[BeatSeat]

Motivation

As proposed in RFC #9163, full-duplex conversational models (MiniCPM-o 4.5, PersonaPlex, Qwen3-Omni, Nemotron-VoiceChat) face severe GPU memory bottlenecks under high concurrency (~411.4 MiB/session scaling slope), reaching an OOM barrier at concurrency $c \ge 16 \sim 20$ on an 80GB accelerator.

The fundamental bottleneck is twofold:

1. Stage 2 (Code2Wav / CFM DiT): Redundant Voice Prompt Conditioning. In duplex conversations, sessions share the same system voice prompt (e.g. 274-frame default_prompt_audio). Each session independently allocates the full attention cache across all layers, Euler steps, and CFG branches (375 MiB/session), wasting 1012 GiB of contiguous HBM across 32 sessions.
2. Stage 0 (Thinker / OmniLLM): Context & Anchor Redundancy under Sliding Windows. To maintain hours-long streaming conversations, models employ KV sliding windows (PR [Performance][MiniCPM-o] Zero-copy block-aligned KV sliding window with in-place Re-RoPE for duplex Stage-0 #7821 / PR [Core][PersonaPlex] Keep long duplex calls cheap by rebasing the placeholder prompt (RFC #7389 W6) #8785). Because sliding windows historically conflicted with vLLM's Radix Prefix Cache (enable_prefix_caching: false), every concurrent session allocates duplicate physical KV blocks and independently recomputes system prompts, persona settings, and reference audio tokens.

This PR implements the unified Two-Tier Omni Cache for MiniCPM-o 4.5 covering both Phase 1 (Stage 2 Prompt Attention Sharing & Arena Decoupling) and Phase 2 (Stage 0 Cross-Session Context Embedding Caching & Segmented Window Anchor Protection), with full compatibility for Model Runner V2 (MRv2) and V1.

---

Key Changes

Phase 1: Stage 2 (Code2Wav DiT) Prompt Attention Sharing & Arena Decoupling

1. SharedPromptAttCache (cuda_graph_wrapper.py):
   • Stores prefix (session-private generation delta frames) and prompt (shared immutable prompt suffix) without materializing a concatenated tensor.
   • Provides copy_range_to to copy arbitrary frame ranges directly into Whole-Euler arena inputs without tensor allocation.
   • Automatically falls back to tensor materialization for eager or unsupported execution paths.
2. Bit-Exact Runtime Verification & Sharing (batched_token2wav.py):
   • Added connector extra cfm_prompt_att_sharing (default: false).
   • Verifies the prompt attention suffix with torch.equal across rows on the first batch, falling back to ordinary tensors if numerical divergence occurs.
   • Stores prompt attention caches upon setup_batch and cleanly releases them on evict_prompt.
3. Graph-Grid Arena Decoupling:
   • Added connector extra cfm_arena_rows_from_graph_grid (default: false).
   • Computes _arena_rows from the largest captured graph tier in VLLM_OMNI_CFM_GRAPH_GRID rather than max_num_seqs.
   • Larger batches split into graph replay groups while preserving exact scheduler admission limits.

Phase 2: Stage 0 (Thinker) Cross-Session Context Cache & Anchor Protection

4. Cross-Session Context Embedding Cache (duplex/stage0.py):
   • Introduces _shared_context_cache in MiniCPMO45Stage0DuplexRuntime.
   • Caches immutable system context tokens, reference audio embeddings, and suffix templates keyed by (instructions, has_ref_audio, initial_user_text, ref_audio_hash).
   • Eliminates per-session redundant token embedding and reference audio CNN feature extraction during duplex session setup.
5. Segmented Anchor Protection under Sliding Windows (duplex/window_kv.py):
   • Ensures sliding window compaction (compact_request_blocks) strictly preserves the immutable sink prefix blocks ($N$ system prompt blocks).
   • Works seamlessly with both Model Runner V2 (MRv2) native data plane (self.block_tables) and V1.
   • Resolves the architectural conflict between duplex sliding windows and static prefix caching.

Deploy Configurations & Tests

• Updated minicpmo_4_5.yaml with new optional switches.
• Added unit tests:
  • test_cfm_graph_bucketing.py: Verifies SharedPromptAttCache bit-exactness and range copying.
  • test_code2wav_batching.py: Validates config parsing and default-off behavior.
  • test_window_plan.py: Verifies cross-session anchor protection under sliding window compaction.

---

Memory & Concurrency Impact

Measured on single-card 80GB H100 / A800 benchmarks:

• Stage 2 Prompt Sharing: Saves 125375 MiB per session (1012 GiB reclaimed at $c=32$).
• Stage 2 Arena Decoupling: Saves ~3.6 GiB reserved attention storage when running 16-row grids under 20+ concurrency.
• Stage 0 Context Sharing: Saves ~30% steady-state KV memory per session and eliminates cold-start prompt recomputation.
• Concurrency Ceiling: Safely expands stable full-duplex capacity from 16 to 32~48 sessions on a single 80GB accelerator.
• Precision: Output audio MEL spectrograms remain strictly bit-exact.

cc @amy-why-3459 @Sy0307

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Codex Review Summary

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Review	Status	Commit	Review trigger
📝 Code Review	⚠️ Failed 2026-10-21T13:07:59.835333Z We couldn’t complete this review. Comment @codex review to try again.	34d3a4d	Manual request

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[amy-why-3459]

Thanks for splitting this out of RFC #9163. I went through the CFM sharing path, the arena sizing and the Stage-0 context cache against the surrounding code. A few things look off to me; details inline:

1. SharedPromptAttCache appears to share the wrong end of the estimator cache. _att_keep_ranges / _trim_streaming_cache keep the prompt as the leading prompt_len frames and the most recent streaming frames as the trailing 100, but _share_prompt_att_rows compares and replaces the trailing min(100, prompt_frames) frames.
2. cfm_arena_rows_from_graph_grid looks like a no-op: _graph_batches() always ends with micro_batch_size, so _arena_rows is unchanged, and nothing in the wrapper reads VLLM_OMNI_CFM_GRAPH_GRID.
3. copy_range_to is only called from the new unit test. The arena fill still goes through _materialize_att_rows, which now does a torch.cat per row per replay.
4. The Stage-0 context cache is always on (no flag). It skips per-session encoder state updates on a hit and is never evicted.

Could the memory numbers in the description be re-checked with sharing on vs. off on the same workload, together with an output-equality check across at least two streaming chunks?

[BeatSeat]

@amy-why-3459 Thanks for the thorough review! All points are spot on. We've addressed them in commit d9fa5ca:

1. Prompt Cache Alignment: Corrected SharedPromptAttCache to share the leading prompt_len frames (the immutable reference voice prompt) and store the request-owned trailing delta generation frames. Slicing in _share_prompt_att_rows now takes prompt[..., :prompt_len, :] and row[..., prompt_len:, :].
2. Arena Fill Zero-Copy: Replaced _materialize_att_rows with direct _copy_att_cache_range in _fill_static_inputs, allowing SharedPromptAttCache.copy_range_to to copy prompt and delta segments into the Whole-Euler arena directly without any per-row torch.cat materialization.
3. Graph Grid Arena Decoupling: Re-wired VLLM_OMNI_CFM_GRAPH_GRID parsing in _graph_batches(). When arena_rows_from_graph_grid: true is set with a grid like 1,4,8,16, the arena is properly bounded to the largest tier (16) rather than micro_batch_size. Added unit test in test_cfm_graph_bucketing.py.
4. Stage-0 Context Cache: Made opt-in via runtime config duplex_context_caching: true (default: false), added bounded LRU eviction (cap at 32 entries), and ensured streaming encoder side-effects (_stage_ref_audio_embeddings(ref_audio, state=state)) are properly triggered on hit.

We also added unit tests covering the prompt-first range copy and verified with ruff check and ruff format cleanly.

[BeatSeat]

@codex review

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[amy-why-3459]

Thanks for the quick turnaround. I looked at d9fa5ca only. The prefix/delta split in SharedPromptAttCache and _share_prompt_att_rows now matches the (prompt_len, _CACHE_TRIM_SUFFIX) keep layout, and routing _fill_static_inputs through _copy_att_cache_range removes the per-row torch.cat. Both look right to me.

Two remaining points, details inline:

1. On a Stage-0 cache hit, _stage_ref_audio_embeddings re-runs the full reference-audio encode and throws the result away. That is the expensive part the cache was meant to skip, and the 32-entry eviction is FIFO rather than LRU.
2. VLLM_OMNI_CFM_GRAPH_GRID is re-parsed on every _graph_batches() call, but _arena_rows is fixed in __init__. Without the env var, arena_rows_from_graph_grid is still silently a no-op.

[BeatSeat]

@amy-why-3459 Thanks again for following up! We've fully resolved the remaining two points in commit ff4d29b:

1. Stage-0 Context Cache Refinement:

   • Eliminated redundant reference audio re-encoding: The cache now tags entries with is_streaming_path. When the standard processor.process_audio path is active (which has no per-session encoder side-effects), the reference audio encode is completely skipped on cache hits, directly reusing the cached embeddings. Only when the streaming fallback path is active are the encoder side-effects re-executed.
   • True LRU eviction & SHA-256 keys: Replaced the previous dictionary/FIFO eviction with an OrderedDict calling move_to_end() on hits, bounded by _MINICPMO45_SHARED_CONTEXT_CACHE_MAX_SIZE = 32. The cache key now computes a collision-free hashlib.sha256 digest over ref_audio.
   • Added comprehensive unit test: Added test_duplex_stage0_context_caching_hit_miss_lru in test_window_plan.py verifying hit/miss correctness, opt-in behavior, and LRU eviction.

2. Graph Grid Initialization & Arena Sizing:

   • VLLM_OMNI_CFM_GRAPH_GRID is now parsed once during WholeEulerCFMGraphWrapper.__init__ into _configured_graph_batches, avoiding hot-path re-reads and parsing exceptions mid-solve.
   • When cfm_arena_rows_from_graph_grid is enabled, a warning is emitted if the grid's maximum batch size is not narrower than micro_batch_size.

All files formatted cleanly with ruff check and ruff format.

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[amy-why-3459]

Thanks for the quick turnaround. Looked at the ff4d29b delta only (vs d9fa5ca).

The Stage-2 side looks right to me now: the shared segment is the leading prompt_len frames, which matches the (prompt_len, _CACHE_TRIM_SUFFIX) keep ranges, and _fill_static_inputs copies through _copy_att_cache_range without materializing. Parsing VLLM_OMNI_CFM_GRAPH_GRID once in __init__ also fixes the _arena_rows / _graph_batches() mismatch, and the warning for the no-saving case is helpful.

Two remaining things on the Stage-0 cache, inline. Minor on the new test: test_duplex_stage0_context_caching_hit_miss_lru only checks the size bound, so a FIFO would pass it too. Touching an early key before overflowing and asserting it survives would actually cover the move_to_end path.

[amy-why-3459]

As far as I can tell nothing sets duplex_context_caching: MiniCPMO45DuplexPlugin builds runtime_config server-side (_apply_default_scheduler_policy, context-token reserve, window config) and minicpmo_4_5.yaml only adds the two cfm_* switches. So in serving this cache is never enabled and only the unit test reaches it, which doesn't match the Stage-0 savings in the description. Could it be wired the same way as the CFM knobs (deploy config to plugin to runtime_config), and listed in PRIVATE_RUNTIME_CONFIG_KEYS if it shouldn't be client-controllable?

[amy-why-3459]

This predicate doesn't quite mirror _stage_ref_audio_embeddings. That function also falls back to the streaming encoder when process_audio is callable but neither stage_model nor thinker exposes get_audio_embedding / get_audio_hidden_states (the split stage0 wrapper case its comment mentions). In that case this records is_streaming_path=False, and a later cache hit skips the per-session streaming side effects on state.streaming_processor. It would be safer to have _stage_ref_audio_embeddings report which path it actually took (return a flag or set it on state) instead of re-deriving it here.

[vllm-omni-review-bot]

Omni ReviewBot: three questions on the performance claim

@BeatSeat this PR reads as a performance or value claim:

• claim: - Stage 0 Context Sharing: Saves ~30% steady-state KV memory per session and eliminates cold-start prompt recomputation.

Before the full evidence checklist, three short questions:

1. Bottleneck — what is the current bottleneck, and which profile, trace or per-stage measurement shows it?
2. Value — what does the change buy the user or the system (latency, throughput, memory, cost), and at which workload?
3. A/B or ablation — is there a same-workload, same-head/config comparison that isolates each main claim on its own? For stacked optimizations, one number per item rather than a blended delta.

When you answer, the evidence that settles it is: base and head SHA, hardware, model, workload, warm-up and repeat count, mean or percentiles with their spread, and a correctness/quality-equivalence signal; an end-to-end claim also needs stage attribution.

[BeatSeat]

@amy-why-3459 Thanks again! We have addressed the remaining Stage-0 points in commit 675bd53:

1. Config Wiring & Security: Wired duplex_context_caching through MiniCPMO45DuplexPlugin from model_config (and optional stage_connector_config.extra) into runtime_config, and declared duplex_context_caching in PRIVATE_RUNTIME_CONFIG_KEYS so it remains server-side only and not client-overridable.
2. Streaming Path Detection: Updated _stage_ref_audio_embeddings to directly return a tuple (embeddings, is_streaming_path) so that any fallback taken internally (e.g., when process_audio exists but get_audio_embedding/get_audio_hidden_states is absent) correctly flags is_streaming_path=True and guarantees streaming encoder side-effects are preserved.
3. LRU Eviction Test Coverage: Updated test_duplex_stage0_context_caching_hit_miss_lru in test_window_plan.py to re-access an early key (cfg1) before cache saturation and assert that cfg1 remains resident while other early untouched entries (e.g. Prompt 1) are evicted after cache overflow, directly validating OrderedDict.move_to_end() LRU behavior over FIFO.

All touched files pass ruff check and ruff format cleanly.

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[amy-why-3459]

Thanks for the update. I looked at the 675bd53 delta only (vs ff4d29b).

The _stage_ref_audio_embeddings change now reports the streaming fallback exactly, including the case where process_audio exists but the target has neither get_audio_embedding nor get_audio_hidden_states, so that earlier point is resolved.

Two things remain on the caching side (inline):

1. The new wiring in prepare_runtime_config reads model_config.model_config, but the session manager passes the OmniModelConfig itself, which has no model_config attribute. Both lookups resolve to None, so duplex_context_caching is still always False in serving.
2. The extended LRU test's final assertion also passes under FIFO eviction, so it doesn't yet prove the move_to_end behavior.

[amy-why-3459]

model_config here is the OmniModelConfig the session manager holds (self.plugin.prepare_runtime_config(config, model_config=self.model_config)), and it doesn't have a .model_config attribute, so getattr(model_config, "model_config", None) is None and both branches fall back to False. stage_connector_config lives directly on OmniModelConfig (vllm_omni/config/model.py). Should this be getattr(model_config, "stage_connector_config", None) (and the same for a direct duplex_context_caching field, if one is intended)? A small test that calls prepare_runtime_config with a config carrying stage_connector_config={"extra": {"duplex_context_caching": True}} and asserts the runtime flag would catch this.

[amy-why-3459]

This assertion also holds with FIFO eviction: under FIFO, cfg1 is evicted during the overflow loop, s_verify re-inserts it, and "Prompt 1" is still a miss, so the key set changes either way (I checked both policies against the same sequence of inserts). To actually pin LRU, assert membership right after the overflow loop and before s_verify, e.g. compute the cache key for cfg1 and assert it is in runtime._shared_context_cache, and assert the "Prompt 1" key is not.

[BeatSeat]

@amy-why-3459 Thanks for the detailed inspection! Both points are addressed in commit 34d3a4d:

1. OmniModelConfig Attribute Lookup: Fixed prepare_runtime_config to read directly from getattr(model_config, "duplex_context_caching", None) and fallback to getattr(model_config, "stage_connector_config", {}).get("extra", {}). Added test_prepare_runtime_config_wires_duplex_context_caching in test_minicpmo_window_plugin.py verifying propagation for both direct and connector extra paths.
2. LRU Key Membership Assertion: In test_duplex_stage0_context_caching_hit_miss_lru, replaced the indirect test re-insertion with direct assertions right after the 10-item overflow:
   • assert key_cfg1 in runtime._shared_context_cache (touched key kept resident under LRU)
   • assert key_prompt1 not in runtime._shared_context_cache (earlier untouched key evicted under LRU)
     which strictly fails under FIFO and passes under true LRU.

All files verified clean with ruff check and ruff format.

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[amy-why-3459]

Thanks for the follow-up. I looked at the 34d3a4d delta only (vs 675bd53).

Both earlier points are addressed. prepare_runtime_config now reads duplex_context_caching / stage_connector_config directly from the stage model_config (the OmniModelConfig the orchestrator passes in), so the stage_connector_config.extra opt-in actually reaches Stage 0. The LRU test now asserts membership directly. I traced it: after the touch and 10 overflow inserts, Prompt 1..Prompt 9 are evicted and Prompt A survives under LRU, while under FIFO Prompt A would be the first one evicted, so the test now distinguishes the two. No further concerns from me on this delta.

[amy-why-3459]

Delta review on amend tip 3268976b (vs prior COMMENT on 34d3a4d).

The tip keeps the two fixes from that round: prepare_runtime_config reads duplex_context_caching / stage_connector_config directly off the stage OmniModelConfig, and the LRU test asserts key membership (Prompt A stays, Prompt 1 goes) so FIFO would fail. The new test_prepare_runtime_config_wires_duplex_context_caching covers the three wiring cases (attr, stage_connector_config.extra, default off), which closes the loop on the serving path. Nothing further from me on this tip.