`v0.0.28.post3` - build for PyTorch 2.5.1

[0.0.28.post3] - 2024-10-30

Pre-built binary wheels require PyTorch 2.5.1

`v0.0.28.post2` - build for PyTorch 2.5.0

[0.0.28.post2] - 2024-10-18

Pre-built binary wheels require PyTorch 2.5.0

`0.0.28.post1` - fixing upload for cuda 12.4 wheels

[0.0.28.post1] - 2024-09-13

Properly upload wheels for cuda 12.4

FAv3, profiler update & AMD

Pre-built binary wheels require PyTorch 2.4.1

Added

• Added wheels for cuda 12.4
• Added conda builds for python 3.11
• Added wheels for rocm 6.1

Improved

• Profiler: Fix computation of FLOPS for the attention when using xFormers
• Profiler: Fix MFU/HFU calculation when multiple dtypes are used
• Profiler: Trace analysis to compute MFU & HFU is now much faster
• fMHA/splitK: Fixed nan in the output when using a torch.Tensor bias where a lot of consecutive keys are masked with -inf
• Update Flash-Attention version to v2.6.3 when building from scratch
• When using the most recent version of Flash-Attention, it is no longer possible to mix it with the cutlass backend. In other words, it is no longer possible to use the cutlass Fw with the flash Bw.

Removed

• fMHA: Removed decoder and small_k backends
• profiler: Removed DetectSlowOpsProfiler profiler
• Removed compatibility with PyTorch < 2.4
• Removed conda builds for python 3.9
• Removed windows pip wheels for cuda 12.1 and 11.8

torch.compile support, bug fixes & more

Pre-built binary wheels require PyTorch 2.4.0

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

torch.compile support, bug fixes & more

Pre-built binary wheels require PyTorch 2.4.0

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

[v0.0.27] torch.compile support, bug fixes & more

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

2:4 sparsity & `torch.compile`-ing memory_efficient_attention

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

`v0.0.25.post1`: Building binaries for PyTorch 2.2.2

Pre-built binary wheels require PyTorch 2.2.2

2:4 sparsity, fused sequence parallel, torch compile & more

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.
• 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