Fix the torchax llama405b OOM at model init time #24
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
Instead of holding a large global `weight_jax` array, we hold the meta tensor, and create a local jax array whose size and dtype correspond to that of a shard. The program still OOMs the host memory space later while compiling the training step but that will be addressed separately.
qihqi
approved these changes
Jan 11, 2025
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Outdated suggestions cannot be applied.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.
Instead of holding a large global
weight_jaxarray, we hold the meta tensor, and create a local jax array whose size and dtype correspond to that of a shard.The program still OOMs the host memory space later while compiling the training step but that will be addressed separately.