[charxwu]

Overview

Implementation of RFC: Scheduler-Aware Multi-Tier KV Caching for vLLM with MORI-UMBP. It adds one UMBPStoreConnector that offloads KV blocks to MORI-UMBP DRAM and restores them after they leave GPU memory. Embedded mode keeps a private pool in each worker. Standalone mode attaches to a node-local server so engines on the host share one pool.

This updates the earlier embedded-only snapshot on this PR with the shared connector, standalone mode, lazy offload, and the Mooncake comparison from charxwu/vllm#7. Each commit is one review slice under Modifications.

• charxwu/vllm#4 is the previous combined review branch (review/umbp-merged-series).

Claims

• One V1 connector serves embedded and standalone UMBP. Runtime topology stays behind IUMBPRuntime.
• vLLM keeps scheduling, GPU blocks, prefix hashes, and cache-group rules. UMBP owns DRAM allocation, lookup, publication, and eviction.
• Transfers use registered GPU ranges. The connector does not build a contiguous host staging copy.
• Missing, evicted, timed-out, or partial objects are cache misses and can be recomputed (kv_load_failure_policy: recompute).
• On the recorded MI355X run, embedded UMBP store overhead stays close to no-offload, and forced external restore is faster than Mooncake RDMA on the same GPU. See Validation for the exact tree and the cases Mooncake wins.

Modifications

Each box is one commit, and implementation is split from its tests. The review slices are:

• #14: Shared connector, runtime contracts, and request lifecycle. 2948e7ec7f.
• #10: Tests for that connector contract. a96996d08a.
• #16: Shared lookup, transfer, hybrid and Mamba coordination, events, and metrics. 8f4d767122.
• #9: Tests for async lookup, hybrid cache, events, and metrics. 3aa5afefdf.
• #13: MORI embedded DRAM runtime, GPU registration, and ranged transfer. 063282ba40.
• #8: Tests for the embedded runtime and MORI integration. be10fb2257.
• #15: Standalone mode backed by a node-local server. e02b5bccd8.
• #11: Tests for standalone mode. 43211677d8.
• #17: Lazy GPU-cache offload. 47a1beb0c8.
• #12: Tests for lazy offload and token restore. a8c19a5fdd.

Validation

Correctness

From the PIT MI355X report on tree b5c7ee7613 plus a local unpushed fix. That tree is the parent series of this split, not a byte-identical tree of head a8c19a5fdd. Connector code under v1/umbp still differs (scheduler, worker, data, embedded runtime, and several unit tests). Treat these as results for that series, and rerun on this head before any upstream update.

UMBP + Mooncake Store unit tests: 536 passed
MORI GPU/runtime integration: 9 passed
Qwen3-0.6B token-level eager/lazy E2E: 2 passed

Head a8c19a5fdd was rerun on one MI350X node (gfx950) with MORI v1.2.3.post1. The Mooncake serving comparison below was not rerun on this head.

UMBP unit tests (tests/v1/kv_connector/unit/test_umbp_*.py): 291 passed
UMBP integration on 2 GPUs (tests/v1/kv_connector/umbp_integration): 11 passed
UMBP integration on 1 GPU: 6 passed, 5 skipped (need 2 GPUs)

Coverage includes lookup/store/load, async completion, KV events, hybrid/Mamba boundaries, embedded TP2, standalone cross-process and server failure, and token restore. Pipeline parallelism is rejected.

Qwen3-0.6B long output, single KV group. UMBP used block size 32 and load_async=false. Mooncake RDMA cannot disable async load, so it used block size 16 and load_async=true. External restore matched each side's own GPU-cache token ids, and the sampled-logprob max diff was 0.

Metric	UMBP	Mooncake RDMA
GPU cached tokens	192	192
External cached tokens	192	192
External restore + 32-token decode	306.6 ms	308.7 ms

Repeated-prefix batch (16 requests, 4 prefixes): external restore batch latency was 123.5 ms for UMBP and 573.7 ms for Mooncake. Token-branch differences on external restore did not exceed the GPU prefix-cache path (UMBP 3/16, Mooncake 4/16). This is a single-batch diagnostic, not the 512-request serving table.

Speed

Lower latency is better. Hardware: AMD Instinct MI355X, physical GPU4, NUMA 1, CPU affinity 96-111. Model openai/gpt-oss-20b, TP1 / PP1. GPU KV cache 40 GiB. External DRAM pool 80 GiB. Block size 32.

UMBP connector settings for these tables: num_workers=8, lookup_workers=2, lookup_async=false, load_async=true, lazy_offload=false, dram_numa_node=1, dram_prefault=false. Mooncake used local rdma4 and a local master. Embedded Mooncake contributed an 80 GiB RDMA segment. Standalone Mooncake used an external 80 GiB owner and global_segment_size=0.

Random-store serving measures store overhead with zero external hits. Input/output 8192/1024, 2 req/s, 512 measured requests, prefix caching on.

Embedded	No offload	UMBP	Mooncake RDMA
Completed / failed	512 / 0	512 / 0	512 / 0
Mean TTFT	98.44 ms	98.73 ms	102.25 ms
Mean TPOT	4.190 ms	4.214 ms	4.174 ms
Mean ITL	4.193 ms	4.218 ms	4.178 ms
Output throughput	1,992.40 tok/s	1,992.62 tok/s	1,992.57 tok/s

Embedded UMBP mean TTFT is 3.44% below Mooncake and 0.29 ms above no-offload. Mooncake TPOT/ITL is about 1% lower. Throughput differs by less than 0.01%.

Standalone	UMBP	Mooncake standalone-store
Completed / failed	512 / 0	512 / 0
Mean TTFT	101.71 ms	101.05 ms
Mean TPOT	4.224 ms	4.194 ms
Mean ITL	4.227 ms	4.200 ms
Output throughput	1,992.67 tok/s	1,992.31 tok/s

Mooncake standalone mean TTFT is about 0.66% lower. Throughput matches.

Forced restore checks that the latency is a real external DRAM load: GPU-local hits are 0, external hits and native loads are non-zero, transfer errors are 0, and cold/replay text matches. Prompt 10,001 tokens, output 1 token.

Embedded restore	UMBP	Mooncake RDMA
Verified	10/10	10/10
Restore mean	27.04 ms	32.47 ms
Output text equal	10/10	10/10

UMBP embedded restore mean is 16.7% lower.

Standalone restore	UMBP	Mooncake standalone-store
Steady samples	8	8
Restore mean	27.91 ms	29.47 ms
Restore median	28.30 ms	29.69 ms
Output text equal	8/8	8/8

Standalone drops the first two replays on both sides as connection and registration warmup. UMBP steady restore mean is 5.3% lower.

Moving the owner out of the engine costs UMBP about 3.0% mean TTFT (98.73 ms to 101.71 ms) and 3.2% restore mean (27.04 ms to 27.91 ms). The standalone gain is shared lifetime and cross-engine reuse, not a lower TTFT.

Native data-path microbenchmark

Contiguous embedded objects, same GPU and NUMA node, byte-identical source and destination. Store/load GB/s after warmup.

Payload	UMBP store/load	Mooncake RDMA store/load
1 MiB × 1	10.8 / 11.8	10.6 / 12.0
1 MiB × 8	29.8 / 32.9	31.5 / 30.2
16 MiB × 1	45.9 / 47.6	35.2 / 26.0
16 MiB × 8	47.8 / 52.6	42.8 / 30.0
64 MiB × 1	51.4 / 52.9	41.7 / 27.8
64 MiB × 8	53.1 / 54.4	42.1 / 29.6

Small 1 MiB objects are close. Larger objects favor UMBP GPU-to-DRAM bulk copy, especially loads.

Duplicate check

Searched open vllm-project/vllm PRs for UMBP on 2026-10-15. This is the author's existing UMBP PR, updated in place from the embedded-only snapshot to the shared, embedded, and standalone series. #59275, #59276, and #59281 were opened by a co-author and stay separate. #61351 was an empty preview of this branch and is closed.

AI assistance

AI assistance was used to split the review branches and draft this description. The human submitter still needs to review every changed line and rerun the suites on this head.

• Human review of every changed line.
• Rerun unit, MORI, and the Mooncake serving comparison on a8c19a5fdd.

---

Pull Request Checklist

• I used vLLM's /pr-checklist skill. (Mandatory for agents, optional for humans).

• AI assistance was used during the creation of this PR.

• Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.

• Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.

• Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.

• Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.

[mergify]

Documentation preview: https://vllm--58568.org.readthedocs.build/en/58568/

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[BadrBasowid]

This converts the full prompt to a tuple per KV block, which adds significant overhead for long contexts. I tried materialising token_ids before the for loop, that got up to 40% reduction in latency.

[charxwu]

In store plans, we no longer convert the full prompt to a tuple.

[BadrBasowid]

The scheduler currently spreads one request across several dictionaries, such as _lookup_states, _load_specs, _requests, _request_trackers, etc... . Do you think it would be better if instead we have one request-level object that owns all of these values for each request? Perhaps something that looks like this:

@dataclass
class RequestContext:
    request: Request
    generation: int
    lookup_state: LookupState
    lookup_future: Future | None
    load_spec: LoadSpec | None
    tracker: RequestTracker
    pending_loads: list[BlockTransferPlan]
    partial_tails: list[PartialTailPlan]

And then the scheduler can own own one dict :

_requests: dict[str, RequestContext]

For asynchronous and multi-rank transfers, we should also keep another state with a separate lifecycle as in:

@dataclass
class TransferLease:
    job_id: int
    request_id: str
    generation: int
    block_ids: tuple[int, ...]
    pending_ranks: set[int]
    status: TransferStatus

It can also be done with a different approach, but the main point is to simplify states in the scheduler

[YukioZzz]

Thanks for the base PR. While preparing the standalone runtime on top of this PR, I noticed two embedded-mode issues that I think are worth fixing here rather than later:

1. Lookup socket paths collide across DP ranks / engines on the same host

_lookup_socket_path() (runtime/embedded.py) derives the path from sha256(namespace:rank_namespace) only, where rank_namespace is (tp, pcp, dcp, pp). Neither the DP rank nor any engine identity is part of it, and UMBPNamespace is intentionally engine-independent (model/layout/topology only).

So with data_parallel_size=2 on one node — or two independent embedded engines serving the same model — worker tp0 of each engine resolves to the same /tmp/vllm-umbp-<digest>.sock. _MoriLookupServer.start() unlinks an existing path before binding, so the second engine silently takes over the socket instead of failing with EADDRINUSE. From then on, DP0's scheduler queries DP1's private pool. The result is false-positive hits followed by load failures (or recompute loops, depending on kv_load_failure_policy), while DP1's own pool is never consulted by DP0.

The existing tests cover TP=4 within a single engine, so this doesn't surface there.

For reference, MooncakeStoreConnector handles the same problem in get_zmq_rpc_path_lookup() by including the hostname and parallel_config.data_parallel_index in the IPC path, plus an optional lookup_rpc_port to separate independent engines:

f"ipc://{base_url}/lookup_rpc_port_{rpc_port}_host_{hostname}_dp_rank{dp_rank}"

Suggested fix: include data_parallel_index (and an optional user-provided instance id / port) in the digest input, and default lookup_dir to envs.VLLM_RPC_BASE_PATH for consistency with other connectors. It would also be safer to fail loudly if the path is already bound by a live listener rather than unlinking it unconditionally. A unit test with two RankTopologys that differ only in DP rank would pin this.

2. The scheduler process allocates a DRAM pool it never uses

UMBPStoreConnector.__init__ calls UMBPRuntimeFactory.build() for both roles, and EmbeddedRuntime.from_config() constructs UMBPClient(client_config) eagerly. In the scheduler process that client is never used: _MoriSchedulerHandle only talks to the workers over the lookup sockets.

This isn't just a handle. In MORI, the embedded client is a local-only DistributedClient whose DRAM PageBackend self-allocates at Init(), and UMBPDramConfig::prefault defaults to true (it's only overridden here when dram_prefault is set explicitly). So each engine's scheduler process maps and prefaults one full per-rank capacity — 64 GiB by default, or total_capacity_bytes / rank_count. With DP>1 this repeats once per engine. It also means host RAM is effectively over-committed relative to what total_capacity_bytes claims to budget.

Suggested fix: make client construction lazy, e.g. keep UMBPConfig on _MoriEmbeddedRuntime and create the UMBPClient in create_worker_handle(), so create_scheduler_handle() never touches MORI allocation. A quick check is the EngineCore process RSS before and after the change with a small capacity_bytes and dram_prefault left at its default.

[YukioZzz]

Thanks for the update. I rebased the standalone/distributed stack (#59275, #59276, #59281) onto 37832fa99; the lazy scheduler client and the load-only failed_block_ids already cover two of my earlier fixes, so those are dropped. Re-reviewing the new head turned up three issues in the shared/embedded code. Each has a fix plus test at the bottom of #59275, and you're welcome to take them into this PR:

1. Hybrid (Mamba-style) models with load_async=false read stale state. Embedded mode advertises layerwise_load=True, so a synchronous load is split per layer. Linear-attention/Mamba layers never call wait_for_layer_load, and vLLM copies the restored Mamba state into the running slot while preparing inputs, before the load lands. On Kimi-Linear-48B-A3B GSM8K (256 questions, conc 64) restored prompts fell from 89% to 52% with load_async=false. Waiting in start_load_kv does not help, because the copy has already happened. The Qwen3.5 checks in the description use async load, so they do not cover this. Fix: the scheduler forces async load when any group is a MambaSpec and warns (commit).

2. With load_async=true, every forward step waits for all in-flight restores. load_blocks jobs are stored as {None: job}, and _drain_load_jobs(wait=True, layer_name=...) always includes None in selected. So the first attention layer of each step blocks until every other request's asynchronous load finishes, even though those requests are not in the batch and nothing reads their blocks. This puts restores back on the running batch's critical path. Single-request restore tests and random-prompt serving don't show it. Fix: wait_for_layer_load returns immediately for async loads, and preemption drains the jobs directly (commit).

3. Lookup socket collision (from my earlier comment, still present): the path now also hashes data_parallel_index and an optional lookup_instance. A live listener on the path fails startup instead of being unlinked and taken over (commit).

A note on the new poll() behavior: raising TimeoutError once a store passes its deadline fails the engine step from get_finished. That is a reasonable fail-stop for an in-process DRAM pool. For the shared-pool modes, where a stalled server or master is an expected failure, #59275 overrides poll() so the store stays pending and the store event keeps its source blocks pinned, while new transfers are refused. If you prefer the base class to own that policy, a per-runtime capability flag would work too.

[claude]

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @charxwu.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @charxwu.

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