fixed a bug

Author: Haozhe Qi

llava/model/language_model/llava_qwen.py

@@ -92,7 +92,6 @@ def forward(
         dpo_forward: Optional[bool] = False,
         cache_position=None,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
-        print ('actions', actions)
         if inputs_embeds is None:
             (input_ids, position_ids, attention_mask, past_key_values, inputs_embeds, labels, action_idx) = self.prepare_inputs_labels_for_multimodal(input_ids, position_ids, attention_mask, past_key_values, labels, images, modalities, image_sizes)
 
@@ -233,8 +232,7 @@ def forward(
                     action_logits = action_logits.to(device)
                     actions = actions.to(device)
 
-                    vision_supervision_loss = 0.0
-                    
+                    vision_supervision_loss = 0.0                    
 
                     triples = list(zip(other_verb_logits_list, other_noun_logits_list, other_action_logits_list))
 
@@ -245,39 +243,39 @@ def forward(
                     elif getattr(self.config, 'vision_token_training', None) and self.config.vision_token_training == 'all_layers':
                         pass
                     # by default, distilaltion uses all layers            
-                # First check if any process has valid examples across all triples
-                world_has_valid = torch.tensor(actions[:, 0].any() > 0, device=actions.device)
-                torch.distributed.all_reduce(world_has_valid, op=torch.distributed.ReduceOp.MAX)
-
-                if world_has_valid:  # If any process has valid examples
-                    for other_verb_logits, other_noun_logits, other_action_logits in triples:
-                        valid_mask = actions[:, 0] > 0
-                        
-                        if valid_mask.any():  # This process has valid examples
-                            valid_verb_logits = other_verb_logits[valid_mask]
-                            valid_noun_logits = other_noun_logits[valid_mask]
-                            valid_action_logits = other_action_logits[valid_mask]
-                            
-                            valid_verb_targets = actions[valid_mask, 0]
-                            valid_noun_targets = actions[valid_mask, 1]
-                            valid_action_targets = actions[valid_mask, 2]
+                    # First check if any process has valid examples across all triples
+                    world_has_valid = torch.tensor(actions[:, 0].any() > 0, device=actions.device)
+                    torch.distributed.all_reduce(world_has_valid, op=torch.distributed.ReduceOp.MAX)               
 
-                            other_verb_loss = loss_fct(valid_verb_logits, valid_verb_targets)
-                            other_noun_loss = loss_fct(valid_noun_logits, valid_noun_targets)
-                            other_action_loss = loss_fct(valid_action_logits, valid_action_targets)
+                    if world_has_valid:  # If any process has valid examples
+                        for other_verb_logits, other_noun_logits, other_action_logits in triples:
+                            valid_mask = actions[:, 0] > 0
 
-                            vision_supervision_loss += 0.5 * other_verb_loss + 0.5 * other_noun_loss + 0.1 * other_action_loss
-                        else:  # This process has no valid examples but others do
-                            # Add dummy loss to maintain gradient flow
-                            vision_supervision_loss += 0.0 * (other_verb_logits.sum() + other_noun_logits.sum() + other_action_logits.sum())
-
-                    vision_supervision_loss /= (len(triples) + 1)
-                    loss += vision_supervision_loss * 0.1
-                else:
-                    # If no process has valid examples, add dummy loss to prevent hanging
-                    dummy_loss = sum(sum(t.sum() * 0.0 for t in triple) for triple in triples)
-                    vision_supervision_loss = dummy_loss / (len(triples) + 1)
-                    loss += vision_supervision_loss * 0.1
+                            if valid_mask.any():  # This process has valid examples
+                                valid_verb_logits = other_verb_logits[valid_mask]
+                                valid_noun_logits = other_noun_logits[valid_mask]
+                                valid_action_logits = other_action_logits[valid_mask]
+                                
+                                valid_verb_targets = actions[valid_mask, 0]
+                                valid_noun_targets = actions[valid_mask, 1]
+                                valid_action_targets = actions[valid_mask, 2]
+
+                                other_verb_loss = loss_fct(valid_verb_logits, valid_verb_targets)
+                                other_noun_loss = loss_fct(valid_noun_logits, valid_noun_targets)
+                                other_action_loss = loss_fct(valid_action_logits, valid_action_targets)
+                                
+                                vision_supervision_loss += 0.5 * other_verb_loss + 0.5 * other_noun_loss + 0.1 * other_action_loss
+                            else:  # This process has no valid examples but others do
+                                # Add dummy loss to maintain gradient flow
+                                vision_supervision_loss += 0.0 * (other_verb_logits.sum() + other_noun_logits.sum() + other_action_logits.sum())
+
+                        vision_supervision_loss /= (len(triples) + 1)
+                        loss += vision_supervision_loss * 0.1
+                    else:
+                        # If no process has valid examples, add dummy loss to prevent hanging
+                        dummy_loss = sum(sum(t.sum() * 0.0 for t in triple) for triple in triples)
+                        vision_supervision_loss = dummy_loss / (len(triples) + 1)
+                        loss += vision_supervision_loss * 0.1
 
                 if getattr(self.config, 'vision_token_training', None) and  'distillation' in self.config.vision_token_training: