering_impl(
             auto this_batch = threads::UnitRange{
                 batch * batchsize, std::min((batch + 1) * batchsize, data.size())};
             auto data_batch_view = data::make_view(data, this_batch);
-            auto all_assignments_convert =
-                timer.push_back("level1 all assignments convert");
             convert_data(data_batch_view, data_batch, threadpool);
-            all_assignments_convert.finish();
             centroid_assignment(
                 data_batch,
                 data_norm,
@@ -199,7 +194,6 @@ auto hierarchical_kmeans_clustering_impl(
         clusters_level1_all =
             group_assignments(assignments_level1_all, num_level1_clusters, data);
         all_assignments_cluster.finish();
-        all_assignments_time.finish();
     } else {
         // For train_only, create empty clusters
         clusters_level1_all.resize(num_level1_clusters);