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GitHub - kernelmachine/cbtm: Code repository for the c-BTM paper
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1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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dir=\"auto\"\u003eCluster-Branch-Train-Merge (c-BTM)\u003c/h1\u003e\u003ca id=\"user-content-cluster-branch-train-merge-c-btm\" class=\"anchor\" aria-label=\"Permalink: Cluster-Branch-Train-Merge (c-BTM)\" href=\"#cluster-branch-train-merge-c-btm\"\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\"\u003eCode for the paper \u003cem\u003e\u003ca href=\"https://arxiv.org/abs/2303.14177\" rel=\"nofollow\"\u003eScaling Expert Language Models with Unsupervised Domain Discovery\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis repository is a fork of \u003ca href=\"https://github.com/facebookresearch/metaseq/\"\u003emetaseq\u003c/a\u003e.\u003c/p\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 this code, please consider citing our work:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@article{cbtm,\n author = {Suchin Gururangan and Margaret Li and Mike Lewis and Weijia Shi and Tim Althoff and Noah A. Smith and Luke Zettlemoyer},\n title = {Scaling Expert Language Models with Unsupervised Domain Discovery},\n journal={arXiv preprint arXiv:2303.14177},\n year = {2023}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@article{cbtm,\n author = {Suchin Gururangan and Margaret Li and Mike Lewis and Weijia Shi and Tim Althoff and Noah A. Smith and Luke Zettlemoyer},\n title = {Scaling Expert Language Models with Unsupervised Domain Discovery},\n journal={arXiv preprint arXiv:2303.14177},\n year = {2023}\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\"\u003eSet up environment\u003c/h2\u003e\u003ca id=\"user-content-set-up-environment\" class=\"anchor\" aria-label=\"Permalink: Set up environment\" href=\"#set-up-environment\"\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\"\u003eClone this repository\u003c/h3\u003e\u003ca id=\"user-content-clone-this-repository\" class=\"anchor\" aria-label=\"Permalink: Clone this repository\" href=\"#clone-this-repository\"\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=\"git clone https://github.com/kernelmachine/cbtm.git\ncd cbtm\"\u003e\u003cpre\u003egit clone https://github.com/kernelmachine/cbtm.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e cbtm\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreate a new conda env (recommended)\u003c/h3\u003e\u003ca id=\"user-content-create-a-new-conda-env-recommended\" class=\"anchor\" aria-label=\"Permalink: Create a new conda env (recommended)\" href=\"#create-a-new-conda-env-recommended\"\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=\"conda create -n cbtm python=3.9\nconda activate cbtm\n\"\u003e\u003cpre\u003econda create -n cbtm python=3.9\nconda activate cbtm\n\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAlternatively, we supply an \u003ccode\u003eenvironment.yml\u003c/code\u003e file; which can be used to create a conda environment with python 3.9 and a variety of dependencies. This will take a few minutes, and may not work for all versions of conda.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda env create -n cbtm -f environment.yml\nconda activate cbtm\"\u003e\u003cpre\u003econda env create -n cbtm -f environment.yml\nconda activate cbtm\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall PyTorch\u003c/h3\u003e\u003ca id=\"user-content-install-pytorch\" class=\"anchor\" aria-label=\"Permalink: Install PyTorch\" href=\"#install-pytorch\"\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\"\u003eWe tested this code with torch compiled with Cuda 11.3.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip3 install torch==1.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\"\u003e\u003cpre\u003epip3 install torch==1.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall Megatron\u003c/h3\u003e\u003ca id=\"user-content-install-megatron\" class=\"anchor\" aria-label=\"Permalink: Install Megatron\" href=\"#install-megatron\"\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\"\u003eMake sure you have a GPU and CUDA visible for this step.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd ..\ngit clone --branch fairseq_v2 https://github.com/ngoyal2707/Megatron-LM.git\ncd Megatron-LM\npip3 install six regex numpy\npip3 install -e .\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\ngit clone --branch fairseq_v2 https://github.com/ngoyal2707/Megatron-LM.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Megatron-LM\npip3 install six regex numpy\npip3 install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall fairscale\u003c/h3\u003e\u003ca id=\"user-content-install-fairscale\" class=\"anchor\" aria-label=\"Permalink: Install fairscale\" href=\"#install-fairscale\"\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=\"cd ..\ngit clone https://github.com/facebookresearch/fairscale.git\ncd fairscale\ngit checkout prefetch_fsdp_params_simple\npip3 install -e .\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\ngit clone https://github.com/facebookresearch/fairscale.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e fairscale\ngit checkout prefetch_fsdp_params_simple\npip3 install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall balanced-kmeans\u003c/h3\u003e\u003ca id=\"user-content-install-balanced-kmeans\" class=\"anchor\" aria-label=\"Permalink: Install balanced-kmeans\" href=\"#install-balanced-kmeans\"\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=\"cd ..\ngit clone https://github.com/kernelmachine/balanced-kmeans.git\ncd balanced-kmeans\npip3 install -e .\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\ngit clone https://github.com/kernelmachine/balanced-kmeans.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e balanced-kmeans\npip3 install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e(Optional) Install Apex\u003c/h3\u003e\u003ca id=\"user-content-optional-install-apex\" class=\"anchor\" aria-label=\"Permalink: (Optional) Install Apex\" href=\"#optional-install-apex\"\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\"\u003eApex may not be compatible with all GPUs. In particular, if you're seeing that CUDA doesn't support your model during the forward pass, you might want to try uninstalling Apex and trying again.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd ..\ngit clone https://github.com/NVIDIA/apex.git\ncd apex\ngit checkout e2083df5eb96643c61613b9df48dd4eea6b07690\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ..\ngit clone https://github.com/NVIDIA/apex.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apex\ngit checkout e2083df5eb96643c61613b9df48dd4eea6b07690\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDepending on your hardware, you may need to comment out lines 101-107 in setup.py before running the next pip install.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip3 install -v --no-cache-dir --global-option=\u0026quot;--cpp_ext\u0026quot; --global-option=\u0026quot;--cuda_ext\u0026quot; --global-option=\u0026quot;--deprecated_fused_adam\u0026quot; --global-option=\u0026quot;--xentropy\u0026quot; --global-option=\u0026quot;--fast_multihead_attn\u0026quot; ./\"\u003e\u003cpre\u003epip3 install -v --no-cache-dir --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--cpp_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--cuda_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--deprecated_fused_adam\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--xentropy\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--fast_multihead_attn\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ./\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall c-BTM library\u003c/h3\u003e\u003ca id=\"user-content-install-c-btm-library\" class=\"anchor\" aria-label=\"Permalink: Install c-BTM library\" href=\"#install-c-btm-library\"\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\"\u003eBuild the c-BTM library.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd ../cbtm\npip3 install -e .\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e ../cbtm\npip3 install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFix setuptools version\u003c/h3\u003e\u003ca id=\"user-content-fix-setuptools-version\" class=\"anchor\" aria-label=\"Permalink: Fix setuptools version\" href=\"#fix-setuptools-version\"\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\"\u003eThere is a known compatibility problem between the above torch version and setuptools\u0026gt;=59.6.0, so we roll this package back:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip3 install setuptools==59.5.0\"\u003e\u003cpre\u003epip3 install setuptools==59.5.0\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ec-BTM Training and Evaluation\u003c/h1\u003e\u003ca id=\"user-content-c-btm-training-and-evaluation\" class=\"anchor\" aria-label=\"Permalink: c-BTM Training and Evaluation\" href=\"#c-btm-training-and-evaluation\"\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\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep 0: Set up data, models, and directories\u003c/h2\u003e\u003ca id=\"user-content-step-0-set-up-data-models-and-directories\" class=\"anchor\" aria-label=\"Permalink: Step 0: Set up data, models, and directories\" href=\"#step-0-set-up-data-models-and-directories\"\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\"\u003eWe'll use the following environment variables in this tutorial, for simplicity. You can set these to whatever you want.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export CBTM_DIR=~/cbtm; \nexport DATA_DIR=${CBTM_DIR}/data;\nexport SERIALIZATION_DIR=${CBTM_DIR}/experiments;\nexport KMEANS_DIR=${CBTM_DIR}/clusterers;\nexport CLUSTERS_DIR=${CBTM_DIR}/clusters;\nexport VOCAB_DIR=$CBTM_DIR/vocab\nexport PRETRAINED_MODELS_DIR=${CBTM_DIR}/pretrained_models;\nmkdir -p ${CBTM_DIR} ${DATA_DIR} ${SERIALIZATION_DIR} ${KMEANS_DIR} ${CLUSTERS_DIR} ${VOCAB_DIR} ${PRETRAINED_MODELS_DIR};\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CBTM_DIR=\u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/cbtm\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e DATA_DIR=\u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e/data\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e SERIALIZATION_DIR=\u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e/experiments\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e KMEANS_DIR=\u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e/clusterers\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CLUSTERS_DIR=\u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e/clusters\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e VOCAB_DIR=\u003cspan class=\"pl-smi\"\u003e$CBTM_DIR\u003c/span\u003e/vocab\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e PRETRAINED_MODELS_DIR=\u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e/pretrained_models\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nmkdir -p \u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${DATA_DIR}\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${SERIALIZATION_DIR}\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${CLUSTERS_DIR}\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${VOCAB_DIR}\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConfigure cbtm_constants.py\u003c/h3\u003e\u003ca id=\"user-content-configure-cbtm_constantspy\" class=\"anchor\" aria-label=\"Permalink: Configure cbtm_constants.py\" href=\"#configure-cbtm_constantspy\"\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\"\u003eNext, the constants necessary to make this repo work are at \u003ccode\u003emetaseq/cbtm_constants.py\u003c/code\u003e. Modify these to suit your local environment.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMake sure the variables in \u003ccode\u003emetaseq/cbtm_constants.py\u003c/code\u003e are consistent with the paths you set as environment variables above.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDownload vocab files and seed models\u003c/h3\u003e\u003ca id=\"user-content-download-vocab-files-and-seed-models\" class=\"anchor\" aria-label=\"Permalink: Download vocab files and seed models\" href=\"#download-vocab-files-and-seed-models\"\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\"\u003eWe use the GPT-2 vocabulary:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"wget -O $VOCAB_DIR/gpt2-vocab.json http://s3.wasabisys.com/c4-example/vocab/gpt2-vocab.json\nwget -O $VOCAB_DIR/gpt2-merges.txt http://s3.wasabisys.com/c4-example/vocab/gpt2-merges.txt\"\u003e\u003cpre\u003ewget -O \u003cspan class=\"pl-smi\"\u003e$VOCAB_DIR\u003c/span\u003e/gpt2-vocab.json http://s3.wasabisys.com/c4-example/vocab/gpt2-vocab.json\nwget -O \u003cspan class=\"pl-smi\"\u003e$VOCAB_DIR\u003c/span\u003e/gpt2-merges.txt http://s3.wasabisys.com/c4-example/vocab/gpt2-merges.txt\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDownload the OPT-1.3B and OPT-6.7B checkpoints, which we use as our seed models. If you don't plan to use the 6.7B model, you can skip the last 2 wgets, which will take a while to complete:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"mkdir -p ${PRETRAINED_MODELS_DIR}/opt/1.3b/\nmkdir -p ${PRETRAINED_MODELS_DIR}/opt/6.7b/\nwget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/1.3B/sharded_for_ddp.tgz | tar xvz --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/1.3b/\nwget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_0.tgz | tar xvz --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/6.7b/\nwget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_1.tgz | tar xvz --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/6.7b/\n\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/opt/1.3b/\nmkdir -p \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/opt/6.7b/\nwget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/1.3B/sharded_for_ddp.tgz \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e tar xvz --strip-components 6 -C \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/opt/1.3b/\nwget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_0.tgz \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e tar xvz --strip-components 6 -C \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/opt/6.7b/\nwget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_1.tgz \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e tar xvz --strip-components 6 -C \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/opt/6.7b/\n\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDownload data\u003c/h3\u003e\u003ca id=\"user-content-download-data\" class=\"anchor\" aria-label=\"Permalink: Download data\" href=\"#download-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\"\u003eWe provide some sample C4 data to get you started. Our model only expects (sharded) line-separated jsonl files, split into train and validation data. If you'd like to train on your own data, just follow the overall data layout in the example.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"mkdir -p ${DATA_DIR}/c4_example/\nwget -qO- http://s3.wasabisys.com/c4-example/c4_example.tar.gz | tar xvz -C ${DATA_DIR}/\"\u003e\u003cpre\u003emkdir -p \u003cspan class=\"pl-smi\"\u003e${DATA_DIR}\u003c/span\u003e/c4_example/\nwget -qO- http://s3.wasabisys.com/c4-example/c4_example.tar.gz \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e tar xvz -C \u003cspan class=\"pl-smi\"\u003e${DATA_DIR}\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis example dataset is a single shard of C4 and a small sample of data from the validation dataset.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can download the full C4 dataset from Huggingface datasets at the following link: \u003ca href=\"https://huggingface.co/datasets/c4\" rel=\"nofollow\"\u003ehttps://huggingface.co/datasets/c4\u003c/a\u003e. Keep in mind that the dataset is very large, and comes as \u003ccode\u003ejson.gz\u003c/code\u003e files. Our code expects raw jsonl files in the structure from the example directory, so make sure you have enough space (in total, it's about 1.4 terabytes of data uncompressed).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMetaseq expects the data to be in this format:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"{\u0026quot;text\u0026quot;: \u0026quot;this is a document\u0026quot;, \u0026quot;id\u0026quot;: 0}\n{\u0026quot;text\u0026quot;: \u0026quot;this is another document\u0026quot;, \u0026quot;id\u0026quot;: 1}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e{\"text\": \"this is a document\", \"id\": 0}\n{\"text\": \"this is another document\", \"id\": 1}\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\"\u003eStep 1: Train Clusterer\u003c/h2\u003e\u003ca id=\"user-content-step-1-train-clusterer\" class=\"anchor\" aria-label=\"Permalink: Step 1: Train Clusterer\" href=\"#step-1-train-clusterer\"\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\"\u003eThis command trains a balanced k-means clusterer on a single shard of the C4 training data. Here we use k=8, and give as an argument a folder which contains a file called \u003ccode\u003eC4.jsonl\u003c/code\u003e, as described above.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eMake sure you have access to a GPU here, to speed up training!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"NUM_CLUSTERS=8;\nDATASET=c4_example;\npython -m metaseq.scripts.train_clusterer \\\n--data-dir ${DATA_DIR}/${DATASET}/train/00000 \\\n--num-clusters ${NUM_CLUSTERS} \\\n--balanced \\\n--output-dir ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/\"\u003e\u003cpre\u003eNUM_CLUSTERS=8\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nDATASET=c4_example\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\npython -m metaseq.scripts.train_clusterer \\\n--data-dir \u003cspan class=\"pl-smi\"\u003e${DATA_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/train/00000 \\\n--num-clusters \u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e \\\n--balanced \\\n--output-dir \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will create \u003ccode\u003etfidf.pkl\u003c/code\u003e (a tf-idf embedder) and \u003ccode\u003ekmeans.pkl\u003c/code\u003e (a kmeans clusterer) pickle files at \u003ccode\u003e${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep 2: Cluster data\u003c/h2\u003e\u003ca id=\"user-content-step-2-cluster-data\" class=\"anchor\" aria-label=\"Permalink: Step 2: Cluster data\" href=\"#step-2-cluster-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\"\u003eThis code uses your trained clusterer to cluster the dataset's documents. This is \u003cem\u003esubstantially\u003c/em\u003e faster if you can parallelize it as slurm jobs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe will do this automatically for you via \u003ccode\u003esubmitit\u003c/code\u003e, just provide your slurm account and partition (either as a flag to the program, or in \u003ccode\u003emetaseq/cbtm_constants.py\u003c/code\u003e)\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you don't have access to slurm, you can cluster your data locally with the flag \u003ccode\u003e--run local\u003c/code\u003e, but it might take some time!\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"DATASET=c4_example;\nNUM_CLUSTERS=8;\n\n# Cluster train data\npython -m metaseq.scripts.cluster \\\n--job-dir ${CBTM_DIR}/cluster_logs \\\n--data-dir ${DATA_DIR}/${DATASET} \\\n--path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \\\n--num-clusters ${NUM_CLUSTERS} \\\n--output-prefix ${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \\\n--split train \\\n--run slurm; \n\n# Cluster validation data\npython -m metaseq.scripts.cluster \\\n--job-dir ${CBTM_DIR}/cluster_logs \\\n--data-dir ${DATA_DIR}/${DATASET} \\\n--path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \\\n--num-clusters ${NUM_CLUSTERS} \\\n--output-prefix ${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \\\n--split valid/C4_small \\\n--run slurm;\"\u003e\u003cpre\u003eDATASET=c4_example\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nNUM_CLUSTERS=8\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cluster train data\u003c/span\u003e\npython -m metaseq.scripts.cluster \\\n--job-dir \u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e/cluster_logs \\\n--data-dir \u003cspan class=\"pl-smi\"\u003e${DATA_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e \\\n--path-to-clusterer \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/ \\\n--num-clusters \u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e \\\n--output-prefix \u003cspan class=\"pl-smi\"\u003e${CLUSTERS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/ \\\n--split train \\\n--run slurm\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Cluster validation data\u003c/span\u003e\npython -m metaseq.scripts.cluster \\\n--job-dir \u003cspan class=\"pl-smi\"\u003e${CBTM_DIR}\u003c/span\u003e/cluster_logs \\\n--data-dir \u003cspan class=\"pl-smi\"\u003e${DATA_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e \\\n--path-to-clusterer \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/ \\\n--num-clusters \u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e \\\n--output-prefix \u003cspan class=\"pl-smi\"\u003e${CLUSTERS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/ \\\n--split valid/C4_small \\\n--run slurm\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eLogs for these clustering jobs appear in \u003ccode\u003e${CBTM_DIR}/cluster_logs\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAfter these jobs complete, open files in ${CLUSTERS_DIR}, e.g., \u003ccode\u003e${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/train/00000/C4.jsonl\u003c/code\u003e. You should see lines like the following:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"{\u0026quot;sp_id\u0026quot;:\u0026quot;\\/gscratch\\/zlab\\/sg01\\/data\\/c4_example\\/train\\/00000\\/C4.jsonl|0\u0026quot;,\u0026quot;cluster\u0026quot;:5}\n{\u0026quot;sp_id\u0026quot;:\u0026quot;\\/gscratch\\/zlab\\/sg01\\/data\\/c4_example\\/train\\/00000\\/C4.jsonl|1\u0026quot;,\u0026quot;cluster\u0026quot;:3}\n{\u0026quot;sp_id\u0026quot;:\u0026quot;\\/gscratch\\/zlab\\/sg01\\/data\\/c4_example\\/train\\/00000\\/C4.jsonl|2\u0026quot;,\u0026quot;cluster\u0026quot;:2}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e{\"sp_id\":\"\\/gscratch\\/zlab\\/sg01\\/data\\/c4_example\\/train\\/00000\\/C4.jsonl|0\",\"cluster\":5}\n{\"sp_id\":\"\\/gscratch\\/zlab\\/sg01\\/data\\/c4_example\\/train\\/00000\\/C4.jsonl|1\",\"cluster\":3}\n{\"sp_id\":\"\\/gscratch\\/zlab\\/sg01\\/data\\/c4_example\\/train\\/00000\\/C4.jsonl|2\",\"cluster\":2}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe field \u003ccode\u003esp_id\u003c/code\u003e indicates a line (i.e., document) within a file, and the field \u003ccode\u003ecluster\u003c/code\u003e indicates its predicted cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStep 3: Train Models\u003c/h2\u003e\u003ca id=\"user-content-step-3-train-models\" class=\"anchor\" aria-label=\"Permalink: Step 3: Train Models\" href=\"#step-3-train-models\"\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\"\u003eNow we'll use the clustered data to train experts. You'll need at least 4 GPUs simultaneously to train each model.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis tutorial uses our \u003ccode\u003etrain_cbtm\u003c/code\u003e script, which interfaces with SLURM. By default, training jobs will request 8 CPUs per GPU and 48G of memory. These resource parameters can be customized in the \u003ccode\u003etrain_cbtm.sh\u003c/code\u003e script.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe have also provided an example sbatch script, if desired, in \u003ccode\u003emetaseq/scripts/example_sbatch.sh\u003c/code\u003e. You may need to edit this example sbatch command to include any additional slurm arguments you might need to get it working on your system.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTrain experts\u003c/h3\u003e\u003ca id=\"user-content-train-experts\" class=\"anchor\" aria-label=\"Permalink: Train experts\" href=\"#train-experts\"\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\"\u003eThe following command will train 8 expert models with 4 GPUs each for 50 steps (increase to 10000 steps to replicate our paper).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"DATASET=c4_example;\nNUM_CLUSTERS=8;\nNUM_GPUS_PER_EXPERT=4;\n\nif [[ \u0026quot;$NUM_GPUS_PER_EXPERT\u0026quot; -lt 8 ]]; then\n NUM_NODES=1\n NUM_GPUS_PER_NODE=$NUM_GPUS\nelse \n NUM_NODES=$((NUM_GPUS/8))\n NUM_GPUS_PER_NODE=8\nfi\npython -m metaseq.scripts.train_cbtm \\\n --model-size 1.3b \\\n --run slurm \\\n --path-to-clusters-dir $CLUSTERS_DIR/${DATASET}/$NUM_CLUSTERS/ \\\n --num-clusters $NUM_CLUSTERS \\\n --num-nodes $NUM_NODES \\\n --num-gpus $NUM_GPUS_PER_NODE \\\n --data-name ${DATASET} \\\n --path-to-data $DATA_DIR/${DATASET} \\\n --learning-rate 2e-4 \\\n --max-steps 50 \\\n --valid-subset valid/C4_small \\\n --train-subset train\"\u003e\u003cpre\u003eDATASET=c4_example\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nNUM_CLUSTERS=8\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nNUM_GPUS_PER_EXPERT=4\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e [[ \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$NUM_GPUS_PER_EXPERT\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e-lt\u003c/span\u003e 8 ]]\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n NUM_NODES=1\n NUM_GPUS_PER_NODE=\u003cspan class=\"pl-smi\"\u003e$NUM_GPUS\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e \n NUM_NODES=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$((\u003c/span\u003eNUM_GPUS\u003cspan class=\"pl-k\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e))\u003c/span\u003e\u003c/span\u003e\n NUM_GPUS_PER_NODE=8\n\u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\npython -m metaseq.scripts.train_cbtm \\\n --model-size 1.3b \\\n --run slurm \\\n --path-to-clusters-dir \u003cspan class=\"pl-smi\"\u003e$CLUSTERS_DIR\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$NUM_CLUSTERS\u003c/span\u003e/ \\\n --num-clusters \u003cspan class=\"pl-smi\"\u003e$NUM_CLUSTERS\u003c/span\u003e \\\n --num-nodes \u003cspan class=\"pl-smi\"\u003e$NUM_NODES\u003c/span\u003e \\\n --num-gpus \u003cspan class=\"pl-smi\"\u003e$NUM_GPUS_PER_NODE\u003c/span\u003e \\\n --data-name \u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e \\\n --path-to-data \u003cspan class=\"pl-smi\"\u003e$DATA_DIR\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e \\\n --learning-rate 2e-4 \\\n --max-steps 50 \\\n --valid-subset valid/C4_small \\\n --train-subset train\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo train on a specific cluster(s), you can add the flag \u003ccode\u003e--train-cluster 1,3,5\u003c/code\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo debug locally, change the \u003ccode\u003erun\u003c/code\u003e flag to \u003ccode\u003e--run local\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis command will output checkpoints and logs to \u003ccode\u003e${SERIALIZATION_DIR}/8_clusters/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDense training\u003c/h3\u003e\u003ca id=\"user-content-dense-training\" class=\"anchor\" aria-label=\"Permalink: Dense training\" href=\"#dense-training\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe following command will train a dense model with 4 GPUs for 50 steps (increase to 10000 steps to replicate our paper).\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"DATASET=c4_example;\nNUM_GPUS_PER_EXPERT=4;\n\nif [[ \u0026quot;$NUM_GPUS_PER_EXPERT\u0026quot; -lt 8 ]]; then\n NUM_NODES=1\n NUM_GPUS_PER_NODE=$NUM_GPUS\nelse \n NUM_NODES=$((NUM_GPUS/8))\n NUM_GPUS_PER_NODE=8\nfi\npython -m metaseq.scripts.train_cbtm \\\n --num-clusters 1 \\\n --model-size 1.3b \\\n --run slurm \\\n --num-nodes $NUM_NODES \\\n --num-gpus $NUM_GPUS_PER_NODE \\\n --data-name ${DATASET} \\\n --path-to-data $DATA_DIR/$DATASET \\\n --learning-rate 2e-4 \\\n --max-steps 50 \\\n --valid-subset valid/C4_small \\\n --train-subset train\"\u003e\u003cpre\u003eDATASET=c4_example\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nNUM_GPUS_PER_EXPERT=4\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e [[ \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$NUM_GPUS_PER_EXPERT\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e-lt\u003c/span\u003e 8 ]]\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n NUM_NODES=1\n NUM_GPUS_PER_NODE=\u003cspan class=\"pl-smi\"\u003e$NUM_GPUS\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eelse\u003c/span\u003e \n NUM_NODES=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$((\u003c/span\u003eNUM_GPUS\u003cspan class=\"pl-k\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e8\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e))\u003c/span\u003e\u003c/span\u003e\n NUM_GPUS_PER_NODE=8\n\u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\npython -m metaseq.scripts.train_cbtm \\\n --num-clusters 1 \\\n --model-size 1.3b \\\n --run slurm \\\n --num-nodes \u003cspan class=\"pl-smi\"\u003e$NUM_NODES\u003c/span\u003e \\\n --num-gpus \u003cspan class=\"pl-smi\"\u003e$NUM_GPUS_PER_NODE\u003c/span\u003e \\\n --data-name \u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e \\\n --path-to-data \u003cspan class=\"pl-smi\"\u003e$DATA_DIR\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e$DATASET\u003c/span\u003e \\\n --learning-rate 2e-4 \\\n --max-steps 50 \\\n --valid-subset valid/C4_small \\\n --train-subset train\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo debug locally, change the \u003ccode\u003erun\u003c/code\u003e flag to \u003ccode\u003e--run local\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis command will output checkpoints to \u003ccode\u003e${SERIALIZATION_DIR}/1_clusters/\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEvaluation\u003c/h2\u003e\u003ca id=\"user-content-evaluation\" class=\"anchor\" aria-label=\"Permalink: Evaluation\" href=\"#evaluation\"\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\"\u003eTo evaluate your models, first consolidate your shards into a single checkpoint file.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe following script depends on the \u003ca href=\"https://www.gnu.org/software/parallel/\" rel=\"nofollow\"\u003e\u003ccode\u003egnu-parallel\u003c/code\u003e\u003c/a\u003e package. You may need to modify the last line of \u003ccode\u003emetaseq/scripts /consolidate_fsdp_shards.sh\u003c/code\u003e to point to your gnu-parallel path.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"NUM_CLUSTERS=8;\nNUM_GPUS_PER_EXPERT=4;\nbash metaseq/scripts/consolidate_fsdp_shards.sh ${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/ \u0026quot;*ngpu${NUM_GPUS_PER_EXPERT}\u0026quot;\"\u003e\u003cpre\u003eNUM_CLUSTERS=8\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nNUM_GPUS_PER_EXPERT=4\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\nbash metaseq/scripts/consolidate_fsdp_shards.sh \u003cspan class=\"pl-smi\"\u003e${SERIALIZATION_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e_clusters/ \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*ngpu\u003cspan class=\"pl-smi\"\u003e${NUM_GPUS_PER_EXPERT}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will create a \u003ccode\u003econsolidated.pt\u003c/code\u003e checkpoint in each model's folder.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNow the checkpoints are ready for eval. To launch on slurm:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"export NUM_CLUSTERS=8;\n# we want as many GPUs as we have clusters\nexport NUM_GPUS=${NUM_CLUSTERS};\nexport DATASET=c4_example;\nexport EVAL_DIR=${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/eval\n\nmkdir -p ${EVAL_DIR};\n\n# get model checkpoints\nCONSOLIDATED_MODEL_PATHS=;\n# this function gets all model checkpoint directories and sorts them by the cluster ID\n# modify the folder pattern to match the directories names of your models, if you need.\nFOLDER_PATTERN=\u0026quot;cbtm\\.c4_example\\.*ngpu4\u0026quot;\nmapfile -t MODEL_FOLDERS \u0026lt; \u0026lt;(find ${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/ -type d -name $FOLDER_PATTERN -name \u0026quot;*\\.cluster*\u0026quot; -printf \u0026quot;%f|%p\\n\u0026quot; | sort -t \u0026quot;|\u0026quot; -k1,1 -t \u0026quot;|\u0026quot; -k2,2 | cut -d \u0026quot;|\u0026quot; -f 2)\nfor folder in \u0026quot;${MODEL_FOLDERS[@]}\u0026quot;; do\n # check if there are any consolidated.pt files in the model folder \n if test -f \u0026quot;${folder}/consolidated.pt\u0026quot;; then\n CONSOLIDATED_MODEL_PATHS+=\u0026quot;${folder}/consolidated.pt \u0026quot;;\n fi;\ndone\n# function to join checkpoints into comma separated string\nfunction join { local IFS=\u0026quot;,\u0026quot;; echo \u0026quot;$*\u0026quot;; }\n\n# these model paths should be ordered by cluster ID!\nJOINED_MODEL_PATHS=$(join ${CONSOLIDATED_MODEL_PATHS[@]})\n\npython -m metaseq_cli.eval_cbtm \\\n --data-dir ${DATA_DIR}/${DATASET} \\\n --data-subset valid/C4_small \\\n --path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \\\n --model-paths $(join ${CONSOLIDATED_MODEL_PATHS[@]}) \\\n --job-dir ${EVAL_DIR} \\\n --temperature 0.1 \\\n --max-valid-steps 200 \\\n --ensemble-type clustering \\\n --submitit\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NUM_CLUSTERS=8\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e we want as many GPUs as we have clusters\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NUM_GPUS=\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e DATASET=c4_example\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e EVAL_DIR=\u003cspan class=\"pl-smi\"\u003e${SERIALIZATION_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e_clusters/eval\n\nmkdir -p \u003cspan class=\"pl-smi\"\u003e${EVAL_DIR}\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get model checkpoints\u003c/span\u003e\nCONSOLIDATED_MODEL_PATHS=\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e this function gets all model checkpoint directories and sorts them by the cluster ID\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e modify the folder pattern to match the directories names of your models, if you need.\u003c/span\u003e\nFOLDER_PATTERN=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ecbtm\\.c4_example\\.*ngpu4\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\nmapfile -t MODEL_FOLDERS \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\u0026lt;(\u003c/span\u003efind \u003cspan class=\"pl-smi\"\u003e${SERIALIZATION_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e_clusters/ -type d -name \u003cspan class=\"pl-smi\"\u003e$FOLDER_PATTERN\u003c/span\u003e -name \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e*\\.cluster*\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -printf \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e%f|%p\\n\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e sort -t \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e|\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -k1,1 -t \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e|\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -k2,2 \u003cspan class=\"pl-k\"\u003e|\u003c/span\u003e cut -d \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e|\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e -f 2\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003efolder\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${MODEL_FOLDERS[@]}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e check if there are any consolidated.pt files in the model folder \u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003eif\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003etest\u003c/span\u003e -f \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${folder}\u003c/span\u003e/consolidated.pt\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ethen\u003c/span\u003e\n CONSOLIDATED_MODEL_PATHS+=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e${folder}\u003c/span\u003e/consolidated.pt \u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efi\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e function to join checkpoints into comma separated string\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efunction\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ejoin\u003c/span\u003e { \u003cspan class=\"pl-k\"\u003elocal\u003c/span\u003e IFS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e,\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eecho\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003e$*\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e }\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e these model paths should be ordered by cluster ID!\u003c/span\u003e\nJOINED_MODEL_PATHS=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ejoin \u003cspan class=\"pl-smi\"\u003e${CONSOLIDATED_MODEL_PATHS[@]}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\n\npython -m metaseq_cli.eval_cbtm \\\n --data-dir \u003cspan class=\"pl-smi\"\u003e${DATA_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e \\\n --data-subset valid/C4_small \\\n --path-to-clusterer \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/ \\\n --model-paths \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003ejoin \u003cspan class=\"pl-smi\"\u003e${CONSOLIDATED_MODEL_PATHS[@]}\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e \\\n --job-dir \u003cspan class=\"pl-smi\"\u003e${EVAL_DIR}\u003c/span\u003e \\\n --temperature 0.1 \\\n --max-valid-steps 200 \\\n --ensemble-type clustering \\\n --submitit\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can check out logs for your slurm job at \u003ccode\u003e${EVAL_DIR}\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo launch locally, remove the flag \u003ccode\u003e--submitit\u003c/code\u003e in the command above. Make sure you have \u003ccode\u003e$NUM_CLUSTERS\u003c/code\u003e GPUs visible though!\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis will output perplexity results to \u003ccode\u003e${EVAL_DIR}/result.json\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUse the same command as above to evaluate your dense models, just change the environment variable \u003ccode\u003eNUM_CLUSTERS=1\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOn our machine we get \u003ccode\u003eppl: 17.86\u003c/code\u003e for the 8-cluster model from this tutorial, and \u003ccode\u003eppl: 18.56\u003c/code\u003e for the 1-cluster model. (Note that these perplexities are only for reproducibility purposes; increase the step size to 10000 to replicate our runs from the paper).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMoE baseline training via sparse upcycling\u003c/h2\u003e\u003ca id=\"user-content-moe-baseline-training-via-sparse-upcycling\" class=\"anchor\" aria-label=\"Permalink: MoE baseline training via sparse upcycling\" href=\"#moe-baseline-training-via-sparse-upcycling\"\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\"\u003eSee our \u003ca href=\"https://github.com/kernelmachine/moe-fairseq\"\u003efairseq fork\u003c/a\u003e for instructions and code to train the sparse upcycling MoE baseline.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOpen-sourced pretrained models\u003c/h2\u003e\u003ca id=\"user-content-open-sourced-pretrained-models\" class=\"anchor\" aria-label=\"Permalink: Open-sourced pretrained models\" href=\"#open-sourced-pretrained-models\"\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\"\u003eDownloading clusterers and embedders\u003c/h3\u003e\u003ca id=\"user-content-downloading-clusterers-and-embedders\" class=\"anchor\" aria-label=\"Permalink: Downloading clusterers and embedders\" href=\"#downloading-clusterers-and-embedders\"\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\"\u003eTo get a pretrained clusterer and embedder from the paper, you can run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# c4 or s2orc\nexport DATASET=c4\n# 2, 4, 8, 16, 32, 64, 128\nexport NUM_CLUSTERS=4\nmkdir -p ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/\nwget -O ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/tfidf.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/${DATASET}/${NUM_CLUSTERS}/tfidf.pkl\nwget -O ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/kmeans.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/${DATASET}/${NUM_CLUSTERS}/kmeans.pkl\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e c4 or s2orc\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e DATASET=c4\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 2, 4, 8, 16, 32, 64, 128\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NUM_CLUSTERS=4\nmkdir -p \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/\nwget -O \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/tfidf.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/tfidf.pkl\nwget -O \u003cspan class=\"pl-smi\"\u003e${KMEANS_DIR}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/kmeans.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e/kmeans.pkl\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDownloading language models\u003c/h3\u003e\u003ca id=\"user-content-downloading-language-models\" class=\"anchor\" aria-label=\"Permalink: Downloading language models\" href=\"#downloading-language-models\"\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\"\u003eTo access the language models we trained in our paper, you can run the following command:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# c4 or s2orc\nexport DATASET=c4\n# opt1.3b or opt6.7b\nexport MODEL_ARCH=opt1.3b\n# 4, 8, 16, 32, 64, 128, 256, 512, 1024\nexport GPUS_PER_EXPERT=4\n# 2, 4, 8, 16, 32, 64, 128\nexport NUM_CLUSTERS=64\n# anything from 0 to $NUM_CLUSTERS-1\nexport CLUSTER_NUMBER=28\nFOLDER=${DATASET}.${MODEL_ARCH}.${NUM_CLUSTERS}_clusters.cluster${CLUSTER_NUMBER}.ngpu${GPUS_PER_EXPERT}/\nmkdir -p ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER\nwget -O ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/$DATASET/${MODEL_ARCH}/${NUM_CLUSTERS}_clusters/ngpu${GPUS_PER_EXPERT}/${CLUSTER_NUMBER}/consolidated.pt\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e c4 or s2orc\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e DATASET=c4\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e opt1.3b or opt6.7b\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MODEL_ARCH=opt1.3b\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 4, 8, 16, 32, 64, 128, 256, 512, 1024\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GPUS_PER_EXPERT=4\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 2, 4, 8, 16, 32, 64, 128\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NUM_CLUSTERS=64\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e anything from 0 to $NUM_CLUSTERS-1\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e CLUSTER_NUMBER=28\nFOLDER=\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${MODEL_ARCH}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e_clusters.cluster\u003cspan class=\"pl-smi\"\u003e${CLUSTER_NUMBER}\u003c/span\u003e.ngpu\u003cspan class=\"pl-smi\"\u003e${GPUS_PER_EXPERT}\u003c/span\u003e/\nmkdir -p \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/cbtm/\u003cspan class=\"pl-smi\"\u003e$FOLDER\u003c/span\u003e\nwget -O \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/cbtm/\u003cspan class=\"pl-smi\"\u003e$FOLDER\u003c/span\u003e/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/\u003cspan class=\"pl-smi\"\u003e$DATASET\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${MODEL_ARCH}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e_clusters/ngpu\u003cspan class=\"pl-smi\"\u003e${GPUS_PER_EXPERT}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${CLUSTER_NUMBER}\u003c/span\u003e/consolidated.pt\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo get all n experts for an n-cluster c-BTM model, you can do a for loop:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# c4 or s2orc\nexport DATASET=c4\n# opt1.3b or opt6.7b\nexport MODEL_ARCH=opt1.3b\n# 4, 8, 16, 32, 64, 128, 256, 512, 1024\nexport GPUS_PER_EXPERT=4\n# 2, 4, 8, 16, 32, 64, 128\nexport NUM_CLUSTERS=4\nfor CLUSTER_NUMBER in $(seq 0 $((NUM_CLUSTERS-1))); do \n FOLDER=${DATASET}.${MODEL_ARCH}.${NUM_CLUSTERS}_clusters.cluster${CLUSTER_NUMBER}.ngpu${GPUS_PER_EXPERT}/\n mkdir -p ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER\n wget -O ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/$DATASET/${MODEL_ARCH}/${NUM_CLUSTERS}_clusters/ngpu${GPUS_PER_EXPERT}/${CLUSTER_NUMBER}/consolidated.pt\ndone\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e c4 or s2orc\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e DATASET=c4\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e opt1.3b or opt6.7b\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e MODEL_ARCH=opt1.3b\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 4, 8, 16, 32, 64, 128, 256, 512, 1024\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e GPUS_PER_EXPERT=4\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e 2, 4, 8, 16, 32, 64, 128\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eexport\u003c/span\u003e NUM_CLUSTERS=4\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eCLUSTER_NUMBER\u003c/span\u003e \u003cspan class=\"pl-k\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$(\u003c/span\u003eseq 0 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e$((\u003c/span\u003eNUM_CLUSTERS\u003cspan class=\"pl-k\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e))\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e)\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"pl-k\"\u003e;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003edo\u003c/span\u003e \n FOLDER=\u003cspan class=\"pl-smi\"\u003e${DATASET}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${MODEL_ARCH}\u003c/span\u003e.\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e_clusters.cluster\u003cspan class=\"pl-smi\"\u003e${CLUSTER_NUMBER}\u003c/span\u003e.ngpu\u003cspan class=\"pl-smi\"\u003e${GPUS_PER_EXPERT}\u003c/span\u003e/\n mkdir -p \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/cbtm/\u003cspan class=\"pl-smi\"\u003e$FOLDER\u003c/span\u003e\n wget -O \u003cspan class=\"pl-smi\"\u003e${PRETRAINED_MODELS_DIR}\u003c/span\u003e/cbtm/\u003cspan class=\"pl-smi\"\u003e$FOLDER\u003c/span\u003e/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/\u003cspan class=\"pl-smi\"\u003e$DATASET\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${MODEL_ARCH}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${NUM_CLUSTERS}\u003c/span\u003e_clusters/ngpu\u003cspan class=\"pl-smi\"\u003e${GPUS_PER_EXPERT}\u003c/span\u003e/\u003cspan class=\"pl-smi\"\u003e${CLUSTER_NUMBER}\u003c/span\u003e/consolidated.pt\n\u003cspan class=\"pl-k\"\u003edone\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAll models for which we show results in the core results section of the paper can be downloaded here. The highest budget (168B token) 1.3B parameter models have \u003ccode\u003e${NUM_CLUSTERS} * $GPUS_PER_EXPERT = 1024\u003c/code\u003e, resulting in \u003ccode\u003e(${NUM_CLUSTERS}, $GPUS_PER_EXPERT) = (1, 1024), (2, 512) ... (128, 8)\u003c/code\u003e. Settings for smaller compute budget models can be found with a proportionally small product. E.g., for 42B token 1.3B parameter models, we have \u003ccode\u003e${NUM_CLUSTERS} * $GPUS_PER_EXPERT = 256\u003c/code\u003e, resulting in \u003ccode\u003e(${NUM_CLUSTERS}, $GPUS_PER_EXPERT) = (1, 256), (2, 128) ... (64, 4)\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThese models can be evaluated using the command given above, under \"Cluster BTM Training and Evaluation\". However, to continue training any of these experts using model parallel, you will need to reshard them, check the \u003ccode\u003emetaseq\u003c/code\u003e library to learn more on how to do this.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee this \u003ca href=\"https://l.facebook.com/l.php?u=https%3A%2F%2Fdl.fbaipublicfiles.com%2FREADME\u0026amp;h=AT1F-a_xXIhZseEwKETbvCNYQIJlBJOLEU2_MkOfjxpaCML8sQz-hm7qGMpUAwJ-Zd3F-P3x3ZfrPCnxP2gME5jGSGZo8c7pCXB_NP1CyxkxJYQWGGPm2ZiTsur2Qt29FxgAF4V4IAoCDStOkr8y8hH0\" rel=\"nofollow\"\u003eREADME\u003c/a\u003e for details on rate-limiting when downloading models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConverting from metaseq to Huggingface\u003c/h3\u003e\u003ca id=\"user-content-converting-from-metaseq-to-huggingface\" class=\"anchor\" aria-label=\"Permalink: Converting from metaseq to Huggingface\" href=\"#converting-from-metaseq-to-huggingface\"\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\"\u003eWe have provided a script to convert all metaseq models to Huggingface transformers compatible checkpoints.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFirst, you have to install huggingface transformers \u003ca href=\"https://huggingface.co/docs/transformers/installation#install-from-source\" rel=\"nofollow\"\u003efrom source\u003c/a\u003e, and make sure you consolidate your metaseq checkpoint using the \u003ccode\u003econsolidate_fsdp_shards.sh\u003c/code\u003e first (see above).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThen run:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"PATH_TO_TRANSFORMERS=/path/to/installed/transformers/library/\n# path to directory containing consolidated.pt checkpoint\nINPUT_DIR=/path/to/metaseq/model/dir\nOUTPUT_DIR=my_huggingface_model\n\nbash metaseq/scripts/convert_hf.sh $PATH_TO_TRANSFORMERS $INPUT_DIR $OUTPUT_DIR\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003ePATH_TO_TRANSFORMERS=/path/to/installed/transformers/library/\n# path to directory containing consolidated.pt checkpoint\nINPUT_DIR=/path/to/metaseq/model/dir\nOUTPUT_DIR=my_huggingface_model\n\nbash metaseq/scripts/convert_hf.sh $PATH_TO_TRANSFORMERS $INPUT_DIR $OUTPUT_DIR\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis will output a checkpoint in $OUTPUT_DIR you can use in any huggingface transformers pipeline.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Cluster-Branch-Train-Merge (c-BTM)","anchor":"cluster-branch-train-merge-c-btm","htmlText":"Cluster-Branch-Train-Merge (c-BTM)"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"},{"level":2,"text":"Set up environment","anchor":"set-up-environment","htmlText":"Set up environment"},{"level":3,"text":"Clone this repository","anchor":"clone-this-repository","htmlText":"Clone this repository"},{"level":3,"text":"Create a new conda env (recommended)","anchor":"create-a-new-conda-env-recommended","htmlText":"Create a new conda env (recommended)"},{"level":3,"text":"Install PyTorch","anchor":"install-pytorch","htmlText":"Install PyTorch"},{"level":3,"text":"Install Megatron","anchor":"install-megatron","htmlText":"Install Megatron"},{"level":3,"text":"Install fairscale","anchor":"install-fairscale","htmlText":"Install fairscale"},{"level":3,"text":"Install balanced-kmeans","anchor":"install-balanced-kmeans","htmlText":"Install balanced-kmeans"},{"level":3,"text":"(Optional) Install Apex","anchor":"optional-install-apex","htmlText":"(Optional) Install Apex"},{"level":3,"text":"Install c-BTM library","anchor":"install-c-btm-library","htmlText":"Install c-BTM library"},{"level":3,"text":"Fix setuptools version","anchor":"fix-setuptools-version","htmlText":"Fix setuptools version"},{"level":1,"text":"c-BTM Training and Evaluation","anchor":"c-btm-training-and-evaluation","htmlText":"c-BTM Training and Evaluation"},{"level":2,"text":"Step 0: Set up data, models, and directories","anchor":"step-0-set-up-data-models-and-directories","htmlText":"Step 0: Set up data, models, and directories"},{"level":3,"text":"Configure cbtm_constants.py","anchor":"configure-cbtm_constantspy","htmlText":"Configure cbtm_constants.py"},{"level":3,"text":"Download vocab files and seed models","anchor":"download-vocab-files-and-seed-models","htmlText":"Download vocab files and seed models"},{"level":3,"text":"Download data","anchor":"download-data","htmlText":"Download data"},{"level":2,"text":"Step 1: Train Clusterer","anchor":"step-1-train-clusterer","htmlText":"Step 1: Train Clusterer"},{"level":2,"text":"Step 2: Cluster data","anchor":"step-2-cluster-data","htmlText":"Step 2: Cluster data"},{"level":2,"text":"Step 3: Train Models","anchor":"step-3-train-models","htmlText":"Step 3: Train Models"},{"level":3,"text":"Train experts","anchor":"train-experts","htmlText":"Train experts"},{"level":3,"text":"Dense training","anchor":"dense-training","htmlText":"Dense training"},{"level":2,"text":"Evaluation","anchor":"evaluation","htmlText":"Evaluation"},{"level":2,"text":"MoE baseline training via sparse upcycling","anchor":"moe-baseline-training-via-sparse-upcycling","htmlText":"MoE baseline training via sparse upcycling"},{"level":2,"text":"Open-sourced pretrained models","anchor":"open-sourced-pretrained-models","htmlText":"Open-sourced pretrained models"},{"level":3,"text":"Downloading clusterers and embedders","anchor":"downloading-clusterers-and-embedders","htmlText":"Downloading clusterers and embedders"},{"level":3,"text":"Downloading language models","anchor":"downloading-language-models","htmlText":"Downloading language models"},{"level":3,"text":"Converting from metaseq to Huggingface","anchor":"converting-from-metaseq-to-huggingface","htmlText":"Converting from metaseq to Huggingface"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fkernelmachine%2Fcbtm"}},{"displayName":"LICENSE","repoName":"cbtm","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fkernelmachine%2Fcbtm"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> <div 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fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M8 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3ZM1.5 9a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Zm13 0a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path></svg></button></div></div></div><div class="Box-sc-g0xbh4-0 vIPPs"><div data-hpc="true"><button hidden="" data-testid="focus-next-element-button" data-hotkey="j"></button><button hidden="" data-testid="focus-previous-element-button" data-hotkey="k"></button><h2 class="sr-only prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading" id="folders-and-files">Folders and files</h2><table aria-labelledby="folders-and-files" class="Box-sc-g0xbh4-0 fdROMU"><thead class="Box-sc-g0xbh4-0 jGKpsv"><tr class="Box-sc-g0xbh4-0 jdgHnn"><th colSpan="2" class="Box-sc-g0xbh4-0 bQivRW"><span class="text-bold">Name</span></th><th colSpan="1" class="Box-sc-g0xbh4-0 ldkMIO"><span class="text-bold">Name</span></th><th class="hide-sm"><div title="Last commit message" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit message</span></div></th><th colSpan="1" class="Box-sc-g0xbh4-0 jMbWeI"><div title="Last commit date" class="Truncate__StyledTruncate-sc-23o1d2-0 liVpTx width-fit"><span class="text-bold">Last commit date</span></div></th></tr></thead><tbody><tr class="Box-sc-g0xbh4-0 gpqjiB"><td colSpan="3" class="bgColor-muted p-1 rounded-top-2"><div class="Box-sc-g0xbh4-0 dzCJzi"><h2 class="sr-only prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">Latest commit</h2><div style="width:120px" class="Skeleton Skeleton--text" data-testid="loading"> </div><div class="d-flex flex-shrink-0 gap-2"><div data-testid="latest-commit-details" class="d-none d-sm-flex flex-items-center"></div><div class="d-flex gap-2"><h2 class="sr-only prc-Heading-Heading-6CmGO" data-testid="screen-reader-heading">History</h2><a href="/kernelmachine/cbtm/commits/main/" class="types__StyledButton-sc-ws60qy-0 dPmZyJ d-none d-lg-flex LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" aria-describedby=":Raqj8pab:-loading-announcement"><span data-component="buttonContent" class="Box-sc-g0xbh4-0 gUkoLg"><span data-component="leadingVisual" class="Box-sc-g0xbh4-0 hzSPyu"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 1.358A.25.25 0 0 1 3.896 6H.25A.25.25 0 0 1 0 5.75V2.104a.25.25 0 0 1 .427-.177ZM7.75 4a.75.75 0 0 1 .75.75v2.992l2.028.812a.75.75 0 0 1-.557 1.392l-2.5-1A.751.751 0 0 1 7 8.25v-3.5A.75.75 0 0 1 7.75 4Z"></path></svg></span><span data-component="text"><span class="fgColor-default">79 Commits</span></span></span></a><div class="d-sm-none"></div><div class="d-flex d-lg-none"><span role="tooltip" aria-label="79 Commits" id="history-icon-button-tooltip" class="Tooltip__TooltipBase-sc-17tf59c-0 hWlpPn tooltipped-n"><a href="/kernelmachine/cbtm/commits/main/" class="types__StyledButton-sc-ws60qy-0 dPmZyJ LinkButton-module__code-view-link-button--xvCGA flex-items-center fgColor-default" data-loading="false" data-size="small" aria-describedby=":R1iqj8pab:-loading-announcement history-icon-button-tooltip"><span data-component="buttonContent" class="Box-sc-g0xbh4-0 gUkoLg"><span data-component="leadingVisual" class="Box-sc-g0xbh4-0 hzSPyu"><svg aria-hidden="true" focusable="false" class="octicon octicon-history" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="m.427 1.927 1.215 1.215a8.002 8.002 0 1 1-1.6 5.685.75.75 0 1 1 1.493-.154 6.5 6.5 0 1 0 1.18-4.458l1.358 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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">Code for the paper <em><a href="https://arxiv.org/abs/2303.14177" rel="nofollow">Scaling Expert Language Models with Unsupervised Domain Discovery</a></em></p> <p dir="auto">This repository is a fork of <a href="https://github.com/facebookresearch/metaseq/">metaseq</a>.</p> <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 this code, please consider citing our work:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@article{cbtm, author = {Suchin Gururangan and Margaret Li and Mike Lewis and Weijia Shi and Tim Althoff and Noah A. Smith and Luke Zettlemoyer}, title = {Scaling Expert Language Models with Unsupervised Domain Discovery}, journal={arXiv preprint arXiv:2303.14177}, year = {2023} }"><pre class="notranslate"><code>@article{cbtm, author = {Suchin Gururangan and Margaret Li and Mike Lewis and Weijia Shi and Tim Althoff and Noah A. Smith and Luke Zettlemoyer}, title = {Scaling Expert Language Models with Unsupervised Domain Discovery}, journal={arXiv preprint arXiv:2303.14177}, year = {2023} } </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Set up environment</h2><a id="user-content-set-up-environment" class="anchor" aria-label="Permalink: Set up environment" href="#set-up-environment"><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">Clone this repository</h3><a id="user-content-clone-this-repository" class="anchor" aria-label="Permalink: Clone this repository" href="#clone-this-repository"><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="git clone https://github.com/kernelmachine/cbtm.git cd cbtm"><pre>git clone https://github.com/kernelmachine/cbtm.git <span class="pl-c1">cd</span> cbtm</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Create a new conda env (recommended)</h3><a id="user-content-create-a-new-conda-env-recommended" class="anchor" aria-label="Permalink: Create a new conda env (recommended)" href="#create-a-new-conda-env-recommended"><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="conda create -n cbtm python=3.9 conda activate cbtm "><pre>conda create -n cbtm python=3.9 conda activate cbtm </pre></div> <p dir="auto">Alternatively, we supply an <code>environment.yml</code> file; which can be used to create a conda environment with python 3.9 and a variety of dependencies. This will take a few minutes, and may not work for all versions of conda.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda env create -n cbtm -f environment.yml conda activate cbtm"><pre>conda env create -n cbtm -f environment.yml conda activate cbtm</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install PyTorch</h3><a id="user-content-install-pytorch" class="anchor" aria-label="Permalink: Install PyTorch" href="#install-pytorch"><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">We tested this code with torch compiled with Cuda 11.3.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip3 install torch==1.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html"><pre>pip3 install torch==1.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install Megatron</h3><a id="user-content-install-megatron" class="anchor" aria-label="Permalink: Install Megatron" href="#install-megatron"><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">Make sure you have a GPU and CUDA visible for this step.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd .. git clone --branch fairseq_v2 https://github.com/ngoyal2707/Megatron-LM.git cd Megatron-LM pip3 install six regex numpy pip3 install -e ."><pre><span class="pl-c1">cd</span> .. git clone --branch fairseq_v2 https://github.com/ngoyal2707/Megatron-LM.git <span class="pl-c1">cd</span> Megatron-LM pip3 install six regex numpy pip3 install -e <span class="pl-c1">.</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install fairscale</h3><a id="user-content-install-fairscale" class="anchor" aria-label="Permalink: Install fairscale" href="#install-fairscale"><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="cd .. git clone https://github.com/facebookresearch/fairscale.git cd fairscale git checkout prefetch_fsdp_params_simple pip3 install -e ."><pre><span class="pl-c1">cd</span> .. git clone https://github.com/facebookresearch/fairscale.git <span class="pl-c1">cd</span> fairscale git checkout prefetch_fsdp_params_simple pip3 install -e <span class="pl-c1">.</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install balanced-kmeans</h3><a id="user-content-install-balanced-kmeans" class="anchor" aria-label="Permalink: Install balanced-kmeans" href="#install-balanced-kmeans"><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="cd .. git clone https://github.com/kernelmachine/balanced-kmeans.git cd balanced-kmeans pip3 install -e ."><pre><span class="pl-c1">cd</span> .. git clone https://github.com/kernelmachine/balanced-kmeans.git <span class="pl-c1">cd</span> balanced-kmeans pip3 install -e <span class="pl-c1">.</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">(Optional) Install Apex</h3><a id="user-content-optional-install-apex" class="anchor" aria-label="Permalink: (Optional) Install Apex" href="#optional-install-apex"><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">Apex may not be compatible with all GPUs. In particular, if you're seeing that CUDA doesn't support your model during the forward pass, you might want to try uninstalling Apex and trying again.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd .. git clone https://github.com/NVIDIA/apex.git cd apex git checkout e2083df5eb96643c61613b9df48dd4eea6b07690"><pre><span class="pl-c1">cd</span> .. git clone https://github.com/NVIDIA/apex.git <span class="pl-c1">cd</span> apex git checkout e2083df5eb96643c61613b9df48dd4eea6b07690</pre></div> <p dir="auto">Depending on your hardware, you may need to comment out lines 101-107 in setup.py before running the next pip install.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip3 install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--deprecated_fused_adam" --global-option="--xentropy" --global-option="--fast_multihead_attn" ./"><pre>pip3 install -v --no-cache-dir --global-option=<span class="pl-s"><span class="pl-pds">"</span>--cpp_ext<span class="pl-pds">"</span></span> --global-option=<span class="pl-s"><span class="pl-pds">"</span>--cuda_ext<span class="pl-pds">"</span></span> --global-option=<span class="pl-s"><span class="pl-pds">"</span>--deprecated_fused_adam<span class="pl-pds">"</span></span> --global-option=<span class="pl-s"><span class="pl-pds">"</span>--xentropy<span class="pl-pds">"</span></span> --global-option=<span class="pl-s"><span class="pl-pds">"</span>--fast_multihead_attn<span class="pl-pds">"</span></span> ./</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install c-BTM library</h3><a id="user-content-install-c-btm-library" class="anchor" aria-label="Permalink: Install c-BTM library" href="#install-c-btm-library"><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">Build the c-BTM library.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd ../cbtm pip3 install -e ."><pre><span class="pl-c1">cd</span> ../cbtm pip3 install -e <span class="pl-c1">.</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Fix setuptools version</h3><a id="user-content-fix-setuptools-version" class="anchor" aria-label="Permalink: Fix setuptools version" href="#fix-setuptools-version"><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">There is a known compatibility problem between the above torch version and setuptools>=59.6.0, so we roll this package back:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip3 install setuptools==59.5.0"><pre>pip3 install setuptools==59.5.0</pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">c-BTM Training and Evaluation</h1><a id="user-content-c-btm-training-and-evaluation" class="anchor" aria-label="Permalink: c-BTM Training and Evaluation" href="#c-btm-training-and-evaluation"><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"><h2 tabindex="-1" class="heading-element" dir="auto">Step 0: Set up data, models, and directories</h2><a id="user-content-step-0-set-up-data-models-and-directories" class="anchor" aria-label="Permalink: Step 0: Set up data, models, and directories" href="#step-0-set-up-data-models-and-directories"><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">We'll use the following environment variables in this tutorial, for simplicity. You can set these to whatever you want.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export CBTM_DIR=~/cbtm; export DATA_DIR=${CBTM_DIR}/data; export SERIALIZATION_DIR=${CBTM_DIR}/experiments; export KMEANS_DIR=${CBTM_DIR}/clusterers; export CLUSTERS_DIR=${CBTM_DIR}/clusters; export VOCAB_DIR=$CBTM_DIR/vocab export PRETRAINED_MODELS_DIR=${CBTM_DIR}/pretrained_models; mkdir -p ${CBTM_DIR} ${DATA_DIR} ${SERIALIZATION_DIR} ${KMEANS_DIR} ${CLUSTERS_DIR} ${VOCAB_DIR} ${PRETRAINED_MODELS_DIR};"><pre><span class="pl-k">export</span> CBTM_DIR=<span class="pl-k">~</span>/cbtm<span class="pl-k">;</span> <span class="pl-k">export</span> DATA_DIR=<span class="pl-smi">${CBTM_DIR}</span>/data<span class="pl-k">;</span> <span class="pl-k">export</span> SERIALIZATION_DIR=<span class="pl-smi">${CBTM_DIR}</span>/experiments<span class="pl-k">;</span> <span class="pl-k">export</span> KMEANS_DIR=<span class="pl-smi">${CBTM_DIR}</span>/clusterers<span class="pl-k">;</span> <span class="pl-k">export</span> CLUSTERS_DIR=<span class="pl-smi">${CBTM_DIR}</span>/clusters<span class="pl-k">;</span> <span class="pl-k">export</span> VOCAB_DIR=<span class="pl-smi">$CBTM_DIR</span>/vocab <span class="pl-k">export</span> PRETRAINED_MODELS_DIR=<span class="pl-smi">${CBTM_DIR}</span>/pretrained_models<span class="pl-k">;</span> mkdir -p <span class="pl-smi">${CBTM_DIR}</span> <span class="pl-smi">${DATA_DIR}</span> <span class="pl-smi">${SERIALIZATION_DIR}</span> <span class="pl-smi">${KMEANS_DIR}</span> <span class="pl-smi">${CLUSTERS_DIR}</span> <span class="pl-smi">${VOCAB_DIR}</span> <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span><span class="pl-k">;</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Configure cbtm_constants.py</h3><a id="user-content-configure-cbtm_constantspy" class="anchor" aria-label="Permalink: Configure cbtm_constants.py" href="#configure-cbtm_constantspy"><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">Next, the constants necessary to make this repo work are at <code>metaseq/cbtm_constants.py</code>. Modify these to suit your local environment.</p> <p dir="auto">Make sure the variables in <code>metaseq/cbtm_constants.py</code> are consistent with the paths you set as environment variables above.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Download vocab files and seed models</h3><a id="user-content-download-vocab-files-and-seed-models" class="anchor" aria-label="Permalink: Download vocab files and seed models" href="#download-vocab-files-and-seed-models"><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">We use the GPT-2 vocabulary:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="wget -O $VOCAB_DIR/gpt2-vocab.json http://s3.wasabisys.com/c4-example/vocab/gpt2-vocab.json wget -O $VOCAB_DIR/gpt2-merges.txt http://s3.wasabisys.com/c4-example/vocab/gpt2-merges.txt"><pre>wget -O <span class="pl-smi">$VOCAB_DIR</span>/gpt2-vocab.json http://s3.wasabisys.com/c4-example/vocab/gpt2-vocab.json wget -O <span class="pl-smi">$VOCAB_DIR</span>/gpt2-merges.txt http://s3.wasabisys.com/c4-example/vocab/gpt2-merges.txt</pre></div> <p dir="auto">Download the OPT-1.3B and OPT-6.7B checkpoints, which we use as our seed models. If you don't plan to use the 6.7B model, you can skip the last 2 wgets, which will take a while to complete:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="mkdir -p ${PRETRAINED_MODELS_DIR}/opt/1.3b/ mkdir -p ${PRETRAINED_MODELS_DIR}/opt/6.7b/ wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/1.3B/sharded_for_ddp.tgz | tar xvz --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/1.3b/ wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_0.tgz | tar xvz --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/6.7b/ wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_1.tgz | tar xvz --strip-components 6 -C ${PRETRAINED_MODELS_DIR}/opt/6.7b/ "><pre>mkdir -p <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/opt/1.3b/ mkdir -p <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/opt/6.7b/ wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/1.3B/sharded_for_ddp.tgz <span class="pl-k">|</span> tar xvz --strip-components 6 -C <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/opt/1.3b/ wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_0.tgz <span class="pl-k">|</span> tar xvz --strip-components 6 -C <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/opt/6.7b/ wget -qO- dl.fbaipublicfiles.com/cbtm/opt_models/6.7B/sharded_for_ddp_part_1.tgz <span class="pl-k">|</span> tar xvz --strip-components 6 -C <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/opt/6.7b/ </pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Download data</h3><a id="user-content-download-data" class="anchor" aria-label="Permalink: Download data" href="#download-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">We provide some sample C4 data to get you started. Our model only expects (sharded) line-separated jsonl files, split into train and validation data. If you'd like to train on your own data, just follow the overall data layout in the example.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="mkdir -p ${DATA_DIR}/c4_example/ wget -qO- http://s3.wasabisys.com/c4-example/c4_example.tar.gz | tar xvz -C ${DATA_DIR}/"><pre>mkdir -p <span class="pl-smi">${DATA_DIR}</span>/c4_example/ wget -qO- http://s3.wasabisys.com/c4-example/c4_example.tar.gz <span class="pl-k">|</span> tar xvz -C <span class="pl-smi">${DATA_DIR}</span>/</pre></div> <p dir="auto">This example dataset is a single shard of C4 and a small sample of data from the validation dataset.</p> <p dir="auto">You can download the full C4 dataset from Huggingface datasets at the following link: <a href="https://huggingface.co/datasets/c4" rel="nofollow">https://huggingface.co/datasets/c4</a>. Keep in mind that the dataset is very large, and comes as <code>json.gz</code> files. Our code expects raw jsonl files in the structure from the example directory, so make sure you have enough space (in total, it's about 1.4 terabytes of data uncompressed).</p> <p dir="auto">Metaseq expects the data to be in this format:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="{"text": "this is a document", "id": 0} {"text": "this is another document", "id": 1}"><pre class="notranslate"><code>{"text": "this is a document", "id": 0} {"text": "this is another document", "id": 1} </code></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Step 1: Train Clusterer</h2><a id="user-content-step-1-train-clusterer" class="anchor" aria-label="Permalink: Step 1: Train Clusterer" href="#step-1-train-clusterer"><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">This command trains a balanced k-means clusterer on a single shard of the C4 training data. Here we use k=8, and give as an argument a folder which contains a file called <code>C4.jsonl</code>, as described above.</p> <p dir="auto">Make sure you have access to a GPU here, to speed up training!</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="NUM_CLUSTERS=8; DATASET=c4_example; python -m metaseq.scripts.train_clusterer \ --data-dir ${DATA_DIR}/${DATASET}/train/00000 \ --num-clusters ${NUM_CLUSTERS} \ --balanced \ --output-dir ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/"><pre>NUM_CLUSTERS=8<span class="pl-k">;</span> DATASET=c4_example<span class="pl-k">;</span> python -m metaseq.scripts.train_clusterer \ --data-dir <span class="pl-smi">${DATA_DIR}</span>/<span class="pl-smi">${DATASET}</span>/train/00000 \ --num-clusters <span class="pl-smi">${NUM_CLUSTERS}</span> \ --balanced \ --output-dir <span class="pl-smi">${KMEANS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/</pre></div> <p dir="auto">This will create <code>tfidf.pkl</code> (a tf-idf embedder) and <code>kmeans.pkl</code> (a kmeans clusterer) pickle files at <code>${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Step 2: Cluster data</h2><a id="user-content-step-2-cluster-data" class="anchor" aria-label="Permalink: Step 2: Cluster data" href="#step-2-cluster-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">This code uses your trained clusterer to cluster the dataset's documents. This is <em>substantially</em> faster if you can parallelize it as slurm jobs.</p> <p dir="auto">We will do this automatically for you via <code>submitit</code>, just provide your slurm account and partition (either as a flag to the program, or in <code>metaseq/cbtm_constants.py</code>)</p> <p dir="auto">If you don't have access to slurm, you can cluster your data locally with the flag <code>--run local</code>, but it might take some time!</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="DATASET=c4_example; NUM_CLUSTERS=8; # Cluster train data python -m metaseq.scripts.cluster \ --job-dir ${CBTM_DIR}/cluster_logs \ --data-dir ${DATA_DIR}/${DATASET} \ --path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \ --num-clusters ${NUM_CLUSTERS} \ --output-prefix ${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \ --split train \ --run slurm; # Cluster validation data python -m metaseq.scripts.cluster \ --job-dir ${CBTM_DIR}/cluster_logs \ --data-dir ${DATA_DIR}/${DATASET} \ --path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \ --num-clusters ${NUM_CLUSTERS} \ --output-prefix ${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \ --split valid/C4_small \ --run slurm;"><pre>DATASET=c4_example<span class="pl-k">;</span> NUM_CLUSTERS=8<span class="pl-k">;</span> <span class="pl-c"><span class="pl-c">#</span> Cluster train data</span> python -m metaseq.scripts.cluster \ --job-dir <span class="pl-smi">${CBTM_DIR}</span>/cluster_logs \ --data-dir <span class="pl-smi">${DATA_DIR}</span>/<span class="pl-smi">${DATASET}</span> \ --path-to-clusterer <span class="pl-smi">${KMEANS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/ \ --num-clusters <span class="pl-smi">${NUM_CLUSTERS}</span> \ --output-prefix <span class="pl-smi">${CLUSTERS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/ \ --split train \ --run slurm<span class="pl-k">;</span> <span class="pl-c"><span class="pl-c">#</span> Cluster validation data</span> python -m metaseq.scripts.cluster \ --job-dir <span class="pl-smi">${CBTM_DIR}</span>/cluster_logs \ --data-dir <span class="pl-smi">${DATA_DIR}</span>/<span class="pl-smi">${DATASET}</span> \ --path-to-clusterer <span class="pl-smi">${KMEANS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/ \ --num-clusters <span class="pl-smi">${NUM_CLUSTERS}</span> \ --output-prefix <span class="pl-smi">${CLUSTERS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/ \ --split valid/C4_small \ --run slurm<span class="pl-k">;</span></pre></div> <p dir="auto">Logs for these clustering jobs appear in <code>${CBTM_DIR}/cluster_logs</code>.</p> <p dir="auto">After these jobs complete, open files in ${CLUSTERS_DIR}, e.g., <code>${CLUSTERS_DIR}/${DATASET}/${NUM_CLUSTERS}/train/00000/C4.jsonl</code>. You should see lines like the following:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="{"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|0","cluster":5} {"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|1","cluster":3} {"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|2","cluster":2}"><pre class="notranslate"><code>{"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|0","cluster":5} {"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|1","cluster":3} {"sp_id":"\/gscratch\/zlab\/sg01\/data\/c4_example\/train\/00000\/C4.jsonl|2","cluster":2} </code></pre></div> <p dir="auto">The field <code>sp_id</code> indicates a line (i.e., document) within a file, and the field <code>cluster</code> indicates its predicted cluster.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Step 3: Train Models</h2><a id="user-content-step-3-train-models" class="anchor" aria-label="Permalink: Step 3: Train Models" href="#step-3-train-models"><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">Now we'll use the clustered data to train experts. You'll need at least 4 GPUs simultaneously to train each model.</p> <p dir="auto">This tutorial uses our <code>train_cbtm</code> script, which interfaces with SLURM. By default, training jobs will request 8 CPUs per GPU and 48G of memory. These resource parameters can be customized in the <code>train_cbtm.sh</code> script.</p> <p dir="auto">We have also provided an example sbatch script, if desired, in <code>metaseq/scripts/example_sbatch.sh</code>. You may need to edit this example sbatch command to include any additional slurm arguments you might need to get it working on your system.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Train experts</h3><a id="user-content-train-experts" class="anchor" aria-label="Permalink: Train experts" href="#train-experts"><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">The following command will train 8 expert models with 4 GPUs each for 50 steps (increase to 10000 steps to replicate our paper).</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="DATASET=c4_example; NUM_CLUSTERS=8; NUM_GPUS_PER_EXPERT=4; if [[ "$NUM_GPUS_PER_EXPERT" -lt 8 ]]; then NUM_NODES=1 NUM_GPUS_PER_NODE=$NUM_GPUS else NUM_NODES=$((NUM_GPUS/8)) NUM_GPUS_PER_NODE=8 fi python -m metaseq.scripts.train_cbtm \ --model-size 1.3b \ --run slurm \ --path-to-clusters-dir $CLUSTERS_DIR/${DATASET}/$NUM_CLUSTERS/ \ --num-clusters $NUM_CLUSTERS \ --num-nodes $NUM_NODES \ --num-gpus $NUM_GPUS_PER_NODE \ --data-name ${DATASET} \ --path-to-data $DATA_DIR/${DATASET} \ --learning-rate 2e-4 \ --max-steps 50 \ --valid-subset valid/C4_small \ --train-subset train"><pre>DATASET=c4_example<span class="pl-k">;</span> NUM_CLUSTERS=8<span class="pl-k">;</span> NUM_GPUS_PER_EXPERT=4<span class="pl-k">;</span> <span class="pl-k">if</span> [[ <span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">$NUM_GPUS_PER_EXPERT</span><span class="pl-pds">"</span></span> <span class="pl-k">-lt</span> 8 ]]<span class="pl-k">;</span> <span class="pl-k">then</span> NUM_NODES=1 NUM_GPUS_PER_NODE=<span class="pl-smi">$NUM_GPUS</span> <span class="pl-k">else</span> NUM_NODES=<span class="pl-s"><span class="pl-pds">$((</span>NUM_GPUS<span class="pl-k">/</span><span class="pl-c1">8</span><span class="pl-pds">))</span></span> NUM_GPUS_PER_NODE=8 <span class="pl-k">fi</span> python -m metaseq.scripts.train_cbtm \ --model-size 1.3b \ --run slurm \ --path-to-clusters-dir <span class="pl-smi">$CLUSTERS_DIR</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">$NUM_CLUSTERS</span>/ \ --num-clusters <span class="pl-smi">$NUM_CLUSTERS</span> \ --num-nodes <span class="pl-smi">$NUM_NODES</span> \ --num-gpus <span class="pl-smi">$NUM_GPUS_PER_NODE</span> \ --data-name <span class="pl-smi">${DATASET}</span> \ --path-to-data <span class="pl-smi">$DATA_DIR</span>/<span class="pl-smi">${DATASET}</span> \ --learning-rate 2e-4 \ --max-steps 50 \ --valid-subset valid/C4_small \ --train-subset train</pre></div> <p dir="auto">To train on a specific cluster(s), you can add the flag <code>--train-cluster 1,3,5</code></p> <p dir="auto">To debug locally, change the <code>run</code> flag to <code>--run local</code>.</p> <p dir="auto">This command will output checkpoints and logs to <code>${SERIALIZATION_DIR}/8_clusters/</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Dense training</h3><a id="user-content-dense-training" class="anchor" aria-label="Permalink: Dense training" href="#dense-training"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The following command will train a dense model with 4 GPUs for 50 steps (increase to 10000 steps to replicate our paper).</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="DATASET=c4_example; NUM_GPUS_PER_EXPERT=4; if [[ "$NUM_GPUS_PER_EXPERT" -lt 8 ]]; then NUM_NODES=1 NUM_GPUS_PER_NODE=$NUM_GPUS else NUM_NODES=$((NUM_GPUS/8)) NUM_GPUS_PER_NODE=8 fi python -m metaseq.scripts.train_cbtm \ --num-clusters 1 \ --model-size 1.3b \ --run slurm \ --num-nodes $NUM_NODES \ --num-gpus $NUM_GPUS_PER_NODE \ --data-name ${DATASET} \ --path-to-data $DATA_DIR/$DATASET \ --learning-rate 2e-4 \ --max-steps 50 \ --valid-subset valid/C4_small \ --train-subset train"><pre>DATASET=c4_example<span class="pl-k">;</span> NUM_GPUS_PER_EXPERT=4<span class="pl-k">;</span> <span class="pl-k">if</span> [[ <span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">$NUM_GPUS_PER_EXPERT</span><span class="pl-pds">"</span></span> <span class="pl-k">-lt</span> 8 ]]<span class="pl-k">;</span> <span class="pl-k">then</span> NUM_NODES=1 NUM_GPUS_PER_NODE=<span class="pl-smi">$NUM_GPUS</span> <span class="pl-k">else</span> NUM_NODES=<span class="pl-s"><span class="pl-pds">$((</span>NUM_GPUS<span class="pl-k">/</span><span class="pl-c1">8</span><span class="pl-pds">))</span></span> NUM_GPUS_PER_NODE=8 <span class="pl-k">fi</span> python -m metaseq.scripts.train_cbtm \ --num-clusters 1 \ --model-size 1.3b \ --run slurm \ --num-nodes <span class="pl-smi">$NUM_NODES</span> \ --num-gpus <span class="pl-smi">$NUM_GPUS_PER_NODE</span> \ --data-name <span class="pl-smi">${DATASET}</span> \ --path-to-data <span class="pl-smi">$DATA_DIR</span>/<span class="pl-smi">$DATASET</span> \ --learning-rate 2e-4 \ --max-steps 50 \ --valid-subset valid/C4_small \ --train-subset train</pre></div> <p dir="auto">To debug locally, change the <code>run</code> flag to <code>--run local</code>.</p> <p dir="auto">This command will output checkpoints to <code>${SERIALIZATION_DIR}/1_clusters/</code>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Evaluation</h2><a id="user-content-evaluation" class="anchor" aria-label="Permalink: Evaluation" href="#evaluation"><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">To evaluate your models, first consolidate your shards into a single checkpoint file.</p> <p dir="auto">The following script depends on the <a href="https://www.gnu.org/software/parallel/" rel="nofollow"><code>gnu-parallel</code></a> package. You may need to modify the last line of <code>metaseq/scripts /consolidate_fsdp_shards.sh</code> to point to your gnu-parallel path.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="NUM_CLUSTERS=8; NUM_GPUS_PER_EXPERT=4; bash metaseq/scripts/consolidate_fsdp_shards.sh ${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/ "*ngpu${NUM_GPUS_PER_EXPERT}""><pre>NUM_CLUSTERS=8<span class="pl-k">;</span> NUM_GPUS_PER_EXPERT=4<span class="pl-k">;</span> bash metaseq/scripts/consolidate_fsdp_shards.sh <span class="pl-smi">${SERIALIZATION_DIR}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>_clusters/ <span class="pl-s"><span class="pl-pds">"</span>*ngpu<span class="pl-smi">${NUM_GPUS_PER_EXPERT}</span><span class="pl-pds">"</span></span></pre></div> <p dir="auto">This will create a <code>consolidated.pt</code> checkpoint in each model's folder.</p> <p dir="auto">Now the checkpoints are ready for eval. To launch on slurm:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="export NUM_CLUSTERS=8; # we want as many GPUs as we have clusters export NUM_GPUS=${NUM_CLUSTERS}; export DATASET=c4_example; export EVAL_DIR=${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/eval mkdir -p ${EVAL_DIR}; # get model checkpoints CONSOLIDATED_MODEL_PATHS=; # this function gets all model checkpoint directories and sorts them by the cluster ID # modify the folder pattern to match the directories names of your models, if you need. FOLDER_PATTERN="cbtm\.c4_example\.*ngpu4" mapfile -t MODEL_FOLDERS < <(find ${SERIALIZATION_DIR}/${NUM_CLUSTERS}_clusters/ -type d -name $FOLDER_PATTERN -name "*\.cluster*" -printf "%f|%p\n" | sort -t "|" -k1,1 -t "|" -k2,2 | cut -d "|" -f 2) for folder in "${MODEL_FOLDERS[@]}"; do # check if there are any consolidated.pt files in the model folder if test -f "${folder}/consolidated.pt"; then CONSOLIDATED_MODEL_PATHS+="${folder}/consolidated.pt "; fi; done # function to join checkpoints into comma separated string function join { local IFS=","; echo "$*"; } # these model paths should be ordered by cluster ID! JOINED_MODEL_PATHS=$(join ${CONSOLIDATED_MODEL_PATHS[@]}) python -m metaseq_cli.eval_cbtm \ --data-dir ${DATA_DIR}/${DATASET} \ --data-subset valid/C4_small \ --path-to-clusterer ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ \ --model-paths $(join ${CONSOLIDATED_MODEL_PATHS[@]}) \ --job-dir ${EVAL_DIR} \ --temperature 0.1 \ --max-valid-steps 200 \ --ensemble-type clustering \ --submitit"><pre><span class="pl-k">export</span> NUM_CLUSTERS=8<span class="pl-k">;</span> <span class="pl-c"><span class="pl-c">#</span> we want as many GPUs as we have clusters</span> <span class="pl-k">export</span> NUM_GPUS=<span class="pl-smi">${NUM_CLUSTERS}</span><span class="pl-k">;</span> <span class="pl-k">export</span> DATASET=c4_example<span class="pl-k">;</span> <span class="pl-k">export</span> EVAL_DIR=<span class="pl-smi">${SERIALIZATION_DIR}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>_clusters/eval mkdir -p <span class="pl-smi">${EVAL_DIR}</span><span class="pl-k">;</span> <span class="pl-c"><span class="pl-c">#</span> get model checkpoints</span> CONSOLIDATED_MODEL_PATHS=<span class="pl-k">;</span> <span class="pl-c"><span class="pl-c">#</span> this function gets all model checkpoint directories and sorts them by the cluster ID</span> <span class="pl-c"><span class="pl-c">#</span> modify the folder pattern to match the directories names of your models, if you need.</span> FOLDER_PATTERN=<span class="pl-s"><span class="pl-pds">"</span>cbtm\.c4_example\.*ngpu4<span class="pl-pds">"</span></span> mapfile -t MODEL_FOLDERS <span class="pl-k"><</span> <span class="pl-s"><span class="pl-pds"><(</span>find <span class="pl-smi">${SERIALIZATION_DIR}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>_clusters/ -type d -name <span class="pl-smi">$FOLDER_PATTERN</span> -name <span class="pl-s"><span class="pl-pds">"</span>*\.cluster*<span class="pl-pds">"</span></span> -printf <span class="pl-s"><span class="pl-pds">"</span>%f|%p\n<span class="pl-pds">"</span></span> <span class="pl-k">|</span> sort -t <span class="pl-s"><span class="pl-pds">"</span>|<span class="pl-pds">"</span></span> -k1,1 -t <span class="pl-s"><span class="pl-pds">"</span>|<span class="pl-pds">"</span></span> -k2,2 <span class="pl-k">|</span> cut -d <span class="pl-s"><span class="pl-pds">"</span>|<span class="pl-pds">"</span></span> -f 2<span class="pl-pds">)</span></span> <span class="pl-k">for</span> <span class="pl-smi">folder</span> <span class="pl-k">in</span> <span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">${MODEL_FOLDERS[@]}</span><span class="pl-pds">"</span></span><span class="pl-k">;</span> <span class="pl-k">do</span> <span class="pl-c"><span class="pl-c">#</span> check if there are any consolidated.pt files in the model folder </span> <span class="pl-k">if</span> <span class="pl-c1">test</span> -f <span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">${folder}</span>/consolidated.pt<span class="pl-pds">"</span></span><span class="pl-k">;</span> <span class="pl-k">then</span> CONSOLIDATED_MODEL_PATHS+=<span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">${folder}</span>/consolidated.pt <span class="pl-pds">"</span></span><span class="pl-k">;</span> <span class="pl-k">fi</span><span class="pl-k">;</span> <span class="pl-k">done</span> <span class="pl-c"><span class="pl-c">#</span> function to join checkpoints into comma separated string</span> <span class="pl-k">function</span> <span class="pl-en">join</span> { <span class="pl-k">local</span> IFS=<span class="pl-s"><span class="pl-pds">"</span>,<span class="pl-pds">"</span></span><span class="pl-k">;</span> <span class="pl-c1">echo</span> <span class="pl-s"><span class="pl-pds">"</span><span class="pl-smi">$*</span><span class="pl-pds">"</span></span><span class="pl-k">;</span> } <span class="pl-c"><span class="pl-c">#</span> these model paths should be ordered by cluster ID!</span> JOINED_MODEL_PATHS=<span class="pl-s"><span class="pl-pds">$(</span>join <span class="pl-smi">${CONSOLIDATED_MODEL_PATHS[@]}</span><span class="pl-pds">)</span></span> python -m metaseq_cli.eval_cbtm \ --data-dir <span class="pl-smi">${DATA_DIR}</span>/<span class="pl-smi">${DATASET}</span> \ --data-subset valid/C4_small \ --path-to-clusterer <span class="pl-smi">${KMEANS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/ \ --model-paths <span class="pl-s"><span class="pl-pds">$(</span>join <span class="pl-smi">${CONSOLIDATED_MODEL_PATHS[@]}</span><span class="pl-pds">)</span></span> \ --job-dir <span class="pl-smi">${EVAL_DIR}</span> \ --temperature 0.1 \ --max-valid-steps 200 \ --ensemble-type clustering \ --submitit</pre></div> <p dir="auto">You can check out logs for your slurm job at <code>${EVAL_DIR}</code>.</p> <p dir="auto">To launch locally, remove the flag <code>--submitit</code> in the command above. Make sure you have <code>$NUM_CLUSTERS</code> GPUs visible though!</p> <p dir="auto">This will output perplexity results to <code>${EVAL_DIR}/result.json</code>.</p> <p dir="auto">Use the same command as above to evaluate your dense models, just change the environment variable <code>NUM_CLUSTERS=1</code>.</p> <p dir="auto">On our machine we get <code>ppl: 17.86</code> for the 8-cluster model from this tutorial, and <code>ppl: 18.56</code> for the 1-cluster model. (Note that these perplexities are only for reproducibility purposes; increase the step size to 10000 to replicate our runs from the paper).</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">MoE baseline training via sparse upcycling</h2><a id="user-content-moe-baseline-training-via-sparse-upcycling" class="anchor" aria-label="Permalink: MoE baseline training via sparse upcycling" href="#moe-baseline-training-via-sparse-upcycling"><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">See our <a href="https://github.com/kernelmachine/moe-fairseq">fairseq fork</a> for instructions and code to train the sparse upcycling MoE baseline.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Open-sourced pretrained models</h2><a id="user-content-open-sourced-pretrained-models" class="anchor" aria-label="Permalink: Open-sourced pretrained models" href="#open-sourced-pretrained-models"><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">Downloading clusterers and embedders</h3><a id="user-content-downloading-clusterers-and-embedders" class="anchor" aria-label="Permalink: Downloading clusterers and embedders" href="#downloading-clusterers-and-embedders"><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">To get a pretrained clusterer and embedder from the paper, you can run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# c4 or s2orc export DATASET=c4 # 2, 4, 8, 16, 32, 64, 128 export NUM_CLUSTERS=4 mkdir -p ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/ wget -O ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/tfidf.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/${DATASET}/${NUM_CLUSTERS}/tfidf.pkl wget -O ${KMEANS_DIR}/${DATASET}/${NUM_CLUSTERS}/kmeans.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/${DATASET}/${NUM_CLUSTERS}/kmeans.pkl"><pre><span class="pl-c"><span class="pl-c">#</span> c4 or s2orc</span> <span class="pl-k">export</span> DATASET=c4 <span class="pl-c"><span class="pl-c">#</span> 2, 4, 8, 16, 32, 64, 128</span> <span class="pl-k">export</span> NUM_CLUSTERS=4 mkdir -p <span class="pl-smi">${KMEANS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/ wget -O <span class="pl-smi">${KMEANS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/tfidf.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/tfidf.pkl wget -O <span class="pl-smi">${KMEANS_DIR}</span>/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/kmeans.pkl https://dl.fbaipublicfiles.com/cbtm/clusterers/<span class="pl-smi">${DATASET}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>/kmeans.pkl</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Downloading language models</h3><a id="user-content-downloading-language-models" class="anchor" aria-label="Permalink: Downloading language models" href="#downloading-language-models"><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">To access the language models we trained in our paper, you can run the following command:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# c4 or s2orc export DATASET=c4 # opt1.3b or opt6.7b export MODEL_ARCH=opt1.3b # 4, 8, 16, 32, 64, 128, 256, 512, 1024 export GPUS_PER_EXPERT=4 # 2, 4, 8, 16, 32, 64, 128 export NUM_CLUSTERS=64 # anything from 0 to $NUM_CLUSTERS-1 export CLUSTER_NUMBER=28 FOLDER=${DATASET}.${MODEL_ARCH}.${NUM_CLUSTERS}_clusters.cluster${CLUSTER_NUMBER}.ngpu${GPUS_PER_EXPERT}/ mkdir -p ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER wget -O ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/$DATASET/${MODEL_ARCH}/${NUM_CLUSTERS}_clusters/ngpu${GPUS_PER_EXPERT}/${CLUSTER_NUMBER}/consolidated.pt"><pre><span class="pl-c"><span class="pl-c">#</span> c4 or s2orc</span> <span class="pl-k">export</span> DATASET=c4 <span class="pl-c"><span class="pl-c">#</span> opt1.3b or opt6.7b</span> <span class="pl-k">export</span> MODEL_ARCH=opt1.3b <span class="pl-c"><span class="pl-c">#</span> 4, 8, 16, 32, 64, 128, 256, 512, 1024</span> <span class="pl-k">export</span> GPUS_PER_EXPERT=4 <span class="pl-c"><span class="pl-c">#</span> 2, 4, 8, 16, 32, 64, 128</span> <span class="pl-k">export</span> NUM_CLUSTERS=64 <span class="pl-c"><span class="pl-c">#</span> anything from 0 to $NUM_CLUSTERS-1</span> <span class="pl-k">export</span> CLUSTER_NUMBER=28 FOLDER=<span class="pl-smi">${DATASET}</span>.<span class="pl-smi">${MODEL_ARCH}</span>.<span class="pl-smi">${NUM_CLUSTERS}</span>_clusters.cluster<span class="pl-smi">${CLUSTER_NUMBER}</span>.ngpu<span class="pl-smi">${GPUS_PER_EXPERT}</span>/ mkdir -p <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/cbtm/<span class="pl-smi">$FOLDER</span> wget -O <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/cbtm/<span class="pl-smi">$FOLDER</span>/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/<span class="pl-smi">$DATASET</span>/<span class="pl-smi">${MODEL_ARCH}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>_clusters/ngpu<span class="pl-smi">${GPUS_PER_EXPERT}</span>/<span class="pl-smi">${CLUSTER_NUMBER}</span>/consolidated.pt</pre></div> <p dir="auto">To get all n experts for an n-cluster c-BTM model, you can do a for loop:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# c4 or s2orc export DATASET=c4 # opt1.3b or opt6.7b export MODEL_ARCH=opt1.3b # 4, 8, 16, 32, 64, 128, 256, 512, 1024 export GPUS_PER_EXPERT=4 # 2, 4, 8, 16, 32, 64, 128 export NUM_CLUSTERS=4 for CLUSTER_NUMBER in $(seq 0 $((NUM_CLUSTERS-1))); do FOLDER=${DATASET}.${MODEL_ARCH}.${NUM_CLUSTERS}_clusters.cluster${CLUSTER_NUMBER}.ngpu${GPUS_PER_EXPERT}/ mkdir -p ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER wget -O ${PRETRAINED_MODELS_DIR}/cbtm/$FOLDER/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/$DATASET/${MODEL_ARCH}/${NUM_CLUSTERS}_clusters/ngpu${GPUS_PER_EXPERT}/${CLUSTER_NUMBER}/consolidated.pt done"><pre><span class="pl-c"><span class="pl-c">#</span> c4 or s2orc</span> <span class="pl-k">export</span> DATASET=c4 <span class="pl-c"><span class="pl-c">#</span> opt1.3b or opt6.7b</span> <span class="pl-k">export</span> MODEL_ARCH=opt1.3b <span class="pl-c"><span class="pl-c">#</span> 4, 8, 16, 32, 64, 128, 256, 512, 1024</span> <span class="pl-k">export</span> GPUS_PER_EXPERT=4 <span class="pl-c"><span class="pl-c">#</span> 2, 4, 8, 16, 32, 64, 128</span> <span class="pl-k">export</span> NUM_CLUSTERS=4 <span class="pl-k">for</span> <span class="pl-smi">CLUSTER_NUMBER</span> <span class="pl-k">in</span> <span class="pl-s"><span class="pl-pds">$(</span>seq 0 <span class="pl-s"><span class="pl-pds">$((</span>NUM_CLUSTERS<span class="pl-k">-</span><span class="pl-c1">1</span><span class="pl-pds">))</span></span><span class="pl-pds">)</span></span><span class="pl-k">;</span> <span class="pl-k">do</span> FOLDER=<span class="pl-smi">${DATASET}</span>.<span class="pl-smi">${MODEL_ARCH}</span>.<span class="pl-smi">${NUM_CLUSTERS}</span>_clusters.cluster<span class="pl-smi">${CLUSTER_NUMBER}</span>.ngpu<span class="pl-smi">${GPUS_PER_EXPERT}</span>/ mkdir -p <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/cbtm/<span class="pl-smi">$FOLDER</span> wget -O <span class="pl-smi">${PRETRAINED_MODELS_DIR}</span>/cbtm/<span class="pl-smi">$FOLDER</span>/consolidated.pt https://dl.fbaipublicfiles.com/cbtm/cbtm_models/<span class="pl-smi">$DATASET</span>/<span class="pl-smi">${MODEL_ARCH}</span>/<span class="pl-smi">${NUM_CLUSTERS}</span>_clusters/ngpu<span class="pl-smi">${GPUS_PER_EXPERT}</span>/<span class="pl-smi">${CLUSTER_NUMBER}</span>/consolidated.pt <span class="pl-k">done</span></pre></div> <p dir="auto">All models for which we show results in the core results section of the paper can be downloaded here. The highest budget (168B token) 1.3B parameter models have <code>${NUM_CLUSTERS} * $GPUS_PER_EXPERT = 1024</code>, resulting in <code>(${NUM_CLUSTERS}, $GPUS_PER_EXPERT) = (1, 1024), (2, 512) ... (128, 8)</code>. Settings for smaller compute budget models can be found with a proportionally small product. E.g., for 42B token 1.3B parameter models, we have <code>${NUM_CLUSTERS} * $GPUS_PER_EXPERT = 256</code>, resulting in <code>(${NUM_CLUSTERS}, $GPUS_PER_EXPERT) = (1, 256), (2, 128) ... (64, 4)</code>.</p> <p dir="auto">These models can be evaluated using the command given above, under "Cluster BTM Training and Evaluation". However, to continue training any of these experts using model parallel, you will need to reshard them, check the <code>metaseq</code> library to learn more on how to do this.</p> <p dir="auto">See this <a href="https://l.facebook.com/l.php?u=https%3A%2F%2Fdl.fbaipublicfiles.com%2FREADME&h=AT1F-a_xXIhZseEwKETbvCNYQIJlBJOLEU2_MkOfjxpaCML8sQz-hm7qGMpUAwJ-Zd3F-P3x3ZfrPCnxP2gME5jGSGZo8c7pCXB_NP1CyxkxJYQWGGPm2ZiTsur2Qt29FxgAF4V4IAoCDStOkr8y8hH0" rel="nofollow">README</a> for details on rate-limiting when downloading models.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Converting from metaseq to Huggingface</h3><a id="user-content-converting-from-metaseq-to-huggingface" class="anchor" aria-label="Permalink: Converting from metaseq to Huggingface" href="#converting-from-metaseq-to-huggingface"><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">We have provided a script to convert all metaseq models to Huggingface transformers compatible checkpoints.</p> <p dir="auto">First, you have to install huggingface transformers <a href="https://huggingface.co/docs/transformers/installation#install-from-source" rel="nofollow">from source</a>, and make sure you consolidate your metaseq checkpoint using the <code>consolidate_fsdp_shards.sh</code> first (see above).</p> <p dir="auto">Then run:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="PATH_TO_TRANSFORMERS=/path/to/installed/transformers/library/ # path to directory containing consolidated.pt checkpoint INPUT_DIR=/path/to/metaseq/model/dir OUTPUT_DIR=my_huggingface_model bash metaseq/scripts/convert_hf.sh $PATH_TO_TRANSFORMERS $INPUT_DIR $OUTPUT_DIR"><pre class="notranslate"><code>PATH_TO_TRANSFORMERS=/path/to/installed/transformers/library/ # path to directory containing consolidated.pt checkpoint INPUT_DIR=/path/to/metaseq/model/dir OUTPUT_DIR=my_huggingface_model bash metaseq/scripts/convert_hf.sh $PATH_TO_TRANSFORMERS $INPUT_DIR $OUTPUT_DIR </code></pre></div> <p dir="auto">This will output a checkpoint in $OUTPUT_DIR you can use in any huggingface transformers pipeline.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> 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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>