Merge pull request #253 from RaulPPelaez/check_errors

Add check_errors option

docs/source/usage.rst

@@ -62,6 +62,16 @@ These requirements correspond to a particular rotation of the system and reduced
 
 .. note:: The box defined by the vectors :math:`\vec{a} = (L_x, 0, 0)`, :math:`\vec{b} = (0, L_y, 0)`, and :math:`\vec{c} = (0, 0, L_z)` correspond to a rectangular box. In this case, the input option in the :ref:`configuration file <configuration-file>` would be ``box-vecs: [[L_x, 0, 0], [0, L_y, 0], [0, 0, L_z]]``.
 
+
+CUDA Graphs
+============
+
+TensorNet is capturable into a `CUDA graph <https://developer.nvidia.com/blog/cuda-graphs/>`_ with the right options. This can dramatically increase performance during inference. The dynamically-shaped nature of training makes CUDA graphs not an option in most practical cases.
+
+For TensorNet to be CUDA-graph compatible, `check_errors` must be `False` and `static_shapes` must be `True`. Manually capturing a piece of code can be challenging, instead, to take advantage of CUDA graphs you can use :py:mod:`torchmdnet.calculators.External`, which helps integrating a Torchmd-NET model into another code, or `OpenMM-Torch <https://github.com/openmm/openmm-torch>`_ if you are using OpenMM.
+
+
+
 Multi-Node Training
 ===================
 
@@ -85,6 +95,7 @@ In order to train models on multiple nodes some environment variables have to be
 	  - Due to the way PyTorch Lightning calculates the number of required DDP processes, all nodes must use the same number of GPUs. Otherwise training will not start or crash.
 	  - We observe a 50x decrease in performance when mixing nodes with different GPU architectures (tested with RTX 2080 Ti and RTX 3090).
 
+
 Developer Guide
 ---------------
 

tests/test_model.py

@@ -107,6 +107,8 @@ def test_cuda_graph_compatible(model_name):
             "prior_model": None,
             "atom_filter": -1,
             "derivative": True,
+            "check_error": False,
+            "static_shapes": True,
             "output_model": "Scalar",
             "reduce_op": "sum",
             "precision": 32 }

torchmdnet/models/model.py

@@ -32,6 +32,10 @@ def create_model(args, prior_model=None, mean=None, std=None):
     dtype = dtype_mapping[args["precision"]]
     if "box_vecs" not in args:
         args["box_vecs"] = None
+    if "check_errors" not in args:
+        args["check_errors"] = True
+    if "static_shapes" not in args:
+        args["static_shapes"] = False
     shared_args = dict(
         hidden_channels=args["embedding_dimension"],
         num_layers=args["num_layers"],
@@ -42,8 +46,11 @@ def create_model(args, prior_model=None, mean=None, std=None):
         cutoff_lower=args["cutoff_lower"],
         cutoff_upper=args["cutoff_upper"],
         max_z=args["max_z"],
+        check_errors=args["check_errors"],
         max_num_neighbors=args["max_num_neighbors"],
-        box_vecs=torch.tensor(args["box_vecs"], dtype=dtype) if args["box_vecs"] is not None else None,
+        box_vecs=torch.tensor(args["box_vecs"], dtype=dtype)
+        if args["box_vecs"] is not None
+        else None,
         dtype=dtype,
     )
 
@@ -87,6 +94,7 @@ def create_model(args, prior_model=None, mean=None, std=None):
         is_equivariant = False
         representation_model = TensorNet(
             equivariance_invariance_group=args["equivariance_invariance_group"],
+            static_shapes=args["static_shapes"],
             **shared_args,
         )
     else:
@@ -153,11 +161,21 @@ def load_model(filepath, args=None, device="cpu", **kwargs):
     # The following are for backward compatibility with models created when atomref was
     # the only supported prior.
     if "prior_model.initial_atomref" in state_dict:
+        warnings.warn(
+            "prior_model.initial_atomref is deprecated and will be removed in a future version. Use prior_model.0.initial_atomref instead.",
+            category=DeprecationWarning,
+            stacklevel=2,
+        )
         state_dict["prior_model.0.initial_atomref"] = state_dict[
             "prior_model.initial_atomref"
         ]
         del state_dict["prior_model.initial_atomref"]
     if "prior_model.atomref.weight" in state_dict:
+        warnings.warn(
+            "prior_model.atomref.weight is deprecated and will be removed in a future version. Use prior_model.0.atomref.weight instead.",
+            category=DeprecationWarning,
+            stacklevel=2,
+        )
         state_dict["prior_model.0.atomref.weight"] = state_dict[
             "prior_model.atomref.weight"
         ]
@@ -201,7 +219,7 @@ def create_prior_models(args, dataset=None):
 
 
 class TorchMD_Net(nn.Module):
-    """ The main TorchMD-Net model.
+    """The main TorchMD-Net model.
 
     The TorchMD_Net class combines a given representation model (such as the equivariant transformer),
     an output model (such as the scalar output module), and a prior model (such as the atomref prior).
@@ -311,15 +329,15 @@ def forward(
 
         Args:
             z (Tensor): Atomic numbers of the atoms in the molecule. Shape: (N,).
-    	    pos (Tensor): Atomic positions in the molecule. Shape: (N, 3).
-    	    batch (Tensor, optional): Batch indices for the atoms in the molecule. Shape: (N,).
+            pos (Tensor): Atomic positions in the molecule. Shape: (N, 3).
+            batch (Tensor, optional): Batch indices for the atoms in the molecule. Shape: (N,).
             box (Tensor, optional): Box vectors. Shape (3, 3).
             The vectors defining the periodic box.  This must have shape `(3, 3)`,
             where `box_vectors[0] = a`, `box_vectors[1] = b`, and `box_vectors[2] = c`.
             If this is omitted, periodic boundary conditions are not applied.
-    	    q (Tensor, optional): Atomic charges in the molecule. Shape: (N,).
-    	    s (Tensor, optional): Atomic spins in the molecule. Shape: (N,).
-    	    extra_args (Dict[str, Tensor], optional): Extra arguments to pass to the prior model.
+            q (Tensor, optional): Atomic charges in the molecule. Shape: (N,).
+            s (Tensor, optional): Atomic spins in the molecule. Shape: (N,).
+            extra_args (Dict[str, Tensor], optional): Extra arguments to pass to the prior model.
 
         Returns:
             Tuple[Tensor, Optional[Tensor]]: The output of the model and the derivative of the output with respect to the positions if derivative is True, None otherwise.
@@ -330,7 +348,9 @@ def forward(
         if self.derivative:
             pos.requires_grad_(True)
         # run the potentially wrapped representation model
-        x, v, z, pos, batch = self.representation_model(z, pos, batch, box=box, q=q, s=s)
+        x, v, z, pos, batch = self.representation_model(
+            z, pos, batch, box=box, q=q, s=s
+        )
         # apply the output network
         x = self.output_model.pre_reduce(x, v, z, pos, batch)
 

torchmdnet/models/tensornet.py

@@ -116,7 +116,9 @@ class TensorNet(nn.Module):
             If this is omitted, periodic boundary conditions are not applied.
             (default: :obj:`None`)
         static_shapes (bool, optional): Whether to enforce static shapes.
-            Makes the model CUDA-graph compatible.
+            Makes the model CUDA-graph compatible if check_errors is set to False.
+            (default: :obj:`True`)
+        check_errors (bool, optional): Whether to check for errors in the distance module.
             (default: :obj:`True`)
     """
 
@@ -134,6 +136,7 @@ def __init__(
         max_z=128,
         equivariance_invariance_group="O(3)",
         static_shapes=True,
+        check_errors=True,
         dtype=torch.float32,
         box_vecs=None,
     ):
@@ -202,7 +205,7 @@ def __init__(
             max_num_pairs=-max_num_neighbors,
             return_vecs=True,
             loop=True,
-            check_errors=False,
+            check_errors=check_errors,
             resize_to_fit=not self.static_shapes,
             box=box_vecs,
             long_edge_index=True,

torchmdnet/models/torchmd_et.py

@@ -75,6 +75,8 @@ class TorchMD_ET(nn.Module):
             where `box_vectors[0] = a`, `box_vectors[1] = b`, and `box_vectors[2] = c`.
             If this is omitted, periodic boundary conditions are not applied.
             (default: :obj:`None`)
+        check_errors (bool, optional): Whether to check for errors in the distance module.
+            (default: :obj:`True`)
 
     """
 
@@ -94,6 +96,7 @@ def __init__(
         cutoff_upper=5.0,
         max_z=100,
         max_num_neighbors=32,
+        check_errors=True,
         box_vecs=None,
         dtype=torch.float32,
     ):
@@ -140,6 +143,7 @@ def __init__(
             loop=True,
             box=box_vecs,
             long_edge_index=True,
+            check_errors=check_errors,
         )
         self.distance_expansion = rbf_class_mapping[rbf_type](
             cutoff_lower, cutoff_upper, num_rbf, trainable_rbf

torchmdnet/models/torchmd_gn.py

@@ -84,6 +84,8 @@ class TorchMD_GN(nn.Module):
             where `box_vectors[0] = a`, `box_vectors[1] = b`, and `box_vectors[2] = c`.
             If this is omitted, periodic boundary conditions are not applied.
             (default: :obj:`None`)
+        check_errors (bool, optional): Whether to check for errors in the distance module.
+            (default: :obj:`True`)
 
     """
 
@@ -101,6 +103,7 @@ def __init__(
         cutoff_upper=5.0,
         max_z=100,
         max_num_neighbors=32,
+        check_errors=True,
         aggr="add",
         dtype=torch.float32,
         box_vecs=None,
@@ -144,6 +147,7 @@ def __init__(
             max_num_pairs=-max_num_neighbors,
             box=box_vecs,
             long_edge_index=True,
+            check_errors=check_errors,
         )
 
         self.distance_expansion = rbf_class_mapping[rbf_type](

torchmdnet/models/torchmd_t.py

@@ -74,6 +74,8 @@ class TorchMD_T(nn.Module):
             where `box_vectors[0] = a`, `box_vectors[1] = b`, and `box_vectors[2] = c`.
             If this is omitted, periodic boundary conditions are not applied.
             (default: :obj:`None`)
+        check_errors (bool, optional): Whether to check for errors in the distance module.
+            (default: :obj:`True`)
 
     """
 
@@ -91,6 +93,7 @@ def __init__(
         distance_influence="both",
         cutoff_lower=0.0,
         cutoff_upper=5.0,
+        check_errors=True,
         max_z=100,
         max_num_neighbors=32,
         dtype=torch.float,
@@ -133,7 +136,8 @@ def __init__(
             max_num_pairs=-max_num_neighbors,
             loop=True,
             box=box_vecs,
-            long_edge_index=True
+            long_edge_index=True,
+            check_errors=check_errors,
         )
 
         self.distance_expansion = rbf_class_mapping[rbf_type](

torchmdnet/scripts/train.py

@@ -98,7 +98,10 @@ def get_argparse():
         `a[1] = a[2] = b[2] = 0`;`a[0] >= 2*cutoff, b[1] >= 2*cutoff, c[2] >= 2*cutoff`;`a[0] >= 2*b[0]`;`a[0] >= 2*c[0]`;`b[1] >= 2*c[1]`;
         These requirements correspond to a particular rotation of the system and reduced form of the vectors, as well as the requirement that the cutoff be no larger than half the box width.
     Example: [[1,0,0],[0,1,0],[0,0,1]]""")
+    parser.add_argument('--static_shapes', type=bool, default=False, help='If true, TensorNet will use statically shaped tensors for the network, making it capturable into a CUDA graphs. In some situations static shapes can lead to a speedup, but it increases memory usage.')
+
     # other args
+    parser.add_argument('--check_errors', type=bool, default=True, help='Will check if max_num_neighbors is not enough to contain all neighbors. This is incompatible with CUDA graphs.')
     parser.add_argument('--derivative', default=False, type=bool, help='If true, take the derivative of the prediction w.r.t coordinates')
     parser.add_argument('--cutoff-lower', type=float, default=0.0, help='Lower cutoff in model')
     parser.add_argument('--cutoff-upper', type=float, default=5.0, help='Upper cutoff in model')