# Changelog All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [0.0.28] - TBD ### Added ### Improved ### Removed ## [0.0.27] - 2024-07-10 Pre-built binary wheels require PyTorch 2.3.1 ### Added - fMHA: `PagedBlockDiagonalGappyKeysMask` - fMHA: heterogeneous queries in `triton_splitk` - fMHA: support for paged attention in flash - fMHA: Added backwards pass for `merge_attentions` - fMHA: Added `torch.compile` support for 3 biases (`LowerTriangularMask`, `LowerTriangularMaskWithTensorBias` and `BlockDiagonalMask`) - some might require PyTorch 2.4 - fMHA: Added `torch.compile` support in `memory_efficient_attention` when passing the flash operator explicitely (eg `memory_efficient_attention(..., op=(flash.FwOp, flash.BwOp))`) - fMHA: `memory_efficient_attention` now expects its `attn_bias` argument to be on the same device as the other input tensor. Previously, it would convert the bias to the right device. - fMHA: `AttentionBias` subclasses are now constructed by default on the `cuda` device if available - they used to be created on the CPU device - 2:4 sparsity: Added `xformers.ops.sp24.sparsify24_ste` for Straight Through Estimator (STE) with options to rescale the gradient differently for masked out/kept values ### Improved - fMHA: Fixed out-of-bounds reading for Split-K triton implementation - Profiler: fix bug with modules that take a single tuple as argument - Profiler: Added manual trigger for a profiling step, by creating a `trigger` file in the profiling directory ### Removed - Removed support for PyTorch version older than 2.2 ## [0.0.26] - 2024-04-29 Pre-built binary wheels require PyTorch 2.3.0 ### Added - [2:4 sparsity] Added support for Straight-Through Estimator for `sparsify24` gradient (`GRADIENT_STE`) - [2:4 sparsity] `sparsify24_like` now supports the cuSparseLt backend, and the STE gradient - Basic support for `torch.compile` for the `memory_efficient_attention` operator. Currently only supports Flash-Attention, and without any bias provided. We want to expand this coverage progressively. ### Improved - merge_attentions no longer needs inputs to be stacked. - fMHA: triton_splitk now supports additive bias - fMHA: benchmark cleanup ## [0.0.25.post1] - 2024-03-29 Pre-built binary wheels require PyTorch 2.2.2 ## [0.0.25] - 2024-03-14 Pre-built binary wheels require PyTorch 2.2.1 ### Added - New `merge_attentions` function - fMHA: New gappy attention biases. ### Improved - fMHA: Updated Flash-Attention to v2.5.6: this has a performance improvement for multiquery. - fMHA: triton_splitk changed and expanded. Now amalgamates using LSE. Can autotune, supports causal with a small number of queries - not just 1. Experimental support for paged attention. - `rope_padded`: Fixed CUDA error with many queries (more than 65k) - `rmsnorm`: Fixed CUDA error with large inputs (enables 512k+ sequence length on Llama2 70B) ### Removed - fMHA: Removed triton operator (`fmha.triton.*`, `xformers.ops.MemoryEfficientAttentionTritonFwdFlashBwOp`, `xformers.ops.TritonFlashAttentionOp`), as it has correctness issues under some conditions, and is slower than other implementations. ## [0.0.24] - 2024-01-31 Pre-built binary wheels require PyTorch 2.2.0 ### Added - Added components for model/sequence parallelism, as near-drop-in replacements for FairScale/Megatron Column&RowParallelLinear modules. They support fusing communication and computation for sequence parallelism, thus making the communication effectively free. [Read more](https://twitter.com/d_haziza/status/1753030654118211593) - Added kernels for training models with 2:4-sparsity. We introduced a very fast kernel for converting a matrix A into 24-sparse format, which can be used during training to sparsify weights dynamically, activations etc... xFormers also provides an API that is compatible with torch-compile, see `xformers.ops.sparsify24`. ### Improved - Make selective activation checkpointing be compatible with torch.compile. ### Removed - Triton kernels now require a GPU with compute capability 8.0 at least (A100 or newer). This is due to newer versions of triton not supporting older GPUs correctly - Removed support for PyTorch version older than 2.1.0 ## [0.0.23] - 2023-12-05 Pre-built binary wheels require PyTorch 2.1.1 (xFormers `0.0.23`) or PyTorch 2.1.2 (xFormers `0.0.23.post1`). ### Fixed - fMHA: Fixed a bug in cutlass backend forward pass where the logsumexp was not correctly calculated, resulting in wrong results in the BW pass. This would happen with MQA when one sequence has a query with `length%64 == 1` - fMHA: Updated Flash-Attention to v2.3.6 - this fixes a performance regression in causal backward passes, and now supports `BlockDiagonalCausalWithOffsetPaddedKeysMask` ### Added - fMHA: Added `LocalAttentionFromBottomRightMask` (local) - fMHA: Added `LowerTriangularFromBottomRightMask` (causal) - fMHA: Added `LowerTriangularFromBottomRightLocalAttentionMask` (local + causal) ### Removed - Removed `xformers.triton.sum_strided` ## [0.0.22] - 2023-09-27 ### Fixed - fMHA: Backward pass now works in PyTorch deterministic mode (although slower) ### Added - fMHA: Added experimental support for Multi-Query Attention and Grouped-Query Attention. This is handled by passing 5-dimensional inputs to `memory_efficient_attention`, see the documentation for more details - fMHA: Added experimental support for Local Attention biases to `memory_efficient_attention` - Added an example of efficient [LLaMa decoding](https://github.com/facebookresearch/xformers/tree/main/examples/llama_inference) using xformers operators - Added Flash-Decoding for faster attention during Large Language Model (LLM) decoding - up to 50x faster for long sequences (token decoding up to 8x faster end-to-end) - Added an efficient rope implementation in triton, to be used in LLM decoding - Added selective activation checkpointing, which gives fine-grained control of which activations to keep and which activations to recompute - `xformers.info` now indicates the Flash-Attention version used ### Removed - fMHA: Removed `smallK` backend support for CPU. `memory_efficient_attention` only works for CUDA/GPU tensors now - **DEPRECATION**: Many classes in `xformers.factory`, `xformers.triton` and `xformers.components` have been or will be deprecated soon (see tracking issue facebookresearch/xformers#848) ## [0.0.21] - 2023-08-18 ### Improved - fMHA: Updated [flash-attention](https://github.com/Dao-AILab/flash-attention) to v2, with massive performance improvements for both the forward pass and backward pass. This implementation is now used by default when it's available ### Bug fixes - fMHA/cutlass: Fix potential race condition in the FW/BW passes - fMHA/cutlass: Fix `attn_bias` stride overflow for very long sequences (>32k) - `LowerTriangularMask` is now backward compatible with older xformers versions ### Breaking changes - `memory_efficient_attention` now expects the `attn_bias` argument to have a head dimension - `memory_efficient_attention` no longer broadcasts the batch/head dimensions of `attn_bias`. Please use `.expand` if you need to broadcast the bias - Remove `causal_diagonal` argument from `BlockDiagonalCausalWithOffsetPaddedKeysMask` ### Added - Binary wheels on pypi/conda now contain H100 kernels - fMHA: Added backend specialized for decoding that does not use TensorCores - useful when not using multiquery **NOTE**: Binary wheels are now provided only for PyTorch 2 with cuda 11.8. It is still possible to use xFormers with older versions of PyTorch by building from source or using conda. ## [0.0.20] - 2023-05-23 ### Improved - fMHA/cutlass (backward): Massive performance improvements when `batch_size * num_heads` is low (10x+) - fMHA/cutlass: Further performance improvements for both the forward & backward kernels - fMHA (backward): Now dispatching to cutlass when `embed_dim>64` - fMHA: Updated Flash-Attention to `v1.0.5` ### Added - fMHA now runs on H100 (support is experimental) ## [0.0.19] - 2023-04-28 ### Added - Display `nvcc` version used to compile `xformers` in `python -m xformers.info` ### Fixed - Fixed performance regression with `nvcc>11.6` (facebookresearch/xformers#712) - fMHA/cutlass: Fixed `nan` in the output when using a `torch.Tensor` with `-inf` prefixes as `attn_bias` (facebookresearch/xformers#722) - fMHA/cutlass: Fixed `nan` in the output when the sequence length is larger than `2 ** 15` (facebookresearch/xformers#719) - fMHA/cutlass: Significative performance improvements (up to 2x) for both the forward pass and backward pass - fMHA/cutlass: The kernel are now deterministic - fMHA/cutlass: Fixed backward pass correctness when using dropout (facebookresearch/xformers#724) ## [0.0.18] - 2023-03-31 ### Added - Added `xformers.ops.index_select_cat` and `xformers.ops.scaled_index_add` - those are experimental functions that only work with a few shapes, and can be used to write efficient stochastic depth in transformer architectures for instance ### Fixed - fMHA: `memory_efficient_attention` now accepts `torch.Tensor` as attention bias for any seqlen, although there are still requirements on the alignment of the bias tensor (see facebookresearch/xformers#683) ## [0.0.17] - 2023-03-28 ### Fixed - fMHA: Fixed BW pass on Sm86/Sm89 GPUs when `K > 64` (RTX 3090, RTX 4090, A6000, ..) [facebookresearch/xformers#631] ### Added - fMHA/CUTLASS: Added tensor attn bias support [facebookresearch/xformers#587] - contribution from [@jfc4050](https://github.com/jfc4050) - fMHA/CUTLASS: Added tensor attn bias grad support [facebookresearch/xformers#587] - contribution from [@jfc4050](https://github.com/jfc4050) - fMHA/CUTLASS: Added dropout support [facebookresearch/xformers#587] - contribution from [@jfc4050](https://github.com/jfc4050) - fMHA: Added support for varying sequence lengths [facebookresearch/xformers#500] ## [0.0.16] - 2023-01-31 ### Fixed - Updated triton dependency [facebookresearch/xformers#418] - Stripe lineinfo from binaries, reducing the binary size [facebookresearch/xformers#549] - Added support for pip wheels [facebookresearch/xformers#588, facebookresearch/xformers#573, facebookresearch/xformers#534, facebookresearch/xformers#523, ...] big thanks to [@AbdBarho](https://github.com/AbdBarho)! - Fixed compatibility with Python 3.7 [facebookresearch/xformers#541] - thanks to [@susumuota](https://github.com/susumuota) - fMHA: Fixed strides for QKV gradients for cutlass attention [facebookresearch/xformers#535] - fMHA: Stricter inputs validation to avoid CUDA errors for unsupported inputs [facebookresearch/xformers#592] - fMHA/Flash-Attention: Updated to https://github.com/HazyResearch/flash-attention/commit/a1f49a2b92b6fa022379bbebafed9d7f5e96a675 with multiple changes from [@TriDao](https://github.com/tridao) that make the operator up to 20% faster - fMHA/Flash-Attention: Fixed backward pass wrapper, where non-contiguous gradients could give the wrong result [facebookresearch/xformers#548] - fMHA: Separate each operator into forward and backward operators. It's now possible to use any combination of forward+backward (for instance Triton forward and Flash-Attention backward) [facebookresearch/xformers#560] ### Added - fMHA: Added Triton operator for forward pass from [Flash-Attention](https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attn_triton.py) authored by [@TriDao](https://github.com/tridao), will be automatically used on A100 when compatible - fMHA: Added [`xformers.ops.memory_efficient_attention_forward`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention_forward), [`xformers.ops.memory_efficient_attention_forward_requires_grad`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention_forward_requires_grad), [`xformers.ops.memory_efficient_attention_backward`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention_backward) for power-users who write custom autograd functions [facebookresearch/xformers#560] - fMHA: Support for custom scaling for the CUTLASS-based kernel [facebookresearch/xformers#530] - contribution from [@comaniac](https://github.com/comaniac) ## [0.0.15] - Skipped ## [0.0.14] - 2022-11-10 ### Fixed - fMHA/CUTLASS: The current CUDA stream is now used by the kernel [facebookresearch/xformers#491] - fMHA/CUTLASS: Improve overall performance ### Added - SwiGLU: Added `xformers.ops.SwiGLU` and its functional counterpart (`xformers.ops.swiglu`) [facebookresearch/xformers#490] - fMHA: Possible to combine CUTLASS's forward with flash-attention's backward pass [facebookresearch/xformers#469] - improves performance on A100 for K = 128 - fMHA: Add custom `xformers.ops.unbind` operator to avoid a cat in the attention block [facebookresearch/xformers#458] ## [0.0.13] - 2022-09-26 ### Added - fMHA: Added CUTLASS-based kernel for `xformers.ops.memory_efficient_attention`. This kernel is automatically depending on the inputs, and works on any GPU after P100 [facebookresearch/xformers#362] ## [0.0.12] - 2022-08-08 ### Fixed - Removed duplicated biases in the FusedMLP layers [facebookresearch/xformers#317] - Rotary embeddings respecting input types [facebookresearch/xformers#326] - Poolformer style instantiating useless projection layers [facebookresearch/xformers#349] - Fix layer position not being properly tracked, causing extra layernorms for programmatic xformers [facebookresearch/xformers#348] - Pass use_triton flag to LayerNorm module [facebookresearch/xformers#336] ### Added - Four blocksparsity layouts from DeepSpeed [facebookresearch/xformers#320] - Support several initialization options [facebookresearch/xformers#312] - Conv2DFeedforward feedforward part [facebookresearch/xformers#321] - VisualAttention [facebookresearch/xformers#329] - Automatic blocksparse for causal attention [facebookresearch/xformers#334] - Better hierarchical transformer generation [facebookresearch/xformers#345] - Fused operations with AOTAutograd/NVFuser, integration into MLP [facebookresearch/xformers#357] - Refactor LRA code to use Pytorch Lightning [facebookresearch/xformers#343] ## [0.0.11] - 2022-05-30 ### Fixed - Fix some torchscriptability [facebookresearch/xformers#246] - Fix FourierMix being compatible with AMP [facebookresearch/xformers#258] - Better asserts on QKV dimensions [facebookresearch/xformers#264] - Better perfs for FusedMLP and FusedLinearLayer [facebookresearch/xformers#283] - Deepnorm init missing self-attention [facebookresearch/xformers#284] ### Added - Simplicial Embeddings [facebookresearch/xformers#259] - Mem efficient attention, FW pass [facebookresearch/xformers#267] - MHA benchmark - MLP benchmark - Move all triton kernels to triton v2 [facebookresearch/xformers#272] - Mem efficient attention, BW pass [facebookresearch/xformers#281] - Metaformer support [facebookresearch/xformers#294] ## [0.0.10] - 2022-03-14 ### Fixed - Expose bias flag for feedforwards, same default as Timm [facebookresearch/xformers#220] - Update eps value for layernorm, same default as torch [facebookresearch/xformers#221] - PreNorm bugfix, only one input was normalized [facebookresearch/xformers#233] - Fix bug where embedding dimensions that did not match model dim would lead to a crash [facebookresearch/xformers#244] ### Added - Add DeepNet (DeepNorm) residual path and init [facebookresearch/xformers#227] ## [0.0.9] - 2022-02-09 ### Added - Compositional Attention [facebookresearch/xformers#41] - Experimental Ragged attention [facebookresearch/xformers#189] - Mixture of Experts [facebookresearch/xformers#181] - BlockSparseTensor [facebookresearch/xformers#202] - Nd-tensor support for triton softmax [facebookresearch/xformers#210] ### Fixed - Bugfix Favor, single feature map [facebookresearch/xformers#183] - Sanity check blocksparse settings [facebookresearch/xformers#207] - Fixed some picklability [facebookresearch/xformers#204] ## [0.0.8] - 2022-01-07 ### Fixed - Much faster fused dropout [facebookresearch/xformers#164] - Fused dropout repeatability [facebookresearch/xformers#173] ### Added - Embedding weight tying option [facebookresearch/xformers#172] ## [0.0.7] - 2021-11-30 ### Fixed - Dropout setting not properly passed in many attentions [facebookresearch/xformers#123] ## [0.0.6] - 2021-11-24 ### Fixed - Fix self attention optimization not being triggered, broken residual path [facebookresearch/xformers#119] - Improve speed by not using contiguous Tensors when not needed [facebookresearch/xformers#119] ### Added - Attention mask wrapper [facebookresearch/xformers#113] - ViT comparison benchmark [facebookresearch/xformers#117] ## [0.0.4] - 2021-11-16 ### Fixed - Homogenizing the masks, additive or bool [facebookresearch/xformers#79][facebookresearch/xformers#85][facebookresearch/xformers#86] - Fix causality flag not being respected [facebookresearch/xformers#103] - Enabling FusedLayerNorm by default in the factory if Triton is available - Fixing Favor with fp16 - Fixing Favor trainability ### Added - Fused dropout/bias/activation layer [facebookresearch/xformers#58] - Fused layernorm used by default in the factory [facebookresearch/xformers#92] ## [0.0.3] - 2021-11-01 ### Fixed - Nystrom causal attention [facebookresearch/xformers#75] ## [0.0.2] - 2021-11-01 ### Fixed - More robust blocksparse [facebookresearch/xformers#24] ### Added - Rotary embeddings [facebookresearch/xformers#32] - More flexible layernorm [facebookresearch/xformers#50]