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Make memory admission and prefix caching hybrid-state aware #22

Description

@randomvariable

Idea

Make request admission, memory accounting, eviction, and prefix-cache ownership aware of non-KV state in current hybrid multimodal models.

Why It Matters

Current models can combine several state classes that do not share ordinary paged-KV semantics:

  • variable media-token expansion plus vision, audio, or video encoder/projector residency;
  • full-attention and sliding-window KV with different retention bounds;
  • convolutional, DeltaNet, Mamba, or other recurrent state;
  • native or external speculative-draft state;
  • prefix-cache entries whose safe reuse depends on modality inputs and state-transition boundaries.

Weight-plus-KV estimates can therefore over-admit work, evict the wrong state, or incorrectly alias prefixes even when each subsystem works in isolation.

Current Architecture Candidates

  • Step-3.7-Flash: multimodal MoE, mixed full/SWA attention, and three NEXTN layers.
  • MiMo-V2.5: multimodal MoE, 128-token SWA/global attention, and one-million-token context.
  • Nemotron 3 Nano Omni: multimodal Mamba/attention MoE.
  • LFM2.5: convolution/attention MoE with explicit convolution state.
  • Qwen3.6 and Nemotron 3 Ultra: recurrent-or-linear/full-attention hybrids with draft-state concerns.

“Architecture candidate” means only that model structure matches this experiment's conceptual target. It does not assert current vLLM model support, quantization compatibility, kernel availability, graph capture, speculative-decoding integration, or acceptable performance. Each must be demonstrated by exact revision, backend-selection evidence, correctness tests, and benchmark results.

Possible vLLM Work

  • Define a state inventory API that reports persistent and per-request bytes, lifetime, mutability, eviction rules, and whether state is prefix-shareable.
  • Include processor/encoder residency, media expansion, per-layer KV retention, recurrent/conv state, and drafter state in admission estimates.
  • Make prefix-cache keys and reuse validation include modality identity and state-transition compatibility.
  • Extend scheduler traces so estimated state can be reconciled with allocator and cache telemetry.
  • Keep architecture adapters explicit; do not treat recurrent or convolutional state as paged KV.

Acceptance Criteria

  • A test matrix covers at least one mixed full/SWA model, one recurrent-or-convolutional hybrid, one multimodal model, and one speculative-draft model.
  • Admission estimates do not undercount state allocations observed by the instrumented runtime for the tested request shapes.
  • Prefix reuse is rejected when modality inputs or state-transition boundaries differ, and accepted for compatible identical prefixes.
  • Eviction never drops required recurrent, convolutional, sparse-index, or draft state while retaining dependent cache entries.
  • Scheduler throughput and p95 latency differ by no more than 2% from the existing path when all requests use ordinary dense text-only KV and memory pressure is absent.

Related Issues

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Duplicate Check

Searched the fork backlog for multimodal admission, hybrid-state accounting, prefix-cache ownership, recurrent state, and media-token expansion. Existing issues cover individual memory, KV, prefix, and state-update mechanisms, but none owns their cross-request accounting and cache-sharing contract.

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