Add 4-bit Groupwise Weight-Only Quantization for Core ML #4866
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/4866
Note: Links to docs will display an error until the docs builds have been completed. ⏳ No Failures, 30 PendingAs of commit ac94cd9 with merge base d3da92d ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
facebook-github-bot
added
the
CLA Signed
digantdesai
reviewed
Aug 23, 2024
|
cc @kimishpatel |
digantdesai
added
triaged
|
Turns out, we may prefer Core ML quantization at this moment. Landed the alternative in #5228 Locally confirmed performance gain & accuracy preservation |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
4-bit groupwise weight-only quantization is well supported in Core ML. Since torchao offers the same quantization, let's use torchao to quantize llama then delegate to Core ML.