Quantization leads to performance degradation
Nobody has claimed this yet.
- Dominant language
- C++
- Stars
- 4.7k
- Forks
- 536
- Avg merge
- 12h 12m
- Merged PRs (30d)
- 4
Description
When converting the model, I enable the quantization to 'int8', but I noticed a decrease in performance of the converted model by 5 points in terms of BLEU.
Therefore, I would like to inquire if there are any specific considerations I should be aware of when performing quantization.
command: ct2-fairseq-converter --model_path . --quantization int8 -output_dir .
Environment:
ctranslate2=3.14.0
fairseq=0.10.2
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 reproducing the supplied ct2-fairseq-converter command with CTranslate2 3.14.0 and fairseq 0.10.2, comparing the quantized model's BLEU with the non-quantized model. Determine whether the reported 5-point degradation is expected for int8 quantization and document the relevant considerations or behavior that needs correction.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100