592661e787
Add EPD doc and load-balance proxy example
- vLLM version: v0.14.0
- vLLM main:
d68209402d
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Signed-off-by: 李少鹏 <lishaopeng21@huawei.com>
3.8 KiB
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:
- 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.
- 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.
- 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
Usage
The current reference pathway is ExampleConnector. Below ready-to-run scripts shows 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/
Development
Disaggregated encoding is implemented by running two parts:
- Encoder instance – a vLLM instance to performs 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.
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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 referred 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
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/feature_guide/disaggregated_prefill.md shows the brief idea about the disaggregated prefill.
Limitations
-
Disable
--mm-processor-cache-gb 0if you want to use cross-process caching -
For the PD disaggregation part, refer to the limitations of PD decomposition