Flexattention pytorch. 5. 5及以上版本中新引入的FlexAttention和BlockMask功能来实现因果注意力机制与填充输入的处理。 鉴于目前网络上缺乏关于FlexAttention处理填充输入序列的完整代码示例和技术讨论，本文将… UMass-Embodied-AGI / FlexAttention Public Notifications You must be signed in to change notification settings Fork 6 Star 46 main Aug 12, 2024 · Error using flex attention on RTX 4090: shared memory out of resource. This function computes the scaled dot product attention between query, key, and value tensors with a user-defined attention score modification function. attention. functional. Aug 7, 2025 · PyTorch FlexAttention tutorial: Building a minimal vLLM-style inference engine from scratch with paged attention Lightning Talk: FlexAttention - The Flexibility of PyTorch + The Performance of FlashAttention - Yanbo Liang & Horace He, Meta Introducing a novel abstraction leveraging the PyTorch compiler stack Dec 29, 2024 · Hello, Really interested in flex attention but still do not fully understand it yet. 22. 7镜像，快速搭建AI开发环境。该镜像支持NVIDIA Blackwell GPU架构和Mega Cache功能，特别适合深度学习模型训练和大型语言模型 (LLM)开发，帮助开发者高效实现AI应用。 Mar 21, 2025 · f"Compiling flex_attention failed with error: '{e}', " "Updating your pytorch version to nightlies may solve it, or you can set" "in your config dataset. I get the mask from a preprocessing step and it is 95% sparse and will appear Jul 29, 2024 · Current high-resolution vision-language models encode images as high-resolution image tokens and exhaustively take all these tokens to compute attention, which significantly increases the computational cost. 7是一个开源的Python机器学习库，基于Torch库开发，底层由C++实现，广泛应用于人工智能领域，特别是计算机视觉和自然语言处理方向。这个预配置的镜像包含了PyTorch 2. Feb 7, 2025 · The issue seems to boil down (I could be missing a lot) where I have to do two checks (1) is the query neighbors with the key and (2) a lookup mapping global index (kv_idx) to local index (the index of the neighbor in edge_bias corresponding to kv_idx. flex_attention for ML researchers who’d like to customize their attention kernels without writing kernel code. At inference time the length of the sequence can vary (but the mask is otherwise not data dependent). These should have the torch. FlexAttention es el kernel de atención a nivel de bloques de PyTorch que llegó en PyTorch 2. 0 版本中将其作为一个原型功能发布。 我们在这里没有涵盖如何将 FlexAttention 用于推理（或如何实现 PagedAttention）——我们将在以后的帖子中介绍这些。 Feb 26, 2026 · flex_attention_tutorial. packed=False to avoid using flex attention. Dec 22, 2024 · On August 8th, Horace He and the Meta PyTorch Team released FlexAttention, a critical API for creating non-causal attention layers without losing speed. parallel import DistributedDataParallel Mar 21, 2025 · f"Compiling flex_attention failed with error: '{e}', " "Updating your pytorch version to nightlies may solve it, or you can set" "in your config dataset. flex_attention. Does the Pytorch integration copy-paste/pull from the original FlashAttention repo, or there are implementation changes made along with the integration? Thanks! Oct 7, 2024 · Even though I havent tried it out myself you should be able to leverage pytorch flexattention api for this. flex_attention torch. In this work, we introduce a novel integration of PagedAttention with PyTorch’s FlexAttention, addressing internal fragmentation and inefficiencies associated with monolithic KV cache allocations. Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch Aug 12, 2024 · Pytorch’s new API, FlexAttention, brings more flexibility by allowing easy implementation of various attention variants with just a few lines of code. 尽管 FlexAttention 在更改时不需要重新编译，但如果你不注意缓存它，它可能会导致显著的减速（查看常见问题解答以获取最佳实践建议）。 尽管TFlops大致相同，但mask_mod版本的执行时间快了2倍！ 这表明我们可以利用BlockMask提供的稀疏性，而不会损失硬件效率。 Aug 7, 2025 · PyTorch FlexAttention tutorial: Building a minimal vLLM-style inference engine from scratch with paged attention ICML投完了不想干活，来写个blog，记录一下赶paper之前使用FlexAttention的经历。这个API特别好用，完全不用写CUDA（甚至连triton都不用写），所以用起来很爽。 FlexAttention简介FlexAttention是pytorch team开发… Oct 14, 2024 · 对FlexAttention的常见API的使用方法做一个解读，博客来源： [链接] ，在此基础上我对部分代码添加了一些解释，修复了几个代码中的bug并使用PyTorch的nightly版本运行了示例，得到了每个custom attention的输出，展示在了下面的每个示例代码后面。最后还补充了一下torch compile inductor后端中实现FlexAttention的 Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources Jan 4, 2025 · 本文介绍了如何利用torch 2. When not using flex_attention I can just trim the mask to the sequence length (inference is batch size 1) mask = dense_mask[:, :, (S - L) : S, :S] (and if I enable capture scalars this even plays nice with torch. Since flex_attention is a higher-order function, it seems to conflict with DDP’s optimizer. Nov 19, 2025 · PyTorch FlexAttention 块掩码创建：create_block_mask () 详解与实践 create_block_mask () 函数的作用是基于一个用户自定义的布尔函数（称为 mask_mod）来预计算注意力矩阵中的稀疏模式，并将其转换为一种高效的、适合 FlexAttention 使用的块掩码数据结构。 FlexAttention PyTorch का block-level attention kernel है जो PyTorch 2. 7镜像简介 PyTorch 2. Aug 7, 2024 · To solve this hypercube problem once and for all, we introduce FlexAttention, a new PyTorch API. Nov 27, 2025 · 这个函数是 PyTorch 2. This repository aims to provide a playground for experimenting with various attention mechanisms using the FlexAttention API. Oct 22, 2024 · I have recently been playing with Flex attention, trying to replace some of my custom triton kernels. 0 で導入されたブロック単位の Attention カーネルです。SDPA 計算をプログラマブルなループに書き換えることで、CUDA を書かずにマスク戦略を表現できます。Diffusers は新しい attention_backend ディスパッチャ経由で公開しており、SimpleTuner はこれを --attention_mechanism=flex に接続し PyTorch’s 𝗙𝗹𝗲𝘅𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 API is designed exactly for this (custom score modifications + block masks, compiled to optimized kernels). 0 में आया था। यह SDPA गणना को एक प्रोग्रामेबल लूप के रूप में लिखता है ताकि आप CUDA लिखे बिना masking रणनीतियाँ This repository aims to provide a playground for experimenting with various attention mechanisms using the FlexAttention API. In order to trigger it, you need all of the following: BlockMask slightly larger than actual first run's seq_len return_lse = True Torch compile import to Apr 24, 2025 · PyTorch 2. compile for custom ops, Mega Cache, FlexAttention enhancements, Intel GPU optimizations, new APIs for improved ML performance, and other enhancements. Sep 19, 2024 · Introducing a novel abstraction leveraging the PyTorch compiler stack to enable custom, user-defined attention mechanisms. 8 pre-built wheels, torch. Tensor): Attention mask for packed and padded sequences of shape (batch_size, total_seq_len). Many existing attention Dec 12, 2024 · E. Implemented within IBM’s Jun 8, 2025 · Large Language Models (LLMs) encounter severe memory inefficiencies during long-context inference due to conventional handling of key-value (KV) caches. batch, head, q_idx, k_idx: Scalar tensors indicating the batch index, query head index, query index, and key/value index, respectively. generate boolean mask from a BlockMask? (for mask visualization / checking that the impl is correct) I can see there exists `create_mask` torch. To previously use attention variants like document masking, sliding window attention, softcap, and Alibi, a user would need to spend weeks handcrafting their own kernel in CUDA/HIP language Helpful tools and examples for working with flex-attention - meta-pytorch/attention-gym May 10, 2025 · FlexAttention is a compiler-driven programming model that enables the implementation of a wide range of attention mechanisms using just a few lines of PyTorch code. To solve this hypercube problem once and for all, we introduce FlexAttention, a new PyTorch API. I started looking at the torch profile for a single forward pass on the complied version after the model warmup and Aug 9, 2024 · I saw the newly released Flex Attention FlexAttention: The Flexibility of PyTorch with the Performance of FlashAttention | PyTorch and I have a question. 5及以上版本中新引入的FlexAttention和BlockMask功能来实现因果注意力机制与填充输入的处理。 ICML投完了不想干活，来写个blog，记录一下赶paper之前使用FlexAttention的经历。这个API特别好用，完全不用写CUDA（甚至连triton都不用写），所以用起来很爽。 FlexAttention简介FlexAttention是pytorch team开发… Nov 27, 2025 · 这个函数是 PyTorch 2. We provide a flexible API that allows implementing many attention variants (including all the ones mentioned in the blog post so far) in a few lines of idiomatic PyTorch code. scaled_dot_product_attention Utils # May 10, 2025 · FlexAttention is a compiler-driven programming model that enables the implementation of a wide range of attention mechanisms using just a few lines of PyTorch code. Many existing attention Aug 8, 2024 · Always had the curiosity to put something together with pytorch but it always seemed either a steep learning curve or there wasn't a big motivator (project, problem to solve, something in my daily routine to optimize). attention # Created On: Jan 24, 2024 | Last Updated On: Nov 12, 2025 This module contains functions and classes that alter the behavior of torch. To address this problem, we propose FlexAttention, a flexible attention mechanism for efficient high-resolution vision-language models. Contribute to BBuf/how-to-optim-algorithm-in-cuda development by creating an account on GitHub. Aug 31, 2024 · I’ve been experimenting with the new flex_attention module and encountered an issue when trying to integrate it with DistributedDataParallel (DDP). Nov 2, 2024 · Result from flex attention seem extremely slow, however, while it comes from flex attention compiling, I tried running the same function multiple time, just in case the first run had the compiling run. I use something similar to the Tri Dao flash attention kernel, but with bias. If you Nov 14, 2025 · Flex Attention, an advanced variant of the attention mechanism, offers enhanced flexibility and adaptability in handling diverse data types and problem scenarios. This design choice supports adoption and reproducibility by relying on Flex Attention rather than bespoke CUDA code. Jan 24, 2024 · torch. 1 Our Approach We present FlexAttention, a novel compiler-driven programming model that allows implementing the majority of attention variants in a few lines of idiomatic PyTorch code. These end-to-end implementations showcase how to integrate various attention mechanisms into your models. It rewrites the SDPA computation as a programmable loop so you can express masking strategies without writing CUDA. PyTorch, a popular deep learning framework, provides a powerful and user-friendly environment to implement Flex Attention. We introduce FlexAttention, a novel compiler-driven programming model that allows implementing the majority of attention variants in a few lines of idiomatic PyTorch code. of shape: [batch, 1, 256, 256]. 3 版本引入的，它提供了一个高性能且灵活的自注意力（Self-Attention）实现，可以利用硬件（如 NVIDIA GPU 上的 FlashAttention 或 Memory-Efficient Attention）加速。不过，由于它相对较新，并且依赖特定的环境和后端，在使用时确实可能会遇到一些常见问题。 Nov 14, 2025 · PyTorch integrated interface: The CuTe DSL implementation of FlexAttention is also integrated into PyTorch when built from source; it will be incorporated into the stable build in the near future. Reescribe el cómputo SDPA como un bucle programable para que puedas expresar estrategias de máscara sin escribir CUDA. Resources FlexAttention: The Flexibility of PyTorch with the Performance of FlashAttention Mar 25, 2025 · I’m using flex_attention (or scaled_dot_product_attention). 7 introduces NVIDIA Blackwell GPU support, CUDA 12. It includes implementations of different attention variants, performance comparisons, and utility functions to help researchers and developers explore and optimize attention mechanisms in their models. search_sorted get around the problem? Any input or thoughts would be appreciated, thank you! 如果你想在后训练 / 推理流程中轻松使用 FlexAttention，PyTorch 原生的后训练库 torchtune 和推理代码库 gpt-fast 已经集成了 FlexAttention，快去试试吧！ 我们很高兴 FlexAttention 的论文已被接受，将在 5 月 12 日至 15 日于加利福尼亚州圣克拉拉举行的 MLSys2025 会议上展示。 本文介绍了如何利用torch 2. Try reducing block sizes or `num_stages` to resolve the issue. I am mostly interesting in benefits from sparsity, this is my usecase: I do cross-attention between two sequences of length 256 with a boolean mask that is identical for all heads i. 7镜像部署与使用教程 1. py Diffusers 通过新的 attention_backend 分发器暴露该能力，SimpleTuner 则将其连接到 --attention_mechanism=flex。 ⚠️ FlexAttention 在上游仍标记为 “prototype”。 当你更换驱动、CUDA 版本或 PyTorch 构建时，可能需要重新编译。 FlexAttention is PyTorch’s block-level attention kernel that landed in PyTorch 2. Diffusers lo expone a través del nuevo dispatcher attention_backend, y FlexAttention は PyTorch 2. 9 documentation , which is very related, but does there exist anything for converting directly a BlockMask object into a tensor mask? This problem is exacerbated by the dificulty of writing eficient fused attention kernels, resisting traditional compiler-based approaches. Flexible Programming Model. . We’ll explore how it integrates with PyTorch’s compilation system and discuss current technical limitations Jun 8, 2025 · Abstract Large Language Models (LLMs) encounter severe memory inefficiencies during long-context inference due to conventional handling of key–value (KV) caches. flex_attention — PyTorch 2. Dec 17, 2024 · Hi! I am wondering if and how I should replace scaled_dot_product_attention with FlexAttention. BlockMask is essential for performant computation of flex attention. Aug 12, 2024 · Error using flex attention on RTX 4090: shared memory out of resource. GitHub Gist: instantly share code, notes, and snippets. Introducing FlexAttention: a novel PyTorch API that enables custom, user-defined attention mechanisms with performance comparable to state-of-the-art solut ZXHY-82 / LI-MSV-TidyVoice2026 Public Notifications You must be signed in to change notification settings Fork 0 Star 4 Code Issues0 Pull requests0 Actions Projects Security0 Insights Code Issues Pull requests Actions Projects Security Insights Files master LI-MSV-TidyVoice2026 / deeplab / pretrained / audio2vector / module / transformers / src / transformers / integrations flex_attention. In this work, we introduce a novel integration of PagedAttention with PyTorch's FlexAttention, addressing internal fragmentation and inefficiencies associated with monolithic KV cache allocations. However, the ease of use with F. int data type and be located on the same device as the score tensor. It includes implementations of different attention variants, performance comparisons, and utility functions to help researchers and developers explore and optimize attention Apr 30, 2025 · Overview In PyTorch 2. Below is a minimal example of my current setup: import os import time import math import torch from torch. sdpa and typical Jul 16, 2024 · torch. This blog introduces our decoding backend optimized for inference, supporting GQA and PagedAttention, along with feature updates including performance tuning guides and trainable biases support. This post examines FlexAttention’s core functionality and implementation details at a high level. compile). 45 PM1494×578 48 This problem is exacerbated by the dificulty of writing eficient fused attention kernels, resisting traditional compiler-based approaches. This new API supports dynamic modifications to attention scores within SDPA, providing both runtime and memory efficiency through kernel fusion with the FlashAttention algorithm. However, my project also requires a highly customized add_score matrix to be added to the score matrix. flex_attention (q, k, v, block_mask=block_mask). ", Apr 28, 2025 · Hi, I am building a project where the attention matrix is sparse and I got a huge performance boost from using FlexAttention by specifying block_mask. Unfortunately Flex attention ones were substantially (around 5x) slower than the custom one I tried. The add_score matrix of shape [batch][num_heads][seq_len][seq_len] is fully pre-computed by the dataloader and its value cannot easily Jan 15, 2025 · Support kernel options when flex_attention compiled with dynamic=True #144815 Closed sjain-profluent opened on Jan 14, 2025 · edited by pytorch-bot Sep 17, 2024 · 🐛 Describe the bug We've came across a NaN in the backward pass of Flex Attention. e. nn. I 文章浏览阅读5k次，点赞22次，收藏31次。FlexAttention 提供了一个灵活的 API，允许使用几行惯用的 PyTorch 代码实现多种 Attention 变体_flexattention score: A scalar tensor representing the attention score, with the same data type and device as the query, key, and value tensors. Diffusers exposes it through the new attention_backend dispatcher, and SimpleTuner wires that dispatcher to --attention_mechanism=flex. Jul 16, 2024 · This function implements scaled dot product attention with an arbitrary attention score modification function. PyTorch 2. Aug 12, 2024 · Pytorch’s new API, FlexAttention, brings more flexibility by allowing easy implementation of various attention variants with just a few lines of code. Dec 6, 2024 · We introduce FlexAttention, a novel compiler-driven programming model that allows implementing the majority of attention variants in a few lines of idiomatic PyTorch code. Nov 19, 2025 · PyTorch FlexAttention 块掩码创建：create_block_mask () 详解与实践 create_block_mask () 函数的作用是基于一个用户自定义的布尔函数（称为 mask_mod）来预计算注意力矩阵中的稀疏模式，并将其转换为一种高效的、适合 FlexAttention 使用的块掩码数据结构。 Diffusers 通过新的 attention_backend 分发器暴露该能力，SimpleTuner 则将其连接到 --attention_mechanism=flex。 ⚠️ FlexAttention 在上游仍标记为 “prototype”。 当你更换驱动、CUDA 版本或 PyTorch 构建时，可能需要重新编译。 FlexAttention is PyTorch’s block-level attention kernel that landed in PyTorch 2. Does anybody have a good starting point to learn with hands-on projects and also that could accommodate for flexattention? May 30, 2025 · For comprehensive examples of using FlexAttention in real-world scenarios, explore the examples/ directory. Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch Nov 18, 2024 · Use FlexAttention: A recent addition to PyTorch, FlexAttention empowers users to implement a wide variety of attention variants without needing to compromise on performance. 2 days ago · This page documents PyTorch's suite of attention implementations, ranging from the standard Scaled Dot Product Attention (SDPA) to advanced, customizable mechanisms like FlexAttention and variable-length (varlen) attention. Have a look at the tutorial here https://pytorch. e. However, when we measure overall gpu memory use and compare with manual implementation of sliding-window mask, flex attention doesn’t show improvement in running speed: Screen Shot 2024-10-30 at 11. g. py. torch. As I understand it the best way to use flex attention is essentially with “batch size” 1 and flattening the entire input with particular block masks. 2 days ago · 从零开始学PyTorch：2. Lightning Talk: FlexAttention - The Flexibility of PyTorch + The Performance of FlashAttention - Yanbo Liang & Horace He, Meta Introducing a novel abstraction leveraging the PyTorch compiler stack 12 hours ago · 本文介绍了如何在星图GPU平台上自动化部署PyTorch 2. parallel import DistributedDataParallel Jan 8, 2026 · In PyTorch 2. 0 版本中将其作为一个原型功能发布。 我们在这里没有涵盖如何将 FlexAttention 用于推理（或如何实现 PagedAttention）——我们将在以后的帖子中介绍这些。 The implementation avoids custom low-level kernels and slots into PyTorch primitives by leveraging an existing operator called Flex Attention. Note Attention Gym is under active development, and we do not currently offer any backward compatibility guarantees. I am wondering if it is possible to write to some globally scoped tensor the way that the alibi bias example in the link above reads from a globally scoped tensor. Implemented within IBM's Foundation Model This repository aims to provide a playground for experimenting with various attention mechanisms using the FlexAttention API. compile 將其降低為融合的 FlashAttention 核心，產生一個 FlashAttention 核心，該核心不會具體化任何額外的記憶體，並且具有與手寫核心競爭的效能 how to optimize some algorithm in cuda. ", 我們提供了一個彈性的 API，允許使用幾行慣用的 PyTorch 程式碼來實作許多注意力機制變體 (包括到目前為止在部落格文章中提到的所有變體)。 我們透過 torch. FlexAttention enables users to specify custom modifications to attention scores within fused kernels, supporting various attention patterns like causal masking, sliding windows, and custom 尽管 FlexAttention 在更改时不需要重新编译，但如果你不注意缓存它，它可能会导致显著的减速（查看常见问题解答以获取最佳实践建议）。 尽管TFlops大致相同，但mask_mod版本的执行时间快了2倍！ 这表明我们可以利用BlockMask提供的稀疏性，而不会损失硬件效率。 Jun 24, 2024 · Hi, I’m trying to experiment and make tweaks and potential upgrades to FlashAttention, and wondering where’s the best place to start. Have had great luck speed running GPT2 or ESM2 with it and want to continue using it during pretraining. See: https://pytorch. 0 release, we introduced FlexAttention torch. org/blog/flexattention/. flex_attention(query, key, value, score_mod=None, block_mask=None, scale=None, enable_gqa=False, return_lse=False, kernel_options=None) [源][源] 此函数实现了带任意注意力分数修改函数的缩放点积注意力。 此函数计算查询、键和值张量之间的缩放点积注意力，并应用用户定义的 The resultant BlockMask is a compressed representation of the full (causal) block mask. Jan 16, 2026 · This blog aims to provide a detailed overview of Flex Attention in PyTorch, covering its fundamental concepts, usage methods, common practices, and best practices. 7和CUDA工具包，能够直接调用GPU加速模型训练和推理。 这个 Flex Attention is a novel compiler-driven programming model that allows implementing the majority of attention variants in a few lines of idiomatic PyTorch code. 0. flex_attention(query, key, value, score_mod=None, block_mask=None, scale=None, enable_gqa=False, return_lse=False, kernel_options=None, *, return_aux=None) [源代码] # 此函数实现了带有任意注意力得分修改函数的缩放点积注意力。 此函数计算查询、键和值张量之间的缩放点积注意力，并带有用户定义的注意力 Oct 31, 2024 · and attention output computation with: y = torch. Say I wanted to retrieve all scores from the model to plot the attention 1. Sep 20, 2025 · Here the ideas from Mixture of Recursion come in - ideally we want to control the hierarchy of heads and the features they learn. This document covers the FlexAttention API from PyTorch and how attention-gym integrates with it to provide efficient, customizable attention mechanisms. 9, all FlexAttention scenarios for both forward and backward are natively supported on Intel GPUs, aligned with PyTorch’s standard GPU behavior. 局限性和未来工作 FlexAttention 目前可在 PyTorch 的 nightly 版本中使用，我们计划在 2. Would precomputing the lookup instead of using torch. Specifically, a high-resolution image is encoded Dec 5, 2024 · Launched in early 2024, FlexAttention enables researchers to modify attention mechanisms without writing complex GPU kernels, while maintaining FlashAttention’s performance benefits. 3 版本引入的，它提供了一个高性能且灵活的自注意力（Self-Attention）实现，可以利用硬件（如 NVIDIA GPU 上的 FlashAttention 或 Memory-Efficient Attention）加速。不过，由于它相对较新，并且依赖特定的环境和后端，在使用时确实可能会遇到一些常见问题。 Aug 31, 2024 · I’ve been experimenting with the new flex_attention module and encountered an issue when trying to integrate it with DistributedDataParallel (DDP). Dec 7, 2024 · This problem is exacerbated by the difficulty of writing efficient fused attention kernels, resisting traditional compiler-based approaches. org/blog/flexattention/ Args: attention_mask_2d (torch. In this talk, we demonstrate that This problem is exacerbated by the dificulty of writing eficient fused attention kernels, resisting traditional compiler-based approaches. bqbgf tlrmfdl ogsrty vwqdk txva adtiy vbrd ukopuqm rdpji clv