--other=remove print info

train.py

@@ -251,7 +251,7 @@ def rrhf_loss(self, scores, idxs, rw_scores):
         # rw_diff = rw_scores.unsqueeze(0) - rw_scores.unsqueeze(-1) # b * b  # batch * cand
         # aval = torch.bitwise_and(rw_diff > 0, diff < 0)[0]
         # return -diff[aval].sum()
-        print(scores.shape)  # score shape (batch * cand)
+
         cand = rw_scores.shape[1]
         new_scores = scores.reshape(-1, cand)   # batch * cand
         diff = new_scores.unsqueeze(1) - new_scores.unsqueeze(-1) # batch * cand * cand
@@ -263,11 +263,9 @@ def sft_loss(self, logit_label, idxs, rw_scores): #  (batch * cand) *L
         max_idx = torch.argmax(rw_scores, dim=1)  # batch
         # 每个task的response个数均相同
         cand = rw_scores.shape[1]
-        print("logit_label:", logit_label.shape)
         logit_label_batch = torch.reshape(logit_label, (-1, cand, logit_label.shape[-1]))  # batch * cand * L
         expert_response_logit_label = logit_label_batch[:1, max_idx].squeeze() # batch * L
         return -torch.sum(expert_response_logit_label.mean())
-        #return -logit_label[max_idx].mean()
 
     def compute_loss(self, model, inputs, return_outputs=False):
         if self.args.only_use_provide: