Lightning-AI / Lightning-AI/lightning-thunder
Quantize and dequantizing model weights in trace
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- Dominant language
- Python
- Stars
- 1.5k
- Forks
- 121
- PR merge metrics
- No merged PRs in 30d
Description
🚀 Feature
Quantize an arbitrary model weight and have that dequantized on the fly in the traces.
Pitch
Take a list of weight names and have a corresponding transform_module and transform_initial_graph. Maybe have options for the quantization (bitsandbytes with both 8 bit and 4 bit). May also need epliogue for updating things.
Additional context
Discussed with @t-vi .
Contributor guide
No contributing guide indexed for this repository
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
The issue names transform_module and transform_initial_graph as possible entry points, but provides no files or tests. Start by locating those entry points and clarifying the weight-name inputs and quantization options; done means model weights can be quantized and dequantized on the fly in traces with the requested options covered.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- compilers, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100