Lightning-AI / Lightning-AI/litgpt
Batched inference on a single node with multiple GPUs
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- Dominant language
- Python
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Description
How to infer a batch of encoded tensors (shape = (B, T)) on 4 GPUs, getting 3~4x tokens/s through put compared to on single GPU? (it's for a small model which can be fit into a GPU's mem)
I've tried launching fabric with strategy='dp', 'ddp', 'fsdp' as in commit 7130a36 (2023/12/14 #818). But failed for various reasons.
Meanwhile, generate/sequentially.py is slower than single GPU and tp.py doesn't work for bached inputs out of the box.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by comparing the approaches in generate/sequentially.py and tp.py with the Fabric strategies referenced from commit 7130a36. Reproduce the batched encoded-tensor case on four GPUs and identify why dp, ddp, fsdp, and tp.py fail or underperform. Done means batched inference works on one node with near-linear throughput improvement over a single GPU.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- distributed-systems, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100