Hi, I’m trying to do subsampling of the ELBO in a ProdLDA model, but keep getting the following error: ‘subsample_size does not match len(subsample), 1000 vs 32. Did you accidentally use different subsample_size in the model and guide?’

I was reading that you only need to specify the subsample_size in the guide when there is a plate in both, as the backend will automatically use the same indices in the model. But I tried specifying in both, then in just the model, and then in just the guide… but I keep getting the same error.

The code for the model is below, it’s just the tutorial model. But I can’t figure out how to correctly do subsampling with this because the dataset I’m using is too large to use batch ELBO gradients.

```
class ProdLDA(nn.Module):
def __init__(self, vocab_size, num_topics, hidden, dropout):
super().__init__()
self.vocab_size = vocab_size
self.num_topics = num_topics
self.encoder = Encoder(vocab_size, num_topics, hidden, dropout)
self.decoder = Decoder(vocab_size, num_topics, dropout)
def model(self, docs):
pyro.module('decoder', self.decoder)
with pyro.plate('documents', docs.shape[0]):
logtheta_loc = docs.new_zeros((docs.shape[0], self.num_topics))
logtheta_scale = docs.new_ones((docs.shape[0], self.num_topics))
logtheta = pyro.sample('logtheta', dist.Normal(logtheta_loc, logtheta_scale).to_event(1))
theta = F.softmax(logtheta, -1)
count_param = self.decoder(theta)
total_count = int(docs.sum(-1).max())
pyro.sample('obs', dist.Multinomial(total_count, count_param), obs=docs)
def guide(self, docs):
pyro.module('encoder', self.encoder)
with pyro.plate('documents', docs.shape[0], subsample_size=1000) as ind:
logtheta_loc, logtheta_scale = self.encoder(docs.index_select(0, ind))
logtheta = pyro.sample('logtheta', dist.Normal(logtheta_loc, logtheta_scale).to_event(1))
```