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config.json

@@ -105,7 +105,7 @@
   },
   "time_delta_monotonic_inversion": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.46.3",
+  "transformers_version": "4.49.0",
   "vision_config": {
     "_attn_implementation_autoset": false,
     "_name_or_path": "aehrc/uniformer_base_tl_384",

dataset.py

@@ -33,4 +33,4 @@ class PriorsDataset:
         batch = self.__getitem__(keys)
         n_examples = len(batch[next(iter(batch))])
         return [{col: array[i] for col, array in batch.items()} for i in range(n_examples)]
-    
+    

generation_config.json

@@ -3,5 +3,5 @@
   "bos_token_id": 1,
   "eos_token_id": 2,
   "pad_token_id": 4,
-  "transformers_version": "4.46.3"
+  "transformers_version": "4.49.0"
 }

modelling_cxrmate_ed.py

@@ -796,8 +796,29 @@ class CXRMateEDModel(transformers.LlavaForConditionalGeneration):
         position_ids[row_indices, col_indices.flatten()] = torch.arange(num_cols, device=time_deltas.device)[None, :].expand(num_rows, -1).flatten()
         position_ids.masked_fill_(attention_mask == 0, 1)  # Following: https://github.com/huggingface/transformers/blob/c5f0288bc7d76f65996586f79f69fba8867a0e67/src/transformers/models/llama/modeling_llama.py#L1285
         
+        for i in range(position_ids.shape[0]):
+            assert self.validate_position_ids(position_ids[i])
+            
         return position_ids
     
+    @staticmethod
+    def validate_position_ids(tensor, repeat_value=1):
+        unique, counts = torch.unique(tensor, return_counts=True)
+
+        # Check if all integers from 0 to tensor.max() exist:
+        full_range = torch.arange(0, tensor.max() + 1, device=tensor.device)
+        if not torch.equal(unique.sort()[0], full_range):
+            return False
+
+        # Check for repeated values except for repeat_value:
+        repeated = unique[counts > 1]
+        if repeated.nelement() == 0:
+            return True
+        if not (repeated.numel() == 1 and repeated.item() == repeat_value):
+            return False
+
+        return True
+    
     def prepare_index_value_feats(self, table, batch):
                
         index_value_columns = (self.tables[table].get('index_columns', []) + self.tables[table].get('value_columns', []))