Add multithread search to BF index (#425)

* Add multithread search for BF index

python_bindings/bindings.cpp

@@ -719,6 +719,7 @@ class BFIndex {
     int dim;
     bool index_inited;
     bool normalize;
+    int num_threads_default;
 
     hnswlib::labeltype cur_l;
     hnswlib::BruteforceSearch<dist_t>* alg;
@@ -739,6 +740,8 @@ class BFIndex {
         }
         alg = NULL;
         index_inited = false;
+
+        num_threads_default = std::thread::hardware_concurrency();
     }
 
 
@@ -749,6 +752,21 @@ class BFIndex {
     }
 
 
+    size_t getMaxElements() const {
+        return alg->maxelements_;
+    }
+
+
+    size_t getCurrentCount() const {
+        return alg->cur_element_count;
+    }
+
+
+    void set_num_threads(int num_threads) {
+        this->num_threads_default = num_threads;
+    }
+
+
     void init_new_index(const size_t maxElements) {
         if (alg) {
             throw std::runtime_error("The index is already initiated.");
@@ -820,15 +838,19 @@ class BFIndex {
     py::object knnQuery_return_numpy(
         py::object input,
         size_t k = 1,
+        int num_threads = -1,
         const std::function<bool(hnswlib::labeltype)>& filter = nullptr) {
         py::array_t < dist_t, py::array::c_style | py::array::forcecast > items(input);
         auto buffer = items.request();
         hnswlib::labeltype *data_numpy_l;
         dist_t *data_numpy_d;
         size_t rows, features;
+
+        if (num_threads <= 0)
+            num_threads = num_threads_default;
+
         {
             py::gil_scoped_release l;
-
             get_input_array_shapes(buffer, &rows, &features);
 
             data_numpy_l = new hnswlib::labeltype[rows * k];
@@ -837,16 +859,16 @@ class BFIndex {
             CustomFilterFunctor idFilter(filter);
             CustomFilterFunctor* p_idFilter = filter ? &idFilter : nullptr;
 
-            for (size_t row = 0; row < rows; row++) {
+            ParallelFor(0, rows, num_threads, [&](size_t row, size_t threadId) {
                 std::priority_queue<std::pair<dist_t, hnswlib::labeltype >> result = alg->searchKnn(
-                        (void *) items.data(row), k, p_idFilter);
+                    (void*)items.data(row), k, p_idFilter);
                 for (int i = k - 1; i >= 0; i--) {
-                    auto &result_tuple = result.top();
+                    auto& result_tuple = result.top();
                     data_numpy_d[row * k + i] = result_tuple.first;
                     data_numpy_l[row * k + i] = result_tuple.second;
                     result.pop();
                 }
-            }
+            });
         }
 
         py::capsule free_when_done_l(data_numpy_l, [](void *f) {
@@ -957,13 +979,22 @@ PYBIND11_PLUGIN(hnswlib) {
         py::class_<BFIndex<float>>(m, "BFIndex")
         .def(py::init<const std::string &, const int>(), py::arg("space"), py::arg("dim"))
         .def("init_index", &BFIndex<float>::init_new_index, py::arg("max_elements"))
-        .def("knn_query", &BFIndex<float>::knnQuery_return_numpy, py::arg("data"), py::arg("k") = 1, py::arg("filter") = py::none())
+        .def("knn_query",
+            &BFIndex<float>::knnQuery_return_numpy,
+            py::arg("data"),
+            py::arg("k") = 1,
+            py::arg("num_threads") = -1,
+            py::arg("filter") = py::none())
         .def("add_items", &BFIndex<float>::addItems, py::arg("data"), py::arg("ids") = py::none())
         .def("delete_vector", &BFIndex<float>::deleteVector, py::arg("label"))
+        .def("set_num_threads", &BFIndex<float>::set_num_threads, py::arg("num_threads"))
         .def("save_index", &BFIndex<float>::saveIndex, py::arg("path_to_index"))
         .def("load_index", &BFIndex<float>::loadIndex, py::arg("path_to_index"), py::arg("max_elements") = 0)
         .def("__repr__", [](const BFIndex<float> &a) {
             return "<hnswlib.BFIndex(space='" + a.space_name + "', dim="+std::to_string(a.dim)+")>";
-        });
+        })
+        .def("get_max_elements", &BFIndex<float>::getMaxElements)
+        .def("get_current_count", &BFIndex<float>::getCurrentCount)
+        .def_readwrite("num_threads", &BFIndex<float>::num_threads_default);
         return m.ptr();
 }

python_bindings/tests/bindings_test_bf_index.py

@@ -0,0 +1,49 @@
+import unittest
+
+import numpy as np
+
+import hnswlib
+
+
+class RandomSelfTestCase(unittest.TestCase):
+    def testBFIndex(self):
+
+        dim = 16
+        num_elements = 10000
+        num_queries = 1000
+        k = 20
+
+        # Generating sample data
+        data = np.float32(np.random.random((num_elements, dim)))
+
+        # Declaring index
+        bf_index = hnswlib.BFIndex(space='l2', dim=dim)  # possible options are l2, cosine or ip
+        bf_index.init_index(max_elements=num_elements)
+
+        num_threads = 8
+        bf_index.set_num_threads(num_threads)  # by default using all available cores
+
+        print(f"Adding all elements {num_elements}")
+        bf_index.add_items(data)
+
+        self.assertEqual(bf_index.num_threads, num_threads)
+        self.assertEqual(bf_index.get_max_elements(), num_elements)
+        self.assertEqual(bf_index.get_current_count(), num_elements)
+
+        queries = np.float32(np.random.random((num_queries, dim)))
+        print("Searching nearest neighbours")
+        labels, distances = bf_index.knn_query(queries, k=k)
+
+        print("Checking results")
+        for i in range(num_queries):
+            query = queries[i]
+            sq_dists = (data - query)**2
+            dists = np.sum(sq_dists, axis=1)
+            labels_gt = np.argsort(dists)[:k]
+            dists_gt = dists[labels_gt]
+            dists_bf = distances[i]
+            # we can compare labels but because of numeric errors in distance calculation in C++ and numpy
+            # sometimes we get different order of labels, therefore we compare distances
+            max_diff_with_gt = np.max(np.abs(dists_gt - dists_bf))
+
+            self.assertTrue(max_diff_with_gt < 1e-5)