Merge branch 'develop'

Author: vmarkovtsev

.travis.yml

@@ -11,4 +11,7 @@ before_script:
   - cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-8.0 ..
 
 script:
-  - make -j2 VERBOSE=1
+  - make -j2 VERBOSE=1
+
+notifications:
+  email: false

CMakeLists.txt

@@ -14,7 +14,7 @@ if (NOT DEFINED CUDA_ARCH)
   set(CUDA_ARCH "61")
 endif()
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -march=native -Wall -Werror -DCUDA_ARCH=${CUDA_ARCH} -std=c++11 ${OpenMP_CXX_FLAGS}")
-set(SOURCE_FILES kmcuda.cc kmcuda.h wrappers.h private.h fp_abstraction.h
+set(SOURCE_FILES kmcuda.cc kmcuda.h wrappers.h private.h fp_abstraction.h tricks.cuh
                  metric_abstraction.h kmeans.cu knn.cu)
 if (NOT DISABLE_PYTHON)
   list(APPEND SOURCE_FILES python.cc)
@@ -29,6 +29,7 @@ if (CMAKE_MAJOR_VERSION LESS 4 AND CMAKE_MINOR_VERSION LESS 3)
   set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS} --std c++11")
 endif()
 cuda_add_library(KMCUDA SHARED ${SOURCE_FILES} OPTIONS ${NVCC_FLAGS})
+target_link_libraries(KMCUDA ${CUDA_curand_LIBRARY})
 if(PYTHONLIBS_FOUND)
   include_directories(${PYTHON_INCLUDE_DIRS} ${NUMPY_INCLUDES})
   target_link_libraries(KMCUDA ${PYTHON_LIBRARIES})

README.md

@@ -25,14 +25,17 @@ have several days and 12 GB of GPU memory); 300K samples are grouped
 into 5000 clusters in 4½ minutes on NVIDIA Titan X (15 iterations); 3M samples
 and 1000 clusters take 20 minutes (33 iterations). Yinyang can be
 turned off to save GPU memory but the slower Lloyd will be used then.
-Three centroid initialization schemes are supported: random, k-means++ and import.
-Two distance metrics are supported: L2 (the usual one) and angular (refined cosine).
+Four centroid initialization schemes are supported: random, k-means++,
+[AFKMC2](http://olivierbachem.ch/files/afkmcmc-oral-pdf.pdf) and import.
+Two distance metrics are supported: L2 (the usual one) and angular
+(arccos of the scalar product). L1 is in development.
 16-bit float support delivers 2x memory compression. If you've got several GPUs,
 they can be utilized together and it gives the corresponding linear speedup
 either for Lloyd or Yinyang.
 
 The code has been thoroughly tested to yield bit-to-bit identical
-results from Yinyang and Lloyd.
+results from Yinyang and Lloyd. AFKMC2 was converted from
+[the reference code](https://github.com/obachem/kmc2).
 
 Read the articles: [1](http://blog.sourced.tech/post/towards_kmeans_on_gpu/),
 [2](https://blog.sourced.tech/post/kmcuda4/).

kmcuda.cc

@@ -2,10 +2,10 @@
 #include <cstdlib>
 #include <cstring>
 #include <cinttypes>
-#include <cfloat>
 #include <cmath>
 #include <cassert>
 #include <algorithm>
+#include <map>
 #include <memory>
 
 #include <cuda_runtime_api.h>
@@ -189,11 +189,11 @@ static KMCUDAResult print_memory_stats(const std::vector<int> &devs) {
 extern "C" {
 
 KMCUDAResult kmeans_init_centroids(
-    KMCUDAInitMethod method, uint32_t samples_size, uint16_t features_size,
-    uint32_t clusters_size, KMCUDADistanceMetric metric, uint32_t seed,
-    const std::vector<int> &devs, int device_ptrs, int fp16x2, int32_t verbosity,
-    const float *host_centroids, const udevptrs<float> &samples,
-    udevptrs<float> *dists, udevptrs<float> *dev_sums, udevptrs<float> *centroids) {
+    KMCUDAInitMethod method, const void *init_params, uint32_t samples_size,
+    uint16_t features_size, uint32_t clusters_size, KMCUDADistanceMetric metric,
+    uint32_t seed, const std::vector<int> &devs, int device_ptrs, int fp16x2,
+    int32_t verbosity, const float *host_centroids, const udevptrs<float> &samples,
+    udevptrs<float> *dists, udevptrs<float> *aux, udevptrs<float> *centroids) {
   srand(seed);
   switch (method) {
     case kmcudaInitMethodImport:
@@ -237,7 +237,7 @@ KMCUDAResult kmeans_init_centroids(
       );
       break;
     }
-    case kmcudaInitMethodPlusPlus:
+    case kmcudaInitMethodPlusPlus: {
       INFO("performing kmeans++...\n");
       float smoke = NAN;
       uint32_t first_offset;
@@ -253,9 +253,9 @@ KMCUDAResult kmeans_init_centroids(
         printf("kmeans++: dump %" PRIu32 " %" PRIu32 " %p\n",
                samples_size, features_size, host_dists.get());
         FOR_EACH_DEVI(
-          printf("kmeans++: dev #%d: %p %p %p %p\n", devs[devi],
+          printf("kmeans++: dev #%d: %p %p %p\n", devs[devi],
                  samples[devi].get(), (*centroids)[devi].get(),
-                 (*dists)[devi].get(), (*dev_sums)[devi].get());
+                 (*dists)[devi].get());
         );
       }
       for (uint32_t i = 1; i < clusters_size; i++) {
@@ -267,7 +267,7 @@ KMCUDAResult kmeans_init_centroids(
         float dist_sum = 0;
         RETERR(kmeans_cuda_plus_plus(
             samples_size, features_size, i, metric, devs, fp16x2, verbosity,
-            samples, centroids, dists, dev_sums, host_dists.get(), &dist_sum),
+            samples, centroids, dists, host_dists.get(), &dist_sum),
                DEBUG("\nkmeans_cuda_plus_plus failed\n"));
         if (dist_sum != dist_sum) {
           assert(dist_sum == dist_sum);
@@ -307,21 +307,79 @@ KMCUDAResult kmeans_init_centroids(
                            (j - 1) * features_size, features_size);
       }
       break;
+    }
+    case kmcudaInitMethodAFKMC2: {
+      uint32_t m = *reinterpret_cast<const uint32_t*>(init_params);
+      if (m == 0) {
+        m = 200;
+      } else if (m > samples_size / 2) {
+        INFO("afkmc2: m > %" PRIu32 " is not supported (got %" PRIu32 ")\n",
+             samples_size / 2, m);
+        return kmcudaInvalidArguments;
+      }
+      float smoke = NAN;
+      uint32_t first_offset;
+      while (smoke != smoke) {
+        first_offset = (rand() % samples_size) * features_size;
+        cudaSetDevice(devs[0]);
+        CUCH(cudaMemcpy(&smoke, samples[0].get() + first_offset, sizeof(float),
+                        cudaMemcpyDeviceToHost), kmcudaMemoryCopyError);
+      }
+      INFO("afkmc2: calculating q (c0 = %" PRIu32 ")... ",
+           first_offset / features_size);
+      CUMEMCPY_D2D_ASYNC(*centroids, 0, samples, first_offset, features_size);
+      auto q = std::unique_ptr<float[]>(new float[samples_size]);
+      kmeans_cuda_afkmc2_calc_q(
+          samples_size, features_size, first_offset / features_size, metric,
+          devs, fp16x2, verbosity, samples, dists, q.get());
+      INFO("done\n");
+      auto cand_ind = std::unique_ptr<uint32_t[]>(new uint32_t[m]);
+      auto rand_a = std::unique_ptr<float[]>(new float[m]);
+      auto p_cand = std::unique_ptr<float[]>(new float[m]);
+      for (uint32_t k = 1; k < clusters_size; k++) {
+        if (verbosity > 1 || (verbosity > 0 && (
+              clusters_size < 100 || k % (clusters_size / 100) == 0))) {
+          printf("\rstep %d", k);
+          fflush(stdout);
+        }
+        RETERR(kmeans_cuda_afkmc2_random_step(
+            k, m, seed, verbosity, dists->back().get(),
+            reinterpret_cast<uint32_t*>(aux->back().get()),
+            cand_ind.get(), aux->back().get() + m, rand_a.get()));
+        RETERR(kmeans_cuda_afkmc2_min_dist(
+            k, m, metric, fp16x2, verbosity, samples.back().get(),
+            reinterpret_cast<uint32_t*>(aux->back().get()),
+            centroids->back().get(), aux->back().get() + m, p_cand.get()));
+        float curr_prob = 0;
+        uint32_t curr_ind = 0;
+        for (uint32_t j = 0; j < m; j++) {
+          auto cand_prob = p_cand[j] / q[cand_ind[j]];
+          if (curr_prob == 0 || cand_prob / curr_prob > rand_a[j]) {
+            curr_ind = j;
+            curr_prob = cand_prob;
+          }
+        }
+        CUMEMCPY_D2D_ASYNC(*centroids, k * features_size,
+                           samples, cand_ind[curr_ind] * features_size,
+                           features_size);
+      }
+      break;
+    }
   }
   INFO("\rdone            \n");
   return kmcudaSuccess;
 }
 
 KMCUDAResult kmeans_cuda(
-    KMCUDAInitMethod init, float tolerance, float yinyang_t,
+    KMCUDAInitMethod init, const void *init_params, float tolerance, float yinyang_t,
     KMCUDADistanceMetric metric, uint32_t samples_size, uint16_t features_size,
     uint32_t clusters_size, uint32_t seed, uint32_t device, int32_t device_ptrs,
     int32_t fp16x2, int32_t verbosity, const float *samples, float *centroids,
     uint32_t *assignments, float *average_distance) {
-  DEBUG("arguments: %d %.3f %.2f %d %" PRIu32 " %" PRIu16 " %" PRIu32 " %"
-        PRIu32 " %" PRIu32 " %d %" PRIi32 " %p %p %p %p\n", init, tolerance,
-        yinyang_t, metric, samples_size, features_size, clusters_size, seed,
-        device, fp16x2, verbosity, samples, centroids, assignments,
+  DEBUG("arguments: %d %p %.3f %.2f %d %" PRIu32 " %" PRIu16 " %" PRIu32 " %"
+        PRIu32 " %" PRIu32 " %d %" PRIi32 " %p %p %p %p\n", init, init_params,
+        tolerance, yinyang_t, metric, samples_size, features_size, clusters_size,
+        seed, device, fp16x2, verbosity, samples, centroids, assignments,
         average_distance);
   RETERR(check_kmeans_args(
       tolerance, yinyang_t, samples_size, features_size, clusters_size,
@@ -392,8 +450,8 @@ KMCUDAResult kmeans_cuda(
   FOR_EACH_DEV(cudaProfilerStart());
   #endif
   RETERR(kmeans_init_centroids(
-      init, samples_size, features_size, clusters_size, metric, seed, devs,
-      device_ptrs, fp16x2, verbosity, centroids, device_samples,
+      init, init_params, samples_size, features_size, clusters_size, metric,
+      seed, devs, device_ptrs, fp16x2, verbosity, centroids, device_samples,
       reinterpret_cast<udevptrs<float>*>(&device_assignments),
       reinterpret_cast<udevptrs<float>*>(&device_assignments_prev),
       &device_centroids),

kmcuda.h

@@ -15,6 +15,7 @@ enum KMCUDAResult {
 enum KMCUDAInitMethod {
   kmcudaInitMethodRandom = 0,
   kmcudaInitMethodPlusPlus,
+  kmcudaInitMethodAFKMC2,
   kmcudaInitMethodImport
 };
 
@@ -27,6 +28,10 @@ extern "C" {
 
 /// @brief Performs K-means clustering on GPU / CUDA.
 /// @param init centroids initialization method.
+/// @param init_params pointer to a struct / number with centroid initialization
+///                    parameters. Ignored unless init == kmcudaInitMethodAFKMC2.
+///                    In case with kmcudaInitMethodAFKMC2 it is expected to be
+///                    uint32_t* to m; m == 0 means the default value (200).
 /// @param tolerance if the number of reassignments drop below this ratio, stop.
 /// @param yinyang_t the relative number of cluster groups, usually 0.1.
 /// @param metric the distance metric to use. The default is Euclidean (L2), can be
@@ -53,7 +58,7 @@ extern "C" {
 ///                         the corresponding centroids. If nullptr, not calculated.
 /// @return KMCUDAResult.
 KMCUDAResult kmeans_cuda(
-    KMCUDAInitMethod init, float tolerance, float yinyang_t,
+    KMCUDAInitMethod init, const void *init_params, float tolerance, float yinyang_t,
     KMCUDADistanceMetric metric, uint32_t samples_size, uint16_t features_size,
     uint32_t clusters_size, uint32_t seed, uint32_t device, int32_t device_ptrs,
     int32_t fp16x2, int32_t verbosity, const float *samples, float *centroids,