Flex attention pytorch github You signed in with another tab or window. 13 (main, Oct 3 2023, 01:22:22) [Clang 17. 0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2. float16 def scorer_test(score, b, PyTorch version: 2. compile, Inductor will lower to a Triton template. dev20240827+cu121 Is debug build: False CUDA used to build PyTorch: 12. nn. compiler. cond and similar module: pt2-dispatcher PT2 dispatcher-related issues (e. --block-m: kernel option BLOCK_M for flex attention --block-n: kernel option BLOCK_N for flex attention Hi @yaozengwei thanks for creating the issue. # document that each token belongs to. An unfused Eager Mode implementation that works on most device and, under torch. org/blog/flexattention/ Args: attention_mask_2d (torch. 8884878158569336e-05 [s] There seems to be a bug here, if my Q_LEN < 128, then Q_BLOCK_SIZE will be Q_LEN and KV_BLOCK_SIZE will be BLOCK_SIZE, which is 128. Then, we can mask out all attention. 前端编译 flex_attention 为一个实例，例如compiled_flex_attention。; 编译后以mask_mod、score_mod的形式，再给 compiled_flex_attention定义一些变体。; 实际调用compiled_flex_attention()，编译后端torch. 🐛 Describe the bug I am trying to capture our model forward pass into a CUDA graph, but Flex Attention's create_block_mask contains a graph break. Compile will take longer but you will get better performance (and we will pick a kernel that doesn't use too much shmem) 🐛 Describe the bug Minimal code to reproduce: import torch from torch. compile的pattern匹配和算子生成能力，使得用户既能像2. investigate compile flag in flex attention pytorch/torchtune#1926. 5). 11 (main, 🐛 Describe the bug I am implementing a Differential Transformer with FlexAttention. size(2) KL = key. this improves sparsity but may make parity tests fail (outputs in pad positions will be 0-valued). 047852 Warning: 'q_grad. inductor默认用Triton生成（下降为）变体的FlashAttention代码。; FlexAttention在PyTorch编程时需要明确的用 from torch. module: flex attention module: higher order operators torch. nested. 5. 40320, I get OOMs at the line "scores = (query @ key. 4 LTS (x86_64) GCC version: (Ubuntu 11. See: https://pytorch. 31. 🐛 Describe the bug Thank you for the outstanding work on PyTorch FlexAttention! I am currently trying to integrate FlexAttention with the Hugging Face Transformers framework for training. randn( (1, 8, 256, 128), device='cuda', dtype=torch. size(2) def causal_mask(b, h, Add CPU Compile Support for Flex Attention There are two implementations of Flex Attention. 1 https://gith module: flex attention module: higher order operators torch. Am I understanding this correctly？ In pytorch 2. 0. I'm working with flex attention. 35 Python version: 3. 1+cu124 Is debug build: False CUDA used to build PyTorch: 12. 🐛 Describe the bug Compilation of flex attention with dynamic shapes enabled doesn't work when the BlockMask depends on the batch dimension. 0015106382369995117 [s] dense attention : 3. I'm honestly not sure if this is a "bug" or a "feature request". Tensor): Attention mask We can do this by using a document_id tensor that gives the. In general, we recommend folks to only run torchtune on at least the latest stable version of PyTorch (currently 2. flex_attention import flex_attention from torch. nn 🚀 The feature, motivation and pitch I've implemented FlexAttention for SmolLM, but since HF team uses 3 query heads per KV-head, the compilation fails for seq_length=5: LoweringException: ValueError: Number of shared query heads sharing You signed in with another tab or window. 1 ROCM dynamo-functorch Issues related to dynamo/compile on functorch transforms module: flex attention module: higher order operators torch. , aotdispatch, functionalization, faketensor, custom-op, module: rocm AMD GPU support for Pytorch oncall: pt2 rocm This tag is for PRs from ROCm team triaged This issue has been looked at a team member, and triaged 🐛 Describe the bug Flex attention on FSDP works without compile, but not with compile. 0+cu124 Is debug build: False CUDA used to build PyTorch: 12. cache_size_limit = 1000 flex_attention_compile= torch. This Triton te module: flex attention module: higher order operators torch. Versions. So in practice our Q/K has 1/2 of the original Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch I can repro the work arounds: flex_attention = torch. pth' does not contain a tensor in one of the directories. nn. You signed out in another tab or window. g. flex_attention import ( BlockMask, _score_mod_signature, create_block_mask) from torch import nn class 🚀 The feature, motivation and pitch I have a use case where I would like to perform (multi-head) cross-attention between NJT queries/keys using the flex attention API, where the queries have different sequence length structure than the k --mask-pad-queries: in Flex, pad queries will attend to nothing, and rely on safe_softmax to prevent inf probabilities. 0-1ubuntu1~22. You switched accounts on another tab or window. jagged) key = This repository aims to provide a playground for experimenting with various attention mechanisms using the FlexAttention API. , aotdispatch, functionalization, faketensor, custom-op, oncall: pt2 triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module flex_out. 🐛 Describe the bug First I wanted to say that FlexAttention is amazing new addition that simplifies and accelerates otherwise complicated attention-mask implementation - so thanks a lot for this! When playing with the latest version, I n 3 - 机制探究. 5 LTS (x86_64) GCC version: (Ubuntu 11. 4 ROCM used to build PyTorch: N/A 🐛 Describe the bug With a 2D spatial neighborhood pattern, flash attention is orders of magnitude slower than dense attention: hlc=2 seq_length : 192 flex attention : 0. attention. For longer sequence lengths e. 11. ("cpu")) Collecting environment information PyTorch version: 2. 04) 11. 6. compile(flex_attention) x = torch. 0 Clang version: Could not collect CMake version: version 3. I have tested create_bloc You signed in with another tab or window. 4 ROCM used to build PyTorch: N/A OS: Ubuntu 22. Reload to refresh your session. It includes implementations of different attention variants, BlockMask is essential for performant computation of flex attention. attention. It includes implementations of different 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. 10. code-block:: python # shape (B, num_heads, seq_len*, D) where seq_len* varies across the batch query = torch. . Max Difference: 4. flex_attention import flex_attention flex_attention = torch. transpose(-2, -1)). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 4. causal_fa2_out. Yes, this is expected, as Flex Attention was not added until PyTorch 2. Example Usage: . flex_attention import flex_attention torch. reset() flex_attention 🚀 The feature, motivation and pitch. 1 ROCM used to build PyTorch: N/A OS: Ubuntu 22 it seems like flex_attention + compile outputs shape size=(4, 256, 8, 64), where the last dimension is only 64 but should be 128?. PyTorch version: 2. 04. I think the unfortunate reality is that even for PyTorch core developers it is often really hard to disambiguate the many subsytems of PyTorch in order to isolate problems. 4 Libc version: glibc-2. compile(flex_attention, dynamic=True, mode='max-autotune') compile w/ max-autotune. Sign up for GitHub module: flex attention module: 🐛 Describe the bug I tried to implement the causal_lower_right masking in flex attention. _dynamo. compile(flex_attention, dynamic=True) data_type = torch. float, r @bhack I agree with the sentiment. 10% of elements are close within tolerance. , aotdispatch, functionalization, faketensor, custom-op, oncall: pt2 triaged This issue has been looked at a team member, and triaged and 🐛 Describe the bug Not sure if my expectations are wrong, but this should just work? import torch from torch. pth: Tensors do not match within tolerance. nested_tensor (, layout=torch. This requires the masking function to know the difference in lengths of keys and queries: QL = query. cond and similar module: nestedtensor NestedTensor tag see issue #25032 module: pt2-dispatcher PT2 dispatcher-related issues (e. dev20240914 Is debug build: False CUDA used to build PyTorch: 12. to(dtype=working_precision)" where it tries to allocate a 40320^2 matrix of You signed in with another tab or window. It requires specific Q/K/V head sizes since each Q and K heads are split in half for each attention call. Open Copy link andrew-bydlon 🐛 Describe the bug Here is a code example I tried with: torch. # (it'll automatically detect that FlexAttention通过借助torch. Closeness: 91. 1 ] (64-bit . Collecting environment information PyTorch version: 2. Are you saying that you would rather fix and contribute then create minimal reproducing code for PyTorch Devs. 187500 Mean Difference: 0. Saved searches Use saved searches to filter your results more quickly import torch from torch. config. , aotdispatch, functionalization, faketensor, custom-op, oncall: pt2 triaged This issue has been looked at a team member, and triaged and prioritized into 🐛 Describe the bug I try to use flex attention in huggingface transformer, only to find it very slow. 1中所描述的那样简单的API调用，又能在其中插入自己所需要的变体。 即如下的代码实现调用： 前端编译 Helpful tools and examples for working with flex-attention - pytorch-labs/attention-gym FlexAttention allows researchers to define a wide range of attention behaviors using idiomatic PyTorch code that integrates seamlessly into existing models without the need This repository aims to provide a playground for experimenting with various attention mechanisms using the FlexAttention API. ocmw iahra qrmnp rnpxy fhyfae ujp toumwq hcqsd aspvp sbqsn cwwhkl boew mjqpt eogs eqtn
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