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GitHub - facebookresearch/Qinco: Residual Quantization with Implicit Neural Codebooks
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3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="feedback-dialog-title"> <div data-view-component="true" class="Overlay-body"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="wj0gRJL63iMkzN/KCD12USlP20Y0Wb9jIc3jfrJi8A81ztaGCEe86K12UV7VY+nNGXwHuZLpl9wK6gtqG4SSOA==" /> <p>We read every piece of feedback, and take your input very seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> 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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":747602081,"defaultBranch":"main","name":"Qinco","ownerLogin":"facebookresearch","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2024-01-24T09:12:00.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/16943930?v=4","public":true,"private":false,"isOrgOwned":true},"currentUser":null,"refInfo":{"name":"main","listCacheKey":"v0:1736238234.0","canEdit":false,"refType":"branch","currentOid":"8d75f94b1f136a9738a95764d00b267defaf4e33"},"tree":{"items":[{"name":"config","path":"config","contentType":"directory"},{"name":"data","path":"data","contentType":"directory"},{"name":"qinco","path":"qinco","contentType":"directory"},{"name":"qinco_v1","path":"qinco_v1","contentType":"directory"},{"name":".gitignore","path":".gitignore","contentType":"file"},{"name":"CODE_OF_CONDUCT.md","path":"CODE_OF_CONDUCT.md","contentType":"file"},{"name":"CONTRIBUTING.md","path":"CONTRIBUTING.md","contentType":"file"},{"name":"LICENSE","path":"LICENSE","contentType":"file"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"environment.yml","path":"environment.yml","contentType":"file"},{"name":"pyproject.toml","path":"pyproject.toml","contentType":"file"},{"name":"requirements.txt","path":"requirements.txt","contentType":"file"},{"name":"run.py","path":"run.py","contentType":"file"},{"name":"run.sh","path":"run.sh","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":14,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/facebookresearch/Qinco/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/facebookresearch/Qinco.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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Compression and Search with Improved Implicit Neural Codebooks (QINCo2)\u003c/h1\u003e\u003ca id=\"user-content-vector-compression-and-search-with-improved-implicit-neural-codebooks-qinco2\" class=\"anchor\" aria-label=\"Permalink: Vector Compression and Search with Improved Implicit Neural Codebooks (QINCo2)\" href=\"#vector-compression-and-search-with-improved-implicit-neural-codebooks-qinco2\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eThis repository has been updated with the code from QINCo2. To access the original QINCo1 code, see \u003ca href=\"/facebookresearch/Qinco/blob/main/qinco_v1/README.md\"\u003eqinco_v1 directory\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis code repository corresponds to the paper \u003ca href=\"https://arxiv.org/abs/2501.03078\" rel=\"nofollow\"\u003eQINCo2: Vector Compression and Search with Improved Implicit Neural Codebooks\u003c/a\u003e, introducing an improved quantization process over QINCo.\nWe also include code reproducing the ICML'24 paper \u003ca href=\"https://arxiv.org/pdf/2401.14732.pdf\" rel=\"nofollow\"\u003eResidual Quantization with Implicit Neural Codebooks\u003c/a\u003e (\u003ca href=\"/facebookresearch/Qinco/blob/main/qinco_v1/README.md\"\u003eqinco_v1 directory\u003c/a\u003e), in which Quantization with Implicit Neural Codebooks (QINCo) was proposed. Please read both papers to learn about QINCo and QINCo2.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eQINCo is a neurally-augmented algorithm for multi-codebook vector quantization, specifically residual quantization (RQ). Instead of using a fixed codebook per quantization step, QINCo uses a neural network to predict a codebook for the next quantization step, conditioned upon the quantized vector so far. In other words, the codebooks to be used depend on the Voronoi cells selected previously. This greatly enhances the capacity of the compression system, without the need to store more codebook vectors explicitly. An additional advantage of QINCo is its modularity. Thanks to training each quantization step with its own quantization error, the trained system for a certain compression rate, can also be exploited for lower compression rates, making QINCo a dynamic rate quantizer.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eQINCo2 introduces several key novelties:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eA fast approximate encoding method, yielding similar MSE for a much faster training and encoding time.\u003c/li\u003e\n\u003cli\u003eIntegration of beam search to the encoding process, reaching much lower compression errors than QINCo1 for a similar encoding time when combined to approximate encoding.\u003c/li\u003e\n\u003cli\u003eA new (optional) module to the large-scale retrieval pipeline, improving accuracy using a pairwise decoder.\u003c/li\u003e\n\u003cli\u003eAn overall upgrade of the architecture and training process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use QINCo in a research work please cite our paper:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@misc{vallaeys2025qinco2,\n title={Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks},\n author={Théophane Vallaeys and Matthew Muckley and Jakob Verbeek and Matthijs Douze},\n year={2025},\n eprint={2501.03078},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@misc{vallaeys2025qinco2,\n title={Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks},\n author={Théophane Vallaeys and Matthew Muckley and Jakob Verbeek and Matthijs Douze},\n year={2025},\n eprint={2501.03078},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSetup\u003c/h2\u003e\u003ca id=\"user-content-setup\" class=\"anchor\" aria-label=\"Permalink: Setup\" href=\"#setup\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h3\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eQINCo2 requires python3 and packages from the \u003ccode\u003erequirements.txt\u003c/code\u003e file. They can be installed using conda:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/facebookresearch/Qinco2\ncd Qinco2\nconda env create -f environment.yml\"\u003e\u003cpre\u003egit clone https://github.com/facebookresearch/Qinco2\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Qinco2\nconda env create -f environment.yml\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDownloading the data\u003c/h3\u003e\u003ca id=\"user-content-downloading-the-data\" class=\"anchor\" aria-label=\"Permalink: Downloading the data\" href=\"#downloading-the-data\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDownload the datasets used in the paper by running the corresponding bash scripts:\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBigANN\u003c/h4\u003e\u003ca id=\"user-content-bigann\" class=\"anchor\" aria-label=\"Permalink: BigANN\" href=\"#bigann\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"./data/bigann/download_data.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e./data/bigann/download_data.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eor if you have limited storage space, you can also download only the first 1M database vectors, and the first 10M training vectors using:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"./data/bigann/download_data.sh -small\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e./data/bigann/download_data.sh -small\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeep1B\u003c/h4\u003e\u003ca id=\"user-content-deep1b\" class=\"anchor\" aria-label=\"Permalink: Deep1B\" href=\"#deep1b\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"./data/deep1b/download_data.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e./data/deep1b/download_data.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eor if you have limited storage space, you can also download only the first 1M database vectors, and the first 10M training vectors using:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"./data/deep1b/download_data.sh -small\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e./data/deep1b/download_data.sh -small\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContriever\u003c/h4\u003e\u003ca id=\"user-content-contriever\" class=\"anchor\" aria-label=\"Permalink: Contriever\" href=\"#contriever\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"./data/contriever/download_data.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e./data/contriever/download_data.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFB-ssnpp\u003c/h4\u003e\u003ca id=\"user-content-fb-ssnpp\" class=\"anchor\" aria-label=\"Permalink: FB-ssnpp\" href=\"#fb-ssnpp\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"./data/fb_ssnpp/download_data.sh\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e./data/fb_ssnpp/download_data.sh\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePretrained checkpoints\u003c/h2\u003e\u003ca id=\"user-content-pretrained-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Pretrained checkpoints\" href=\"#pretrained-checkpoints\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBase experiments checkpoints\u003c/h3\u003e\u003ca id=\"user-content-base-experiments-checkpoints\" class=\"anchor\" aria-label=\"Permalink: Base experiments checkpoints\" href=\"#base-experiments-checkpoints\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBelow are the checkpoints for the QINCo1 and QINCo2-L models, trained on all four datasets. Instructions below show how to use and evaluate them. The commands suppose that the files are stored inside the \u003ccode\u003emodels/\u003c/code\u003e directory.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eBigANN1M\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDeep1M\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eContriever1M\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eFB-ssnpp1M\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco1 (8 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-bigann1M-8x8.pt\" rel=\"nofollow\"\u003eqinco1-bigann1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-deep1M-8x8.pt\" rel=\"nofollow\"\u003eqinco1-deep1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-contriever1M-8x8.pt\" rel=\"nofollow\"\u003eqinco1-contriever1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-FB_ssnpp1M-8x8.pt\" rel=\"nofollow\"\u003eqinco1-FB_ssnpp1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco1 (16 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-bigann1M-16x8.pt\" rel=\"nofollow\"\u003eqinco1-bigann1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-deep1M-16x8.pt\" rel=\"nofollow\"\u003eqinco1-deep1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-contriever1M-16x8.pt\" rel=\"nofollow\"\u003eqinco1-contriever1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-FB_ssnpp1M-16x8.pt\" rel=\"nofollow\"\u003eqinco1-FB_ssnpp1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-L (8 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-bigann1M-8x8.pt\" rel=\"nofollow\"\u003eqinco2_L-bigann1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-deep1M-8x8.pt\" rel=\"nofollow\"\u003eqinco2_L-deep1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-contriever1M-8x8.pt\" rel=\"nofollow\"\u003eqinco2_L-contriever1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-FB_ssnpp1M-8x8.pt\" rel=\"nofollow\"\u003eqinco2_L-FB_ssnpp1M-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-L (16 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-bigann1M-16x8.pt\" rel=\"nofollow\"\u003eqinco2_L-bigann1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-deep1M-16x8.pt\" rel=\"nofollow\"\u003eqinco2_L-deep1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-contriever1M-16x8.pt\" rel=\"nofollow\"\u003eqinco2_L-contriever1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-FB_ssnpp1M-16x8.pt\" rel=\"nofollow\"\u003eqinco2_L-FB_ssnpp1M-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIVF models checkpoints\u003c/h3\u003e\u003ca id=\"user-content-ivf-models-checkpoints\" class=\"anchor\" aria-label=\"Permalink: IVF models checkpoints\" href=\"#ivf-models-checkpoints\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThese models are trained with and additional codebook of \u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"58d03d87f954d6c3122322a409725efc\"\u003e$K_{IVF}=2^20=1048576$\u003c/math-renderer\u003e codewords, corresponding to the IVF step. They can be used to evaluate large-scale search.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eBigANN1B\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDeep1B\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-S (8 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-bigann1B-8x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_S-bigann1B-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-deep1B-8x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_S-deep1B-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-S (16 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-bigann1B-16x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_S-bigann1B-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-deep1B-16x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_S-deep1B-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-S (32 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-bigann1B-32x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_S-bigann1B-32x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-deep1B-32x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_S-deep1B-32x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-M (8 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-bigann1B-8x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_M-bigann1B-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-deep1B-8x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_M-deep1B-8x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-M (16 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-bigann1B-16x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_M-bigann1B-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-deep1B-16x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_M-deep1B-16x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eQinco2-M (32 bytes)\u003c/strong\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-bigann1B-32x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_M-bigann1B-32x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-deep1B-32x8.pt\" rel=\"nofollow\"\u003eIVF-qinco2_M-deep1B-32x8.pt\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eWe also provide the IVF centroids used to create these models:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/ivf_centroids_bigann1B_1048576.npy\" rel=\"nofollow\"\u003eivf_centroids_bigann1B_1048576.npy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://dl.fbaipublicfiles.com/QINCo/qinco2-models/ivf_centroids_deep1B_1048576.npy\" rel=\"nofollow\"\u003eivf_centroids_deep1B_1048576.npy\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEvery command uses the \u003ccode\u003erun.py\u003c/code\u003e endpoint. It can be run on multiple GPUs with \u003ca href=\"https://huggingface.co/docs/accelerate/index\" rel=\"nofollow\"\u003eaccelerate\u003c/a\u003e using the \u003ccode\u003e./run.sh\u003c/code\u003e script. If you are familiar with accelerate, you can use it directly to launch \u003ccode\u003erun.py\u003c/code\u003e.\nCommand-line arguments are parsed using the \u003ca href=\"https://hydra.cc/\" rel=\"nofollow\"\u003eHydra\u003c/a\u003e format. You can find the default configuration and all overloadable parameters inside the \u003ccode\u003econfig/qinco_cfg.yaml\u003c/code\u003e file.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRunning on an single GPU or on CPU\u003c/strong\u003e: In any of the following commands, you can replace \u003ccode\u003e./run.sh\u003c/code\u003e by \u003ccode\u003epython run.py\u003c/code\u003e to use a single GPU. Set the \u003ccode\u003ecpu\u003c/code\u003e argument to true (\u003ccode\u003epython run.py cpu=true\u003c/code\u003e) to run on CPU instead.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eDatasets\u003c/strong\u003e: for all commands below, you can either use your own data using the \u003ccode\u003edb\u003c/code\u003e, \u003ccode\u003etrainset\u003c/code\u003e, \u003ccode\u003equeries\u003c/code\u003e and \u003ccode\u003equeries_gt\u003c/code\u003e arguments, or use one of the \u003cem\u003edefault\u003c/em\u003e dataset that is used within the paper. To use one of theses datasets, replace these arguments by \u003ccode\u003edb=\u0026lt;name of the dataset\u0026gt;\u003c/code\u003e. The paths will be automatically populated. Be sure to download the corresponding datasets beforehand (see above).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eAvailable default datasets\u003c/strong\u003e: \u003ccode\u003edb=FB_ssnpp1M\u003c/code\u003e, \u003ccode\u003edb=contriever1M\u003c/code\u003e, \u003ccode\u003edb=bigann1M\u003c/code\u003e, \u003ccode\u003edb=bigann1B\u003c/code\u003e, \u003ccode\u003edb=bigann1B\u003c/code\u003e, \u003ccode\u003edb=deep1B\u003c/code\u003e.\nThe \u003ccode\u003e1M\u003c/code\u003e datasets are intended to be used for most tasks, while the \u003ccode\u003e1B\u003c/code\u003e datasets should only be used to build and search a index for large-scale search.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUsing your own data\u003c/strong\u003e: you will need at least a vector database (\u003ccode\u003edb\u003c/code\u003e) and a set of training vectors (\u003ccode\u003etrainset\u003c/code\u003e) to train and evaluate the MSE of the model.\nAvailable data formats are: \u003ccode\u003ebvecs\u003c/code\u003e, \u003ccode\u003efvecs\u003c/code\u003e, \u003ccode\u003eivecs\u003c/code\u003e, \u003ccode\u003enpy\u003c/code\u003e, and the format should be a single matrix of dimensions \u003ccode\u003e(N_samples, D)\u003c/code\u003e.\nDuring training, the last 10,000 vectors of the training set will set appart at the validation set. The database is the test set.\nAdditionally, for nearest-neighbour search, you need a set of queries (\u003ccode\u003equeries\u003c/code\u003e, matrix of dimension \u003ccode\u003e(N_queries, D)\u003c/code\u003e) and the id of their answers in the database (\u003ccode\u003equeries_gt\u003c/code\u003e, matrix of dimension \u003ccode\u003e(N_queries, 1)\u003c/code\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUsing a subset of the data files\u003c/strong\u003e: you can use only a subset of the training set and/or database, which can be usefull for testing or using limited data. Use \u003ccode\u003eds.trainset=\u0026lt;...\u0026gt;\u003c/code\u003e and \u003ccode\u003eds.db=\u0026lt;...\u0026gt;\u003c/code\u003e to limit their size. Similarly, you can control the number of validation samples extracted from the training set with \u003ccode\u003eds.valset=\u0026lt;...\u0026gt;\u003c/code\u003e, and the number of samples from the training set used at each epoch with \u003ccode\u003eds.loop=\u0026lt;...\u0026gt;\u003c/code\u003e. If not specified, the default values are \u003ccode\u003eds.valset=10_000\u003c/code\u003e and \u003ccode\u003eds.loop=10_000_000\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTraining\u003c/h3\u003e\u003ca id=\"user-content-training\" class=\"anchor\" aria-label=\"Permalink: Training\" href=\"#training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"./run.sh task=train \\\n output=\u0026lt;model_weights.pt\u0026gt; \\\n [resume=false] \\\n [model_args=\u0026lt;model preset\u0026gt;] \\\n [L=\u0026lt;L\u0026gt; dh=\u0026lt;dh\u0026gt; M=\u0026lt;M\u0026gt; K=\u0026lt;K\u0026gt;] [A=\u0026lt;A\u0026gt;] [B=\u0026lt;B\u0026gt;] [de=\u0026lt;de\u0026gt;] \\\n [db=\u0026lt;db_name\u0026gt;] [trainset=\u0026lt;trainset_path\u0026gt;] \\\n [ivf_centroids=\u0026lt;path_to_ivf_centroids\u0026gt;] \\\n [ds.loop=\u0026lt;epoch_size\u0026gt;] [ds.trainset=\u0026lt;trainset_max_size\u0026gt;] [ds.valset=\u0026lt;validation_set_max_size\u0026gt;] \\\n [lr=0.0008] [batch=1024] [epochs=60] [grad_accumulate=1] [grad_clip=0.1] \\\n [verbose=true] \\\n [tensorboard=\u0026lt;path_to_directory\u0026gt;] \\\n [model=\u0026lt;path_to_resume_from\u0026gt;]\"\u003e\u003cpre\u003e./run.sh task=train \\\n output=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emodel_weights.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [resume\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003efalse] \\\n [model_args\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003emodel preset\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [L\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eL\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e dh\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003edh\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e M\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eM\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e K\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eK\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [A\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eA\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [B\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eB\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [de\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003ede\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003edb_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003etrainset_path\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [ivf_centroids\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003epath_to_ivf_centroids\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [ds.loop\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eepoch_size\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [ds.trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003etrainset_max_size\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [ds.valset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003evalidation_set_max_size\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [lr\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e0.0008] [batch\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e1024] [epochs\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e60] [grad_accumulate\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e1] [grad_clip\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e0.1] \\\n [verbose\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003etrue] \\\n [tensorboard\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003epath_to_directory\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [model\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003epath_to_resume_from\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command trains a new model and accepts a set of required and optional arguments:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe \u003ccode\u003eoutput\u003c/code\u003e argument should specify the path to the \u003ccode\u003e.pt\u003c/code\u003e checkpoint file that will be created during training.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIf the \u003ccode\u003eresume\u003c/code\u003e argument is set to \u003ccode\u003etrue\u003c/code\u003e (default is \u003ccode\u003efalse\u003c/code\u003e), the training will resume from the \u003ccode\u003eoutput\u003c/code\u003e path \u003cem\u003eif it exists\u003c/em\u003e, or starts from scracth otherwise.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eYou need to specify the models arguments \u003ccode\u003eK\u003c/code\u003e (codebook size, set to 256 in the paper), \u003ccode\u003eM\u003c/code\u003e (number of QINCo2 steps, and bytes if \u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"58d03d87f954d6c3122322a409725efc\"\u003e$K=256$\u003c/math-renderer\u003e), \u003ccode\u003edh\u003c/code\u003e (hidden dimension \u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"58d03d87f954d6c3122322a409725efc\"\u003e$d_h$\u003c/math-renderer\u003e), \u003ccode\u003ede\u003c/code\u003e (embedding dimension \u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"58d03d87f954d6c3122322a409725efc\"\u003e$d_e$\u003c/math-renderer\u003e), \u003ccode\u003eL\u003c/code\u003e (number of residual blocks in each step), \u003ccode\u003eA\u003c/code\u003e (number of fast pre-selected candidates) and \u003ccode\u003eB\u003c/code\u003e (size of beam search). See the paper for reference.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe values of \u003ccode\u003eA\u003c/code\u003e, \u003ccode\u003eB\u003c/code\u003e and \u003ccode\u003ede\u003c/code\u003e can be set to \u003ccode\u003e0\u003c/code\u003e or \u003ccode\u003enull\u003c/code\u003e (default value) to disable these components. \u003ccode\u003eA=null\u003c/code\u003e implies no beam search, \u003ccode\u003eB=0\u003c/code\u003e implies no candidates pre-selection, and \u003ccode\u003ede\u003c/code\u003e implies an embedding dimension of same size as the data dimension.\u003c/li\u003e\n\u003cli\u003eYou can also use a \u003cstrong\u003epreset\u003c/strong\u003e, using \u003ccode\u003emodel_args=\u0026lt;model-name\u0026gt;\u003c/code\u003e. Available models are: \u003ccode\u003eqinco1\u003c/code\u003e, \u003ccode\u003eqinco2-S\u003c/code\u003e, \u003ccode\u003eqinco2-M\u003c/code\u003e, \u003ccode\u003eqinco2-L\u003c/code\u003e, which will set all these arguments if not specified manually.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eYou need to specify a path to the training data file the \u003ccode\u003etrainset\u003c/code\u003e argument, or using \u003ccode\u003edb=\u0026lt;db_name\u0026gt;\u003c/code\u003e (see \"Datasets\" above).\u003c/li\u003e\n\u003cli\u003eIf you want to train an IVF model (e.g. \u003ccode\u003eIVF-qinco2-S\u003c/code\u003e), you can optionnaly specifiy \u003ccode\u003eivf_centroids\u003c/code\u003e with a path to an IVF centroid file. See below how to generate one.\u003c/li\u003e\n\u003cli\u003eFor arguments \u003ccode\u003eds.loop\u003c/code\u003e, \u003ccode\u003eds.trainset\u003c/code\u003e, \u003ccode\u003eds.valset\u003c/code\u003e, see above (\"Using a subset of the data files\"). \u003ccode\u003eds.loop\u003c/code\u003e will set the size of an epoch.\u003c/li\u003e\n\u003cli\u003eYou can control training by overloading the learning rate (\u003ccode\u003elr\u003c/code\u003e), batch size \u003cstrong\u003eper\u003c/strong\u003e GPU (\u003ccode\u003ebatch\u003c/code\u003e), the number of epochs (\u003ccode\u003eepochs\u003c/code\u003e), and also \u003ccode\u003egrad_accumulate\u003c/code\u003e and \u003ccode\u003egrad_clip\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eYou can set \u003ccode\u003everbose\u003c/code\u003e to false to print only at the beginning and end of an epoch, instead of printing a line at each batch iteration.\u003c/li\u003e\n\u003cli\u003eThe \u003ccode\u003etensorboard\u003c/code\u003e argument can be specified to log training curves inside a directory. They can be displayed using tensorboard, see [\u003ca href=\"https://www.tensorflow.org/tensorboard](tensorboard\" rel=\"nofollow\"\u003ehttps://www.tensorflow.org/tensorboard](tensorboard\u003c/a\u003e documentation) (usually with \u003ccode\u003etensorboard --logdir=path_to_directory\u003c/code\u003e).\u003c/li\u003e\n\u003cli\u003eYou can load a previous model and resume training from it by specifying a path with \u003ccode\u003emodel\u003c/code\u003e. It should resume from the last epoch, and it will automatically set the model arguments.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eWarning\u003c/strong\u003e: The MSE error shown is the validation error, and can differ significantly from the test error (see \"Evaluating a model\") as a smaller subset is used to compute this estimate.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsage examples:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Train a qinco2-S model on deep1M, with 16 bytes. Resume if the output file already exists.\n./run.sh task=train model_args=qinco2-S db=deep1M M=16 output=runs/weights/qinco2_S-deep1M-16x8.pt resume=true\n\n# Train a very small model (qinco2-S with some overloaded arguments) on a custom dataset, with small epochs of 100_000 samples\n./run.sh task=train model_args=qinco2-S L=1 dh=32 de=64 A=4 B=4 \\\n output=my_super_small_model-my_dataset-8x8.pt \\\n trainset=my_dataset-trainset.fvecs \\\n ds.loop=100_000 ds.valset=10_000\n\n# Resume training from a previous model on a single CPU, with low verbosity\npython run.py cpu=true task=train model=runs/weights/qinco2_S-deep1M-8x8.pt output=runs/weights/qinco2_S-deep1M-8x8-2.pt db=deep1M verbose=false\n\n# Train a qinco1 model on deep1M, with 8 bytes. It doesn't use beam search or candidates pre-selection.\n./run.sh task=train model_args=qinco1 db=deep1M output=runs/weights/qinco1-deep1M-8x8.pt\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Train a qinco2-S model on deep1M, with 16 bytes. Resume if the output file already exists.\u003c/span\u003e\n./run.sh task=train model_args=qinco2-S db=deep1M M=16 output=runs/weights/qinco2_S-deep1M-16x8.pt resume=true\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Train a very small model (qinco2-S with some overloaded arguments) on a custom dataset, with small epochs of 100_000 samples\u003c/span\u003e\n./run.sh task=train model_args=qinco2-S L=1 dh=32 de=64 A=4 B=4 \\\n output=my_super_small_model-my_dataset-8x8.pt \\\n trainset=my_dataset-trainset.fvecs \\\n ds.loop=100_000 ds.valset=10_000\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Resume training from a previous model on a single CPU, with low verbosity\u003c/span\u003e\npython run.py cpu=true task=train model=runs/weights/qinco2_S-deep1M-8x8.pt output=runs/weights/qinco2_S-deep1M-8x8-2.pt db=deep1M verbose=false\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Train a qinco1 model on deep1M, with 8 bytes. It doesn't use beam search or candidates pre-selection.\u003c/span\u003e\n./run.sh task=train model_args=qinco1 db=deep1M output=runs/weights/qinco1-deep1M-8x8.pt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluating a model\u003c/h3\u003e\u003ca id=\"user-content-evaluating-a-model\" class=\"anchor\" aria-label=\"Permalink: Evaluating a model\" href=\"#evaluating-a-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluating on the test set\u003c/h4\u003e\u003ca id=\"user-content-evaluating-on-the-test-set\" class=\"anchor\" aria-label=\"Permalink: Evaluating on the test set\" href=\"#evaluating-on-the-test-set\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"./run.sh task=eval \\\n model=\u0026lt;model_path.pt\u0026gt; \\\n db=\u0026lt;database_path or db_name\u0026gt; \\\n [A=\u0026lt;A\u0026gt;] [B=\u0026lt;B\u0026gt;] \\\n [ds.db=\u0026lt;limit_for_data\u0026gt;]\"\u003e\u003cpre\u003e./run.sh task=eval \\\n model=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emodel_path.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n db=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edatabase_path or db_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [A\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eA\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [B\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eB\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [ds.db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_data\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command evaluates the MSE on the full database. It should yield MSEs comparables to the ones from the paper (Table 3).\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003emodel\u003c/code\u003e should specify a path to a model, either trained using the command above, or downloaded from the \"Pretrained checkpoints\" section.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003edb\u003c/code\u003e should be a path to the dataset, or the name of a pre-defined one (see \"Datasets\" above).\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eA\u003c/code\u003e and \u003ccode\u003eB\u003c/code\u003e can optionally be overloaded to change the run-time beam size and candidates pre-selection size. If not overloaded, the values from training are used.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eds.db\u003c/code\u003e can be used to limit the amount of data used to evaluate the model.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eUsage examples:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Gives the MSE from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam\n./run.sh task=eval model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64\n\n# Evaluates a small custom model on 100_000 samples from a custom dataset\n./run.sh task=eval model=my_super_small_model-my_dataset-8x8.pt db=my_dataset-db.fvecs ds.db=100_000\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Gives the MSE from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam\u003c/span\u003e\n./run.sh task=eval model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Evaluates a small custom model on 100_000 samples from a custom dataset\u003c/span\u003e\n./run.sh task=eval model=my_super_small_model-my_dataset-8x8.pt db=my_dataset-db.fvecs ds.db=100_000\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluating retrieval accuracy\u003c/h4\u003e\u003ca id=\"user-content-evaluating-retrieval-accuracy\" class=\"anchor\" aria-label=\"Permalink: Evaluating retrieval accuracy\" href=\"#evaluating-retrieval-accuracy\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run.py task=search \\\n model=\u0026lt;model_path.pt\u0026gt; \\\n db=\u0026lt;database_path or db_name\u0026gt; \\\n [A=\u0026lt;A\u0026gt;] [B=\u0026lt;B\u0026gt;] \\\n [ds.db=\u0026lt;limit_for_data\u0026gt;]\"\u003e\u003cpre\u003epython run.py task=search \\\n model=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emodel_path.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n db=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edatabase_path or db_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [A\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eA\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [B\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eB\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [ds.db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_data\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command returns the retrieval accuracy (R@1 from table 3, but also R@10 and R@100) on a dataset, with full decoding of the database using QINCo2.\nIt \u003cstrong\u003edoes not\u003c/strong\u003e evaluate large-scale search using the custom pipeline shown in Figure 3. Arguments are similar to the \u003ccode\u003eeval\u003c/code\u003e command.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSingle GPU process\u003c/strong\u003e: this command should be ran using a single process (\u003ccode\u003epython run.py\u003c/code\u003e), and will use a single GPU.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsage example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Gives the R@1 from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam\npython run.py task=search model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Gives the R@1 from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam\u003c/span\u003e\npython run.py task=search model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluating on the validation set\u003c/h4\u003e\u003ca id=\"user-content-evaluating-on-the-validation-set\" class=\"anchor\" aria-label=\"Permalink: Evaluating on the validation set\" href=\"#evaluating-on-the-validation-set\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"./run.sh task=eval_valset \\\n model=\u0026lt;model_path.pt\u0026gt; \\\n [db=\u0026lt;db_name\u0026gt;] [trainset=\u0026lt;trainset_path\u0026gt;] \\\n [A=\u0026lt;A\u0026gt;] [B=\u0026lt;B\u0026gt;] \\\n [ds.valset=\u0026lt;\u0026gt;]\"\u003e\u003cpre\u003e./run.sh task=eval_valset \\\n model=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emodel_path.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003edb_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003etrainset_path\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [A\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eA\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [B\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eB\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [ds.valset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command can be used to evaluate on the validation set (extracted from the training set) and get the MSE also obtained during training.\nIt works similarly to \u003ccode\u003etask=eval\u003c/code\u003e, but takes the trainset path (and optionally, thevalidation set size) as arguments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing QINCo2 with IVF\u003c/h3\u003e\u003ca id=\"user-content-using-qinco2-with-ivf\" class=\"anchor\" aria-label=\"Permalink: Using QINCo2 with IVF\" href=\"#using-qinco2-with-ivf\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuilding IVF centroids\u003c/h4\u003e\u003ca id=\"user-content-building-ivf-centroids\" class=\"anchor\" aria-label=\"Permalink: Building IVF centroids\" href=\"#building-ivf-centroids\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run.py task=ivf_centroids \\\n ivf_K=\u0026lt;IVF_codebooks_size\u0026gt; \\\n output=\u0026lt;centroids_weights.npy\u0026gt; \\\n [db=\u0026lt;db_name\u0026gt;] [trainset=\u0026lt;trainset_path\u0026gt;] \\\n [ds.trainset=100_000] [ds.valset=10_000]\"\u003e\u003cpre\u003epython run.py task=ivf_centroids \\\n ivf_K=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eIVF_codebooks_size\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n output=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003ecentroids_weights.npy\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003edb_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003etrainset_path\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [ds.trainset\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e100_000] [ds.valset\u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e10_000]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBefore using the IVF centroids, you need to create them with this command, or use one of the pre-trained centroids from below.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eivf_K\u003c/code\u003e sets the number of centroids used. In the paper, we use \u003ccode\u003eivf_K=1048576\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eoutput\u003c/code\u003e should be a \u003ccode\u003e.npy\u003c/code\u003e path.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eds.trainset\u003c/code\u003e can be used to train on a smaller set of vectors, if the training takes too long.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSingle GPU process\u003c/strong\u003e: this command should be ran using a single process (\u003ccode\u003epython run.py\u003c/code\u003e), and will use a single GPU.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsage examples:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Trains centroids 1048576 on deep1M, as in the paper. It should give a result similar to the `ivf_centroids_deep1B_1048576.npy` file.\npython run.py task=ivf_centroids ivf_K=1048576 db=deep1M output=runs/ivf_centroids/ivf_centroids-deep1M-1048576.npy\n\n# Trains only 700 centroids on a custom dataset\npython run.py task=ivf_centroids ivf_K=700 \\\n trainset=my_dataset-trainset.fvecs \\\n output=runs/ivf_centroids/ivf_centroids-my_dataset-K=700.npy\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains centroids 1048576 on deep1M, as in the paper. It should give a result similar to the `ivf_centroids_deep1B_1048576.npy` file.\u003c/span\u003e\npython run.py task=ivf_centroids ivf_K=1048576 db=deep1M output=runs/ivf_centroids/ivf_centroids-deep1M-1048576.npy\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Trains only 700 centroids on a custom dataset\u003c/span\u003e\npython run.py task=ivf_centroids ivf_K=700 \\\n trainset=my_dataset-trainset.fvecs \\\n output=runs/ivf_centroids/ivf_centroids-my_dataset-K=700.npy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing centroids when training a model\u003c/h4\u003e\u003ca id=\"user-content-using-centroids-when-training-a-model\" class=\"anchor\" aria-label=\"Permalink: Using centroids when training a model\" href=\"#using-centroids-when-training-a-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can train a model with an additional IVF first step, which use the IVF centroids with no beam search, following the same instructions as above for training.\nYou need to add the \u003ccode\u003eivf_centroids\u003c/code\u003e parameter.\nThese models can be evaluated in the same way as other model to obtain the MSE / retrieval accuracy on the database, while using only\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsage example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Train an IVF-qinco2-S model on deep1M, with 8 bytes.\n./run.sh task=train model_args=qinco2-S db=deep1M output=runs/weights/IVF-qinco2_S-deep1M-8x8.pt ivf_centroids=models/ivf_centroids-deep1M-1048576.npy\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Train an IVF-qinco2-S model on deep1M, with 8 bytes.\u003c/span\u003e\n./run.sh task=train model_args=qinco2-S db=deep1M output=runs/weights/IVF-qinco2_S-deep1M-8x8.pt ivf_centroids=models/ivf_centroids-deep1M-1048576.npy\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEncode the training set and database\u003c/h4\u003e\u003ca id=\"user-content-encode-the-training-set-and-database\" class=\"anchor\" aria-label=\"Permalink: Encode the training set and database\" href=\"#encode-the-training-set-and-database\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"./run.sh task=encode \\\n model=\u0026lt;model_path.pt\u0026gt; \\\n output=\u0026lt;encoded_db_path.npz\u0026gt; \\\n db=\u0026lt;database_path or db_name\u0026gt; \\\n [encode_trainset=\u0026lt;false\u0026gt;] \\\n [A=\u0026lt;A\u0026gt;] [B=\u0026lt;B\u0026gt;] \\\n [ds.db=\u0026lt;limit_for_data\u0026gt;] [ds.trainset=\u0026lt;limit_for_trainset\u0026gt;]\"\u003e\u003cpre\u003e./run.sh task=encode \\\n model=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emodel_path.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n output=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003eencoded_db_path.npz\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n db=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003edatabase_path or db_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [encode_trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003efalse\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [A\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eA\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [B\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eB\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [ds.db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_data\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [ds.trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_trainset\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command encodes a set of vectors using the specified QINCo2 model.\nYou should encode both the \u003cem\u003etraining set\u003c/em\u003e and the \u003cem\u003edatabase\u003c/em\u003e.\nWhen using a predefined dataset (e.g. \u003ccode\u003edb=deep1B\u003c/code\u003e), add the argument \u003ccode\u003eencode_trainset=true\u003c/code\u003e to encode the training set instead of the database.\nAs this step can take a very long time on a billion-scale database, it is recommended to launch this command with multiple GPUs available.\nIt will do a \u003cstrong\u003eparallel encoding\u003c/strong\u003e of the database, where each GPU work on a substep of it (e.g. use 100 GPUs for a 100x acceleration of the encoding process).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsage example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Encode the 1B vectors from deep1B dataset using an IVF-QINCo-S model\n# Note that we are using `db=deep1B` here instead of `db=deep1M`, to encode all 1B vectors\n./run.sh task=encode db=deep1B model=models/IVF-qinco2_S-deep1B-8x8.pt \\\n output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_db.npz\n\n# Encode the training set from deep1B\n./run.sh task=encode db=deep1B encode_trainset=true model=models/IVF-qinco2_S-deep1B-8x8.pt \\\n output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_trainset.npz\n\n# Encode the training set and database for a custom dataset\n./run.sh task=encode db=my_dataset-trainset.fvecs model=my-custom-IVF-model.pt output=my_encoded_trainset.npz\n./run.sh task=encode db=my_dataset-db.fvecs model=my-custom-IVF-model.pt output=my_encoded_db.npz\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Encode the 1B vectors from deep1B dataset using an IVF-QINCo-S model\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Note that we are using `db=deep1B` here instead of `db=deep1M`, to encode all 1B vectors\u003c/span\u003e\n./run.sh task=encode db=deep1B model=models/IVF-qinco2_S-deep1B-8x8.pt \\\n output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_db.npz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Encode the training set from deep1B\u003c/span\u003e\n./run.sh task=encode db=deep1B encode_trainset=true model=models/IVF-qinco2_S-deep1B-8x8.pt \\\n output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_trainset.npz\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Encode the training set and database for a custom dataset\u003c/span\u003e\n./run.sh task=encode db=my_dataset-trainset.fvecs model=my-custom-IVF-model.pt output=my_encoded_trainset.npz\n./run.sh task=encode db=my_dataset-db.fvecs model=my-custom-IVF-model.pt output=my_encoded_db.npz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTrain a pairwise decoder\u003c/h4\u003e\u003ca id=\"user-content-train-a-pairwise-decoder\" class=\"anchor\" aria-label=\"Permalink: Train a pairwise decoder\" href=\"#train-a-pairwise-decoder\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run.py task=train_pairwise_decoder \\\n ivf_centroids=\u0026lt;path_to_ivf_centroids\u0026gt; \\\n output=\u0026lt;path_for_pairwise_decoder.pt\u0026gt; \\\n [trainset=\u0026lt;trainset_path\u0026gt;] [db=\u0026lt;db_name\u0026gt;] \\\n encoded_trainset=\u0026lt;path_to_encoded_trainset.npz\u0026gt; \\\n [ds.trainset=\u0026lt;limit_for_trainset\u0026gt;] [ds.valset=\u0026lt;limit_for_valset\u0026gt;]\"\u003e\u003cpre\u003epython run.py task=train_pairwise_decoder \\\n ivf_centroids=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_ivf_centroids\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n output=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_for_pairwise_decoder.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003etrainset_path\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003edb_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n encoded_trainset=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_encoded_trainset.npz\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [ds.trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_trainset\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [ds.valset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_valset\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command builds a pairwise additive decoder (\"Pairwise additive decoding\", section 3.3 in the QINCo2 paper) that can be used to improve performances of large-scale search within an index (see below).\nIt requires both the encoded as well as unencoded training set, and the IVF centroids.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSingle GPU process\u003c/strong\u003e: this command should be ran using a single process (\u003ccode\u003epython run.py\u003c/code\u003e), and will use a single GPU.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsage example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Use the provided IVF centroids for deep1M and the previously encoded deep1M/deep1B training set to create a pairwise encoded\npython run.py task=train_pairwise_decoder db=deep1B \\\n ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \\\n output=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt \\\n encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Use the provided IVF centroids for deep1M and the previously encoded deep1M/deep1B training set to create a pairwise encoded\u003c/span\u003e\npython run.py task=train_pairwise_decoder db=deep1B \\\n ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \\\n output=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt \\\n encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuild a search index\u003c/h4\u003e\u003ca id=\"user-content-build-a-search-index\" class=\"anchor\" aria-label=\"Permalink: Build a search index\" href=\"#build-a-search-index\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run.py task=build_index \\\n ivf_centroids=\u0026lt;path_to_ivf_centroids\u0026gt; \\\n output=\u0026lt;paht_to_store_index.faissindex\u0026gt; \\\n [trainset=\u0026lt;trainset_path\u0026gt;] [db=\u0026lt;db_name\u0026gt;] \\\n encoded_trainset=\u0026lt;path_to_encoded_trainset.npz\u0026gt; \\\n encoded_db=\u0026lt;path_to_encoded_db.npz\u0026gt; \\\n [ds.db=\u0026lt;limit_for_data\u0026gt;] [ds.trainset=\u0026lt;limit_for_trainset\u0026gt;] [ds.valset=\u0026lt;limit_for_valset\u0026gt;]\"\u003e\u003cpre\u003epython run.py task=build_index \\\n ivf_centroids=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_ivf_centroids\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n output=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epaht_to_store_index.faissindex\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003etrainset_path\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003edb_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n encoded_trainset=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_encoded_trainset.npz\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n encoded_db=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_encoded_db.npz\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n [ds.db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_data\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [ds.trainset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_trainset\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [ds.valset\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003elimit_for_valset\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command creates a \u003ca href=\"https://github.com/facebookresearch/faiss\"\u003efaiss\u003c/a\u003e index to efficiently search within billion-scale databases, using the previously encoded database. The training set is used to train a set of AQ codebooks for the first fast approximative shortlist.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSingle GPU process\u003c/strong\u003e: this command should be ran using a single process (\u003ccode\u003epython run.py\u003c/code\u003e), and will use a single GPU.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUsage example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run.py task=build_index db=deep1B \\\n ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \\\n output=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \\\n encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz \\\n encoded_db=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_db.npz \\\n ds.db=300_000 ds.trainset=100_000 ds.valset=10_000\"\u003e\u003cpre\u003epython run.py task=build_index db=deep1B \\\n ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \\\n output=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \\\n encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz \\\n encoded_db=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_db.npz \\\n ds.db=300_000 ds.trainset=100_000 ds.valset=10_000\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSearch inside an index\u003c/h4\u003e\u003ca id=\"user-content-search-inside-an-index\" class=\"anchor\" aria-label=\"Permalink: Search inside an index\" href=\"#search-inside-an-index\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python run.py cpu=true task=search \\\n model=\u0026lt;model_path.pt\u0026gt; \\\n index=runs/index/index-qinco2s-ivf-deep1M-8x8.faissindex \\\n [queries=\u0026lt;path_to_queries\u0026gt;] [queries_gt=\u0026lt;path_to_groundtruth\u0026gt;] [db=\u0026lt;db_name\u0026gt;] \\\n [pairwise_decoder=\u0026lt;path_for_pairwise_decoder.pt\u0026gt;] \\\n [output=\u0026lt;output_logs.json\u0026gt;] [resume=\u0026lt;false/true\u0026gt;]\"\u003e\u003cpre\u003epython run.py cpu=true task=search \\\n model=\u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003emodel_path.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \\\n index=runs/index/index-qinco2s-ivf-deep1M-8x8.faissindex \\\n [queries\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003epath_to_queries\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [queries_gt\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003epath_to_groundtruth\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [db\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003edb_name\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [pairwise_decoder\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003epath_for_pairwise_decoder.pt\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] \\\n [output\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003eoutput_logs.json\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e] [resume\u003cspan class=\"pl-k\"\u003e=\u0026lt;\u003c/span\u003efalse/true\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e]\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis command search over a faiss index using the optimized search pipeline shown in Figure 3 in the paper.\nIt will explore different search parameters to find a pareto-optimal frontier for the speed/accuracy tradeoff shown in Figure 6 of the paper.\nThe model will only be used to decode elements at the end of the search pipeline.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eSingle process, on CPUs\u003c/strong\u003e: this command should be ran using a single process, and only on (up to 32) CPUs (\u003ccode\u003epython run.py cpu=true\u003c/code\u003e). Our experiments in the paper used 32 CPUs for the timing.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003epairwise_decoder\u003c/code\u003e: optional argument. If specified, the search will also explore the use (or not) of the pairwise decoder within the pipeline to increase search speed.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eoutput\u003c/code\u003e: optional argument. If specified, all the explored search settings with their corresponding accuracies and timings will be loged into the file as a JSON object.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003eresume\u003c/code\u003e if specified witht the \u003ccode\u003eoutput\u003c/code\u003e argument, will continue exploration of search settings from a previously uncompleted \u003ccode\u003esearch\u003c/code\u003e command.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe queries and ground-truth answers for those should be stored as \u003ccode\u003e(N_queries, D)\u003c/code\u003e (floats or integers) and \u003ccode\u003e(N_queries, 1)\u003c/code\u003e (integers: id of the correct nearest neighbour in the database) arrays.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eYou can instead use a default database using the \u003ccode\u003edb=\u0026lt;db_name\u0026gt;\u003c/code\u003e argument, which will automatically give the queries and desired answers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eUsage example:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Gives the search speed and accuracies within the deep1B database, for a set of search parameters.\npython run.py cpu=true task=search db=deep1B \\\n model=models/IVF-qinco2_S-deep1B-8x8.pt \\\n index=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \\\n output=runs/logs/search_results-IVF-qinco2_S-deep1B-8x8_v2.json resume=true \\\n pairwise_decoder=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Gives the search speed and accuracies within the deep1B database, for a set of search parameters.\u003c/span\u003e\npython run.py cpu=true task=search db=deep1B \\\n model=models/IVF-qinco2_S-deep1B-8x8.pt \\\n index=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \\\n output=runs/logs/search_results-IVF-qinco2_S-deep1B-8x8_v2.json resume=true \\\n pairwise_decoder=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLegal\u003c/h2\u003e\u003ca id=\"user-content-legal\" class=\"anchor\" aria-label=\"Permalink: Legal\" href=\"#legal\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eQinco2 is licenced under CC-BY-NC, please refer to the LICENSE file in the top level directory.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCopyright © Meta Platforms, Inc. See the Terms of Use and Privacy Policy for this project.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Vector Compression and Search with Improved Implicit Neural Codebooks (QINCo2)","anchor":"vector-compression-and-search-with-improved-implicit-neural-codebooks-qinco2","htmlText":"Vector Compression and Search with Improved Implicit Neural Codebooks (QINCo2)"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"Setup","anchor":"setup","htmlText":"Setup"},{"level":3,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Downloading the data","anchor":"downloading-the-data","htmlText":"Downloading the data"},{"level":4,"text":"BigANN","anchor":"bigann","htmlText":"BigANN"},{"level":4,"text":"Deep1B","anchor":"deep1b","htmlText":"Deep1B"},{"level":4,"text":"Contriever","anchor":"contriever","htmlText":"Contriever"},{"level":4,"text":"FB-ssnpp","anchor":"fb-ssnpp","htmlText":"FB-ssnpp"},{"level":2,"text":"Pretrained checkpoints","anchor":"pretrained-checkpoints","htmlText":"Pretrained checkpoints"},{"level":3,"text":"Base experiments checkpoints","anchor":"base-experiments-checkpoints","htmlText":"Base experiments checkpoints"},{"level":3,"text":"IVF models checkpoints","anchor":"ivf-models-checkpoints","htmlText":"IVF models checkpoints"},{"level":2,"text":"Usage","anchor":"usage","htmlText":"Usage"},{"level":3,"text":"Training","anchor":"training","htmlText":"Training"},{"level":3,"text":"Evaluating a model","anchor":"evaluating-a-model","htmlText":"Evaluating a model"},{"level":4,"text":"Evaluating on the test set","anchor":"evaluating-on-the-test-set","htmlText":"Evaluating on the test set"},{"level":4,"text":"Evaluating retrieval accuracy","anchor":"evaluating-retrieval-accuracy","htmlText":"Evaluating retrieval accuracy"},{"level":4,"text":"Evaluating on the validation set","anchor":"evaluating-on-the-validation-set","htmlText":"Evaluating on the validation set"},{"level":3,"text":"Using QINCo2 with IVF","anchor":"using-qinco2-with-ivf","htmlText":"Using QINCo2 with IVF"},{"level":4,"text":"Building IVF centroids","anchor":"building-ivf-centroids","htmlText":"Building IVF centroids"},{"level":4,"text":"Using centroids when training a model","anchor":"using-centroids-when-training-a-model","htmlText":"Using centroids when training a model"},{"level":4,"text":"Encode the training set and database","anchor":"encode-the-training-set-and-database","htmlText":"Encode the training set and database"},{"level":4,"text":"Train a pairwise decoder","anchor":"train-a-pairwise-decoder","htmlText":"Train a pairwise decoder"},{"level":4,"text":"Build a search index","anchor":"build-a-search-index","htmlText":"Build a search index"},{"level":4,"text":"Search inside an index","anchor":"search-inside-an-index","htmlText":"Search inside an index"},{"level":2,"text":"Legal","anchor":"legal","htmlText":"Legal"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Ffacebookresearch%2FQinco"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":"Qinco","refName":"main","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of 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class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Vector Compression and Search with Improved Implicit Neural Codebooks (QINCo2)</h1><a id="user-content-vector-compression-and-search-with-improved-implicit-neural-codebooks-qinco2" class="anchor" aria-label="Permalink: Vector Compression and Search with Improved Implicit Neural Codebooks (QINCo2)" href="#vector-compression-and-search-with-improved-implicit-neural-codebooks-qinco2"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><em>This repository has been updated with the code from QINCo2. To access the original QINCo1 code, see <a href="/facebookresearch/Qinco/blob/main/qinco_v1/README.md">qinco_v1 directory</a>.</em></p> <p dir="auto">This code repository corresponds to the paper <a href="https://arxiv.org/abs/2501.03078" rel="nofollow">QINCo2: Vector Compression and Search with Improved Implicit Neural Codebooks</a>, introducing an improved quantization process over QINCo. We also include code reproducing the ICML'24 paper <a href="https://arxiv.org/pdf/2401.14732.pdf" rel="nofollow">Residual Quantization with Implicit Neural Codebooks</a> (<a href="/facebookresearch/Qinco/blob/main/qinco_v1/README.md">qinco_v1 directory</a>), in which Quantization with Implicit Neural Codebooks (QINCo) was proposed. Please read both papers to learn about QINCo and QINCo2.</p> <p dir="auto">QINCo is a neurally-augmented algorithm for multi-codebook vector quantization, specifically residual quantization (RQ). Instead of using a fixed codebook per quantization step, QINCo uses a neural network to predict a codebook for the next quantization step, conditioned upon the quantized vector so far. In other words, the codebooks to be used depend on the Voronoi cells selected previously. This greatly enhances the capacity of the compression system, without the need to store more codebook vectors explicitly. An additional advantage of QINCo is its modularity. Thanks to training each quantization step with its own quantization error, the trained system for a certain compression rate, can also be exploited for lower compression rates, making QINCo a dynamic rate quantizer.</p> <p dir="auto">QINCo2 introduces several key novelties:</p> <ul dir="auto"> <li>A fast approximate encoding method, yielding similar MSE for a much faster training and encoding time.</li> <li>Integration of beam search to the encoding process, reaching much lower compression errors than QINCo1 for a similar encoding time when combined to approximate encoding.</li> <li>A new (optional) module to the large-scale retrieval pipeline, improving accuracy using a pairwise decoder.</li> <li>An overall upgrade of the architecture and training process.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you use QINCo in a research work please cite our paper:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@misc{vallaeys2025qinco2, title={Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks}, author={Théophane Vallaeys and Matthew Muckley and Jakob Verbeek and Matthijs Douze}, year={2025}, eprint={2501.03078}, archivePrefix={arXiv}, primaryClass={cs.LG} }"><pre class="notranslate"><code>@misc{vallaeys2025qinco2, title={Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks}, author={Théophane Vallaeys and Matthew Muckley and Jakob Verbeek and Matthijs Douze}, year={2025}, eprint={2501.03078}, archivePrefix={arXiv}, primaryClass={cs.LG} } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Setup</h2><a id="user-content-setup" class="anchor" aria-label="Permalink: Setup" href="#setup"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Installation</h3><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">QINCo2 requires python3 and packages from the <code>requirements.txt</code> file. They can be installed using conda:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/facebookresearch/Qinco2 cd Qinco2 conda env create -f environment.yml"><pre>git clone https://github.com/facebookresearch/Qinco2 <span class="pl-c1">cd</span> Qinco2 conda env create -f environment.yml</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Downloading the data</h3><a id="user-content-downloading-the-data" class="anchor" aria-label="Permalink: Downloading the data" href="#downloading-the-data"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Download the datasets used in the paper by running the corresponding bash scripts:</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">BigANN</h4><a id="user-content-bigann" class="anchor" aria-label="Permalink: BigANN" href="#bigann"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="./data/bigann/download_data.sh"><pre class="notranslate"><code>./data/bigann/download_data.sh </code></pre></div> <p dir="auto">or if you have limited storage space, you can also download only the first 1M database vectors, and the first 10M training vectors using:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="./data/bigann/download_data.sh -small"><pre class="notranslate"><code>./data/bigann/download_data.sh -small </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Deep1B</h4><a id="user-content-deep1b" class="anchor" aria-label="Permalink: Deep1B" href="#deep1b"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="./data/deep1b/download_data.sh"><pre class="notranslate"><code>./data/deep1b/download_data.sh </code></pre></div> <p dir="auto">or if you have limited storage space, you can also download only the first 1M database vectors, and the first 10M training vectors using:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="./data/deep1b/download_data.sh -small"><pre class="notranslate"><code>./data/deep1b/download_data.sh -small </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Contriever</h4><a id="user-content-contriever" class="anchor" aria-label="Permalink: Contriever" href="#contriever"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="./data/contriever/download_data.sh"><pre class="notranslate"><code>./data/contriever/download_data.sh </code></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">FB-ssnpp</h4><a id="user-content-fb-ssnpp" class="anchor" aria-label="Permalink: FB-ssnpp" href="#fb-ssnpp"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="./data/fb_ssnpp/download_data.sh"><pre class="notranslate"><code>./data/fb_ssnpp/download_data.sh </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Pretrained checkpoints</h2><a id="user-content-pretrained-checkpoints" class="anchor" aria-label="Permalink: Pretrained checkpoints" href="#pretrained-checkpoints"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Base experiments checkpoints</h3><a id="user-content-base-experiments-checkpoints" class="anchor" aria-label="Permalink: Base experiments checkpoints" href="#base-experiments-checkpoints"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Below are the checkpoints for the QINCo1 and QINCo2-L models, trained on all four datasets. Instructions below show how to use and evaluate them. The commands suppose that the files are stored inside the <code>models/</code> directory.</p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th><strong>BigANN1M</strong></th> <th><strong>Deep1M</strong></th> <th><strong>Contriever1M</strong></th> <th><strong>FB-ssnpp1M</strong></th> </tr> </thead> <tbody> <tr> <td><strong>Qinco1 (8 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-bigann1M-8x8.pt" rel="nofollow">qinco1-bigann1M-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-deep1M-8x8.pt" rel="nofollow">qinco1-deep1M-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-contriever1M-8x8.pt" rel="nofollow">qinco1-contriever1M-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-FB_ssnpp1M-8x8.pt" rel="nofollow">qinco1-FB_ssnpp1M-8x8.pt</a></td> </tr> <tr> <td><strong>Qinco1 (16 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-bigann1M-16x8.pt" rel="nofollow">qinco1-bigann1M-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-deep1M-16x8.pt" rel="nofollow">qinco1-deep1M-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-contriever1M-16x8.pt" rel="nofollow">qinco1-contriever1M-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco1-FB_ssnpp1M-16x8.pt" rel="nofollow">qinco1-FB_ssnpp1M-16x8.pt</a></td> </tr> <tr> <td><strong>Qinco2-L (8 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-bigann1M-8x8.pt" rel="nofollow">qinco2_L-bigann1M-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-deep1M-8x8.pt" rel="nofollow">qinco2_L-deep1M-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-contriever1M-8x8.pt" rel="nofollow">qinco2_L-contriever1M-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-FB_ssnpp1M-8x8.pt" rel="nofollow">qinco2_L-FB_ssnpp1M-8x8.pt</a></td> </tr> <tr> <td><strong>Qinco2-L (16 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-bigann1M-16x8.pt" rel="nofollow">qinco2_L-bigann1M-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-deep1M-16x8.pt" rel="nofollow">qinco2_L-deep1M-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-contriever1M-16x8.pt" rel="nofollow">qinco2_L-contriever1M-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/qinco2_L-FB_ssnpp1M-16x8.pt" rel="nofollow">qinco2_L-FB_ssnpp1M-16x8.pt</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">IVF models checkpoints</h3><a id="user-content-ivf-models-checkpoints" class="anchor" aria-label="Permalink: IVF models checkpoints" href="#ivf-models-checkpoints"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">These models are trained with and additional codebook of <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="58d03d87f954d6c3122322a409725efc">$K_{IVF}=2^20=1048576$</math-renderer> codewords, corresponding to the IVF step. They can be used to evaluate large-scale search.</p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th><strong>BigANN1B</strong></th> <th><strong>Deep1B</strong></th> </tr> </thead> <tbody> <tr> <td><strong>Qinco2-S (8 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-bigann1B-8x8.pt" rel="nofollow">IVF-qinco2_S-bigann1B-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-deep1B-8x8.pt" rel="nofollow">IVF-qinco2_S-deep1B-8x8.pt</a></td> </tr> <tr> <td><strong>Qinco2-S (16 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-bigann1B-16x8.pt" rel="nofollow">IVF-qinco2_S-bigann1B-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-deep1B-16x8.pt" rel="nofollow">IVF-qinco2_S-deep1B-16x8.pt</a></td> </tr> <tr> <td><strong>Qinco2-S (32 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-bigann1B-32x8.pt" rel="nofollow">IVF-qinco2_S-bigann1B-32x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_S-deep1B-32x8.pt" rel="nofollow">IVF-qinco2_S-deep1B-32x8.pt</a></td> </tr> <tr> <td><strong>Qinco2-M (8 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-bigann1B-8x8.pt" rel="nofollow">IVF-qinco2_M-bigann1B-8x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-deep1B-8x8.pt" rel="nofollow">IVF-qinco2_M-deep1B-8x8.pt</a></td> </tr> <tr> <td><strong>Qinco2-M (16 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-bigann1B-16x8.pt" rel="nofollow">IVF-qinco2_M-bigann1B-16x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-deep1B-16x8.pt" rel="nofollow">IVF-qinco2_M-deep1B-16x8.pt</a></td> </tr> <tr> <td><strong>Qinco2-M (32 bytes)</strong></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-bigann1B-32x8.pt" rel="nofollow">IVF-qinco2_M-bigann1B-32x8.pt</a></td> <td><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/IVF-qinco2_M-deep1B-32x8.pt" rel="nofollow">IVF-qinco2_M-deep1B-32x8.pt</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">We also provide the IVF centroids used to create these models:</p> <ul dir="auto"> <li><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/ivf_centroids_bigann1B_1048576.npy" rel="nofollow">ivf_centroids_bigann1B_1048576.npy</a></li> <li><a href="https://dl.fbaipublicfiles.com/QINCo/qinco2-models/ivf_centroids_deep1B_1048576.npy" rel="nofollow">ivf_centroids_deep1B_1048576.npy</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Usage</h2><a id="user-content-usage" class="anchor" aria-label="Permalink: Usage" href="#usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Every command uses the <code>run.py</code> endpoint. It can be run on multiple GPUs with <a href="https://huggingface.co/docs/accelerate/index" rel="nofollow">accelerate</a> using the <code>./run.sh</code> script. If you are familiar with accelerate, you can use it directly to launch <code>run.py</code>. Command-line arguments are parsed using the <a href="https://hydra.cc/" rel="nofollow">Hydra</a> format. You can find the default configuration and all overloadable parameters inside the <code>config/qinco_cfg.yaml</code> file.</p> <p dir="auto"><strong>Running on an single GPU or on CPU</strong>: In any of the following commands, you can replace <code>./run.sh</code> by <code>python run.py</code> to use a single GPU. Set the <code>cpu</code> argument to true (<code>python run.py cpu=true</code>) to run on CPU instead.</p> <p dir="auto"><strong>Datasets</strong>: for all commands below, you can either use your own data using the <code>db</code>, <code>trainset</code>, <code>queries</code> and <code>queries_gt</code> arguments, or use one of the <em>default</em> dataset that is used within the paper. To use one of theses datasets, replace these arguments by <code>db=<name of the dataset></code>. The paths will be automatically populated. Be sure to download the corresponding datasets beforehand (see above).</p> <p dir="auto"><strong>Available default datasets</strong>: <code>db=FB_ssnpp1M</code>, <code>db=contriever1M</code>, <code>db=bigann1M</code>, <code>db=bigann1B</code>, <code>db=bigann1B</code>, <code>db=deep1B</code>. The <code>1M</code> datasets are intended to be used for most tasks, while the <code>1B</code> datasets should only be used to build and search a index for large-scale search.</p> <p dir="auto"><strong>Using your own data</strong>: you will need at least a vector database (<code>db</code>) and a set of training vectors (<code>trainset</code>) to train and evaluate the MSE of the model. Available data formats are: <code>bvecs</code>, <code>fvecs</code>, <code>ivecs</code>, <code>npy</code>, and the format should be a single matrix of dimensions <code>(N_samples, D)</code>. During training, the last 10,000 vectors of the training set will set appart at the validation set. The database is the test set. Additionally, for nearest-neighbour search, you need a set of queries (<code>queries</code>, matrix of dimension <code>(N_queries, D)</code>) and the id of their answers in the database (<code>queries_gt</code>, matrix of dimension <code>(N_queries, 1)</code>).</p> <p dir="auto"><strong>Using a subset of the data files</strong>: you can use only a subset of the training set and/or database, which can be usefull for testing or using limited data. Use <code>ds.trainset=<...></code> and <code>ds.db=<...></code> to limit their size. Similarly, you can control the number of validation samples extracted from the training set with <code>ds.valset=<...></code>, and the number of samples from the training set used at each epoch with <code>ds.loop=<...></code>. If not specified, the default values are <code>ds.valset=10_000</code> and <code>ds.loop=10_000_000</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Training</h3><a id="user-content-training" class="anchor" aria-label="Permalink: Training" href="#training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="./run.sh task=train \ output=<model_weights.pt> \ [resume=false] \ [model_args=<model preset>] \ [L=<L> dh=<dh> M=<M> K=<K>] [A=<A>] [B=<B>] [de=<de>] \ [db=<db_name>] [trainset=<trainset_path>] \ [ivf_centroids=<path_to_ivf_centroids>] \ [ds.loop=<epoch_size>] [ds.trainset=<trainset_max_size>] [ds.valset=<validation_set_max_size>] \ [lr=0.0008] [batch=1024] [epochs=60] [grad_accumulate=1] [grad_clip=0.1] \ [verbose=true] \ [tensorboard=<path_to_directory>] \ [model=<path_to_resume_from>]"><pre>./run.sh task=train \ output=<span class="pl-k"><</span>model_weights.pt<span class="pl-k">></span> \ [resume<span class="pl-k">=</span>false] \ [model_args<span class="pl-k">=<</span>model preset<span class="pl-k">></span>] \ [L<span class="pl-k">=<</span>L<span class="pl-k">></span> dh<span class="pl-k">=<</span>dh<span class="pl-k">></span> M<span class="pl-k">=<</span>M<span class="pl-k">></span> K<span class="pl-k">=<</span>K<span class="pl-k">></span>] [A<span class="pl-k">=<</span>A<span class="pl-k">></span>] [B<span class="pl-k">=<</span>B<span class="pl-k">></span>] [de<span class="pl-k">=<</span>de<span class="pl-k">></span>] \ [db<span class="pl-k">=<</span>db_name<span class="pl-k">></span>] [trainset<span class="pl-k">=<</span>trainset_path<span class="pl-k">></span>] \ [ivf_centroids<span class="pl-k">=<</span>path_to_ivf_centroids<span class="pl-k">></span>] \ [ds.loop<span class="pl-k">=<</span>epoch_size<span class="pl-k">></span>] [ds.trainset<span class="pl-k">=<</span>trainset_max_size<span class="pl-k">></span>] [ds.valset<span class="pl-k">=<</span>validation_set_max_size<span class="pl-k">></span>] \ [lr<span class="pl-k">=</span>0.0008] [batch<span class="pl-k">=</span>1024] [epochs<span class="pl-k">=</span>60] [grad_accumulate<span class="pl-k">=</span>1] [grad_clip<span class="pl-k">=</span>0.1] \ [verbose<span class="pl-k">=</span>true] \ [tensorboard<span class="pl-k">=<</span>path_to_directory<span class="pl-k">></span>] \ [model<span class="pl-k">=<</span>path_to_resume_from<span class="pl-k">></span>]</pre></div> <p dir="auto">This command trains a new model and accepts a set of required and optional arguments:</p> <ul dir="auto"> <li>The <code>output</code> argument should specify the path to the <code>.pt</code> checkpoint file that will be created during training. <ul dir="auto"> <li>If the <code>resume</code> argument is set to <code>true</code> (default is <code>false</code>), the training will resume from the <code>output</code> path <em>if it exists</em>, or starts from scracth otherwise.</li> </ul> </li> <li>You need to specify the models arguments <code>K</code> (codebook size, set to 256 in the paper), <code>M</code> (number of QINCo2 steps, and bytes if <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="58d03d87f954d6c3122322a409725efc">$K=256$</math-renderer>), <code>dh</code> (hidden dimension <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="58d03d87f954d6c3122322a409725efc">$d_h$</math-renderer>), <code>de</code> (embedding dimension <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="58d03d87f954d6c3122322a409725efc">$d_e$</math-renderer>), <code>L</code> (number of residual blocks in each step), <code>A</code> (number of fast pre-selected candidates) and <code>B</code> (size of beam search). See the paper for reference. <ul dir="auto"> <li>The values of <code>A</code>, <code>B</code> and <code>de</code> can be set to <code>0</code> or <code>null</code> (default value) to disable these components. <code>A=null</code> implies no beam search, <code>B=0</code> implies no candidates pre-selection, and <code>de</code> implies an embedding dimension of same size as the data dimension.</li> <li>You can also use a <strong>preset</strong>, using <code>model_args=<model-name></code>. Available models are: <code>qinco1</code>, <code>qinco2-S</code>, <code>qinco2-M</code>, <code>qinco2-L</code>, which will set all these arguments if not specified manually.</li> </ul> </li> <li>You need to specify a path to the training data file the <code>trainset</code> argument, or using <code>db=<db_name></code> (see "Datasets" above).</li> <li>If you want to train an IVF model (e.g. <code>IVF-qinco2-S</code>), you can optionnaly specifiy <code>ivf_centroids</code> with a path to an IVF centroid file. See below how to generate one.</li> <li>For arguments <code>ds.loop</code>, <code>ds.trainset</code>, <code>ds.valset</code>, see above ("Using a subset of the data files"). <code>ds.loop</code> will set the size of an epoch.</li> <li>You can control training by overloading the learning rate (<code>lr</code>), batch size <strong>per</strong> GPU (<code>batch</code>), the number of epochs (<code>epochs</code>), and also <code>grad_accumulate</code> and <code>grad_clip</code>.</li> <li>You can set <code>verbose</code> to false to print only at the beginning and end of an epoch, instead of printing a line at each batch iteration.</li> <li>The <code>tensorboard</code> argument can be specified to log training curves inside a directory. They can be displayed using tensorboard, see [<a href="https://www.tensorflow.org/tensorboard](tensorboard" rel="nofollow">https://www.tensorflow.org/tensorboard](tensorboard</a> documentation) (usually with <code>tensorboard --logdir=path_to_directory</code>).</li> <li>You can load a previous model and resume training from it by specifying a path with <code>model</code>. It should resume from the last epoch, and it will automatically set the model arguments.</li> </ul> <p dir="auto"><strong>Warning</strong>: The MSE error shown is the validation error, and can differ significantly from the test error (see "Evaluating a model") as a smaller subset is used to compute this estimate.</p> <p dir="auto">Usage examples:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Train a qinco2-S model on deep1M, with 16 bytes. Resume if the output file already exists. ./run.sh task=train model_args=qinco2-S db=deep1M M=16 output=runs/weights/qinco2_S-deep1M-16x8.pt resume=true # Train a very small model (qinco2-S with some overloaded arguments) on a custom dataset, with small epochs of 100_000 samples ./run.sh task=train model_args=qinco2-S L=1 dh=32 de=64 A=4 B=4 \ output=my_super_small_model-my_dataset-8x8.pt \ trainset=my_dataset-trainset.fvecs \ ds.loop=100_000 ds.valset=10_000 # Resume training from a previous model on a single CPU, with low verbosity python run.py cpu=true task=train model=runs/weights/qinco2_S-deep1M-8x8.pt output=runs/weights/qinco2_S-deep1M-8x8-2.pt db=deep1M verbose=false # Train a qinco1 model on deep1M, with 8 bytes. It doesn't use beam search or candidates pre-selection. ./run.sh task=train model_args=qinco1 db=deep1M output=runs/weights/qinco1-deep1M-8x8.pt"><pre><span class="pl-c"><span class="pl-c">#</span> Train a qinco2-S model on deep1M, with 16 bytes. Resume if the output file already exists.</span> ./run.sh task=train model_args=qinco2-S db=deep1M M=16 output=runs/weights/qinco2_S-deep1M-16x8.pt resume=true <span class="pl-c"><span class="pl-c">#</span> Train a very small model (qinco2-S with some overloaded arguments) on a custom dataset, with small epochs of 100_000 samples</span> ./run.sh task=train model_args=qinco2-S L=1 dh=32 de=64 A=4 B=4 \ output=my_super_small_model-my_dataset-8x8.pt \ trainset=my_dataset-trainset.fvecs \ ds.loop=100_000 ds.valset=10_000 <span class="pl-c"><span class="pl-c">#</span> Resume training from a previous model on a single CPU, with low verbosity</span> python run.py cpu=true task=train model=runs/weights/qinco2_S-deep1M-8x8.pt output=runs/weights/qinco2_S-deep1M-8x8-2.pt db=deep1M verbose=false <span class="pl-c"><span class="pl-c">#</span> Train a qinco1 model on deep1M, with 8 bytes. It doesn't use beam search or candidates pre-selection.</span> ./run.sh task=train model_args=qinco1 db=deep1M output=runs/weights/qinco1-deep1M-8x8.pt</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Evaluating a model</h3><a id="user-content-evaluating-a-model" class="anchor" aria-label="Permalink: Evaluating a model" href="#evaluating-a-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Evaluating on the test set</h4><a id="user-content-evaluating-on-the-test-set" class="anchor" aria-label="Permalink: Evaluating on the test set" href="#evaluating-on-the-test-set"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="./run.sh task=eval \ model=<model_path.pt> \ db=<database_path or db_name> \ [A=<A>] [B=<B>] \ [ds.db=<limit_for_data>]"><pre>./run.sh task=eval \ model=<span class="pl-k"><</span>model_path.pt<span class="pl-k">></span> \ db=<span class="pl-k"><</span>database_path or db_name<span class="pl-k">></span> \ [A<span class="pl-k">=<</span>A<span class="pl-k">></span>] [B<span class="pl-k">=<</span>B<span class="pl-k">></span>] \ [ds.db<span class="pl-k">=<</span>limit_for_data<span class="pl-k">></span>]</pre></div> <p dir="auto">This command evaluates the MSE on the full database. It should yield MSEs comparables to the ones from the paper (Table 3).</p> <ul dir="auto"> <li><code>model</code> should specify a path to a model, either trained using the command above, or downloaded from the "Pretrained checkpoints" section.</li> <li><code>db</code> should be a path to the dataset, or the name of a pre-defined one (see "Datasets" above).</li> <li><code>A</code> and <code>B</code> can optionally be overloaded to change the run-time beam size and candidates pre-selection size. If not overloaded, the values from training are used.</li> <li><code>ds.db</code> can be used to limit the amount of data used to evaluate the model.</li> </ul> <p dir="auto">Usage examples:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Gives the MSE from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam ./run.sh task=eval model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64 # Evaluates a small custom model on 100_000 samples from a custom dataset ./run.sh task=eval model=my_super_small_model-my_dataset-8x8.pt db=my_dataset-db.fvecs ds.db=100_000"><pre><span class="pl-c"><span class="pl-c">#</span> Gives the MSE from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam</span> ./run.sh task=eval model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64 <span class="pl-c"><span class="pl-c">#</span> Evaluates a small custom model on 100_000 samples from a custom dataset</span> ./run.sh task=eval model=my_super_small_model-my_dataset-8x8.pt db=my_dataset-db.fvecs ds.db=100_000</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Evaluating retrieval accuracy</h4><a id="user-content-evaluating-retrieval-accuracy" class="anchor" aria-label="Permalink: Evaluating retrieval accuracy" href="#evaluating-retrieval-accuracy"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run.py task=search \ model=<model_path.pt> \ db=<database_path or db_name> \ [A=<A>] [B=<B>] \ [ds.db=<limit_for_data>]"><pre>python run.py task=search \ model=<span class="pl-k"><</span>model_path.pt<span class="pl-k">></span> \ db=<span class="pl-k"><</span>database_path or db_name<span class="pl-k">></span> \ [A<span class="pl-k">=<</span>A<span class="pl-k">></span>] [B<span class="pl-k">=<</span>B<span class="pl-k">></span>] \ [ds.db<span class="pl-k">=<</span>limit_for_data<span class="pl-k">></span>]</pre></div> <p dir="auto">This command returns the retrieval accuracy (R@1 from table 3, but also R@10 and R@100) on a dataset, with full decoding of the database using QINCo2. It <strong>does not</strong> evaluate large-scale search using the custom pipeline shown in Figure 3. Arguments are similar to the <code>eval</code> command.</p> <p dir="auto"><strong>Single GPU process</strong>: this command should be ran using a single process (<code>python run.py</code>), and will use a single GPU.</p> <p dir="auto">Usage example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Gives the R@1 from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam python run.py task=search model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64"><pre><span class="pl-c"><span class="pl-c">#</span> Gives the R@1 from the paper for the QINCo2-L model on bigann1M (8 bytes) with a larger beam</span> python run.py task=search model=models/qinco2_L-bigann1M-8x8.pt db=bigann1M A=32 B=64</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Evaluating on the validation set</h4><a id="user-content-evaluating-on-the-validation-set" class="anchor" aria-label="Permalink: Evaluating on the validation set" href="#evaluating-on-the-validation-set"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="./run.sh task=eval_valset \ model=<model_path.pt> \ [db=<db_name>] [trainset=<trainset_path>] \ [A=<A>] [B=<B>] \ [ds.valset=<>]"><pre>./run.sh task=eval_valset \ model=<span class="pl-k"><</span>model_path.pt<span class="pl-k">></span> \ [db<span class="pl-k">=<</span>db_name<span class="pl-k">></span>] [trainset<span class="pl-k">=<</span>trainset_path<span class="pl-k">></span>] \ [A<span class="pl-k">=<</span>A<span class="pl-k">></span>] [B<span class="pl-k">=<</span>B<span class="pl-k">></span>] \ [ds.valset<span class="pl-k">=<></span>]</pre></div> <p dir="auto">This command can be used to evaluate on the validation set (extracted from the training set) and get the MSE also obtained during training. It works similarly to <code>task=eval</code>, but takes the trainset path (and optionally, thevalidation set size) as arguments.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Using QINCo2 with IVF</h3><a id="user-content-using-qinco2-with-ivf" class="anchor" aria-label="Permalink: Using QINCo2 with IVF" href="#using-qinco2-with-ivf"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Building IVF centroids</h4><a id="user-content-building-ivf-centroids" class="anchor" aria-label="Permalink: Building IVF centroids" href="#building-ivf-centroids"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run.py task=ivf_centroids \ ivf_K=<IVF_codebooks_size> \ output=<centroids_weights.npy> \ [db=<db_name>] [trainset=<trainset_path>] \ [ds.trainset=100_000] [ds.valset=10_000]"><pre>python run.py task=ivf_centroids \ ivf_K=<span class="pl-k"><</span>IVF_codebooks_size<span class="pl-k">></span> \ output=<span class="pl-k"><</span>centroids_weights.npy<span class="pl-k">></span> \ [db<span class="pl-k">=<</span>db_name<span class="pl-k">></span>] [trainset<span class="pl-k">=<</span>trainset_path<span class="pl-k">></span>] \ [ds.trainset<span class="pl-k">=</span>100_000] [ds.valset<span class="pl-k">=</span>10_000]</pre></div> <p dir="auto">Before using the IVF centroids, you need to create them with this command, or use one of the pre-trained centroids from below.</p> <ul dir="auto"> <li><code>ivf_K</code> sets the number of centroids used. In the paper, we use <code>ivf_K=1048576</code>.</li> <li><code>output</code> should be a <code>.npy</code> path.</li> <li><code>ds.trainset</code> can be used to train on a smaller set of vectors, if the training takes too long.</li> </ul> <p dir="auto"><strong>Single GPU process</strong>: this command should be ran using a single process (<code>python run.py</code>), and will use a single GPU.</p> <p dir="auto">Usage examples:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Trains centroids 1048576 on deep1M, as in the paper. It should give a result similar to the `ivf_centroids_deep1B_1048576.npy` file. python run.py task=ivf_centroids ivf_K=1048576 db=deep1M output=runs/ivf_centroids/ivf_centroids-deep1M-1048576.npy # Trains only 700 centroids on a custom dataset python run.py task=ivf_centroids ivf_K=700 \ trainset=my_dataset-trainset.fvecs \ output=runs/ivf_centroids/ivf_centroids-my_dataset-K=700.npy"><pre><span class="pl-c"><span class="pl-c">#</span> Trains centroids 1048576 on deep1M, as in the paper. It should give a result similar to the `ivf_centroids_deep1B_1048576.npy` file.</span> python run.py task=ivf_centroids ivf_K=1048576 db=deep1M output=runs/ivf_centroids/ivf_centroids-deep1M-1048576.npy <span class="pl-c"><span class="pl-c">#</span> Trains only 700 centroids on a custom dataset</span> python run.py task=ivf_centroids ivf_K=700 \ trainset=my_dataset-trainset.fvecs \ output=runs/ivf_centroids/ivf_centroids-my_dataset-K=700.npy</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Using centroids when training a model</h4><a id="user-content-using-centroids-when-training-a-model" class="anchor" aria-label="Permalink: Using centroids when training a model" href="#using-centroids-when-training-a-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can train a model with an additional IVF first step, which use the IVF centroids with no beam search, following the same instructions as above for training. You need to add the <code>ivf_centroids</code> parameter. These models can be evaluated in the same way as other model to obtain the MSE / retrieval accuracy on the database, while using only</p> <p dir="auto">Usage example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Train an IVF-qinco2-S model on deep1M, with 8 bytes. ./run.sh task=train model_args=qinco2-S db=deep1M output=runs/weights/IVF-qinco2_S-deep1M-8x8.pt ivf_centroids=models/ivf_centroids-deep1M-1048576.npy"><pre><span class="pl-c"><span class="pl-c">#</span> Train an IVF-qinco2-S model on deep1M, with 8 bytes.</span> ./run.sh task=train model_args=qinco2-S db=deep1M output=runs/weights/IVF-qinco2_S-deep1M-8x8.pt ivf_centroids=models/ivf_centroids-deep1M-1048576.npy</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Encode the training set and database</h4><a id="user-content-encode-the-training-set-and-database" class="anchor" aria-label="Permalink: Encode the training set and database" href="#encode-the-training-set-and-database"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="./run.sh task=encode \ model=<model_path.pt> \ output=<encoded_db_path.npz> \ db=<database_path or db_name> \ [encode_trainset=<false>] \ [A=<A>] [B=<B>] \ [ds.db=<limit_for_data>] [ds.trainset=<limit_for_trainset>]"><pre>./run.sh task=encode \ model=<span class="pl-k"><</span>model_path.pt<span class="pl-k">></span> \ output=<span class="pl-k"><</span>encoded_db_path.npz<span class="pl-k">></span> \ db=<span class="pl-k"><</span>database_path or db_name<span class="pl-k">></span> \ [encode_trainset<span class="pl-k">=<</span>false<span class="pl-k">></span>] \ [A<span class="pl-k">=<</span>A<span class="pl-k">></span>] [B<span class="pl-k">=<</span>B<span class="pl-k">></span>] \ [ds.db<span class="pl-k">=<</span>limit_for_data<span class="pl-k">></span>] [ds.trainset<span class="pl-k">=<</span>limit_for_trainset<span class="pl-k">></span>]</pre></div> <p dir="auto">This command encodes a set of vectors using the specified QINCo2 model. You should encode both the <em>training set</em> and the <em>database</em>. When using a predefined dataset (e.g. <code>db=deep1B</code>), add the argument <code>encode_trainset=true</code> to encode the training set instead of the database. As this step can take a very long time on a billion-scale database, it is recommended to launch this command with multiple GPUs available. It will do a <strong>parallel encoding</strong> of the database, where each GPU work on a substep of it (e.g. use 100 GPUs for a 100x acceleration of the encoding process).</p> <p dir="auto">Usage example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Encode the 1B vectors from deep1B dataset using an IVF-QINCo-S model # Note that we are using `db=deep1B` here instead of `db=deep1M`, to encode all 1B vectors ./run.sh task=encode db=deep1B model=models/IVF-qinco2_S-deep1B-8x8.pt \ output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_db.npz # Encode the training set from deep1B ./run.sh task=encode db=deep1B encode_trainset=true model=models/IVF-qinco2_S-deep1B-8x8.pt \ output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_trainset.npz # Encode the training set and database for a custom dataset ./run.sh task=encode db=my_dataset-trainset.fvecs model=my-custom-IVF-model.pt output=my_encoded_trainset.npz ./run.sh task=encode db=my_dataset-db.fvecs model=my-custom-IVF-model.pt output=my_encoded_db.npz"><pre><span class="pl-c"><span class="pl-c">#</span> Encode the 1B vectors from deep1B dataset using an IVF-QINCo-S model</span> <span class="pl-c"><span class="pl-c">#</span> Note that we are using `db=deep1B` here instead of `db=deep1M`, to encode all 1B vectors</span> ./run.sh task=encode db=deep1B model=models/IVF-qinco2_S-deep1B-8x8.pt \ output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_db.npz <span class="pl-c"><span class="pl-c">#</span> Encode the training set from deep1B</span> ./run.sh task=encode db=deep1B encode_trainset=true model=models/IVF-qinco2_S-deep1B-8x8.pt \ output=runs/encoded_db/IVF-qinco2_S-deep1B-8x8_trainset.npz <span class="pl-c"><span class="pl-c">#</span> Encode the training set and database for a custom dataset</span> ./run.sh task=encode db=my_dataset-trainset.fvecs model=my-custom-IVF-model.pt output=my_encoded_trainset.npz ./run.sh task=encode db=my_dataset-db.fvecs model=my-custom-IVF-model.pt output=my_encoded_db.npz</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Train a pairwise decoder</h4><a id="user-content-train-a-pairwise-decoder" class="anchor" aria-label="Permalink: Train a pairwise decoder" href="#train-a-pairwise-decoder"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run.py task=train_pairwise_decoder \ ivf_centroids=<path_to_ivf_centroids> \ output=<path_for_pairwise_decoder.pt> \ [trainset=<trainset_path>] [db=<db_name>] \ encoded_trainset=<path_to_encoded_trainset.npz> \ [ds.trainset=<limit_for_trainset>] [ds.valset=<limit_for_valset>]"><pre>python run.py task=train_pairwise_decoder \ ivf_centroids=<span class="pl-k"><</span>path_to_ivf_centroids<span class="pl-k">></span> \ output=<span class="pl-k"><</span>path_for_pairwise_decoder.pt<span class="pl-k">></span> \ [trainset<span class="pl-k">=<</span>trainset_path<span class="pl-k">></span>] [db<span class="pl-k">=<</span>db_name<span class="pl-k">></span>] \ encoded_trainset=<span class="pl-k"><</span>path_to_encoded_trainset.npz<span class="pl-k">></span> \ [ds.trainset<span class="pl-k">=<</span>limit_for_trainset<span class="pl-k">></span>] [ds.valset<span class="pl-k">=<</span>limit_for_valset<span class="pl-k">></span>]</pre></div> <p dir="auto">This command builds a pairwise additive decoder ("Pairwise additive decoding", section 3.3 in the QINCo2 paper) that can be used to improve performances of large-scale search within an index (see below). It requires both the encoded as well as unencoded training set, and the IVF centroids.</p> <p dir="auto"><strong>Single GPU process</strong>: this command should be ran using a single process (<code>python run.py</code>), and will use a single GPU.</p> <p dir="auto">Usage example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Use the provided IVF centroids for deep1M and the previously encoded deep1M/deep1B training set to create a pairwise encoded python run.py task=train_pairwise_decoder db=deep1B \ ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \ output=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt \ encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz"><pre><span class="pl-c"><span class="pl-c">#</span> Use the provided IVF centroids for deep1M and the previously encoded deep1M/deep1B training set to create a pairwise encoded</span> python run.py task=train_pairwise_decoder db=deep1B \ ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \ output=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt \ encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Build a search index</h4><a id="user-content-build-a-search-index" class="anchor" aria-label="Permalink: Build a search index" href="#build-a-search-index"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run.py task=build_index \ ivf_centroids=<path_to_ivf_centroids> \ output=<paht_to_store_index.faissindex> \ [trainset=<trainset_path>] [db=<db_name>] \ encoded_trainset=<path_to_encoded_trainset.npz> \ encoded_db=<path_to_encoded_db.npz> \ [ds.db=<limit_for_data>] [ds.trainset=<limit_for_trainset>] [ds.valset=<limit_for_valset>]"><pre>python run.py task=build_index \ ivf_centroids=<span class="pl-k"><</span>path_to_ivf_centroids<span class="pl-k">></span> \ output=<span class="pl-k"><</span>paht_to_store_index.faissindex<span class="pl-k">></span> \ [trainset<span class="pl-k">=<</span>trainset_path<span class="pl-k">></span>] [db<span class="pl-k">=<</span>db_name<span class="pl-k">></span>] \ encoded_trainset=<span class="pl-k"><</span>path_to_encoded_trainset.npz<span class="pl-k">></span> \ encoded_db=<span class="pl-k"><</span>path_to_encoded_db.npz<span class="pl-k">></span> \ [ds.db<span class="pl-k">=<</span>limit_for_data<span class="pl-k">></span>] [ds.trainset<span class="pl-k">=<</span>limit_for_trainset<span class="pl-k">></span>] [ds.valset<span class="pl-k">=<</span>limit_for_valset<span class="pl-k">></span>]</pre></div> <p dir="auto">This command creates a <a href="https://github.com/facebookresearch/faiss">faiss</a> index to efficiently search within billion-scale databases, using the previously encoded database. The training set is used to train a set of AQ codebooks for the first fast approximative shortlist.</p> <p dir="auto"><strong>Single GPU process</strong>: this command should be ran using a single process (<code>python run.py</code>), and will use a single GPU.</p> <p dir="auto">Usage example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run.py task=build_index db=deep1B \ ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \ output=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \ encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz \ encoded_db=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_db.npz \ ds.db=300_000 ds.trainset=100_000 ds.valset=10_000"><pre>python run.py task=build_index db=deep1B \ ivf_centroids=models/ivf_centroids-deep1M-1048576.npy \ output=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \ encoded_trainset=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_trainset.npz \ encoded_db=runs/encoded_db/IVF-qinco2_S-deep1M-8x8_db.npz \ ds.db=300_000 ds.trainset=100_000 ds.valset=10_000</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Search inside an index</h4><a id="user-content-search-inside-an-index" class="anchor" aria-label="Permalink: Search inside an index" href="#search-inside-an-index"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python run.py cpu=true task=search \ model=<model_path.pt> \ index=runs/index/index-qinco2s-ivf-deep1M-8x8.faissindex \ [queries=<path_to_queries>] [queries_gt=<path_to_groundtruth>] [db=<db_name>] \ [pairwise_decoder=<path_for_pairwise_decoder.pt>] \ [output=<output_logs.json>] [resume=<false/true>]"><pre>python run.py cpu=true task=search \ model=<span class="pl-k"><</span>model_path.pt<span class="pl-k">></span> \ index=runs/index/index-qinco2s-ivf-deep1M-8x8.faissindex \ [queries<span class="pl-k">=<</span>path_to_queries<span class="pl-k">></span>] [queries_gt<span class="pl-k">=<</span>path_to_groundtruth<span class="pl-k">></span>] [db<span class="pl-k">=<</span>db_name<span class="pl-k">></span>] \ [pairwise_decoder<span class="pl-k">=<</span>path_for_pairwise_decoder.pt<span class="pl-k">></span>] \ [output<span class="pl-k">=<</span>output_logs.json<span class="pl-k">></span>] [resume<span class="pl-k">=<</span>false/true<span class="pl-k">></span>]</pre></div> <p dir="auto">This command search over a faiss index using the optimized search pipeline shown in Figure 3 in the paper. It will explore different search parameters to find a pareto-optimal frontier for the speed/accuracy tradeoff shown in Figure 6 of the paper. The model will only be used to decode elements at the end of the search pipeline.</p> <p dir="auto"><strong>Single process, on CPUs</strong>: this command should be ran using a single process, and only on (up to 32) CPUs (<code>python run.py cpu=true</code>). Our experiments in the paper used 32 CPUs for the timing.</p> <ul dir="auto"> <li><code>pairwise_decoder</code>: optional argument. If specified, the search will also explore the use (or not) of the pairwise decoder within the pipeline to increase search speed.</li> <li><code>output</code>: optional argument. If specified, all the explored search settings with their corresponding accuracies and timings will be loged into the file as a JSON object. <ul dir="auto"> <li><code>resume</code> if specified witht the <code>output</code> argument, will continue exploration of search settings from a previously uncompleted <code>search</code> command.</li> </ul> </li> <li>The queries and ground-truth answers for those should be stored as <code>(N_queries, D)</code> (floats or integers) and <code>(N_queries, 1)</code> (integers: id of the correct nearest neighbour in the database) arrays. <ul dir="auto"> <li>You can instead use a default database using the <code>db=<db_name></code> argument, which will automatically give the queries and desired answers.</li> </ul> </li> </ul> <p dir="auto">Usage example:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Gives the search speed and accuracies within the deep1B database, for a set of search parameters. python run.py cpu=true task=search db=deep1B \ model=models/IVF-qinco2_S-deep1B-8x8.pt \ index=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \ output=runs/logs/search_results-IVF-qinco2_S-deep1B-8x8_v2.json resume=true \ pairwise_decoder=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt"><pre><span class="pl-c"><span class="pl-c">#</span> Gives the search speed and accuracies within the deep1B database, for a set of search parameters.</span> python run.py cpu=true task=search db=deep1B \ model=models/IVF-qinco2_S-deep1B-8x8.pt \ index=runs/index/index-IVF-qinco2_S-deep1B-8x8.faissindex \ output=runs/logs/search_results-IVF-qinco2_S-deep1B-8x8_v2.json resume=true \ pairwise_decoder=runs/weights/qinco2s-ivf-deep1B-8x8_pairwise_decoder.pt</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Legal</h2><a id="user-content-legal" class="anchor" aria-label="Permalink: Legal" href="#legal"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Qinco2 is licenced under CC-BY-NC, please refer to the LICENSE file in the top level directory.</p> <p dir="auto">Copyright © Meta Platforms, Inc. 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1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>