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fix: prevent global logger modification#4

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justinwangx merged 1 commit into
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prevent-global-logger-modification
Aug 13, 2024
Merged

fix: prevent global logger modification#4
justinwangx merged 1 commit into
mainfrom
prevent-global-logger-modification

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@justinwangx justinwangx merged commit 2b0867a into main Aug 13, 2024
@justinwangx justinwangx deleted the prevent-global-logger-modification branch August 13, 2024 12:32
justinwangx pushed a commit that referenced this pull request Feb 3, 2025
* chore: housekeeping

* chore: linter/formatter

* chore: more linter/formatter + default model

* feat: probe sampling for nanoGCG - plumbing (#3)

* feat: probe sampling - checkpointing

* a bit more cleanups

* feat: probe sampling for nanoGCG - parallelization (#4)

Major thing is the parallelization. It's actually relatively straightforward, what took the most time was actually debugging the cuda device-side assertions.

* feat: allow retrying (#5)

During probe sampling, quite a noticeable bunch of iterations don't actually bring down the losses. Adding a retry functionality to hopefully tackle the issue.

* fix: correct condition of buffer.size == 0

* docs: Update README.md

* docs: wordsmithing on README

* debug: try using another pad token

* fix: correct `optim_ids` assignment (#6)

* A trivial that unfortunately blocked me for a couple of days. 
* Symptom: When probe sampling is enabled, the losses don't seem to be optimized at all.
* Debugging: Manually tweaked `R` such that probe sampling effectively looks at all `B` candidates, the issue still persists.
* Approach: In the end a silly mistake was discovered during the `current_loss, optim_ids` assignment stage where in probe sampling, the indices in `sampled_ids` don't match the actual calculated optimal candidate. 
* Fixed by making probe sampling function return the optimal candidate.

* chore: undo retry feature (#7)

Thanks to psyclaudeZ#6, the retry mechanism is probably not helpful. Hence reverting.

* chore: code cleanup (#8)

* refactor: better capture draft mode with a dedicated config

* docs: comments

* perf: GPU memory cleanup (#9)

* perf: actually might not need the cleanup

* perf: no_grad for draft loss calculation (#10)

* docs: revert changes to README

* address feedback: excessive logger.debug, linter, simply.py

* address feedback: pad tokens, deps

* chore: lowerbound for transformers dep
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