Reformatting MACE #307
Merged
Reformatting MACE #307
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
allaffa
approved these changes
Nov 8, 2024
RylieWeaver
added a commit
to RylieWeaver/HydraGNN
that referenced
this pull request
Nov 8, 2024
* reformatting MACE * move irreps_string tool * Comment * adjusting position accounting for batch
RylieWeaver
added a commit
to RylieWeaver/HydraGNN
that referenced
this pull request
Nov 12, 2024
* reformatting MACE * move irreps_string tool * Comment * adjusting position accounting for batch
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Outdated suggestions cannot be applied.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.
Reworking of MACE for cleanliness, robustness to hyperparameter configurations, and numerical stability in decoders.
Main Changes:
(1) Many changes to cleanliness and comments, moving some blocks to the utils.
(2) Handle configurations with only one convolutional layer where both first_layer and last_layer are true
(3) Do not append multiple layers in the decoder block when doing a purely linear decoding. This is because multiple linear layers stacked on top of each other without activations can be cause more erratic training / numerical instability.