adasplash 0.1.1b0

AdaSplash: Efficient Adaptive Sparse Attention in Triton

AdaSplash: Adaptive Sparse Flash Attention

AdaSplash, aka flash entmax attention, is an efficient adaptive sparse attention mechanism implemented in Triton. Check out our paper: https://arxiv.org/abs/2502.12082.

Installation

You can install AdaSplash via pip:

pip install adasplash

Alternatively, install the latest development version directly from GitHub:

pip install git+https://github.com/deep-spin/adasplash.git

Usage

AdaSplash provides three main functions, all available via from adasplash import ...:

Triton Entmax (Optimized Entmax Activation)

from adasplash import triton_entmax
import torch

x = torch.randn(128, 256).cuda()
y = triton_entmax(x, alpha=1.5, n_iter=10, fast_math=True)

• Uses Halley's method + bisection instead of pure bisection.
• Faster and more efficient than traditional Entmax implementations.

AdaSplash with Block Masking

from adasplash import adasplash

q = torch.randn(1, 8, 128, 64, device="cuda")
k = torch.randn(1, 8, 128, 64, device="cuda")
v = torch.randn(1, 8, 128, 64, device="cuda")

output = adasplash(q, k, v, alpha=1.5, niter=10, is_causal=True, varlen=None)

• Leverages adaptive sparsity for efficiency in both forward and backward passes.
• Requires O(Tr × Tc) bits of extra memory for storing a binary mask per block.

AdaSplash without Block Masking

from adasplash import adasplash_no_block_mask

output = adasplash_no_block_mask(q, k, v, alpha=1.5, niter=10, is_causal=True, varlen=None)

• Does not use block masking but still benefits from tiling and fused ops for efficiency.
• Requires less memory than the block-masked version.

Key Features

Variable Length Sequences:

varlen = torch.tensor([34, 128], device='cuda')  # Actual sequence lengths
output = adasplash(q, k, v, varlen=varlen)

Adaptive Sparsity Control:

# Control sparsity via alpha parameter
output = adasplash(q, k, v, alpha=1.333)  # More dense
output = adasplash(q, k, v, alpha=2.0)  # More sparse

Causal and Non-causal Masking:

output = adasplash(q, k, v, is_causal=True)  # Causal masking
output = adasplash(q, k, v, is_causal=False)  # Non-causal masking

Benchmark

Testing

To ensure the library works as expected, install the development dependencies and run tests:

pip install -r requirements-dev.txt
pytest

Citation

If you use AdaSplash in your research, please cite:

@article{goncalves2025adasplash,
  title={AdaSplash: Adaptive Sparse Flash Attention},
  author={Nuno Gonçalves and Marcos Treviso and André F. T. Martins},
  journal={arXiv preprint arXiv:2502.12082},
  url={https://arxiv.org/abs/2502.12082},
  year={2025}
}

License

AdaSplash is licensed under the MIT License. See the LICENSE file for details.