add trim_attn_metadata comment

vllm/worker/habana_model_runner.py

@@ -803,14 +803,34 @@ def _seq_len(self, attn_metadata):
             return attn_metadata.block_tables.size(1) * self.block_size
 
     def trim_attn_metadata(self, metadata: AttentionMetadata) -> object:
-        prefill_metadata = subtuple(metadata,
-                                    'TrimmedAttentionMetadata',
-                                    ['block_tables',
-                                     'seq_lens_tensor',
-                                     'attn_bias',
-                                     'slot_mapping',
-                                     'is_prompt'])
-        return prefill_metadata
+        # NOTE(kzawora): To anyone working on this in the future:
+        # Trimming metadata is required when using HPUGraphs.
+        # Attention metadata is going to be hashed by PT bridge, and
+        # appropriate HPUGraphs will be matched based on all inputs' hash.
+
+        # Before you put more keys in here, make sure you know their 
+        # value type and make sure you know how it's going to be hashed. 
+        # You can find that information in input_hash function 
+        # in habana_frameworks/torch/hpu/graphs.py. You can also hash
+        # it manually with torch.hpu.graphs.input_hash(attention_metadata)
+
+        # If you use primitive types here - they will get hashed based
+        # on their value. You *will* get lots of excessive graph captures
+        # (and an OOM eventually) if you decide to put something like
+        # seq_len int here. 
+        # If you absolutely need a scalar, put it in a tensor. Tensors 
+        # get hashed using their metadata, not their values:
+        # input_hash(torch.tensor(123)) == input_hash(torch.tensor(321))
+        # input_hash(123) != input_hash(321)
+        # input_hash("abc") != input_hash("cba")
+        attention_metadata = subtuple(metadata,
+                                      'TrimmedAttentionMetadata',
+                                      ['block_tables',
+                                       'seq_lens_tensor',
+                                       'attn_bias',
+                                       'slot_mapping',
+                                       'is_prompt'])
+        return attention_metadata
 
     @torch.inference_mode()
     def execute_model(