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* Add initial pixi environment all tests pass, predictions seem to be correct corresponds to a modernized conda environment following best practices * Reorder dependencies for easier read * Add openfold3 as an editable dependency * Sync cuda-python pin between pypi package and the conda environment * Comments Comments Overcommenting issues * Add explicitly a conda yml version of the pixi environment * Improve some wordings * Update pixi lockfile * Vendoring pieces of deepspeed incomplete, we might not need the native sources from upstream commit df59f203f40c8a292dd019ae68c9e6c88f107026 * Swap ninja verification with pytorch's * Vendoring pieces of deepspeed incomplete, we might not need the native sources from upstream commit df59f203f40c8a292dd019ae68c9e6c88f107026 * Use vendored deepspeed evoformer builder Use vendored deepspeed in the attention primitives * Add symlink to vendored deepspeed as in upstream * Vendor also op_builder.__init__ from deepspeed * Import explicitly EvoformerAttnBuilder, avoiding broken introspection magic * Add a ignore mechanism for cutlass detection in vendored deepspeed * Apply cutlass detection workaround and remove all nvidia-cutlass tricks from pixi environment * Remove nvidia-cutlass from openfold-3 dependencies (fix later) * Remove pypi ninja dependency in pixi workspace * No need for cutlass hacks * Add pixi config to .gitattributes * Remove deepspeed hacks for good * Update pixi lockfile * Update pixi conda environment * Remove MKL from pypi dependencies, as it is unused * Remove aria2 from pypi dependencies, unused and not so much of a convenience * Update lockfile Update lockfile * Re-enable pure PyPI install * Disable hack when conda is active * More comments on cutlass python API deprecation and pytorch * Make pixi environments (CPU, CUDA12, CUDA13, for all major platforms) * Increase LMDB map size to make test pass in osx-arm64 * Better comments of TODOs in pixi.toml Better comments of TODOs in pixi.toml Better comments of TODOs in pixi.toml * Pin cuequivariance until test failure is investigated * Move deepspeed to optional dependency also in pyproject * Pyproject: extend python version support * Pyproject: move dependencies table together with optional-dependencies * Pyproject: document future decision on dependency-groups * Pyproject: reformat to consolidate indent to 4 spaces * Pyproject: reorder dependencies for easier read * Pixi: add scipy * Pixi: add comment on CUDA13 * Pixi: make cuequivariance CUDA generic for its conda packages * Pixi: add reminder about devel install * Pyproject: fix and improve readability, add URLs * pixi.toml: make more readable by showing first envs, then base, then variants * pixi.toml: pin deepspeed to 0.18.3, first one with ninja detection fixed * pixi.toml: fully enable aarch64 and cuda13, revamp docs * pixi.lock: update * pixi.toml: add triton to cuequivariance dependencies for CUDA13 * pixi.lock: update * pixi.toml: include pip to allow users to play * pixi.toml: formatting for better readability * pixi.toml: restrict cuequivariance-cu13 to linux-64 until we unpin to >=0.8 * pixi.toml: formatting for better readability * pixi.toml: make pytorch-gpu an isolated environment feature in this way we can more easily express when a package is not ready yet in CF * pixi.toml: add environments that combine mostly pypi-based deps with CUDA from conda * pixi.toml: add openfold3-editable-full and account for lack of cuequivariance for python=3.14 * pixi.toml: brief documentation of the pypi-dominant environments * pixi.toml: add also the dev optional dependency group to openfold3-full * pyproject.toml: pin cuequivariance to <0.8 until we adapt tests * pixi.toml: add kalign to required non-pypi dependencies * pixi.toml: add more bioinformatics tools to non-pypi * pixi.toml: make env setup be part of the deepspeed-build feature * pixi.toml: simplify management of pypi features * pixi.lock: update, all tests pass A100,B300 x CUDA12,CUDA13 * pixi.toml: add table of what works and what needs test * pixi.toml: add tasks for exporting to regular conda environment yamls * conda environments: delete outdated modernized conda env, use new tasks instead * pixi.toml: bump min pixi version * pixi.toml: remove unnecessary comments * pixi.toml: remove unnecessary envvar definition for isolating extension builds * pixi.toml: better definition of maintenance environment pixi.toml: better definition of maintenance environment pixi.toml: better definition of maintenance environment * pixi.toml: add simple task to run test and save rsults to an environment-specific dir * of3: enable pickling regardless of forking strategy and platform * of3: enable multiple data loader workers in osx mps backed * Vendor improved deepspeed builder from upstream PR See: deepspeedai/DeepSpeed#7760 * pixi.lock: update * pixi.toml: remove some comment noise * of3: fix multiprocessing configuration corner case in osx * docker: move outdated example dockerfiles to docker/pixi-examples * examples: add example runner for osx inference * pixi.toml: ensure we get the right pytorch from pypi something smilar should actually be supported in pyproject.toml * pixi.lock: update, fixed torch cuda missmatch in pypi environments * pixi.toml: fix lock export + make default environment be maintenance * pixi.toml: use a more consitent name for environment arg * pixi.lock: update * pixi.toml: workaround for no-default-feature breaking the test task (pixi bug) * pixi.toml: issue with pixi pypi resolution seems solved * Revert "pixi.toml: issue with pixi pypi resolution seems solved" This reverts commit ded3482. * pixi.toml: better document problem and workaround * pixi.toml: make the test task present in all relevant environments this I feel makes less surprising its use, as opposed to passing the environment as an arg to a dependent task * pixi.toml: let CUDA13 flow freely * pixi.lock: update for initial pytorch 2.10, cuda 13.1 support * pixi.toml: add safe cuda environments (no accelerators) * of3: remove deepspeed hacks note that there are still some in __init__.py * of3: unvendor deepspeed * pixi.toml: simplify deepspeed dependency after our changes made it to CF/pypi * pixi.toml: remove safe environments as we are not maintaining them * pixi.toml: enable pytorch-coda in cuda 13 env after 2.10 release * pyproject.toml: pin deepspeed to >0.18.5, improved evoformer compilation * Add awscrt to dependencies, missing from recent PR * pixi.toml: setup correctly path to PTXAS_BLACKWELL for triton >=3.6.0 * pixi.toml: add -safe environments, at the moment just without cuequivariance these are also conda-pure environments * pixi.lock: update after consolidation (no vendor, pytorch 2.10 + CF cuda13) * pixi.toml: update outdated comments * updates with GB10 tests (#2) * updates with GB10 tests * cleanup * harmonize * linting data_module.py * speculative changes * pixi.toml: remove safe environments * pixi.lock: update after removal of safe environments * Remove pixi docker examples, to rework * Comment-out workaround for hard to reproduce ABI mismatch problem * pixi.toml: bump pixi, improve conda export by including all env variables * pixi.toml: unpin biotite * pixi.toml: python has its own feature * pixi.toml: bump deepspeed * pyproject.toml: bump deepspeed to version without Evoformer build bug * pixi.toml: detail on workaround * pixi.lock: update * pixi.toml: add example task to update safely the lockfile * pixi.toml: remove kalign2 * tests: fix test depending on unspecified glob return order * pixi.toml: better metadata * docs: wip * pixi.lock: update * Allow to configure multiprocessing start and set safe defaults We would still need to document this for users * Fix capitalization error * Fix capitalization error * Fix typo * pixi.lock: update --------- Co-authored-by: Tim Adler <tim.adler@bayer.com> Co-authored-by: Jan Domański <jan.domanski@omsf.io>
@sdvillal I tried to get something for the docs that gives people an overview of this feature – what do you think about this? |
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I'm not in love with this – might open a side-car PR to clean this up. LMDB roundtrip test fails for me without this hack. We just need a better way to cleanup these resources
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| map_size = 200 * 1024 |
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We bumped into this while working on another problem, this should just be tied to the page size on the os – otherwise we're hacking around with random numbers
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@sdvillal can you take a look here? I've messed around a bit Manually triggered the test workflow from the branch, otherwise it picks-up the workflow form main (points to files that no longer exists) https://github.com/aqlaboratory/openfold-3/actions/runs/24134205657 update all the above tests passed, I think this is ready to go! LMDB is the only loose-end but it's being addressed in other PRs already. Another batch of tests https://github.com/aqlaboratory/openfold-3/actions/runs/24185282518 🛑 failed, missing dep https://github.com/aqlaboratory/openfold-3/actions/runs/24186689907 🟢 |
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Your spidy-sense was correct @jnwei – the pixi envs include cueq but the tests are getting skipped (locally, DGX – on the AWS builders we don't have a GPU). Otherwise the branch is all green https://github.com/aqlaboratory/openfold-3/actions/runs/24453803143 🟢 |
IIRC, we setup so that cueq is not installed in ARM systems. For me, cueq tests were run successfully in both A100s and B300 - we needed to pin it down for the tests to pass. Will give a look later. |
Inference works like a charm in CPU only - in my limited experience at least. What kind of limitations are you thinking about? |
This looks cool; I would maybe simplify by (it is already good enough as is):
Fine for me to merge as is and I can give a hand with more accurate docs later, if needed. As a note, one of the limitations (or design choices) from pixi makes it difficult to have more modular environments (e.g., that you can opt out deepspeed with a command line option). I discussed with the pixi folks and they seem to have some ideas on how to bring this modularity back without fully compromising locking. It would be a great addition for our complex setup. |
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@sdvillal that's good feedback thanks!!
well about that :P #181 – it's available for 0.8 and above, but it required the tests to be fixed. inference works for me in this branch too.
sorry it's late – maybe you can unpack this for me? BTW there is a source .excalidraw in the repo, and a vscode extension if you want to add your chef's kiss! |
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Below is a record of the systems I tested with pixi. A few notes:
These are the regression tests for triangular attention. We should revisit how we store the regression records. Perhaps we should have a record per device. But I don't think we need to fix this here.
@jandom and @sdvillal if this matches with your expectations of what we should accomplish in this PR, I am happy to approve and merge |
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Hi Jennifer, Thanks for testing! Regarding osx and openmp, things work again if you disable the check, which I guess is risky but so far it allowed me to run tests and predictions succesfuly. Let's document it with a warning admonition: Note that this is a problem with how the wheels are created (using Possible solutions are indeed getting the conda-forge package accepted (stuck in a strange limbo), which is an example why the conda ecosystem is better suited technically for healthy environments (all dependencies linking to the same openmp runtime in this case, which is just not possible to achieve with pypi at the moment) or going back to calling kalign via subprocess. |
My expectations are that:
As a note, tests also pass in B300 |
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Thank you @sdvillal for the suggestion. Unfortunately, I seem to have a slightly different error; adding the KMP_DUPLICATE_LIB_OK=TRUE flag did not fix my issue on M2. The full stack trace is below. If my understanding is correct, I think the underlying issue in this build is that it is missing any copy of libomp, e.g. kalign-python build failure on macOS ARM64pixi run -e openfold3-cpu setup_openfoldError: × Failed to update PyPI packages for environment 'openfold3-cpu' Details |
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Thanks for the info @jnwei. I suspect you have a system for which there is no precompiled kalign-python wheel and so pixi is trying to use the source distribution. This tries to compile and fails, finding cmake in your homebrew install but not finding there the needed openmp library. It is better that this fails, as it violates the environment isolation. This highlights another risk for using kalign-python from pypi. If we look at what is available there, we see that there are wheels available:
Python 3.14 wheels are also not available for linux, so we might eventually see people complaining about it. I assume there is not an inherent incompatibility between python 3.14 and kalign-python, so we should ask upstream to create these wheels. About the macos compatibility, I am less knowledgeable if there is any easy path for wheels. I would propose to document these constraints, open a new issue and then we have two options:
I suggest to not block this PR and open a new one. I might give a try to the fun option if I find some bandwidth. |
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Thanks Santi, what you propose makes sense. I wrote issue #192 with specifically the Apple Silicon issue, and we can discuss more about next steps there. I suspect it may be easier to wait for the conda-forge package of kalign-python. Happy to proceed with merging this PR |
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The GHA errors because of a Dockerfile rename, everything is green when triggering manually https://github.com/aqlaboratory/openfold-3/actions/runs/24785787396 |
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@sdvillal – massive thanks for this, this is such a terrific enhancement |


Completing the work started in #34
Remaining tasks