[EC Transfer] Add MooncakeStoreECConnector for multimodal hidden-state transfer

[kanceler]

Summary

This PR adds MooncakeStoreECConnector, a vLLM Encoder Cache connector backed by Mooncake Store. It is designed for Disaggregated Encoder / EPD deployments where multimodal encoder hidden states need to be shared between physically separated Encoder and Prefill vLLM instances.

The connector fills the Encoder -> Prefill hidden-state transfer path:

client
  -> EPD proxy
    -> encoder: MooncakeStoreECConnector, ec_producer
    -> prefill: MooncakeStoreECConnector + MooncakeStoreConnector,
                ec_consumer + kv_producer
    -> decode:  MooncakeStoreConnector, kv_consumer

Prefill -> Decode KV cache transfer intentionally continues to use the existing MooncakeStoreConnector. This PR focuses only on multimodal hidden-state transfer and keeps the integration inside vLLM's existing ECConnectorBase lifecycle.

The design follows three principles:

1. Minimal intrusion into vLLM: reuse the native EC connector scheduler/worker hooks without changing scheduler core logic, model execution, or multimodal model code.
2. Separate Hidden State from KV Cache semantics: hidden states are stored as complete encoder-output tensor objects, not KV blocks or rank-sharded KV objects.
3. Store-compatible tensor transfer: use Mooncake Store object APIs with a stable key namespace, tensor metadata, range reads, and buffer registration where available.

A full EPD demo, proxy contract, run scripts, and verified server results are maintained separately:

https://github.com/kanceler/epd-vllm-mooncake-demo

Motivation

Native vLLM encoder-cache reuse is primarily local to one process/runtime. In EPD deployments, the Vision Encoder and LLM Prefill stages may run in different processes, on different GPUs, or on different nodes. The Prefill stage needs encoder hidden states before executing the language model prefill, but vLLM does not currently provide a distributed hidden-state cache connector for this path.

Mooncake Store already provides distributed object storage and high-performance transfer primitives. This PR adapts those capabilities to vLLM's EC connector abstraction so that multimodal hidden states can be produced by an Encoder instance and consumed by a Prefill instance through Mooncake Store.

This is different from a generic tensor-transfer helper because the connector participates in vLLM's scheduling lifecycle:

• Scheduler side decides whether a hidden object is available and builds load/save metadata.
• Worker side performs actual Store reads/writes.
• Producer and consumer roles remain isolated.
• Hidden State transfer does not interfere with the existing KV connector path.

Scope

This PR implements:

• A new MooncakeStoreECConnector.
• Hidden-state key construction and namespace isolation.
• Scheduler-side hidden lookup and load/save planning.
• Worker-side hidden tensor load/save.
• Asynchronous save completion tracking.
• Store client wrappers for tensor metadata, payload transfer, and API compatibility.
• Unit tests for key protocol, connector lifecycle, worker behavior, and Store client behavior.

This PR does not implement:

• EPD request proxying inside vLLM.
• Prefill -> Decode KV transfer changes.
• Hidden-aware eviction policy in vLLM.
• Agent state cloning.
• Hidden State prefix caching.
• Omni pipeline scheduling.

Those are separate system-level concerns. The full EPD demo and orchestration layer are kept in the external demo repository linked above.

Architecture

New module:

vllm/distributed/ec_transfer/ec_connector/mooncake_store_hidden/
+-- __init__.py
+-- connector.py      # ECConnectorBase implementation and role dispatch
+-- data.py           # Hidden key metadata, load/save metadata, tensor metadata
+-- keys.py           # Hidden Store key escaping and construction helpers
+-- store_client.py   # Mooncake Store tensor object put/get/lookup wrapper
+-- worker.py         # Worker-side load/save logic, lookup RPC, async save thread

Factory registration:

vllm/distributed/ec_transfer/ec_connector/factory.py

The connector is registered as:

MooncakeStoreECConnector

ec_connector_module_path continues to allow explicit module loading when users want to bypass the built-in registry.

Scheduler-Side Design

The scheduler side owns cache availability decisions and metadata planning, but does not perform tensor transfer.

For the consumer role, the scheduler path works as follows:

1. Extract multimodal feature identifiers from the current scheduling window.
2. Deduplicate identifiers.
3. Use a local lookup client to query the worker-side lookup server.
4. Cache lookup results in scheduler memory.
5. Let has_cache_item() remain local-only and cheap.
6. After allocation, convert eligible cache hits into load plans.
7. Emit MooncakeStoreConnectorMetadata for the worker.

The design separates lookup from actual load:

• ensure_cache_available() may issue asynchronous remote existence checks.
• has_cache_item() only reads local lookup results.
• update_state_after_alloc() records candidates after resource allocation.
• build_connector_meta() commits only non-preempted candidates.

This avoids doing remote Store work directly in the scheduler hot path and keeps actual tensor movement on the worker side.

Worker-Side Design

The worker side owns all Mooncake Store data-plane operations.

For the producer role:

• save_caches(encoder_cache, mm_hash) finds the hidden tensor in local encoder_cache.
• A save request is enqueued to a background sending thread.
• The sending thread checks whether the object already exists.
• If missing, it stores the tensor into Mooncake Store.
• get_finished() returns only identifiers whose background saves completed successfully.
• Save failures are logged and are not reported as completed.

For the consumer role:

• start_load_caches() receives scheduler metadata.
• For each loadable hidden object, the worker checks local encoder_cache first.
• If absent, it reads tensor metadata from Store.
• It allocates the target tensor.
• It reads the payload into the target tensor buffer.
• It validates shape, dtype, and byte size before inserting the tensor into encoder_cache.

Load is synchronous because Prefill execution depends on hidden states being ready before model execution. Save is asynchronous because the Encoder instance does not need to consume the saved hidden state locally.

Hidden Object Model

Hidden states are represented as complete encoder-output tensor objects.

Current storage layout:

replicated_object

Current tensor layout:

tensor

A single multimodal identifier maps to one complete hidden tensor object in Mooncake Store. This matches vLLM's current encoder-cache object granularity and avoids mixing Hidden State semantics with KV block/page semantics.

The design intentionally keeps storage layout as an explicit key field so that future layouts can be added without changing the object identity model, for example:

• rank-sharded hidden objects
• segmented hidden objects
• prefix-cached hidden fragments
• stage-output tensors for broader multimodal pipelines

Hidden Key Namespace

The hidden key is structured and escaped field by field.

Current format:

<cache_prefix>@hidden
@kind:<kind>
@model:<model_name>
@encoder:<encoder_config_hash>
@storage:<storage_layout>
@parallel:<parallel_fingerprint>
@tensor_layout:<tensor_layout>
@id:<multimodal_identifier>

Example:

epd-demo@hidden@kind:encoder_output@model:Qwen2.5-VL-7B-Instruct@encoder:<hash>@storage:replicated_object@parallel:tp%3A1%40pp%3A1%40pcp%3A1%40dcp%3A1%40mm_tp%3Aweights@tensor_layout:tensor@id:<identifier>

The namespace includes:

• deployment/cache prefix
• object kind
• model name
• multimodal encoder configuration hash
• storage layout
• parallel layout
• tensor layout
• vLLM multimodal identifier

It intentionally excludes:

• request id
• modality
• writer instance id
• temporary runtime state

This keeps hidden reuse stable across requests while still isolating incompatible model, encoder, and parallel configurations.

Tensor Object Layout

For Mooncake Store versions that expose buffer-based tensor object APIs, the connector stores hidden tensors as:

[304-byte Mooncake tensor metadata][contiguous tensor payload]

The metadata records:

• protocol/layout information
• shape
• dtype
• payload byte size
• payload offset

Save path:

1. Check object existence.
2. If the tensor is contiguous, use the original tensor buffer.
3. If not, perform one contiguous() normalization.
4. Encode fixed-size tensor metadata.
5. Register metadata and payload buffers.
6. Store both buffers through Mooncake Store.
7. Unregister buffers in finally.

Load path:

1. Read the fixed-size metadata range.
2. Decode shape, dtype, payload offset, and byte size.
3. Allocate the target tensor.
4. Register the target tensor buffer.
5. Read payload directly into the target buffer.
6. Validate the loaded tensor.
7. Insert into local encoder_cache.

Older Mooncake bindings are supported through fallback tensor APIs such as pub_tensor / put_tensor. Those paths preserve functional compatibility, while the buffer-based path is the preferred path for the explicit metadata + payload protocol.

Mooncake Store Compatibility

The Store client wrapper handles API differences across Mooncake versions.

When available, it uses:

• batch_is_exist
• batch_put_from_multi_buffers
• get_into_ranges
• register_buffer
• unregister_buffer

When ObjectDataType.HIDDEN_STATE is exposed by the Mooncake Python binding, hidden objects are written with that data type. For older bindings, the connector falls back to ObjectDataType.TENSOR or leaves the data type unset if neither is available.

This keeps the connector usable with older Store deployments while allowing newer Store versions to apply hidden-state-aware accounting or eviction policy.

Failure Handling and Lifecycle

The connector treats save and load failures differently.

Save failures:

• Save is a cache population optimization.
• Failed saves are logged.
• Failed identifiers are not reported as finished.
• Future requests naturally fall back to recomputing Encoder output if the hidden object is absent.

Load failures:

• Load happens after the scheduler has decided that the hidden object should be used.
• Prefill cannot proceed without the hidden tensor.
• The connector logs detailed failure context and raises the load error.
• Failed loads do not write partial data into encoder_cache.

Resource lifecycle:

• Background save thread supports close/join.
• Lookup client/server close ZMQ sockets and contexts.
• Store client close is best-effort across available close/teardown/finalize-style APIs.
• Registered buffers are unregistered in success and failure paths.

Configuration Notes

The connector expects normal vLLM EC transfer configuration with:

ec_connector = MooncakeStoreECConnector
ec_role      = ec_producer | ec_consumer

Optional connector extra config includes:

hidden_cache_prefix
cache_prefix
lookup_async
hidden_lookup_rpc_port
lookup_rpc_port
soft_pin_video_hidden

Mooncake Store connection settings are loaded through the existing Mooncake configuration path, including:

MOONCAKE_CONFIG_PATH

The complete runnable EPD setup, including Encoder / Prefill / Decode launch commands, proxy behavior, and Mooncake Store configuration, is documented in:

https://github.com/kanceler/epd-vllm-mooncake-demo

Tests and Reproduction

This PR includes targeted unit tests for the connector implementation.

The full unit test command, EPD smoke test, Qwen2.5-VL runbook, result JSON files, and reproduction instructions are maintained in the demo repository:

https://github.com/kanceler/epd-vllm-mooncake-demo

The vLLM PR intentionally keeps contest/demo orchestration out of the code tree and focuses on the reusable connector implementation.

Limitations

• The connector currently stores one complete hidden tensor per multimodal identifier.
• Complex TP/PP/multi-producer hidden sharding is not implemented in this PR.
• Load is synchronous before model execution.
• Save is asynchronous from save_caches().
• Non-contiguous tensors require one contiguous() copy before storing.
• Hidden-aware eviction policy is implemented on the Mooncake Store side, not in this vLLM connector.
• vLLM's current ECConnectorBase does not expose rich per-hidden value metrics, so this connector only supports a lightweight soft_pin_video_hidden hint when modality is available.

Related

• Design document for this submission: DESIGN.md
• Demo, proxy contract, runbook, and verified results: https://github.com/kanceler/epd-vllm-mooncake-demo
• Mooncake Store project: https://github.com/kvcache-ai/Mooncake
• Existing vLLM KV connector: MooncakeStoreConnector

[claude]

Claude Code Review

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[mergify]

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

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[gty111]

Why do we need this change?

[kanceler]

This change is not needed for this PR. It was accidentally left over from an earlier version where I was experimenting with using the module path as part of the Mooncake Store key. That approach was later removed, but I missed reverting this factory change.

I will remove this change and keep the connector loading behavior unchanged. I will also do another pass over the PR to make sure the remaining diff is scoped to the Mooncake Store EC connector and does not include similar leftover changes.

[gty111]

Can we rename “hidden” to “embedding” in this PR? “Hidden states” typically refer to the intermediate representations within or across layers.

[kanceler]

This naming was originally taken from the project title description of the Mooncake community open‑source call for proposals, hence I reused the term "hidden" in early‑stage implementation. Within the vLLM context, "embedding" is indeed the more appropriate naming.

[gty111]

Thanks for the great work! We really need this feature. Do we have some performance comparison between this PR and ECExampleConnector?

[kanceler]

Thanks for the great work! We really need this feature. Do we have some performance comparison between this PR and ECExampleConnector?

The main purpose of this PR is not to replace an existing production-grade connector with the same functionality, but to add a Mooncake Store backed implementation path for the EC connector. This allows producer / consumer vLLM instances to share multimodal encoder outputs / embeddings through Mooncake Store and integrate with Mooncake's distributed storage / transfer capability.

I currently do not have access to a Mooncake RDMA environment. My existing tests are mainly based on TCP transfer, so I do not want to report inaccurate or misleading benchmark numbers. If the community thinks this direction is worth continuing, I am willing to keep improving this PR, including optimizing the implementation, adding a benchmark plan / script, and providing real performance numbers once a suitable Mooncake/RDMA environment is available.

[gty111]

The main purpose of this PR is not to replace an existing production-grade connector with the same functionality, but to add a Mooncake Store backed implementation path for the EC connector. This allows producer / consumer vLLM instances to share multimodal encoder outputs / embeddings through Mooncake Store and integrate with Mooncake's distributed storage / transfer capability.

I currently do not have access to a Mooncake RDMA environment. My existing tests are mainly based on TCP transfer, so I do not want to report inaccurate or misleading benchmark numbers. If the community thinks this direction is worth continuing, I am willing to keep improving this PR, including optimizing the implementation, adding a benchmark plan / script, and providing real performance numbers once a suitable Mooncake/RDMA environment is available.

Thanks for the clarification. For the initial stage, we can start with TCP-based testing, including functionality validation and benchmarks. Later, we can add RDMA-based benchmarks and performance tests once a suitable Mooncake/RDMA environment is available.

[kanceler]

The main purpose of this PR is not to replace an existing production-grade connector with the same functionality, but to add a Mooncake Store backed implementation path for the EC connector. This allows producer / consumer vLLM instances to share multimodal encoder outputs / embeddings through Mooncake Store and integrate with Mooncake's distributed storage / transfer capability.
I currently do not have access to a Mooncake RDMA environment. My existing tests are mainly based on TCP transfer, so I do not want to report inaccurate or misleading benchmark numbers. If the community thinks this direction is worth continuing, I am willing to keep improving this PR, including optimizing the implementation, adding a benchmark plan / script, and providing real performance numbers once a suitable Mooncake/RDMA environment is available.

Thanks for the clarification. For the initial stage, we can start with TCP-based testing, including functionality validation and benchmarks. Later, we can add RDMA-based benchmarks and performance tests once a suitable Mooncake/RDMA environment is available.

I do have a set of TCP-only evaluation results from the original Mooncake submission project. The reproduction repository is here:
https://github.com/kanceler/epd-vllm-mooncake-demo

The setup was a single-machine 3-GPU EPD deployment with Qwen/Qwen2.5-VL-7B-Instruct, using Mooncake Store over TCP transport. In the correctness matrix, 6/6 multimodal cases passed; 2/2 encoder-output roundtrip cases observed Store put / scheduler hit / Store get; and 6/6 outputs exactly matched the single-process vLLM baseline.

The TCP transfer metrics from the same stable run were:

• Encoder -> Store write: 694.414 MiB/s
• Store -> Prefill load: 912.206 MiB/s
• KV Prefill -> Store save_put: 315.887 MiB/s
• successful requests: 6/6

I also noticed #41567. My understanding is that #41567 is closer to an EC-over-Mooncake-TransferEngine P2P path, while this PR is closer to an EC-over-Mooncake-Store shared-store path. They may be complementary, similar to the relationship between MooncakeConnector and MooncakeStoreConnector on the KV side. I will continue looking into #41567 and try to keep the naming, documentation, and common Mooncake logic aligned with it.

[gty111]

Maybe we can rebase or merge to vllm main?

[kanceler]

Maybe we can rebase or merge to vllm main?

Sure, rebasing onto vllm‑main works for me. Besides, discussing some details in English here would be verbose. Could we sync up via private channels in Chinese offline, and post our conclusions back here afterwards?

[gty111]

Maybe we can rebase or merge to vllm main?

Sure, rebasing onto vllm‑main works for me. Besides, discussing some details in English here would be verbose. Could we sync up via private channels in Chinese offline, and post our conclusions back here afterwards?

Yes, could you please send me an email with your contact information and the best way to reach you?

[Akine-Ko]

Thanks for the great work! The demo repository provides TCP-only results, but I could not find a controlled performance comparison isolating the impact of MooncakeStoreECConnector. Do you have benchmark results comparing EPD with and without this connector, preferably under RDMA, including TTFT, throughput, and transfer latency at different concurrency levels?

[mergify]

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

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork