Merge pull request #348 from torchmd/fix_torch_warnings

Fixed pytorch deprecations warnings

tests/test_optimize.py

@@ -4,14 +4,23 @@
 
 import pytest
 from pytest import mark
-import torch as pt
-from torchmdnet.models.model import create_model
-from torchmdnet.optimize import optimize
-from torchmdnet.models.utils import dtype_mapping
 
+try:
+    import NNPOps
+
+    nnpops_available = True
+except ImportError:
+    nnpops_available = False
+
+
+@pytest.mark.skipif(not nnpops_available, reason="NNPOps not available")
 @mark.parametrize("device", ["cpu", "cuda"])
 @mark.parametrize("num_atoms", [10, 100])
 def test_gn(device, num_atoms):
+    import torch as pt
+    from torchmdnet.models.model import create_model
+    from torchmdnet.optimize import optimize
+    from torchmdnet.models.utils import dtype_mapping
 
     if not pt.cuda.is_available() and device == "cuda":
         pytest.skip("No GPU")

torchmdnet/extensions/__init__.py

@@ -145,12 +145,12 @@ def get_neighbor_pairs_fwd_meta(
 
 
 if torch.__version__ >= "2.2.0":
-    from torch.library import impl_abstract
+    from torch.library import register_fake
 
-    impl_abstract(
+    register_fake(
         "torchmdnet_extensions::get_neighbor_pairs_bkwd", get_neighbor_pairs_bkwd_meta
     )
-    impl_abstract(
+    register_fake(
         "torchmdnet_extensions::get_neighbor_pairs_fwd", get_neighbor_pairs_fwd_meta
     )
 elif torch.__version__ < "2.2.0" and torch.__version__ >= "2.0.0":

torchmdnet/extensions/neighbors/neighbors_cpu.cpp

@@ -8,7 +8,7 @@
 using std::tuple;
 using torch::arange;
 using torch::div;
-using torch::frobenius_norm;
+using torch::linalg_vector_norm;
 using torch::full;
 using torch::hstack;
 using torch::index_select;
@@ -99,7 +99,7 @@ forward_impl(const std::string& strategy, const Tensor& positions, const Tensor&
         deltas.index_put_({Slice(), 0}, deltas.index({Slice(), 0}) -
                                             scale1 * box_vectors.index({pair_batch, 0, 0}));
     }
-    distances = frobenius_norm(deltas, 1);
+    distances = linalg_vector_norm(deltas, 2, 1);
     mask = (distances < cutoff_upper) * (distances >= cutoff_lower);
     neighbors = neighbors.index({Slice(), mask});
     deltas = deltas.index({mask, Slice()});

torchmdnet/models/model.py

@@ -209,7 +209,7 @@ def load_model(filepath, args=None, device="cpu", return_std=False, **kwargs):
             filepath, args=args, device=device, return_std=return_std, **kwargs
         )
     assert isinstance(filepath, str)
-    ckpt = torch.load(filepath, map_location="cpu")
+    ckpt = torch.load(filepath, map_location="cpu", weights_only=False)
     if args is None:
         args = ckpt["hyper_parameters"]