GH-1079: Increase CPU training speed by pinning tensors #1082
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
|
👍 |
1 similar comment
|
👍 |
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Outdated suggestions cannot be applied.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.
This PR makes minor modifications to the
.to()method of theDataPointbase class and all implementing classes, namely adding the option of moving a data point tensor to pinned memory. Pinning a tensor is a one-time cost but then allows all subsequents GPU tensor copy operations to work faster. When training a model withembeddings_storage_mode = 'cpu', we at each epoch move tensors from CPU to GPU, so this PR increases overall training speed (closes #1079).This PR also adds a check if the
.to()operation is even necessary (not needed if a tensor is already on the relevant device), leading to a small increase in training speed.