Quality of life and helper callback functions

examples/callbacks/autocorrelation.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+import pytorch_lightning as pl
+from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger
+
+from matsciml.datasets.transforms import DistancesTransform, PointCloudToGraphTransform
+from matsciml.lightning.data_utils import MatSciMLDataModule
+from matsciml.lightning.callbacks import ModelAutocorrelation
+from matsciml.models import SchNet
+from matsciml.models.base import ScalarRegressionTask
+
+"""
+This script demonstrates the use of the `ModelAutocorrelation` callback.
+
+The main utility of this callback is to monitor the degree of correlation
+in model parameters and optionally gradients over a time span. The idea
+is that for optimization trajectories, steps are ideally as de-correlated
+as possible (at least within reason), and indeed is actually a major
+assumption of Adam-like optimizers.
+
+There is no hard coded heuristic for identifying "too much correlation"
+yet, however this callback can help do the data collection for you to
+develop a sense for yourself. One method for trying this out is to
+set varying learning rates, and seeing how the autocorrelation spectra
+are different.
+"""
+
+# construct a scalar regression task with SchNet encoder
+task = ScalarRegressionTask(
+    encoder_class=SchNet,
+    # kwargs to be passed into the creation of SchNet model
+    encoder_kwargs={
+        "encoder_only": True,
+        "hidden_feats": [128, 128, 128],
+        "atom_embedding_dim": 128,
+    },
+    # which keys to use as targets
+    task_keys=["energy_relaxed"],
+    log_embeddings=False,
+)
+# Use IS2RE devset to test workflow
+# SchNet uses RBFs, and expects edge features corresponding to atom-atom distances
+dm = MatSciMLDataModule.from_devset(
+    "IS2REDataset",
+    dset_kwargs={
+        "transforms": [
+            PointCloudToGraphTransform(
+                "dgl",
+                cutoff_dist=20.0,
+                node_keys=["pos", "atomic_numbers"],
+            ),
+            DistancesTransform(),
+        ],
+    },
+)
+
+# tensorboard logging if working purely locally, otherwise wandb
+logger = WandbLogger(
+    name="helper-callback", offline=False, project="matsciml", log_model="all"
+)
+logger = TensorBoardLogger("./")
+
+# run a quick training loop
+trainer = pl.Trainer(max_epochs=30, logger=logger, callbacks=[ModelAutocorrelation()])
+trainer.fit(task, datamodule=dm)

examples/callbacks/helper.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+
+import pytorch_lightning as pl
+from pytorch_lightning.loggers import WandbLogger
+
+from matsciml.datasets.transforms import DistancesTransform, PointCloudToGraphTransform
+from matsciml.lightning.data_utils import MatSciMLDataModule
+from matsciml.lightning.callbacks import TrainingHelperCallback
+from matsciml.models import SchNet
+from matsciml.models.base import ScalarRegressionTask
+
+"""
+This script demonstrates the use of the ``TrainingHelperCallback``
+callback. The purpose of this callback is to provide some
+helpful heuristics into the training process by identifying
+some common issues like unused weights, small gradients,
+and oversmoothed embeddings.
+"""
+
+# construct a scalar regression task with SchNet encoder
+task = ScalarRegressionTask(
+    encoder_class=SchNet,
+    # kwargs to be passed into the creation of SchNet model
+    encoder_kwargs={
+        "encoder_only": True,
+        "hidden_feats": [128, 128, 128],
+        "atom_embedding_dim": 128,
+    },
+    # which keys to use as targets
+    task_keys=["energy_relaxed"],
+    log_embeddings=True,
+)
+# Use IS2RE devset to test workflow
+# SchNet uses RBFs, and expects edge features corresponding to atom-atom distances
+dm = MatSciMLDataModule.from_devset(
+    "IS2REDataset",
+    dset_kwargs={
+        "transforms": [
+            PointCloudToGraphTransform(
+                "dgl",
+                cutoff_dist=20.0,
+                node_keys=["pos", "atomic_numbers"],
+            ),
+            DistancesTransform(),
+        ],
+    },
+)
+
+# tensorboard logging if working purely locally
+# logger = TensorBoardLogger("./")
+logger = WandbLogger(
+    name="helper-callback", offline=False, project="matsciml", log_model="all"
+)
+
+# run a quick training loop
+trainer = pl.Trainer(max_epochs=10, logger=logger, callbacks=[TrainingHelperCallback()])
+trainer.fit(task, datamodule=dm)

matsciml/datasets/utils.py

@@ -9,6 +9,7 @@
 
 import lmdb
 import torch
+import numpy as np
 from einops import einsum, rearrange
 from joblib import Parallel, delayed
 from pymatgen.core import Lattice, Structure
@@ -302,11 +303,11 @@ def get_lmdb_keys(
             "Both `ignore_keys` and `_lambda` were passed; arguments are mutually exclusive.",
         )
     if ignore_keys:
-        _lambda = lambda x: x not in ignore_keys
+        _lambda = lambda x: x not in ignore_keys  # noqa: E731
     else:
         if not _lambda:
             # escape case where we basically don't filter
-            _lambda = lambda x: x
+            _lambda = lambda x: x  # noqa: E731
     # convert to a sorted list of keys
     keys = sorted(list(filter(_lambda, keys)))
     return keys
@@ -529,7 +530,7 @@ def divide_data_chunks(
     assert all(
         [length != 0 for length in lengths],
     ), "Too many processes specified and not enough data to split over multiple LMDB files. Decrease `num_procs!`"
-    p = Parallel(num_procs)(
+    _ = Parallel(num_procs)(
         delayed(write_chunk)(chunk, target_dir, index, metadata)
         for chunk, index in zip(chunks, lmdb_indices)
     )
@@ -693,6 +694,11 @@ def calculate_periodic_shifts(
         include_index=True,
         include_image=True,
     )
+    # check to make sure the cell definition is valid
+    if np.any(structure.frac_coords > 1.0):
+        raise ValueError(
+            f"Structure has fractional coordinates greater than 1! Check structure:\n{structure}"
+        )
 
     def _all_sites_have_neighbors(neighbors):
         return all([len(n) for n in neighbors])
@@ -729,12 +735,13 @@ def _all_sites_have_neighbors(neighbors):
     cell = torch.from_numpy(cell.copy()).float()
     # get coordinates as well, for standardization
     frac_coords = torch.from_numpy(structure.frac_coords).float()
+    coords = torch.from_numpy(structure.cart_coords).float()
     return_dict = {
         "src_nodes": torch.LongTensor(all_src),
         "dst_nodes": torch.LongTensor(all_dst),
         "images": torch.FloatTensor(all_images),
         "cell": cell,
-        "pos": frac_coords,
+        "pos": coords,
     }
     # now calculate offsets based on each image for a lattice
     return_dict["offsets"] = einsum(return_dict["images"], cell, "v i, n i j -> v j")