-
Notifications
You must be signed in to change notification settings - Fork 11.2k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add initial AVX512 support for dot product on Linux #320
Merged
Conversation
This file contains 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
* Update Makefile to detect AVX512 support and add compiler flags if it's available * Based on existing AVX2 implementation, dot product on one 32-value block of 4-bit quantized ints at a time * Perform 8 bit -> 16 bit sign extension and multiply+add on 32 values at time instead of 16 * Use built-in AVX512 horizontal reduce add to get sum at the end * Manual unrolling on inner dot product loop to reduce loop counter overhead
sw
reviewed
Mar 20, 2023
* Rename it to make it more clear that it's used for that dot product function
sw
reviewed
Mar 21, 2023
sw
approved these changes
Mar 21, 2023
Thank you for your effort! It also works on Windows and gives a little boost on my i7-11700F, from ~208 ms/token to 195 ms/token or sometimes even 185 ms/token on Alpaca7B. |
mudler
pushed a commit
to go-skynet/llama
that referenced
this pull request
Mar 21, 2023
* Update Makefile to detect AVX512 support and add compiler flags if it's available * Based on existing AVX2 implementation, dot product on one 32-value block of 4-bit quantized ints at a time * Perform 8 bit -> 16 bit sign extension and multiply+add on 32 values at time instead of 16 * Use built-in AVX512 horizontal reduce add to get sum at the end * Manual unrolling on inner dot product loop to reduce loop counter overhead
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
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.
NOTE: I am seeing different outputs when running with these changes. They seem of equal quality, but this isn't something I observed when first testing this out on alpaca.cpp.
It's possible that some rounding behavior is happening slightly differently or something like that. If this is a dealbreaker, I can try to figure out what is causing the difference and check if it's possible to get rid of it.
Changes
Performance Impact
I initially implemented this over on alpaca.cpp where I saw an ~10% speedup to inferrence.
Before:
After:
I was hoping for more, but some other stuff I tried like converting the
bytesFromNibbles
function to operate on two blocks at a time by using AVX512 were not successful.