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data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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<label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow 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class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLiger Kernel: Efficient Triton Kernels for LLM Training\u003c/h1\u003e\u003ca id=\"user-content-liger-kernel-efficient-triton-kernels-for-llm-training\" class=\"anchor\" aria-label=\"Permalink: Liger Kernel: Efficient Triton Kernels for LLM Training\" href=\"#liger-kernel-efficient-triton-kernels-for-llm-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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003cth colspan=\"2\"\u003eStable\u003c/th\u003e\n \u003cth colspan=\"2\"\u003eNightly\u003c/th\u003e\n \u003cth\u003eDiscord\u003c/th\u003e\n \u003cth\u003eGurubase (experimental)\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca href=\"https://pepy.tech/project/liger-kernel\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/50c50bd91ad37ce1c00b8f982948f7df8aa1013738786da269d1c9ec7ba384f9/68747470733a2f2f7374617469632e706570792e746563682f62616467652f6c696765722d6b65726e656c\" alt=\"Downloads (Stable)\" data-canonical-src=\"https://static.pepy.tech/badge/liger-kernel\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca 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\u003ctd\u003e\n \u003ca href=\"https://gurubase.io/g/liger-kernel\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/85eb8df9534351c8c466c321f7fbeafb7beeedc390e6ab64a6a29b6eb0ad1adc/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f47757275626173652d41736b2532304c696765722532304b65726e656c253230477572752d303036424646\" alt=\"Ask Liger Kernel Guru\" data-canonical-src=\"https://img.shields.io/badge/Gurubase-Ask%20Liger%20Kernel%20Guru-006BFF\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/logo-banner.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/logo-banner.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e | \u003ca href=\"#getting-started\"\u003eGetting Started\u003c/a\u003e | \u003ca href=\"#examples\"\u003eExamples\u003c/a\u003e | \u003ca href=\"#apis\"\u003eAPIs\u003c/a\u003e | \u003ca href=\"#cite-this-work\"\u003eCite our work\u003c/a\u003e\u003c/p\u003e\n\u003cdetails\u003e\n \u003csummary\u003eLatest News 🔥\u003c/summary\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2024/11/6] We release \u003ca href=\"https://github.com/linkedin/Liger-Kernel/releases/tag/v0.4.0\"\u003ev0.4.0\u003c/a\u003e: Full AMD support, Tech Report, Modal CI, Llama-3.2-Vision!\u003c/li\u003e\n\u003cli\u003e[2024/10/21] We have released the tech report of Liger Kernel on Arxiv: \u003ca href=\"https://arxiv.org/pdf/2410.10989\" rel=\"nofollow\"\u003ehttps://arxiv.org/pdf/2410.10989\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/9/6] We release v0.2.1 (\u003ca href=\"https://x.com/liger_kernel/status/1832168197002510649\" rel=\"nofollow\"\u003eX post\u003c/a\u003e). 2500+ Stars, 10+ New Contributors, 50+ PRs, 50k Downloads in two weeks!\u003c/li\u003e\n\u003cli\u003e[2024/8/31] CUDA MODE talk, \u003ca href=\"https://youtu.be/gWble4FreV4?si=dxPeIchhkJ36Mbns\" rel=\"nofollow\"\u003eLiger-Kernel: Real-world Triton kernel for LLM Training\u003c/a\u003e, \u003ca href=\"https://github.com/cuda-mode/lectures?tab=readme-ov-file#lecture-28-liger-kernel\"\u003eSlides\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/8/23] Official release: check out our \u003ca href=\"https://x.com/hsu_byron/status/1827072737673982056\" rel=\"nofollow\"\u003eX post\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eLiger Kernel\u003c/strong\u003e is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU \u003cstrong\u003etraining throughput by 20%\u003c/strong\u003e and reduces \u003cstrong\u003ememory usage by 60%\u003c/strong\u003e. We have implemented \u003cstrong\u003eHugging Face Compatible\u003c/strong\u003e \u003ccode\u003eRMSNorm\u003c/code\u003e, \u003ccode\u003eRoPE\u003c/code\u003e, \u003ccode\u003eSwiGLU\u003c/code\u003e, \u003ccode\u003eCrossEntropy\u003c/code\u003e, \u003ccode\u003eFusedLinearCrossEntropy\u003c/code\u003e, and more to come. The kernel works out of the box with \u003ca href=\"https://github.com/Dao-AILab/flash-attention\"\u003eFlash Attention\u003c/a\u003e, \u003ca href=\"https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html\" rel=\"nofollow\"\u003ePyTorch FSDP\u003c/a\u003e, and \u003ca href=\"https://github.com/microsoft/DeepSpeed\"\u003eMicrosoft DeepSpeed\u003c/a\u003e. We welcome contributions from the community to gather the best kernels for LLM training.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupercharge Your Model with Liger Kernel\u003c/h2\u003e\u003ca id=\"user-content-supercharge-your-model-with-liger-kernel\" class=\"anchor\" aria-label=\"Permalink: Supercharge Your Model with Liger Kernel\" href=\"#supercharge-your-model-with-liger-kernel\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/banner.GIF\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/banner.GIF\" alt=\"Banner\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWith one line of code, Liger Kernel can increase throughput by more than 20% and reduce memory usage by 60%, thereby enabling longer context lengths, larger batch sizes, and massive vocabularies.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSpeed Up\u003c/th\u003e\n\u003cth\u003eMemory Reduction\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-tps.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-tps.png\" alt=\"Speed up\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-memory.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-memory.png\" alt=\"Memory\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBenchmark conditions: LLaMA 3-8B, Batch Size = 8, Data Type = \u003ccode\u003ebf16\u003c/code\u003e, Optimizer = AdamW, Gradient Checkpointing = True, Distributed Strategy = FSDP1 on 8 A100s.\u003c/li\u003e\n\u003cli\u003eHugging Face models start to OOM at a 4K context length, whereas Hugging Face + Liger Kernel scales up to 16K.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/blockquote\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eUse Case\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/linkedin/Liger-Kernel/tree/main/examples/huggingface\"\u003e\u003cstrong\u003eHugging Face Trainer\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eTrain LLaMA 3-8B ~20% faster with over 40% memory reduction on Alpaca dataset using 4 A100s with FSDP\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/linkedin/Liger-Kernel/tree/main/examples/lightning\"\u003e\u003cstrong\u003eLightning Trainer\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eIncrease 15% throughput and reduce memory usage by 40% with LLaMA3-8B on MMLU dataset using 8 A100s with DeepSpeed ZeRO3\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/linkedin/Liger-Kernel/tree/main/examples/medusa\"\u003e\u003cstrong\u003eMedusa Multi-head LLM (Retraining Phase)\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eReduce memory usage by 80% with 5 LM heads and improve throughput by 40% using 8 A100s with FSDP\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/linkedin/Liger-Kernel/tree/main/examples/huggingface/run_qwen2_vl.sh\"\u003e\u003cstrong\u003eVision-Language Model SFT\u003c/strong\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFinetune Qwen2-VL on image-text data using 4 A100s with FSDP\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKey Features\u003c/h2\u003e\u003ca id=\"user-content-key-features\" class=\"anchor\" aria-label=\"Permalink: Key Features\" href=\"#key-features\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eEase of use:\u003c/strong\u003e Simply patch your Hugging Face model with one line of code, or compose your own model using our Liger Kernel modules.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTime and memory efficient:\u003c/strong\u003e In the same spirit as Flash-Attn, but for layers like \u003cstrong\u003eRMSNorm\u003c/strong\u003e, \u003cstrong\u003eRoPE\u003c/strong\u003e, \u003cstrong\u003eSwiGLU\u003c/strong\u003e, and \u003cstrong\u003eCrossEntropy\u003c/strong\u003e! Increases multi-GPU training throughput by 20% and reduces memory usage by 60% with \u003cstrong\u003ekernel fusion\u003c/strong\u003e, \u003cstrong\u003ein-place replacement\u003c/strong\u003e, and \u003cstrong\u003echunking\u003c/strong\u003e techniques.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eExact:\u003c/strong\u003e Computation is exact—no approximations! Both forward and backward passes are implemented with rigorous unit tests and undergo convergence testing against training runs without Liger Kernel to ensure accuracy.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLightweight:\u003c/strong\u003e Liger Kernel has minimal dependencies, requiring only Torch and Triton—no extra libraries needed! Say goodbye to dependency headaches!\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMulti-GPU supported:\u003c/strong\u003e Compatible with multi-GPU setups (PyTorch FSDP, DeepSpeed, DDP, etc.).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTrainer Framework Integration\u003c/strong\u003e: \u003ca href=\"https://github.com/axolotl-ai-cloud/axolotl\"\u003eAxolotl\u003c/a\u003e, \u003ca href=\"https://github.com/hiyouga/LLaMA-Factory\"\u003eLLaMa-Factory\u003c/a\u003e, \u003ca href=\"https://github.com/huggingface/trl/releases/tag/v0.10.1\"\u003eSFTTrainer\u003c/a\u003e, \u003ca href=\"https://github.com/huggingface/transformers/pull/32860\" data-hovercard-type=\"pull_request\" data-hovercard-url=\"/huggingface/transformers/pull/32860/hovercard\"\u003eHugging Face Trainer\u003c/a\u003e, \u003ca href=\"https://github.com/modelscope/ms-swift\"\u003eSWIFT\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDependencies\u003c/h3\u003e\u003ca id=\"user-content-dependencies\" class=\"anchor\" aria-label=\"Permalink: Dependencies\" href=\"#dependencies\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCUDA\u003c/h4\u003e\u003ca id=\"user-content-cuda\" class=\"anchor\" aria-label=\"Permalink: CUDA\" href=\"#cuda\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003etorch \u0026gt;= 2.1.2\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003etriton \u0026gt;= 2.3.0\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eROCm\u003c/h4\u003e\u003ca id=\"user-content-rocm\" class=\"anchor\" aria-label=\"Permalink: ROCm\" href=\"#rocm\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003etorch \u0026gt;= 2.5.0\u003c/code\u003e Install according to the instruction in Pytorch official webpage.\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003etriton \u0026gt;= 3.0.0\u003c/code\u003e Install from pypi. (e.g. \u003ccode\u003epip install triton==3.0.0\u003c/code\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOptional Dependencies\u003c/h3\u003e\u003ca id=\"user-content-optional-dependencies\" class=\"anchor\" aria-label=\"Permalink: Optional Dependencies\" href=\"#optional-dependencies\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ccode\u003etransformers \u0026gt;= 4.x\u003c/code\u003e: Required if you plan to use the transformers models patching APIs. The specific model you are working will dictate the minimum version of transformers.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote:\u003c/strong\u003e\nOur kernels inherit the full spectrum of hardware compatibility offered by \u003ca href=\"https://github.com/triton-lang/triton\"\u003eTriton\u003c/a\u003e.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"auto\"\u003eTo install the stable version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install liger-kernel\"\u003e\u003cpre\u003e$ pip install liger-kernel\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install the nightly version:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install liger-kernel-nightly\"\u003e\u003cpre\u003e$ pip install liger-kernel-nightly\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install from source:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/linkedin/Liger-Kernel.git\ncd Liger-Kernel\npip install -e .\n# or if using transformers\npip install -e .[transformers]\"\u003e\u003cpre\u003egit clone https://github.com/linkedin/Liger-Kernel.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e Liger-Kernel\npip install -e \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or if using transformers\u003c/span\u003e\npip install -e .[transformers]\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\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 are a couple of ways to apply Liger kernels, depending on the level of customization required.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1. Use AutoLigerKernelForCausalLM\u003c/h3\u003e\u003ca id=\"user-content-1-use-autoligerkernelforcausallm\" class=\"anchor\" aria-label=\"Permalink: 1. Use AutoLigerKernelForCausalLM\" href=\"#1-use-autoligerkernelforcausallm\"\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\"\u003eUsing the \u003ccode\u003eAutoLigerKernelForCausalLM\u003c/code\u003e is the simplest approach, as you don't have to import a model-specific patching API. If the model type is supported, the modeling code will be automatically patched using the default settings.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from liger_kernel.transformers import AutoLigerKernelForCausalLM\n\n# This AutoModel wrapper class automatically monkey-patches the\n# model with the optimized Liger kernels if the model is supported.\nmodel = AutoLigerKernelForCausalLM.from_pretrained(\u0026quot;path/to/some/model\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eliger_kernel\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoLigerKernelForCausalLM\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# This AutoModel wrapper class automatically monkey-patches the\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# model with the optimized Liger kernels if the model is supported.\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoLigerKernelForCausalLM\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efrom_pretrained\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"path/to/some/model\"\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\"\u003e2. Apply Model-Specific Patching APIs\u003c/h3\u003e\u003ca id=\"user-content-2-apply-model-specific-patching-apis\" class=\"anchor\" aria-label=\"Permalink: 2. Apply Model-Specific Patching APIs\" href=\"#2-apply-model-specific-patching-apis\"\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\"\u003eUsing the \u003ca href=\"#patching\"\u003epatching APIs\u003c/a\u003e, you can swap Hugging Face models with optimized Liger Kernels.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import transformers\nfrom liger_kernel.transformers import apply_liger_kernel_to_llama\n\n# 1a. Adding this line automatically monkey-patches the model with the optimized Liger kernels\napply_liger_kernel_to_llama()\n\n# 1b. You could alternatively specify exactly which kernels are applied\napply_liger_kernel_to_llama(\n rope=True,\n swiglu=True,\n cross_entropy=True,\n fused_linear_cross_entropy=False,\n rms_norm=False\n)\n\n# 2. Instantiate patched model\nmodel = transformers.AutoModelForCausalLM(\u0026quot;path/to/llama/model\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eliger_kernel\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eapply_liger_kernel_to_llama\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# 1a. Adding this line automatically monkey-patches the model with the optimized Liger kernels\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eapply_liger_kernel_to_llama\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# 1b. You could alternatively specify exactly which kernels are applied\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eapply_liger_kernel_to_llama\u003c/span\u003e(\n \u003cspan class=\"pl-s1\"\u003erope\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003eswiglu\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003ecross_entropy\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003efused_linear_cross_entropy\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003erms_norm\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e\n)\n\n\u003cspan class=\"pl-c\"\u003e# 2. Instantiate patched model\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eAutoModelForCausalLM\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"path/to/llama/model\"\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\"\u003e3. Compose Your Own Model\u003c/h3\u003e\u003ca id=\"user-content-3-compose-your-own-model\" class=\"anchor\" aria-label=\"Permalink: 3. Compose Your Own Model\" href=\"#3-compose-your-own-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can take individual \u003ca href=\"#kernels\"\u003ekernels\u003c/a\u003e to compose your models.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from liger_kernel.transformers import LigerFusedLinearCrossEntropyLoss\nimport torch.nn as nn\nimport torch\n\nmodel = nn.Linear(128, 256).cuda()\n\n# fuses linear + cross entropy layers together and performs chunk-by-chunk computation to reduce memory\nloss_fn = LigerFusedLinearCrossEntropyLoss()\n\ninput = torch.randn(4, 128, requires_grad=True, device=\u0026quot;cuda\u0026quot;)\ntarget = torch.randint(256, (4, ), device=\u0026quot;cuda\u0026quot;)\n\nloss = loss_fn(model.weight, input, target)\nloss.backward()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eliger_kernel\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etransformers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eLigerFusedLinearCrossEntropyLoss\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enn\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enn\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e\n\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enn\u003c/span\u003e.\u003cspan class=\"pl-v\"\u003eLinear\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e128\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e256\u003c/span\u003e).\u003cspan class=\"pl-en\"\u003ecuda\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# fuses linear + cross entropy layers together and performs chunk-by-chunk computation to reduce memory\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eloss_fn\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eLigerFusedLinearCrossEntropyLoss\u003c/span\u003e()\n\n\u003cspan class=\"pl-s1\"\u003einput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003erandn\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e128\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003erequires_grad\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"cuda\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003erandint\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e256\u003c/span\u003e, (\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e, ), \u003cspan class=\"pl-s1\"\u003edevice\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"cuda\"\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eloss_fn\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eweight\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einput\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebackward\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAPIs\u003c/h2\u003e\u003ca id=\"user-content-apis\" class=\"anchor\" aria-label=\"Permalink: APIs\" href=\"#apis\"\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\"\u003eAutoModel\u003c/h3\u003e\u003ca id=\"user-content-automodel\" class=\"anchor\" aria-label=\"Permalink: AutoModel\" href=\"#automodel\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eAutoModel Variant\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eAPI\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eAutoModelForCausalLM\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.AutoLigerKernelForCausalLM\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePatching\u003c/h3\u003e\u003ca id=\"user-content-patching\" class=\"anchor\" aria-label=\"Permalink: Patching\" href=\"#patching\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eAPI\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eSupported Operations\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA 2 \u0026amp; 3\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_llama\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA 3.2-Vision\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_mllama\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMistral\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_mistral\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMixtral\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_mixtral\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGemma1\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_gemma\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGemma2\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_gemma2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen2 \u0026amp; Qwen2.5\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_qwen2\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen2-VL\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_qwen2_vl\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRMSNorm, LayerNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePhi3 \u0026amp; Phi3.5\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.apply_liger_kernel_to_phi3\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKernels\u003c/h3\u003e\u003ca id=\"user-content-kernels\" class=\"anchor\" aria-label=\"Permalink: Kernels\" href=\"#kernels\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eKernel\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eAPI\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eRMSNorm\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerRMSNorm\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLayerNorm\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerLayerNorm\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRoPE\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.liger_rotary_pos_emb\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSwiGLU\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerSwiGLUMLP\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGeGLU\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerGEGLUMLP\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCrossEntropy\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerCrossEntropyLoss\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFusedLinearCrossEntropy\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerFusedLinearCrossEntropyLoss\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eKLDivergence\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerKLDIVLoss\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eJSD\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerJSD\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFusedLinearJSD\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.LigerFusedLinearJSD\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eRMSNorm\u003c/strong\u003e: \u003ca href=\"https://arxiv.org/pdf/1910.07467\" rel=\"nofollow\"\u003eRMSNorm\u003c/a\u003e, which normalizes activations using their root mean square, is implemented by fusing the normalization and scaling steps into a single Triton kernel, and achieves ~3X speedup with ~3X peak memory reduction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLayerNorm\u003c/strong\u003e: \u003ca href=\"https://arxiv.org/pdf/1607.06450\" rel=\"nofollow\"\u003eLayerNorm\u003c/a\u003e, which centers and normalizes activations across the feature dimension, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and achieves ~2X speedup.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGroupNorm\u003c/strong\u003e: \u003ca href=\"https://arxiv.org/pdf/1803.08494\" rel=\"nofollow\"\u003eGroupNorm\u003c/a\u003e, which normalizes activations across the group dimension for a given sample. Channels are grouped in K groups over which the normalization is performed, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and can achieve up to ~2X speedup as the number of channels/groups increases.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eRoPE\u003c/strong\u003e: \u003ca href=\"https://arxiv.org/pdf/2104.09864\" rel=\"nofollow\"\u003eRotary Positional Embedding\u003c/a\u003e is implemented by fusing the query and key embedding rotary into a single kernel with inplace replacement, and achieves ~3X speedup with ~3X peak memory reduction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSwiGLU\u003c/strong\u003e: \u003ca href=\"https://arxiv.org/pdf/2002.05202\" rel=\"nofollow\"\u003eSwish Gated Linear Units\u003c/a\u003e, given by\n\u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"9568ff752b8d5dad9a604248fd08c60f\"\u003e$$\\text{SwiGLU}(x)=\\text{Swish}_{\\beta}(xW+b)\\otimes(xV+c)$$\u003c/math-renderer\u003e\n, is implemented by fusing the elementwise multiplication (denoted by \u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"9568ff752b8d5dad9a604248fd08c60f\"\u003e$\\otimes$\u003c/math-renderer\u003e) into a single kernel with inplace replacement, and achieves parity speed with ~1.5X peak memory reduction.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGeGLU\u003c/strong\u003e: \u003ca href=\"https://arxiv.org/pdf/2002.05202\" rel=\"nofollow\"\u003eGELU Gated Linear Units\u003c/a\u003e, given by\n\u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"9568ff752b8d5dad9a604248fd08c60f\"\u003e$$\\text{GeGLU}(x)=\\text{GELU}(xW+b)\\otimes(xV+c)$$\u003c/math-renderer\u003e\n, is implemented by fusing the elementwise multiplication into a single kernel with inplace replacement, and achieves parity speed with ~1.5X peak memory reduction. Note that the \u003ca href=\"https://pytorch.org/docs/stable/generated/torch.nn.GELU.html\" rel=\"nofollow\"\u003etanh approximation form of GELU\u003c/a\u003e is used.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCrossEntropy\u003c/strong\u003e: \u003ca href=\"https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html\" rel=\"nofollow\"\u003eCross entropy loss\u003c/a\u003e is implemented by computing both the loss and gradient in the forward pass with inplace replacement of input to reduce the peak memory by avoiding simultaneous materialization of both input logits and gradient. It achieves \u0026gt;2X speedup and \u0026gt;4X memory reduction for common vocab sizes (e.g., 32K, 128K, etc.).\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cstrong\u003eFusedLinearCrossEntropy\u003c/strong\u003e: Peak memory usage of cross entropy loss is further improved by fusing the model head with the CE loss and chunking the input for block-wise loss and gradient calculation, a technique inspired by \u003ca href=\"https://github.com/mgmalek/efficient_cross_entropy\"\u003eEfficient Cross Entropy\u003c/a\u003e. It achieves \u0026gt;4X memory reduction for 128k vocab size. \u003cstrong\u003eThis is highly effective for large batch size, large sequence length, and large vocabulary sizes.\u003c/strong\u003e Please refer to the \u003ca href=\"https://github.com/linkedin/Liger-Kernel/tree/main/examples/medusa\"\u003eMedusa example\u003c/a\u003e for individual kernel usage.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eKLDivergence\u003c/strong\u003e: \u003ca href=\"https://pytorch.org/docs/stable/generated/torch.nn.KLDivLoss.html\" rel=\"nofollow\"\u003eKL Divergence\u003c/a\u003e is implemented by fusing the forward into a single triton kernel, with reduction done outside the kernel. It achieves ~1.5X speed and ~15% memory reduction for 128K vocab size.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eJSD\u003c/strong\u003e: \u003ca href=\"https://arxiv.org/pdf/2306.13649\" rel=\"nofollow\"\u003eGeneralized JSD\u003c/a\u003e (Jensen-Shannon divergence), is implemented by computing both the loss and gradient in the forward pass. It achieves ~1.5X speed and ~54% memory reduction for 128k vocab size. \u003cstrong\u003eNOTE\u003c/strong\u003e: It implements forward/reverse KL when \u003ccode\u003ebeta\u003c/code\u003e equals 0 and 1 respectively.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFusedLinearJSD\u003c/strong\u003e: Peak memory usage of JSD loss is further improved by fusing the model head with the JSD and chunking the input for block-wise loss and gradient calculation. It achieves ~85% memory reduction for 128k vocab size where batch size \u003cmath-renderer class=\"js-inline-math\" style=\"display: inline-block\" data-static-url=\"https://github.githubassets.com/static\" data-run-id=\"9568ff752b8d5dad9a604248fd08c60f\"\u003e$\\times$\u003c/math-renderer\u003e sequence length is 8192. \u003cstrong\u003eNOTE\u003c/strong\u003e: It implements forward/reverse KL when \u003ccode\u003ebeta\u003c/code\u003e equals 0 and 1 respectively.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExperimental Kernels\u003c/h3\u003e\u003ca id=\"user-content-experimental-kernels\" class=\"anchor\" aria-label=\"Permalink: Experimental Kernels\" href=\"#experimental-kernels\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eKernel\u003c/strong\u003e\u003c/th\u003e\n\u003cth\u003e\u003cstrong\u003eAPI\u003c/strong\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eEmbedding\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.experimental.LigerEmbedding\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMatmul int2xint8\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003eliger_kernel.transformers.experimental.matmul\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eEmbedding\u003c/strong\u003e: \u003ca href=\"https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html\" rel=\"nofollow\"\u003eEmbedding\u003c/a\u003e is implemented by fusing embedding lookup and output operations. It achieves a peak speedup of ~1.5x in the forward pass and an overall speedup of ~1.1x.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMatmul int2xint8\u003c/strong\u003e: is implemented by using the cache tiled matrix multiplication and by fusing the matmul with the unpacking process which achieves a considerable speed up and performs on par with @torch.compile\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing, Acknowledgements, and License\u003c/h2\u003e\u003ca id=\"user-content-contributing-acknowledgements-and-license\" class=\"anchor\" aria-label=\"Permalink: Contributing, Acknowledgements, and License\" href=\"#contributing-acknowledgements-and-license\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/linkedin/Liger-Kernel/blob/main/docs/CONTRIBUTING.md\"\u003eContributing Guidelines\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/linkedin/Liger-Kernel/blob/main/docs/Acknowledgement.md\"\u003eAcknowledgements\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/linkedin/Liger-Kernel/blob/main/docs/License.md\"\u003eLicense Information\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContact\u003c/h2\u003e\u003ca id=\"user-content-contact\" class=\"anchor\" aria-label=\"Permalink: Contact\" href=\"#contact\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor issues, create a Github ticket in this repository\u003c/li\u003e\n\u003cli\u003eFor open discussion, join \u003ca href=\"https://discord.gg/gpumode\" rel=\"nofollow\"\u003eour discord channel\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eFor formal collaboration, send an email to \u003ca href=\"mailto:byhsu@linkedin.com\"\u003ebyhsu@linkedin.com\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCite this work\u003c/h2\u003e\u003ca id=\"user-content-cite-this-work\" class=\"anchor\" aria-label=\"Permalink: Cite this work\" href=\"#cite-this-work\"\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\"\u003eBiblatex entry:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@article{hsu2024ligerkernelefficienttriton,\n title={Liger Kernel: Efficient Triton Kernels for LLM Training},\n author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},\n year={2024},\n eprint={2410.10989},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2410.10989},\n journal={arXiv preprint arXiv:2410.10989},\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@article\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003ehsu2024ligerkernelefficienttriton\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eLiger Kernel: Efficient Triton Kernels for LLM Training\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003ePin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and 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Training","anchor":"liger-kernel-efficient-triton-kernels-for-llm-training","htmlText":"Liger Kernel: Efficient Triton Kernels for LLM Training"},{"level":2,"text":"Supercharge Your Model with Liger Kernel","anchor":"supercharge-your-model-with-liger-kernel","htmlText":"Supercharge Your Model with Liger Kernel"},{"level":2,"text":"Examples","anchor":"examples","htmlText":"Examples"},{"level":2,"text":"Key Features","anchor":"key-features","htmlText":"Key Features"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":3,"text":"Dependencies","anchor":"dependencies","htmlText":"Dependencies"},{"level":4,"text":"CUDA","anchor":"cuda","htmlText":"CUDA"},{"level":4,"text":"ROCm","anchor":"rocm","htmlText":"ROCm"},{"level":3,"text":"Optional Dependencies","anchor":"optional-dependencies","htmlText":"Optional Dependencies"},{"level":2,"text":"Getting Started","anchor":"getting-started","htmlText":"Getting Started"},{"level":3,"text":"1. Use AutoLigerKernelForCausalLM","anchor":"1-use-autoligerkernelforcausallm","htmlText":"1. Use AutoLigerKernelForCausalLM"},{"level":3,"text":"2. Apply Model-Specific Patching APIs","anchor":"2-apply-model-specific-patching-apis","htmlText":"2. Apply Model-Specific Patching APIs"},{"level":3,"text":"3. Compose Your Own Model","anchor":"3-compose-your-own-model","htmlText":"3. Compose Your Own Model"},{"level":2,"text":"APIs","anchor":"apis","htmlText":"APIs"},{"level":3,"text":"AutoModel","anchor":"automodel","htmlText":"AutoModel"},{"level":3,"text":"Patching","anchor":"patching","htmlText":"Patching"},{"level":3,"text":"Kernels","anchor":"kernels","htmlText":"Kernels"},{"level":3,"text":"Experimental Kernels","anchor":"experimental-kernels","htmlText":"Experimental Kernels"},{"level":2,"text":"Contributing, Acknowledgements, and License","anchor":"contributing-acknowledgements-and-license","htmlText":"Contributing, Acknowledgements, and License"},{"level":2,"text":"Contact","anchor":"contact","htmlText":"Contact"},{"level":2,"text":"Cite this work","anchor":"cite-this-work","htmlText":"Cite this work"},{"level":2,"text":"Star History","anchor":"star-history","htmlText":"Star History"},{"level":2,"text":"Contributors","anchor":"contributors","htmlText":"Contributors"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Flinkedin%2FLiger-Kernel"}},{"displayName":"LICENSE","repoName":"Liger-Kernel","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"BSD-2-Clause","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Flinkedin%2FLiger-Kernel"}}],"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,"accessible_code_button":true}}}}</script> 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class="heading-element" dir="auto">Liger Kernel: Efficient Triton Kernels for LLM Training</h1><a id="user-content-liger-kernel-efficient-triton-kernels-for-llm-training" class="anchor" aria-label="Permalink: Liger Kernel: Efficient Triton Kernels for LLM Training" href="#liger-kernel-efficient-triton-kernels-for-llm-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> <markdown-accessiblity-table><table> <tbody><tr> <th colspan="2">Stable</th> <th colspan="2">Nightly</th> <th>Discord</th> <th>Gurubase (experimental)</th> </tr> <tr> <td> <a href="https://pepy.tech/project/liger-kernel" rel="nofollow"> <img src="https://camo.githubusercontent.com/50c50bd91ad37ce1c00b8f982948f7df8aa1013738786da269d1c9ec7ba384f9/68747470733a2f2f7374617469632e706570792e746563682f62616467652f6c696765722d6b65726e656c" alt="Downloads (Stable)" data-canonical-src="https://static.pepy.tech/badge/liger-kernel" style="max-width: 100%;"> </a> </td> <td> <a href="https://pypi.org/project/liger-kernel" rel="nofollow"> <img alt="PyPI - Version" src="https://camo.githubusercontent.com/5455c705ed24c447f72991a850169312a19be2032454f506b46485ca2029a61a/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c696765722d6b65726e656c3f636f6c6f723d677265656e" data-canonical-src="https://img.shields.io/pypi/v/liger-kernel?color=green" style="max-width: 100%;"> </a> </td> <td> <a 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href="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/logo-banner.png"><img src="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/logo-banner.png" style="max-width: 100%;"></a></p> <p dir="auto"><a href="#installation">Installation</a> | <a href="#getting-started">Getting Started</a> | <a href="#examples">Examples</a> | <a href="#apis">APIs</a> | <a href="#cite-this-work">Cite our work</a></p> <details> <summary>Latest News 🔥</summary> <ul dir="auto"> <li>[2024/11/6] We release <a href="https://github.com/linkedin/Liger-Kernel/releases/tag/v0.4.0">v0.4.0</a>: Full AMD support, Tech Report, Modal CI, Llama-3.2-Vision!</li> <li>[2024/10/21] We have released the tech report of Liger Kernel on Arxiv: <a href="https://arxiv.org/pdf/2410.10989" rel="nofollow">https://arxiv.org/pdf/2410.10989</a></li> <li>[2024/9/6] We release v0.2.1 (<a href="https://x.com/liger_kernel/status/1832168197002510649" rel="nofollow">X post</a>). 2500+ Stars, 10+ New Contributors, 50+ PRs, 50k Downloads in two weeks!</li> <li>[2024/8/31] CUDA MODE talk, <a href="https://youtu.be/gWble4FreV4?si=dxPeIchhkJ36Mbns" rel="nofollow">Liger-Kernel: Real-world Triton kernel for LLM Training</a>, <a href="https://github.com/cuda-mode/lectures?tab=readme-ov-file#lecture-28-liger-kernel">Slides</a></li> <li>[2024/8/23] Official release: check out our <a href="https://x.com/hsu_byron/status/1827072737673982056" rel="nofollow">X post</a></li> </ul> </details> <p dir="auto"><strong>Liger Kernel</strong> is a collection of Triton kernels designed specifically for LLM training. It can effectively increase multi-GPU <strong>training throughput by 20%</strong> and reduces <strong>memory usage by 60%</strong>. We have implemented <strong>Hugging Face Compatible</strong> <code>RMSNorm</code>, <code>RoPE</code>, <code>SwiGLU</code>, <code>CrossEntropy</code>, <code>FusedLinearCrossEntropy</code>, and more to come. The kernel works out of the box with <a href="https://github.com/Dao-AILab/flash-attention">Flash Attention</a>, <a href="https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html" rel="nofollow">PyTorch FSDP</a>, and <a href="https://github.com/microsoft/DeepSpeed">Microsoft DeepSpeed</a>. We welcome contributions from the community to gather the best kernels for LLM training.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Supercharge Your Model with Liger Kernel</h2><a id="user-content-supercharge-your-model-with-liger-kernel" class="anchor" aria-label="Permalink: Supercharge Your Model with Liger Kernel" href="#supercharge-your-model-with-liger-kernel"><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"><a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/banner.GIF"><img src="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/banner.GIF" alt="Banner" data-animated-image="" style="max-width: 100%;"></a></p> <p dir="auto">With one line of code, Liger Kernel can increase throughput by more than 20% and reduce memory usage by 60%, thereby enabling longer context lengths, larger batch sizes, and massive vocabularies.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Speed Up</th> <th>Memory Reduction</th> </tr> </thead> <tbody> <tr> <td><a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-tps.png"><img src="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-tps.png" alt="Speed up" style="max-width: 100%;"></a></td> <td><a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-memory.png"><img src="https://raw.githubusercontent.com/linkedin/Liger-Kernel/main/docs/images/e2e-memory.png" alt="Memory" style="max-width: 100%;"></a></td> </tr> </tbody> </table></markdown-accessiblity-table> <blockquote> <p dir="auto"><strong>Note:</strong></p> <ul dir="auto"> <li>Benchmark conditions: LLaMA 3-8B, Batch Size = 8, Data Type = <code>bf16</code>, Optimizer = AdamW, Gradient Checkpointing = True, Distributed Strategy = FSDP1 on 8 A100s.</li> <li>Hugging Face models start to OOM at a 4K context length, whereas Hugging Face + Liger Kernel scales up to 16K.</li> </ul> </blockquote> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Examples</h2><a id="user-content-examples" class="anchor" aria-label="Permalink: Examples" href="#examples"><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> <markdown-accessiblity-table><table> <thead> <tr> <th><strong>Use Case</strong></th> <th><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td><a href="https://github.com/linkedin/Liger-Kernel/tree/main/examples/huggingface"><strong>Hugging Face Trainer</strong></a></td> <td>Train LLaMA 3-8B ~20% faster with over 40% memory reduction on Alpaca dataset using 4 A100s with FSDP</td> </tr> <tr> <td><a href="https://github.com/linkedin/Liger-Kernel/tree/main/examples/lightning"><strong>Lightning Trainer</strong></a></td> <td>Increase 15% throughput and reduce memory usage by 40% with LLaMA3-8B on MMLU dataset using 8 A100s with DeepSpeed ZeRO3</td> </tr> <tr> <td><a href="https://github.com/linkedin/Liger-Kernel/tree/main/examples/medusa"><strong>Medusa Multi-head LLM (Retraining Phase)</strong></a></td> <td>Reduce memory usage by 80% with 5 LM heads and improve throughput by 40% using 8 A100s with FSDP</td> </tr> <tr> <td><a href="https://github.com/linkedin/Liger-Kernel/tree/main/examples/huggingface/run_qwen2_vl.sh"><strong>Vision-Language Model SFT</strong></a></td> <td>Finetune Qwen2-VL on image-text data using 4 A100s with FSDP</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Key Features</h2><a id="user-content-key-features" class="anchor" aria-label="Permalink: Key Features" href="#key-features"><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> <ul dir="auto"> <li><strong>Ease of use:</strong> Simply patch your Hugging Face model with one line of code, or compose your own model using our Liger Kernel modules.</li> <li><strong>Time and memory efficient:</strong> In the same spirit as Flash-Attn, but for layers like <strong>RMSNorm</strong>, <strong>RoPE</strong>, <strong>SwiGLU</strong>, and <strong>CrossEntropy</strong>! Increases multi-GPU training throughput by 20% and reduces memory usage by 60% with <strong>kernel fusion</strong>, <strong>in-place replacement</strong>, and <strong>chunking</strong> techniques.</li> <li><strong>Exact:</strong> Computation is exact—no approximations! Both forward and backward passes are implemented with rigorous unit tests and undergo convergence testing against training runs without Liger Kernel to ensure accuracy.</li> <li><strong>Lightweight:</strong> Liger Kernel has minimal dependencies, requiring only Torch and Triton—no extra libraries needed! Say goodbye to dependency headaches!</li> <li><strong>Multi-GPU supported:</strong> Compatible with multi-GPU setups (PyTorch FSDP, DeepSpeed, DDP, etc.).</li> <li><strong>Trainer Framework Integration</strong>: <a href="https://github.com/axolotl-ai-cloud/axolotl">Axolotl</a>, <a href="https://github.com/hiyouga/LLaMA-Factory">LLaMa-Factory</a>, <a href="https://github.com/huggingface/trl/releases/tag/v0.10.1">SFTTrainer</a>, <a href="https://github.com/huggingface/transformers/pull/32860" data-hovercard-type="pull_request" data-hovercard-url="/huggingface/transformers/pull/32860/hovercard">Hugging Face Trainer</a>, <a href="https://github.com/modelscope/ms-swift">SWIFT</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Dependencies</h3><a id="user-content-dependencies" class="anchor" aria-label="Permalink: Dependencies" href="#dependencies"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">CUDA</h4><a id="user-content-cuda" class="anchor" aria-label="Permalink: CUDA" href="#cuda"><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> <ul dir="auto"> <li><code>torch >= 2.1.2</code></li> <li><code>triton >= 2.3.0</code></li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">ROCm</h4><a id="user-content-rocm" class="anchor" aria-label="Permalink: ROCm" href="#rocm"><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> <ul dir="auto"> <li><code>torch >= 2.5.0</code> Install according to the instruction in Pytorch official webpage.</li> <li><code>triton >= 3.0.0</code> Install from pypi. (e.g. <code>pip install triton==3.0.0</code>)</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Optional Dependencies</h3><a id="user-content-optional-dependencies" class="anchor" aria-label="Permalink: Optional Dependencies" href="#optional-dependencies"><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> <ul dir="auto"> <li><code>transformers >= 4.x</code>: Required if you plan to use the transformers models patching APIs. The specific model you are working will dictate the minimum version of transformers.</li> </ul> <blockquote> <p dir="auto"><strong>Note:</strong> Our kernels inherit the full spectrum of hardware compatibility offered by <a href="https://github.com/triton-lang/triton">Triton</a>.</p> </blockquote> <p dir="auto">To install the stable version:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install liger-kernel"><pre>$ pip install liger-kernel</pre></div> <p dir="auto">To install the nightly version:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install liger-kernel-nightly"><pre>$ pip install liger-kernel-nightly</pre></div> <p dir="auto">To install from source:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/linkedin/Liger-Kernel.git cd Liger-Kernel pip install -e . # or if using transformers pip install -e .[transformers]"><pre>git clone https://github.com/linkedin/Liger-Kernel.git <span class="pl-c1">cd</span> Liger-Kernel pip install -e <span class="pl-c1">.</span> <span class="pl-c"><span class="pl-c">#</span> or if using transformers</span> pip install -e .[transformers]</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting Started" href="#getting-started"><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 are a couple of ways to apply Liger kernels, depending on the level of customization required.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">1. Use AutoLigerKernelForCausalLM</h3><a id="user-content-1-use-autoligerkernelforcausallm" class="anchor" aria-label="Permalink: 1. Use AutoLigerKernelForCausalLM" href="#1-use-autoligerkernelforcausallm"><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">Using the <code>AutoLigerKernelForCausalLM</code> is the simplest approach, as you don't have to import a model-specific patching API. If the model type is supported, the modeling code will be automatically patched using the default settings.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from liger_kernel.transformers import AutoLigerKernelForCausalLM # This AutoModel wrapper class automatically monkey-patches the # model with the optimized Liger kernels if the model is supported. model = AutoLigerKernelForCausalLM.from_pretrained("path/to/some/model")"><pre><span class="pl-k">from</span> <span class="pl-s1">liger_kernel</span>.<span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-v">AutoLigerKernelForCausalLM</span> <span class="pl-c"># This AutoModel wrapper class automatically monkey-patches the</span> <span class="pl-c"># model with the optimized Liger kernels if the model is supported.</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-v">AutoLigerKernelForCausalLM</span>.<span class="pl-en">from_pretrained</span>(<span class="pl-s">"path/to/some/model"</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">2. Apply Model-Specific Patching APIs</h3><a id="user-content-2-apply-model-specific-patching-apis" class="anchor" aria-label="Permalink: 2. Apply Model-Specific Patching APIs" href="#2-apply-model-specific-patching-apis"><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">Using the <a href="#patching">patching APIs</a>, you can swap Hugging Face models with optimized Liger Kernels.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import transformers from liger_kernel.transformers import apply_liger_kernel_to_llama # 1a. Adding this line automatically monkey-patches the model with the optimized Liger kernels apply_liger_kernel_to_llama() # 1b. You could alternatively specify exactly which kernels are applied apply_liger_kernel_to_llama( rope=True, swiglu=True, cross_entropy=True, fused_linear_cross_entropy=False, rms_norm=False ) # 2. Instantiate patched model model = transformers.AutoModelForCausalLM("path/to/llama/model")"><pre><span class="pl-k">import</span> <span class="pl-s1">transformers</span> <span class="pl-k">from</span> <span class="pl-s1">liger_kernel</span>.<span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-s1">apply_liger_kernel_to_llama</span> <span class="pl-c"># 1a. Adding this line automatically monkey-patches the model with the optimized Liger kernels</span> <span class="pl-en">apply_liger_kernel_to_llama</span>() <span class="pl-c"># 1b. You could alternatively specify exactly which kernels are applied</span> <span class="pl-en">apply_liger_kernel_to_llama</span>( <span class="pl-s1">rope</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">swiglu</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">cross_entropy</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">fused_linear_cross_entropy</span><span class="pl-c1">=</span><span class="pl-c1">False</span>, <span class="pl-s1">rms_norm</span><span class="pl-c1">=</span><span class="pl-c1">False</span> ) <span class="pl-c"># 2. Instantiate patched model</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-s1">transformers</span>.<span class="pl-v">AutoModelForCausalLM</span>(<span class="pl-s">"path/to/llama/model"</span>)</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">3. Compose Your Own Model</h3><a id="user-content-3-compose-your-own-model" class="anchor" aria-label="Permalink: 3. Compose Your Own Model" href="#3-compose-your-own-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can take individual <a href="#kernels">kernels</a> to compose your models.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from liger_kernel.transformers import LigerFusedLinearCrossEntropyLoss import torch.nn as nn import torch model = nn.Linear(128, 256).cuda() # fuses linear + cross entropy layers together and performs chunk-by-chunk computation to reduce memory loss_fn = LigerFusedLinearCrossEntropyLoss() input = torch.randn(4, 128, requires_grad=True, device="cuda") target = torch.randint(256, (4, ), device="cuda") loss = loss_fn(model.weight, input, target) loss.backward()"><pre><span class="pl-k">from</span> <span class="pl-s1">liger_kernel</span>.<span class="pl-s1">transformers</span> <span class="pl-k">import</span> <span class="pl-v">LigerFusedLinearCrossEntropyLoss</span> <span class="pl-k">import</span> <span class="pl-s1">torch</span>.<span class="pl-s1">nn</span> <span class="pl-k">as</span> <span class="pl-s1">nn</span> <span class="pl-k">import</span> <span class="pl-s1">torch</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-s1">nn</span>.<span class="pl-v">Linear</span>(<span class="pl-c1">128</span>, <span class="pl-c1">256</span>).<span class="pl-en">cuda</span>() <span class="pl-c"># fuses linear + cross entropy layers together and performs chunk-by-chunk computation to reduce memory</span> <span class="pl-s1">loss_fn</span> <span class="pl-c1">=</span> <span class="pl-v">LigerFusedLinearCrossEntropyLoss</span>() <span class="pl-s1">input</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">randn</span>(<span class="pl-c1">4</span>, <span class="pl-c1">128</span>, <span class="pl-s1">requires_grad</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">device</span><span class="pl-c1">=</span><span class="pl-s">"cuda"</span>) <span class="pl-s1">target</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-en">randint</span>(<span class="pl-c1">256</span>, (<span class="pl-c1">4</span>, ), <span class="pl-s1">device</span><span class="pl-c1">=</span><span class="pl-s">"cuda"</span>) <span class="pl-s1">loss</span> <span class="pl-c1">=</span> <span class="pl-en">loss_fn</span>(<span class="pl-s1">model</span>.<span class="pl-s1">weight</span>, <span class="pl-s1">input</span>, <span class="pl-s1">target</span>) <span class="pl-s1">loss</span>.<span class="pl-en">backward</span>()</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">APIs</h2><a id="user-content-apis" class="anchor" aria-label="Permalink: APIs" href="#apis"><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">AutoModel</h3><a id="user-content-automodel" class="anchor" aria-label="Permalink: AutoModel" href="#automodel"><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> <markdown-accessiblity-table><table> <thead> <tr> <th><strong>AutoModel Variant</strong></th> <th><strong>API</strong></th> </tr> </thead> <tbody> <tr> <td>AutoModelForCausalLM</td> <td><code>liger_kernel.transformers.AutoLigerKernelForCausalLM</code></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Patching</h3><a id="user-content-patching" class="anchor" aria-label="Permalink: Patching" href="#patching"><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> <markdown-accessiblity-table><table> <thead> <tr> <th><strong>Model</strong></th> <th><strong>API</strong></th> <th><strong>Supported Operations</strong></th> </tr> </thead> <tbody> <tr> <td>LLaMA 2 & 3</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_llama</code></td> <td>RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>LLaMA 3.2-Vision</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_mllama</code></td> <td>RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>Mistral</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_mistral</code></td> <td>RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>Mixtral</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_mixtral</code></td> <td>RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>Gemma1</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_gemma</code></td> <td>RoPE, RMSNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>Gemma2</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_gemma2</code></td> <td>RoPE, RMSNorm, GeGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>Qwen2 & Qwen2.5</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_qwen2</code></td> <td>RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>Qwen2-VL</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_qwen2_vl</code></td> <td>RMSNorm, LayerNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> <tr> <td>Phi3 & Phi3.5</td> <td><code>liger_kernel.transformers.apply_liger_kernel_to_phi3</code></td> <td>RoPE, RMSNorm, SwiGLU, CrossEntropyLoss, FusedLinearCrossEntropy</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Kernels</h3><a id="user-content-kernels" class="anchor" aria-label="Permalink: Kernels" href="#kernels"><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> <markdown-accessiblity-table><table> <thead> <tr> <th><strong>Kernel</strong></th> <th><strong>API</strong></th> </tr> </thead> <tbody> <tr> <td>RMSNorm</td> <td><code>liger_kernel.transformers.LigerRMSNorm</code></td> </tr> <tr> <td>LayerNorm</td> <td><code>liger_kernel.transformers.LigerLayerNorm</code></td> </tr> <tr> <td>RoPE</td> <td><code>liger_kernel.transformers.liger_rotary_pos_emb</code></td> </tr> <tr> <td>SwiGLU</td> <td><code>liger_kernel.transformers.LigerSwiGLUMLP</code></td> </tr> <tr> <td>GeGLU</td> <td><code>liger_kernel.transformers.LigerGEGLUMLP</code></td> </tr> <tr> <td>CrossEntropy</td> <td><code>liger_kernel.transformers.LigerCrossEntropyLoss</code></td> </tr> <tr> <td>FusedLinearCrossEntropy</td> <td><code>liger_kernel.transformers.LigerFusedLinearCrossEntropyLoss</code></td> </tr> <tr> <td>KLDivergence</td> <td><code>liger_kernel.transformers.LigerKLDIVLoss</code></td> </tr> <tr> <td>JSD</td> <td><code>liger_kernel.transformers.LigerJSD</code></td> </tr> <tr> <td>FusedLinearJSD</td> <td><code>liger_kernel.transformers.LigerFusedLinearJSD</code></td> </tr> </tbody> </table></markdown-accessiblity-table> <ul dir="auto"> <li> <strong>RMSNorm</strong>: <a href="https://arxiv.org/pdf/1910.07467" rel="nofollow">RMSNorm</a>, which normalizes activations using their root mean square, is implemented by fusing the normalization and scaling steps into a single Triton kernel, and achieves ~3X speedup with ~3X peak memory reduction.</li> <li> <strong>LayerNorm</strong>: <a href="https://arxiv.org/pdf/1607.06450" rel="nofollow">LayerNorm</a>, which centers and normalizes activations across the feature dimension, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and achieves ~2X speedup.</li> <li> <strong>GroupNorm</strong>: <a href="https://arxiv.org/pdf/1803.08494" rel="nofollow">GroupNorm</a>, which normalizes activations across the group dimension for a given sample. Channels are grouped in K groups over which the normalization is performed, is implemented by fusing the centering, normalization and scaling steps into a single Triton kernel, and can achieve up to ~2X speedup as the number of channels/groups increases.</li> <li> <strong>RoPE</strong>: <a href="https://arxiv.org/pdf/2104.09864" rel="nofollow">Rotary Positional Embedding</a> is implemented by fusing the query and key embedding rotary into a single kernel with inplace replacement, and achieves ~3X speedup with ~3X peak memory reduction.</li> <li> <strong>SwiGLU</strong>: <a href="https://arxiv.org/pdf/2002.05202" rel="nofollow">Swish Gated Linear Units</a>, given by <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="9568ff752b8d5dad9a604248fd08c60f">$$\text{SwiGLU}(x)=\text{Swish}_{\beta}(xW+b)\otimes(xV+c)$$</math-renderer> , is implemented by fusing the elementwise multiplication (denoted by <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="9568ff752b8d5dad9a604248fd08c60f">$\otimes$</math-renderer>) into a single kernel with inplace replacement, and achieves parity speed with ~1.5X peak memory reduction.</li> <li> <strong>GeGLU</strong>: <a href="https://arxiv.org/pdf/2002.05202" rel="nofollow">GELU Gated Linear Units</a>, given by <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="9568ff752b8d5dad9a604248fd08c60f">$$\text{GeGLU}(x)=\text{GELU}(xW+b)\otimes(xV+c)$$</math-renderer> , is implemented by fusing the elementwise multiplication into a single kernel with inplace replacement, and achieves parity speed with ~1.5X peak memory reduction. Note that the <a href="https://pytorch.org/docs/stable/generated/torch.nn.GELU.html" rel="nofollow">tanh approximation form of GELU</a> is used.</li> <li> <strong>CrossEntropy</strong>: <a href="https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html" rel="nofollow">Cross entropy loss</a> is implemented by computing both the loss and gradient in the forward pass with inplace replacement of input to reduce the peak memory by avoiding simultaneous materialization of both input logits and gradient. It achieves >2X speedup and >4X memory reduction for common vocab sizes (e.g., 32K, 128K, etc.).</li> </ul> <ul dir="auto"> <li> <strong>FusedLinearCrossEntropy</strong>: Peak memory usage of cross entropy loss is further improved by fusing the model head with the CE loss and chunking the input for block-wise loss and gradient calculation, a technique inspired by <a href="https://github.com/mgmalek/efficient_cross_entropy">Efficient Cross Entropy</a>. It achieves >4X memory reduction for 128k vocab size. <strong>This is highly effective for large batch size, large sequence length, and large vocabulary sizes.</strong> Please refer to the <a href="https://github.com/linkedin/Liger-Kernel/tree/main/examples/medusa">Medusa example</a> for individual kernel usage.</li> <li> <strong>KLDivergence</strong>: <a href="https://pytorch.org/docs/stable/generated/torch.nn.KLDivLoss.html" rel="nofollow">KL Divergence</a> is implemented by fusing the forward into a single triton kernel, with reduction done outside the kernel. It achieves ~1.5X speed and ~15% memory reduction for 128K vocab size.</li> <li> <strong>JSD</strong>: <a href="https://arxiv.org/pdf/2306.13649" rel="nofollow">Generalized JSD</a> (Jensen-Shannon divergence), is implemented by computing both the loss and gradient in the forward pass. It achieves ~1.5X speed and ~54% memory reduction for 128k vocab size. <strong>NOTE</strong>: It implements forward/reverse KL when <code>beta</code> equals 0 and 1 respectively.</li> <li> <strong>FusedLinearJSD</strong>: Peak memory usage of JSD loss is further improved by fusing the model head with the JSD and chunking the input for block-wise loss and gradient calculation. It achieves ~85% memory reduction for 128k vocab size where batch size <math-renderer class="js-inline-math" style="display: inline-block" data-static-url="https://github.githubassets.com/static" data-run-id="9568ff752b8d5dad9a604248fd08c60f">$\times$</math-renderer> sequence length is 8192. <strong>NOTE</strong>: It implements forward/reverse KL when <code>beta</code> equals 0 and 1 respectively.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Experimental Kernels</h3><a id="user-content-experimental-kernels" class="anchor" aria-label="Permalink: Experimental Kernels" href="#experimental-kernels"><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> <markdown-accessiblity-table><table> <thead> <tr> <th><strong>Kernel</strong></th> <th><strong>API</strong></th> </tr> </thead> <tbody> <tr> <td>Embedding</td> <td><code>liger_kernel.transformers.experimental.LigerEmbedding</code></td> </tr> <tr> <td>Matmul int2xint8</td> <td><code>liger_kernel.transformers.experimental.matmul</code></td> </tr> </tbody> </table></markdown-accessiblity-table> <ul dir="auto"> <li><strong>Embedding</strong>: <a href="https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html" rel="nofollow">Embedding</a> is implemented by fusing embedding lookup and output operations. It achieves a peak speedup of ~1.5x in the forward pass and an overall speedup of ~1.1x.</li> <li><strong>Matmul int2xint8</strong>: is implemented by using the cache tiled matrix multiplication and by fusing the matmul with the unpacking process which achieves a considerable speed up and performs on par with @torch.compile</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributing, Acknowledgements, and License</h2><a id="user-content-contributing-acknowledgements-and-license" class="anchor" aria-label="Permalink: Contributing, Acknowledgements, and License" href="#contributing-acknowledgements-and-license"><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> <ul dir="auto"> <li><a href="https://github.com/linkedin/Liger-Kernel/blob/main/docs/CONTRIBUTING.md">Contributing Guidelines</a></li> <li><a href="https://github.com/linkedin/Liger-Kernel/blob/main/docs/Acknowledgement.md">Acknowledgements</a></li> <li><a href="https://github.com/linkedin/Liger-Kernel/blob/main/docs/License.md">License Information</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contact</h2><a id="user-content-contact" class="anchor" aria-label="Permalink: Contact" href="#contact"><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> <ul dir="auto"> <li>For issues, create a Github ticket in this repository</li> <li>For open discussion, join <a href="https://discord.gg/gpumode" rel="nofollow">our discord channel</a></li> <li>For formal collaboration, send an email to <a href="mailto:byhsu@linkedin.com">byhsu@linkedin.com</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Cite this work</h2><a id="user-content-cite-this-work" class="anchor" aria-label="Permalink: Cite this work" href="#cite-this-work"><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">Biblatex entry:</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@article{hsu2024ligerkernelefficienttriton, title={Liger Kernel: Efficient Triton Kernels for LLM Training}, author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen}, year={2024}, eprint={2410.10989}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2410.10989}, journal={arXiv preprint arXiv:2410.10989}, }"><pre><span class="pl-k">@article</span>{<span class="pl-en">hsu2024ligerkernelefficienttriton</span>, <span class="pl-s">title</span>=<span class="pl-s"><span class="pl-pds">{</span>Liger Kernel: Efficient Triton Kernels for LLM Training<span class="pl-pds">}</span></span>, <span class="pl-s">author</span>=<span class="pl-s"><span class="pl-pds">{</span>Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen<span class="pl-pds">}</span></span>, <span class="pl-s">year</span>=<span class="pl-s"><span class="pl-pds">{</span>2024<span class="pl-pds">}</span></span>, <span class="pl-s">eprint</span>=<span class="pl-s"><span class="pl-pds">{</span>2410.10989<span class="pl-pds">}</span></span>, <span class="pl-s">archivePrefix</span>=<span class="pl-s"><span class="pl-pds">{</span>arXiv<span class="pl-pds">}</span></span>, <span class="pl-s">primaryClass</span>=<span class="pl-s"><span class="pl-pds">{</span>cs.LG<span class="pl-pds">}</span></span>, <span class="pl-s">url</span>=<span class="pl-s"><span class="pl-pds">{</span>https://arxiv.org/abs/2410.10989<span class="pl-pds">}</span></span>, <span class="pl-s">journal</span>=<span class="pl-s"><span class="pl-pds">{</span>arXiv preprint arXiv:2410.10989<span class="pl-pds">}</span></span>, }</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Star History</h2><a 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