Lightning Refactor: pytorch_lightning -> lightning.pytorch

Security.md

@@ -1,5 +1,5 @@
-# Security Policy
-Intel is committed to rapidly addressing security vulnerabilities affecting our customers and providing clear guidance on the solution, impact, severity and mitigation.
-
-## Reporting a Vulnerability
-Please report any security vulnerabilities in this project [utilizing the guidelines here](https://www.intel.com/content/www/us/en/security-center/vulnerability-handling-guidelines.html).
+# Security Policy
+Intel is committed to rapidly addressing security vulnerabilities affecting our customers and providing clear guidance on the solution, impact, severity and mitigation.
+
+## Reporting a Vulnerability
+Please report any security vulnerabilities in this project [utilizing the guidelines here](https://www.intel.com/content/www/us/en/security-center/vulnerability-handling-guidelines.html).

examples/callbacks/SAM.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import DistancesTransform, PointCloudToGraphTransform
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/callbacks/autocorrelation.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
-from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger
+import lightning.pytorch as pl
+from lightning.pytorch.loggers import TensorBoardLogger, WandbLogger
 
 from matsciml.datasets.transforms import DistancesTransform, PointCloudToGraphTransform
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/callbacks/code-carbon.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import (
     PointCloudToGraphTransform,

examples/callbacks/exponential_moving_average.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
-from pytorch_lightning.callbacks import StochasticWeightAveraging
+import lightning.pytorch as pl
+from lightning.pytorch.callbacks import StochasticWeightAveraging
 
 from matsciml.datasets.transforms import DistancesTransform, PointCloudToGraphTransform
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/callbacks/helper.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
-from pytorch_lightning.loggers import WandbLogger
+import lightning.pytorch as pl
+from lightning.pytorch.loggers import WandbLogger
 
 from matsciml.datasets.transforms import DistancesTransform, PointCloudToGraphTransform
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/callbacks/loss_scheduling.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import (
     PeriodicPropertiesTransform,

examples/datasets/alexandria/single_task_base.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import (

examples/datasets/alexandria/single_task_devset.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import (
     PointCloudToGraphTransform,

examples/datasets/alexandria/single_task_egnn.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import (

examples/datasets/alexandria/single_task_gala.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/datasets/alexandria/single_task_mpnn_dgl.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import (
     DistancesTransform,

examples/datasets/carolina_db/single_task_devset.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform

examples/datasets/colabfit/single_task_devset.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform

examples/datasets/materials_project/single_task_base.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform

examples/datasets/materials_project/single_task_devset.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/datasets/materials_project/single_task_egnn.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform

examples/datasets/materials_project/single_task_gala.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/datasets/materials_project/single_task_symmetry.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform

examples/datasets/nomad/single_task_devset.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import (

examples/datasets/oqmd/single_task_devset.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch.nn import LayerNorm, SiLU
 
 from matsciml.datasets.transforms import (

examples/devices/xpu_ddp_mpi.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from torch import nn
 from torch import distributed as dist
 from mace.modules import RealAgnosticInteractionBlock

examples/devices/xpu_ddp_slurm.py

@@ -2,7 +2,7 @@
 
 import os
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 from matsciml.datasets.transforms import (
     PointCloudToGraphTransform,
     PeriodicPropertiesTransform,

examples/devices/xpu_example.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform
 from matsciml.lightning.data_utils import MatSciMLDataModule

examples/interfaces/ase_from_scratch.py

@@ -2,7 +2,7 @@
 
 from ase import Atoms, units
 from ase.md.verlet import VelocityVerlet
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.lightning import MatSciMLDataModule
 from matsciml.models.base import ForceRegressionTask

examples/leaderboard_format_example.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.lightning.callbacks import LeaderboardWriter
 from matsciml.lightning.data_utils import IS2REDGLDataModule, is2re_devset

examples/model_demos/cdvae/cdvae.py

@@ -7,7 +7,7 @@
 import sys
 from pathlib import Path
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 import torch
 from tqdm import tqdm
 

examples/model_demos/cdvae/cdvae_inference.py

@@ -6,11 +6,9 @@
 import os
 import sys
 import time
-from functools import partial
 from pathlib import Path
 from types import SimpleNamespace
 
-import pytorch_lightning as pl
 import torch
 from torch.optim import Adam
 from torch_geometric.data import Batch

examples/model_demos/cdvae/cdvae_metrics.py

@@ -7,11 +7,9 @@
 import os
 import sys
 from collections import Counter
-from functools import partial
 from pathlib import Path
 
 import numpy as np
-import pytorch_lightning as pl
 from matminer.featurizers.composition.composite import ElementProperty
 from matminer.featurizers.site.fingerprint import CrystalNNFingerprint
 from p_tqdm import p_map
@@ -39,7 +37,6 @@
         compute_cov,
         get_crystals_list,
         get_fp_pdist,
-        load_config,
         load_data,
         prop_model_eval,
         smact_validity,

examples/model_demos/egnn_pyg.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import PointCloudToGraphTransform
 from matsciml.lightning.data_utils import MatSciMLDataModule
@@ -33,5 +33,5 @@
 )
 
 # run a quick training loop
-trainer = pl.Trainer(fast_dev_run=10)
+trainer = pl.Trainer(fast_dev_run=10, accelerator="cpu")
 trainer.fit(task, datamodule=dm)

examples/model_demos/faenet_pyg.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.transforms import (
     FrameAveraging,
@@ -25,7 +25,7 @@
         "out_dim": 128,
         "tag_hidden_channels": 0,
     },
-     output_kwargs={"lazy": False, "input_dim": 128, "hidden_dim": 128},
+    output_kwargs={"lazy": False, "input_dim": 128, "hidden_dim": 128},
     task_keys=["force"],
 )
 
@@ -47,4 +47,4 @@
 
 # run a quick training loop
 trainer = pl.Trainer(fast_dev_run=10)
-trainer.fit(task, datamodule=dm)
+trainer.fit(task, datamodule=dm)

examples/model_demos/m3gnet_dgl.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import pytorch_lightning as pl
+import lightning.pytorch as pl
 
 from matsciml.datasets.utils import element_types
 from matsciml.lightning.data_utils import MatSciMLDataModule