memory usage reduced

ud.py

@@ -84,10 +84,13 @@ class UniversalDependenciesCausalPipeline(BellmanFordTokenClassificationPipeline
  m.append(e[x,:].sum(axis=0))
  m.append(e[self.tokenizer.sep_token_id,:])
  m.append(e[self.tokenizer.pad_token_id,:])
- m=torch.stack(m)
+ m=torch.stack(m).to(self.device)
  k=list(range(len(d)+1))
+ e=[]
  with torch.no_grad():
- e=self.model(inputs_embeds=torch.stack([m[k+list(range(i,len(d)))+[-1]*i,:] for i in range(len(d))]).to(self.device)).logits[:,-len(d):,:].cpu().numpy()
+ for i in range(len(d)):
+ e.append(self.model(inputs_embeds=torch.unsqueeze(m[k+list(range(i,len(d)))+[-1]*i,:],0)).logits[0,-len(d):,:])
+ e=torch.stack(e).cpu().numpy()
  for i in range(len(d)):
  for j in range(i):
  e[-j-1,-i-1],e[-i-1,-j-1]=e[-i-1,i-j]+self.left_arc,e[-i-1,i-j]+self.right_arc