[AutoDeploy] Enhance checkpoint loading pipeline
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AutoDeploy
- Dominant language
- Python
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Description
More robust usage of load_checkpoint_in_model
- Investigate load_checkpoint_in_model's load_hook applying issue:
- understand the difference between set_model_state_dict and the torch-native module.load_state_dict, the first one works for the DS FP4 case while the second doesn't
- Add unit tests for our use case(e.g. load_hook) and guard around version updates
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 in tensorrt_llm/_torch/auto_deploy/models/hf.py at load_checkpoint_in_model and trace its load_hook behavior. Compare set_model_state_dict with torch-native module.load_state_dict, especially for the DS FP4 case. Done means the load_hook use case is covered by unit tests and version updates have appropriate guards.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100