Binary cloning and GPU range search

contrib/exhaustive_search.py

@@ -60,12 +60,18 @@ def range_search_gpu(xq, r2, index_gpu, index_cpu, gpu_k=1024):
     - None. In that case, at most gpu_k results will be returned
     """
     nq, d = xq.shape
-    k = min(index_gpu.ntotal, gpu_k)
+    is_binary_index = isinstance(index_gpu, faiss.IndexBinary)
     keep_max = faiss.is_similarity_metric(index_gpu.metric_type)
-    LOG.debug(f"GPU search {nq} queries with {k=:}")
+    r2 = int(r2) if is_binary_index else float(r2)
+    k = min(index_gpu.ntotal, gpu_k)
+    LOG.debug(
+        f"GPU search {nq} queries with {k=:} {is_binary_index=:} {keep_max=:}")
     t0 = time.time()
     D, I = index_gpu.search(xq, k)
     t1 = time.time() - t0
+    if is_binary_index:
+        assert d * 8 < 32768  # let's compact the distance matrix
+        D = D.astype('int16')
     t2 = 0
     lim_remain = None
     if index_cpu is not None:
@@ -79,14 +85,24 @@ def range_search_gpu(xq, r2, index_gpu, index_cpu, gpu_k=1024):
             if isinstance(index_cpu, np.ndarray):
                 # then it in fact an array that we have to make flat
                 xb = index_cpu
-                index_cpu = faiss.IndexFlat(d, index_gpu.metric_type)
+                if is_binary_index:
+                    index_cpu = faiss.IndexBinaryFlat(d * 8)
+                else:
+                    index_cpu = faiss.IndexFlat(d, index_gpu.metric_type)
                 index_cpu.add(xb)
             lim_remain, D_remain, I_remain = index_cpu.range_search(xq[mask], r2)
+            if is_binary_index:
+                D_remain = D_remain.astype('int16')
             t2 = time.time() - t0
     LOG.debug("combine")
     t0 = time.time()
 
-    combiner = faiss.CombinerRangeKNN(nq, k, float(r2), keep_max)
+    CombinerRangeKNN = (
+        faiss.CombinerRangeKNNint16 if is_binary_index else
+        faiss.CombinerRangeKNNfloat
+    )
+
+    combiner = CombinerRangeKNN(nq, k, r2, keep_max)
     if True:
         sp = faiss.swig_ptr
         combiner.I = sp(I)
@@ -101,7 +117,7 @@ def range_search_gpu(xq, r2, index_gpu, index_cpu, gpu_k=1024):
         L_res = np.empty(nq + 1, dtype='int64')
         combiner.compute_sizes(sp(L_res))
         nres = L_res[-1]
-        D_res = np.empty(nres, dtype='float32')
+        D_res = np.empty(nres, dtype=D.dtype)
         I_res = np.empty(nres, dtype='int64')
         combiner.write_result(sp(D_res), sp(I_res))
     else:
@@ -251,6 +267,7 @@ def range_search_max_results(index, query_iterator, radius,
     """
     # TODO: all result manipulations are in python, should move to C++ if perf
     # critical
+    is_binary_index = isinstance(index, faiss.IndexBinary)
 
     if min_results is None:
         assert max_results is not None
@@ -268,6 +285,8 @@ def range_search_max_results(index, query_iterator, radius,
         co = faiss.GpuMultipleClonerOptions()
         co.shard = shard
         index_gpu = faiss.index_cpu_to_all_gpus(index, co=co, ngpu=ngpu)
+    else:
+        index_gpu = None
 
     t_start = time.time()
     t_search = t_post_process = 0
@@ -276,7 +295,8 @@ def range_search_max_results(index, query_iterator, radius,
 
     for xqi in query_iterator:
         t0 = time.time()
-        if ngpu > 0:
+        LOG.debug(f"searching {len(xqi)} vectors")
+        if index_gpu:
             lims_i, Di, Ii = range_search_gpu(xqi, radius, index_gpu, index)
         else:
             lims_i, Di, Ii = index.range_search(xqi, radius)
@@ -286,8 +306,7 @@ def range_search_max_results(index, query_iterator, radius,
         qtot += len(xqi)
 
         t1 = time.time()
-        if xqi.dtype != np.float32:
-            # for binary indexes
+        if is_binary_index:
             # weird Faiss quirk that returns floats for Hamming distances
             Di = Di.astype('int16')
 
@@ -299,7 +318,7 @@ def range_search_max_results(index, query_iterator, radius,
                      (totres, max_results))
             radius, totres = apply_maxres(
                 res_batches, min_results,
-                keep_max=faiss.is_similarity_metric(index.metric_type)
+                keep_max=index.metric_type == faiss.METRIC_INNER_PRODUCT
             )
         t2 = time.time()
         t_search += t1 - t0
@@ -315,7 +334,7 @@ def range_search_max_results(index, query_iterator, radius,
     if clip_to_min and totres > min_results:
         radius, totres = apply_maxres(
             res_batches, min_results,
-            keep_max=faiss.is_similarity_metric(index.metric_type)
+            keep_max=index.metric_type == faiss.METRIC_INNER_PRODUCT
         )
 
     nres = np.hstack([nres_i for nres_i, dis_i, ids_i in res_batches])

faiss/IndexBinary.cpp

@@ -15,6 +15,11 @@
 
 namespace faiss {
 
+IndexBinary::IndexBinary(idx_t d, MetricType metric)
+        : d(d), code_size(d / 8), metric_type(metric) {
+    FAISS_THROW_IF_NOT(d % 8 == 0);
+}
+
 IndexBinary::~IndexBinary() {}
 
 void IndexBinary::train(idx_t, const uint8_t*) {
@@ -51,7 +56,7 @@ void IndexBinary::reconstruct(idx_t, uint8_t*) const {
 
 void IndexBinary::reconstruct_n(idx_t i0, idx_t ni, uint8_t* recons) const {
     for (idx_t i = 0; i < ni; i++) {
-        reconstruct(i0 + i, recons + i * d);
+        reconstruct(i0 + i, recons + i * code_size);
     }
 }
 
@@ -70,10 +75,10 @@ void IndexBinary::search_and_reconstruct(
         for (idx_t j = 0; j < k; ++j) {
             idx_t ij = i * k + j;
             idx_t key = labels[ij];
-            uint8_t* reconstructed = recons + ij * d;
+            uint8_t* reconstructed = recons + ij * code_size;
             if (key < 0) {
                 // Fill with NaNs
-                memset(reconstructed, -1, sizeof(*reconstructed) * d);
+                memset(reconstructed, -1, code_size);
             } else {
                 reconstruct(key, reconstructed);
             }

faiss/IndexBinary.h

@@ -5,8 +5,6 @@
  * LICENSE file in the root directory of this source tree.
  */
 
-// -*- c++ -*-
-
 #ifndef FAISS_INDEX_BINARY_H
 #define FAISS_INDEX_BINARY_H
 
@@ -16,7 +14,6 @@
 #include <typeinfo>
 
 #include <faiss/Index.h>
-#include <faiss/impl/FaissAssert.h>
 
 namespace faiss {
 
@@ -35,27 +32,19 @@ struct IndexBinary {
     using component_t = uint8_t;
     using distance_t = int32_t;
 
-    int d;         ///< vector dimension
-    int code_size; ///< number of bytes per vector ( = d / 8 )
-    idx_t ntotal;  ///< total nb of indexed vectors
-    bool verbose;  ///< verbosity level
+    int d = 0;            ///< vector dimension
+    int code_size = 0;    ///< number of bytes per vector ( = d / 8 )
+    idx_t ntotal = 0;     ///< total nb of indexed vectors
+    bool verbose = false; ///< verbosity level
 
     /// set if the Index does not require training, or if training is done
     /// already
-    bool is_trained;
+    bool is_trained = true;
 
     /// type of metric this index uses for search
-    MetricType metric_type;
-
-    explicit IndexBinary(idx_t d = 0, MetricType metric = METRIC_L2)
-            : d(d),
-              code_size(d / 8),
-              ntotal(0),
-              verbose(false),
-              is_trained(true),
-              metric_type(metric) {
-        FAISS_THROW_IF_NOT(d % 8 == 0);
-    }
+    MetricType metric_type = METRIC_L2;
+
+    explicit IndexBinary(idx_t d = 0, MetricType metric = METRIC_L2);
 
     virtual ~IndexBinary();
 

faiss/IndexBinaryFromFloat.cpp

@@ -9,6 +9,7 @@
 
 #include <faiss/IndexBinaryFromFloat.h>
 
+#include <faiss/impl/FaissAssert.h>
 #include <faiss/utils/utils.h>
 #include <algorithm>
 #include <memory>

faiss/IndexIDMap.cpp

@@ -21,18 +21,31 @@
 
 namespace faiss {
 
+namespace {
+
+// IndexBinary needs to update the code_size when d is set...
+
+void sync_d(Index* index) {}
+
+void sync_d(IndexBinary* index) {
+    FAISS_THROW_IF_NOT(index->d % 8 == 0);
+    index->code_size = index->d / 8;
+}
+
+} // anonymous namespace
+
 /*****************************************************
  * IndexIDMap implementation
  *******************************************************/
 
 template <typename IndexT>
-IndexIDMapTemplate<IndexT>::IndexIDMapTemplate(IndexT* index)
-        : index(index), own_fields(false) {
+IndexIDMapTemplate<IndexT>::IndexIDMapTemplate(IndexT* index) : index(index) {
     FAISS_THROW_IF_NOT_MSG(index->ntotal == 0, "index must be empty on input");
     this->is_trained = index->is_trained;
     this->metric_type = index->metric_type;
     this->verbose = index->verbose;
     this->d = index->d;
+    sync_d(this);
 }
 
 template <typename IndexT>

faiss/IndexIDMap.h

@@ -22,8 +22,8 @@ struct IndexIDMapTemplate : IndexT {
     using component_t = typename IndexT::component_t;
     using distance_t = typename IndexT::distance_t;
 
-    IndexT* index;   ///! the sub-index
-    bool own_fields; ///! whether pointers are deleted in destructo
+    IndexT* index = nullptr; ///! the sub-index
+    bool own_fields = false; ///! whether pointers are deleted in destructo
     std::vector<idx_t> id_map;
 
     explicit IndexIDMapTemplate(IndexT* index);

faiss/IndexReplicas.cpp

@@ -12,17 +12,34 @@
 
 namespace faiss {
 
+namespace {
+
+// IndexBinary needs to update the code_size when d is set...
+
+void sync_d(Index* index) {}
+
+void sync_d(IndexBinary* index) {
+    FAISS_THROW_IF_NOT(index->d % 8 == 0);
+    index->code_size = index->d / 8;
+}
+
+} // anonymous namespace
+
 template <typename IndexT>
 IndexReplicasTemplate<IndexT>::IndexReplicasTemplate(bool threaded)
         : ThreadedIndex<IndexT>(threaded) {}
 
 template <typename IndexT>
 IndexReplicasTemplate<IndexT>::IndexReplicasTemplate(idx_t d, bool threaded)
-        : ThreadedIndex<IndexT>(d, threaded) {}
+        : ThreadedIndex<IndexT>(d, threaded) {
+    sync_d(this);
+}
 
 template <typename IndexT>
 IndexReplicasTemplate<IndexT>::IndexReplicasTemplate(int d, bool threaded)
-        : ThreadedIndex<IndexT>(d, threaded) {}
+        : ThreadedIndex<IndexT>(d, threaded) {
+    sync_d(this);
+}
 
 template <typename IndexT>
 void IndexReplicasTemplate<IndexT>::onAfterAddIndex(IndexT* index) {
@@ -168,6 +185,8 @@ void IndexReplicasTemplate<IndexT>::syncWithSubIndexes() {
     }
 
     auto firstIndex = this->at(0);
+    this->d = firstIndex->d;
+    sync_d(this);
     this->metric_type = firstIndex->metric_type;
     this->is_trained = firstIndex->is_trained;
     this->ntotal = firstIndex->ntotal;
@@ -181,28 +200,6 @@ void IndexReplicasTemplate<IndexT>::syncWithSubIndexes() {
     }
 }
 
-// No metric_type for IndexBinary
-template <>
-void IndexReplicasTemplate<IndexBinary>::syncWithSubIndexes() {
-    if (!this->count()) {
-        this->is_trained = false;
-        this->ntotal = 0;
-
-        return;
-    }
-
-    auto firstIndex = this->at(0);
-    this->is_trained = firstIndex->is_trained;
-    this->ntotal = firstIndex->ntotal;
-
-    for (int i = 1; i < this->count(); ++i) {
-        auto index = this->at(i);
-        FAISS_THROW_IF_NOT(this->d == index->d);
-        FAISS_THROW_IF_NOT(this->is_trained == index->is_trained);
-        FAISS_THROW_IF_NOT(this->ntotal == index->ntotal);
-    }
-}
-
 // explicit instantiations
 template struct IndexReplicasTemplate<Index>;
 template struct IndexReplicasTemplate<IndexBinary>;