Merge branch 'branch-25.10' into issues/1447/linear_regression

Author: virchan

ci/release/update-version.sh

@@ -57,7 +57,7 @@ DEPENDENCIES=(
 )
 for DEP in "${DEPENDENCIES[@]}"; do
   for FILE in dependencies.yaml conda/environments/*.yaml; do
-    sed_runner "/-.* ${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0/g" "${FILE}"
+    sed_runner "/-.* ${DEP}\(-cu[[:digit:]]\{2\}\)\{0,1\}\(\[.*\]\)\{0,1\}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0/g" "${FILE}"
   done
   for FILE in python/*/pyproject.toml; do
     sed_runner "/\"${DEP}==/ s/==.*\"/==${NEXT_SHORT_TAG_PEP440}.*,>=0.0.0a0\"/g" "${FILE}"

cpp/src/umap/simpl_set_embed/algo.cuh

@@ -21,18 +21,24 @@
 #include <cuml/common/logger.hpp>
 #include <cuml/manifold/umapparams.h>
 
+#include <raft/linalg/init.cuh>
 #include <raft/linalg/unary_op.cuh>
 #include <raft/sparse/coo.hpp>
 #include <raft/sparse/op/filter.cuh>
 #include <raft/util/cudart_utils.hpp>
 
+#include <rmm/device_scalar.hpp>
 #include <rmm/device_uvector.hpp>
 #include <rmm/exec_policy.hpp>
 
+#include <thrust/device_ptr.h>
 #include <thrust/iterator/constant_iterator.h>
 #include <thrust/iterator/discard_iterator.h>
+#include <thrust/iterator/zip_iterator.h>
 #include <thrust/reduce.h>
+#include <thrust/shuffle.h>
 #include <thrust/system/cuda/execution_policy.h>
+#include <thrust/tuple.h>
 
 #include <curand.h>
 #include <math.h>
@@ -185,6 +191,47 @@ T create_gradient_rounding_factor(
   return create_rounding_factor(max_abs, n_edges);
 }
 
+template <typename nnz_t>
+CUML_KERNEL void compute_degrees_kernel(const int* rows, nnz_t nnz, int* degrees)
+{
+  nnz_t i = blockIdx.x * blockDim.x + threadIdx.x;
+  if (i < nnz) {
+    int row = rows[i];
+    atomicAdd(&degrees[row], 1);
+  }
+}
+
+CUML_KERNEL void check_threshold_kernel(const int* degrees,
+                                        int n_vertices,
+                                        int threshold,
+                                        bool* flag)
+{
+  int i = blockIdx.x * blockDim.x + threadIdx.x;
+  if (i < n_vertices) {
+    if (degrees[i] > threshold) { *flag = true; }
+  }
+}
+
+template <typename nnz_t, int TPB_X>
+bool check_outliers(const int* rows, int m, nnz_t nnz, int threshold, cudaStream_t stream)
+{
+  rmm::device_uvector<int> graph_degree_head(m, stream);
+  raft::linalg::zero(graph_degree_head.data(), m, stream);
+
+  dim3 grid_nnz(raft::ceildiv(nnz, static_cast<nnz_t>(TPB_X)), 1, 1);
+  dim3 blk(TPB_X, 1, 1);
+  compute_degrees_kernel<<<grid_nnz, blk, 0, stream>>>(rows, nnz, graph_degree_head.data());
+
+  rmm::device_scalar<bool> has_outlier_d(0, stream);  // initialize to 0
+
+  dim3 grid_head_n(raft::ceildiv(static_cast<nnz_t>(m), static_cast<nnz_t>(TPB_X)), 1, 1);
+  check_threshold_kernel<<<grid_head_n, blk, 0, stream>>>(
+    graph_degree_head.data(), m, threshold, has_outlier_d.data());
+  cudaStreamSynchronize(stream);
+  bool has_outlier_h = has_outlier_d.value(stream);
+  return has_outlier_h;
+}
+
 /**
  * Runs gradient descent using sampling weights defined on
  * both the attraction and repulsion vectors.
@@ -199,8 +246,8 @@ void optimize_layout(T* head_embedding,
                      int head_n,
                      T* tail_embedding,
                      int tail_n,
-                     const int* head,
-                     const int* tail,
+                     int* head,
+                     int* tail,
                      nnz_t nnz,
                      T* epochs_per_sample,
                      float gamma,
@@ -213,6 +260,39 @@ void optimize_layout(T* head_embedding,
   T alpha         = params->initial_alpha;
 
   auto stream_view = rmm::cuda_stream_view(stream);
+
+  T rounding = create_gradient_rounding_factor<T, nnz_t>(head, nnz, head_n, alpha, stream_view);
+
+  auto min_n                = min(head_n, tail_n);
+  int threshold_for_outlier = 1024;  // this is a heuristic value.
+  // for smaller datasets, could be a dense point even with a smaller graph degree
+  if (min_n <= 100000) {
+    threshold_for_outlier = 256;
+  } else if (min_n <= 1000000) {
+    threshold_for_outlier = 512;
+  }
+
+  bool has_outlier = check_outliers<nnz_t, TPB_X>(head, head_n, nnz, threshold_for_outlier, stream);
+  if (move_other && !has_outlier) {
+    has_outlier = check_outliers<nnz_t, TPB_X>(tail, tail_n, nnz, threshold_for_outlier, stream);
+  }
+
+  if (has_outlier) {
+    // Shuffling is necessary when outliers may be present (i.e., dense points that undergo many
+    // updates). It is critical to avoid having too many threads update the same embedding vector
+    // simultaneously, as this can affect correctness. By shuffling, potential outlier points are
+    // distributed across threads, rather than being processed by consecutive threads that are
+    // scheduled together. This approach relies on the GPU's inability to physically schedule all
+    // nnz edges at once.
+    auto first =
+      thrust::make_zip_iterator(thrust::make_tuple(thrust::device_pointer_cast(head),
+                                                   thrust::device_pointer_cast(tail),
+                                                   thrust::device_pointer_cast(epochs_per_sample)));
+
+    thrust::default_random_engine rng(params->random_state);
+    thrust::shuffle(first, first + nnz, rng);
+  }
+
   rmm::device_uvector<T> epoch_of_next_negative_sample(nnz, stream);
   T nsr_inv = T(1.0) / params->negative_sample_rate;
   raft::linalg::unaryOp<T>(
@@ -250,8 +330,6 @@ void optimize_layout(T* head_embedding,
   dim3 blk(TPB_X, 1, 1);
   uint64_t seed = params->random_state;
 
-  T rounding = create_gradient_rounding_factor<T, nnz_t>(head, nnz, head_n, alpha, stream_view);
-
   for (int n = 0; n < n_epochs; n++) {
     call_optimize_batch_kernel<T, nnz_t, TPB_X>(head_embedding,
                                                 d_head_buffer,

cpp/src/umap/simpl_set_embed/optimize_batch_kernel.cuh

@@ -156,7 +156,8 @@ CUML_KERNEL void optimize_batch_kernel_reg(T const* head_embedding,
   for (int d = 0; d < n_components; d++) {
     auto diff   = current_reg[d] - other_reg[d];
     auto grad_d = clip<T>(attractive_grad_coeff * diff, T(-4.0), T(4.0));
-    grads[d]    = grad_d * alpha;
+    current_reg[d] += grad_d * alpha;
+    grads[d] = grad_d * alpha;
   }
   // storing gradients for negative samples back to global memory
   if (move_other) {
@@ -200,6 +201,7 @@ CUML_KERNEL void optimize_batch_kernel_reg(T const* head_embedding,
         grad_d = clip<T>(repulsive_grad_coeff * diff, T(-4.0), T(4.0));
       else
         grad_d = T(4.0);
+      current_reg[d] += grad_d * alpha;
       grads[d] += grad_d * alpha;
     }
   }
@@ -252,8 +254,17 @@ CUML_KERNEL void optimize_batch_kernel(T const* head_embedding,
   T* cur_write = head_buffer + (j * n_components);
   T* oth_write = tail_buffer + (k * n_components);
 
+  // for reducing access to global memory. load values from global memory, and accumulate grads onto
+  // this shared memory position instead of reading from global memory every time.
   T* current_buffer{nullptr};
-  if (use_shared_mem) { current_buffer = (T*)embedding_shared_mem_updates + threadIdx.x; }
+  // for keeping track of grads, final write to global memory
+  T* grads_buffer{nullptr};
+  if constexpr (use_shared_mem) {
+    // n_components for thread0, then the next n_components for thread1 ...
+    current_buffer = (T*)embedding_shared_mem_updates + threadIdx.x * n_components;
+    // TPB_X for first component, then another TPB_X for the next component for better coalescing...
+    grads_buffer = (T*)embedding_shared_mem_updates + TPB_X * n_components + threadIdx.x;
+  }
   auto dist_squared = rdist<T>(current, other, n_components);
   // Attractive force between the two vertices, since they
   // are connected by an edge in the 1-skeleton.
@@ -267,10 +278,13 @@ CUML_KERNEL void optimize_batch_kernel(T const* head_embedding,
    * performing unsupervised training).
    */
   for (int d = 0; d < n_components; d++) {
-    auto grad_d = clip<T>(attractive_grad_coeff * (current[d] - other[d]), T(-4.0), T(4.0));
+    T current_val = current[d];
+    if constexpr (use_shared_mem) { current_buffer[d] = current_val; }
+    auto grad_d = clip<T>(attractive_grad_coeff * (current_val - other[d]), T(-4.0), T(4.0));
     grad_d *= alpha;
-    if (use_shared_mem) {
-      current_buffer[d * TPB_X] = grad_d;
+    if constexpr (use_shared_mem) {
+      current_buffer[d] += grad_d;
+      grads_buffer[d * TPB_X] = grad_d;
     } else {
       raft::myAtomicAdd<T>((T*)cur_write + d, truncate_gradient(rounding, grad_d));
       if (move_other) {  // happens only during unsupervised training
@@ -282,7 +296,7 @@ CUML_KERNEL void optimize_batch_kernel(T const* head_embedding,
   if (use_shared_mem && move_other) {
     __syncthreads();
     for (int d = 0; d < n_components; d++) {
-      auto grad = current_buffer[d * TPB_X];
+      auto grad = grads_buffer[d * TPB_X];
       raft::myAtomicAdd<T>((T*)oth_write + d, truncate_gradient(rounding, -grad));
     }
   }
@@ -299,7 +313,11 @@ CUML_KERNEL void optimize_batch_kernel(T const* head_embedding,
     gen.next(r);
     nnz_t t                  = r % tail_n;
     T const* negative_sample = tail_embedding + (t * n_components);
-    dist_squared             = rdist<T>(current, negative_sample, n_components);
+    if constexpr (use_shared_mem) {
+      dist_squared = rdist<T>(current_buffer, negative_sample, n_components);
+    } else {
+      dist_squared = rdist<T>(current, negative_sample, n_components);
+    }
     // repulsive force between two vertices
     auto repulsive_grad_coeff = T(0.0);
     if (dist_squared > T(0.0)) {
@@ -313,25 +331,31 @@ CUML_KERNEL void optimize_batch_kernel(T const* head_embedding,
      */
     for (int d = 0; d < n_components; d++) {
       auto grad_d = T(0.0);
-      if (repulsive_grad_coeff > T(0.0))
-        grad_d = clip<T>(repulsive_grad_coeff * (current[d] - negative_sample[d]), T(-4.0), T(4.0));
-      else
+      if (repulsive_grad_coeff > T(0.0)) {
+        if constexpr (use_shared_mem) {
+          grad_d = clip<T>(
+            repulsive_grad_coeff * (current_buffer[d] - negative_sample[d]), T(-4.0), T(4.0));
+        } else {
+          grad_d =
+            clip<T>(repulsive_grad_coeff * (current[d] - negative_sample[d]), T(-4.0), T(4.0));
+        }
+      } else
         grad_d = T(4.0);
       grad_d *= alpha;
-      if (use_shared_mem) {
-        current_buffer[d * TPB_X] += grad_d;
+      if constexpr (use_shared_mem) {
+        current_buffer[d] += grad_d;
+        grads_buffer[d * TPB_X] += grad_d;
       } else {
         raft::myAtomicAdd<T>((T*)cur_write + d, truncate_gradient(rounding, grad_d));
       }
     }
   }
 
   // storing gradients for positive samples back to global memory
-  if (use_shared_mem) {
+  if constexpr (use_shared_mem) {
     __syncthreads();
     for (int d = 0; d < n_components; d++) {
-      raft::myAtomicAdd<T>((T*)cur_write + d,
-                           truncate_gradient(rounding, current_buffer[d * TPB_X]));
+      raft::myAtomicAdd<T>((T*)cur_write + d, truncate_gradient(rounding, grads_buffer[d * TPB_X]));
     }
   }
   epoch_of_next_negative_sample[row] =
@@ -373,7 +397,7 @@ void call_optimize_batch_kernel(T const* head_embedding,
                                 cudaStream_t& stream,
                                 T rounding)
 {
-  std::size_t requiredSize = TPB_X * params->n_components;
+  std::size_t requiredSize = TPB_X * params->n_components * 2;
   requiredSize *= sizeof(T);
   bool use_shared_mem = requiredSize < static_cast<std::size_t>(raft::getSharedMemPerBlock());
   T nsr_inv           = T(1.0) / params->negative_sample_rate;