From 04d2d72fd09dc8c0ada72d22a40f2b785f87a062 Mon Sep 17 00:00:00 2001 From: "zhirong.xzr" Date: Sat, 16 Apr 2022 18:02:14 +0800 Subject: [PATCH] Publish CurvFaiss --- README.md | 3 +- README_CN.md | 1 + docs/sample.png | Bin 196687 -> 162693 bytes examples/curvfaiss/README.md | 47 ++++++++++++++++ examples/curvfaiss/customized.md | 18 ++++++ examples/curvfaiss/knn.py | 91 +++++++++++++++++++++++++++++++ 6 files changed, 159 insertions(+), 1 deletion(-) create mode 100644 examples/curvfaiss/README.md create mode 100644 examples/curvfaiss/customized.md create mode 100644 examples/curvfaiss/knn.py diff --git a/README.md b/README.md index 2edb735..b8b0a86 100644 --- a/README.md +++ b/README.md @@ -5,8 +5,9 @@ English | [简体中文](README_CN.md) #### *NEWS!* +- [April 16] [*CurvFaiss*](examples/curvfaiss/README.md), the nearest neighbor search tool in the non-Euclidean space, is available now! - [March 29] Our paper *[AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System](https://arxiv.org/abs/2203.14683)* has been accepted by ICDE'22! -- [November 03] The [Detailed explanation](https://mp.weixin.qq.com/s/uP_wU5nnd7faBoo5B_7Xfw?spm=ata.21736010.0.0.5b9d2c7eQ67WZg) is posted on Alimama Tech Blog! +- [November 03] The [detailed explanation](https://mp.weixin.qq.com/s/uP_wU5nnd7faBoo5B_7Xfw?spm=ata.21736010.0.0.5b9d2c7eQ67WZg) is posted on Alimama Tech Blog! ## Why Non-Euclidean Geometry diff --git a/README_CN.md b/README_CN.md index 3ef0395..c1257d1 100644 --- a/README_CN.md +++ b/README_CN.md @@ -5,6 +5,7 @@ [English](./README.md) | 简体中文 #### *更新!* +- [04/16] 非欧最近邻检索工具[*CurvFaiss*](examples/curvfaiss/README.md)现已发布! - [03/29] [*AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System*](https://arxiv.org/abs/2203.14683) 发表在ICDE'22! - [11/03] [*曲率学习框架详解*](https://mp.weixin.qq.com/s/uP_wU5nnd7faBoo5B_7Xfw?spm=ata.21736010.0.0.5b9d2c7eQ67WZg) 发布在阿里妈妈技术公众号! diff --git a/docs/sample.png b/docs/sample.png index 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z?NI3u|FiMIKK2Lc>wwr%Qg3{`Kj!MMZR6$l3SW@u_N^WKpUK6q{~6 0 +index = curvfaiss.IndexFlatStereographic(dim, curvature) +index.add(embedding) + +# knn search +knn_distance, knn_index = index.search(query, topk) + +print(knn_distance, knn_index) +``` + +See the [*full demo*](knn.py) here! \ No newline at end of file diff --git a/examples/curvfaiss/customized.md b/examples/curvfaiss/customized.md new file mode 100644 index 0000000..4221edc --- /dev/null +++ b/examples/curvfaiss/customized.md @@ -0,0 +1,18 @@ +# Tutorial on developing customized distance metric in Faiss + +## Build the environment +1) build the docker from [*Dockerfile*](https://github.com/facebookresearch/faiss/blob/main/Dockerfile). +2) install swig4, cmake, anaconda with py=3.7/numpy. +3) follow the installation [*wiki*](https://github.com/facebookresearch/faiss/wiki/Installing-Faiss#compiling-the-python-interface-within-an-anaconda-install), [*readme*](https://github.com/facebookresearch/faiss/blob/main/INSTALL.md#building-from-source) to verify the correctness. + + +## Develop the new metric +Here is an [*example*](https://github.com/XuZhirong/faiss/commit/a7d5466884d924226aa1d43dd5a41dd44606817c). + +The main modifications include: declaring the index in ```IndexFlat```, implementing the metric in ```utils```. + +## Compile and Publish +1) remove the build files and rebuild. +2) put *.so files to the proper location. +3) write [*setup.py*](https://github.com/facebookresearch/faiss/blob/main/faiss/python/setup.py), where the ```package_data``` field should include the *.so files. +4) upload to pypi/docker. [*Instructions*](https://zhuanlan.zhihu.com/p/61174349) are here. \ No newline at end of file diff --git a/examples/curvfaiss/knn.py b/examples/curvfaiss/knn.py new file mode 100644 index 0000000..ba7e2bf --- /dev/null +++ b/examples/curvfaiss/knn.py @@ -0,0 +1,91 @@ +# Copyright 2020 Alibaba Group Holding Limited. 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. +# ============================================================================== + +from absl import app +from absl import flags + +import numpy as np +import curvfaiss +from curvlearn import manifolds +import tensorflow as tf + +FLAGS = flags.FLAGS +flags.DEFINE_float("curvature", 0.0, "Curvature of stereographic model, default is 0") +flags.DEFINE_integer("batch", 16, "Embedding batch size") +flags.DEFINE_integer("dim", 8, "Embedding dimension") +flags.DEFINE_integer("topk", 10, "Find the top k nearest neighbors, default is 10") + + +def generate_sample(): + np.random.seed(2022) + embedding = np.random.random((FLAGS.batch, FLAGS.dim)).astype('float32') + id = np.arange(FLAGS.batch) + + def curvlearn_op(emb): + manifold = manifolds.Stereographic() + emb = manifold.to_manifold(tf.constant(emb, dtype=tf.float32), FLAGS.curvature) + + # src = [emb[0],..,emb[0],emb[1],...,emb[1],...] + # dst = [emb[0],emb[1],...,emb[batch],emb[0],...] + src = tf.reshape(tf.tile(emb, [1, FLAGS.batch]), [-1, FLAGS.dim]) + dst = tf.tile(emb, [FLAGS.batch, 1]) + distance = tf.reshape(manifold.distance(src, dst, FLAGS.curvature), [FLAGS.batch, FLAGS.batch]) + + sess = tf.Session() + emb, distance = sess.run([emb, distance]) + + return emb, distance + + embedding, distance = curvlearn_op(embedding) + + knn_index = np.argsort(distance, axis=-1) + knn_id = np.array([[id[col] for col in row[:FLAGS.topk]] for row in knn_index]) + + return embedding, id, knn_id + + +def faiss_knn(emb, id, query=None): + def build_index(embedding): + """ + Stereographic distance only supports brute-force indexing method for now. + """ + n, d = embedding.shape[0], embedding.shape[1] + + index = curvfaiss.IndexFlatStereographic(d, FLAGS.curvature) + index.add(embedding) + + return index + + if query is None: + query = emb + + index = build_index(emb) + D, I = index.search(query, FLAGS.topk) + I = np.array([[id[col] for col in row] for row in I]) + + return I + + +def main(argv): + del argv + + embedding, id, golden_knn = generate_sample() + knn = faiss_knn(embedding, id) + + print("curvfaiss sanity check: {}!".format("passed" if np.array_equal(golden_knn, knn) else "failed")) + + +if __name__ == "__main__": + app.run(main) \ No newline at end of file