xformers/CHANGELOG.md

# 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]