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…equest With CUDA graphs enabled, capture_model() signals dummy PLE outputs and then runs real steps through execute_model() that submit real requests to the offload worker. The first real wait passes on the dummy signal (the buffer is still zero), its release resets the flag, and the worker's copy for that step raises the flag for the *next* step. From then on every forward consumes the previous step's per-layer embeddings; only identical consecutive requests hide it. cudagraph_mode=NONE never signals dummy outputs outside execute_model and is unaffected. Reset every layer's semaphore on the model stream before a real request is launched, so the GPU-side wait can only be satisfied by this step's copy. The reset runs per rank, before the tp_rank check in _launch, because each TP rank owns its buffer and semaphore. Found on GB10 (sm_121, TP=1): identical sequential requests fell into two classes (cold vs warm), the PLE output buffer read back with the previous step's rows, and the semaphore trace showed the flag raised before the first real request. With the reset, every real step consumes exactly its own rows. Written with AI assistance (Claude Code); reviewed by the author. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Jürgen Schmied <juergenschmied70@gmail.com>
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…er7/vllm#13) (#28) With CUDA graphs on, capture_model() signals dummy PLE outputs and the first real wait passes on that signal, leaving the semaphore one step ahead for the life of the server: every forward consumes the previous step's PLE rows. The 11-line fix is filed against #53899's branch and not yet in its head, so carry it as patch 0002 (apply-clean on base+0001) until the PR picks it up. VERSION 20260906.1 so the tags are distinct from the unpatched build. Claude-Session: https://claude.ai/code/session_01B6LcUAV5vBtQDxNdUFbYgy
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Purpose
Fix a one-step-behind read of the PLE CPU-offload outputs whenever CUDA graphs are enabled (
cudagraph_modePIECEWISE, FULL_DECODE_ONLY, …) on this branch.capture_model()signals dummy PLE outputs and then runs real steps throughexecute_model()that submit real requests to the offload worker. The first real wait passes on the dummy signal (the buffer is still zero), its release resets the flag, and the worker's copy for that step raises the flag for the next step. From then on every forward consumes the previous step's per-layer embeddings; only identical consecutive requests hide it.cudagraph_mode=NONEnever signals dummy outputs outsideexecute_modeland is correct.Fix: reset every layer's semaphore on the model stream before a real request is launched (
PleOffloadConnector.prepare_forward), so the GPU-sideple_offload_waitcan only be satisfied by this step's copy. Per rank, before thetp_rankcheck in_launch, because each TP rank owns its buffer and semaphore. 11 lines; the worker already waits for the reset before copying, so its protocol is unchanged.Test Plan
GB10 (sm_121, TP=1), FP8 PLE shards,
VLLM_PLE_CPU_OFFLOAD=1, no speculation, prefix cache off,cudagraph_mode=PIECEWISE(default) andNONEas the reference:prompt_logprobs=5, full per-position vector hashed and grouped into classes.Test Result
Unfixed, PIECEWISE: first real step 0 non-zero rows; a cold 1,460-token request 32 rows (= the previous step); the next identical request 1,460 rows; a cold 1,999-token request exactly 1,460 rows. 16 identical requests fall into two classes (cold first, then 15 bit-identical). Trace:
signal via signal_dummy_outputs <- capture_model, thenexecute_modelsteps at 32/16/2/1 tokens with worker requests; the 32-token wait sees flag=1 and 0 rows; the 16-token wait blocks and is released by the worker's signal for the 32-token step; every later release is followed by the previous step's late signal.cudagraph_mode=NONE: every step exactly its own rows; the NONE buffer hash equals PIECEWISE's warm hash, i.e. the warm class is the correct computation.Fixed, PIECEWISE: every real step consumes exactly its own rows from the first one (32/16/2/1 at init, then 1,460 ×3, 1,999 ×2), hashes equal to the NONE run's; the cold first request gives the same first-token logprob as the warm ones (−0.2638); 16 identical requests = one class; the position-resolved set is bit-exact sequentially at 1,460 / 1,999 / 5,960 tokens (0 flips, spread 0.000, 1/8 distinct completions each). The concurrent batches keep 0 / 416 / 665 flips, identical to the NONE run — the batch-shape axis, unrelated to this fix.
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supported_models.mdandexamplesfor a new model.This PR includes AI-generated code (Claude Code); every changed line was reviewed and the behavior validated end-to-end by the author.