C++ k-means implementation

.github/workflows/build_wheels.yml

@@ -4,7 +4,6 @@ on:
   push:
     branches:
       - release-*
-      - '*wheel*'  # must quote since "*" is a YAML reserved character; we want a string
     tags:
       - '*'
   pull_request:

Dockerfile

@@ -8,7 +8,7 @@ RUN conda config --prepend channels conda-forge
 # Install mamba for faster installations
 RUN conda install mamba
 
-RUN mamba install -y -c tiledb 'tiledb>=2.17,<2.18' tiledb-py cmake pybind11 pytest c-compiler cxx-compiler ninja openblas-devel "pip>22"
+RUN mamba install -y -c tiledb 'tiledb>=2.16,<2.17' tiledb-py cmake pybind11 pytest c-compiler cxx-compiler ninja openblas-devel "pip>22"
 
 COPY . TileDB-Vector-Search/
 

apis/python/CMakeLists.txt

@@ -49,7 +49,10 @@ find_package(pybind11 CONFIG REQUIRED)
 
 set(VSPY_TARGET_NAME _tiledbvspy)
 
-python_add_library(${VSPY_TARGET_NAME} MODULE "src/tiledb/vector_search/module.cc" WITH_SOABI)
+python_add_library(${VSPY_TARGET_NAME} MODULE
+    "src/tiledb/vector_search/module.cc"
+    "src/tiledb/vector_search/kmeans.cc"
+    WITH_SOABI)
 
 target_link_libraries(${VSPY_TARGET_NAME}
   PRIVATE

apis/python/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "tiledb-vector-search"
-version = "0.0.14"
+version = "0.0.10"
 #dynamic = ["version"]
 description = "TileDB Vector Search Python client"
 license = { text = "MIT" }

apis/python/src/tiledb/vector_search/index.py

@@ -1,5 +1,3 @@
-import concurrent.futures as futures
-import os
 import numpy as np
 import sys
 
@@ -22,7 +20,6 @@ class Index:
     config: Optional[Mapping[str, Any]]
         config dictionary, defaults to None
     """
-
     def __init__(
         self,
         uri: str,
@@ -39,28 +36,16 @@ def __init__(
         self.storage_version = self.group.meta.get("storage_version", "0.1")
         self.update_arrays_uri = None
         self.index_version = self.group.meta.get("index_version", "")
-        self.thread_executor = futures.ThreadPoolExecutor()
+
 
     def query(self, queries: np.ndarray, k, **kwargs):
+        updated_ids = set(self.read_updated_ids())
+        retrieval_k = k
+        if len(updated_ids) > 0:
+            retrieval_k = 2*k
+        internal_results_d, internal_results_i = self.query_internal(queries, retrieval_k, **kwargs)
         if self.update_arrays_uri is None:
-            return self.query_internal(queries, k, **kwargs)
-
-        # Query with updates
-        # Perform the queries in parallel
-        retrieval_k = 2 * k
-        kwargs["nthreads"] = int(os.cpu_count() / 2)
-        future = self.thread_executor.submit(
-            Index.query_additions,
-            queries,
-            k,
-            self.dtype,
-            self.update_arrays_uri,
-            int(os.cpu_count() / 2),
-        )
-        internal_results_d, internal_results_i = self.query_internal(
-            queries, retrieval_k, **kwargs
-        )
-        addition_results_d, addition_results_i, updated_ids = future.result()
+            return internal_results_d[:, 0:k], internal_results_i[:, 0:k]
 
         # Filter updated vectors
         query_id = 0
@@ -70,137 +55,119 @@ def query(self, queries: np.ndarray, k, **kwargs):
                 if res in updated_ids:
                     internal_results_d[query_id, res_id] = MAX_FLOAT_32
                     internal_results_i[query_id, res_id] = MAX_UINT64
-                if (
-                    internal_results_d[query_id, res_id] == 0
-                    and internal_results_i[query_id, res_id] == 0
-                ):
-                    internal_results_d[query_id, res_id] = MAX_FLOAT_32
-                    internal_results_i[query_id, res_id] = MAX_UINT64
                 res_id += 1
             query_id += 1
         sort_index = np.argsort(internal_results_d, axis=1)
         internal_results_d = np.take_along_axis(internal_results_d, sort_index, axis=1)
         internal_results_i = np.take_along_axis(internal_results_i, sort_index, axis=1)
 
         # Merge update results
+        addition_results_d, addition_results_i = self.query_additions(queries, k)
         if addition_results_d is None:
             return internal_results_d[:, 0:k], internal_results_i[:, 0:k]
 
         query_id = 0
         for query in addition_results_d:
             res_id = 0
             for res in query:
-                if (
-                    addition_results_d[query_id, res_id] == 0
-                    and addition_results_i[query_id, res_id] == 0
-                ):
+                if addition_results_d[query_id, res_id] == 0 and addition_results_i[query_id, res_id] == 0:
                     addition_results_d[query_id, res_id] = MAX_FLOAT_32
                     addition_results_i[query_id, res_id] = MAX_UINT64
                 res_id += 1
             query_id += 1
 
+
         results_d = np.hstack((internal_results_d, addition_results_d))
         results_i = np.hstack((internal_results_i, addition_results_i))
         sort_index = np.argsort(results_d, axis=1)
         results_d = np.take_along_axis(results_d, sort_index, axis=1)
         results_i = np.take_along_axis(results_i, sort_index, axis=1)
         return results_d[:, 0:k], results_i[:, 0:k]
 
-    @staticmethod
-    def query_additions(
-        queries: np.ndarray, k, dtype, update_arrays_uri, nthreads=8
-    ):
+    def query_internal(self, queries: np.ndarray, k, **kwargs):
+        raise NotImplementedError
+
+    def query_additions(self, queries: np.ndarray, k):
         assert queries.dtype == np.float32
-        additions_vectors, additions_external_ids, updated_ids = Index.read_additions(
-            update_arrays_uri
-        )
+        additions_vectors, additions_external_ids = self.read_additions()
         if additions_vectors is None:
-            return None, None, updated_ids
-
+            return None, None
         queries_m = array_to_matrix(np.transpose(queries))
         d, i = query_vq_heap_pyarray(
-            array_to_matrix(np.transpose(additions_vectors).astype(dtype)),
+            array_to_matrix(np.transpose(additions_vectors).astype(self.dtype)),
             queries_m,
             StdVector_u64(additions_external_ids),
             k,
-            nthreads,
-        )
-        return np.transpose(np.array(d)), np.transpose(np.array(i)), updated_ids
-
-    @staticmethod
-    def read_additions(update_arrays_uri) -> (np.ndarray, np.array):
-        if update_arrays_uri is None:
-            return None, None, np.array([], np.uint64)
-        updates_array = tiledb.open(update_arrays_uri, mode="r")
-        q = updates_array.query(attrs=("vector",), coords=True)
-        data = q[:]
-        updates_array.close()
-        updated_ids = data["external_id"]
-        additions_filter = [len(item) > 0 for item in data["vector"]]
-        if len(data["external_id"][additions_filter]) > 0:
-            return (
-                np.vstack(data["vector"][additions_filter]),
-                data["external_id"][additions_filter],
-                updated_ids
-            )
-        else:
-            return None, None, updated_ids
-
-    def query_internal(self, queries: np.ndarray, k, **kwargs):
-        raise NotImplementedError
+            8)
+        return np.transpose(np.array(d)), np.transpose(np.array(i))
 
     def update(self, vector: np.array, external_id: np.uint64):
         updates_array = self.open_updates_array()
-        vectors = np.empty((1), dtype="O")
+        vectors = np.empty((1), dtype='O')
         vectors[0] = vector
-        updates_array[external_id] = {"vector": vectors}
+        updates_array[external_id] = {'vector': vectors}
         updates_array.close()
         self.consolidate_update_fragments()
 
     def update_batch(self, vectors: np.ndarray, external_ids: np.array):
         updates_array = self.open_updates_array()
-        updates_array[external_ids] = {"vector": vectors}
+        updates_array[external_ids] = {'vector': vectors}
         updates_array.close()
         self.consolidate_update_fragments()
 
     def delete(self, external_id: np.uint64):
         updates_array = self.open_updates_array()
-        deletes = np.empty((1), dtype="O")
+        deletes = np.empty((1), dtype='O')
         deletes[0] = np.array([], dtype=self.dtype)
-        updates_array[external_id] = {"vector": deletes}
+        updates_array[external_id] =  {'vector': deletes}
         updates_array.close()
         self.consolidate_update_fragments()
 
     def delete_batch(self, external_ids: np.array):
         updates_array = self.open_updates_array()
-        deletes = np.empty((len(external_ids)), dtype="O")
+        deletes = np.empty((len(external_ids)), dtype='O')
         for i in range(len(external_ids)):
             deletes[i] = np.array([], dtype=self.dtype)
-        updates_array[external_ids] = {"vector": deletes}
+        updates_array[external_ids] = {'vector': deletes}
         updates_array.close()
         self.consolidate_update_fragments()
 
     def consolidate_update_fragments(self):
         fragments_info = tiledb.array_fragments(self.update_arrays_uri)
-        if len(fragments_info) > 10:
+        if(len(fragments_info) > 10):
             tiledb.consolidate(self.update_arrays_uri)
             tiledb.vacuum(self.update_arrays_uri)
 
     def get_updates_uri(self):
         return self.update_arrays_uri
 
+    def read_additions(self) -> (np.ndarray, np.array):
+        if self.update_arrays_uri is None:
+            return None, None
+        updates_array = tiledb.open(self.update_arrays_uri, mode="r")
+        q = updates_array.query(attrs=('vector',), coords=True)
+        data = q[:]
+        additions_filter = [len(item) > 0 for item in data["vector"]]
+        if len(data["external_id"][additions_filter]) > 0:
+            return np.vstack(data["vector"][additions_filter]), data["external_id"][additions_filter]
+        else:
+            return None, None
+    def read_updated_ids(self) -> np.array:
+        if self.update_arrays_uri is None:
+            return np.array([], np.uint64)
+        updates_array = tiledb.open(self.update_arrays_uri, mode="r")
+        q = updates_array.query(attrs=('vector',), coords=True)
+        data = q[:]
+        return data["external_id"]
+
     def open_updates_array(self):
         if self.update_arrays_uri is None:
-            updates_array_name = storage_formats[self.storage_version][
-                "UPDATES_ARRAY_NAME"
-            ]
+            updates_array_name = storage_formats[self.storage_version]["UPDATES_ARRAY_NAME"]
             updates_array_uri = f"{self.group.uri}/{updates_array_name}"
             if tiledb.array_exists(updates_array_uri):
                 raise RuntimeError(f"Array {updates_array_uri} already exists.")
             external_id_dim = tiledb.Dim(
-                name="external_id",
-                domain=(0, MAX_UINT64 - 1),
-                dtype=np.dtype(np.uint64),
+                name="external_id", domain=(0, MAX_UINT64-1), dtype=np.dtype(np.uint64)
             )
             dom = tiledb.Domain(external_id_dim)
             vector_attr = tiledb.Attr(name="vector", dtype=self.dtype, var=True)
@@ -221,18 +188,13 @@ def open_updates_array(self):
 
     def consolidate_updates(self):
         from tiledb.vector_search.ingestion import ingest
-
         new_index = ingest(
             index_type=self.index_type,
             index_uri=self.uri,
             size=self.size,
             source_uri=self.db_uri,
             external_ids_uri=self.ids_uri,
-            updates_uri=self.update_arrays_uri,
+            updates_uri=self.update_arrays_uri
         )
         tiledb.Array.delete_array(self.update_arrays_uri)
-        self.group.close()
-        self.group = tiledb.Group(self.uri, "w", ctx=tiledb.Ctx(self.config))
-        self.group.remove(self.update_arrays_uri)
-        self.group.close()
         return new_index