[Bugfix][MoRIIO] Proxy robustness and write-task deferral for async KV transfer#41242
[Bugfix][MoRIIO] Proxy robustness and write-task deferral for async KV transfer#41242jaeyoun98 wants to merge 1 commit into
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Documentation preview: https://vllm--41242.org.readthedocs.build/en/41242/ |
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Code Review
This pull request adds a /health endpoint to the toy proxy server and implements logic to handle max_completion_tokens. It also introduces task deferral in the Moriio engine when remote block IDs are unavailable. Review feedback identifies a critical bug in the Moriio engine where appending to _deferred_tasks during iteration leads to task loss and potential busy loops. Additionally, the token decrement logic in the proxy server needs to prioritize max_completion_tokens over max_tokens to comply with OpenAI API precedence rules.
| task.request_id, | ||
| task.transfer_id, | ||
| ) | ||
| self._deferred_tasks.append(task) |
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Appending to self._deferred_tasks here introduces a critical bug when called from _process_deferred_tasks. In _process_deferred_tasks (lines 142-154), the code iterates over self._deferred_tasks and then overwrites it with a local still_deferred list at the end. Any task appended to self._deferred_tasks during the iteration (via this call to _execute_write_task) will be lost when the overwrite occurs at line 154.
Furthermore, if _is_remote_ready returns True (because the entry exists in the dictionary) but block_ids is still None, this will cause a busy loop in the background thread, as _process_deferred_tasks will repeatedly call _execute_write_task for the same task. The readiness check should be updated to ensure block_ids are present before attempting execution.
| if "max_tokens" in req_data: | ||
| req_data["max_tokens"] -= 1 | ||
| elif "max_completion_tokens" in req_data: | ||
| req_data["max_completion_tokens"] -= 1 |
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According to the OpenAI API documentation, if both max_tokens and max_completion_tokens are specified, max_completion_tokens takes precedence. The current implementation checks for max_tokens first, which means if both are present, max_tokens will be decremented while the backend (vLLM) will use the original, non-decremented max_completion_tokens. This results in the model generating one extra token beyond the user's requested limit.
| if "max_tokens" in req_data: | |
| req_data["max_tokens"] -= 1 | |
| elif "max_completion_tokens" in req_data: | |
| req_data["max_completion_tokens"] -= 1 | |
| if "max_completion_tokens" in req_data: | |
| req_data["max_completion_tokens"] -= 1 | |
| elif "max_tokens" in req_data: | |
| req_data["max_tokens"] -= 1 |
…V transfer This PR bundles three independent fixes for the MoRIIO-based P/D disaggregation path (no overlap with open PRs vllm-project#40344, vllm-project#39276, vllm-project#32630, vllm-project#39835). 1. Proxy: handle max_completion_tokens for chat completions API. The OpenAI Chat Completions API uses max_completion_tokens; the toy proxy was only decrementing max_tokens, dropping the field on forward and causing the backend to use its default. When both fields are present, max_completion_tokens takes precedence per the OpenAI spec, so decrement that one first; fall back to max_tokens otherwise. 2. Proxy: add /health endpoint. Benchmark harnesses (e.g. vllm bench serve) perform health probes against the proxy before sending traffic. Without this endpoint the probe fails and the benchmark aborts. Add a minimal 200-OK responder that also reports the current instance counts. 3. Engine: defer write task when remote block_ids is None. When a prefill-side write task arrives before the scheduler has populated the remote block_ids (async handshake race), the previous code silently dropped the task. The fix is in _is_remote_ready: it now returns False when block_ids is still None, so the existing outer deferral path (in _write_worker_loop and _process_deferred_tasks) re-queues the task safely. The inner None-check inside _execute_write_task is removed; appending to self._deferred_tasks from inside the iteration in _process_deferred_tasks would have been clobbered by the still_deferred overwrite at end of loop. All three are narrow, independent fixes. They do not modify the scheduler's hot path or introduce new control flow. Verified to apply cleanly on upstream/main as of 2026-04-22. This change was developed with AI assistance; the author has reviewed and tested each hunk and is accountable for the behavior. Signed-off-by: Chaemin Lim <chaemin.lim@mangoboost.io>
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Purpose
This PR bundles three independent fixes for the MoRIIO-based P/D disaggregation path (no overlap with open PRs #40344, 39276, #32630, #39835).
Proxy: handle
max_completion_tokensfor chat completions API.The OpenAI Chat Completions API uses
max_completion_tokens; the toy proxy was only decrementingmax_tokens, dropping the field on forward and causing the backend to use its default. Decrement whichever field the client sent.Proxy: add
/healthendpoint.Benchmark harnesses (e.g.
vllm bench serve) perform health probes against the proxy before sending traffic. Without this endpoint the probe fails and the benchmark aborts. Add a minimal 200-OK responder that also reports the current instance counts.Engine: defer write task when remote
block_idsisNone.When a prefill-side write task arrives before the scheduler has populated the remote
block_ids(async handshake race), the current code silently drops the task. Instead, append it to the existingself._deferred_tasksqueue so it is retried once the mapping becomes available, preventing lost KV transfers.All three are narrow, independent fixes. They do not modify the scheduler's hot path or introduce new control flow. Verified to apply cleanly on upstream/main as of 2026-04-22.
Test Plan
These fixes were developed and verified on:
bnxt_re)bnxt_re)We use these as a precondition for our vLLM P/D disaggregated serving stack with the MoRI-IO connector. Without these fixes, MoRI-IO failed at startup.
Test Result
This change was developed with AI assistance; the author has reviewed and tested each hunk and is accountable for the behavior.
Signed-off-by: Chaemin Lim chaemin.lim@mangoboost.io
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