xc-llm-ascend/docs/source/user_guide/feature_guide/epd_disaggregation.md

# Disaggregated-encoder

## Why disaggregated-encoder?

A **disaggregated encoder** runs the vision-encoder stage of a multimodal LLM in a process that is separate from the pre-fill / decoder stage. Deploying these two stages in independent vLLM instances brings three practical benefits:

1. **Independent, fine-grained scaling**  

   * Vision encoders are lightweight, while language models are orders of magnitude larger.  
   * The language model can be parallelised without affecting the encoder fleet.  
   * Encoder nodes can be added or removed independently.

2. **Lower time-to-first-token (TTFT)**

   * Language-only requests bypass the vision encoder entirely.  
   * Encoder output is injected only at required attention layers, shortening the pre-fill critical path.  

3. **Cross-process reuse and caching of encoder outputs**

   * In-process encoders confine reuse to a single worker.  
   * A remote, shared cache lets any worker retrieve existing embeddings, eliminating redundant computation.

Design doc: <https://docs.google.com/document/d/1aed8KtC6XkXtdoV87pWT0a8OJlZ-CpnuLLzmR8l9BAE>

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## Usage

The current reference pathway is **ExampleConnector**.
The ready-to-run scripts below show the workflow:

1 Encoder instance + 1 PD instance:
`examples/online_serving/disaggregated_encoder/disagg_1e1pd/`

1 Encoder instance + 1 Prefill instance + 1 Decode instance:
`examples/online_serving/disaggregated_encoder/disagg_1e1p1d/`

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## Development

![alt text](<./images/epd_disaggregation.jpg>)

Disaggregated encoding is implemented by running two parts:

* **Encoder instance** – a vLLM instance to perform vision encoding.
* **Prefill/Decode (PD) instance(s)** – runs language pre-fill and decode.
    * PD can be in either a single normal instance with (E + PD) or in disaggregated instances with (E + P + D)

A connector transfers encoder-cache (EC) embeddings from the encoder instance to the PD instance.  
All related code is under `vllm/distributed/ec_transfer`.

## Key abstractions

* **ECConnector** – interface for retrieving EC caches produced by the encoder.  
    * *Scheduler role* – checks cache existence and schedules loads.  
    * *Worker role* – loads the embeddings into memory.

* **EPD Load Balance Proxy** -
    * *Multi-Path Scheduling Strategy* - dynamically diverts the multimodal request or text requests to the corresponding inference path
    * *Instance-Level Dynamic Load Balancing* -  dispatches multimodal requests based on a least-loaded strategy, using a priority queue to balance the active token workload across instances.
  
We create the example setup with the **MooncakeLayerwiseConnector** from `vllm_ascend/distributed/kv_transfer/kv_p2p/mooncake_layerwise_connector.py` and refer to the `examples/disaggregated_prefill_v1/load_balance_proxy_layerwise_server_example.py` to facilitate the kv transfer between P and D. For step-by-step deployment and configuration of Mooncake, refer to the following guide:  
[https://docs.vllm.ai/projects/ascend/en/latest/tutorials/pd_disaggregation_mooncake_multi_node.html](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/features/pd_disaggregation_mooncake_multi_node.html)

For the PD disaggregation part, when using MooncakeLayerwiseConnector: The request first enters the Decoder instance,the Decoder triggers a remote prefill task in reverse via the Metaserver. The Prefill node then executes inference and pushes KV Cache layer-wise to the Decoder, overlapping computation with transmission. Once the transfer is complete, the Decoder seamlessly continues with the subsequent token generation.
`docs/source/developer_guide/Design_Documents/disaggregated_prefill.md` shows the brief idea about the disaggregated prefill.

## Limitations

* Disable `--mm-processor-cache-gb 0` if you want to use cross-process caching

* For the PD disaggregation part, refer to the limitations of PD decomposition