[Bugfix] Skip PP sampled-token receive on last rank during async scheduling

[wi-adam]

Summary

• avoid calling the PP sampled-token receive path on the last pipeline-parallel rank during async scheduling
• keep the receive path on non-last PP ranks, which still need sampled token IDs broadcast by the last rank
• preserve the existing lazy PP-group lookup when async scheduling is disabled
• add focused regression tests for the empty execute_model_state path in GPUModelRunner.sample_tokens()

Motivation / validation

We found this while bringing up Gemma 4 31B FP8 with TurboQuant KV cache on AMD RDNA4: RedHatAI/gemma-4-31B-it-FP8-block on 2x AMD Radeon AI PRO R9700 GPUs (gfx1201, 32 GiB each), vLLM v0.19.0 on our wi-adam/vllm RDNA4 branch, TheRock/ROCm 7.13, tq-k8v4 KV cache, and pipeline parallel size 2.

In that setup, sample_tokens() could call _pp_receive_prev_sampled_token_ids_to_input_batch() on every PP rank in the empty execute_model_state path. That helper asserts not pp.is_last_rank, but the last PP rank is the rank that broadcasts sampled token IDs, so it should not enter the receive path. The fix is to keep the receive path only for non-last PP ranks while preserving the old behavior that avoids even looking up the PP group when async scheduling is disabled.

We carried this patch in our RDNA4/Gemma 4 deployment and verified the patched stack serving with PP=2 in that environment. The unit tests added here cover the rank-selection contract directly without requiring the production GPU setup.

Duplicate check

This is not duplicating an existing upstream PR. I checked open PRs with these searches and found no matches:

• _pp_receive_prev_sampled_token_ids_to_input_batch
• use_async_scheduling is_last_rank sample_tokens
• pipeline parallel async scheduling receive sampled token ids last rank

No upstream issue number is referenced by this patch.

Tests

• .venv/bin/python -m pytest tests/v1/worker/test_gpu_model_runner.py::test_sample_tokens_receives_pp_sampled_ids_only_on_non_last_rank tests/v1/worker/test_gpu_model_runner.py::test_sample_tokens_skips_pp_group_lookup_without_async_scheduling -q -> 3 passed
• .venv/bin/pre-commit run --files vllm/v1/worker/gpu_model_runner.py tests/v1/worker/test_gpu_model_runner.py -> passed

AI assistance

AI assistance was used to prepare this draft PR. The submitting human should review every changed line and validate the fix before marking ready for review.

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[gemini-code-assist]

Code Review

This pull request updates the sample_tokens method in GPUModelRunner to ensure that only non-last ranks in a pipeline parallel group attempt to receive sampled token IDs. A new unit test has been added to verify that this call is correctly skipped on the last rank. I have no feedback to provide.

[njhill]

@wi-adam I don't think it reaches here on the last rank anyhow, there is an assert inside the _pp_receive_prev_sampled_token_ids_to_input_batch method:

        assert not pp.is_last_rank
``

[wi-adam]

@njhill - I reproduced the original failure on our RDNA4/R9700 deployment by building the pre-fix image and running RedHatAI/gemma-4-31B-it-FP8-block with async scheduling and pipeline_parallel_size=2. The first chat completion returned HTTP 500, and Worker_PP1 hit sample_tokens -> _pp_receive_prev_sampled_token_ids_to_input_batch -> assert not pp.is_last_rank.

I traced the repro path. This is not the normal successful last-rank path. With async PP, EngineCore queues execute_model and sample_tokens back-to-back as non-blocking RPCs. The RPC is broadcast to all workers. In our repro, Worker_PP1 first failed in execute_model while syncing Gemma4 PP intermediate tensors, before execute_model_state was set. WorkerProc logs that exception but continues processing queued RPCs, so Worker_PP1 then ran sample_tokens with execute_model_state still None. The pre-fix branch checked async scheduling + PP world size only, so the last PP rank called _pp_receive_prev_sampled_token_ids_to_input_batch(), which asserts not pp.is_last_rank.

So this guard is preventing an invalid receive on the last PP rank in the async no-state path. It also avoids masking the original execute_model failure with the secondary PP receive assertion.

[he-yufeng]

FYI a second independent repro just landed in #41612 — different hardware (NVIDIA RTX 3060 x3, not RDNA4) and a different model (Qwen3.6-27B-GPTQ-Pro-4bit with --kv-offloading-backend native, --enable-prefix-caching, --mamba-cache-mode align). Same assert not pp.is_last_rank at gpu_model_runner.py:4374, same call site at the early-return path of sample_tokens() (line ~4131).

This addresses @njhill's concern that the path may not be reachable on the last rank: it is, whenever the connector returns a no-op output (execute_model_state is None) on the last PP stage. Both repros land via the connector path — KV offloading on @tibrezus's side, FP8-block / TurboQuant on yours.

[njhill]

Thanks @wi-adam @he-yufeng, makes sense, I think this fix looks fine.

[njhill]

@wi-adam could you take it out of draft if you think it's ready?

[claude]

Claude Code Review

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