Hello everyone,

Can you help me to train a temporal GNN with Opacus?

I try to set up Differential Private learning on a Graph Temporal Neural Network, using opacus for the differential privacy and A3TGCN2 from the pytorch-gemoemtric-temporal library for the TemporalGNN part. In particular I try to combine the guides for opacus and for a3tgcn training.

To check if the resulting Model is opacus compliant, i test it with

the opacus ModuleValidator and got no errors. So I assume the model is compliant.

Unfortunately I still run into errors. Could you please help me understand where my errors come from, how I can fix them and optimally provide a minimal working example?

Here are the relevant parts of my code:

Using the privacy engine:

```
from opacus import PrivacyEngine
secure_mode = False privacy_engine = PrivacyEngine(secure_mode=secure_mode)
# Privacy engine hyper-parameters
MAX_GRAD_NORM = 1.5
DELTA = 1e-5
EPSILON = 50.0
EPOCHS = 20 priv_model, priv_optimizer, priv_train_loader =
privacy_engine.make_private_with_epsilon(
module=model,
optimizer=optimizer,
data_loader=train_loader,
max_grad_norm=MAX_GRAD_NORM,
target_delta=DELTA,
target_epsilon=EPSILON,
epochs=EPOCHS )
```

Attempting to train the Model:

```
model.train()
snapshot = next(iter(train_set))
edge_index = snapshot.edge_index.to(device)
for epoch in tqdm(range(EPOCHS)):
loss = 0
loss_list = []
for encoder_inputs, labels in priv_train_loader:
y_hat = priv_model(encoder_inputs, edge_index)
loss = criterion(y_hat, labels)
loss.backward()
priv_optimizer.step()
priv_optimizer.zero_grad()
```

The error message:

```
ValueError: Poisson sampling is not compatible with grad accumulation. You need to call optimizer.step() after every forward/backward pass or consider using BatchMemoryManager
```

Attempt with BatchMemoryManager

```
for epoch in tqdm(range(EPOCHS)):
loss = 0
loss_list = []
with BatchMemoryManager( data_loader=priv_train_loader, max_physical_batch_size=2, optimizer=optimizer) as new_data_loader:
for encoder_inputs, labels in new_data_loader:
y_hat = priv_model(encoder_inputs, edge_index)
loss = criterion(y_hat, labels)
loss.backward()
priv_optimizer.step()
priv_optimizer.zero_grad()
```

The second error message:

```
AttributeError: 'SGD' object has no attribute 'signal_skip_step'
```