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[Bugfix][Disaggregated] patch the inflight batching on the decode node in SimpleConnector to avoid hangs in SimpleBuffer (nccl based) #13987

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Merged
merged 1 commit into from
Feb 28, 2025
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[Bugfix][Disaggregated] patch the inflight batching on the decode node in SimpleConnector to avoid hangs in SimpleBuffer (nccl based) #13987

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17 changes: 15 additions & 2 deletions vllm/distributed/kv_transfer/kv_connector/simple_connector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -214,6 +214,7 @@ def recv_kv_caches_and_hidden_states(

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 = []
Expand All @@ -225,9 +226,21 @@ def recv_kv_caches_and_hidden_states(
# 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

Expand Down Expand Up @@ -288,7 +301,7 @@ def recv_kv_caches_and_hidden_states(
# 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
Expand Down