IVFUtilsSelect2.cu

/**
 * Copyright (c) 2015-present, Facebook, Inc.
 * All rights reserved.
 *
 * This source code is licensed under the BSD+Patents license found in the
 * LICENSE file in the root directory of this source tree.
 */


#include "IVFUtils.cuh"
#include "../utils/DeviceUtils.h"
#include "../utils/Limits.cuh"
#include "../utils/Select.cuh"
#include "../utils/StaticUtils.h"
#include "../utils/Tensor.cuh"

//
// This kernel is split into a separate compilation unit to cut down
// on compile time
//

namespace faiss { namespace gpu {

// This is warp divergence central, but this is really a final step
// and happening a small number of times
inline __device__ int binarySearchForBucket(int* prefixSumOffsets,
                                            int size,
                                            int val) {
  int start = 0;
  int end = size;

  while (end - start > 0) {
    int mid = start + (end - start) / 2;

    int midVal = prefixSumOffsets[mid];

    // Find the first bucket that we are <=
    if (midVal <= val) {
      start = mid + 1;
    } else {
      end = mid;
    }
  }

  // We must find the bucket that it is in
  assert(start != size);

  return start;
}

template <int ThreadsPerBlock,
          int NumWarpQ,
          int NumThreadQ,
          bool Dir>
__global__ void
pass2SelectLists(Tensor<float, 2, true> heapDistances,
                 Tensor<int, 2, true> heapIndices,
                 void** listIndices,
                 Tensor<int, 2, true> prefixSumOffsets,
                 Tensor<int, 2, true> topQueryToCentroid,
                 int k,
                 IndicesOptions opt,
                 Tensor<float, 2, true> outDistances,
                 Tensor<long, 2, true> outIndices) {
  constexpr int kNumWarps = ThreadsPerBlock / kWarpSize;

  __shared__ float smemK[kNumWarps * NumWarpQ];
  __shared__ int smemV[kNumWarps * NumWarpQ];

  constexpr auto kInit = Dir ? kFloatMin : kFloatMax;
  BlockSelect<float, int, Dir, Comparator<float>,
            NumWarpQ, NumThreadQ, ThreadsPerBlock>
    heap(kInit, -1, smemK, smemV, k);

  auto queryId = blockIdx.x;
  int num = heapDistances.getSize(1);
  int limit = utils::roundDown(num, kWarpSize);

  int i = threadIdx.x;
  auto heapDistanceStart = heapDistances[queryId];

  // BlockSelect add cannot be used in a warp divergent circumstance; we
  // handle the remainder warp below
  for (; i < limit; i += blockDim.x) {
    heap.add(heapDistanceStart[i], i);
  }

  // Handle warp divergence separately
  if (i < num) {
    heap.addThreadQ(heapDistanceStart[i], i);
  }

  // Merge all final results
  heap.reduce();

  for (int i = threadIdx.x; i < k; i += blockDim.x) {
    outDistances[queryId][i] = smemK[i];

    // `v` is the index in `heapIndices`
    // We need to translate this into an original user index. The
    // reason why we don't maintain intermediate results in terms of
    // user indices is to substantially reduce temporary memory
    // requirements and global memory write traffic for the list
    // scanning.
    // This code is highly divergent, but it's probably ok, since this
    // is the very last step and it is happening a small number of
    // times (#queries x k).
    int v = smemV[i];
    long index = -1;

    if (v != -1) {
      // `offset` is the offset of the intermediate result, as
      // calculated by the original scan.
      int offset = heapIndices[queryId][v];

      // In order to determine the actual user index, we need to first
      // determine what list it was in.
      // We do this by binary search in the prefix sum list.
      int probe = binarySearchForBucket(prefixSumOffsets[queryId].data(),
                                        prefixSumOffsets.getSize(1),
                                        offset);

      // This is then the probe for the query; we can find the actual
      // list ID from this
      int listId = topQueryToCentroid[queryId][probe];

      // Now, we need to know the offset within the list
      // We ensure that before the array (at offset -1), there is a 0 value
      int listStart = *(prefixSumOffsets[queryId][probe].data() - 1);
      int listOffset = offset - listStart;

      // This gives us our final index
      if (opt == INDICES_32_BIT) {
        index = (long) ((int*) listIndices[listId])[listOffset];
      } else if (opt == INDICES_64_BIT) {
        index = ((long*) listIndices[listId])[listOffset];
      } else {
        index = ((long) listId << 32 | (long) listOffset);
      }
    }

    outIndices[queryId][i] = index;
  }
}

void
runPass2SelectLists(Tensor<float, 2, true>& heapDistances,
                    Tensor<int, 2, true>& heapIndices,
                    thrust::device_vector<void*>& listIndices,
                    IndicesOptions indicesOptions,
                    Tensor<int, 2, true>& prefixSumOffsets,
                    Tensor<int, 2, true>& topQueryToCentroid,
                    int k,
                    bool chooseLargest,
                    Tensor<float, 2, true>& outDistances,
                    Tensor<long, 2, true>& outIndices,
                    cudaStream_t stream) {
  constexpr auto kThreadsPerBlock = 128;

  auto grid = dim3(topQueryToCentroid.getSize(0));
  auto block = dim3(kThreadsPerBlock);

#define RUN_PASS(NUM_WARP_Q, NUM_THREAD_Q, DIR)                         \
  do {                                                                  \
    pass2SelectLists<kThreadsPerBlock,                                  \
                     NUM_WARP_Q, NUM_THREAD_Q, DIR>                     \
      <<<grid, block, 0, stream>>>(heapDistances,                       \
                                   heapIndices,                         \
                                   listIndices.data().get(),            \
                                   prefixSumOffsets,                    \
                                   topQueryToCentroid,                  \
                                   k,                                   \
                                   indicesOptions,                      \
                                   outDistances,                        \
                                   outIndices);                         \
    CUDA_TEST_ERROR();                                                  \
    return; /* success */                                               \
  } while (0)

#define RUN_PASS_DIR(DIR)                                \
  do {                                                   \
    if (k == 1) {                                        \
      RUN_PASS(1, 1, DIR);                               \
    } else if (k <= 32) {                                \
      RUN_PASS(32, 2, DIR);                              \
    } else if (k <= 64) {                                \
      RUN_PASS(64, 3, DIR);                              \
    } else if (k <= 128) {                               \
      RUN_PASS(128, 3, DIR);                             \
    } else if (k <= 256) {                               \
      RUN_PASS(256, 4, DIR);                             \
    } else if (k <= 512) {                               \
      RUN_PASS(512, 8, DIR);                             \
    } else if (k <= 1024) {                              \
      RUN_PASS(1024, 8, DIR);                            \
    }                                                    \
  } while (0)

  if (chooseLargest) {
    RUN_PASS_DIR(true);
  } else {
    RUN_PASS_DIR(false);
  }

  // unimplemented / too many resources
  FAISS_ASSERT_FMT(false, "unimplemented k value (%d)", k);

#undef RUN_PASS_DIR
#undef RUN_PASS
}

} } // namespace