[jiaran-king]

Motivation and design

When an image reaches an Encoder that lacks its output locally, a shared Store
can reuse an embedding already computed by another Encoder. This change adds
cross-Encoder output reuse in normal EPD serving, with Mooncake Store as the
cache backend and the existing ECMooncakeConnector for P2P delivery. Normal
serving requests populate the Store as they compute outputs.

The Store resolves content identifiers against Encoder-derived tensor contracts
before encoding. Hits skip the Encoder; misses retain normal computation. Both
sources enter the local cache; serving requests then use the configured EC
transport. Store policy accepts tensors and output contracts, without P2P
reservations or destinations.
Only the Mooncake integration is enabled in this PR.

Serving requests arrive with their Prefill destination already selected.

                      Encoder scheduler
                local miss → normal allocation
                              │
                              ▼
               Encoder worker: Store lookup/load
                  (shared Mooncake Store, RAM)
                              │
                ┌─────────────┴─────────────┐
                ▼                           ▼
            Store hit              Miss / recoverable failure
         reuse embedding              Encoder forward
                │                           ├── async PUT → shared Store
                └─────────────┬─────────────┘
                              ▼
                  Local Encoder output cache
                              │
                              ▼
                   Configured EC transport
                              │
                              ▼
                           Prefill

Current serving integration: Configured EC transport = ECMooncakeConnector P2P.
Store resolution and publication are invoked by the Mooncake Encoder worker.

PUT lazily registers one reusable CPU header per client; payload registration
remains per embedding. The publisher drains before header unregistration.

Publication is bounded, asynchronous and best-effort; request completion does
not acknowledge Store readiness. Concurrent cold requests may still duplicate
computation. Hits are read with native batch_get_into through one reusable
registered buffer, pinned for CUDA outputs. The synchronous resolver chunks
reads by byte capacity, validates full objects and copies into independent
tensors before reusing staging. A rejected registration unwinds before falling
back. Batch GET rejections with INVALID_PARAMS (-600), INVALID_REPLICA
(-702), and completed GETs with
LEASE_EXPIRED (-707) fall back to encoding; a PUT rejected for
TENANT_QUOTA_EXCEEDED (-1700) skips publication. Unconfirmed native I/O or
unregistration retains buffer owners and fails explicitly.

Usage

Set cross_encoder_cache: true in the producer's ec_connector_extra_config
and configure a RAM Store through MOONCAKE_CONFIG_PATH. Initial support is
images, MRv2, Encoder TP1, contiguous 2D FP16/BF16/FP32 outputs and no dynamic LoRA.
Shared reuse requires --mm-processor-cache-gb greater than zero to preserve
content identifiers; the processor-cache-off combination is rejected at startup.
store_read_buffer_bytes controls read staging separately from the publication
budget (default 128 MiB). It is allocated on the first Store hit; full misses
allocate none. Objects larger than the buffer fall back to encoding.
Prefill keeps its P2P configuration. EC and KV share Store configuration/setup.
embedding_model_identity overrides only the Store key's model field; matching
vLLM multimodal identifiers are still required, so this override alone does not
enable reuse across different configured model paths.
See docs/features/cross_encoder_cache.md for configuration and failure semantics.

Validation

Self-contained CPU tests, with native Store I/O faked:

.venv/bin/python -m pytest --confcutdir=tests/v1/ec_connector/unit -q \
  tests/v1/ec_connector/unit/test_cross_encoder_cache.py \
  tests/v1/ec_connector/unit/test_worker_ec_connector.py

51 cases passed (45 focused Store cases and 6 existing wrapper cases), covering
MRv2 miss/hit/partial-hit serving, cross-Encoder reuse, registration reuse,
byte-capacity chunking, explicit CPU staging under non-CPU default devices,
independent output storage, mixed batch results and over-capacity returns,
malformed-object isolation, mid-copy/event-record/completion failures,
shared-storage publication budgets,
consumed failure release, completed-publication reaping before the next load
(including empty inputs), and native I/O/shutdown ownership. The tests use their
own fixtures and omit the repository's global accelerator teardown.

71 affected proxy/shared-Store configuration and setup tests passed (27 proxy
and 44 shared-Store cases). Changed-file pre-commit, including mypy 3.10, passed.

Real Mooncake 0.3.12.post1 tests on H20 TCP passed 19 checks, including one
registration over interior slots, bounded chunk reuse, bit-exact FP16/BF16/FP32
GPU outputs, missing/short/oversized/malformed-object isolation and final buffer
unregistration. Full-object reads return actual short lengths; oversized reads
return INVALID_PARAMS (-600).

Seven alternating serial/batch resolver measurements per warm condition used
one or four objects of 16 KiB, 1 MiB and 30 MiB, with full hits, partial hits and
misses. Four-object full-hit p50 changed from 3.05 → 0.41 ms, 4.51 → 1.14 ms and
29.44 → 20.59 ms respectively. Four hits changed eight read calls/registrations
per request into one batch read with no registrations after initial allocation.
Full misses perform no reads or staging allocation. These small same-node
measurements cover lookup, I/O and completed copies, not serving throughput.

Serving validation with Qwen3.5-4B BF16, MRv2 and TP1 passed 45 assertions:
full hits skipped Encoder work, partial hits recomputed only the missing image,
and actual P2P-delivered tensors were bit-exact against cold computation.
All 32 generated token IDs matched. Full and partial hits each used one native
batch read and reused the same pinned staging buffer. The completed cold
request from job 3020 was preserved; job 3027 restored those exact tensors into
the Store and completed the serial/batch comparisons and partial-hit phase.
SLURM job 3027 completed with verified task process/port/GPU/IPC cleanup.

Three warm serving requests per implementation had Encoder-request medians of
21.62 ms (serial) and 18.21 ms (batch); approximate E→PD TTFT medians were
97.94 ms and 96.43 ms. The first batch request was slower (623.71 vs 596.08 ms
approximate E→PD TTFT). These are small supplementary samples; TTFT here sums
Encoder HTTP duration and PD time to first nonempty content.

GPU validation reused the 6e1d051fc PR-base wheel with the candidate's 10
EC/config/shared Store Python modules, without a native rebuild. The tested
batch-read diff is unchanged after rebasing onto the independently published
consumed-traceback fix.
Follow-ups enforce each GET destination capacity, explicitly allocate staging
on CPU, and reap completed publications before the next Store resolution or
Encoder computation. The current 52 CPU cases and changed-file pre-commit
passed; duplicate tests were consolidated. GPU measurements above cover the
preceding batch-read implementation.

PUT header-only follow-up: on H20 / Mooncake 0.3.12.post1 / same-node TCP,
a no-eviction rerun with a 16 GiB Store used three alternating rounds
(30 warm samples per case/path). Median PUT processing time fell from 1.466 to
1.183 ms for 1 × 1 MiB and from 5.088 to 4.118 ms for 4 × 1 MiB (about 19%).
Small-object gains held across all three rounds. For 30 MiB objects the medians
were 15.678 to 15.378 ms (one object) and 61.846 to 61.600 ms (four objects);
the four-object result was noisy and does not establish a stable improvement.
All 780 PUT payloads and headers were verified outside timing. Each candidate
client registered its header once and unregistered it after draining; payload
registration was unchanged. Final Store usage was 11.81/16 GiB, with zero
eviction attempts and zero allocation failures. This supersedes the earlier
6 GiB run, which encountered eviction. These are asynchronous publication
processing measurements, not TTFT.

Controlled performance result

Setup and comparison

We evaluated InternVL3_5-38B-HF BF16 on six H20 GPUs on one node, using common
CPU single-tile preprocessing. Each measured request contained four images,
approximately 400 text tokens and 32 output tokens. All instances used TP1;
EPD delivery used Mooncake P2P over TCP.

Configuration	Six-GPU deployment	Encoder output caching
M6 (non-EPD)	Six monolithic instances, each running Encoder, Prefill and Decode	Existing instance-local cache
EL (local-only EPD)	Two Encoders + four Prefill/Decode instances (2E4PD)	Existing Encoder-local cache
ES (shared-cache EPD)	Same 2E4PD topology as EL	Existing Encoder-local cache + shared RAM Store

EL → ES is the primary comparison: topology, inputs, arrival times and
forced E/PD routes are identical; ES adds shared output reuse. M6 provides an
equal-GPU system reference, showing how the EPD configurations compare with
monolithic serving. M6 → EL/ES also changes resource partitioning and delivery,
so it does not isolate the cache's effect.

Each configuration replayed the same fixed, open-loop Poisson arrival trace
at nominal 2 requests/s: 128 measured requests and 512 distinct images. The
client did not wait for one response before sending the next request. All
128 requests completed successfully in each configuration.

Reassignment workload

We use seed → ownership swap → measurement. During seeding, EL and ES process
the same images, and ES publishes the outputs to the shared Store. E0 and E1
then exchange image ownership so each target Encoder has a local-cache miss.
EL recomputes the embedding, while ES reuses the shared output and skips
Encoder execution. Each of the 512 images is measured once after reassignment
to avoid subsequent local-cache hits.

Results

Encoder GPU time is cumulative CUDA-event time during measurement; it excludes
Store access and does not represent reclaimable whole-GPU time.

Metric	M6 (non-EPD)	EL (local-only EPD)	ES (shared-cache EPD)
Completed requests	128/128	128/128	128/128
Re-encoded images	512	512	0
Encoder GPU time	53.661 s	55.484 s	0 s
TTFT p50	1.173 s	1.209 s	0.819 s
TTFT p90	1.197 s	1.656 s	0.891 s
TPOT p50	19.17 ms	19.33 ms	19.33 ms
TPOT p90	54.53 ms	41.54 ms	41.39 ms

Relative to EL, ES eliminates the 512 repeated Encoder executions and reduces
TTFT p50/p90 by 32.3%/46.2%, with essentially unchanged TPOT. The E fan-out
completion span falls from 364.40/823.80 ms to 48.39/69.84 ms at p50/p90.
TTFT is measured from scheduled arrival to the first nonempty generated SSE;
client dispatch lag p90 was below 1.7 ms in all three configurations.

Cold-miss publication overhead

During the closed-loop seeding phase, EL and ES compute the same 512 Encoder
outputs; ES additionally publishes 1.25 GiB of embeddings asynchronously.

Seeding metric	EL	ES	Observed change
Wall-clock time	129.71 s	130.01 s	+0.23%
Encoder GPU time	56.821 s	57.131 s	+0.55%
Store data published	0	1.25 GiB	—

All 512 publications completed successfully, with no pending writes at phase
completion. Per-request cold-miss TTFT was not measured separately.

Reduced Encoder work

The 2E4PD topology concentrates vision-encoder work on two E workers, while
M6 distributes it across six monolithic instances. Cross-Encoder reuse
eliminates repeated forwards and reduces demand on the Encoder tier,
shortening the embedding-preparation path in this experiment. This can ease
Encoder capacity pressure when reusable outputs are available.

Scope. These results use one run per configuration in a controlled 100%
Encoder-reassignment workload on one model over same-node TCP. They quantify
the benefit when cross-Encoder reuse opportunities exist; production reuse
frequency is not evaluated here.

Cost. Shared reuse requires additional Store RAM and I/O: ES writes
1.25 GiB during population and reads 1.25 GiB during reuse. Asynchronous
population showed a +0.23% aggregate wall-clock increase in this experiment.
The prototype performance experiment was not repeated after the final refactor.

Related work and assistance

Related to the EPD tracker #52409.

This builds on Mooncake P2P #41567.
Store client operations were originally adapted from
Store EC #47302. The current
v3 namespace uses its own 24-byte header for complete 2D embeddings.
#47302 focuses on Encoder → Prefill transfer through Mooncake Store, while this PR focuses on
cross-Encoder output reuse to avoid redundant Encoder computation. Store is
used as a shared computation cache here, while the existing
ECMooncakeConnector remains the E→P delivery path.

Overlapping Store helpers require coordination during upstream integration.

Processor cache #53809 caches
multimodal preprocessing results. This PR caches Encoder outputs after model
execution and does not replace that preprocessing cache.

AI assistance was used for implementation, testing, and documentation.

[claude]

Claude Code Review

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[mergify]

Documentation preview: https://vllm--56242.org.readthedocs.build/en/56242/

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[gty111]

A design question: On an embedding cache hit, could the P/D instance fetch the embedding directly from the store, instead of having the E instance fetch and forward it?

[jiaran-king]

A design question: On an embedding cache hit, could the P/D instance fetch the embedding directly from the store, instead of having the E instance fetch and forward it?

Yes, P/D could fetch the embedding directly from the shared Store, avoiding the intermediate payload transfer through E.

This PR deliberately separates Encoder-output reuse from E→P/D delivery. The Store logic operates on output identities and tensor contracts, without depending on P2P reservations or destinations; resolved outputs then follow the existing delivery path. Only Mooncake is integrated in this PR, but the Store logic is intended to be reusable in other integrations.

Direct P/D reads would introduce a second delivery source alongside the existing connector, requiring coordination of per-item source selection, completion, and fallback after a recoverable Store read failure. We would prefer to address that in a follow-up while keeping this PR focused on cross-Encoder reuse. The Store object and publication logic would remain reusable. This could also fit naturally into the further E/P responsibility split discussed in #55639: P could own the decision and consumption of reusable embeddings, including loading directly from the shared Store on a hit, while E remains responsible for computing and publishing missing Encoder outputs.

[jiangkuaixue123]

Could cache-aware routing at the router layer be a viable alternative to the shared Store? For example, routing requests based on image content identifiers could increase the chance that repeated images reach an Encoder instance that already has their embeddings in its local cache.

How do you see the trade-offs between these two approaches, particularly in terms of cache hit rate, load balancing and hot spots, requests containing multiple images, and Encoder scaling or failures? It seems routing could avoid Store I/O and additional shared storage, while the Store would allow reuse independently of which Encoder receives the request. Would you consider them alternatives or complementary optimizations? @jiaran-king @gty111

[jiaran-king]

Could cache-aware routing at the router layer be a viable alternative to the shared Store? For example, routing requests based on image content identifiers could increase the chance that repeated images reach an Encoder instance that already has their embeddings in its local cache.

How do you see the trade-offs between these two approaches, particularly in terms of cache hit rate, load balancing and hot spots, requests containing multiple images, and Encoder scaling or failures? It seems routing could avoid Store I/O and additional shared storage, while the Store would allow reuse independently of which Encoder receives the request. Would you consider them alternatives or complementary optimizations? @jiaran-king @gty111

I think these two approaches can be enabled together and are complementary rather than mutually exclusive.

Cache-aware routing can improve locality by increasing the chance that repeated images are sent to an Encoder that already has the output locally, while the shared Store allows reuse when the request is routed to a different Encoder.

So routing can reduce unnecessary Store accesses, and the Store can still preserve reuse when routing affinity cannot be maintained due to load balancing, scaling, failures, or other reasons.

This PR focuses on the shared Store side, and cache-aware routing could be added independently on top of it.

[gty111]

Could PUT and GET reuse the producer’s pre-registered GPU send pool, with registration for the Store client as needed? PUT currently registers/unregisters each embedding and header. GET already reuses a registered CPU buffer, but still copies embeddings to GPU before P2P staging. Reading directly into the GPU send pool could avoid those payload copies and let PUT reuse the same allocation. Pool regions would need to remain owned until all Store/P2P operations and tensor users are finished.

[jiaran-king]

Could PUT and GET reuse the producer’s pre-registered GPU send pool, with registration for the Store client as needed? PUT currently registers/unregisters each embedding and header. GET already reuses a registered CPU buffer, but still copies embeddings to GPU before P2P staging. Reading directly into the GPU send pool could avoid those payload copies and let PUT reuse the same allocation. Pool regions would need to remain owned until all Store/P2P operations and tensor users are finished.

Thanks for the suggestion. Each client now registers one reusable CPU header buffer, updates its contents between completed PUTs, and unregisters it only at shutdown. This removes one header register/unregister pair per subsequent PUT.

In a no-eviction H20/Mooncake TCP test, warm PUT processing time improved by ~19%:

• 1 × 1 MiB: 1.466 → 1.183 ms
• 4 × 1 MiB: 5.088 → 4.118 ms

Unlike the fixed header buffer, payloads come from different tensor allocations, so one persistent registration cannot cover them. P2P staging is independent of publication and is not always available; sharing its regions would require retaining them until both operations finish. I left that coordination out of this PR, keeping the cache independent of Mooncake P2P’s pool lifecycle for future connector integrations.

For GET, the GPU-destination prototype showed no repeatable benefit across three no-eviction runs on H20/Mooncake TCP. For 4 × 30 MiB, GET-to-P2P-ready latency was 29.733 ms, versus 20.807 ms with CPU staging, so I kept the current path in this PR.

[gty111]

LGTM. I think we can left some optimizations in the future PRs.

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