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feat(vl): SGLang VL end-to-end prediction and recommendation - #302
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Carry per-image placeholder spans and encoder shapes on replay and engine requests so the SGLang scheduler can see which prompt tokens an image occupies. Synthetic traffic lays images out ahead of the text with identity-keyed placeholder blocks, so a reused image shares prompt tokens the way SGLang's padded input IDs do, and the KV-load concurrency budget counts the visual tokens. No scheduling behavior changes; text-only replay is unchanged. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Add an optional scheduler-thread model to the SGLang core. With `sglang.host` set, one pass is one overlap-loop iteration: requests delivered during an iteration are received at the next one, batch selection and kernel launch are charged on the scheduler thread, the forward starts when its launch begins and the GPU is free, and the previous forward's outputs, terminals and KV release become visible only after this iteration's launch. A request that finished in the previous forward stays a ghost member of this iteration's batch, as SGLang's `filter_batch` only sees `finished()` after the result is processed. Costs are affine `CostFn`s (receive, select, launch_extend, launch_decode, result, tp_sync) lowered from a measured host profile; the all-zero configuration reproduces the legacy token times exactly, and `host: None` leaves the existing pass model untouched. Per-request reports gain `scheduler_received_ms`, `selected_ms` and `prefill_complete_ms` through a new `LifecycleEvent::HostStage`, which aggregated replay now forwards to the trace collector. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
…atch Prefill batches now encode the cache-miss images whose placeholders overlap the chunk being computed: one encoder call per batch, deduplicated by (identity, visual tokens) and served from an LRU of encoder outputs (`sglang.vlm_cache_bytes`, default 100 MiB like SGLANG_VLM_CACHE_SIZE_MB). Its device time is added to the forward, and `sglang.host.launch_vision` charges the scheduler thread for the encoder launch, feature copies and embedding placement. `TimingModel::predict_vision_ms` prices one batch by image shape; latency-only providers keep the default and image requests fail closed. The AIC provider (`vision: true`) evaluates the model's encoder ops through the native engine's ad-hoc op-list API, with the Python model layer serializing the ops by shape class (`encoder_op_groups_json`); the estimator itself is still constructed through `ForwardPassPerfModel::best_available`. Encoder operations join the prefill phase evidence under their `encoder_*` names. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Requests can now cross configurable frontend worker pools before they reach the scheduler inbox. `EngineConfig.frontend` lists stages over four host resources (tokenizer-manager loop, image decode threads, processor workers, Rust multimodal workers): image stages fan one job per image out and join, request stages run one job, jobs sharing a resource are repriced by its load, and a synchronous tokenizer-manager stage stalls the dispatch of arrivals and continuations while executor work already submitted keeps running. The pools live inside `SglangCore` as internal work: `is_ready` excludes requests still in the frontend so no effect-free pass runs, and the pools wake the rank through `next_internal_deadline_ms` / `process_internal_work`. Ready requests are delivered to the host-loop inbox and the per-request report gains `frontend_ready_ms`. Requires `sglang.host`. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
…uration Image workloads no longer require an analytical encoder pool. An aggregated SGLang worker with `host` configured encodes them itself: - `workers.<role>.host` lowers scheduler-thread costs (`CostFnConfig` tables) to `sglang.host`; `frontend` lowers worker pools and stages to the engine `frontend`; `vision.cache_mb` sizes the embedding cache; `scheduler.max_prefill_tokens` reaches `sglang.max_prefill_tokens`. On this path `max_batched_tokens` becomes SGLang's `chunked_prefill_size`; other SGLang predictions keep their bytes. - `traffic.source.images` gains `encoding` and `identity` (unique or a pool). The runner lowers the profile to placeholder geometry through `image_geometry`, which shares the encoder-phase token math, so the workload driver lays images out inside every prompt. - The rank-level `vision` flag replaces the timing-config flag: the AIC provider loads encoder ops when the rank hosts the tower, and the KV budget deducts the encoder weights and the embedding cache (`colocated_encoder` / `reserved_bytes` through `sdk.memory`). KV load ratios size in-flight requests on the effective prompt. - Reports carry the mean time to first token split by host stage (`mean_frontend_ms`, `mean_scheduler_receive_ms`, `mean_selection_wait_ms`, `mean_forward_ms`, `mean_result_wait_ms`) without per-request capture. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
A measured host profile (`aisimulate.vl.profile.HostProfile`) carries the
same scheduler-thread and frontend tables the public configuration accepts
explicitly, plus the identity they were measured under (SGLang revision,
model, frontend kind, image encoding), per-tensor-parallel `tp_sync_ms`,
the costs the sampling could not measure, and free-form provenance.
`workers.<role>.host_profile: {path, frontend}` replaces explicit `host`
and `frontend` tables. The compiler matches the identity against the
prediction and fails closed on any difference, on unmeasured costs, and on
tensor-parallel sizes without a synchronization entry, then lowers the
profile to the same engine arguments an explicit configuration produces.
The report records the profile digest under `vl.host_profile_id`.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
`python -m aisimulate.vl.calibrate` samples one image workload into a `HostProfile`. Frontend pools are measured by wrapping the real worker functions of SGLang v0.5.19 inside their own executors (image decode on the IO pool, the Hugging Face processor call, and the synchronous remainder on the tokenizer-manager loop); the Rust frontend is lowered from the JSONL the `tools/frontend/sglang-v0.5.19-timing.patch` worker timing writes. Service curves per concurrency become a constant cost plus a sharing scale, using only samples whose measured overlap reached the targeted concurrency. Scheduler `receive` runs the real feature hashing on one thread; batch-level launch and result costs come from an operator-supplied measurement and are listed as missing otherwise, so predictions fail closed. `host_profile.on_missing: calibrate` samples into the configured path from a fresh interpreter without the supervision thread variables. The patch, its upstream license and the notices for the behavioral models and read-only wrappers are added under `tools/frontend/` and `THIRD_PARTY_NOTICES.md`. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Recommendations accept the same `host`, `frontend`, `host_profile`, `vision` and `scheduler.max_prefill_tokens` on the aggregated worker as predictions. They are measured data rather than search dimensions: the lowering pins them in the search space (a profile is resolved once for a single rank and each candidate applies its tensor-parallel synchronization entry, failing closed when the profile has none), the sweeper emits the same SGLang engine arguments the compiler does, and the KV load ratio sizes requests on the effective prompt. Every candidate's prediction-ready configuration keeps naming its profile or carrying its tables; `validate_vl_prediction_mapping` recompiles it and requires the image workload, load, topology, host tables, profile identity and SLA to match the scored replay, so `aisimulate predict` on a saved recommendation reproduces the candidate metrics. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Describe native VL replay for users and maintainers: the per-iteration scheduling contract with its stage timestamps, the vision embedding cache, frontend pools, measured cost tables and profiles, scope limits, and the SGLang v0.5.19 behavior sources and tests behind them. The user guide gains the image, scheduler, host, frontend, profile and vision fields plus a "Native SGLang VL prediction" section; the core API doc notes how encoder timing reaches the timing provider through the existing ad-hoc op-list API. CODEOWNERS covers the new root test and `tools/frontend/`. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
…measure The host-aware SGLang path reported scheduler-thread "stages" whose names promised less than they contained: `HostStage` also marks a GPU event (prefill completion), `mean_scheduler_receive_ms` is the wait between leaving the frontend and being drained from the inbox, not receive CPU work, `mean_selection_wait_ms` includes receive preparation, TP sync and selection, `mean_forward_ms` spans every prefill chunk with the host work between them, and `mean_result_wait_ms` includes result processing. Rename them to `TtftMilestone`, `mean_scheduler_inbox_wait_ms`, `mean_receive_to_admit_ms`, `mean_prefill_elapsed_ms` and `mean_result_observation_delay_ms`; the per-request timestamps keep their names. `vision.cache_mb` becomes `cache_mib` (the value is shifted by 20) and the profile identifier becomes `host_profile_digest`, a content digest defined by the following commit. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
…zero-image requests `FrontendRuntime::submit` and `cancel` advanced the clock without first completing the jobs due before that instant, so a worker freed in the past started its next job only when the scheduler next looked, and past completions were re-dated to the arrival: three 10 ms jobs on one worker arriving at 0, 5 and 30 left at 30, 40 and 50 instead of 10, 20 and 40. A request without images whose last stage is image-unit was dropped outright, because the synchronous `finish_stage` result was discarded. Every entry point now settles the completions due before its instant, and requests leaving the last stage park in an exit buffer with their true exit time until `advance` collects them. `next_deadline_ms` reports a parked request so the core wakes up for it, and `cancel` can withdraw one. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
The host loop delayed only the output tokens of a forward by one iteration. Everything else a forward changes on the scheduler was still applied when it launched: a completed prefill's prefix entered the radix cache before `maybe_cache_unfinished_req` would have run, so a request selected one iteration later already reused it; a request cancelled with its forward in flight still emitted that forward's token one iteration later; a request needing no output token completed, released and cached its KV at launch; and a finished row kept in the next decode batch (`filter_batch` has not seen `finished()` yet) was charged device time but no KV slot, although `prepare_for_decode` allocates and commits one for every row. Outputs, prefix-cache commits and finishes now travel with the in-flight batch (`ForwardOutputs`) and become visible together when the batch is observed; `cancel` drops the request's share while the device work stays charged; ghost decode rows allocate their slot, which `release_kv_cache` then caches with the rest of the request's committed KV, page aligned, when the finish is observed. Launch costs are split into `prepare_extend`/`prepare_vision`, the input preparation the first kernel waits for, and `launch_*`, kernel enqueueing that overlaps the forward: 100 ms of preparation plus 10 ms of device work takes 110 ms, not 100. The new tables default to zero, so existing profiles keep their arithmetic. Behavioral source: sgl-project/sglang v0.5.19 (`0bcd822`), `scheduler.py`, `schedule_batch.py` (`prepare_for_decode`, `filter_batch`), `mem_cache/allocation.py` (`alloc_for_decode`), `mem_cache/common.py` (`release_kv_cache`) and `scheduler_components/batch_result_processor.py`. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
`ForwardPassPerfModelConfig` gains `encoder_parallel`: `tp`, SGLang's default layout of the ViT over the attention tensor-parallel group, or `dp` for `--mm-enable-dp-encoder`. `compile_engine` builds the model with that layout and writes its encoder ops, grouped by the token count they run on, into the new `EngineSpec` `vision` section (schema 20); `ForwardPassPerfModel::vision_operations` / `predict_vision_ms` and `RustForwardPassPerfModel.vision_operations` price a call over `EncoderImageShape` groups. The replay binding only converts shapes: the Python op-grouping helper it imported is gone, the vision estimator handle is separate from the diagnostic handle replay drops, and the identity, cache keys and provenance of a VL estimator now include the tower layout. Memory sizing and KV capacity read the same `encoder_parallel`, so timing, collectives and per-rank tower weights follow one setting. Image geometry keeps the processor's structure instead of one `patches` number: `ImageSpec` / `ImageGeometry` carry the encoder sequences per image and the per-sequence patch, transformer and output tokens. A 1024x1024 Llama 4 Scout image is 17 sequences of 576/577/144 tokens behind a 2467-token placeholder, not one 9809-token sequence. Qwen `smart_resize` applies the checkpoint's `min_pixels`/`max_pixels` budget (Qwen3-VL: 65536 and 16777216 pixels, from the checkpoints' `preprocessor_config.json`), so a 32x32 image resolves to 64 visual tokens and an 8192x8192 image to 16384; a checkpoint with unknown bounds is rejected, and `traffic.source.images` accepts `min_pixels`/`max_pixels` for a served processor with another budget. Signed-off-by: wenpengw-nv <wenpengw@nvidia.com> Video frames keep the stride-only geometry: the video processor budgets pixels per clip, which is not modeled, so the image budget must not rescale frames. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Calibration: the decode curve is sampled through the recorded class-level entry point (the previous wrapper never saw the executor's calls); the processor stage is the full `process_and_combine_mm_data` executor unit and is attributed to the tokenizer-manager loop when SGLang runs it there (one worker); loop time subtracts the interval union of the pool spans instead of their sum and adds the shared-memory wrap and msgpack encode the loop runs before the scheduler handoff; the Rust worker's service time is its `rust_worker` boundary span; scheduler receive is sampled per frontend path on the objects the scheduler really receives (Python: decode, shared-memory materialization, `hash_feature`, padding; Rust: header decode, `RustMmProcessor.build_output`, precomputed hash, padding); a Rust recording must cover every concurrency up to `mm_workers`, or the lowering fails naming the missing levels instead of emitting a scale the engine rejects; the CPU-only guard looks at what the decode stage produced, so an nvJPEG decode on a CUDA host is rejected; and a sampler started from a supervised run gets the host's CPU mask back and records its nice level. Profiles: the identity now covers the sampled workload (image shape, count, encoding, processor pixel budget, text length), the processor, CPU and thread budget, and `profile_digest` identifies the resolved content; matching lists every mismatch; the calibrator samples under the workload's `min_pixels`/`max_pixels` through SGLang's `--mm-process-config`. `recommend` resolves a profile once and writes the tables the runner executed, tensor-parallel sync applied, into each saved candidate, so a later change to the profile file cannot alter what the saved YAML predicts. Entry points: KV-load resolution sizes native VL requests on the same placeholder geometry the workload driver lays out (pixel budget included) and against the capacity the runtime uses (tower weights and embedding cache deducted under the same `encoder_parallel`); host and frontend tables reach recommend candidates for text-only workloads exactly as predict keeps them, and the saved-candidate validation follows the workload instead of requiring images; and the runner rejects image workloads on the materialized-request path, which lays out no placeholders. The calibrators follow sgl-project/sglang v0.5.19 (`0bcd822`) source but were not run against a serving host in this tree. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Document the observation contract (deferred prefix-cache commits, ghost KV slots, cancellation), the prepare/launch split, the canonical `encoder_parallel` tower, processor-driven geometry, profile identity and digest, the renamed TTFT milestone means, and the boundaries that remain: latencies end at scheduler observation, every replica is assumed to have its own CPU, and the support matrix separates what is exercised end to end from what is covered by contract tests only. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
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| Check name | Status | Explanation | Resolution |
|---|---|---|---|
| Cross-Layer Contract |  |
The standing cross-layer contract failure remains unresolved. test_collect_cli.py:48-71 covers only saved-recording lowering and --show; :74-118 covers only three measure_rust startup failures… |
Add focused mocked tests for aisimulate.vl.collect.__main__.main covering --config, recording identity mismatches, worker nonzero exit, empty output, and measurement failures. Add fake-SGLang tests for frontend_worker.main, `_sglang_v… |
| Modeling And Data Evidence | |
The PR adds model provenance and collection safeguards, but it does not supply the required comparative evidence. host_loop.rs:6-12 and docs/replay/engine/sglang-vl-host-loop.md:371-407 identify t… |
Add an immutable machine-readable evidence artifact for aggregated, disaggregated-prefill, and native-encoder paths. Include model, SGLang revision, hardware, workload, host-profile digest, sample counts, observations, anomaly checks, and p… |
| Compatibility Boundaries | |
The downstream Rust consumer remains incompatible. This PR adds required Request.images: Vec<ImageSpec> in crates/core/src/engine/protocol.rs, but Dynamo HEAD `8e7863af7a88d36fe52cfc4957487557bdaa… |
Update the supported Dynamo consumer and its compatibility fixture to initialize Request.images to Vec::new() for text-only requests, and handle or explicitly reject TtftMilestone. Update DYNAMO_REVISION, all reviewed source hashes,… |
| Review Evidence |  |
The visible description names relevant commands and results, including Cargo tests, pytest suites, formatting checks, and unchanged parity goldens. It does not clearly distinguish local, fake-runner, … | Provide the complete PR description, or provide an evidence section that labels local, fake-runner, parity, and production results separately and identifies any hosted workflow by an active workflow run. |
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Check name Status Explanation Title check The title precisely describes the main behavioral change: end-to-end SGLang vision-language prediction and recommendation. Description check The description is detailed, on-topic, and covers the required rationale, review map, contracts, compatibility, evidence, provenance, and tracking. It does not explicitly provide separate Fast CI, Ful… Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request. Full details: Description check
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Explanation
The description is detailed, on-topic, and covers the required rationale, review map, contracts, compatibility, evidence, provenance, and tracking. It does not explicitly provide separate Fast CI, Full CI, CodeRabbit commit, or Codex commit fields, but the evidence is otherwise substantially complete.
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Full details: Cross-Layer Contract
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Explanation
The standing cross-layer contract failure remains unresolved.
test_collect_cli.py:48-71covers only saved-recording lowering and--show;:74-118covers only threemeasure_ruststartup failures. No tests cover__main__.main --config, recording identity mismatch, worker nonzero/empty output,_sglang_version, image generation, emitted recording schema,closed_loop,measure_levels,measure_python, successfulmeasure_rust, or inline/shared-memory success paths. These are new changed paths in__main__.py:73-231andfrontend_worker.py:62-524, and the repository-wide test search found no other tests for those symbols.Resolution
Add focused mocked tests for
aisimulate.vl.collect.__main__.maincovering--config, recording identity mismatches, worker nonzero exit, empty output, and measurement failures. Add fake-SGLang tests forfrontend_worker.main,_sglang_version, image encoding, emitted recording schema,measure_python, and successfulmeasure_rust, including inline/shared-memory transport and failure paths. Add an integration-style test that consumes worker JSON throughfrontend_rowand stores it withupdate_table.
Full details: Modeling And Data Evidence
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Explanation
The PR adds model provenance and collection safeguards, but it does not supply the required comparative evidence.
host_loop.rs:6-12anddocs/replay/engine/sglang-vl-host-loop.md:371-407identify the pinned SGLang revision and describe serving-host collection.samples.py:62-83checks steady sample counts, andtable.py:44-74,99-130records environment metadata and row digests. However, no PR file changesbenchmarks/evidenceor adds an immutable aggregated, disaggregated-prefill, or native-encoder observation artifact. The documentation states that the supported path uses hand-written stages and that Llama 4 and TP 2/4 lack end-to-end serving comparisons (sglang-vl-host-loop.md:362-369). The new tests use internally generated values such asservice_ms=3.0and assert replay behavior (tests/test_vl_cli.py:25-55,140-185); they provide no independent oracle, held-out comparison, predicted-versus-observed deltas, or before/after golden diff. This remains binary-only validation for the new model outputs.Resolution
Add an immutable machine-readable evidence artifact for aggregated, disaggregated-prefill, and native-encoder paths. Include model, SGLang revision, hardware, workload, host-profile digest, sample counts, observations, anomaly checks, and predicted-versus-observed deltas. Add a documented regeneration command and a test or CI check that compares regenerated output with the artifact. Add an explained before/after or independent-oracle comparison for changed text-only and host-loop behavior, and state the unsupported Llama 4 comparison scope explicitly.
Full details: Compatibility Boundaries
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Explanation
The downstream Rust consumer remains incompatible. This PR adds required
Request.images: Vec<ImageSpec>incrates/core/src/engine/protocol.rs, but Dynamo HEAD8e7863af7a88d36fe52cfc4957487557bdaadfa5still constructsRequestwithoutimagesinlib/mocker/src/grouped_scheduler/compatibility.rs:42-56(linked repository, commit8e7863af7a88d36fe52cfc4957487557bdaadfa5). Its lifecycle conversion also handles only the pre-existing variants at lines 120-148. The new adapter logic only supports the separately pinned Dynamo revisiondef3b79...and rejects any other revision atscripts/performance/build_simulation_dynamo.py:14,407-417; the added tests use synthetic fixtures, not the current downstream checkout (tests/test_ci_workflow_contracts.py:3464-3505). The PR therefore does not synchronize the public Rust surface with the supported downstream consumer.Resolution
Update the supported Dynamo consumer and its compatibility fixture to initialize
Request.imagestoVec::new()for text-only requests, and handle or explicitly rejectTtftMilestone. UpdateDYNAMO_REVISION, all reviewed source hashes, and the compatibility records inscripts/performance/build_simulation_dynamo.py. Add a compile-contract test against that exact supported Dynamo revision. Retain the serde defaults for JSON compatibility.
Full details: Review Evidence
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Explanation
The visible description names relevant commands and results, including Cargo tests, pytest suites, formatting checks, and unchanged parity goldens. It does not clearly distinguish local, fake-runner, parity, and production evidence. The description is explicitly truncated, so the omitted text could contain the required distinctions or a claim that an inactive nested workflow supplied hosted CI evidence. No complete description is available in the repository checkout.
Resolution
Provide the complete PR description, or provide an evidence section that labels local, fake-runner, parity, and production results separately and identifies any hosted workflow by an active workflow run.
Comment @coderabbitai help to get the list of available commands.
…ables
The SGLang host loop keeps its iteration structure (requests received at
iteration boundaries, a forward's outputs, terminals and prefix-cache commits
visible one iteration later, ghost decode rows) but no longer prices the
scheduler thread: `HostLoopConfig` and its seven cost tables become
`SglangConfig::host_loop`, and the decode launch synchronizing with the
previous forward is a constant of SGLang 0.5.19. Frontend stages are black-box
pools (`resource: pool | tm_loop`, `workers`, request-level cost,
`concurrency_scale`); the scheduler's per-request receive preparation is the
last, single-worker pool stage of the Python frontend.
`python -m aisimulate.vl.collect` measures one workload shape into a host
cost table (`vl/table.py`, schema 3) without a GPU: a Python 3.10 worker
script runs under the serving host's SGLang interpreter, times the real
multimodal processor (`process_mm_data_async`), the tokenizer-manager send
continuation and the receive functions on the produced payload, or launches
the embedded Rust server alone and times each request from its HTTP send to
the scheduler-side drain. Rows are keyed by CPU, SGLang revision, model,
frontend and shape; a prediction whose shape has no row fails closed with
the exact collect command. `host_profile: {path, frontend}` resolves a row
into `frontend` stages and implies `host_loop`.
Removed: `vl/calibrate/` (batch costs read from an operator JSON nothing
produced, in-process re-implementation of the receive path, the automatic
calibration subprocess), `vl/profile.py`, and `tools/frontend/` with the Rust
timing patch. Sweeper samples carry `agg_host_loop` and `agg_frontend`; the
`agg_host` and `agg_tp_sync_ms` keys are gone.
Omitted CPU costs are documented: pixel-value H2D before the encoder,
eager kernel launch, decode-step launch, selection and result processing,
tensor-parallel synchronization. Saved configurations and profiles written
with the previous `host` tables are rejected by validation.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
EngineSpec schema moves to 21: main's 20 appended FpmForwardOp.original_fmha_quant_mode, this branch's optional vision section now follows it on the bincode wire. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
…served outputs
- FrontendStage is {workers, service_ms, concurrency_scale}: the tokenizer-manager loop
and the scheduler's receive preparation are single-worker pools; the TmLoop resource,
its dispatch queue and the affine CostFn (only const_ms was ever filled) are gone.
- HostLoop::plan takes the launched forward; with a free scheduler thread EXTEND and
DECODE launches share one timeline, so LaunchKind was redundant.
- A retracted request drops the token the forward in flight sampled for it and never
receives that signal (SGLang skips is_retracted results); cancel/retract paths share
HostLoop::discard_request.
- FPM telemetry under the host loop records the forward's device time, not the iteration.
- BoundedSummary accumulates the TTFT stage means, so predictions without per-request
capture still report them.
- Tests: retract under the host loop, first-token finish caches only computed KV,
vision DP ceil share and empty token classes, vision section bincode round trip,
forward duration telemetry.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
…st_loop, one lowering
Configuration
- FrontendStageConfig is {workers, service_ms, concurrency_scale}; the affine CostFnConfig
the collector never filled is gone. FrontendPredictionConfig carries measured_for: the
frontend, feature transport and image workload the stages were measured on, and the
compiler refuses to price another workload or tensor width with them.
- frontend/host_profile imply host_loop; host_loop, frontend, host_profile and vision live
on the aggregated worker only. Image workloads without an encoder pool take the native VL
path on an aggregated SGLang worker whether or not the host loop is on; analytical EPD
rejects min_pixels/max_pixels it cannot honor.
- scheduler.max_prefill_tokens leaves the public schema (unused by the H20 alignment,
inconsistently lowered for P/D); the Rust default stays.
- Vision defaults are materialized once in the compiler; compiler and sweeper share
sglang_host_engine_args; the canonical timing payload omits encoder_parallel when unset;
the naive KV fallback refuses encoder deductions it cannot model; Llama 4 rejects
encoder_parallel=dp, which sglang 0.5.19 does not implement for it.
- The candidate mapping check compares the compiled workload, goal and language execution
instead of a second list of fields.
Host cost table v1 and collector
- One table per serving environment (cpu, host, threads, sglang_version, python); rows keyed
by model and measurement (frontend, feature_transport, image shape); text length is
provenance. Locked read-modify-write, unique temporary files, readable errors.
- Rust rows are measured per feature transport: --tp launches the standalone Rust server
under that tensor-parallel width (inline on one rank, POSIX shm across ranks) and adds
the scheduler's receive preparation on the drained requests; recommend resolves one row
per transport its TP candidates need and each candidate takes its own.
- Worker: pool widths come from the serving objects, --levels only bounds sampling;
cleanup runs outside the timed regions; receive is timed with torch at one thread as the
scheduler process runs it; HTTP errors and drain deadlines fail closed; control messages
in the drain are skipped; progress per level on stderr; a failed lowering keeps the raw
recording. `collect --config prediction.yaml` reads the workload from the prediction.
Tests, docs, examples updated; kv_load test restored to main's layout plus the image case.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
…domain, collector hygiene - The recommend-to-predict mapping check normalizes both specs through the sweeper's Workload model and the runner's rules (resolved concurrency, request count of a relative stop) and compares them in both directions; relative stops no longer fail as "request_count vs None" and a dropped setting is a change too. - Image workloads that enable the vision encoder implicitly go through the same pipeline/attention-DP gate as explicit host_loop/vision (require_native_vl_parallelism). - Host cost rows are keyed by the full measurement including the model; a saved candidate refuses another model as it refuses another image shape. The table environment is the stable identity (cpu, sglang release, python); host, threads and sampling time travel per row. The sglang release is compared exactly, local build suffixes dropped. - recommend resolves frontend rows for the feature transports the TP domain actually contains (a log range contributes its bounds), not both unconditionally. - Collector: drained requests that are not kept release their shared-memory features at once and kept ones are released on any exit; send/receive samples are prepared, timed and cleaned one at a time; environment is recorded before the Rust server narrows the affinity; --levels is gone (sampling covers the pool width); --recording lowers a saved recording; clearer errors for a YAML without host_profile and a silent worker. - Replay FPM snapshots carry the forward's device time (fpm.duration_ms), not the pass. - Rust: discard_request counts token-bearing signals only; a disabled vision cache stores nothing; the cache lookup follows the batch validation a provider may reject; capacity.py reports a missing vlm_cache_bytes instead of a KeyError; dead EncoderShape::total_patch_tokens removed. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
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The first prediction on a new serving host names the collection that creates the table instead of reporting an unreadable file. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Extend the host-aware SGLang VL path from the aggregated worker to disaggregated deployments and add an event-level encoder pool. Disaggregated prefill: the `host_loop`, `frontend`, `host_profile` and `vision` fields move to a shared `HostWorkerPredictionConfig` used by the aggregated and the prefill worker; the compiler, runner, recommend and sweeper lower them per role. The Rust prefill rank drops the aggregated-only gate, keeps a finished extend out of the next batch under the host loop (SGLang's `disagg_prefill_inflight_queue`) and holds the KV source when the next iteration observes the forward; the disaggregated replay gains the internal-work pump that wakes it for frontend deadlines. The decode rank is the text-PD decode rank; its concurrent image preprocessing, resident tower and loop are documented approximations. Native encoder disaggregation: `engine.workers.encoder.mode: native` replays SGLang's `--encoder-only` servers (`replay/components/encoder.rs`): one serial loop per instance, greedy batches up to the cap, CPU preprocessing from the `process` stage of the encoder host's cost table, the AIC encoder forward at every batch size, and per-request embedding transfers that share the KV handoff's bytes-over-bandwidth arithmetic. A request reaches the `--language-only` language worker once its embeddings arrived; the report adds `encoder_latency_ms` and per-request `encoder_ready_ms`. The analytical overlay stays the default and is unchanged. Signed-off-by: wenpengw-nv <wenpengw@nvidia.com> Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Native encoder pools (`workers.encoder.mode: native`) now follow SGLang's encoder disaggregation more closely and align with the analytical overlay's contracts: - The language side splits a request's images evenly over the encoder servers and admits it when the last part arrived (`_assign_items_by_modality`); each instance keeps its own queue and takes its next batch at its own end. - The forward is priced at replay by the canonical timing model at the pool's tensor width (`TimingModel::predict_vision_ms`), the same entry a language rank uses for its tower; the per-batch latency curve and the `run_encoder_static` bypass are gone. The pool's power evidence enters the report like a rank's. - CPU preprocessing is extrapolated per image from the host table's `process` stage and labeled `preprocess_source: frontend_process_extrapolated`. - Image geometry, including `min_pixels`/`max_pixels`, is shared by the prompt layout, the forward and the transfer. - The pool's GPUs count in `gpu_hours`, `encoder_gpus` and `total_gpus`; parked requests are registered at arrival so cut-short runs report them. - Saved recommendations pin the resolved encoder terms and the host table's digest; a changed table is rejected. `rate_degradation` is no longer saved for native pools; a single encoder tp that does not fit no longer aborts the catalog. - Validators: native pools honor the pixel budget, need no `op_level`, and messages name encoder pools; the collector's `--config` falls back to `workers.encoder.host_profile`, and a kept recording carries its measurement identity. Disaggregated prefill: `CancelSource` settles the frontend pools at the command instant; TTFT attribution gains `mean_handoff_to_first_token_ms` (source release to the decode rank's first token) so the observation span no longer absorbs the KV transfer; `host_loop` on a decode rank is rejected. Prediction-callback validation compares execution meaning: constant-rate loads (compiled as their interval), KV-capacity loads resolved to a concurrency and built-in timing models are accepted as the scored run. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
EngineSpec schema 29 adds the vision section after main's 28 (GLM-5.3-Flash measured ops); the stale-layout test, the public API pin and the Python schema test follow. Requests carry main's session_id next to the image placeholders in every protocol struct and test literal, and main's new ReplaySpec and TurnRuntime literals gain the encoder and images fields. The replay release loops keep the encoder-pool branch; admit_ready takes main's placement session from the replay context and keeps the registered flag for requests the pool already registered. The Python runtime keeps the encoder pool's timing resolution and power source next to main's timing provenance, and run_prediction keeps the encoder modes apart while adopting main's retained native report and the no-completed-requests status. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
`estimate_kv_cache` rejects `colocated_encoder`, `reserved_bytes` and `encoder_parallel` together with an FPM profile instead of returning a budget that dropped them: the profile records the rank's memory as measured, and perfmodel-api.md asks unsupported combinations to fail explicitly. The runner reads the vision-hosting role from the rank whether the Runner specification nests it under `rank` or writes it flat, as the design page documents and the materializer already accepts. The SGLang host loop's forward-pass metrics count the terminal ghost rows the decode forward actually runs, so the reported batch shape matches the duration the timing provider charged; the decode scheduling itself is unchanged. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
… review found `host_loop` and `vision` on a worker require the engine mode that hosts it, so an AFD+P/D companion can no longer carry them into a replay that ignores them. Explicit frontend stages that carry their measurement are checked against the workload and every searched tensor width before the search starts, the way a host-table row is. A native encoder pool's `frontend` requires `measured_for` up front instead of failing at compile with a mismatch message. `encoder_parallel` is refused by the whole-forward estimators at construction, like speculation and DCP, and leaves the serialized identity when unset, as the Core API page says. A colocated tower without an explicit layout is sharded, as SGLang lays it out. Agentic replay refuses an encoder pool, whose parked requests the agentic accounting does not see. The documentation names `frontend_ready_ms` as a frontend-stage milestone and keeps AFD apart from both encoder modes. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
Main's fail-stop SGLang admission replaces the pass-level rollback: the vision encoder's misses are priced and stored on the single admission path, the rejected overlength requests are plain again, and the request index helpers that served the rollback are test-only. The per-worker tokenization stage and the encoder pool both count in flight and install side by side on the replay factory; `engine.tokenization` is rejected together with SGLang frontend stages, which already price the tokenizer manager's work. The FPM profile path keeps rejecting the encoder memory controls while adopting main's speculative-round profiles, and the runtime compiles the vision section next to main's measured execution profile. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
…a measured claim A relative `host_profile.path` resolves against the working directory `collect` and `predict` run in; the Rust frontend's extensions come from an SGLang source tree. An encoder pool runs on `encoder.hardware`, which may differ from the language workers' GPU, with that system's performance data and a host table measured on the encoder host; the native example shows the override. Text length stays row provenance without a measured-grid claim. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Signed-off-by: wenpengw-nv <wenpengw@nvidia.com>
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The three P2 findings from my previous review are addressed in c893d6ae and independently verified on the current head, b2517f9e403d5a74231111d769428b826568dd3d.
- Profile-memory controls: Non-default
reserved_bytes,colocated_encoder, andencoder_parallelnow fail explicitly with an FPM profile. Verification covered declared, grouped, and runtime-memory profiles; the default declared-profile budget remains unchanged. - Nested vision ranks: Aggregated and disaggregated nested
rank.visioninputs now replay with metrics matching the equivalent flat configuration. The helper also normalizes the decode-role check and returns the rank mapping used to retrieve the timing-model identity. - Host-loop metrics: The reported decode rows and KV tokens now match the batch charged by the timing provider. The original control reports
(1 row, 5 KV tokens, 1 ms); both mixed-terminal and all-terminal host-loop examples report(2 rows, 10 KV tokens, 2 ms).
Validation passed: 27 focused Python tests and two Rust tests, including an independent public-Engine reproduction of all three original ghost-batch scenarios. All seven reported GitHub checks are green at this head.
Approving the verified fixes and resolving the three findings from my previous review. This follow-up covers those requested changes; it is not a full review of the other commits added since the original review.
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/ok to test b2517f9 |
Preserve the public CorrectionMode export alongside EncoderImageShape and EncoderParallel while merging hybrid calibration from main. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
Python dataclass serialization reintroduced encoder_parallel=None after Rust omitted it from the canonical identity. Omit the unset layout from to_dict while preserving explicit tp/dp selections, matching the documented payload contract. Add saved round-trip coverage for omitted, null, tp, and dp encoder layouts. The unchanged GLM saved-identity regression and 393 targeted Python tests pass. Ruff lint and format checks pass. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
The pinned Dynamo compatibility literal predates required Request.images, so the optional-field patch rejects the simulation-performance build. Accept only the reviewed public Vec<ImageSpec> field, reject image-bearing input explicitly, and construct an empty image list for the existing text-only benchmark. Keep source digests, unknown-field rejection, and legal notices intact. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
vLLM and TRT-LLM converted image-bearing requests into text-only scheduler state without validating the image work. Reject native images across all three request commands before request or handoff state can change, while preserving SGLang vision timing and separate encoder pools. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
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Historical review and CI results for cf04c9af. The follow-up fixes are now pushed as 9be98f38; see the current exact-commit review for the completed fixes and latest validation.
Follow-up at cf04c9af7a6ac0503b0eeffb3ea2ebd36d0c3952: I added signed-off fixes and merged current main, preserving the author's commits. Independent task reviews and the final branch review covered the changes.
| Issue | Before | After |
|---|---|---|
| Saved GLM configuration identity | Python added encoder_parallel: null after Rust omitted it, failing four unit cases on both architectures. |
Unset values stay omitted; explicit tp and dp survive serialization. |
| Pinned Dynamo adapter | The required Request.images field stopped the simulation benchmark build. |
Text requests explicitly initialize an empty image list. Image input is rejected; upstream hashes and unknown-field checks remain enforced. |
| Native image admission | A vLLM/TRTLLM request with one image completed as text at 1 ms without encoder work. | All three request-bearing commands reject images before scheduler mutation. SGLang image timing and separate encoder pools remain supported. |
| Merge conflict | Public API exports conflicted with main's calibration change. | Both the calibration and vision exports are preserved. |
The native admission regression failed in all six backend/command combinations before the fix. Afterward, 758 engine unit tests, 74 engine integration tests, and both Python native-runtime regression cases passed. Tests cover unchanged scheduler state, reuse of request/handoff IDs after rejection, text behavior, SGLang's 5 ms encoder + 1 ms prefill result, and encoder pools across all three backends. Formatting and legal-file checks passed.
Exact-head CI update: Full CI passed with 56 successful jobs and one expected skip. The Linux forward prediction benchmark passed: 0 confirmed regressions, 27 noisy comparisons, and all 320 paired prediction outputs identical. The previously flagged Qwen3.5 SGLang cold case is stable under the unchanged thresholds (83.698 → 88.517 µs; 0/5 rounds exceeded both thresholds). No benchmark cases, thresholds, or prediction formulas were changed.
The complete Dynamo wheel build reached four additional compiler errors: SglangConfig needs the new host-loop/cache fields, two ReplaySpec initializers need encoder, and the lifecycle adapter needs an explicit policy for TtftMilestone. I am extending the pinned compatibility adapter and its consumer coverage.
The latest review also has a follow-up fix in progress for image-count overflow, missing reserved-memory diagnostics, collector startup cleanup and command quoting, and documentation/error-message corrections. Independent testing did not reproduce the alleged replay clock regression; 36 targeted cases and 74 existing integration tests passed with monotonic-clock assertions. It did expose a separate valid zero-duration frontend case: two simultaneous requests currently cause an aggregated replay error or only one completion in disaggregated replay. That case is included in the fix with regression coverage for both topologies. Merge readiness remains pending that fix, independent re-review, and checks on the resulting commit.
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Actionable comments posted: 8
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Inline comments:
Review comments at @crates/core/src/engine/scheduler/sglang/frontend.rs:
- Around line 109-123: Clamp settlement timestamps to the frontend’s existing
clock before draining due work in settle_due, ensuring parked exits cannot
regress FrontendRuntime time. Also clamp the combined next_internal_deadline_ms
after choose_next_timestamp, and apply the same normalization in disagg.rs so
replay loops never advance to deadlines earlier than now_ms.
Review comments at @crates/core/src/replay/loadgen/trace.rs:
- Around line 852-887: Update the identity calculation in the image-validation
block to avoid unchecked multiplication when deriving the default identity pool.
In the None case of images.identity_pool, use checked multiplication for
num_sessions, turns_per_session, and images.count, and fail with an appropriate
context message on overflow; preserve explicitly provided pool values and the
existing 2^28 validation.
Review comments at @docs/replay/engine/workers.md:
- Around line 138-139: Update the replay documentation note to state that
max_batched_tokens affects SGLang replay results with host_loop or images
encoded on the worker, and identify it as the worker’s chunked_prefill_size in
both cases. Keep the change limited to correcting this behavioral claim.
Review comments at @python/aisimulate/src/aisimulate_core/sdk/engine.py:
- Around line 156-158: Update the schema-history comment for the optional vision
section from version 28 to 29, and revise its merge note to state that it was
renumbered to 29. Leave the surrounding history unchanged.
Review comments at
@python/aisimulate/src/aisimulate_core/sdk/memory/kv_cache.py:
- Line 465: Update _estimate_from_breakdown to include a non-negative
reserved_bytes entry in the returned memory_breakdown, using
breakdown.get("reserved_bytes", 0.0) for backward compatibility. Preserve the
existing handling of weights, activations, runtime overhead, communication
overhead, and CUDA graph bytes.
Review comments at @python/aisimulate/src/aisimulate_core/sdk/utils.py:
- Around line 1992-2004: Update the ValueError raised in the processor-file loop
to use the current architecture value instead of hardcoding “Kimi”, so invalid
processor files report the correct model family for both Kimi and Qwen3-VL
checkpoints.
Review comments at
@python/aisimulate/src/aisimulate/vl/collect/_sglang/frontend_worker.py:
- Around line 408-418: Wrap the post-launch validation checks for server.mm_spec
and feature_shm in a try/except BaseException block, call
server.server.shutdown() before re-raising any failure, and leave the existing
successful startup and later cleanup flow unchanged.
Review comments at @python/aisimulate/src/aisimulate/vl/table.py:
- Line 158: Update the --sglang-python placeholder in the MissingRow message to
be safe for direct POSIX-shell pasting by removing shell metacharacters and
spaces, while retaining the SGLANG_VERSION value; use a plain token such as the
version with dots replaced by underscores.
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python/aisimulate/src/aisimulate/vl/collect/__init__.pypython/aisimulate/src/aisimulate/vl/collect/_sglang/__init__.pypython/aisimulate/src/aisimulate/sweeper/search.pypython/aisimulate/src/aisimulate/sweeper/score.pypython/aisimulate/src/aisimulate/sweeper/sample.pypython/aisimulate/src/aisimulate/vl/__init__.pypython/aisimulate/src/aisimulate/config/common.pypython/aisimulate/src/aisimulate/sdk/sweep.pypython/aisimulate/src/aisimulate/supervision.pypython/aisimulate/src/aisimulate/sweeper/kv_load.pypython/aisimulate/src/aisimulate/sweeper/forward_pass_estimator.pypython/aisimulate/src/aisimulate/predict.pypython/aisimulate/src/aisimulate/main.pypython/aisimulate/src/aisimulate/capacity.pypython/aisimulate/src/aisimulate/config/traffic.pypython/aisimulate/src/aisimulate/config/vl.pypython/aisimulate/src/aisimulate/sweeper/deploy.pypython/aisimulate/src/aisimulate/vl/collect/workload.pypython/aisimulate/src/aisimulate/config/epd.pypython/aisimulate/src/aisimulate/recommend.pypython/aisimulate/src/aisimulate/sweeper/replay.pypython/aisimulate/src/aisimulate/vl/collect/samples.pypython/aisimulate/src/aisimulate/vl/collect/lower.pypython/aisimulate/src/aisimulate/config/cli.pypython/aisimulate/src/aisimulate/compiler.pypython/aisimulate/src/aisimulate/runner.pypython/aisimulate/src/aisimulate/vl/collect/__main__.pypython/aisimulate/src/aisimulate/vl/collect/_sglang/frontend_worker.pypython/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate/sweeper/epd.pypython/aisimulate/src/aisimulate/vl/table.pypython/aisimulate/src/aisimulate/config/engine.py
Enforce the single-oracle and golden-diff rules in python/aisimulate/.claude/rules/rust-core/parity.md.
⚙️ CodeRabbit configuration file
Files:
crates/core/src/perfmodel/mod.rscrates/core/src/perfmodel/common/enums.rscrates/core/src/perfmodel/config.rscrates/core/src/perfmodel/fpm/config.rscrates/core/src/perfmodel/fpm/model.rscrates/core/src/perfmodel/py.rscrates/core/src/perfmodel/engine/spec.rscrates/core/src/perfmodel/engine/runtime.rs
Review scheduler state transitions and event ordering, including cancel, preempt, drain, retry, duplicate, empty, and terminal paths.
⚙️ CodeRabbit configuration file
Files:
crates/core/src/engine/scheduler/sglang/prefill.rscrates/core/src/engine/scheduler/vllm/policy/tests.rscrates/core/src/engine/scheduler/sglang/mod.rscrates/core/src/engine/scheduler/vllm/core.rscrates/core/src/engine/runtime.rscrates/core/src/engine/kv_manager/sglang_backend.rscrates/core/src/engine/scheduler/source_holds.rscrates/core/src/engine/timing.rscrates/core/src/engine/scheduler/sglang/config.rscrates/core/src/engine/scheduler/mod.rscrates/core/src/engine/common/protocols.rscrates/core/src/engine/common/perf_model.rscrates/core/src/engine/handoff.rscrates/core/src/engine/mod.rscrates/core/src/engine/scheduler/sglang/vision.rscrates/core/src/engine/scheduler/sglang/request.rscrates/core/src/engine/config.rscrates/core/src/engine/protocol.rscrates/core/src/engine/scheduler/sglang/tests.rscrates/core/src/engine/scheduler/sglang/host_loop.rscrates/core/src/engine/scheduler/rank.rscrates/core/src/engine/scheduler/vllm/tests.rscrates/core/src/engine/scheduler/sglang/frontend.rscrates/core/src/engine/scheduler/sglang/decode.rscrates/core/src/engine/scheduler/sglang/core.rs
Preserve deterministic Replay semantics and explicit validation.
⚙️ CodeRabbit configuration file
Files:
crates/core/src/replay/loadgen/mod.rscrates/core/src/replay/components/mod.rscrates/core/src/replay/snapshot_tests.rscrates/core/src/replay/disagg_tests.rscrates/core/src/replay/mod.rscrates/core/src/replay/engine.rscrates/core/src/replay/protocol.rscrates/core/src/replay/loadgen/types.rscrates/core/src/replay/components/engine.rscrates/core/src/replay/report/bounded.rscrates/core/src/replay/spec.rscrates/core/src/replay/loadgen/tests.rscrates/core/src/replay/replayer.rscrates/core/src/replay/loadgen/trace.rscrates/core/src/replay/loadgen/driver.rscrates/core/src/replay/agg.rscrates/core/src/replay/components/encoder.rscrates/core/src/replay/report.rscrates/core/src/replay/disagg.rs
Verify upstream provenance, license text, collection and overlay hashes, local modification notices, and generated derivative records remain complete and internally consistent.
⚙️ CodeRabbit configuration file
Files:
THIRD_PARTY_NOTICES.md
Treat top-level exports and bindings as public and release boundaries.
⚙️ CodeRabbit configuration file
Files:
crates/core/src/lib.rscrates/core/src/python.rs
Check examples against executable public behavior.
⚙️ CodeRabbit configuration file
Files:
examples/cli/vl-predict-aggregated.yamlexamples/cli/vl-predict-epd-native.yamlexamples/cli/vl-predict-disaggregated.yamlexamples/cli/vl-recommend.yaml
Require coverage of the changed behavior and its negative or boundary cases.
⚙️ CodeRabbit configuration file
Files:
tests/sweeper/test_core_contracts.pytests/test_afd_cli.pytests/test_cli_config.pytests/test_epd_cli.pytests/sweeper/test_kv_load.pytests/test_runner.pytests/test_ci_workflow_contracts.pytests/sweeper/test_epd.pytests/test_vl_cli.py
Check commands, defaults, supported runtimes, public names, and claims against executable behavior.
⚙️ CodeRabbit configuration file
Files:
docs/replay/README.mddocs/perf-model/api/rust.mddocs/replay/api/rust.mddocs/replay/engine/README.mddocs/replay/features.mddocs/perf-model/configuration.mddocs/getting-started/understand-results.mddocs/replay/workloads.mddocs/sweeper/search-space.mddocs/reference/cli.mddocs/replay/engine/workers.mddocs/replay/engine/sglang-vl-host-loop.mddocs/replay/engine/analytical.md
Read REVIEW.md before commenting.
⚙️ CodeRabbit configuration file
Files:
crates/core/tests/artifacts.rsdocs/replay/README.mdpython/aisimulate/tests/cross_package/test_core_public_api.pypython/aisimulate/src/aisimulate/vl/collect/__init__.pycrates/core/src/engine/scheduler/sglang/prefill.rspython/aisimulate/src/aisimulate/vl/collect/_sglang/__init__.pycrates/core/src/engine/scheduler/vllm/policy/tests.rstests/sweeper/test_core_contracts.pycrates/core/src/replay/loadgen/mod.rspython/aisimulate/src/aisimulate/sweeper/search.pycrates/core/tests/replay_belady.rspython/aisimulate/tests/unit/test_systems_path.pycrates/core/tests/replay_non_finite_timing.rscrates/core/tests/replay_tokenization.rspython/aisimulate/src/aisimulate/sweeper/score.pycrates/core/tests/host_offload_scopes.rscrates/core/src/perfmodel/mod.rspython/aisimulate/tests/unit/sdk/database/test_attention_lanes.pycrates/core/src/engine/scheduler/sglang/mod.rspython/aisimulate/tests/unit/sdk/sweep/test_sweep.pypython/aisimulate/src/aisimulate/sweeper/sample.pycrates/core/tests/runtime_contract.rscrates/core/src/perfmodel/common/enums.rscrates/core/src/replay/components/mod.rstests/test_afd_cli.pypython/aisimulate/src/aisimulate/vl/__init__.pycrates/core/src/replay/snapshot_tests.rsdocs/perf-model/api/rust.mdcrates/core/src/engine/scheduler/vllm/core.rspython/aisimulate/src/aisimulate/config/common.pypython/aisimulate/src/aisimulate/sdk/sweep.pycrates/core/tests/replay_trace_admission.rscrates/core/src/perfmodel/config.rscrates/core/src/replay/disagg_tests.rscrates/core/src/replay/mod.rsdocs/replay/api/rust.mdcrates/core/tests/replay_session_placement.rscrates/core/src/lib.rscrates/core/tests/agentx_qualification.rsdocs/replay/engine/README.mdcrates/core/src/engine/runtime.rscrates/core/src/engine/kv_manager/sglang_backend.rsdocs/replay/features.mdpython/aisimulate/tests/unit/sdk/test_fpm_profile.pycrates/core/src/perfmodel/fpm/config.rscrates/core/src/replay/engine.rspython/aisimulate/src/aisimulate_core/sdk/common.pyCODEOWNERSdocs/perf-model/configuration.mdpython/aisimulate/src/aisimulate/supervision.pypython/aisimulate/src/aisimulate/sweeper/kv_load.pydocs/getting-started/understand-results.mdscripts/performance/build_simulation_dynamo.pyexamples/cli/vl-predict-aggregated.yamldocs/replay/workloads.mdcrates/core/src/engine/scheduler/source_holds.rspython/aisimulate/src/aisimulate/sweeper/forward_pass_estimator.pydocs/sweeper/search-space.mdcrates/tests/public-api/src/lib.rstests/test_cli_config.pycrates/core/src/replay/protocol.rspython/aisimulate/src/aisimulate_core/sdk/models/blocks/vit.pytests/test_epd_cli.pydocs/reference/cli.mdcrates/core/src/engine/timing.rscrates/core/src/engine/scheduler/sglang/config.rspython/aisimulate/src/aisimulate/predict.pypython/aisimulate/src/aisimulate/main.pypython/aisimulate/tests/unit/vl/test_collect_cli.pycrates/core/src/replay/loadgen/types.rscrates/core/src/engine/scheduler/mod.rscrates/core/src/engine/common/protocols.rscrates/core/src/engine/common/perf_model.rscrates/core/src/engine/handoff.rspython/aisimulate/tests/unit/sdk/test_utils.pytests/sweeper/test_kv_load.pycrates/core/src/engine/mod.rstests/test_runner.pypython/aisimulate/src/aisimulate/capacity.pypython/aisimulate/THIRD_PARTY_NOTICES.mdexamples/cli/vl-predict-epd-native.yamlpython/aisimulate/src/aisimulate/config/traffic.pypython/aisimulate/tests/unit/sdk/test_memory_estimation.pypython/aisimulate/tests/unit/vl/test_table.pyexamples/cli/vl-predict-disaggregated.yamlcrates/core/src/perfmodel/fpm/model.rstests/test_ci_workflow_contracts.pycrates/core/src/replay/components/engine.rsexamples/cli/vl-recommend.yamlcrates/core/src/replay/report/bounded.rspython/aisimulate/src/aisimulate_core/sdk/rust_engine_step.pypython/aisimulate/src/aisimulate/config/vl.pypython/aisimulate/tests/unit/vl/test_lower.pycrates/core/src/replay/spec.rspython/aisimulate/src/aisimulate/sweeper/deploy.pycrates/core/src/replay/loadgen/tests.rspython/aisimulate/src/aisimulate/vl/collect/workload.pypython/aisimulate/tests/unit/sdk/test_rust_engine_step.pypython/aisimulate/src/aisimulate/config/epd.pypython/aisimulate/src/aisimulate/recommend.pycrates/core/src/replay/replayer.rscrates/core/src/perfmodel/py.rsdocs/replay/engine/workers.mdcrates/core/src/engine/scheduler/sglang/vision.rscrates/core/src/engine/scheduler/sglang/request.rsTHIRD_PARTY_NOTICES.mddocs/replay/engine/sglang-vl-host-loop.mdpython/aisimulate/src/aisimulate/sweeper/replay.pypython/aisimulate/tests/unit/sdk/backends/test_encoder.pypython/aisimulate/src/aisimulate_core/sdk/backends/base_backend.pypython/aisimulate/src/aisimulate/vl/collect/samples.pypython/aisimulate/src/aisimulate_core/sdk/memory/kv_cache.pypython/aisimulate/src/aisimulate/vl/collect/lower.pycrates/core/src/perfmodel/engine/spec.rspython/aisimulate/src/aisimulate_core/sdk/utils.pytests/sweeper/test_epd.pycrates/core/src/replay/loadgen/trace.rscrates/core/src/engine/config.rscrates/core/src/engine/protocol.rspython/aisimulate/src/aisimulate/config/cli.pycrates/core/src/replay/loadgen/driver.rspython/aisimulate/src/aisimulate/compiler.pycrates/core/src/engine/scheduler/sglang/tests.rscrates/core/src/engine/scheduler/sglang/host_loop.rscrates/core/src/engine/scheduler/rank.rscrates/core/src/replay/agg.rspython/aisimulate/src/aisimulate/runner.pycrates/core/src/engine/scheduler/vllm/tests.rscrates/core/src/perfmodel/engine/runtime.rspython/aisimulate/src/aisimulate_core/sdk/engine.pydocs/replay/engine/analytical.mdpython/aisimulate/src/aisimulate/vl/collect/__main__.pycrates/core/tests/engine.rscrates/core/src/engine/scheduler/sglang/frontend.rspython/aisimulate/src/aisimulate/vl/collect/_sglang/frontend_worker.pytests/test_vl_cli.pycrates/core/src/replay/components/encoder.rscrates/core/src/engine/scheduler/sglang/decode.rscrates/core/src/replay/report.rspython/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate/sweeper/epd.pycrates/core/src/replay/disagg.rspython/aisimulate/src/aisimulate/vl/table.pypython/aisimulate/src/aisimulate/config/engine.pycrates/core/src/python.rscrates/core/src/engine/scheduler/sglang/core.rs
Applicability reference: crates/core/src/engine/scheduler/vllm/AGENTS.md: CLAUDE.md Source excerpt: `core.rs` owns common queue mechanics, KV allocation, state transitions, completion, and event emission.
📄 CodeRabbit inference engine (crates/core/src/engine/scheduler/vllm/CLAUDE.md)
Files:
crates/core/src/engine/scheduler/vllm/policy/tests.rscrates/core/src/engine/scheduler/vllm/core.rscrates/core/src/engine/scheduler/vllm/tests.rs
Source excerpt: Any PR that changes what the compiled engine computes (operators, loaders, interpolation, selection rules, engine composition) MUST in the same PR: Keep `test_engine_step_parity.py` / `test_compile_engine_parity.py` green —...
📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/rust-core/parity.md)
Files:
crates/core/src/perfmodel/mod.rscrates/core/src/perfmodel/common/enums.rscrates/core/src/perfmodel/config.rscrates/core/src/perfmodel/fpm/config.rspython/aisimulate/src/aisimulate_core/sdk/common.pypython/aisimulate/src/aisimulate_core/sdk/models/blocks/vit.pycrates/core/src/perfmodel/fpm/model.rspython/aisimulate/src/aisimulate_core/sdk/rust_engine_step.pycrates/core/src/perfmodel/py.rspython/aisimulate/src/aisimulate_core/sdk/backends/base_backend.pypython/aisimulate/src/aisimulate_core/sdk/memory/kv_cache.pycrates/core/src/perfmodel/engine/spec.rspython/aisimulate/src/aisimulate_core/sdk/utils.pycrates/core/src/perfmodel/engine/runtime.rspython/aisimulate/src/aisimulate_core/sdk/engine.py
Applicability reference: AGENTS.md: # AGENTS Before changing a performance model, its configuration, or a caller in Rust, Python, CLI, Sweeper, Replay, or Planner, MUST read and follow [`perfmodel-api.md`](python/aisimulat...
📄 CodeRabbit inference engine (python/aisimulate/.claude/rules/perfmodel-api.md)
Files:
python/aisimulate/tests/cross_package/test_core_public_api.pypython/aisimulate/tests/unit/test_systems_path.pypython/aisimulate/tests/unit/sdk/database/test_attention_lanes.pypython/aisimulate/tests/unit/sdk/sweep/test_sweep.pypython/aisimulate/tests/unit/sdk/test_fpm_profile.pypython/aisimulate/tests/unit/vl/test_collect_cli.pypython/aisimulate/tests/unit/sdk/test_utils.pypython/aisimulate/tests/unit/sdk/test_memory_estimation.pypython/aisimulate/tests/unit/vl/test_table.pypython/aisimulate/tests/unit/vl/test_lower.pypython/aisimulate/tests/unit/sdk/test_rust_engine_step.pypython/aisimulate/tests/unit/sdk/backends/test_encoder.py
Source excerpt: Keep run logs, investigation notes, and benchmark results out of `docs/`; measured evidence belongs under `benchmarks/evidence/`.
📄 CodeRabbit inference engine (AGENTS.md)
Files:
crates/core/tests/artifacts.rsdocs/replay/README.mdpython/aisimulate/tests/cross_package/test_core_public_api.pypython/aisimulate/src/aisimulate/vl/collect/__init__.pycrates/core/src/engine/scheduler/sglang/prefill.rspython/aisimulate/src/aisimulate/vl/collect/_sglang/__init__.pycrates/core/src/engine/scheduler/vllm/policy/tests.rstests/sweeper/test_core_contracts.pycrates/core/src/replay/loadgen/mod.rspython/aisimulate/src/aisimulate/sweeper/search.pycrates/core/tests/replay_belady.rspython/aisimulate/tests/unit/test_systems_path.pycrates/core/tests/replay_non_finite_timing.rscrates/core/tests/replay_tokenization.rspython/aisimulate/src/aisimulate/sweeper/score.pycrates/core/tests/host_offload_scopes.rscrates/core/src/perfmodel/mod.rspython/aisimulate/tests/unit/sdk/database/test_attention_lanes.pycrates/core/src/engine/scheduler/sglang/mod.rspython/aisimulate/tests/unit/sdk/sweep/test_sweep.pypython/aisimulate/src/aisimulate/sweeper/sample.pycrates/core/tests/runtime_contract.rscrates/core/src/perfmodel/common/enums.rscrates/core/src/replay/components/mod.rstests/test_afd_cli.pypython/aisimulate/src/aisimulate/vl/__init__.pycrates/core/src/replay/snapshot_tests.rsdocs/perf-model/api/rust.mdcrates/core/src/engine/scheduler/vllm/core.rspython/aisimulate/src/aisimulate/config/common.pypython/aisimulate/src/aisimulate/sdk/sweep.pycrates/core/tests/replay_trace_admission.rscrates/core/src/perfmodel/config.rscrates/core/src/replay/disagg_tests.rscrates/core/src/replay/mod.rsdocs/replay/api/rust.mdcrates/core/tests/replay_session_placement.rscrates/core/src/lib.rscrates/core/tests/agentx_qualification.rsdocs/replay/engine/README.mdcrates/core/src/engine/runtime.rscrates/core/src/engine/kv_manager/sglang_backend.rsdocs/replay/features.mdpython/aisimulate/tests/unit/sdk/test_fpm_profile.pycrates/core/src/perfmodel/fpm/config.rscrates/core/src/replay/engine.rspython/aisimulate/src/aisimulate_core/sdk/common.pyCODEOWNERSdocs/perf-model/configuration.mdpython/aisimulate/src/aisimulate/supervision.pypython/aisimulate/src/aisimulate/sweeper/kv_load.pydocs/getting-started/understand-results.mdscripts/performance/build_simulation_dynamo.pyexamples/cli/vl-predict-aggregated.yamldocs/replay/workloads.mdcrates/core/src/engine/scheduler/source_holds.rspython/aisimulate/src/aisimulate/sweeper/forward_pass_estimator.pydocs/sweeper/search-space.mdcrates/tests/public-api/src/lib.rstests/test_cli_config.pycrates/core/src/replay/protocol.rspython/aisimulate/src/aisimulate_core/sdk/models/blocks/vit.pytests/test_epd_cli.pydocs/reference/cli.mdcrates/core/src/engine/timing.rscrates/core/src/engine/scheduler/sglang/config.rspython/aisimulate/src/aisimulate/predict.pypython/aisimulate/src/aisimulate/main.pypython/aisimulate/tests/unit/vl/test_collect_cli.pycrates/core/src/replay/loadgen/types.rscrates/core/src/engine/scheduler/mod.rscrates/core/src/engine/common/protocols.rscrates/core/src/engine/common/perf_model.rscrates/core/src/engine/handoff.rspython/aisimulate/tests/unit/sdk/test_utils.pytests/sweeper/test_kv_load.pycrates/core/src/engine/mod.rstests/test_runner.pypython/aisimulate/src/aisimulate/capacity.pypython/aisimulate/THIRD_PARTY_NOTICES.mdexamples/cli/vl-predict-epd-native.yamlpython/aisimulate/src/aisimulate/config/traffic.pypython/aisimulate/tests/unit/sdk/test_memory_estimation.pypython/aisimulate/tests/unit/vl/test_table.pyexamples/cli/vl-predict-disaggregated.yamlcrates/core/src/perfmodel/fpm/model.rstests/test_ci_workflow_contracts.pycrates/core/src/replay/components/engine.rsexamples/cli/vl-recommend.yamlcrates/core/src/replay/report/bounded.rspython/aisimulate/src/aisimulate_core/sdk/rust_engine_step.pypython/aisimulate/src/aisimulate/config/vl.pypython/aisimulate/tests/unit/vl/test_lower.pycrates/core/src/replay/spec.rspython/aisimulate/src/aisimulate/sweeper/deploy.pycrates/core/src/replay/loadgen/tests.rspython/aisimulate/src/aisimulate/vl/collect/workload.pypython/aisimulate/tests/unit/sdk/test_rust_engine_step.pypython/aisimulate/src/aisimulate/config/epd.pypython/aisimulate/src/aisimulate/recommend.pycrates/core/src/replay/replayer.rscrates/core/src/perfmodel/py.rsdocs/replay/engine/workers.mdcrates/core/src/engine/scheduler/sglang/vision.rscrates/core/src/engine/scheduler/sglang/request.rsTHIRD_PARTY_NOTICES.mddocs/replay/engine/sglang-vl-host-loop.mdpython/aisimulate/src/aisimulate/sweeper/replay.pypython/aisimulate/tests/unit/sdk/backends/test_encoder.pypython/aisimulate/src/aisimulate_core/sdk/backends/base_backend.pypython/aisimulate/src/aisimulate/vl/collect/samples.pypython/aisimulate/src/aisimulate_core/sdk/memory/kv_cache.pypython/aisimulate/src/aisimulate/vl/collect/lower.pycrates/core/src/perfmodel/engine/spec.rspython/aisimulate/src/aisimulate_core/sdk/utils.pytests/sweeper/test_epd.pycrates/core/src/replay/loadgen/trace.rscrates/core/src/engine/config.rscrates/core/src/engine/protocol.rspython/aisimulate/src/aisimulate/config/cli.pycrates/core/src/replay/loadgen/driver.rspython/aisimulate/src/aisimulate/compiler.pycrates/core/src/engine/scheduler/sglang/tests.rscrates/core/src/engine/scheduler/sglang/host_loop.rscrates/core/src/engine/scheduler/rank.rscrates/core/src/replay/agg.rspython/aisimulate/src/aisimulate/runner.pycrates/core/src/engine/scheduler/vllm/tests.rscrates/core/src/perfmodel/engine/runtime.rspython/aisimulate/src/aisimulate_core/sdk/engine.pydocs/replay/engine/analytical.mdpython/aisimulate/src/aisimulate/vl/collect/__main__.pycrates/core/tests/engine.rscrates/core/src/engine/scheduler/sglang/frontend.rspython/aisimulate/src/aisimulate/vl/collect/_sglang/frontend_worker.pytests/test_vl_cli.pycrates/core/src/replay/components/encoder.rscrates/core/src/engine/scheduler/sglang/decode.rscrates/core/src/replay/report.rspython/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate/sweeper/epd.pycrates/core/src/replay/disagg.rspython/aisimulate/src/aisimulate/vl/table.pypython/aisimulate/src/aisimulate/config/engine.pycrates/core/src/python.rscrates/core/src/engine/scheduler/sglang/core.rs
Source excerpt: Binary parquet changes require a machine-readable summary of keys, shapes, units, row counts, coverage, and anomalies plus proof that a real consumer reaches the new data.
📄 CodeRabbit inference engine (REVIEW.md)
Files:
crates/core/tests/artifacts.rsdocs/replay/README.mdpython/aisimulate/tests/cross_package/test_core_public_api.pypython/aisimulate/src/aisimulate/vl/collect/__init__.pycrates/core/src/engine/scheduler/sglang/prefill.rspython/aisimulate/src/aisimulate/vl/collect/_sglang/__init__.pycrates/core/src/engine/scheduler/vllm/policy/tests.rstests/sweeper/test_core_contracts.pycrates/core/src/replay/loadgen/mod.rspython/aisimulate/src/aisimulate/sweeper/search.pycrates/core/tests/replay_belady.rspython/aisimulate/tests/unit/test_systems_path.pycrates/core/tests/replay_non_finite_timing.rscrates/core/tests/replay_tokenization.rspython/aisimulate/src/aisimulate/sweeper/score.pycrates/core/tests/host_offload_scopes.rscrates/core/src/perfmodel/mod.rspython/aisimulate/tests/unit/sdk/database/test_attention_lanes.pycrates/core/src/engine/scheduler/sglang/mod.rspython/aisimulate/tests/unit/sdk/sweep/test_sweep.pypython/aisimulate/src/aisimulate/sweeper/sample.pycrates/core/tests/runtime_contract.rscrates/core/src/perfmodel/common/enums.rscrates/core/src/replay/components/mod.rstests/test_afd_cli.pypython/aisimulate/src/aisimulate/vl/__init__.pycrates/core/src/replay/snapshot_tests.rsdocs/perf-model/api/rust.mdcrates/core/src/engine/scheduler/vllm/core.rspython/aisimulate/src/aisimulate/config/common.pypython/aisimulate/src/aisimulate/sdk/sweep.pycrates/core/tests/replay_trace_admission.rscrates/core/src/perfmodel/config.rscrates/core/src/replay/disagg_tests.rscrates/core/src/replay/mod.rsdocs/replay/api/rust.mdcrates/core/tests/replay_session_placement.rscrates/core/src/lib.rscrates/core/tests/agentx_qualification.rsdocs/replay/engine/README.mdcrates/core/src/engine/runtime.rscrates/core/src/engine/kv_manager/sglang_backend.rsdocs/replay/features.mdpython/aisimulate/tests/unit/sdk/test_fpm_profile.pycrates/core/src/perfmodel/fpm/config.rscrates/core/src/replay/engine.rspython/aisimulate/src/aisimulate_core/sdk/common.pyCODEOWNERSdocs/perf-model/configuration.mdpython/aisimulate/src/aisimulate/supervision.pypython/aisimulate/src/aisimulate/sweeper/kv_load.pydocs/getting-started/understand-results.mdscripts/performance/build_simulation_dynamo.pyexamples/cli/vl-predict-aggregated.yamldocs/replay/workloads.mdcrates/core/src/engine/scheduler/source_holds.rspython/aisimulate/src/aisimulate/sweeper/forward_pass_estimator.pydocs/sweeper/search-space.mdcrates/tests/public-api/src/lib.rstests/test_cli_config.pycrates/core/src/replay/protocol.rspython/aisimulate/src/aisimulate_core/sdk/models/blocks/vit.pytests/test_epd_cli.pydocs/reference/cli.mdcrates/core/src/engine/timing.rscrates/core/src/engine/scheduler/sglang/config.rspython/aisimulate/src/aisimulate/predict.pypython/aisimulate/src/aisimulate/main.pypython/aisimulate/tests/unit/vl/test_collect_cli.pycrates/core/src/replay/loadgen/types.rscrates/core/src/engine/scheduler/mod.rscrates/core/src/engine/common/protocols.rscrates/core/src/engine/common/perf_model.rscrates/core/src/engine/handoff.rspython/aisimulate/tests/unit/sdk/test_utils.pytests/sweeper/test_kv_load.pycrates/core/src/engine/mod.rstests/test_runner.pypython/aisimulate/src/aisimulate/capacity.pypython/aisimulate/THIRD_PARTY_NOTICES.mdexamples/cli/vl-predict-epd-native.yamlpython/aisimulate/src/aisimulate/config/traffic.pypython/aisimulate/tests/unit/sdk/test_memory_estimation.pypython/aisimulate/tests/unit/vl/test_table.pyexamples/cli/vl-predict-disaggregated.yamlcrates/core/src/perfmodel/fpm/model.rstests/test_ci_workflow_contracts.pycrates/core/src/replay/components/engine.rsexamples/cli/vl-recommend.yamlcrates/core/src/replay/report/bounded.rspython/aisimulate/src/aisimulate_core/sdk/rust_engine_step.pypython/aisimulate/src/aisimulate/config/vl.pypython/aisimulate/tests/unit/vl/test_lower.pycrates/core/src/replay/spec.rspython/aisimulate/src/aisimulate/sweeper/deploy.pycrates/core/src/replay/loadgen/tests.rspython/aisimulate/src/aisimulate/vl/collect/workload.pypython/aisimulate/tests/unit/sdk/test_rust_engine_step.pypython/aisimulate/src/aisimulate/config/epd.pypython/aisimulate/src/aisimulate/recommend.pycrates/core/src/replay/replayer.rscrates/core/src/perfmodel/py.rsdocs/replay/engine/workers.mdcrates/core/src/engine/scheduler/sglang/vision.rscrates/core/src/engine/scheduler/sglang/request.rsTHIRD_PARTY_NOTICES.mddocs/replay/engine/sglang-vl-host-loop.mdpython/aisimulate/src/aisimulate/sweeper/replay.pypython/aisimulate/tests/unit/sdk/backends/test_encoder.pypython/aisimulate/src/aisimulate_core/sdk/backends/base_backend.pypython/aisimulate/src/aisimulate/vl/collect/samples.pypython/aisimulate/src/aisimulate_core/sdk/memory/kv_cache.pypython/aisimulate/src/aisimulate/vl/collect/lower.pycrates/core/src/perfmodel/engine/spec.rspython/aisimulate/src/aisimulate_core/sdk/utils.pytests/sweeper/test_epd.pycrates/core/src/replay/loadgen/trace.rscrates/core/src/engine/config.rscrates/core/src/engine/protocol.rspython/aisimulate/src/aisimulate/config/cli.pycrates/core/src/replay/loadgen/driver.rspython/aisimulate/src/aisimulate/compiler.pycrates/core/src/engine/scheduler/sglang/tests.rscrates/core/src/engine/scheduler/sglang/host_loop.rscrates/core/src/engine/scheduler/rank.rscrates/core/src/replay/agg.rspython/aisimulate/src/aisimulate/runner.pycrates/core/src/engine/scheduler/vllm/tests.rscrates/core/src/perfmodel/engine/runtime.rspython/aisimulate/src/aisimulate_core/sdk/engine.pydocs/replay/engine/analytical.mdpython/aisimulate/src/aisimulate/vl/collect/__main__.pycrates/core/tests/engine.rscrates/core/src/engine/scheduler/sglang/frontend.rspython/aisimulate/src/aisimulate/vl/collect/_sglang/frontend_worker.pytests/test_vl_cli.pycrates/core/src/replay/components/encoder.rscrates/core/src/engine/scheduler/sglang/decode.rscrates/core/src/replay/report.rspython/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate/sweeper/epd.pycrates/core/src/replay/disagg.rspython/aisimulate/src/aisimulate/vl/table.pypython/aisimulate/src/aisimulate/config/engine.pycrates/core/src/python.rscrates/core/src/engine/scheduler/sglang/core.rs
Applicability reference: AGENTS.md: # AGENTS Document a new or changed configuration field on the page for the YAML block it belongs to, such as `traffic` in [`docs/replay/workloads.md`](docs/replay/workloads.md) or `engine.*`
📄 CodeRabbit inference engine (docs/replay/engine/README.md)
Files:
docs/replay/engine/README.mddocs/replay/engine/workers.mddocs/replay/engine/sglang-vl-host-loop.mddocs/replay/engine/analytical.md
Applicability reference: AGENTS.md: # AGENTS **Lint:** `python/aisimulate/.venv/bin/ruff check --config python/aisimulate/pyproject.toml python/aisimulate tests` and `python/aisimulate/.venv/bin/ruff format --check --config python/aisimulat...
📄 CodeRabbit inference engine (DEVELOPMENT.md)
Files:
python/aisimulate/src/aisimulate/vl/collect/__init__.pypython/aisimulate/src/aisimulate/vl/collect/_sglang/__init__.pypython/aisimulate/src/aisimulate/sweeper/search.pypython/aisimulate/src/aisimulate/sweeper/score.pypython/aisimulate/src/aisimulate/sweeper/sample.pypython/aisimulate/src/aisimulate/vl/__init__.pypython/aisimulate/src/aisimulate/config/common.pypython/aisimulate/src/aisimulate/sdk/sweep.pypython/aisimulate/src/aisimulate_core/sdk/common.pypython/aisimulate/src/aisimulate/supervision.pypython/aisimulate/src/aisimulate/sweeper/kv_load.pypython/aisimulate/src/aisimulate/sweeper/forward_pass_estimator.pypython/aisimulate/src/aisimulate_core/sdk/models/blocks/vit.pypython/aisimulate/src/aisimulate/predict.pypython/aisimulate/src/aisimulate/main.pypython/aisimulate/src/aisimulate/capacity.pypython/aisimulate/src/aisimulate/config/traffic.pypython/aisimulate/src/aisimulate_core/sdk/rust_engine_step.pypython/aisimulate/src/aisimulate/config/vl.pypython/aisimulate/src/aisimulate/sweeper/deploy.pypython/aisimulate/src/aisimulate/vl/collect/workload.pypython/aisimulate/src/aisimulate/config/epd.pypython/aisimulate/src/aisimulate/recommend.pypython/aisimulate/src/aisimulate/sweeper/replay.pypython/aisimulate/src/aisimulate_core/sdk/backends/base_backend.pypython/aisimulate/src/aisimulate/vl/collect/samples.pypython/aisimulate/src/aisimulate_core/sdk/memory/kv_cache.pypython/aisimulate/src/aisimulate/vl/collect/lower.pypython/aisimulate/src/aisimulate_core/sdk/utils.pypython/aisimulate/src/aisimulate/config/cli.pypython/aisimulate/src/aisimulate/compiler.pypython/aisimulate/src/aisimulate/runner.pypython/aisimulate/src/aisimulate_core/sdk/engine.pypython/aisimulate/src/aisimulate/vl/collect/__main__.pypython/aisimulate/src/aisimulate/vl/collect/_sglang/frontend_worker.pypython/aisimulate/src/aisimulate/sweeper/config.pypython/aisimulate/src/aisimulate/sweeper/epd.pypython/aisimulate/src/aisimulate/vl/table.pypython/aisimulate/src/aisimulate/config/engine.py
🪛 ast-grep (0.45.3)
python/aisimulate/tests/unit/vl/test_collect_cli.py
[info] 44-44: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_recording())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 61-61: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_recording())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
python/aisimulate/tests/unit/sdk/test_utils.py
[info] 2177-2177: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_QWEN3VL_HF_CONFIG)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 2179-2179: use jsonify instead of json.dumps for JSON output
Context: json.dumps({"size": {"shortest_edge": 3136, "longest_edge": 1003520}})
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 2187-2187: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_QWEN3VL_HF_CONFIG)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
tests/test_runner.py
[info] 347-347: use jsonify instead of json.dumps for JSON output
Context: json.dumps(payload)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 362-362: use jsonify instead of json.dumps for JSON output
Context: json.dumps(payload)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
python/aisimulate/tests/unit/vl/test_table.py
[info] 114-114: use jsonify instead of json.dumps for JSON output
Context: json.dumps({"schema_version": 2, "environment": _environment().model_dump(), "rows": []})
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
python/aisimulate/tests/unit/sdk/test_rust_engine_step.py
[info] 1004-1004: use jsonify instead of json.dumps for JSON output
Context: json.dumps(config.to_dict())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 1006-1006: use jsonify instead of json.dumps for JSON output
Context: json.dumps(ForwardPassPerfModelConfig(**resolved).to_dict())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 1025-1025: use jsonify instead of json.dumps for JSON output
Context: json.dumps(config)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
python/aisimulate/src/aisimulate/vl/collect/__main__.py
[error] 107-109: Command coming from incoming request
Context: subprocess.run(
[python, "-c", "import sglang.version as v; print(v.version)"], capture_output=True, text=True, check=False
)
Note: [CWE-78] Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection').
(subprocess-from-request)
[info] 177-177: use jsonify instead of json.dumps for JSON output
Context: json.dumps(recording, indent=1)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[error] 225-225: Use of unsanitized data to create processes
Context: subprocess.run(command, stdout=subprocess.PIPE, text=True, check=False)
Note: [CWE-78] Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection').
(os-system-unsanitized-data)
[error] 225-225: Command coming from incoming request
Context: subprocess.run(command, stdout=subprocess.PIPE, text=True, check=False)
Note: [CWE-78] Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection').
(subprocess-from-request)
tests/test_vl_cli.py
[info] 214-214: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 240-240: use jsonify instead of json.dumps for JSON output
Context: json.dumps(table)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 352-352: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 427-427: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table(rows=rows))
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 644-644: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 725-725: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 731-731: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table(rows=[row]))
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 743-743: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 793-793: use jsonify instead of json.dumps for JSON output
Context: json.dumps(_host_table())
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
python/aisimulate/src/aisimulate/vl/table.py
[info] 74-74: use jsonify instead of json.dumps for JSON output
Context: json.dumps(content, sort_keys=True)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[info] 95-95: use jsonify instead of json.dumps for JSON output
Context: json.dumps(table.model_dump(mode="json"), indent=2, sort_keys=True)
Note: [CWE-116] Improper Encoding or Escaping of Output.
(use-jsonify)
[warning] 108-108: File path is request-/variable-derived; validate and normalize to prevent path traversal.
Context: open(path.with_name(path.name + ".lock"), "w")
Note: [CWE-22] Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal').
(open-filename-from-request)
🪛 Clippy (1.98.1)
crates/core/src/replay/disagg.rs
[warning] 3256-3256: doc list item without indentation
(warning)
🪛 LanguageTool
docs/replay/engine/sglang-vl-host-loop.md
[style] ~166-~166: Consider shortening this phrase to just ‘whether’, unless you mean ‘regardless of whether’.
Context: ... without an encoder pool take this path whether or not the host loop is on. The tower is price...
(WHETHER)
[style] ~193-~193: The words ‘observation’ and ‘observing’ are quite similar. Consider replacing ‘observing’ with a different word.
Context: ...ms` (device completion to the scheduler observing the result: the first token on an aggre...
(VERB_NOUN_SENT_LEVEL_REP)
[style] ~304-~304: Consider using “who” when you are referring to a person instead of an object.
Context: ... decode_context at 1) on the worker that hosts them: workers.aggregated in `mo...
(THAT_WHO)
[style] ~336-~336: The words ‘observes’ and ‘observation’ are quite similar. Consider replacing ‘observes’ with a different word.
Context: ...enizer-manager work after the scheduler observes a token): the reported latencies end at...
(VERB_NOUN_SENT_LEVEL_REP)
🔀 Multi-repo context ai-dynamo/dynamo, ai-dynamo/aiconfigurator
Linked repositories findings
ai-dynamo/dynamo
DynamoReplayRunnerFactory.capabilities()advertises backend/topology support but does not set the new native-EPD or SGLang host-loop capability fields. These therefore remain disabled by default, so Dynamo will reject the PR’s native encoder and host-loop replay specs during compatibility checks. [::ai-dynamo/dynamo::components/src/dynamo/replay/simulation.py:56-99]- Dynamo pins Python and Rust AISimulate dependencies to the exact published version
0.13.0.dev202610160000000073, with tests requiring all dependency locations and lockfiles to match. The PR’s release must update these pins in coordination with Dynamo. [::ai-dynamo/dynamo::pyproject.toml:17] [::ai-dynamo/dynamo::Cargo.toml:61-62] [::ai-dynamo/dynamo::tests/dependencies/test_aisimulate_consistency.py:95-135]
ai-dynamo/aiconfigurator
- Aiconfigurator retains an independent positional
EngineSpecbincode contract at schema version 15 and rejects any other version before decoding. It cannot consume this PR’s schema-29 vision payload; compatibility requires an explicit migration or boundary rather than assuming additive schema compatibility. [::ai-dynamo/aiconfigurator::aic-core/rust/aiconfigurator-core/src/config.rs:18-21,72] [::ai-dynamo/aiconfigurator::aic-core/rust/aiconfigurator-core/src/engine/spec.rs:104-130] - Its public API test also hard-codes schema version 15, confirming that the old contract remains enforced in downstream compatibility checks. [::ai-dynamo/aiconfigurator::aic-core/rust/tests/public-api/src/lib.rs:75-85]
Signed-off-by: Yiming Liu <yimingl@nvidia.com>
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Historical review of 9be98f38. Its two hosted-CI follow-ups are fixed and independently re-reviewed in f78b6429; see the current exact-commit review. The text below preserves the earlier findings and validation history.
Signed-off helper fixes are now pushed through 9be98f38cd90ec59cf49647e9c1f157d0472b1e2, preserving the author's commits and main's calibration/vision exports. The independent whole-branch review and follow-up review are clean on this exact commit.
| Issue | Before | After |
|---|---|---|
| Saved GLM configuration identity | Python added encoder_parallel: null after Rust omitted it, failing four unit cases on both architectures. |
Unset values stay omitted; explicit tp and dp survive serialization. |
| Native image admission | A vLLM/TRTLLM request with one image completed as text at 1 ms without encoder work. | All three request-bearing commands reject images before scheduler mutation. SGLang image timing and separate encoder pools remain supported. |
| Zero-duration frontend stages | Two simultaneous requests caused an aggregated replay error or only one completion in disaggregated replay. | Immediate frontend work is delivered during admission. Both requests complete, with correct milestones, in both topologies; positive and mixed-stage controls pass. |
| Synthetic image identity count | Two sessions with 2^63 images panicked before validation; unwrap_or also evaluated the unused default for explicit pools. |
Checked multiplication returns a validation error, explicit pools are handled lazily, and the 2^28 identity bound remains enforced. |
| Pinned Dynamo compatibility | The request image field, new SGLang controls, replay encoder field, and TTFT event broke downstream compilation. | Exact-source adapters preserve text-only defaults and explicitly reject unsupported image/milestone inputs. The complete pinned Dynamo mocker compiles against both this core and baseline 5275cb1a; source hashes and API-shape checks remain enforced. |
| Reserved-memory diagnostics | A 100 MiB reservation reduced the KV budget by 100 MiB but was absent from the reported breakdown. | Both estimate and block-count diagnostics report reserved_bytes; capacity arithmetic is unchanged. |
| Collector startup cleanup | Missing multimodal configuration or a transport mismatch raised after server launch without shutdown. | Cleanup covers post-launch validation and drainer startup; the original error is preserved. |
| Missing-row collection command | The unquoted interpreter placeholder caused a shell syntax error. | The placeholder is quoted as one argument, and the message tells the user to replace it with the serving interpreter path. |
Also corrected the Qwen3-VL processor error's model family, schema history (vision is version 29), and the documented SGLang batch-budget condition (host loop or images encoded on the worker). The alleged replay clock regression was not reproduced; instrumented checks cover both topologies, and no speculative clock clamp was added. The original clock finding is resolved with its evidence. The separate zero-duration defect discovered during that investigation is fixed above.
Local validation passed: 2,079 core unit tests (one existing data-dependent test ignored), 79 engine integration tests, 730 Python tests, and 377 CI-contract tests (three macOS Bash skips). The original failing probes now pass. The independent reviewer reran core and integration tests, 54 instrumented timing scenarios, 254 focused Python tests, 59 adapter contracts, public memory diagnostics, and a fresh complete Dynamo consumer compile. Added scaling-policy assertions verify actual backend-produced durations for vLLM and the SGLang host loop. Collector cleanup tests use fake external dependencies at the real measure_rust boundary. Formatting, whitespace, and packaged legal-file checks passed.
CodeRabbit completed its review of 9be98f38 on 2026-10-21 at 11:18 UTC. Its summary's encoder/timing claims were independently rechecked: both public image-pool CLI layouts completed all four requests (1,296 total prompt tokens, 11.168 ms encoder latency, one image per request); actual text-only traffic had no encoder latency or GPU charge, and text-only-plus-pool input was rejected. Pool workloads deliberately put their fixed image count in EncoderSpec, with no language-request image placeholders to avoid double encoding. All 11 public encoder CLI tests, 54 instrumented timing cases, and four native zero/positive-stage controls passed again. No new P0/P1/P2 was established. Dynamo HEAD 8e7863af pins the published core exactly to 0.13.0-dev.202610160000000073; it does not currently consume this PR. The supported def3b79 benchmark consumer was independently compiled against this branch. Real serving accuracy remains outside the evidence claimed here.
Exact-head CI: source Fast CI passed. After CodeRabbit and the independent Codex review completed, admission created trusted pull-request/302 at 9be98f38cd90ec59cf49647e9c1f157d0472b1e2. Full CI exposed a missed public-schema update in this helper commit: memory_breakdown.reserved_bytes is emitted but absent from the strict prediction-details JSON schema. The ARM64 contracts job reports five real CLI detail validation failures, with 4,662 tests passing and five skipped. A focused schema fix and independent re-review are underway. Forward performance passed: an independent raw-data audit found zero confirmed regressions (53 OK, 56 improvements, 19 unstable comparisons), all 320 paired prediction outputs identical, and the previously flagged SGLang cold case at 86.864 → 87.211 µs with zero threshold exceedances. The Dynamo Python wheel job reached a separate bindings consumer and failed on rust/llm/replay.rs:2284: its SyntheticTraceSpec initializer is missing images. The mocker-crate compile had not covered the complete Python binding build. The same focused wave will extend the pinned adapter and verify that full consumer. Both architecture contract jobs report the same five schema failures; 53 other Full CI jobs succeeded, one expected job skipped, and the aggregate gate failed correctly. All 44 review threads are resolved. Merge readiness still awaits those results and applicable owner approval. No benchmark cases, thresholds, or prediction formulas were changed.
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@coderabbitai resume |
✅ Action performedReviews resumed and review finished. |
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/ok to test 9be98f3 |
Accept the reserved memory diagnostic as an optional nonnegative byte count in prediction details, preserving strict validation and legacy reports. Keep the pinned Dynamo Python synthetic trace text-only across the new image field with exact source and API-shape guards. Signed-off-by: Yiming Liu <yimingl@nvidia.com>
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The signed follow-up f78b6429aed162eb72aaf19318b46f44601ae088 is pushed to the existing PR branch. Independent specification and code-quality review is clean on this exact commit. It closes both failures found by hosted validation of the preceding helper commit.
| Boundary | Before at 9be98f38 |
Verified fix |
|---|---|---|
| Public prediction-details schema | Five real CLI detail cases failed on each architecture because the emitted memory_breakdown.reserved_bytes field was rejected by the strict JSON schema. |
The schema accepts the field as an optional nonnegative integer. Real native CLI output with 0 and 100 MiB reservations validates; saved JSON matches stdout. Legacy omission remains accepted; negative, fractional, boolean, string, null, and unknown-field inputs remain rejected. |
| Complete Dynamo Python bindings | The mocker crate compiled, but the separate Python-binding workspace failed at rust/llm/replay.rs:2284: its SyntheticTraceSpec initializer lacked images. |
The pinned text-only binding receives images: None only for the reviewed public Option<SyntheticImages> API shape. Exact source hashes, drift rejection, baseline behavior, and mirrored third-party attribution are preserved. The complete bindings consumer now compiles. |
The independent reviewer reproduced the original five schema failures, then passed 249 public CLI tests and 71 builder contracts, verified the immutable upstream source and signed file contents, and compiled a fresh full Dynamo Python-binding checkout with cargo check --locked --features ais-forward-pass --manifest-path lib/bindings/python/Cargo.toml --lib. The fixer additionally passed 640 related Python tests, 389 CI contracts (three existing macOS Bash skips), and full bindings checks against both this PR core and exact main 5275cb1a. Formatting, whitespace, and packaged legal checks passed. The local full-binding checks ran on macOS ARM; the hosted Linux release-wheel build, installation, and simulation workflow have now also passed on this exact commit.
Earlier fixes and concrete examples are recorded in the preceding review. All 44 existing review threads were resolved. The two CodeRabbit summary claims about encoder inputs and frontend clocks were independently rechecked: both supported public image-pool layouts completed correctly, actual text-only traffic incurred no encoder cost, and 54 instrumented timing cases passed. No additional code defect was established from those claims. Measured-serving accuracy is not claimed by these tests.
The final-head forward benchmark passed. An independent audit recomputed all 128 cold/warm classifications across 64 cases: 43 OK, 66 improvements, 19 unstable, zero confirmed regressions. All 320 paired prediction values are identical. The previously affected Qwen3.5 SGLang cold case measured 85.699 → 88.344 µs, with zero of five threshold exceedances. Prediction formulas and benchmark thresholds were not changed by this seven-file follow-up.
Current validation on exact f78b6429: source Fast CI, trusted-branch Fast CI, Full CI, and forward performance all passed. Full CI completed with 56 successful jobs and one expected skip; both architecture-specific contracts jobs passed 4,669 tests, with five existing skips and 138 passing subtests each. All required GitHub checks are green. CodeRabbit completed its exact-head review with no new inline findings; its summary repeats the already rechecked encoder/timing claims.
Maintainer admission copied this exact SHA to trusted pull-request/302. The complete Dynamo wheel/simulation workflow passed all four jobs, including both release builds and installation. Independent artifact verification checked all 16 recorded file hashes, exact source revisions, model/data trees, and matching build settings. The simulation audit found 10/10 cases PASS: 50 paired rounds, all 100 responses valid, and zero slowdown-threshold exceedances in every case. This gate validates measured replay runtime and workload/provenance contracts; it does not establish equivalence of simulated behavior or measured-serving accuracy.
Final status: all required checks, both advisory performance workflows, and independent specification/code-quality review pass on f78b6429; all 44 review threads are resolved. GitHub reports the open PR as approved and cleanly mergeable. The tested comparison base is 5275cb1a. Newer main f76b07e8 (PR #320) also merges without conflicts in a read-only git merge-tree check, but that combined tree was not runtime-tested and is not substituted for the exact CI evidence above.
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/ok to test f78b642 |
2 participants
Why and what changed
SGLang image workloads could only be predicted through the analytical EPD overlay, and the SGLang pass model carried no host time. This PR adds opt-in native VL prediction and recommendation for SGLang workers; text-only runs and other backends are unchanged.
host_loop: the SGLang overlap iteration (result observed one iteration late, retraction, PD prefill hand-off), with per-request TTFT milestones and six stage means.vision: the vision tower priced per prefill batch by the canonical estimator (EngineSpec.vision, schema 29), with the embedding cache in the KV budget.host_profile: the frontend stages a request crosses before the scheduler admits it, measured on the serving host withpython -m aisimulate.vl.collect: image preprocessing, the tokenizer manager's send and the scheduler's receive for the Python frontend; preprocessing and receive for the Rust frontend, one row per feature transport (inline on one rank, shared memory above). A missing row fails closed with the command that adds it.workers.encoder.mode: native: SGLang--encoder-onlyservers replayed event by event, on their ownhardwarewhen it differs from the language workers'; their GPUs count ingpu_hoursandtotal_gpus.recommendsearches load, scheduling, TP and replicas; a saved candidate carries its resolved stages and reproduces throughpredictwithout the table.Not priced or modeled (see
docs/replay/engine/sglang-vl-host-loop.md): the GPU image-processor path (tables describe--image-processor-backend pil), the output side, decode-side host costs, encoder-server data parallelism, encoder timeouts, transfer contention, video and mixed image sizes. Analytical encoder pools build Qwen3-VL and Kimi K3 towers; native pools and on-worker encoding build Qwen3-VL towers only.Review map
crates/core/src/engine/scheduler/sglang/{host_loop,core,frontend,vision}.rs,crates/core/src/replay/components/encoder.rs,python/aisimulate/src/aisimulate/{vl/,compiler.py,recommend.py,sweeper/epd.py}EngineSpecschema 27→29 (the vision section follows main's 28);ForwardPassPerfModelConfig.encoder_parallel, omitted when unset and accepted by op-level timing only; new prediction fieldshost_loop,frontend,host_profile,vision,images.*,encoder.{mode,host_profile,frontend,transfer}; host cost table schema 3, keyed by model, frontend, feature transport and image shape; new report keys (mean_frontend_ms…mean_handoff_to_first_token_ms,encoder_latency_ms,encoder_gpus,total_gpus).engine.tokenizationis exclusive with frontend stages, which already price the tokenizer.kv_load_ratioand applies the processor's pixel budget;estimate_kv_cacherejects the encoder memory controls together with an FPM profile.Evidence
cargo test -p aisimulate-coreall targets,--features embed-python,replay-benchand the public API crate pass; parity goldens identical to main;pytest tests/test_vl_cli.py tests/test_epd_cli.py tests/test_afd_cli.py tests/test_cli_config.py tests/test_runner.py tests/sweeper python/aisimulate/tests/cross_packageand the package unit suite pass; ruff, cargo fmt, legal-file, documentation-link and CODEOWNERS checks pass. Parity goldens hold no image workloads; the pixel-budget change is pinned by the image-geometry unit tests.fix(vl)commits, each with a regression that fails on the previous code.rank.visionRunner input, ghost rows in decode metrics.cache_finished_reqandis_retractedsemantics.Modeling or data provenance
Behavior re-implemented from sgl-project/sglang v0.5.19 (
0bcd822), no source copied (THIRD_PARTY_NOTICES.md). GPU costs come from the packaged perf database; host costs are measured tables. Exercised end to end on packaged H200 SGLang data in aggregated, disaggregated and native-encoder layouts, and the documented collect → predict → recommend flow on a serving host. Accuracy against measured serving is not part of this PR.Tracking