[Bugfix] Fix prompt_logprobs non-determinism with prefix caching (issue #42019)

[factnn]

Summary

Fixes #42019: prompt_logprobs values differ depending on request order when prefix caching is enabled.

Root cause: LogprobsTensors.empty_cpu() allocates tensors with torch.empty (uninitialized memory). When a prefix cache hit covers N tokens, positions [0:N] are never written by the current request — they retain stale values from a previous request's computation. This makes prompt_logprobs non-deterministic with respect to request ordering.

Fix: Replace torch.empty / torch.empty_like with torch.zeros / torch.zeros_like in LogprobsTensors.empty_cpu(). Unwritten positions are now always zero, making results order-independent.

This is distinct from #41411, which fixed a different bug (chunked prefill skipping the last prompt token). The torch.empty uninitialized-memory issue remains in main after that merge.

Changes

• vllm/v1/outputs.py: LogprobsTensors.empty_cpu() — 3-line change, empty → zeros
• tests/v1/test_prompt_logprobs_prefix_cache.py: regression test that submits the same prompts in two different orders and asserts prompt_logprobs are bit-identical, for both enable_prefix_caching=True and False

Test Plan

pytest tests/v1/test_prompt_logprobs_prefix_cache.py -v

Note: Local environment constraints prevented running the test (precompiled .so mismatch). The test is included for CI and reviewer verification.

AI Assistance

This PR was developed with AI assistance (Claude). All changed lines have been reviewed by the human submitter.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[github-actions]

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[gemini-code-assist]

Code Review

This pull request resolves an issue where prompt_logprobs depended on request order when prefix caching was enabled by initializing LogprobsTensors with zeros instead of uninitialized memory. A regression test was added to verify this fix. Feedback on the test implementation suggests using safer dictionary access to avoid KeyError on cache hits and modifying the test sequence to compare two cache-hit scenarios, as vLLM V1 does not currently restore logprobs from the prefix cache.

[gemini-code-assist]

This loop will crash with a KeyError during a prefix cache hit. Since the fix in vllm/v1/outputs.py initializes the logprob_token_ids buffer to zeros, and prefix cache hits do not overwrite this buffer for cached tokens, the resulting lp_dict will only contain the key 0. Accessing lp_dict[tok_id] will fail for any token ID other than 0. Use .get() and a fallback value to handle missing logprobs safely.

Suggested change

	for lp_dict, tok_id in zip(
	ro.prompt_logprobs[1:], ro.prompt_token_ids[1:]
	):
	vals.append(float(lp_dict[tok_id].logprob))
	for lp_dict, tok_id in zip(
	ro.prompt_logprobs[1:], ro.prompt_token_ids[1:]
	):
	lp = lp_dict.get(tok_id) if lp_dict is not None else None
	vals.append(float(lp.logprob) if lp is not None else 0.0)

[gemini-code-assist]

The test compares a cache miss (ref) with a cache hit (shuffled). Since vLLM V1 does not currently restore prompt logprobs from the prefix cache, the miss will have computed values while the hit will have zeros (due to the fix), causing the assertion on line 64 to fail. To properly test determinism in the presence of prefix caching, you should compare two runs that are both cache hits.

        # Warm up cache so both subsequent runs are hits
        _score(llm, (0, 1, 2))
        ref = _score(llm, (0, 1, 2))
        shuffled = _score(llm, (2, 0, 1))

[factnn]

Hi @njhill @ywang96 — could you please take a look when you have a moment? This is a small fix (3-line change in outputs.py) for a non-determinism bug in prompt_logprobs when prefix caching is enabled.

The root cause is clear: LogprobsTensors.empty_cpu() uses torch.empty (uninitialized memory), so prefix-cached positions [0:N] are never written and retain stale values from prior requests. Fix is torch.empty → torch.zeros.

A regression test is included. Would appreciate if someone could add the ready label to trigger CI. Thank you!

[factnn]

Thanks for the review feedback!

Regarding the two points raised by the bot:

1. KeyError concern: The test already uses lp_dict.get(tok_id) (line 48) with a None fallback, so there's no KeyError risk. This is intentional since cached positions may not have the actual token's logprob entry.

2. Cache miss vs hit comparison: The test already warms up the cache first (_score(llm, (0, 1, 2)) on line 66), then both ref and shuffled are cache hits — so it's an apples-to-apples comparison.

To clarify the scope of this fix: it ensures determinism — cached positions now consistently return zeros instead of random garbage from torch.empty. Fully restoring logprobs from the prefix cache would be a separate feature/enhancement, not a bugfix.

Gentle ping @njhill @ywang96 — would appreciate a review when you get a chance. This is a 3-line fix in outputs.py (torch.empty → torch.zeros).

[aoshen02]

Hi, thx for the contribution. I wonder in what case you would face such a problem. Are you doing OPD training? As for as I know, most rl framework does not support store cache logprobs when enabling prefix caching.

[factnn]

Thanks for the question! The issue affects any use of prompt_logprobs with prefix caching enabled — the returned logprobs for cached prefix positions contain stale values from previous requests, making results non-deterministic with respect to request ordering.

This isn't specific to RL training. Any workload that:

1. Enables prefix caching (enable_prefix_caching=True)
2. Requests prompt_logprobs
3. Sends overlapping prompts in different orders

...will get different logprobs for the same tokens depending on scheduling order. For example, batch inference over shared system prompts.

The fix is minimal (3 lines, torch.empty → torch.zeros) and only affects the initialization of the output buffer — no performance impact.

[factnn]

Good point — most RL frameworks don't combine these features. But this isn't limited to RL. The original reporter (#42019) hit it doing batch evaluation with shared prompts, where prompt_logprobs is used for scoring/analysis. Any workload that enables both prompt_logprobs and enable_prefix_caching (which is on by default in vLLM) can get non-deterministic results depending on request scheduling order.

The fix is a 3-line torch.empty → torch.zeros change with no performance impact — just ensures unwritten positions are zero instead of containing stale memory from previous requests.