From 01a673d72e048a60bb45d83dd430f9a51183262b Mon Sep 17 00:00:00 2001 From: Naveen Tatikonda Date: Sun, 16 Jun 2024 22:36:49 -0500 Subject: [PATCH 01/10] Add blog for sqfp16 Signed-off-by: Naveen Tatikonda --- _community_members/navtat.md | 2 +- ...izing-opensearch-with-fp16-quantization.md | 629 ++++++++++++++++++ assets/media/community/members/navtat.jpg | Bin 621497 -> 0 bytes assets/media/community/members/navtat.png | Bin 0 -> 665098 bytes 4 files changed, 630 insertions(+), 1 deletion(-) create mode 100644 _posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md delete mode 100644 assets/media/community/members/navtat.jpg create mode 100644 assets/media/community/members/navtat.png diff --git a/_community_members/navtat.md b/_community_members/navtat.md index 99f9503004..6e96fa9e2c 100644 --- a/_community_members/navtat.md +++ b/_community_members/navtat.md @@ -1,7 +1,7 @@ --- name: Naveen Tatikonda short_name: naveen -photo: '/assets/media/community/members/navtat.jpg' +photo: '/assets/media/community/members/navtat.png' title: 'OpenSearch Community Member: Naveen Tatikonda' primary_title: Naveen Tatikonda breadcrumbs: diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md new file mode 100644 index 0000000000..f96c92413a --- /dev/null +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -0,0 +1,629 @@ +--- +layout: post +title: "Optimizing OpenSearch with FP16 Quantization: Enhancing Memory Efficiency and Cost-Effectiveness" +authors: + - naveen + - vamshin + - tal +date: 2024-06-19 00:00:00 -0700 +categories: + - technical-posts +meta_keywords: FP16 quantization, OpenSearch k-NN plugin, memory optimization, cost-effectiveness +meta_description: Learn how FP16 Quantization in OpenSearch helps to reduce memory requirements upto 50% with a very minimal loss in quality. +has_math: true +--- + +The rise of large language models (LLMs) and generative AI has ushered in a new era of natural language processing capabilities. Vector databases have emerged as a crucial +component in this ecosystem, acting as external databases that can efficiently index, store, and retrieve embeddings generated by LLMs. However, as the scale and complexity +of LLMs continue to grow, the workloads on vector databases have also increased significantly. Ingesting and querying billions of vectors can strain computational resources, +leading to higher memory requirements and increased operational costs. This feature enables you to store vector embeddings with lower precision resulting in cost reduction +by reducing the memory consumption. + +## How does OpenSearch k-NN plugin solves this problem ? + +When you index vectors on [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply a technique called scalar quantization. This technique converts each dimension +of the vector from 32-bit floating-point (fp32) to 16-bit floating-point (fp16) representation. The conversion reduces the precision and memory requirements of your vector by half, +resulting in significant memory savings. + +k-NN plugin integrated Faiss Scalar Quantizer and added support for this Faiss Scalar Quantization FP16 (SQfp16) feature which helps to quantize your 32 bit float vectors into +fp16 vectors by providing memory savings up to 50% with a very minimal loss of recall(refer benchmarks in later sections). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), +SQfp16 quantization can also significantly reduce search latencies and improve indexing throughput. + +## How to use faiss scalar quantization fp16 ? + +To use Faiss scalar quantization, set the k-NN vector field's `method.parameters.encoder.name` to `sq` when creating a k-NN index: + +```json +PUT /test-index +{ + "settings": { + "index": { + "knn": true + } + }, + "mappings": { + "properties": { + "my_vector1": { + "type": "knn_vector", + "dimension": 8, + "method": { + "name": "hnsw", + "engine": "faiss", + "space_type": "l2", + "parameters": { + "encoder": { + "name": "sq", + "parameters": { + "type": "fp16", + "clip": true + } + }, + "ef_construction": 256, + "m": 8 + } + } + } + } + } +} +``` + +Refer to [this k-NN documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#sq-parameters) to know more details about the SQ parameters. + +The `fp16` encoder converts 32-bit vectors into their 16-bit counterparts. For this encoder type, the vector values must be in the **[-65504.0, 65504.0]** range. + +To define how to handle out-of-range values, the above index mapping request specifies the `clip` parameter. + +* By default, this `clip` parameter is `false`, and any vectors containing out-of-range values are rejected. +* When the `clip` is set to `true`, out-of-range vector values are rounded up or down so that they are in the supported range. For example, if the original 32-bit vector is +`[65510.82, -65504.1]`, the vector will be indexed as a 16-bit vector `[65504.0, -65504.0]`. + +**Note** - We recommend setting `clip` to `true` only if very few elements lie outside of the supported range. Rounding the values may cause a drop in recall. + +During ingestion, make sure each dimension of the vector is in the supported range ([-65504.0, 65504.0]): +```json +PUT test-index/_doc/1 +{ + "my_vector1": [-65504.0, 65503.845, 55.82, -65300.456, 34.67, -1278.23, 90.62, 8.36] +} +``` + +During querying, there is no range limitation for the query vector: +```json +GET test-index/_search +{ + "size": 2, + "query": { + "knn": { + "my_vector1": { + "vector": [265436.876, -120906.256, 99.84, 89.45, 100000.45, 9.23, -70.17, 6.93], + "k": 2 + } + } + } +} +``` + +## Memory estimation + +### HNSW memory estimation with fp16 + +The memory required for HNSW is estimated to be `1.1 * (2 * dimension + 8 * M)` bytes/vector. + +As an example, assume that you have 1 million vectors with a dimension of 256 and M of 16. The memory requirement can be estimated as follows: + +`1.1 * (2 * 256 + 8 * 16) * 1,000,000 ~= 0.656 GB` + +To know the memory estimation for IVF with fp16, refer to [this documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-vector-quantization/#memory-estimation-1). + +## Benchmarking Results + +We ran benchmarking tests on some of the popular and trending datasets using our [opensearch-benchmark](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch) tool +to compare the indexing, search performance, and the quality of search results of Faiss SQfp16 against float vectors (using Faiss without any encoding). All these tests were performed with [SIMD](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#simd-optimization-for-the-faiss-engine) +enabled (on x86 architecture with AVX2 optimization). + +**Note** - Without SIMD optimization(AVX2 or NEON) or with AVX2 disabled(on x86 architecture), the quantization process adds an additional overhead which leads to an increase in latencies. +These are the [processors](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2) that supports AVX2. In AWS, for x86 architecture all the community AMIs with [HVM](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) supports AVX2 optimization. + +### Benchmarking results using small workloads + +All these tests were ran on a single node cluster without any replica using these datasets mentioned below. + +#### Configuration + + + + + + + + + + + + + +
mef_constructionef_searchreplica
161001000
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Dataset IDDatasetDimension of vectorData sizeNumber of queriesTraining data rangeQuery data rangeSpace typePrimary shardsIndexing clients
Dataset 1gist-960-euclidean9601,000,0001,000[ 0.0, 1.48 ][ 0.0, 0.729 ]L2816
Dataset 2mnist-784-euclidean78460,00010,000[ 0.0, 255.0 ][ 0.0, 255.0 ]L212
Dataset 3cohere-wiki-simple-embeddings-768768475,85810,000[ -4.1561704, 5.5478516 ][ -4.065383, 5.4902344 ]L248
Dataset 4cohere-ip-1m7681,000,00010,000[ -4.1073565, 5.504557 ][ -4.109505, 5.4809895 ]InnerProduct816
Dataset 5sift-128-euclidean1281,000,00010,000[ 0.0, 218.0 ][ 0.0, 184.0 ]L2816
+ +#### Recall and memory results + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Dataset IDFaiss hnsw recall@100Faiss hnsw-sqfp16 recall@100Faiss hnsw memory estimate (gb)Faiss hnsw-sqfp16 memory estimate (gb)Faiss hnsw memory usage (gb)Faiss hnsw-sqfp16 memory usage (gb)% reduction in memory
Dataset 10.90710.90724.072.103.721.9348.12
Dataset 20.98890.98890.200.100.180.1044.44
Dataset 30.94560.94501.560.811.430.7547.55
Dataset 40.94290.94223.281.703.001.5747.67
Dataset 50.99250.99250.660.390.620.3838.71
+ +#### Indexing and Query results + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Dataset IDFaiss hnsw mean indexing throughput (docs/sec)Faiss hnsw-sqfp16 mean indexing throughput (docs/sec)Faiss hnsw p90 (ms)Faiss hnsw-sqfp16 p90 (ms)Faiss hnsw p99 (ms)Faiss hnsw-sqfp16 p99 (ms)
Dataset 1468146964.975.085.545.50
Dataset 2427145802.012.062.162.21
Dataset 3469046983.353.333.583.57
Dataset 4604461294.614.815.165.37
Dataset 51154991020602.732.682.962.89
+ +#### Analysis + +Comparing the benchmarking results, you can see that: + +* The recall obtained using Faiss HNSW SQfp16 matches that of Faiss HNSW (with negligible difference). +* Using SQfp16, there is a significant reduction in memory usage of up to **48%**, with a slight reduction in disk usage. Based on the results, we can see that a larger dimension of the vector provides better memory reduction. +* The performance metrics using SQfp16 are on par with those of fp32 vectors. + +### Benchmarking results using large workloads + +These tests were ran using some large scale workload, Laion 100M dataset with 768 dimensions to show the comparison between the performance metrics and memory savings obtained for Faiss HNSW SQfp16 against Faiss HNSW. + +#### Configuration + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Faiss HNSW SQfp16Faiss HNSW
OpenSearch version2.132.13
Enginefaissfaiss
Dimension of vector768768
Ingest vectors100M100M
Test vectors1K1K
Primary shards3636
Replica shards00
Data nodes48
Data node instance typer5.4xlarger5.4xlarge
Master nodes33
Master node instance typec5.xlargec5.xlarge
Indexing clients99
Query clients11
Forcemerge clients11
Client instancer5.16xlarger5.16xlarge
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Config IDOptimization strategymef_constructionef_search
hnsw1Default Configuration16100100
hnsw2Balance between latency, memory, and recall16128128
hnsw3Optimize for recall16256256
+ +The Faiss HNSW SQfp16 require 4 data nodes which is half the number of data nodes required for Faiss HNSW (8) which proves that SQfp16 reduces the memory requirements by 50%. +To know more details about the calculation, refer to appendix section. + +#### Recall and memory results + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Experiment IDhnsw-recall@1000hnsw-sqfp16-recall@1000hnsw memory usage (gb)hnsw-sqfp16 memory usage (gb)% reduction in memory
hnsw10.940.94300.28157.2347.64
hnsw20.960.96300.28157.2347.64
hnsw30.980.98300.28157.2347.64
+ +#### Indexing and Query results + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Experiment IDhnsw mean indexing throughput (docs/sec)hnsw-sqfp16 mean indexing throughput (docs/sec)hnsw p90 (ms)hnsw-sqfp16 p90 (ms)hnsw p99 (ms)hnsw-sqfp16 p99 (ms)
hnsw17544765714.0216.9919.1820.83
hnsw27063721914.2117.4418.8621.80
hnsw36004584816.1420.8517.6524.73
+ +#### Analysis + +* For k=1000, the recall is identical for both Faiss HNSW and Faiss HNSW with SQfp16. +* The memory requirements (number of data nodes) are reduced by half, and based on the [k-NN stats API metrics](https://opensearch.org/docs/latest/search-plugins/knn/api/#stats), the memory usage has been reduced by 47.64% using SQfp16. +* In most instances, SQfp16 demonstrated better indexing throughput compared to fp32 vectors. + +## Conclusion + +Faiss Scalar Quantization FP16 (Float16) is a powerful technique that enables significant memory savings while maintaining high recall performance comparable to full-precision vectors. +By quantizing vectors to 16-bit floating-point representation, it can reduce memory requirements by up to 50%. Additionally, when combined with SIMD (Single Instruction Multiple Data) +optimization, Faiss Scalar Quantization FP16 offers enhanced indexing throughput and lower search latencies, resulting in improved overall performance. This technique strikes an excellent +balance between memory efficiency and accuracy, making it a valuable tool for large-scale similarity search applications. + +## Future Scope + +To achieve even greater memory efficiency, we plan to introduce int8 quantization support using [Faiss Scalar Quantizer](https://github.com/opensearch-project/k-NN/issues/1723) and [Lucene Scalar Quantizer](https://github.com/opensearch-project/k-NN/issues/1277). +This advanced technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors, while maintaining high recall performance. +The quantizers will accept FP32 vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for external quantization or extra training steps. + +Furthermore, we aim to release Binary vector support, enabling an unprecedented 32X compression rate. This groundbreaking approach will further reduce memory consumption. +By combining these cutting-edge quantization techniques, we will provide a comprehensive solution for efficient similarity search, balancing memory optimization and +accurate retrieval. + +Our commitment to continuous innovation ensures that our users can leverage state-of-the-art technologies to tackle large-scale similarity search challenges while minimizing resource +requirements and maximizing cost-effectiveness. + +## Appendix + +### Memory estimation and data nodes calculation + +Here is the memory estimation and data nodes calculation for the 100M, 768 dimension large workload benchmarking test. +``` +// Faiss HNSW SQfp16 Memory Estimation +1.1 * (2 * dimension + 8 * M) * num_of_vectors * (1 + num_of_replicas) bytes + +Let m = 16 and num_replicas = 0 + +1.1 * (2 * 768 + 8 * 16) * 100000000 * (1 + 0) = 170.47 gb = 171 gb + +Instance r5.4xlarge has a memory of 128 gb in which 32 gb is used for JVM. +Let us assume circuit breaker limit is 0.5 + +Total available memory = (data node instance memory - jvm memory) * circuit breaker limit +Total available memory = (128 - 32 ) * 0.5 = 48gb + +Number of Data nodes -> 171/48 = 3.56 = 4 +``` + +``` +// Faiss HNSW Memory Estimation +1.1 * (4 * dimension + 8 * M) * num_of_vectors * (1 + num_of_replicas) bytes + +Let m = 16 and num_replicas = 0 + +1.1 * (4 * 768 + 8 * 16) * 100000000 * (1 + 0) = 327.83 gb = 328 gb + +Instance r5.4xlarge has a memory of 128 gb in which 32 gb is used for JVM. +Let us assume circuit breaker limit is 0.5 + +Total available memory = (data node instance memory - jvm memory) * circuit breaker limit +Total available memory = (128 - 32 ) * 0.5 = 48gb + +Number of Data nodes -> 328/48 = 6.83 = 7 + 1(for stability) = 8 +``` + +## References + +* https://github.com/erikbern/ann-benchmarks?tab=readme-ov-file#data-sets +* https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings +* https://dbyiw3u3rf9yr.cloudfront.net/corpora/vectorsearch/cohere-wikipedia-22-12-en-embeddings/documents-1m.hdf5.bz2 +* https://laion.ai/about/ +* Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., & Jitsev, J. (2022). 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literal 0 HcmV?d00001 From ce48905bd486a0e6572c740a10d08eacc9e76405 Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Tue, 25 Jun 2024 17:52:33 -0400 Subject: [PATCH 02/10] Doc review Signed-off-by: Fanit Kolchina --- ...izing-opensearch-with-fp16-quantization.md | 568 ++++-------------- 1 file changed, 113 insertions(+), 455 deletions(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index f96c92413a..3ac0df6618 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -1,6 +1,6 @@ --- layout: post -title: "Optimizing OpenSearch with FP16 Quantization: Enhancing Memory Efficiency and Cost-Effectiveness" +title: "Optimizing OpenSearch with Faiss FP16 scalar quantization: Enhancing memory efficiency and cost-effectiveness" authors: - naveen - vamshin @@ -10,26 +10,20 @@ categories: - technical-posts meta_keywords: FP16 quantization, OpenSearch k-NN plugin, memory optimization, cost-effectiveness meta_description: Learn how FP16 Quantization in OpenSearch helps to reduce memory requirements upto 50% with a very minimal loss in quality. -has_math: true +has_science_table: true --- The rise of large language models (LLMs) and generative AI has ushered in a new era of natural language processing capabilities. Vector databases have emerged as a crucial -component in this ecosystem, acting as external databases that can efficiently index, store, and retrieve embeddings generated by LLMs. However, as the scale and complexity -of LLMs continue to grow, the workloads on vector databases have also increased significantly. Ingesting and querying billions of vectors can strain computational resources, -leading to higher memory requirements and increased operational costs. This feature enables you to store vector embeddings with lower precision resulting in cost reduction -by reducing the memory consumption. +component in this landscape, acting as external databases that can efficiently index, store, and retrieve embeddings generated by LLMs. However, as the scale and complexity +of LLMs continue to grow, vector database workloads have also increased significantly. Ingesting and querying billions of vectors can strain computational resources, +leading to higher memory requirements and increased operational costs. Faiss scalar quantization enables you to generate vector embeddings with lower precision, which reduces memory consumption and, consequently, lowers costs. -## How does OpenSearch k-NN plugin solves this problem ? +## Why use Faiss scalar quantization? -When you index vectors on [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply a technique called scalar quantization. This technique converts each dimension -of the vector from 32-bit floating-point (fp32) to 16-bit floating-point (fp16) representation. The conversion reduces the precision and memory requirements of your vector by half, -resulting in significant memory savings. - -k-NN plugin integrated Faiss Scalar Quantizer and added support for this Faiss Scalar Quantization FP16 (SQfp16) feature which helps to quantize your 32 bit float vectors into -fp16 vectors by providing memory savings up to 50% with a very minimal loss of recall(refer benchmarks in later sections). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), +When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply a technique called _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, you can get up to a 50% memory savings with a very minimal loss of recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), SQfp16 quantization can also significantly reduce search latencies and improve indexing throughput. -## How to use faiss scalar quantization fp16 ? +## How to use Faiss scalar quantization? To use Faiss scalar quantization, set the k-NN vector field's `method.parameters.encoder.name` to `sq` when creating a k-NN index: @@ -68,19 +62,20 @@ PUT /test-index } ``` -Refer to [this k-NN documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#sq-parameters) to know more details about the SQ parameters. +For more information about the SQ parameters, see the [k-NN documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#sq-parameters). The `fp16` encoder converts 32-bit vectors into their 16-bit counterparts. For this encoder type, the vector values must be in the **[-65504.0, 65504.0]** range. -To define how to handle out-of-range values, the above index mapping request specifies the `clip` parameter. +The preceding index mapping request specifies the `clip` parameter, which defines how to handle out-of-range values: -* By default, this `clip` parameter is `false`, and any vectors containing out-of-range values are rejected. -* When the `clip` is set to `true`, out-of-range vector values are rounded up or down so that they are in the supported range. For example, if the original 32-bit vector is +* By default, `clip` is `false`, and any vectors containing out-of-range values are rejected. +* When `clip` is set to `true`, out of-range vector values are rounded up or down so that they are in the supported range. For example, if the original 32-bit vector is `[65510.82, -65504.1]`, the vector will be indexed as a 16-bit vector `[65504.0, -65504.0]`. -**Note** - We recommend setting `clip` to `true` only if very few elements lie outside of the supported range. Rounding the values may cause a drop in recall. +**Note**: We recommend setting `clip` to `true` only if very few elements lie outside of the supported range. Rounding the values may cause a drop in recall. During ingestion, make sure each dimension of the vector is in the supported range ([-65504.0, 65504.0]): + ```json PUT test-index/_doc/1 { @@ -89,6 +84,7 @@ PUT test-index/_doc/1 ``` During querying, there is no range limitation for the query vector: + ```json GET test-index/_search { @@ -104,9 +100,7 @@ GET test-index/_search } ``` -## Memory estimation - -### HNSW memory estimation with fp16 +## HNSW memory estimation The memory required for HNSW is estimated to be `1.1 * (2 * dimension + 8 * M)` bytes/vector. @@ -114,477 +108,142 @@ As an example, assume that you have 1 million vectors with a dimension of 256 an `1.1 * (2 * 256 + 8 * 16) * 1,000,000 ~= 0.656 GB` -To know the memory estimation for IVF with fp16, refer to [this documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-vector-quantization/#memory-estimation-1). +For more information about memory estimation for scalar quantization with IVF, refer to [this documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-vector-quantization/#memory-estimation-1). -## Benchmarking Results +## Benchmarking results We ran benchmarking tests on some of the popular and trending datasets using our [opensearch-benchmark](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch) tool -to compare the indexing, search performance, and the quality of search results of Faiss SQfp16 against float vectors (using Faiss without any encoding). All these tests were performed with [SIMD](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#simd-optimization-for-the-faiss-engine) -enabled (on x86 architecture with AVX2 optimization). +to compare the indexing, search performance, and the quality of search results of Faiss scalar quantization. We compared Faiss scalar quantization against using Faiss with float vectors without any encoding. All tests were performed with [SIMD](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#simd-optimization-for-the-faiss-engine) +enabled on x86 architecture with AVX2 optimization. -**Note** - Without SIMD optimization(AVX2 or NEON) or with AVX2 disabled(on x86 architecture), the quantization process adds an additional overhead which leads to an increase in latencies. -These are the [processors](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2) that supports AVX2. In AWS, for x86 architecture all the community AMIs with [HVM](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) supports AVX2 optimization. +**Note**: Without SIMD optimization (AVX2 or NEON) or with AVX2 disabled(on x86 architecture), the quantization process introduces additional overhead, which leads to an increase in latencies. +For information about processors that support AVX2, see [CPUs with AVX2](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2). In AWS, all community AMIs with [HVM](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) support AVX2 optimization for the x86 architecture. ### Benchmarking results using small workloads -All these tests were ran on a single node cluster without any replica using these datasets mentioned below. +We ran the following tests on a single-node cluster without any replicas using the following datasets. + #### Configuration - - - - - - - - - - - - - -
mef_constructionef_searchreplica
161001000
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Dataset IDDatasetDimension of vectorData sizeNumber of queriesTraining data rangeQuery data rangeSpace typePrimary shardsIndexing clients
Dataset 1gist-960-euclidean9601,000,0001,000[ 0.0, 1.48 ][ 0.0, 0.729 ]L2816
Dataset 2mnist-784-euclidean78460,00010,000[ 0.0, 255.0 ][ 0.0, 255.0 ]L212
Dataset 3cohere-wiki-simple-embeddings-768768475,85810,000[ -4.1561704, 5.5478516 ][ -4.065383, 5.4902344 ]L248
Dataset 4cohere-ip-1m7681,000,00010,000[ -4.1073565, 5.504557 ][ -4.109505, 5.4809895 ]InnerProduct816
Dataset 5sift-128-euclidean1281,000,00010,000[ 0.0, 218.0 ][ 0.0, 184.0 ]L2816
+ +|m |ef_construction |ef_search |replica | +|--- |--- |--- |--- | +|16 |100 |100 |0 | + +

+|Dataset ID |Dataset |Dimension of vector |Data size |Number of queries |Training data range |Query data range |Space type |Primary shards |Indexing clients | +|--- |--- |--- |--- |--- |--- |--- |--- |--- |--- | +|Dataset 1 |cohere-wiki-simple-embeddings-768 |768 |475,858 |10,000 |[ -4.1561704, 5.5478516 ] |[ -4.065383, 5.4902344 ] |L2 |4 |8 | +|Dataset 2 |cohere-ip-1m |768 |1,000,000 |10,000 |[ -4.1073565, 5.504557 ] |[ -4.109505, 5.4809895 ] |innerproduct |8 |16 | +|Dataset 3 |gist-960-euclidean |960 |1,000,000 |1,000 |[ 0.0, 1.48 ] |[ 0.0, 0.729 ] |L2 |8 |16 | +|Dataset 4 |sift-128-euclidean |128 |1,000,000 |10,000 |[ 0.0, 218.0 ] |[ 0.0, 184.0 ] |L2 |8 |16 | +|Dataset 5 |mnist-784-euclidean |784 |60,000 |10,000 |[ 0.0, 255.0 ] |[ 0.0, 255.0 ] |L2 |1 |2 | #### Recall and memory results - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Dataset IDFaiss hnsw recall@100Faiss hnsw-sqfp16 recall@100Faiss hnsw memory estimate (gb)Faiss hnsw-sqfp16 memory estimate (gb)Faiss hnsw memory usage (gb)Faiss hnsw-sqfp16 memory usage (gb)% reduction in memory
Dataset 10.90710.90724.072.103.721.9348.12
Dataset 20.98890.98890.200.100.180.1044.44
Dataset 30.94560.94501.560.811.430.7547.55
Dataset 40.94290.94223.281.703.001.5747.67
Dataset 50.99250.99250.660.390.620.3838.71
- -#### Indexing and Query results - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Dataset IDFaiss hnsw mean indexing throughput (docs/sec)Faiss hnsw-sqfp16 mean indexing throughput (docs/sec)Faiss hnsw p90 (ms)Faiss hnsw-sqfp16 p90 (ms)Faiss hnsw p99 (ms)Faiss hnsw-sqfp16 p99 (ms)
Dataset 1468146964.975.085.545.50
Dataset 2427145802.012.062.162.21
Dataset 3469046983.353.333.583.57
Dataset 4604461294.614.815.165.37
Dataset 51154991020602.732.682.962.89
+|Dataset ID |Faiss hnsw recall@100 |Faiss hnsw-sqfp16 recall@100 |Faiss hnsw memory estimate (gb) |Faiss hnsw-sqfp16 memory estimate (gb) |Faiss hnsw memory usage (gb) |Faiss hnsw-sqfp16 memory usage (gb) |% reduction in memory | +|--- |--- |--- |--- |--- |--- |--- |--- | +|Dataset 1 |0.9456 |0.9450 |1.56 |0.81 |1.43 |0.75 |47.55 | +|Dataset 2 |0.9429 |0.9422 |3.28 |1.70 |3.00 |1.57 |47.67 | +|Dataset 3 |0.9071 |0.9072 |4.07 |2.10 |3.72 |1.93 |48.12 | +|Dataset 4 |0.9925 |0.9925 |0.66 |0.39 |0.62 |0.38 |38.71 | +|Dataset 5 |0.9889 |0.9889 |0.20 |0.10 |0.18 |0.10 |44.44 | + +#### Indexing and query results + +|Dataset ID |Faiss hnsw mean throughput (docs/sec) |Faiss hnsw-sqfp16 mean throughput (docs/sec) |Faiss hnsw p90 (ms) |Faiss hnsw-sqfp16 p90 (ms) |Faiss hnsw p99 (ms) |Faiss hnsw-sqfp16 p99 (ms) | +|--- |--- |--- |--- |--- |--- |--- | +|Dataset 1 |4690 |4698 |3.35 |3.33 |3.58 |3.57 | +|Dataset 2 |6044 |6129 |4.61 |4.81 |5.16 |5.37 | +|Dataset 3 |4681 |4696 |4.97 |5.08 |5.54 |5.50 | +|Dataset 4 |115499 |102060 |2.73 |2.68 |2.96 |2.89 | +|Dataset 5 |4271 |4580 |2.01 |2.06 |2.16 |2.21 | #### Analysis -Comparing the benchmarking results, you can see that: +When comparing the benchmarking results, note that: * The recall obtained using Faiss HNSW SQfp16 matches that of Faiss HNSW (with negligible difference). -* Using SQfp16, there is a significant reduction in memory usage of up to **48%**, with a slight reduction in disk usage. Based on the results, we can see that a larger dimension of the vector provides better memory reduction. -* The performance metrics using SQfp16 are on par with those of fp32 vectors. +* Using SQfp16, there is a significant reduction in memory usage of up to **48%**, with a slight reduction in disk usage. These results indicate that a larger vector dimension leads to greater memory reduction. +* The performance metrics using SQfp16 are on par with those of `fp32` vectors. ### Benchmarking results using large workloads -These tests were ran using some large scale workload, Laion 100M dataset with 768 dimensions to show the comparison between the performance metrics and memory savings obtained for Faiss HNSW SQfp16 against Faiss HNSW. +To compare performance metrics and memory savings, we ran tests on the large-scale [Laion](https://laion.ai/about/) 100M dataset with 768 dimensions, using both Faiss HNSW SQfp16 and Faiss HNSW. #### Configuration - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Faiss HNSW SQfp16Faiss HNSW
OpenSearch version2.132.13
Enginefaissfaiss
Dimension of vector768768
Ingest vectors100M100M
Test vectors1K1K
Primary shards3636
Replica shards00
Data nodes48
Data node instance typer5.4xlarger5.4xlarge
Master nodes33
Master node instance typec5.xlargec5.xlarge
Indexing clients99
Query clients11
Forcemerge clients11
Client instancer5.16xlarger5.16xlarge
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Config IDOptimization strategymef_constructionef_search
hnsw1Default Configuration16100100
hnsw2Balance between latency, memory, and recall16128128
hnsw3Optimize for recall16256256
- -The Faiss HNSW SQfp16 require 4 data nodes which is half the number of data nodes required for Faiss HNSW (8) which proves that SQfp16 reduces the memory requirements by 50%. -To know more details about the calculation, refer to appendix section. +| |Faiss HNSW SQfp16 |Faiss HNSW | +|--- |--- |--- | +|OpenSearch version |2.13 |2.13 | +|engine |faiss |faiss | +|Dimension of vector |768 |768 | +|ingest vectors |100M |100M | +|test vectors |1k |1k | +|primary shards |36 |36 | +|replica shards |0 |0 | +|data nodes |4 |8 | +|data node instance type |r5.4xlarge |r5.4xlarge | +|master nodes |3 |3 | +|master node instance type |c5.xlarge |c5.xlarge | +|indexing clients |9 |9 | +|query clients |1 |1 | +|forcemerge segments |1 |1 | +|client instance |r5.16xlarge |r5.16xlarge | +

+ +Config ID |Optimization Strategy |m |ef_construction |ef_search | +|--- |--- |--- |--- |--- | +|hnsw1 |Default configuration |16 |100 |100 | +|hnsw2 |Balance between latency, memory, and recall |16 |128 |128 | +|hnsw3 |Optimize for recall |16 |256 |256 | + +Faiss HNSW SQfp16 requires 4 data nodes, half the number needed for Faiss HNSW (8). This demonstrates that SQfp16 reduces memory requirements by 50%. +For more information about estimating required memory and number of data nodes, see the [Appendix](#appendix-memory-and-data-node-requirement-estimation). #### Recall and memory results - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Experiment IDhnsw-recall@1000hnsw-sqfp16-recall@1000hnsw memory usage (gb)hnsw-sqfp16 memory usage (gb)% reduction in memory
hnsw10.940.94300.28157.2347.64
hnsw20.960.96300.28157.2347.64
hnsw30.980.98300.28157.2347.64
- -#### Indexing and Query results - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Experiment IDhnsw mean indexing throughput (docs/sec)hnsw-sqfp16 mean indexing throughput (docs/sec)hnsw p90 (ms)hnsw-sqfp16 p90 (ms)hnsw p99 (ms)hnsw-sqfp16 p99 (ms)
hnsw17544765714.0216.9919.1820.83
hnsw27063721914.2117.4418.8621.80
hnsw36004584816.1420.8517.6524.73
+|Experiment ID |hnsw-recall@1000 |hnsw-sqfp16-recall@1000 |hnsw memory usage (gb) |hnsw-sqfp16 memory usage (gb) |% reduction in memory | +|--- |--- |--- |--- |--- |--- | +|hnsw 1 |0.94 |0.94 |300.28 |157.23 |47.64 | +|hnsw 2 |0.96 |0.96 |300.28 |157.23 |47.64 | +|hnsw 3 |0.98 |0.98 |300.28 |157.23 |47.64 | + +#### Indexing and query results + +|Experiment ID |hnsw mean throughput (docs/sec) |hnsw-sqfp16 mean throughput (docs/sec) |hnsw p90 (ms) |hnsw-sqfp16 p90 (ms) |hnsw p99 (ms) |hnsw-sqfp16 p99 (ms) | +|--- |--- |--- |--- |--- |--- |--- | +|hnsw 1 |7544 |7657 |14.02 |16.99 |19.18 |20.83 | +|hnsw 2 |7063 |7219 |14.21 |17.44 |18.86 |21.80 | +|hnsw 3 |6004 |5848 |16.14 |20.85 |17.65 |24.73 | #### Analysis * For k=1000, the recall is identical for both Faiss HNSW and Faiss HNSW with SQfp16. -* The memory requirements (number of data nodes) are reduced by half, and based on the [k-NN stats API metrics](https://opensearch.org/docs/latest/search-plugins/knn/api/#stats), the memory usage has been reduced by 47.64% using SQfp16. -* In most instances, SQfp16 demonstrated better indexing throughput compared to fp32 vectors. +* Faiss HNSW with SQfp16 requires approximately half the memory Faiss HNSW required (as measured by the required number of data nodes). Based on the [k-NN stats API metrics](https://opensearch.org/docs/latest/search-plugins/knn/api/#stats), the memory usage has been reduced by 47.64% using SQfp16. +* In most instances, SQfp16 demonstrated better indexing throughput compared to `fp32` vectors. ## Conclusion -Faiss Scalar Quantization FP16 (Float16) is a powerful technique that enables significant memory savings while maintaining high recall performance comparable to full-precision vectors. -By quantizing vectors to 16-bit floating-point representation, it can reduce memory requirements by up to 50%. Additionally, when combined with SIMD (Single Instruction Multiple Data) -optimization, Faiss Scalar Quantization FP16 offers enhanced indexing throughput and lower search latencies, resulting in improved overall performance. This technique strikes an excellent -balance between memory efficiency and accuracy, making it a valuable tool for large-scale similarity search applications. +Faiss FP16 scalar quantization is a powerful technique that provides significant memory savings while maintaining high recall performance similar to full-precision vectors. By converting vectors to a 16-bit floating-point representation, it can reduce memory requirements by up to 50%. When combined with SIMD (Single Instruction Multiple Data) optimization, FP16 scalar quantization also enhances indexing throughput and reduces search latencies, leading to better overall performance. This method strikes an excellent balance between memory efficiency and accuracy, making it a valuable tool for large-scale similarity search applications. -## Future Scope +## Future scope -To achieve even greater memory efficiency, we plan to introduce int8 quantization support using [Faiss Scalar Quantizer](https://github.com/opensearch-project/k-NN/issues/1723) and [Lucene Scalar Quantizer](https://github.com/opensearch-project/k-NN/issues/1277). +To achieve an even greater memory efficiency, we plan to introduce `int8` quantization support using a [Faiss scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1723) and [Lucene scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1277). This advanced technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors, while maintaining high recall performance. -The quantizers will accept FP32 vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for external quantization or extra training steps. +The quantizers will accept FP32 vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for an external quantization or extra training steps. -Furthermore, we aim to release Binary vector support, enabling an unprecedented 32X compression rate. This groundbreaking approach will further reduce memory consumption. +Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This groundbreaking approach will further reduce memory consumption. By combining these cutting-edge quantization techniques, we will provide a comprehensive solution for efficient similarity search, balancing memory optimization and accurate retrieval. -Our commitment to continuous innovation ensures that our users can leverage state-of-the-art technologies to tackle large-scale similarity search challenges while minimizing resource +Our commitment to continuous innovation delivers state-of-the-art technologies to tackle large-scale similarity search challenges while minimizing resource requirements and maximizing cost-effectiveness. -## Appendix +## Appendix: Memory and data node requirement estimation -### Memory estimation and data nodes calculation +The following calculations provide a memory and number of data nodes estimation for the 100M, 768 dimension large workload benchmarking test. -Here is the memory estimation and data nodes calculation for the 100M, 768 dimension large workload benchmarking test. ``` // Faiss HNSW SQfp16 Memory Estimation 1.1 * (2 * dimension + 8 * M) * num_of_vectors * (1 + num_of_replicas) bytes @@ -621,9 +280,8 @@ Number of Data nodes -> 328/48 = 6.83 = 7 + 1(for stability) = 8 ## References -* https://github.com/erikbern/ann-benchmarks?tab=readme-ov-file#data-sets -* https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings -* https://dbyiw3u3rf9yr.cloudfront.net/corpora/vectorsearch/cohere-wikipedia-22-12-en-embeddings/documents-1m.hdf5.bz2 -* https://laion.ai/about/ -* Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., & Jitsev, J. (2022). LAION-5B: An open large-scale dataset for training next generation image-text models. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2210.08402 -* Douze, Matthijs, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazar'e, Maria Lomeli, Lucas Hosseini and Herv'e J'egou. “The Faiss library.” ArXiv abs/2401.08281 (2024): n. pag. +* [Benchmarking datasets](https://github.com/erikbern/ann-benchmarks?tab=readme-ov-file#data-sets) +* [Cohere/wikipedia-22-12-simple-embeddings ](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings) +* [Laion](https://laion.ai/about/) +* Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., & Jitsev, J. (2022). LAION-5B: An open large-scale dataset for training next generation image-text models. arXiv (Cornell University). [https://doi.org/10.48550/arxiv.2210.08402](https://doi.org/10.48550/arxiv.2210.08402) +* Douze, Matthijs, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazar'e, Maria Lomeli, Lucas Hosseini and Herv'e J'egou. The Faiss library. [https://arxiv.org/abs/2401.08281](https://arxiv.org/abs/2401.08281) From 44ba0d4677480b344d888badce7e922c0c975bf0 Mon Sep 17 00:00:00 2001 From: Fanit Kolchina Date: Tue, 25 Jun 2024 17:53:24 -0400 Subject: [PATCH 03/10] Formatting Signed-off-by: Fanit Kolchina --- .../2024-06-19-optimizing-opensearch-with-fp16-quantization.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index 3ac0df6618..fb40347dc2 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -130,7 +130,6 @@ We ran the following tests on a single-node cluster without any replicas using t |--- |--- |--- |--- | |16 |100 |100 |0 | -

|Dataset ID |Dataset |Dimension of vector |Data size |Number of queries |Training data range |Query data range |Space type |Primary shards |Indexing clients | |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- | |Dataset 1 |cohere-wiki-simple-embeddings-768 |768 |475,858 |10,000 |[ -4.1561704, 5.5478516 ] |[ -4.065383, 5.4902344 ] |L2 |4 |8 | @@ -190,7 +189,6 @@ To compare performance metrics and memory savings, we ran tests on the large-sca |query clients |1 |1 | |forcemerge segments |1 |1 | |client instance |r5.16xlarge |r5.16xlarge | -

Config ID |Optimization Strategy |m |ef_construction |ef_search | |--- |--- |--- |--- |--- | From 29500b92a30e21a058b3c7be0b45fe30d611f2b1 Mon Sep 17 00:00:00 2001 From: Naveen Tatikonda Date: Tue, 25 Jun 2024 20:07:59 -0500 Subject: [PATCH 04/10] Sort the metrics based on dimension of vector Signed-off-by: Naveen Tatikonda --- ...izing-opensearch-with-fp16-quantization.md | 40 ++++++++++--------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index fb40347dc2..a35002a01c 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -100,7 +100,7 @@ GET test-index/_search } ``` -## HNSW memory estimation +## HNSW memory estimation with fp16 The memory required for HNSW is estimated to be `1.1 * (2 * dimension + 8 * M)` bytes/vector. @@ -126,37 +126,39 @@ We ran the following tests on a single-node cluster without any replicas using t #### Configuration -|m |ef_construction |ef_search |replica | +|m |ef_construction |ef_search |replica| |--- |--- |--- |--- | |16 |100 |100 |0 | -|Dataset ID |Dataset |Dimension of vector |Data size |Number of queries |Training data range |Query data range |Space type |Primary shards |Indexing clients | +The dataset details and other configuration are listed below: + +|Dataset ID |Dataset |Dimension of vector |Data size |Number of queries |Training data range |Query data range |Space type |Primary shards |Indexing clients| |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- | -|Dataset 1 |cohere-wiki-simple-embeddings-768 |768 |475,858 |10,000 |[ -4.1561704, 5.5478516 ] |[ -4.065383, 5.4902344 ] |L2 |4 |8 | -|Dataset 2 |cohere-ip-1m |768 |1,000,000 |10,000 |[ -4.1073565, 5.504557 ] |[ -4.109505, 5.4809895 ] |innerproduct |8 |16 | -|Dataset 3 |gist-960-euclidean |960 |1,000,000 |1,000 |[ 0.0, 1.48 ] |[ 0.0, 0.729 ] |L2 |8 |16 | -|Dataset 4 |sift-128-euclidean |128 |1,000,000 |10,000 |[ 0.0, 218.0 ] |[ 0.0, 184.0 ] |L2 |8 |16 | -|Dataset 5 |mnist-784-euclidean |784 |60,000 |10,000 |[ 0.0, 255.0 ] |[ 0.0, 255.0 ] |L2 |1 |2 | +|Dataset 1 |gist-960-euclidean |960 |1,000,000 |1,000 |[ 0.0, 1.48 ] |[ 0.0, 0.729 ] |L2 |8 |16| +|Dataset 2 |mnist-784-euclidean |784 |60,000 |10,000 |[ 0.0, 255.0 ] |[ 0.0, 255.0 ] |L2 |1 |2| +|Dataset 3 |cohere-wiki-simple-embeddings-768 |768 |475,858 |10,000 |[ -4.1561704, 5.5478516 ] |[ -4.065383, 5.4902344 ] |L2 |4 |8| +|Dataset 4 |cohere-ip-1m |768 |1,000,000 |10,000 |[ -4.1073565, 5.504557 ] |[ -4.109505, 5.4809895 ] |innerproduct |8 |16| +|Dataset 5 |sift-128-euclidean |128 |1,000,000 |10,000 |[ 0.0, 218.0 ] |[ 0.0, 184.0 ] |L2 |8 |16| #### Recall and memory results -|Dataset ID |Faiss hnsw recall@100 |Faiss hnsw-sqfp16 recall@100 |Faiss hnsw memory estimate (gb) |Faiss hnsw-sqfp16 memory estimate (gb) |Faiss hnsw memory usage (gb) |Faiss hnsw-sqfp16 memory usage (gb) |% reduction in memory | +|Dataset ID | Faiss hnsw recall@100 |Faiss hnsw-sqfp16 recall@100 |Faiss hnsw memory estimate (gb) |Faiss hnsw-sqfp16 memory estimate (gb) |Faiss hnsw memory usage (gb) |Faiss hnsw-sqfp16 memory usage (gb) |% reduction in memory | |--- |--- |--- |--- |--- |--- |--- |--- | -|Dataset 1 |0.9456 |0.9450 |1.56 |0.81 |1.43 |0.75 |47.55 | -|Dataset 2 |0.9429 |0.9422 |3.28 |1.70 |3.00 |1.57 |47.67 | -|Dataset 3 |0.9071 |0.9072 |4.07 |2.10 |3.72 |1.93 |48.12 | -|Dataset 4 |0.9925 |0.9925 |0.66 |0.39 |0.62 |0.38 |38.71 | -|Dataset 5 |0.9889 |0.9889 |0.20 |0.10 |0.18 |0.10 |44.44 | +|Dataset 1 |0.9071 |0.9072 |4.07 |2.10 |3.72 |1.93 |48.12 | +|Dataset 2 |0.9889 |0.9889 |0.20 |0.10 |0.18 |0.10 |44.44| +|Dataset 3 |0.9456 |0.9450 |1.56 |0.81 |1.43 |0.75 |47.55| +|Dataset 4 |0.9429 |0.9422 |3.28 |1.70 |3.00 |1.57 |47.67| +|Dataset 5 |0.9925 |0.9925 |0.66 |0.39 |0.62 |0.38 |38.71| #### Indexing and query results |Dataset ID |Faiss hnsw mean throughput (docs/sec) |Faiss hnsw-sqfp16 mean throughput (docs/sec) |Faiss hnsw p90 (ms) |Faiss hnsw-sqfp16 p90 (ms) |Faiss hnsw p99 (ms) |Faiss hnsw-sqfp16 p99 (ms) | |--- |--- |--- |--- |--- |--- |--- | -|Dataset 1 |4690 |4698 |3.35 |3.33 |3.58 |3.57 | -|Dataset 2 |6044 |6129 |4.61 |4.81 |5.16 |5.37 | -|Dataset 3 |4681 |4696 |4.97 |5.08 |5.54 |5.50 | -|Dataset 4 |115499 |102060 |2.73 |2.68 |2.96 |2.89 | -|Dataset 5 |4271 |4580 |2.01 |2.06 |2.16 |2.21 | +|Dataset 1 |4681 |4696 |4.97 |5.08 |5.54 |5.50| +|Dataset 2 |4271 |4580 |2.01 |2.06 |2.16 |2.21| +|Dataset 3 |4690 |4698 |3.35 |3.33 |3.58 |3.57| +|Dataset 4 |6044 |6129 |4.61 |4.81 |5.16 |5.37| +|Dataset 5 |115499 |102060 |2.73 |2.68 |2.96 |2.89| #### Analysis From 9aeb84b7d162641b6a88de5e1dbaceb252e7c25b Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Wed, 26 Jun 2024 07:54:14 -0400 Subject: [PATCH 05/10] Apply suggestions from code review Co-authored-by: Nathan Bower Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- ...izing-opensearch-with-fp16-quantization.md | 73 ++++++++++--------- 1 file changed, 37 insertions(+), 36 deletions(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index a35002a01c..a30b55f131 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -23,7 +23,7 @@ leading to higher memory requirements and increased operational costs. Faiss sca When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply a technique called _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, you can get up to a 50% memory savings with a very minimal loss of recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), SQfp16 quantization can also significantly reduce search latencies and improve indexing throughput. -## How to use Faiss scalar quantization? +## How to use Faiss scalar quantization To use Faiss scalar quantization, set the k-NN vector field's `method.parameters.encoder.name` to `sq` when creating a k-NN index: @@ -74,7 +74,7 @@ The preceding index mapping request specifies the `clip` parameter, which define **Note**: We recommend setting `clip` to `true` only if very few elements lie outside of the supported range. Rounding the values may cause a drop in recall. -During ingestion, make sure each dimension of the vector is in the supported range ([-65504.0, 65504.0]): +During ingestion, make sure each dimension of the vector is within the supported range ([-65504.0, 65504.0]): ```json PUT test-index/_doc/1 @@ -108,16 +108,16 @@ As an example, assume that you have 1 million vectors with a dimension of 256 an `1.1 * (2 * 256 + 8 * 16) * 1,000,000 ~= 0.656 GB` -For more information about memory estimation for scalar quantization with IVF, refer to [this documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-vector-quantization/#memory-estimation-1). +For more information about memory estimation for scalar quantization with the inverted file (IVF) algorithm, refer to [this documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-vector-quantization/#memory-estimation-1). ## Benchmarking results -We ran benchmarking tests on some of the popular and trending datasets using our [opensearch-benchmark](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch) tool -to compare the indexing, search performance, and the quality of search results of Faiss scalar quantization. We compared Faiss scalar quantization against using Faiss with float vectors without any encoding. All tests were performed with [SIMD](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#simd-optimization-for-the-faiss-engine) +We ran benchmarking tests on some popular and trending datasets using our [opensearch-benchmark](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch) tool +to compare the indexing, search performance, and quality of search results of Faiss scalar quantization. We compared Faiss scalar quantization against using Faiss with float vectors without any encoding. All tests were performed with [SIMD](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#simd-optimization-for-the-faiss-engine) (Single Instruction Multiple Data). enabled on x86 architecture with AVX2 optimization. -**Note**: Without SIMD optimization (AVX2 or NEON) or with AVX2 disabled(on x86 architecture), the quantization process introduces additional overhead, which leads to an increase in latencies. -For information about processors that support AVX2, see [CPUs with AVX2](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2). In AWS, all community AMIs with [HVM](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) support AVX2 optimization for the x86 architecture. +**Note**: Without SIMD optimization (AVX2 or NEON) or with AVX2 disabled (on x86 architecture), the quantization process introduces additional overhead, which leads to an increase in latency. +For information about processors that support AVX2, see [CPUs with AVX2](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2). In AWS, all community Amazon Machine Images (AMIs) with [HVM](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) support AVX2 optimization for the x86 architecture. ### Benchmarking results using small workloads @@ -130,9 +130,9 @@ We ran the following tests on a single-node cluster without any replicas using t |--- |--- |--- |--- | |16 |100 |100 |0 | -The dataset details and other configuration are listed below: +The dataset and other configuration details are listed in the following table. -|Dataset ID |Dataset |Dimension of vector |Data size |Number of queries |Training data range |Query data range |Space type |Primary shards |Indexing clients| +|Dataset ID |Dataset |Vector dimension |Data size |Number of queries |Training data range |Query data range |Space type |Primary shards |Indexing clients| |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- | |Dataset 1 |gist-960-euclidean |960 |1,000,000 |1,000 |[ 0.0, 1.48 ] |[ 0.0, 0.729 ] |L2 |8 |16| |Dataset 2 |mnist-784-euclidean |784 |60,000 |10,000 |[ 0.0, 255.0 ] |[ 0.0, 255.0 ] |L2 |1 |2| @@ -164,9 +164,10 @@ The dataset details and other configuration are listed below: When comparing the benchmarking results, note that: -* The recall obtained using Faiss HNSW SQfp16 matches that of Faiss HNSW (with negligible difference). +* The recall obtained using Faiss HNSW SQfp16 matches that of Faiss HNSW (with a negligible difference). * Using SQfp16, there is a significant reduction in memory usage of up to **48%**, with a slight reduction in disk usage. These results indicate that a larger vector dimension leads to greater memory reduction. -* The performance metrics using SQfp16 are on par with those of `fp32` vectors. +* When using SQfp16, the performance metrics are similar to those of `fp32` vectors. + ### Benchmarking results using large workloads @@ -177,29 +178,29 @@ To compare performance metrics and memory savings, we ran tests on the large-sca | |Faiss HNSW SQfp16 |Faiss HNSW | |--- |--- |--- | |OpenSearch version |2.13 |2.13 | -|engine |faiss |faiss | -|Dimension of vector |768 |768 | -|ingest vectors |100M |100M | -|test vectors |1k |1k | -|primary shards |36 |36 | -|replica shards |0 |0 | -|data nodes |4 |8 | -|data node instance type |r5.4xlarge |r5.4xlarge | -|master nodes |3 |3 | -|master node instance type |c5.xlarge |c5.xlarge | -|indexing clients |9 |9 | -|query clients |1 |1 | -|forcemerge segments |1 |1 | -|client instance |r5.16xlarge |r5.16xlarge | - -Config ID |Optimization Strategy |m |ef_construction |ef_search | +|Engine |faiss |faiss | +|Vector dimension |768 |768 | +|Ingest vectors |100M |100M | +|Test vectors |1k |1k | +|Primary shards |36 |36 | +|Replica shards |0 |0 | +|Data nodes |4 |8 | +|Data node instance type |r5.4xlarge |r5.4xlarge | +|Cluster manager nodes |3 |3 | +|Cluster manager node instance type |c5.xlarge |c5.xlarge | +|Indexing clients |9 |9 | +|Query clients |1 |1 | +|Force merge segments |1 |1 | +|Client instance |r5.16xlarge |r5.16xlarge | + +Config ID |Optimization strategy |m |ef_construction |ef_search | |--- |--- |--- |--- |--- | |hnsw1 |Default configuration |16 |100 |100 | |hnsw2 |Balance between latency, memory, and recall |16 |128 |128 | |hnsw3 |Optimize for recall |16 |256 |256 | -Faiss HNSW SQfp16 requires 4 data nodes, half the number needed for Faiss HNSW (8). This demonstrates that SQfp16 reduces memory requirements by 50%. -For more information about estimating required memory and number of data nodes, see the [Appendix](#appendix-memory-and-data-node-requirement-estimation). +Faiss HNSW SQfp16 requires 4 data nodes---half the number needed for Faiss HNSW (8). This demonstrates that SQfp16 reduces memory requirements by 50%. +For more information about estimating the required memory and number of data nodes, see the [Appendix](#appendix-memory-and-data-node-requirement-estimation). #### Recall and memory results @@ -220,18 +221,18 @@ For more information about estimating required memory and number of data nodes, #### Analysis * For k=1000, the recall is identical for both Faiss HNSW and Faiss HNSW with SQfp16. -* Faiss HNSW with SQfp16 requires approximately half the memory Faiss HNSW required (as measured by the required number of data nodes). Based on the [k-NN stats API metrics](https://opensearch.org/docs/latest/search-plugins/knn/api/#stats), the memory usage has been reduced by 47.64% using SQfp16. -* In most instances, SQfp16 demonstrated better indexing throughput compared to `fp32` vectors. +* Faiss HNSW with SQfp16 requires approximately half the memory as Faiss HNSW (as measured by the required number of data nodes). Based on the [k-NN stats API metrics](https://opensearch.org/docs/latest/search-plugins/knn/api/#stats), the memory usage was reduced by 47.64% by using SQfp16. +* In most instances, SQfp16 demonstrated better indexing throughput as compared to `fp32` vectors. ## Conclusion -Faiss FP16 scalar quantization is a powerful technique that provides significant memory savings while maintaining high recall performance similar to full-precision vectors. By converting vectors to a 16-bit floating-point representation, it can reduce memory requirements by up to 50%. When combined with SIMD (Single Instruction Multiple Data) optimization, FP16 scalar quantization also enhances indexing throughput and reduces search latencies, leading to better overall performance. This method strikes an excellent balance between memory efficiency and accuracy, making it a valuable tool for large-scale similarity search applications. +Faiss FP16 scalar quantization is a powerful technique that provides significant memory savings while maintaining high recall performance similar to full-precision vectors. By converting vectors to a 16-bit floating-point representation, it can reduce memory requirements by up to 50%. When combined with SIMD optimization, FP16 scalar quantization also enhances indexing throughput and reduces search latencies, leading to better overall performance. This method strikes an excellent balance between memory efficiency and accuracy, making it a valuable tool for large-scale similarity search applications. ## Future scope -To achieve an even greater memory efficiency, we plan to introduce `int8` quantization support using a [Faiss scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1723) and [Lucene scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1277). -This advanced technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors, while maintaining high recall performance. -The quantizers will accept FP32 vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for an external quantization or extra training steps. +To achieve even greater memory efficiency, we plan to introduce `int8` quantization support using a [Faiss scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1723) and [Lucene scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1277). +This advanced technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors while maintaining high recall performance. +The quantizers will accept `fp32` vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for external quantization or extra training steps. Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This groundbreaking approach will further reduce memory consumption. By combining these cutting-edge quantization techniques, we will provide a comprehensive solution for efficient similarity search, balancing memory optimization and @@ -281,7 +282,7 @@ Number of Data nodes -> 328/48 = 6.83 = 7 + 1(for stability) = 8 ## References * [Benchmarking datasets](https://github.com/erikbern/ann-benchmarks?tab=readme-ov-file#data-sets) -* [Cohere/wikipedia-22-12-simple-embeddings ](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings) +* [Cohere/wikipedia-22-12-simple-embeddings](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings) * [Laion](https://laion.ai/about/) * Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S., Crowson, K., Schmidt, L., Kaczmarczyk, R., & Jitsev, J. (2022). LAION-5B: An open large-scale dataset for training next generation image-text models. arXiv (Cornell University). [https://doi.org/10.48550/arxiv.2210.08402](https://doi.org/10.48550/arxiv.2210.08402) * Douze, Matthijs, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazar'e, Maria Lomeli, Lucas Hosseini and Herv'e J'egou. The Faiss library. [https://arxiv.org/abs/2401.08281](https://arxiv.org/abs/2401.08281) From d2268a2af3ad3a462d9746c116448a11a94173dd Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Wed, 26 Jun 2024 07:54:58 -0400 Subject: [PATCH 06/10] Update _posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md Co-authored-by: Nathan Bower Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- .../2024-06-19-optimizing-opensearch-with-fp16-quantization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index a30b55f131..4a7f433b1e 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -243,7 +243,7 @@ requirements and maximizing cost-effectiveness. ## Appendix: Memory and data node requirement estimation -The following calculations provide a memory and number of data nodes estimation for the 100M, 768 dimension large workload benchmarking test. +The following calculations provide an estimation of the memory and number of data nodes for the 100M, 768-dimension large workload benchmarking test: ``` // Faiss HNSW SQfp16 Memory Estimation From b4935a56e4f19bb8f20ee334fb0301644acdee12 Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Wed, 26 Jun 2024 07:55:37 -0400 Subject: [PATCH 07/10] Apply suggestions from code review Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --- .../2024-06-19-optimizing-opensearch-with-fp16-quantization.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index 4a7f433b1e..c71ef01e50 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -243,7 +243,7 @@ requirements and maximizing cost-effectiveness. ## Appendix: Memory and data node requirement estimation -The following calculations provide an estimation of the memory and number of data nodes for the 100M, 768-dimension large workload benchmarking test: +The following calculations provide an estimation of the memory and number of data nodes required for the 100M, 768-dimension large workload benchmarking test: ``` // Faiss HNSW SQfp16 Memory Estimation From 9077afdcb8a095e0b5fc3a8d2e83460cddca4070 Mon Sep 17 00:00:00 2001 From: Naveen Tatikonda Date: Wed, 26 Jun 2024 11:34:27 -0500 Subject: [PATCH 08/10] Address review comments Signed-off-by: Naveen Tatikonda --- ...izing-opensearch-with-fp16-quantization.md | 48 +++++++++---------- 1 file changed, 23 insertions(+), 25 deletions(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index c71ef01e50..32803bfda4 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -8,8 +8,8 @@ authors: date: 2024-06-19 00:00:00 -0700 categories: - technical-posts -meta_keywords: FP16 quantization, OpenSearch k-NN plugin, memory optimization, cost-effectiveness -meta_description: Learn how FP16 Quantization in OpenSearch helps to reduce memory requirements upto 50% with a very minimal loss in quality. +meta_keywords: FP16 quantization, OpenSearch k-NN plugin, memory optimization, cost-effectiveness, performance, search latency, indexing throughput +meta_description: Learn how FP16 Quantization in OpenSearch helps to reduce memory requirements up to 50% with a very minimal loss in quality. has_science_table: true --- @@ -20,7 +20,7 @@ leading to higher memory requirements and increased operational costs. Faiss sca ## Why use Faiss scalar quantization? -When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply a technique called _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, you can get up to a 50% memory savings with a very minimal loss of recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), +When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, you can get up to a 50% memory savings with a very minimal loss of recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), SQfp16 quantization can also significantly reduce search latencies and improve indexing throughput. ## How to use Faiss scalar quantization @@ -64,13 +64,13 @@ PUT /test-index For more information about the SQ parameters, see the [k-NN documentation](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#sq-parameters). -The `fp16` encoder converts 32-bit vectors into their 16-bit counterparts. For this encoder type, the vector values must be in the **[-65504.0, 65504.0]** range. +The `fp16` encoder converts 32-bit vectors into their 16-bit counterparts. For this encoder type, the vector values must be in the range **[-65504.0, 65504.0]**. -The preceding index mapping request specifies the `clip` parameter, which defines how to handle out-of-range values: +The `clip` parameter above specifies how to handle out-of-range values: * By default, `clip` is `false`, and any vectors containing out-of-range values are rejected. * When `clip` is set to `true`, out of-range vector values are rounded up or down so that they are in the supported range. For example, if the original 32-bit vector is -`[65510.82, -65504.1]`, the vector will be indexed as a 16-bit vector `[65504.0, -65504.0]`. +`[65510.82, -65504.1]`, the vector will be indexed in the range `[65504.0, -65504.0]`. **Note**: We recommend setting `clip` to `true` only if very few elements lie outside of the supported range. Rounding the values may cause a drop in recall. @@ -112,16 +112,16 @@ For more information about memory estimation for scalar quantization with the in ## Benchmarking results -We ran benchmarking tests on some popular and trending datasets using our [opensearch-benchmark](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch) tool -to compare the indexing, search performance, and quality of search results of Faiss scalar quantization. We compared Faiss scalar quantization against using Faiss with float vectors without any encoding. All tests were performed with [SIMD](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#simd-optimization-for-the-faiss-engine) (Single Instruction Multiple Data). +We ran benchmarking tests on some popular datasets using our [opensearch-benchmark](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch) tool +to compare the indexing, search performance, and quality of search results of Faiss scalar quantization. We compared Faiss scalar quantization (FP16) against using Faiss with float vectors without any encoding (FP32). All tests were performed with [SIMD](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#simd-optimization-for-the-faiss-engine) (Single Instruction Multiple Data). enabled on x86 architecture with AVX2 optimization. **Note**: Without SIMD optimization (AVX2 or NEON) or with AVX2 disabled (on x86 architecture), the quantization process introduces additional overhead, which leads to an increase in latency. -For information about processors that support AVX2, see [CPUs with AVX2](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2). In AWS, all community Amazon Machine Images (AMIs) with [HVM](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) support AVX2 optimization for the x86 architecture. +For information about processors that support AVX2, see [CPUs with AVX2](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions#CPUs_with_AVX2). In an AWS environment, all community Amazon Machine Images (AMIs) with [HVM](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/virtualization_types.html) support AVX2 optimization for the x86 architecture. ### Benchmarking results using small workloads -We ran the following tests on a single-node cluster without any replicas using the following datasets. +We ran the following tests on a single-node cluster without any replicas. #### Configuration @@ -142,13 +142,13 @@ The dataset and other configuration details are listed in the following table. #### Recall and memory results -|Dataset ID | Faiss hnsw recall@100 |Faiss hnsw-sqfp16 recall@100 |Faiss hnsw memory estimate (gb) |Faiss hnsw-sqfp16 memory estimate (gb) |Faiss hnsw memory usage (gb) |Faiss hnsw-sqfp16 memory usage (gb) |% reduction in memory | -|--- |--- |--- |--- |--- |--- |--- |--- | -|Dataset 1 |0.9071 |0.9072 |4.07 |2.10 |3.72 |1.93 |48.12 | -|Dataset 2 |0.9889 |0.9889 |0.20 |0.10 |0.18 |0.10 |44.44| -|Dataset 3 |0.9456 |0.9450 |1.56 |0.81 |1.43 |0.75 |47.55| -|Dataset 4 |0.9429 |0.9422 |3.28 |1.70 |3.00 |1.57 |47.67| -|Dataset 5 |0.9925 |0.9925 |0.66 |0.39 |0.62 |0.38 |38.71| +|Dataset ID | Faiss hnsw recall@100 | Faiss hnsw-sqfp16 recall@100 |Faiss hnsw memory estimate (gb) |Faiss hnsw-sqfp16 memory estimate (gb) |Faiss hnsw memory usage (gb) |Faiss hnsw-sqfp16 memory usage (gb) |% reduction in memory | +|--- |--------------------|-------------------------|--- |--- |--- |--- |-- | +|Dataset 1 | 0.91 | 0.91 |4.07 |2.10 |3.72 |1.93 |48 | +|Dataset 2 | 0.99 | 0.99 |0.20 |0.10 |0.18 |0.10 |44| +|Dataset 3 | 0.95 | 0.95 |1.56 |0.81 |1.43 |0.75 |48| +|Dataset 4 | 0.94 | 0.94 |3.28 |1.70 |3.00 |1.57 |48| +|Dataset 5 | 0.99 | 0.99 |0.66 |0.39 |0.62 |0.38 |39| #### Indexing and query results @@ -226,24 +226,22 @@ For more information about estimating the required memory and number of data nod ## Conclusion -Faiss FP16 scalar quantization is a powerful technique that provides significant memory savings while maintaining high recall performance similar to full-precision vectors. By converting vectors to a 16-bit floating-point representation, it can reduce memory requirements by up to 50%. When combined with SIMD optimization, FP16 scalar quantization also enhances indexing throughput and reduces search latencies, leading to better overall performance. This method strikes an excellent balance between memory efficiency and accuracy, making it a valuable tool for large-scale similarity search applications. +Faiss SQfp16 scalar quantization is a powerful technique that provides significant memory savings while maintaining high recall performance similar to full-precision vectors. Converting vectors to a 16-bit floating-point representation can reduce memory requirements by up to 50%. When combined with SIMD optimization, SQfp16 scalar quantization also enhances indexing throughput and reduces search latency, leading to better overall performance. This method strikes an excellent balance between memory efficiency and accuracy, making it a valuable tool for large-scale similarity search applications. ## Future scope To achieve even greater memory efficiency, we plan to introduce `int8` quantization support using a [Faiss scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1723) and [Lucene scalar quantizer](https://github.com/opensearch-project/k-NN/issues/1277). -This advanced technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors while maintaining high recall performance. +This technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors and we expect to find minimal reduction in recall. The quantizers will accept `fp32` vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for external quantization or extra training steps. -Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This groundbreaking approach will further reduce memory consumption. -By combining these cutting-edge quantization techniques, we will provide a comprehensive solution for efficient similarity search, balancing memory optimization and -accurate retrieval. +Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This approach will further reduce memory consumption. In +addition to this we will soon add support for avx512 optimization which helps to further reduce search latency. -Our commitment to continuous innovation delivers state-of-the-art technologies to tackle large-scale similarity search challenges while minimizing resource -requirements and maximizing cost-effectiveness. +Our ongoing analysis and tuning of OpenSearch lets you address large-scale similarity search while minimizing resource requirements and maximizing cost-effectiveness. ## Appendix: Memory and data node requirement estimation -The following calculations provide an estimation of the memory and number of data nodes required for the 100M, 768-dimension large workload benchmarking test: +Here are some estimates of the amount of memory and number of data nodes needed for the 100M, 768 dimension large workload benchmarking test: ``` // Faiss HNSW SQfp16 Memory Estimation From a937cc4c413449cbea7d8442c67b4cb62b49f370 Mon Sep 17 00:00:00 2001 From: Naveen Tatikonda Date: Wed, 26 Jun 2024 22:00:43 -0500 Subject: [PATCH 09/10] Update publish date and meta keywords Signed-off-by: Naveen Tatikonda --- ...2024-06-19-optimizing-opensearch-with-fp16-quantization.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index 32803bfda4..d3039d2de6 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -5,10 +5,10 @@ authors: - naveen - vamshin - tal -date: 2024-06-19 00:00:00 -0700 +date: 2024-06-27 00:00:00 -0700 categories: - technical-posts -meta_keywords: FP16 quantization, OpenSearch k-NN plugin, memory optimization, cost-effectiveness, performance, search latency, indexing throughput +meta_keywords: faiss scalar quantization, OpenSearch k-NN plugin, FP16 scalar quantization, vector embeddings meta_description: Learn how FP16 Quantization in OpenSearch helps to reduce memory requirements up to 50% with a very minimal loss in quality. has_science_table: true --- From ce24fbbd7a1c7d591aedbc303b374604d08cb63c Mon Sep 17 00:00:00 2001 From: Naveen Tatikonda Date: Wed, 26 Jun 2024 22:14:24 -0500 Subject: [PATCH 10/10] Rephrase sentences Signed-off-by: Naveen Tatikonda --- ...4-06-19-optimizing-opensearch-with-fp16-quantization.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md index d3039d2de6..6a86b71e5f 100644 --- a/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md +++ b/_posts/2024-06-19-optimizing-opensearch-with-fp16-quantization.md @@ -16,11 +16,11 @@ has_science_table: true The rise of large language models (LLMs) and generative AI has ushered in a new era of natural language processing capabilities. Vector databases have emerged as a crucial component in this landscape, acting as external databases that can efficiently index, store, and retrieve embeddings generated by LLMs. However, as the scale and complexity of LLMs continue to grow, vector database workloads have also increased significantly. Ingesting and querying billions of vectors can strain computational resources, -leading to higher memory requirements and increased operational costs. Faiss scalar quantization enables you to generate vector embeddings with lower precision, which reduces memory consumption and, consequently, lowers costs. +leading to higher memory requirements and increased operational costs. Faiss scalar quantization enables you to store vector embeddings with lower precision, which reduces memory consumption and, consequently, lowers costs. ## Why use Faiss scalar quantization? -When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, you can get up to a 50% memory savings with a very minimal loss of recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), +When you index vectors in [OpenSearch 2.13](https://github.com/opensearch-project/opensearch-build/blob/main/release-notes/opensearch-release-notes-2.13.0.md) or later versions, you can configure your k-NN index to apply _scalar quantization_. Scalar quantization converts each dimension of a vector from a 32-bit floating-point (`fp32`) to a 16-bit floating-point (`fp16`) representation. Using the Faiss scalar quantizer (SQfp16), integrated in the k-NN plugin, saves about 50% of the memory with minimal reduction in recall (see [Benchmarking results](#benchmarking-results)). When used with [SIMD optimization](https://opensearch.org/docs/latest/search-plugins/knn/knn-index#simd-optimization-for-the-faiss-engine), SQfp16 quantization can also significantly reduce search latencies and improve indexing throughput. ## How to use Faiss scalar quantization @@ -234,8 +234,7 @@ To achieve even greater memory efficiency, we plan to introduce `int8` quantizat This technique will enable a remarkable 75% reduction in memory requirements, or 4x compression, compared to full-precision vectors and we expect to find minimal reduction in recall. The quantizers will accept `fp32` vectors as input, perform online training, and quantize the data into byte-sized vectors, eliminating the need for external quantization or extra training steps. -Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This approach will further reduce memory consumption. In -addition to this we will soon add support for avx512 optimization which helps to further reduce search latency. +Furthermore, we aim to release binary vector support, enabling an unprecedented 32x compression rate. This approach will further reduce memory consumption. Moreover, we plan to incorporate AVX-512 optimization, which will contribute to further reducing search latency. Our ongoing analysis and tuning of OpenSearch lets you address large-scale similarity search while minimizing resource requirements and maximizing cost-effectiveness.