lock torch version to 2.7.1

Author: stefdoerr

setup.py

@@ -101,7 +101,7 @@ def set_torch_cuda_arch_list():
         install_requires=[
             "h5py",
             # "nnpops",
-            "torch",
+            "torch==2.7.1",
             "torch_geometric",
             "lightning",
             "tqdm",

torchmdnet/extensions/neighbors/neighbors_cpu.cpp

@@ -218,20 +218,20 @@ TORCH_LIBRARY_IMPL(torchmdnet_extensions, CompositeImplicitAutograd, m) {
            });
 }
 
-// Explicit device backend registrations for PyTorch versions that do not
-// automatically fall back to CompositeImplicitAutograd for device dispatch.
-TORCH_LIBRARY_IMPL(torchmdnet_extensions, CPU, m) {
-    m.impl("get_neighbor_pairs",
-           [](const std::string& strategy, const Tensor& positions, const Tensor& batch,
-              const Tensor& box_vectors, bool use_periodic, const Scalar& cutoff_lower,
-              const Scalar& cutoff_upper, const Scalar& max_num_pairs, bool loop,
-              bool include_transpose) {
-               auto result = NeighborAutograd::apply(strategy, positions, batch, box_vectors,
-                                                     use_periodic, cutoff_lower, cutoff_upper,
-                                                     max_num_pairs, loop, include_transpose);
-               return std::make_tuple(result[0], result[1], result[2], result[3]);
-           });
-}
+// // Explicit device backend registrations for PyTorch versions that do not
+// // automatically fall back to CompositeImplicitAutograd for device dispatch.
+// TORCH_LIBRARY_IMPL(torchmdnet_extensions, CPU, m) {
+//     m.impl("get_neighbor_pairs",
+//            [](const std::string& strategy, const Tensor& positions, const Tensor& batch,
+//               const Tensor& box_vectors, bool use_periodic, const Scalar& cutoff_lower,
+//               const Scalar& cutoff_upper, const Scalar& max_num_pairs, bool loop,
+//               bool include_transpose) {
+//                auto result = NeighborAutograd::apply(strategy, positions, batch, box_vectors,
+//                                                      use_periodic, cutoff_lower, cutoff_upper,
+//                                                      max_num_pairs, loop, include_transpose);
+//                return std::make_tuple(result[0], result[1], result[2], result[3]);
+//            });
+// }
 
 // TORCH_LIBRARY_IMPL(torchmdnet_extensions, CUDA, m) {
 //     m.impl("get_neighbor_pairs",