Add Faiss to the list of supported ANN frameworks

README.md

@@ -134,6 +134,7 @@ One important aspect of deploying recommender model is efficient retrieval via A
 
 | Supported framework | Cornac wrapper | Examples |
 | :---: | :---: | :---: |
+| [meta/faiss](https://github.com/facebookresearch/faiss) | [FaissANN](cornac/models/ann/recom_ann_faiss.py) | [ann_all.ipynb](examples/ann_all.ipynb)
 | [nmslib/hnswlib](https://github.com/nmslib/hnswlib) | [HNSWLibANN](cornac/models/ann/recom_ann_hnswlib.py) | [ann_hnswlib.ipynb](tutorials/ann_hnswlib.ipynb), [ann_all.ipynb](examples/ann_all.ipynb)
 | [google/scann](https://github.com/google-research/google-research/tree/master/scann) | [ScaNNANN](cornac/models/ann/recom_ann_scann.py) | [ann_all.ipynb](examples/ann_all.ipynb)
 

cornac/models/__init__.py

@@ -17,6 +17,7 @@
 from .recommender import NextBasketRecommender
 
 from .amr import AMR
+from .ann import FaissANN
 from .ann import HNSWLibANN
 from .ann import ScaNNANN
 from .baseline_only import BaselineOnly

cornac/models/ann/__init__.py

@@ -1,2 +1,3 @@
+from .recom_ann_faiss import FaissANN
 from .recom_ann_hnswlib import HNSWLibANN
 from .recom_ann_scann import ScaNNANN

cornac/models/ann/recom_ann_faiss.py

@@ -0,0 +1,153 @@
+# Copyright 2023 The Cornac Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+
+import multiprocessing
+import numpy as np
+
+from ..recommender import MEASURE_L2, MEASURE_DOT, MEASURE_COSINE
+from .recom_ann_base import BaseANN
+
+
+class FaissANN(BaseANN):
+    """Approximate Nearest Neighbor Search with Faiss (https://github.com/facebookresearch/faiss).
+    Faiss provides both CPU and GPU implementation. More on the algorithms:
+    https://github.com/facebookresearch/faiss/wiki
+
+    Parameters
+    ----------------
+    model: object: :obj:`cornac.models.Recommender`, required
+        Trained recommender model which to get user/item vectors from.
+
+    nlist: int, default: 100
+        The number of cells used for building the index.
+
+    nprobe: int, default: 50
+        The number of cells (out of nlist) that are visited to perform a search.
+
+    use_gpu : bool, optional
+        Whether or not to run Faiss on GPU. Requires faiss-gpu to be installed
+        instead of faiss-cpu.
+
+    num_threads: int, optional, default: -1
+        Default number of threads used for building index. If num_threads = -1,
+        all cores will be used.
+
+    seed: int, optional, default: None
+        Random seed for reproducibility.
+
+    name: str, required
+        Name of the recommender model.
+
+    verbose: boolean, optional, default: False
+        When True, running logs are displayed.
+    """
+
+    def __init__(
+        self,
+        model,
+        nlist=100,
+        nprobe=50,
+        use_gpu=False,
+        num_threads=-1,
+        seed=None,
+        name="FaissANN",
+        verbose=False,
+    ):
+        super().__init__(model=model, name=name, verbose=verbose)
+
+        self.model = model
+        self.nlist = nlist
+        self.nprobe = nprobe
+        self.use_gpu = use_gpu
+        self.num_threads = (
+            num_threads if num_threads != -1 else multiprocessing.cpu_count()
+        )
+        self.seed = seed
+
+        self.index = None
+        self.ignored_attrs.extend(
+            [
+                "index",  # will be saved separately
+                "item_vectors",  # redundant after index is built
+            ]
+        )
+
+    def build_index(self):
+        """Building index from the base recommender model."""
+        import faiss
+
+        faiss.omp_set_num_threads(self.num_threads)
+
+        SUPPORTED_MEASURES = {
+            MEASURE_L2: faiss.METRIC_L2,
+            MEASURE_DOT: faiss.METRIC_INNER_PRODUCT,
+            MEASURE_COSINE: faiss.METRIC_INNER_PRODUCT,
+        }
+
+        assert self.measure in SUPPORTED_MEASURES
+
+        if self.measure == MEASURE_COSINE:
+            self.item_vectors /= np.linalg.norm(self.item_vectors, axis=1)[
+                :, np.newaxis
+            ]
+
+        self.item_vectors = self.item_vectors.astype("float32")
+
+        self.index = faiss.IndexIVFFlat(
+            faiss.IndexFlat(self.item_vectors.shape[1]),
+            self.item_vectors.shape[1],
+            self.nlist,
+            SUPPORTED_MEASURES[self.measure],
+        )
+
+        if self.use_gpu:
+            self.index = faiss.index_cpu_to_all_gpus(self.index)
+
+        self.index.train(self.item_vectors)
+        self.index.add(self.item_vectors)
+        self.index.nprobe = self.nprobe
+
+    def knn_query(self, query, k):
+        """Implementing ANN search for a given query.
+
+        Returns
+        -------
+        neighbors, distances: numpy.array and numpy.array
+            Array of k-nearest neighbors and corresponding distances for the given query.
+        """
+        distances, neighbors = self.index.search(query, k)
+        return neighbors, distances
+
+    def save(self, save_dir=None):
+        import faiss
+
+        saved_path = super().save(save_dir)
+        idx_path = saved_path + ".index"
+        if self.use_gpu:
+            self.index = faiss.index_gpu_to_cpu(self.index)
+        faiss.write_index(self.index, idx_path)
+        return saved_path
+
+    @staticmethod
+    def load(model_path, trainable=False):
+        import faiss
+
+        ann = BaseANN.load(model_path, trainable)
+        idx_path = ann.load_from + ".index"
+        ann.index = faiss.read_index(idx_path)
+        if ann.use_gpu:
+            ann.index = faiss.index_cpu_to_all_gpus(ann.index)
+        return ann

cornac/models/ann/recom_ann_hnswlib.py

@@ -133,7 +133,7 @@ def knn_query(self, query, k):
 
     def save(self, save_dir=None):
         saved_path = super().save(save_dir)
-        self.index.save_index(saved_path + ".idx")
+        self.index.save_index(saved_path + ".index")
         return saved_path
 
     @staticmethod
@@ -144,7 +144,7 @@ def load(model_path, trainable=False):
         ann.index = hnswlib.Index(
             space=SUPPORTED_MEASURES[ann.measure], dim=ann.user_vectors.shape[1]
         )
-        ann.index.load_index(ann.load_from + ".idx")
+        ann.index.load_index(ann.load_from + ".index")
         ann.index.set_ef(ann.ef)
         ann.index.set_num_threads(ann.num_threads)
         return ann

cornac/models/ann/recom_ann_scann.py

@@ -22,7 +22,11 @@
 from .recom_ann_base import BaseANN
 
 
-SUPPORTED_MEASURES = {MEASURE_L2: "squared_l2", MEASURE_DOT: "dot_product"}
+SUPPORTED_MEASURES = {
+    MEASURE_L2: "squared_l2",
+    MEASURE_DOT: "dot_product",
+    MEASURE_COSINE: "dot_product",
+}
 
 
 class ScaNNANN(BaseANN):
@@ -108,7 +112,6 @@ def build_index(self):
             self.item_vectors /= np.linalg.norm(self.item_vectors, axis=1)[
                 :, np.newaxis
             ]
-            self.measure = MEASURE_DOT
         else:
             self.partition_params["spherical"] = False
 
@@ -149,7 +152,7 @@ def knn_query(self, query, k):
 
     def save(self, save_dir=None):
         saved_path = super().save(save_dir)
-        idx_path = saved_path + ".idx"
+        idx_path = saved_path + ".index"
         os.makedirs(idx_path, exist_ok=True)
         self.index.searcher.serialize(idx_path)
         return saved_path
@@ -159,6 +162,6 @@ def load(model_path, trainable=False):
         from scann.scann_ops.py import scann_ops_pybind
 
         ann = BaseANN.load(model_path, trainable)
-        idx_path = ann.load_from + ".idx"
+        idx_path = ann.load_from + ".index"
         ann.index = scann_ops_pybind.load_searcher(idx_path)
         return ann