pytorch

ad90ab31 - Flash Attention v2 (#105602)

Flash Attention v2 (#105602) # Summary ## PR Dependencies I don't use ghstack :( this is a PR where it would have been helpful. That beings said I am going to peel off some PRs to make reviewing this easier: - [x] Separate build flags for Flash and MemEff: #107985 ### Description This pull request updates the version of _scaled_dot_product_flash_attention from version 1 to version 2. The changes are based on the flash attention code originally authored by @tridao ### Changes Made The majority of the changes in this pull request involve: - Copying over the flash_attention sources. - Updating header files. - Removing padding and slicing code from within the flash_attention kernel and relocating it to the composite implicit region of the SDPA. This was need to make the kernel functional and appease autograd. - Introducing a simple kernel generator to generate different instantiations of the forward and backward flash templates. - Adding conditional compilation (ifdef) to prevent building when nvcc is invoked with gencode < sm80. - Introducing a separate dependent option for mem_eff_attention, as flash_attention v2 lacks support for Windows and cannot be built for sm50 generation codes. - Modifying build.sh to reduce parallelization on sm86 runners and to lower the maximum parallelization on the manywheel builds. This adjustment was made to address out-of-memory issues during the compilation of FlashAttentionV2 sources. - Adding/Updating tests. ### Notes for Reviewers This is not a fun review, and I apologize in advance. Most of the files-changed are in the flash_attn/ folder. The only files of interest here IMO: - aten/src/ATen/native/transformers/cuda/flash_attn/flash_api.cpp - aten/src/ATen/native/transformers/cuda/flash_attn/kernels/generate_kernels.py ( this has been incorporated upstream to flash-attention github) There are a number of files all related to avoiding OOMs in CI/CD. These are typically shell scripts. ### Follow up items - Include the updates from https://github.com/Dao-AILab/flash-attention/commit/e07aa036db0e6cd251b17d9ee2d1720023da2def and https://github.com/Dao-AILab/flash-attention/commit/9e5e8bc91e30af5cdc321362b553f6c0da332e30 | https://github.com/pytorch/pytorch/issues/108108 ### Work Items - [x] I don't think Windows will be supported for 3.1.0 - Need to update cmakee - [x] Let multi_query/attention pass through and test | UPDATE: I have the fast path implemented here: https://github.com/pytorch/pytorch/pull/106730 but since this will require changes to semantics of math to call repeat_interleave, I think this should be done as a followup. - [x] Had to drop cutlass back to 3.0.0 to get it to compile. Need to figure out how to upgrade to 3.1.0 and later. Spoke with Tri and he is going to be taking a look. Note: compiling with clang currently errors for the cute headers. - [x] Update test exercise above codepath - [x] Still need to disable on seq_len % 128 != 0 for backward( Tri beat me to it https://github.com/Dao-AILab/flash-attention/commit/a4f148b6abe077cd387e69e5caae45d63a76bb24) - [x] Add determinism warning to BWD, Tri got to this one as well: 1c41d2b - [x] Update dispatcher to universally prefer FlashV2 - [x] Update tests to exercise new head_dims - [x] Move the head_dim padding from kernel to top level composite implicit function in order to make it purely functional - [x] Create template generator script - [x] Initial cmake support for building kernels/ folder - [x] Replay CudaGraph changes ### Results #### Forward only The TFlops are reported here are on a100 that is underclocked.  #### Forward+Backward Ran a sweep and for large compute bound sizes we do see a ~2x performance increase for forw+back. <img width="1684" alt="Screenshot 2023-07-20 at 3 47 47 PM" src="https://github.com/pytorch/pytorch/assets/32754868/fdd26e07-0077-4878-a417-f3a418b6fb3b"> Pull Request resolved: https://github.com/pytorch/pytorch/pull/105602 Approved by: https://github.com/huydhn, https://github.com/cpuhrsch