perf(r3): accelerate routed-expert transport with packed arrays#1909
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dyurk-lila wants to merge 2 commits into
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perf(r3): accelerate routed-expert transport with packed arrays#1909dyurk-lila wants to merge 2 commits into
dyurk-lila wants to merge 2 commits into
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Fix routed-expert replay (R3) correctness for RL training and introduce a shared token-metadata layout that later routed-expert and sampler-support work builds on. - Scope global RouterReplay state to one Megatron pipeline schedule so a forward-only logprob pass can no longer leak backward replay state into the next training schedule (clear before the schedule and in `finally`). - Keep `rollout_expert_indices` ragged and treat its length as the captured-prefix length. Derive a `router_padding_mask` after left padding that marks alignment padding and the uncaptured trajectory suffix, and carry it through the training data, replay experiences, microbatch padding, and the Megatron model call. - Build one `TokenMetadataLayout` per microbatch and apply it to both routes and the padding mask. Generic construction, alignment, next-token shifting, and packed-output restoration live in `skyrl/utils/token_metadata.py`. - Pass Megatron's `padding_mask` through the model and apply a narrow compatibility shim so `[tokens]` masks broadcast over experts in expert-bias accounting. - Slice every per-trajectory generator field generically during dynamic-sampling replacement and filtering so route metadata stays attached to its trajectory. Synthetic padding rows use distinct dummy experts `[0, ..., topk - 1]`; the mask excludes them from expert-bias accounting while preserving Megatron's dropless `tokens * topk` dispatcher invariant.
Store routed-expert (R3) generation data as compact NumPy arrays instead of large nested Python lists, and send it over the network base64-encoded alongside its shape and dtype. Expert IDs are compacted to the smallest safe uint8/int16/int32 dtype, vLLM responses and client responses use orjson, and preprocessing accepts the decoded NumPy route arrays directly. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This was referenced Jul 16, 2026
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Problem
The routed-expert (R3) generation path returns one expert ID for every generated token, MoE layer, and top-k slot. Serializing that integer tensor as nested JSON lists creates a large HTTP payload and substantial Python/JSON overhead before training sees the data.
Implementation
uint8,int16, orint32NumPy dtype.orjson.This change is intentionally scoped to transport and preprocessing: it does not alter batching,
Experience, device placement, or backend replay setup.Testing
CPU tests run with:
The suite covers route preprocessing, dtype selection, base64 round trips, malformed payloads, and the packed wire codec.
ruffandblack(line length 120) pass on the changed files.This is part of a small series of routed-expert transport improvements.