Fix pbc conv args #297
Closed
Fix pbc conv args #297
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
* fix error name bugs * rm tracking error of node sum * reserved length for error lists
* Treat warnings as errors; Ignore deprecation warnings from tpl * fixup unscale assert
* Split device and device_name functions * Move data to device in loading rather than training (does NOT affect performance)
* frequency relaxed * frequency relaxed Co-authored-by: Massimiliano Lupo Pasini <7ml@ornl.gov>
* mapping of degree tensor to GPU * formatting fixed Co-authored-by: Massimiliano Lupo Pasini <7ml@ornl.gov>
data.batch is remapped to the same device as data.x
* added dashed red diagonal and use of empty dots in scatterplot * formatting fixed Co-authored-by: Massimiliano Lupo Pasini <7ml@ornl.gov>
* save train/val/test to pkls when total provided and reorganize data loading * remove raw in config
* fix an indexing bug in denormalization * add min/max loading from pkl
* wip: init profile * wip: add profile routine * wip: add profile routine * format fixed * Create profiler class * minor fix * Minor changes for merging * Move profile block to an upper level Co-authored-by: Massimiliano Lupo Pasini <7ml@ornl.gov> Co-authored-by: Massimiliano Lupo Pasini <massimiliano.lupo.pasini@gmail.com>
Co-authored-by: User <user@localadmins-Air.homenet.telecomitalia.it>
optimize get_head_indices with tensor operations
Remove num_nodes_list
Check Cuda enabled for profiler
* reformatting * Update create_configurations.py * json file added for Ising model * Update ising_model.json * change in the definition of the spin and change of data format in .json file * requirements file for example added * Rename ising data * Add tqdm * Rename spin and redefine atom_type * removed subtraction of -1 from coordinates * sine used to map atom features into spin * sinusoidal spin used to calculate the energy * setting for creation of dfataset and model changed Co-authored-by: Massimiliano Lupo Pasini <7ml@ornl.gov> Co-authored-by: Sam Reeve <6740307+streeve@users.noreply.github.com>
* reformatting * call to stratified splitting added in ci test input files * collections added * stratified_splitting removed from ci.json * call to stratified splitting added in ci test input files * reformatting * indexing for validation and test set fixed * fix Data object referring issue * reformatting * auxiliary functions created to make the code easier to maintain * removed line from sklearn.model_selection.StratifiedShuffleSplit import StratifiedShuffleSplit * explicit type statement removed from input parameter list * reformatting * reformatting * routines renamed into compositional_data_splitting.py Co-authored-by: Massimiliano Lupo Pasini <7ml@ornl.gov> Co-authored-by: Pei Zhang <zhangp1@ornl.gov>
…ing (ORNL#268) * init commit, tested work on frontier * update black formatting * amend template --------- Co-authored-by: Zhifan Ye <zye327@login07.frontier.olcf.ornl.gov>
* add energy linear regression * remove pdb * remove var_conf * fix for energy per atom * remove debug * fix energy per atom * fix for new energy calc * add npz output * save energy mean and linear regresison term * black * fix adios write * remove emean
* Update deephyper runs Update to capture all errors with try-except block. * Update gfm_deephyper_multi_perlmutter.py * Update distributed.py Use "SLURM_STEP_NODELIST" env, which is needed for HPO.
Adding PNAPlus Stack
* force tests, which required model arg, and some typo fixing in Lennard Jones * Add PNAPlus since it uses positions as well * formatting
* utils renamed and black formatting applied * bug fixed solved for tests * black formatting fixed * examples corrected * test_model_loadpred.py fixed * black formatting fixed * test_loss_and_activation_functions.py fixed * black formatting fixed * reverting inadvertent automated refactoring of dataset forlder into datasets * reverting inadvertent automated refactoring of dataset forlder into datasets * reverting inadvertent automated refactoring of dataset forlder into datasets * reverting inadvertent automated refactoring of dataset forlder into datasets * reverting inadvertent automated refactoring of hydragnn into hhydragnn package * reverting inadvertent automated refactoring of dataset forlder into datasets * reverting inadvertent automated refactoring of dataset forlder into datasets * reverting inadvertent automated refactoring of dataset forlder into datasets * git formatting fixed * Adagrad converted to Adamax * Additional changes to fix bugs and suggestions from erdem * imports fixed for LennardJones example * formatting fixed * imports in LJ_data.py fixed * import of graph utils fixed in LJ_data.py * import of setup.ddp() fixed in LennardJones * setup_log call fixed * get_summary_writer call fixed * additional calls fixed * black formatting fixedf
|
@allaffa This should give an idea of the implementation I've been working with. I can still take a further look at the Facebook implementation you mentioned, but since this one is already working and I'm familiar with I wanted to show you what it would look like first. Let me know your thoughts. Also I am not sure why there are so many commits. Maybe I forgot a rebase or merge |
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 aims to address the issue related to generating conv_args that are in-line with periodic boundary conditions.
The following implementation should be robust under the following assumptions, which I think should always be true:
(1) radius < min(supercell_size) / 2
(2) The edge_index has been generated correctly with periodic boundary conditions.
Currently, only two models are updated to use the new functionality. If this is a good solution, it can be easily extended to the others.