Update modeling_minicpmv.py

modeling_minicpmv.py

@@ -102,7 +102,7 @@ class MiniCPMV(MiniCPMVPreTrainedModel):
                                                                        padding_value=0.0)
                     B, L, _ = all_pixel_values.shape
                     all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)
-
+                    print(B, "BATCH")
                     patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device)
                     for i in range(B):
                         patch_attn_mask[i, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True
@@ -168,7 +168,8 @@ class MiniCPMV(MiniCPMVPreTrainedModel):
                                           cur_vs_hs.view(-1, cur_vs_hs.shape[-1]))
                 elif self.training:
                     cur_vllm_emb += cur_vs_hs[0].mean() * 0
-        
+
+        print(vllm_embedding.shape)
         return vllm_embedding, vision_hidden_states
 
     def forward(self, data, **kwargs):