Implement reconstruct_n for GPU IVFFlat indexes

faiss/gpu/GpuIndexIVFFlat.cu

@@ -356,5 +356,27 @@ void GpuIndexIVFFlat::setIndex_(
     }
 }
 
+void GpuIndexIVFFlat::reconstruct_n(idx_t i0, idx_t ni, float* out) const {
+    FAISS_ASSERT(index_);
+
+    if (ni == 0) {
+        // nothing to do
+        return;
+    }
+
+    FAISS_THROW_IF_NOT_FMT(
+            i0 < this->ntotal,
+            "start index (%zu) out of bounds (ntotal %zu)",
+            i0,
+            this->ntotal);
+    FAISS_THROW_IF_NOT_FMT(
+            i0 + ni - 1 < this->ntotal,
+            "max index requested (%zu) out of bounds (ntotal %zu)",
+            i0 + ni - 1,
+            this->ntotal);
+
+    index_->reconstruct_n(i0, ni, out);
+}
+
 } // namespace gpu
 } // namespace faiss

faiss/gpu/GpuIndexIVFFlat.h

@@ -87,6 +87,8 @@ class GpuIndexIVFFlat : public GpuIndexIVF {
     /// Trains the coarse quantizer based on the given vector data
     void train(idx_t n, const float* x) override;
 
+    void reconstruct_n(idx_t i0, idx_t n, float* out) const override;
+
    protected:
     /// Initialize appropriate index
     void setIndex_(

faiss/gpu/impl/IVFBase.cu

@@ -340,6 +340,10 @@ void IVFBase::copyInvertedListsTo(InvertedLists* ivf) {
     }
 }
 
+void IVFBase::reconstruct_n(idx_t i0, idx_t n, float* out) {
+    FAISS_THROW_MSG("not implemented");
+}
+
 void IVFBase::addEncodedVectorsToList_(
         idx_t listId,
         const void* codes,

faiss/gpu/impl/IVFBase.cuh

@@ -109,9 +109,18 @@ class IVFBase {
             Tensor<idx_t, 2, true>& outIndices,
             bool storePairs) = 0;
 
+    /*  It is used to reconstruct a given number of vectors in an Inverted File
+     * (IVF) index
+     *  @param i0          index of the first vector to reconstruct
+     *  @param n           number of vectors to reconstruct
+     *  @param out         This is a pointer to a buffer where the reconstructed
+     * vectors will be stored.
+     */
+    virtual void reconstruct_n(idx_t i0, idx_t n, float* out);
+
    protected:
-    /// Adds a set of codes and indices to a list, with the representation
-    /// coming from the CPU equivalent
+    /// Adds a set of codes and indices to a list, with the
+    /// representation coming from the CPU equivalent
     virtual void addEncodedVectorsToList_(
             idx_t listId,
             // resident on the host

faiss/gpu/impl/IVFFlat.cu

@@ -283,6 +283,53 @@ void IVFFlat::searchPreassigned(
             storePairs);
 }
 
+void IVFFlat::reconstruct_n(idx_t i0, idx_t ni, float* out) {
+    if (ni == 0) {
+        // nothing to do
+        return;
+    }
+
+    auto stream = resources_->getDefaultStreamCurrentDevice();
+
+    for (idx_t list_no = 0; list_no < numLists_; list_no++) {
+        size_t list_size = deviceListData_[list_no]->numVecs;
+
+        auto idlist = getListIndices(list_no);
+
+        for (idx_t offset = 0; offset < list_size; offset++) {
+            idx_t id = idlist[offset];
+            if (!(id >= i0 && id < i0 + ni)) {
+                continue;
+            }
+
+            // vector data in the non-interleaved format is laid out like:
+            // v0d0 v0d1 ... v0d(dim-1) v1d0 v1d1 ... v1d(dim-1)
+
+            // vector data in the interleaved format is laid out like:
+            // (v0d0 v1d0 ... v31d0) (v0d1 v1d1 ... v31d1)
+            // (v0d(dim - 1) ... v31d(dim-1))
+            // (v32d0 v33d0 ... v63d0) (... v63d(dim-1)) (v64d0 ...)
+
+            // where vectors are chunked into groups of 32, and each dimension
+            // for each of the 32 vectors is contiguous
+
+            auto vectorChunk = offset / 32;
+            auto vectorWithinChunk = offset % 32;
+
+            auto listDataPtr = (float*)deviceListData_[list_no]->data.data();
+            listDataPtr += vectorChunk * 32 * dim_ + vectorWithinChunk;
+
+            for (int d = 0; d < dim_; ++d) {
+                fromDevice<float>(
+                        listDataPtr + 32 * d,
+                        out + (id - i0) * dim_ + d,
+                        1,
+                        stream);
+            }
+        }
+    }
+}
+
 void IVFFlat::searchImpl_(
         Tensor<float, 2, true>& queries,
         Tensor<float, 2, true>& coarseDistances,

faiss/gpu/impl/IVFFlat.cuh

@@ -51,6 +51,8 @@ class IVFFlat : public IVFBase {
             Tensor<idx_t, 2, true>& outIndices,
             bool storePairs) override;
 
+    void reconstruct_n(idx_t i0, idx_t n, float* out) override;
+
    protected:
     /// Returns the number of bytes in which an IVF list containing numVecs
     /// vectors is encoded on the device. Note that due to padding this is not

faiss/gpu/test/TestGpuIndexIVFFlat.cpp

@@ -842,6 +842,71 @@ TEST(TestGpuIndexIVFFlat, LongIVFList) {
 #endif
 }
 
+TEST(TestGpuIndexIVFFlat, Reconstruct_n) {
+    Options opt;
+
+    std::vector<float> trainVecs = faiss::gpu::randVecs(opt.numTrain, opt.dim);
+    std::vector<float> addVecs = faiss::gpu::randVecs(opt.numAdd, opt.dim);
+
+    faiss::IndexFlatL2 cpuQuantizer(opt.dim);
+    faiss::IndexIVFFlat cpuIndex(
+            &cpuQuantizer, opt.dim, opt.numCentroids, faiss::METRIC_L2);
+    cpuIndex.nprobe = opt.nprobe;
+    cpuIndex.train(opt.numTrain, trainVecs.data());
+    cpuIndex.add(opt.numAdd, addVecs.data());
+
+    faiss::gpu::StandardGpuResources res;
+    res.noTempMemory();
+
+    faiss::gpu::GpuIndexIVFFlatConfig config;
+    config.device = opt.device;
+    config.indicesOptions = faiss::gpu::INDICES_64_BIT;
+    config.use_raft = false;
+
+    faiss::gpu::GpuIndexIVFFlat gpuIndex(
+            &res, opt.dim, opt.numCentroids, faiss::METRIC_L2, config);
+    gpuIndex.nprobe = opt.nprobe;
+
+    gpuIndex.train(opt.numTrain, trainVecs.data());
+    gpuIndex.add(opt.numAdd, addVecs.data());
+
+    std::vector<float> gpuVals(opt.numAdd * opt.dim);
+
+    gpuIndex.reconstruct_n(0, gpuIndex.ntotal, gpuVals.data());
+
+    std::vector<float> cpuVals(opt.numAdd * opt.dim);
+
+    cpuIndex.reconstruct_n(0, cpuIndex.ntotal, cpuVals.data());
+
+    EXPECT_EQ(gpuVals, cpuVals);
+
+    config.indicesOptions = faiss::gpu::INDICES_32_BIT;
+
+    faiss::gpu::GpuIndexIVFFlat gpuIndex1(
+            &res, opt.dim, opt.numCentroids, faiss::METRIC_L2, config);
+    gpuIndex1.nprobe = opt.nprobe;
+
+    gpuIndex1.train(opt.numTrain, trainVecs.data());
+    gpuIndex1.add(opt.numAdd, addVecs.data());
+
+    gpuIndex1.reconstruct_n(0, gpuIndex1.ntotal, gpuVals.data());
+
+    EXPECT_EQ(gpuVals, cpuVals);
+
+    config.indicesOptions = faiss::gpu::INDICES_CPU;
+
+    faiss::gpu::GpuIndexIVFFlat gpuIndex2(
+            &res, opt.dim, opt.numCentroids, faiss::METRIC_L2, config);
+    gpuIndex2.nprobe = opt.nprobe;
+
+    gpuIndex2.train(opt.numTrain, trainVecs.data());
+    gpuIndex2.add(opt.numAdd, addVecs.data());
+
+    gpuIndex2.reconstruct_n(0, gpuIndex2.ntotal, gpuVals.data());
+
+    EXPECT_EQ(gpuVals, cpuVals);
+}
+
 int main(int argc, char** argv) {
     testing::InitGoogleTest(&argc, argv);
 

faiss/gpu/test/test_gpu_basics.py

@@ -11,6 +11,7 @@
 import random
 from common_faiss_tests import get_dataset_2
 
+
 class ReferencedObject(unittest.TestCase):
 
     d = 16

faiss/gpu/test/test_gpu_index_ivfflat.py

@@ -0,0 +1,25 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import unittest
+
+import faiss
+import numpy as np
+
+
+class TestGpuIndexIvfflat(unittest.TestCase):
+    def test_reconstruct_n(self):
+        index = faiss.index_factory(4, "IVF10,Flat")
+        x = np.random.RandomState(123).rand(10, 4).astype('float32')
+        index.train(x)
+        index.add(x)
+        res = faiss.StandardGpuResources()
+        res.noTempMemory()
+        config = faiss.GpuIndexIVFFlatConfig()
+        config.use_raft = False
+        index2 = faiss.GpuIndexIVFFlat(res, index, config)
+        recons = index2.reconstruct_n(0, 10)
+
+        np.testing.assert_array_equal(recons, x)

faiss/gpu/test/torch_test_contrib_gpu.py

@@ -108,7 +108,7 @@ def test_train_add_with_ids(self):
         self.assertTrue(np.array_equal(I.reshape(10), ids_np[10:20]))
 
     # tests reconstruct, reconstruct_n
-    def test_reconstruct(self):
+    def test_flat_reconstruct(self):
         d = 32
         res = faiss.StandardGpuResources()
         res.noTempMemory()
@@ -157,6 +157,40 @@ def test_reconstruct(self):
         index.reconstruct_n(50, 10, y)
         self.assertTrue(torch.equal(xb[50:60], y))
 
+    def test_ivfflat_reconstruct(self):
+        d = 32
+        nlist = 5
+        res = faiss.StandardGpuResources()
+        res.noTempMemory()
+        config = faiss.GpuIndexIVFFlatConfig()
+        config.use_raft = False
+
+        index = faiss.GpuIndexIVFFlat(res, d, nlist, faiss.METRIC_L2, config)
+
+        xb = torch.rand(100, d, device=torch.device('cuda', 0), dtype=torch.float32)
+        index.train(xb)
+        index.add(xb)
+
+        # Test reconstruct_n with torch gpu (native return)
+        y = index.reconstruct_n(10, 10)
+        self.assertTrue(y.is_cuda)
+        self.assertTrue(torch.equal(xb[10:20], y))
+
+        # Test reconstruct with numpy output provided
+        y = np.empty((10, d), dtype='float32')
+        index.reconstruct_n(20, 10, y)
+        self.assertTrue(np.array_equal(xb.cpu().numpy()[20:30], y))
+
+        # Test reconstruct_n with torch cpu output provided
+        y = torch.empty(10, d, dtype=torch.float32)
+        index.reconstruct_n(40, 10, y)
+        self.assertTrue(torch.equal(xb[40:50].cpu(), y))
+
+        # Test reconstruct_n with torch gpu output provided
+        y = torch.empty(10, d, device=torch.device('cuda', 0), dtype=torch.float32)
+        index.reconstruct_n(50, 10, y)
+        self.assertTrue(torch.equal(xb[50:60], y))
+
     # tests assign
     def test_assign(self):
         d = 32

faiss/gpu/utils/DeviceVector.cuh

@@ -169,6 +169,8 @@ class DeviceVector {
         T out;
         CUDA_VERIFY(cudaMemcpyAsync(
                 &out, data() + idx, sizeof(T), cudaMemcpyDeviceToHost, stream));
+
+        return out;
     }
 
     // Clean up after oversized allocations, while leaving some space to