PQScanMultiPassPrecomputed.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.
 */

// Copyright 2004-present Facebook. All Rights Reserved.

#include "PQScanMultiPassPrecomputed.cuh"
#include "../GpuResources.h"
#include "PQCodeLoad.cuh"
#include "IVFUtils.cuh"
#include "../utils/ConversionOperators.cuh"
#include "../utils/DeviceTensor.cuh"
#include "../utils/DeviceUtils.h"
#include "../utils/Float16.cuh"
#include "../utils/LoadStoreOperators.cuh"
#include "../utils/MathOperators.cuh"
#include "../utils/StaticUtils.h"
#include <limits>

namespace faiss { namespace gpu {

// For precomputed codes, this calculates and loads code distances
// into smem
template <typename LookupT, typename LookupVecT>
inline __device__ void
loadPrecomputedTerm(LookupT* smem,
                    LookupT* term2Start,
                    LookupT* term3Start,
                    int numCodes) {
  constexpr int kWordSize = sizeof(LookupVecT) / sizeof(LookupT);

  // We can only use vector loads if the data is guaranteed to be
  // aligned. The codes are innermost, so if it is evenly divisible,
  // then any slice will be aligned.
  if (numCodes % kWordSize == 0) {
    constexpr int kUnroll = 2;

    // Load the data by float4 for efficiency, and then handle any remainder
    // limitVec is the number of whole vec words we can load, in terms
    // of whole blocks performing the load
    int limitVec = numCodes / (kUnroll * kWordSize * blockDim.x);
    limitVec *= kUnroll * blockDim.x;

    LookupVecT* smemV = (LookupVecT*) smem;
    LookupVecT* term2StartV = (LookupVecT*) term2Start;
    LookupVecT* term3StartV = (LookupVecT*) term3Start;

    for (int i = threadIdx.x; i < limitVec; i += kUnroll * blockDim.x) {
      LookupVecT vals[kUnroll];

#pragma unroll
      for (int j = 0; j < kUnroll; ++j) {
        vals[j] =
          LoadStore<LookupVecT>::load(&term2StartV[i + j * blockDim.x]);
      }

#pragma unroll
      for (int j = 0; j < kUnroll; ++j) {
        LookupVecT q =
          LoadStore<LookupVecT>::load(&term3StartV[i + j * blockDim.x]);

        vals[j] = Math<LookupVecT>::add(vals[j], q);
      }

#pragma unroll
      for (int j = 0; j < kUnroll; ++j) {
        LoadStore<LookupVecT>::store(&smemV[i + j * blockDim.x], vals[j]);
      }
    }

    // This is where we start loading the remainder that does not evenly
    // fit into kUnroll x blockDim.x
    int remainder = limitVec * kWordSize;

    for (int i = remainder + threadIdx.x; i < numCodes; i += blockDim.x) {
      smem[i] = Math<LookupT>::add(term2Start[i], term3Start[i]);
    }
  } else {
    // Potential unaligned load
    constexpr int kUnroll = 4;

    int limit = utils::roundDown(numCodes, kUnroll * blockDim.x);

    int i = threadIdx.x;
    for (; i < limit; i += kUnroll * blockDim.x) {
      LookupT vals[kUnroll];

#pragma unroll
      for (int j = 0; j < kUnroll; ++j) {
        vals[j] = term2Start[i + j * blockDim.x];
      }

#pragma unroll
      for (int j = 0; j < kUnroll; ++j) {
        vals[j] = Math<LookupT>::add(vals[j], term3Start[i + j * blockDim.x]);
      }

#pragma unroll
      for (int j = 0; j < kUnroll; ++j) {
        smem[i + j * blockDim.x] = vals[j];
      }
    }

    for (; i < numCodes; i += blockDim.x) {
      smem[i] = Math<LookupT>::add(term2Start[i], term3Start[i]);
    }
  }
}

template <int NumSubQuantizers, typename LookupT, typename LookupVecT>
__global__ void
pqScanPrecomputedMultiPass(Tensor<float, 2, true> queries,
                           Tensor<float, 2, true> precompTerm1,
                           Tensor<LookupT, 3, true> precompTerm2,
                           Tensor<LookupT, 3, true> precompTerm3,
                           Tensor<int, 2, true> topQueryToCentroid,
                           void** listCodes,
                           int* listLengths,
                           Tensor<int, 2, true> prefixSumOffsets,
                           Tensor<float, 1, true> distance) {
  // precomputed term 2 + 3 storage
  // (sub q)(code id)
  extern __shared__ char smemTerm23[];
  LookupT* term23 = (LookupT*) smemTerm23;

  // Each block handles a single query
  auto queryId = blockIdx.y;
  auto probeId = blockIdx.x;
  auto codesPerSubQuantizer = precompTerm2.getSize(2);
  auto precompTermSize = precompTerm2.getSize(1) * codesPerSubQuantizer;

  // This is where we start writing out data
  // We ensure that before the array (at offset -1), there is a 0 value
  int outBase = *(prefixSumOffsets[queryId][probeId].data() - 1);
  float* distanceOut = distance[outBase].data();

  auto listId = topQueryToCentroid[queryId][probeId];
  // Safety guard in case NaNs in input cause no list ID to be generated
  if (listId == -1) {
    return;
  }

  unsigned char* codeList = (unsigned char*) listCodes[listId];
  int limit = listLengths[listId];

  constexpr int kNumCode32 = NumSubQuantizers <= 4 ? 1 :
    (NumSubQuantizers / 4);
  unsigned int code32[kNumCode32];
  unsigned int nextCode32[kNumCode32];

  // We double-buffer the code loading, which improves memory utilization
  if (threadIdx.x < limit) {
    LoadCode32<NumSubQuantizers>::load(code32, codeList, threadIdx.x);
  }

  // Load precomputed terms 1, 2, 3
  float term1 = precompTerm1[queryId][probeId];
  loadPrecomputedTerm<LookupT, LookupVecT>(term23,
                                           precompTerm2[listId].data(),
                                           precompTerm3[queryId].data(),
                                           precompTermSize);

  // Prevent WAR dependencies
  __syncthreads();

  // Each thread handles one code element in the list, with a
  // block-wide stride
  for (int codeIndex = threadIdx.x;
       codeIndex < limit;
       codeIndex += blockDim.x) {
    // Prefetch next codes
    if (codeIndex + blockDim.x < limit) {
      LoadCode32<NumSubQuantizers>::load(
        nextCode32, codeList, codeIndex + blockDim.x);
    }

    float dist = term1;

#pragma unroll
    for (int word = 0; word < kNumCode32; ++word) {
      constexpr int kBytesPerCode32 =
        NumSubQuantizers < 4 ? NumSubQuantizers : 4;

      if (kBytesPerCode32 == 1) {
        auto code = code32[0];
        dist = ConvertTo<float>::to(term23[code]);

      } else {
#pragma unroll
        for (int byte = 0; byte < kBytesPerCode32; ++byte) {
          auto code = getByte(code32[word], byte * 8, 8);

          auto offset =
            codesPerSubQuantizer * (word * kBytesPerCode32 + byte);

          dist += ConvertTo<float>::to(term23[offset + code]);
        }
      }
    }

    // Write out intermediate distance result
    // We do not maintain indices here, in order to reduce global
    // memory traffic. Those are recovered in the final selection step.
    distanceOut[codeIndex] = dist;

    // Rotate buffers
#pragma unroll
    for (int word = 0; word < kNumCode32; ++word) {
      code32[word] = nextCode32[word];
    }
  }
}

void
runMultiPassTile(Tensor<float, 2, true>& queries,
                 Tensor<float, 2, true>& precompTerm1,
                 NoTypeTensor<3, true>& precompTerm2,
                 NoTypeTensor<3, true>& precompTerm3,
                 Tensor<int, 2, true>& topQueryToCentroid,
                 bool useFloat16Lookup,
                 int bytesPerCode,
                 int numSubQuantizers,
                 int numSubQuantizerCodes,
                 thrust::device_vector<void*>& listCodes,
                 thrust::device_vector<void*>& listIndices,
                 IndicesOptions indicesOptions,
                 thrust::device_vector<int>& listLengths,
                 Tensor<char, 1, true>& thrustMem,
                 Tensor<int, 2, true>& prefixSumOffsets,
                 Tensor<float, 1, true>& allDistances,
                 Tensor<float, 3, true>& heapDistances,
                 Tensor<int, 3, true>& heapIndices,
                 int k,
                 Tensor<float, 2, true>& outDistances,
                 Tensor<long, 2, true>& outIndices,
                 cudaStream_t stream) {
  // Calculate offset lengths, so we know where to write out
  // intermediate results
  runCalcListOffsets(topQueryToCentroid, listLengths, prefixSumOffsets,
                     thrustMem, stream);

  // Convert all codes to a distance, and write out (distance,
  // index) values for all intermediate results
  {
    auto kThreadsPerBlock = 256;

    auto grid = dim3(topQueryToCentroid.getSize(1),
                     topQueryToCentroid.getSize(0));
    auto block = dim3(kThreadsPerBlock);

    // pq precomputed terms (2 + 3)
    auto smem = sizeof(float);
#ifdef FAISS_USE_FLOAT16
    if (useFloat16Lookup) {
      smem = sizeof(half);
    }
#endif
    smem *= numSubQuantizers * numSubQuantizerCodes;
    FAISS_ASSERT(smem <= getMaxSharedMemPerBlockCurrentDevice());

#define RUN_PQ_OPT(NUM_SUB_Q, LOOKUP_T, LOOKUP_VEC_T)                   \
    do {                                                                \
      auto precompTerm2T = precompTerm2.toTensor<LOOKUP_T>();           \
      auto precompTerm3T = precompTerm3.toTensor<LOOKUP_T>();           \
                                                                        \
      pqScanPrecomputedMultiPass<NUM_SUB_Q, LOOKUP_T, LOOKUP_VEC_T>     \
        <<<grid, block, smem, stream>>>(                                \
          queries,                                                      \
          precompTerm1,                                                 \
          precompTerm2T,                                                \
          precompTerm3T,                                                \
          topQueryToCentroid,                                           \
          listCodes.data().get(),                                       \
          listLengths.data().get(),                                     \
          prefixSumOffsets,                                             \
          allDistances);                                                \
    } while (0)

#ifdef FAISS_USE_FLOAT16
#define RUN_PQ(NUM_SUB_Q)                       \
    do {                                        \
      if (useFloat16Lookup) {                   \
        RUN_PQ_OPT(NUM_SUB_Q, half, Half8);     \
      } else {                                  \
        RUN_PQ_OPT(NUM_SUB_Q, float, float4);   \
      }                                         \
    } while (0)
#else
#define RUN_PQ(NUM_SUB_Q)                       \
    do {                                        \
      RUN_PQ_OPT(NUM_SUB_Q, float, float4);     \
    } while (0)
#endif // FAISS_USE_FLOAT16

    switch (bytesPerCode) {
      case 1:
        RUN_PQ(1);
        break;
      case 2:
        RUN_PQ(2);
        break;
      case 3:
        RUN_PQ(3);
        break;
      case 4:
        RUN_PQ(4);
        break;
      case 8:
        RUN_PQ(8);
        break;
      case 12:
        RUN_PQ(12);
        break;
      case 16:
        RUN_PQ(16);
        break;
      case 20:
        RUN_PQ(20);
        break;
      case 24:
        RUN_PQ(24);
        break;
      case 28:
        RUN_PQ(28);
        break;
      case 32:
        RUN_PQ(32);
        break;
      case 40:
        RUN_PQ(40);
        break;
      case 48:
        RUN_PQ(48);
        break;
      case 56:
        RUN_PQ(56);
        break;
      case 64:
        RUN_PQ(64);
        break;
      case 96:
        RUN_PQ(96);
        break;
      default:
        FAISS_ASSERT(false);
        break;
    }

    CUDA_TEST_ERROR();

#undef RUN_PQ
#undef RUN_PQ_OPT
  }

  // k-select the output in chunks, to increase parallelism
  runPass1SelectLists(prefixSumOffsets,
                      allDistances,
                      topQueryToCentroid.getSize(1),
                      k,
                      false, // L2 distance chooses smallest
                      heapDistances,
                      heapIndices,
                      stream);

  // k-select final output
  auto flatHeapDistances = heapDistances.downcastInner<2>();
  auto flatHeapIndices = heapIndices.downcastInner<2>();

  runPass2SelectLists(flatHeapDistances,
                      flatHeapIndices,
                      listIndices,
                      indicesOptions,
                      prefixSumOffsets,
                      topQueryToCentroid,
                      k,
                      false, // L2 distance chooses smallest
                      outDistances,
                      outIndices,
                      stream);

  CUDA_TEST_ERROR();
}

void runPQScanMultiPassPrecomputed(Tensor<float, 2, true>& queries,
                                   Tensor<float, 2, true>& precompTerm1,
                                   NoTypeTensor<3, true>& precompTerm2,
                                   NoTypeTensor<3, true>& precompTerm3,
                                   Tensor<int, 2, true>& topQueryToCentroid,
                                   bool useFloat16Lookup,
                                   int bytesPerCode,
                                   int numSubQuantizers,
                                   int numSubQuantizerCodes,
                                   thrust::device_vector<void*>& listCodes,
                                   thrust::device_vector<void*>& listIndices,
                                   IndicesOptions indicesOptions,
                                   thrust::device_vector<int>& listLengths,
                                   int maxListLength,
                                   int k,
                                   // output
                                   Tensor<float, 2, true>& outDistances,
                                   // output
                                   Tensor<long, 2, true>& outIndices,
                                   GpuResources* res) {
  constexpr int kMinQueryTileSize = 8;
  constexpr int kMaxQueryTileSize = 128;
  constexpr int kThrustMemSize = 16384;

  int nprobe = topQueryToCentroid.getSize(1);

  auto& mem = res->getMemoryManagerCurrentDevice();
  auto stream = res->getDefaultStreamCurrentDevice();

  // Make a reservation for Thrust to do its dirty work (global memory
  // cross-block reduction space); hopefully this is large enough.
  DeviceTensor<char, 1, true> thrustMem1(
    mem, {kThrustMemSize}, stream);
  DeviceTensor<char, 1, true> thrustMem2(
    mem, {kThrustMemSize}, stream);
  DeviceTensor<char, 1, true>* thrustMem[2] =
    {&thrustMem1, &thrustMem2};

  // How much temporary storage is available?
  // If possible, we'd like to fit within the space available.
  size_t sizeAvailable = mem.getSizeAvailable();

  // We run two passes of heap selection
  // This is the size of the first-level heap passes
  constexpr int kNProbeSplit = 8;
  int pass2Chunks = std::min(nprobe, kNProbeSplit);

  size_t sizeForFirstSelectPass =
    pass2Chunks * k * (sizeof(float) + sizeof(int));

  // How much temporary storage we need per each query
  size_t sizePerQuery =
    2 * // # streams
    ((nprobe * sizeof(int) + sizeof(int)) + // prefixSumOffsets
     nprobe * maxListLength * sizeof(float) + // allDistances
     sizeForFirstSelectPass);

  int queryTileSize = (int) (sizeAvailable / sizePerQuery);

  if (queryTileSize < kMinQueryTileSize) {
    queryTileSize = kMinQueryTileSize;
  } else if (queryTileSize > kMaxQueryTileSize) {
    queryTileSize = kMaxQueryTileSize;
  }

  // FIXME: we should adjust queryTileSize to deal with this, since
  // indexing is in int32
  FAISS_ASSERT(queryTileSize * nprobe * maxListLength <=
         std::numeric_limits<int>::max());

  // Temporary memory buffers
 // Make sure there is space prior to the start which will be 0, and
  // will handle the boundary condition without branches
  DeviceTensor<int, 1, true> prefixSumOffsetSpace1(
    mem, {queryTileSize * nprobe + 1}, stream);
  DeviceTensor<int, 1, true> prefixSumOffsetSpace2(
    mem, {queryTileSize * nprobe + 1}, stream);

  DeviceTensor<int, 2, true> prefixSumOffsets1(
    prefixSumOffsetSpace1[1].data(),
    {queryTileSize, nprobe});
  DeviceTensor<int, 2, true> prefixSumOffsets2(
    prefixSumOffsetSpace2[1].data(),
    {queryTileSize, nprobe});
  DeviceTensor<int, 2, true>* prefixSumOffsets[2] =
    {&prefixSumOffsets1, &prefixSumOffsets2};

  // Make sure the element before prefixSumOffsets is 0, since we
  // depend upon simple, boundary-less indexing to get proper results
  CUDA_VERIFY(cudaMemsetAsync(prefixSumOffsetSpace1.data(),
                              0,
                              sizeof(int),
                              stream));
  CUDA_VERIFY(cudaMemsetAsync(prefixSumOffsetSpace2.data(),
                              0,
                              sizeof(int),
                              stream));

  DeviceTensor<float, 1, true> allDistances1(
    mem, {queryTileSize * nprobe * maxListLength}, stream);
  DeviceTensor<float, 1, true> allDistances2(
    mem, {queryTileSize * nprobe * maxListLength}, stream);
  DeviceTensor<float, 1, true>* allDistances[2] =
    {&allDistances1, &allDistances2};

  DeviceTensor<float, 3, true> heapDistances1(
    mem, {queryTileSize, pass2Chunks, k}, stream);
  DeviceTensor<float, 3, true> heapDistances2(
    mem, {queryTileSize, pass2Chunks, k}, stream);
  DeviceTensor<float, 3, true>* heapDistances[2] =
    {&heapDistances1, &heapDistances2};

  DeviceTensor<int, 3, true> heapIndices1(
    mem, {queryTileSize, pass2Chunks, k}, stream);
  DeviceTensor<int, 3, true> heapIndices2(
    mem, {queryTileSize, pass2Chunks, k}, stream);
  DeviceTensor<int, 3, true>* heapIndices[2] =
    {&heapIndices1, &heapIndices2};

  auto streams = res->getAlternateStreamsCurrentDevice();
  streamWait(streams, {stream});

  int curStream = 0;

  for (int query = 0; query < queries.getSize(0); query += queryTileSize) {
    int numQueriesInTile =
      std::min(queryTileSize, queries.getSize(0) - query);

    auto prefixSumOffsetsView =
      prefixSumOffsets[curStream]->narrowOutermost(0, numQueriesInTile);

    auto coarseIndicesView =
      topQueryToCentroid.narrowOutermost(query, numQueriesInTile);
    auto queryView =
      queries.narrowOutermost(query, numQueriesInTile);
    auto term1View =
      precompTerm1.narrowOutermost(query, numQueriesInTile);
    auto term3View =
      precompTerm3.narrowOutermost(query, numQueriesInTile);

    auto heapDistancesView =
      heapDistances[curStream]->narrowOutermost(0, numQueriesInTile);
    auto heapIndicesView =
      heapIndices[curStream]->narrowOutermost(0, numQueriesInTile);

    auto outDistanceView =
      outDistances.narrowOutermost(query, numQueriesInTile);
    auto outIndicesView =
      outIndices.narrowOutermost(query, numQueriesInTile);

    runMultiPassTile(queryView,
                     term1View,
                     precompTerm2,
                     term3View,
                     coarseIndicesView,
                     useFloat16Lookup,
                     bytesPerCode,
                     numSubQuantizers,
                     numSubQuantizerCodes,
                     listCodes,
                     listIndices,
                     indicesOptions,
                     listLengths,
                     *thrustMem[curStream],
                     prefixSumOffsetsView,
                     *allDistances[curStream],
                     heapDistancesView,
                     heapIndicesView,
                     k,
                     outDistanceView,
                     outIndicesView,
                     streams[curStream]);

    curStream = (curStream + 1) % 2;
  }

  streamWait({stream}, streams);
}

} } // namespace