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[CI] Quantization workflow (#29046)

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* [CI] Quantization workflow

* build dockerfile

* fix dockerfile

* update self-cheduled.yml

* test build dockerfile on push

* fix torch install

* udapte to python 3.10

* update aqlm version

* uncomment build dockerfile

* tests if the scheduler works

* fix docker

* do not trigger on psuh again

* add additional runs

* test again

* all good

* style

* Update .github/workflows/self-scheduled.yml

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* test build dockerfile with torch 2.2.0

* fix extra

* clean

* revert changes

* Revert "revert changes"

This reverts commit 4cb52b8822da9d1786a821a33e867e4fcc00d8fd.

* revert correct change

---------

Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
This commit is contained in:
Marc Sun
2024-02-28 10:09:25 -05:00
committed by GitHub
parent 554e7ada89
commit f54d82cace
5 changed files with 133 additions and 2 deletions
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docs/source/en/hf_quantizer.md
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@@ -66,4 +66,4 @@ For some quantization methods, they may require "pre-quantizing" the models thro
7. Document everything! Make sure your quantization method is documented in the [`docs/source/en/quantization.md`](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/docs/source/en/quantization.md) file.
8. Add tests! You should add tests by first adding the package in our nightly Dockerfile inside `docker/transformers-all-latest-gpu` and then adding a new test file in `tests/quantization/xxx`. Feel free to check out how it is implemented for other quantization methods.
8. Add tests! You should add tests by first adding the package in our nightly Dockerfile inside `docker/transformers-quantization-latest-gpu` and then adding a new test file in `tests/quantization/xxx`. Feel free to check out how it is implemented for other quantization methods.