TorchScript Support — pytorch_geometric documentation (original) (raw)

TorchScript is a way to create serializable and optimizable models from PyTorch code. Any TorchScript program can be saved from a Python process and loaded in a process where there is no Python dependency. If you are unfamilar with TorchScript, we recommend to read the official “Introduction to TorchScript” tutorial first.

Converting GNN Models

Note

From PyG 2.5 (and onwards), GNN layers are now fully compatible with torch.jit.script() without any modification needed. If you are on an earlier version of PyG, consider to convert your GNN layers into “jittable” instances first by calling jittable().

Converting your PyG model to a TorchScript program is straightforward and requires only a few code changes. Let’s consider the following model:

import torch import torch.nn.functional as F from torch_geometric.nn import GCNConv

class GNN(torch.nn.Module): def init(self, in_channels, out_channels): super().init() self.conv1 = GCNConv(in_channels, 64) self.conv2 = GCNConv(64, out_channels)

def forward(self, x, edge_index):
    x = self.conv1(x, edge_index)
    x = F.relu(x)
    x = self.conv2(x, edge_index)
    return F.log_softmax(x, dim=1)

model = GNN(dataset.num_features, dataset.num_classes)

The instantiated model can now be directly passed into torch.jit.script():

model = torch.jit.script(model)

That is all you need to know on how to convert your PyG models to TorchScript programs. You can have a further look at our JIT examples that show-case how to obtain TorchScript programs for node and graph classification models.

Creating Jittable GNN Operators

All PyG MessagePassing operators are tested to be convertible to a TorchScript program. However, if you want your own GNN module to be compatible with torch.jit.script(), you need to account for the following two things:

1. As one would expect, your forward() code may need to be adjusted so that it passes the TorchScript compiler requirements, e.g., by adding type notations.
2. You need to tell the MessagePassing module the types that you pass to its propagate() function. This can be achieved in two different ways:
   1. Declaring the type of propagation arguments in a dictionary called propagate_type:

      from typing import Optional
      from torch import Tensor
      from torch_geometric.nn import MessagePassing

      class MyConv(MessagePassing):
      propagate_type = {'x': Tensor, 'edge_weight': Optional[Tensor] }

      def forward(
      self,

   x: Tensor,  
   edge_index: Tensor,  
   edge_weight: Optional[Tensor] = None,  

) -> Tensor:
return self.propagate(edge_index, x=x, edge_weight=edge_weight)

1. Declaring the type of propagation arguments as a comment inside your module:

   from typing import Optional
   from torch import Tensor
   from torch_geometric.nn import MessagePassing

   class MyConv(MessagePassing):
   def forward(
   self,
   x: Tensor,
   edge_index: Tensor,
   edge_weight: Optional[Tensor] = None,
   ) -> Tensor:
   # propagate_type: (x: Tensor, edge_weight: Optional[Tensor])

   return self.propagate(edge_index, x=x, edge_weight=edge_weight)  

If none of these options are given, the MessagePassing module will infer the arguments of propagate() to be of type torch.Tensor (mimicing the default type that TorchScript is inferring for non-annotated arguments).