perf(sample-support): add packed base64 transport#1915
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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>
Under pipeline parallelism each rank replays only its local router layers, but the Megatron worker eagerly expanded the full global-layer routed-expert tensor to int32 before replay setup, allocating a large device temporary for unused layers. Keep routed-expert IDs in their compact dtype through whole-batch device movement, index_select the current PP stage's router layers before metadata alignment, and perform the single int32 conversion inside _split_replay_indices so only the bounded PP-local slice is materialized as int32. Also validate the 4D replay-indices shape up front. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extract the single-request HTTP generation path out of RemoteInferenceClient into a standalone RemoteInferenceGenerator and a RemoteGenerateResult dataclass. RemoteInferenceClient now owns an internal generator and delegates session management, _post, and _generate_single to it. This is a pure refactor with no functionality change: endpoint routing, retry/backoff, cache_salt handling, serialization, and lifecycle behavior are all preserved. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
For multi-turn rollouts, build routed-expert (R3) trace data incrementally as the conversation grows instead of re-gathering the whole conversation's routes on every turn. - Add `routed_experts_prompt_starts` to `InferenceEngineInput` and thread a per-request `routed_experts_prompt_start` through `RemoteInferenceClient` and `RemoteInferenceGenerator.generate()`, forwarded as a sampling param so the engine only returns routes for the newly generated suffix. - Introduce `TokenMetadataTrace` (token-aligned array accumulator) and `RoutedExpertTrace`, which records each generation's routes and finalizes a full per-token routed-expert array with loss-mask-aware terminal padding. - Track a `RoutedExpertTrace` on `AgentLoopState` and record routes per turn, replacing the previous whole-conversation re-gather in `SkyRLGymGenerator.agent_loop`. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add the core of bounded sampler-support replay across data plumbing, inference capture, and the Megatron dense-path trainer. - Inference: /skyrl/v1/generate gains a return_sample_support flag that requests flat_logprobs from vLLM; the fixed-width rows are decoded into per-token support IDs and surfaced through RemoteInferenceGenerator, RemoteGenerateResult, and InferenceEngineOutput.rollout_sample_support. - Data plumbing: support rows ride the shared TokenMetadataTrace through multi-turn accumulation, dynamic filtering, truncation, replay buffering, and microbatch padding; preprocessing validates support width, trailing -1 padding, int32 vocab IDs, and presence of every loss-bearing token. - Config: reject sampler settings whose rollout distribution cannot be replayed exactly (temperature > 0, top_k > 1, repetition penalty 1.0, no arbitrary additional_kwargs). - Megatron: renormalize sampled-token scores over recorded support with fixed-shape gathers; fused LM-head path projects only selected candidate pairs and chunks by logprobs_chunk_size; controller-packed rows reuse TokenMetadataLayout. New utils/sample_support_replay.py holds the core. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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…om its top-k support (NovaSky-AI#66) * fix(sample-support): repair rows where the sampled token is absent from its top-k support vLLM's approximate top-k/top-p pivot can let a sampled survivor rank just beyond top_k, so it is missing from the captured support row and the downstream sample-support invariant hard-crashes the run. When the sampled id is absent, overwrite the weakest support member with it, preserving width==top_k, single-occurrence, and trailing padding. Emit one aggregated warning per call. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * style(sample-support): apply black formatting to regression test --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extend bounded sampler-support replay to the FSDP backend so policy and reference forwards apply the same support-conditioned normalization as Megatron, keeping recomputed logprobs on the same distribution. - Enable replay for trainer.strategy=fsdp with the existing sampler-support constraints; thread support IDs and the response loss mask through policy training, policy recomputation, and reference forwards only when enabled. - Right-align the response loss mask into the full token layout and keep sampled IDs, support IDs, and the mask aligned through padding removal, next-token shifting, and Ulysses SP slicing; reuse padding-removal indices. - Generalize Ulysses input partitioning to [batch, seq, ...] metadata with a configurable padding value, avoiding a second validity-mask partition. - Share the backend-neutral aligned replay helper for support normalization and synthetic-EOS handling; preserve feature-disabled behavior. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Transport dense sampler-support vocab IDs as a base64-encoded int32 NumPy buffer inside the orjson response envelope, replacing nested JSON integer lists. - keep vLLM's [tokens, top_k] support as a contiguous int32 array and encode it via pybase64 in the response envelope; - add sample_support_set_wire with PackedSampleSupportSet as the wire contract, validating dtype, shape, byte count, ID range, and trailing -1 padding; - decode once in RemoteInferenceGenerator before populating RemoteGenerateResult.sample_support; - add a benchmark and tests covering the codec, malformed payloads, empty token dimensions, legacy-list rejection, and endpoint decode. Trainer representation and replay semantics are unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Problem
Dense sampler-support rows are large integer arrays. Returning them as nested JSON lists materializes one Python integer per candidate and spends substantial controller time formatting decimal JSON.
Implementation
[tokens, top_k]support as a contiguous int32 NumPy array;pybase64inside the existingorjsonresponse envelope;PackedSampleSupportSetthe only wire response contract, with strict dtype, shape, byte-count, ID-range, and trailing-padding validation;RemoteInferenceGeneratorbefore populatingRemoteGenerateResult.sample_support;RemoteInferenceClientconsume the same logical decoded result.This change affects only the physical rollout transport. The trainer-side representation and replay semantics are unchanged.
Performance
The included benchmark (
skyrl/benchmarks/bench_dense_sample_support_wire.py) usesorjsonon both paths and compares equivalent decoded outputs. For 4096-token responses:The packed envelope is primarily a controller-CPU optimization. Sparse top-p rows can be slightly larger because base64 still carries
-1padding.Testing
RemoteGenerateResultand the real/skyrl/v1/generateresponse;uv run --isolated --extra dev --extra fsdp pytestover the changed inference-server test files passes (76 tests);