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Add pipeline parallel support to `TransformersModel`
Merge after:
- https://github.com/huggingface/transformers/pull/36091
- #13403
Tested with Llama 3.1 70B in pp=4 and pp2,tp2 configurations.
The following command can be used for testing:
CUDA_VISIBLE_DEVICES=0,1 vllm serve meta-llama/Llama-3.2-1B-Instruct --disable-log-requests --model-impl transformers --gpu-memory-utilization 0.25 --pipeline-parallel-size 2
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This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hmellor.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
Can only be merged after Transformers v4.49, but marking as ready for review so this can be merged soon after.
This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hmellor.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hmellor.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
Please merge the latest changes from main
This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hmellor.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
Let's update this after #13634 to ensure that nothing is broken.
This pull request has merge conflicts that must be resolved before it can be merged. Please rebase the PR, @hmellor.
https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork
The test passes locally, but I've commented it out so that we are not blocked by https://github.com/vllm-project/vllm/pull/13905
I'd be keen to get this merged because it makes quite a few changes to the TransformersModel without breaking it for non-PP cases. Merging would allow me to commit smaller QoL changes and pave the way for VLM support.