[Bugfix][Disaggregated] patch the inflight batching on the decode node in...

[Bugfix][Disaggregated] patch the inflight batching on the decode node in SimpleConnector to avoid hangs in SimpleBuffer (nccl based) (#13987)
Signed-off-by: Mathis Felardos <mathis@mistral.ai>

vllm/distributed/kv_transfer/kv_connector/simple_connector.py

@@ -214,6 +214,7 @@ class SimpleConnector(KVConnectorBase):
 
         input_tokens_tensor = model_input.input_tokens
         seq_lens = model_input.attn_metadata.seq_lens
+        num_prefill_tokens = model_input.attn_metadata.num_prefill_tokens
         slot_mapping = model_input.attn_metadata.slot_mapping.flatten()
 
         hidden_or_intermediate_states_for_one_req = []
@@ -225,9 +226,21 @@ class SimpleConnector(KVConnectorBase):
         # enumerate different requests
         # FIXME(Kuntai): This impl assumes that all requests are prefill.
         for idx, slen in enumerate(seq_lens):
-
             start_pos = sum(seq_lens[:idx])
             end_pos = start_pos + slen
+
+            if start_pos >= num_prefill_tokens:
+                # This can happen during inflight batching. See:
+                # vllm/worker/model_runner.py::_prepare_model_input_tensors:
+                # - input_tokens[:num_prefill_tokens] contains prefill tokens.
+                # - input_tokens[num_prefill_tokens:] contains decode tokens.
+                logger.warning("You should set --enable_chunked_prefill=False "
+                               "and --max_num_batched_tokens "
+                               "should be equal to max_seq_len_to_capture")
+                bypass_model_exec = False
+                assert start_pos == num_prefill_tokens
+                break
+
             current_tokens = input_tokens_tensor[start_pos:end_pos]
             num_tokens = slen
 
@@ -288,7 +301,7 @@ class SimpleConnector(KVConnectorBase):
             # Here we will fall back to normal model forwarding
             # But optionally you can adjust model_input so that you only do
             # prefilling on those tokens that are missing KV caches.
-            logger.debug(
+            logger.warning(
                 "[rank%d]: Failed to receive all KVs and hidden "
                 "states, redo model forwarding.", torch.distributed.get_rank())
             hidden_or_intermediate_states = None