Reduce Python overhead in the GPTQ column loop
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 48/100
- Issue type
- Refactor
- Clarity
- Mostly clear
- Activity status
- Active
- Domain
- machine-learning, performance
Research direction
Start with src/bitsmith/calib/gptq.py and trace the Python column loop and lazy-update blocks in gptq_quantize; compare the proposed torch.compile and Triton approaches. Read tests/unit/test_calib.py and run its GPTQ checks to establish the current results. Done means Qwen2.5-0.5B GPTQ is at least 3x faster with the same perplexity and results differing only by float rounding.
Written by the indexing model from the issue text.
Description
gptq_quantize loops over columns in Python. On a GPU this makes GPTQ CPU bound: during
the Qwen2.5 runs the GPU sat mostly idle while one CPU core ran at 100%.
Ideas: process each lazy-update block with torch.compile, or write the inner block
loop as a Triton kernel; the update between blocks is already a single matmul.
Files: src/bitsmith/calib/gptq.py, tests/unit/test_calib.py (results must not change
beyond float rounding)
Done when GPTQ on Qwen2.5-0.5B is at least 3x faster with the same perplexity.
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing 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.
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