[Core][Frontend] Add weight version tagging for RL rollouts#49040
[Core][Frontend] Add weight version tagging for RL rollouts#49040ShuoleiWang wants to merge 1 commit into
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Documentation preview: https://vllm--49040.org.readthedocs.build/en/49040/ |
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| The key insight is that requests paused with `mode="keep"` will produce tokens from the **old** weights before the pause and tokens from the **new** weights after resume. The `clear_cache` parameter controls whether the KV cache is invalidated during the pause. When `clear_cache=True`, previously cached key-value entries are discarded, so all tokens generated after resume will be computed entirely with the new weights. When `clear_cache=False`, existing KV cache entries are retained, meaning some tokens in context may still reflect the old weights (stale KV cache). | ||
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| ## Weight Version Tagging |
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Can be simpler, no need to have a separate paragraph.
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Track successful target-policy weight updates in EngineCore and bind requests to the current version at scheduler admission time. Expose the current version through LLM, AsyncLLM, and HTTP APIs, and propagate admitted versions through Python and Rust generation outputs. Part of vllm-project#48306, Section 2.2. Signed-off-by: Shuolei Wang <shuoleiwang123@gmail.com>
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| from vllm.engine.protocol import EngineClient | ||
| from vllm.entrypoints.serve.dev.rlhf.api_router import attach_router | ||
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no need to have a test file here.
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| def _make_engine_core() -> EngineCore: | ||
| engine_core = object.__new__(EngineCore) |
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we only need to add some e2e test, ai-generated unit test is not recommended to add if without any human involved
Purpose
Part of #48306, this PR implements the part of Section 2.2 (Weight Version Tagging) that records a weight version on each request and exposes it through APIs and rollout responses. It builds on the weight-update lifecycle introduced in #39212.
For online RL inference:
EngineCorestarts withweight_version=0. After a target-modelfinish_weight_updatecompletes successfully, thatEngineCoreincrements its counter. Draft-model updates and a failed finish on thatEngineCoredo not increment it.Scheduler.add_request,EngineCorecopies the current counter value intoRequest.weight_version.LLM.get_weight_version(),AsyncLLM.get_weight_version(), or the dev-modeGET /weight_infoendpoint.RequestOutputand in Python and Rust token-in/token-out (TITO)/inference/v1/generateresponses.The request first follows one common path. After
EngineCoreOutputis created, the same value continues through either the Python or Rust output path:Reading the current version is separate from generating a response:
GET /weight_infocalls the server'sEngineClient.get_weight_version()and uses the same async client path shown above.The tag records the counter value assigned when the request enters the scheduler; it does not freeze the model weights for that request. A request kept with
pause(mode="keep")can retain its original tag even if later tokens use newer weights.For
n > 1, vLLM combines the child samples into oneRequestOutput. That aggregated output reportsweight_version=Nonebecause its children may have been assigned different versions.The counter belongs to one
EngineCoreand resets when that core restarts. In multi-DP deployments, the existing weight-update fan-out is unchanged and eachEngineCorekeeps its own counter. This PR does not add distributed rollback, the RFC's multi-replica(dp_rank, version)identity, or router-side version selection.Most of the diff carries the same optional field through the existing request and response types shown above. It does not change model execution, logits, token selection, scheduling policy, or weight-transfer mechanics.
Test Plan
EngineCore's counter.Scheduler.add_request.EngineCoreOutputandRequestOutput.GET /weight_infoendpoint.Test Result
Core, API, and output tests (
6 passed):Scheduler and Python TITO regression tests (
4 passed):git diff --check upstream/mainreported no errors.Rust Frontend Cargo TestsCI job.tests/v1/engine/test_output_processor.py::test_incremental_detokenizationdid not reach its assertions locally becausemeta-llama/Llama-3.2-1Bwas unavailable from Hugging Face in this environment.AI assistance disclosure
OpenAI Codex was used for code exploration, implementation assistance and code review. I reviewed and understood every changed line and ran the tests reported above.