[Bugfix] Fix multimodal streaming-session prefix rehashing

[edwardlong-ctrl]

Purpose

Fixes #49377 for streaming-session suffix replacement.

Scheduler._update_request_as_session() discards an uncomputed token suffix, but
Request.update_block_hashes() only appends hashes. A hash produced for a
discarded token can therefore remain attached to the replacement sequence.

Deleting that stale hash suffix exposes a separate multimodal restart bug. For a
nonzero hash boundary, the incremental hasher starts at the final multimodal
feature. If an earlier feature still overlaps the first recomputed block, its
position-dependent extra key is omitted. For example, with block size 4, feature
A at [4, 6), feature B at [7, 8), and a restart at token 4, the old
incremental path hashes only B while a fresh request hashes A and B.

This change:

• invalidates the request hash suffix at
  floor(num_computed_tokens / hash_block_size) before replacing session tokens;
• uses a binary search over sorted multimodal feature end positions to restart at
  the first feature that can overlap the recomputed range;
• preserves retained-prefix hashes and recomputes every later chained hash under
  the replacement identity.

The scope is deliberately limited to request identity for the uncomputed suffix
replaced by the streaming-session path. It does not add a generic token-mutation
API, remap pool entries for already-computed truncations, or change connector and
offload truncation paths.

Coordination note: #49377 identified both the stale hash-suffix bug and the
multimodal incremental-rehash limitation, including a verified
fresh-vs-incremental mismatch. This PR implements and tests those fixes.
@Change72 confirmed they are not working on a competing fix; @Alex-ai-future can
confirm separately. Maintainers, please advise whether this focused fix should
remain independent of #43372/#43374. #48838's adjacent full-chain helper does not
model position-dependent multimodal extra keys.

This PR includes code and tests developed with non-trivial assistance from
OpenAI Codex.

Test Plan

The CPU regression matrix covers:

• token replacement before, at, and immediately after a full hash boundary;
• invalidation from token zero and from nonzero boundaries;
• retained prefixes and multiple later chained blocks;
• no multimodal features, wholly-before/overlapping/wholly-after features,
  multiple features in one block, exact feature boundaries, and features spanning
  multiple hash blocks;
• both supported hash functions;
• existing adjacent coverage for LoRA, cache-salt, and prompt-embedding identity
  metadata;
• KVCacheManager/BlockPool miss-before-insert and hit-after-legitimate-cache
  behavior for discarded, multimodal-omitted, and correct replacement identities.

Commands:

.venv/bin/python -m pytest tests/v1/core/test_kv_cache_utils.py -q
.venv/bin/python -m pytest tests/v1/streaming_input/test_scheduler_streaming.py -q
.venv/bin/python -m pytest tests/v1/core/test_prefix_caching.py -q
.venv/bin/python -m pytest tests/v1/core/test_single_type_kv_cache_manager.py -q
.venv/bin/python -m pytest tests/v1/core/prefix_cache/test_partial_prefix_cache_primitives.py -q
.venv/bin/python -m pytest tests/v1/core/prefix_cache/test_partial_prefix_cache_hits.py -q
.venv/bin/python -m pytest tests/v1/core/test_async_scheduler.py -q -k prefix_caching
.venv/bin/python -m pytest tests/v1/core/test_scheduler.py -q -k prefix_cache
.venv/bin/pre-commit run --files tests/v1/core/test_kv_cache_utils.py tests/v1/streaming_input/test_scheduler_streaming.py vllm/v1/core/kv_cache_utils.py vllm/v1/core/sched/scheduler.py

Test Result

On macOS arm64 with Python 3.12.13, PyTorch 2.11.0, and no CUDA device:

• test_kv_cache_utils.py: 89 passed;
• test_scheduler_streaming.py: 10 passed, 6 subtests passed;
• test_prefix_caching.py: 89 passed;
• test_single_type_kv_cache_manager.py: 9 passed;
• partial-prefix-cache primitives/hits: 10 passed / 12 passed;
• async scheduler prefix selection: 2 passed, 8 deselected;
• scheduler prefix-cache selection: 9 passed, 123 deselected;
• all applicable pre-commit hooks passed.

The semantic regression reproduces the first multimodal mismatch at hash index 1:
the old incremental calculation uses only B's extra key, while fresh and fixed
calculations use A and B; all later chained hashes then agree after the fix.

The restart is O(log M) only while some multimodal feature can overlap the
recomputed range. The common late-decode path remains O(1) via the existing
last-feature-end check. A focused local Python microbenchmark (not a model
throughput claim) measured about 0.5 microseconds for a binary restart among 4096
features versus about 148 microseconds for a scan from feature zero; the common
post-multimodal path stayed about 1.3-1.5 microseconds per block across 0-4096
features.

No GPU model-output test was run. The pool regression validates deterministic
CPU hash addressability and insertion/lookup identity, not KV tensor contents.

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[Change72]

One attribution correction: the original, unedited #49377 also identified the multimodal incremental-rehash failure and documented the verified fresh-vs-incremental mismatch. Could you update the coordination note to reflect that the issue identified both the stale-suffix bug and the multimodal limitation, while this PR implements and tests the fix?

[edwardlong-ctrl]

Thanks for the correction—you’re right. I’ve updated the coordination note to
state that #49377 identified both the stale-suffix bug and the multimodal
fresh-vs-incremental mismatch, while this PR implements and tests the fixes.
Thanks also for confirming there is no competing work on your side.