Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
76 lines
3.2 KiB
Markdown
76 lines
3.2 KiB
Markdown
# Disaggregated Encoder
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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:
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1. **Independent, fine-grained scaling**
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2. **Lower time-to-first-token (TTFT)**
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3. **Cross-process reuse and caching of encoder outputs**
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Design doc: <https://docs.google.com/document/d/1aed8KtC6XkXtdoV87pWT0a8OJlZ-CpnuLLzmR8l9BAE>
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---
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## 1 Motivation
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### 1. Independent, fine-grained scaling
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* Vision encoders are lightweight, while language models are orders of magnitude larger.
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* The language model can be parallelised without affecting the encoder fleet.
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* Encoder nodes can be added or removed independently.
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### 2. Lower time-to-first-token (TTFT)
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* Language-only requests bypass the vision encoder entirely.
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* Encoder output is injected only at required attention layers, shortening the pre-fill critical path.
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### 3. Cross-process reuse and caching
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* In-process encoders confine reuse to a single worker.
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* A remote, shared cache lets any worker retrieve existing embeddings, eliminating redundant computation.
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---
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## 2 Usage Example
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The current reference pathway is **ExampleConnector**.
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Below ready-to-run scripts shows the workflow:
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1 Encoder instance + 1 PD instance:
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`examples/online_serving/disaggregated_encoder/disagg_1e1pd_example.sh`
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1 Encoder instance + 1 Prefill instance + 1 Decode instance:
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`examples/online_serving/disaggregated_encoder/disagg_1e1p1d_example.sh`
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---
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## 3 Test Script
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Please refer to the directories `tests/v1/ec_connector`
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## 4 Development
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Disaggregated encoding is implemented by running two parts:
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* **Encoder instance** – a vLLM instance to performs vision encoding.
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* **Prefill/Decode (PD) instance(s)** – runs language pre-fill and decode.
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* PD can be in either a single normal instance with `disagg_encoder_example.sh` (E->PD) or in disaggregated instances with `disagg_epd_example.sh` (E->P->D)
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A connector transfers encoder-cache (EC) embeddings from the encoder instance to the PD instance.
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All related code is under `vllm/distributed/ec_transfer`.
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### Key abstractions
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* **ECConnector** – interface for retrieving EC caches produced by the encoder.
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* *Scheduler role* – checks cache existence and schedules loads.
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* *Worker role* – loads the embeddings into memory.
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Here is a figure illustrating disaggregate encoder flow:
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For the PD disaggregation part, the Prefill instance receives cache exactly the same as the disaggregated encoder flow above. Prefill instance executes 1 step (prefill -> 1 token output) and then transfers KV cache to the Decode instance for the remaining execution. The KV transfer part purely happens after the execution of the PD instance.
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`docs/features/disagg_prefill.md` shows the brief idea about the disaggregated prefill (v0)
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We create the example setup with the **NixlConnector** from `vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py` and referred to the `tests/v1/kv_connector/nixl_integration/toy_proxy_server.py` to facilitate the kv transfer between P and D;
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