def test_revgnn_multi_backward(num_groups):
x = torch.randn(4, 32)
edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
lin = Linear(32, 32)
conv = SAGEConv(32 // num_groups, 32 // num_groups)
conv = GroupAddRev(conv, num_groups=num_groups, num_bwd_passes=4)
h = lin(x)
out = conv(h, edge_index)
target = out.mean()
target.backward(retain_graph=True)
target.backward(retain_graph=True)
torch.autograd.grad(outputs=target,
inputs=[h] + list(conv.parameters()),
retain_graph=True)
torch.autograd.grad(outputs=target, inputs=[h] + list(conv.parameters()))
class SAGEConvActor(object):
def __init__(self, in_channels, out_channels):
self.conv = SAGEConv(in_channels, out_channels)
self.local_optim = Adam(self.conv.parameters(), lr=0.01)
def forward(self, x: Union[Tensor, OptPairTensor], edge_index: Adj):
return self.conv(x, edge_index)
def zero_grad(self):
self.local_optim.zero_grad()
def step(self):
self.local_optim.step()