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…ding flash-attention
- make validation and testing more efficient - improve lightning_model code - make alibi mask optional when there no attention mask is passed as parameter
- add tutorial on how to use slide and patient encoding - set chief as default encoder - update readme for new installation steps with uv
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Wow, thanks for fixing this Fabi! |
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Wow!!! Great job Fabi. The installation steps are clear and also the PR description. Unfortunately, I reproduced the steps for installation using the repository with gpu (updated uv and ran the commands one by one) and the models that require flash-attention fail: Tried it on sirius, on this branch, removed previous .venv and ran uv sync step with --refresh. Also took around 20 minutes to build flash-attn. Hope this info helps! |
EzicStar
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Jul 21, 2025
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LGTM! Thank you so much for fixing this :)
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This PR enables to install STAMP with all slide encoders on CPU-only and CUDA systems.
With #75 it happened that dependencies like flash-attn, causal-conv1d or mamba-ssm were installed / compiled using a different version of PyTorch than in the environment which caused errors.
Some changes in this PR are temporary measurements to simplify the installation process. They can be reverted as soon as the underlying issues have been resolved.
The
tritonversion has been fixed until this is resolved. This causedtorchto downgrade to 2.6.0. As the flash-attn wheels for 2.6.0 are broken for my setups, I had to make sure to force flash-attn to build. Unfortunately theno-binary-packagestatement in thepyproject.tomlfile does not ensure that. That is the reason for the flash-attention fork. Can be reverted as soon as fixed.This PR bumps the default python version to 3.12 (but is still compatible with 3.11).
closes #81
Follow-up: #94 (publish PyPI package)