fix(vllm): warm Inkling convolution caches with native prefills

[Arsene12358]

Summary

Inkling decode self-benchmark collection fell back to synthetic KV under tensor parallelism, and for grouped sliding-window caches, borrowing a deeper prefill chain can hit evicted convolution history. This PR adds a native exact-context KV warm-up: each decode point prefills its own requests to ctx - 1 and continues the same requests into decode, so window history and recurrent state are computed by the model instead of borrowed from a chain at another depth.

• Native applies to models whose decode timing depends on KV content (MoE models, or models with Mamba/KDA recurrent-state layers) when every cache group is full attention (including MLA), a finite sliding window (including sliding-window MLA and k-pool tails) or a Mamba/KDA state group, and at least one group is a finite window or a state group. It needs neither expert parallelism nor prefix caching. Dense attention-only models keep synthetic KV, which is correct by construction for them, and their rows read skip:dense_model_content_insensitive. DYN_BENCHMARK_RANDOMIZE_KDA_STATE=true keeps the shared-chain random-state path, and native takes precedence over --benchmark-hybrid-live-state.
• Each execution, including eager warm-up replicas and repeated coordinates, gets its own requests, and plan lookups survive the grid's public renumbering, including when attention data parallelism drops uncovered points. A point whose footprint exceeds the cache pool is recorded in kvwarm.capacity_fallbacks with a warning and measured on synthetic KV outside attention data parallelism. Align-mode Mamba/KDA groups reserve one block each for vLLM's first-decode partial-tail allocation.
• Decode points of a configuration the warm-up gate rejected are no longer stamped kvwarm_fake_fallback: their regime reads skip:<reason>, and the new kvwarm.points_gate_skipped counter counts them. fake_fallback now means an eligible configuration whose point fell back.
• DYN_BENCH_KV_WARMUP and the regime paragraph in the environment-variable reference describe the strategy, which cache groups qualify, attention-DP behavior and the cost: the untimed prefill per sweep is about the sum of total_kv_read_tokens over the decode executions.
• With feat(vllm): add reproducible FPM inputs and measurement provenance #15110 on main, native real_kv rows also record its prompt evidence: benchmark_measurement.prompts holds the stage's prefill prompts, ctx - 1 tokens per request, because the admission token is sampled. The traces doc describes both decode warm-up strategies in its evidence sections.

Artifact changes for consumers such as AISimulate: rows of gate-rejected configurations read skip:<reason> instead of fake_fallback; kvwarm gains initialization_strategy (native only), points_gate_skipped, and per-execution capacity_fallbacks entries with benchmark_id (null when attention data parallelism dropped the point) and total_kv_read_tokens; points_fake_fallback no longer counts gate-skipped points.

Validation

Merge of main 0f01da1926 (vLLM 0.31.0 and #15110) at 5d654ec31. The conflicts were the test imports and one paragraph of environment-variables.mdx, resolved by keeping both sides. #15110 rewrote the same scheduler code, and one integration fix was needed: native decode points stored #15110's prompt evidence as unavailable, because the native resume admits the stage's own requests instead of injecting new ones; they now hash the stage's prefill prompts (new test). Two tests were adapted (a stub needs state #15110 now reads; vLLM 0.31.0 renamed the admission-cap attribute a native test patches), and two docs sentences describe native warm-up in the traces doc. All commits are signed.

• Unit tests on one GB200 node with vLLM 0.31.0 (torch 2.13.0, CUDA 13.0) installed into the dynamo-ci vLLM 0.30.0 image, keeping the image's Dynamo runtime, merged head vs. main 0f01da1926: test_vllm_instrumented_scheduler.py 410 vs. 360 passed; test_benchmark_points.py 19, test_vllm_worker_factory.py 229 (pytest-asyncio 1.3.0), test_gc_policy.py 10 and test_vllm_benchmark_worker.py 9 on both; test_vllm_unit.py 183 passed on both, with the same 2 fixture-path failures in that package-only checkout.
• Live check on GB300 (oci-aga, TP4, Inkling-NVFP4 42a75a99a40eb2ba1e0717db6357a0bf15205044, vLLM 0.31.0 installed the same way, with FlashInfer 0.7.0.post1 and its sm103a prebuilt modules, V2 model runner, eager, synchronous scheduling) at 5d654ec31: 15/15 decode rows real_kv, kvwarm.initialization_strategy = "native_exact_context", all 18 native stages succeeded (including three eager replicas), zero fake fallbacks, gate skips and capacity fallbacks, and every row records its prompt evidence (benchmark_measurement.prompts.status = "recorded", one entry per request); ordinary generation afterward returned 2+2=4. The job ran 13 minutes 4 seconds. The campaign-local cache-layout inspection still does not run on vLLM 0.30.0 or later (SlidingWindowSpec has no storage_block_size) and is not a pass criterion.
• black, isort, flake8, ruff, codespell and mypy 1.18.2 (repository configuration) are clean on the changed files, and the pytest marker report passes. An independent review of the merge and the integration with feat(vllm): add reproducible FPM inputs and measurement provenance #15110 found nothing blocking.

Before the merge, on vLLM 0.30.0:

• Rebased onto main 85f15f55e (vLLM 0.30.0), resolving the conflict with fix(fpm kvwarm): hybrid (KDA/Mamba) fixes for the real-KV decode warm-up, validated on GLM-5.3-Flash #14614 in _kvwarm_prepare. All commits are signed.
• Cluster unit run on one GB200 node in the dynamo-ci vLLM 0.30.0 image, compared with main: test_vllm_instrumented_scheduler.py 320 passed, 0 failed (main: 272); test_benchmark_points.py 19/19; test_vllm_worker_factory.py 63/63 with pytest-asyncio 1.3.0; test_vllm_unit.py identical to main (162 passed, and the same 2 fixture-path failures in that package-only checkout). The run used the final code before a last amend that changed only one log string and one docs phrase.
• Live check on GB300 (oci-aga, TP4, Inkling-NVFP4 42a75a99a40eb2ba1e0717db6357a0bf15205044, vLLM 0.30.0, V2 model runner, eager, synchronous scheduling) at 4d72a2a2d: 15/15 decode rows real_kv, kvwarm.initialization_strategy = "native_exact_context", all 18 native stages succeeded (including three eager replicas), zero fake fallbacks, zero gate skips and zero capacity fallbacks; ordinary generation afterward returned 2 + 2 = 4. The job ran 14 minutes 28 seconds. The campaign-local cache-layout inspection used in the first round no longer runs on vLLM 0.30.0 (SlidingWindowSpec has no storage_block_size); it is supporting evidence only and was not part of the pass criteria.

Not covered here: GPU runs of Mamba/KDA layouts (GLM-5.3-Flash, Nemotron-H) and of native warm-up under attention data parallelism, CUDA graphs, and asynchronous scheduling. vLLM 0.31.0 changed the align-mode Mamba block allocation; the native headroom stays an upper bound and has still not run on a Mamba/KDA layout. These timings are not a performance or workload-accuracy qualification.

Where should the reviewer start?

components/src/dynamo/vllm/instrumented_scheduler.py: the gate (_kvwarm_warm_eligible, _kvwarm_native_layout), the per-execution native plan in _kvwarm_prepare, prefill and continuation (_kvwarm_start_stage, _kvwarm_resume_native, _bench_make_steady_step), and the row labels in _bench_step_decode. Tests are in components/src/dynamo/vllm/tests/test_vllm_instrumented_scheduler.py.

Related Issues

• Confirmed — no related issue.

Builds on #14614 (merged; its live-state mode now applies only to layouts that do not qualify for native). #15110 is merged and included here (see Validation).

Summary by CodeRabbit

• New Features
  • KV warm-up benchmarks now support finite sliding-window attention layouts using prefills that match each decode point’s context.
  • Warm-up can proceed without prefix caching or expert parallelism in supported configurations. If capacity is insufficient, the benchmark can fall back to available samples.
• Documentation
  • Clarified warm-up eligibility, initialization strategies, capacity limits, and how repeat counts vary between real and synthetic KV benchmarks.

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Walkthrough

The scheduler adds native exact-context KV warm-up for eligible full-attention and sliding-window cache layouts. It plans, stages, and resumes requests per decode point while retaining shared-prefix warm-up for other eligible configurations. Tests cover staging, capacity fallback, request resumption, and measurement behavior. Documentation describes both strategies and their repeat-count rules.

Changes

KV Warm-up

Layer / File(s)	Summary
Warm-up eligibility components/src/dynamo/vllm/instrumented_scheduler.py, components/src/dynamo/vllm/tests/test_vllm_instrumented_scheduler.py, docs/fern/pages/reference/observability/environment-variables.mdx	The scheduler selects native exact-context warm-up for eligible cache layouts. Tests and documentation cover supported layouts and eligibility conditions.
Per-point plans and capacity checks components/src/dynamo/vllm/instrumented_scheduler.py, components/src/dynamo/vllm/tests/test_vllm_instrumented_scheduler.py	Native plans use each decode point’s admission contexts and capacity estimates. Public benchmark IDs are applied to plan keys, while shared-prefix planning remains batch-based.
Native stage validation components/src/dynamo/vllm/instrumented_scheduler.py, components/src/dynamo/vllm/tests/test_vllm_instrumented_scheduler.py	Native stages build requests at exact contexts and check request progress and available capacity. Stage failures disable the affected point’s plan, and tests cover failure and attention-DP verdict handling.
Request resumption and decode measurement components/src/dynamo/vllm/instrumented_scheduler.py, components/src/dynamo/vllm/tests/test_vllm_instrumented_scheduler.py, docs/fern/pages/reference/observability/environment-variables.mdx	Ready native requests resume for steady-step measurement with runner-state and block-copy handling. Dispatch selects native requests, shared-prefix chains, or synthetic fallback. Tests and documentation cover fallback, cleanup, and repeat limits.

Priority: ⬇️ Low

Estimated code review effort: 4 (Complex) | ~50 minutes

Merge Risk: 🔵 Low · up to ac3ae

Inkling warm-up now uses native prefills, but dense sliding-window models also take this path. They may spend extra setup time before measurement. This can shorten benchmark sweeps under the timeout. The change is mergeable, but the owner should know about this and consider limiting which models take the native path.

Pre-merge checks | 3 | 2

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🧹 Nitpick comments (1)

components/src/dynamo/vllm/instrumented_scheduler.py (1)

4931-4936: 🚀 Performance & Scalability | 🔵 Trivial

Limit native warm-up to layouts that need it.

_kvwarm_native_layout() selects native warm-up for any layout with a positive SlidingWindowSpec and only full-attention or sliding-window specs. This includes dense sliding-window models. The native branch calls _kvwarm_probe_content() before the dense-model check, so these models now resolve and tokenize the warm-up dataset instead of taking dense_model_content_insensitive.

Native planning also creates a separate real prefill for each decode point. It uses max(1, context - 1) tokens per request, then resumes those requests for measurement. With the default 128 batch-size samples and 128 KV-read-token samples, this adds substantial untimed work before measurement. On a cold host without network access, dataset resolution can wait up to the 60-second download timeout. The added work can consume the 900-second soft timeout before the sweep completes.

If native warm-up targets Inkling-style convolution caches, restrict selection to that layout. Otherwise, measure and document the added cost for dense sliding-window models.

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In `@components/src/dynamo/vllm/instrumented_scheduler.py` around lines 4931 -
4936, Restrict _kvwarm_native_layout() to layouts that require Inkling-style
convolution-cache warm-up rather than selecting every layout with a positive
SlidingWindowSpec. Ensure dense sliding-window models bypass the native branch
and retain the dense_model_content_insensitive path.

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Inline comments:
In `@components/src/dynamo/vllm/tests/test_vllm_instrumented_scheduler.py`:
- Around line 110-112: Update _install_test_capacity_preflight to preserve a
caller-provided kv_cache_manager: create the fallback manager only when none
exists, and add a default kv_cache_config only when the existing manager lacks
one. Keep manager-specific accounting available to _bench_blocks_per_req.

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Nitpick comments:
In `@components/src/dynamo/vllm/instrumented_scheduler.py`:
- Around line 4931-4936: Restrict _kvwarm_native_layout() to layouts that
require Inkling-style convolution-cache warm-up rather than selecting every
layout with a positive SlidingWindowSpec. Ensure dense sliding-window models
bypass the native branch and retain the dense_model_content_insensitive path.

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[tianhaox]

Review of ac3ae91e7f. I read this alongside #14614 (mine) and the consumer side in aisimulate #248. Line numbers are in components/src/dynamo/vllm/instrumented_scheduler.py at this head.

Strategy

The native exact-context approach is the simpler and more general design: prefill each point's own requests at exactly ctx-1 and continue the same requests into decode. State is exact by construction, there are no shadows, and it needs neither prefix caching nor expert parallelism. I would rather see this become the default warm-up path than keep growing the shared-chain machinery. Two consequences follow.

1. The eligibility restriction excludes the layouts where borrowing is hardest. _kvwarm_native_layout (:4805-4826) requires every spec to be FullAttentionSpec or SlidingWindowSpec, so MambaSpec layouts (GLM-5.3-Flash, Qwen3-Next, Nemotron-H) fall back to random-KDA or skip. Native prefill computes the exact recurrent state for free, and without shadows the problems #14614 had to fix on hybrids disappear: no 2 * batch request-slot budget, no k-pool circular-table registration, no stale checkpoint fork. _bench_blocks_per_req(apply_admission_cap=True) already accounts for mamba_cache_mode == "align". Suggest relaxing the layout check to Full | SlidingWindow | Mamba when _bench_random_kda is off. I can validate that on GLM-5.3-Flash tep4/dep4 against the GPU-event ground truth from #14614; if it holds, #14614 shrinks to its three warm-up-independent fixes (attention-DP real-KV-only grid, giant-KV measured coordinate, block-aligned prefill axis) and the live-state mode is unnecessary.

Note the two PRs currently conflict in this file (_kvwarm_plan key type, _kvwarm_start_stage, _kvwarm_ready_for); whichever lands second rebases, so agreeing the direction first saves a round.

2. Native now precedes the dense check. _kvwarm_warm_eligible evaluates _kvwarm_native_layout() before has_experts (:4931-4936). Dense sliding-window models (Gemma-3, gpt-oss without EP) move from "synthetic KV is correct by construction" to a full prefill per point with no fidelity gain by this file's own doctrine. Consider requiring experts or state layers for native, or making it opt-in for dense layouts.

Seed-regime contract

The injection path stamps kvwarm_fake_fallback on every decode point whenever the flag is on, including points the gate skipped (_kvwarm_seed_regime's skip:<reason> branch is unreachable for points that reached injection). aisimulate's collector treats fake_fallback as wrong-regime poison and approves only moe_tp_balanced_by_construction as a skip reason, so dense_model_content_insensitive rows are blocked from direct FPM (ai-dynamo/aisimulate#248, 37/37 Llama-3.1-8B decode rows). Since this PR already revises the regime docstring, it is a good place to emit a distinct row-level regime (e.g. skip:<reason>) for gate-skipped decode points, so consumers can separate "wanted real KV, fell back" from "synthetic by design".

Cost

Each execution, including eager replicas and duplicate coordinates, rebuilds its own stage, and cache_salt is per request so no prefix reuse happens. Total untimed prefill is roughly the sum of total_kv_read_tokens over the decode grid. Fine for the 15-point live check; worth documenting the bound in the env-var page and, if cheap, reusing a stage across identical coordinates.

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[Arsene12358]

@tianhaox Thanks for the review. I updated the PR in 4d72a2a2d: it is rebased onto main with #14614, and all commits are now signed, so CI can run. Point by point:

1. Strategy and Mamba. Native now also covers Mamba/KDA state layouts when random-KDA is off. The layout check admits full attention (including MLA), finite sliding windows (including sliding-window MLA and k-pool tails) and Mamba/KDA state groups, as long as at least one group is a window or a state group. Native takes precedence over --benchmark-hybrid-live-state (one INFO line says the flag is unused), and random-KDA keeps the shared-chain path. Each align-mode state group reserves one block for vLLM's first-decode partial-tail allocation. Unit tests cover the gate, capacity, and V1/V2 resume with a state group, but I have no GPU run of a Mamba layout. Could you run the GLM-5.3-Flash tep4/dep4 validation you offered against the fix(fpm kvwarm): hybrid (KDA/Mamba) fixes for the real-KV decode warm-up, validated on GLM-5.3-Flash #14614 ground truth? A k-pool layout would also help if you have one.
2. Dense layouts. Native now requires experts or recurrent-state layers, so dense attention-only models such as Gemma-3 go back to synthetic KV and read skip:dense_model_content_insensitive. TP-only MoE with a finite-window layout (gpt-oss without EP) stays native on purpose. The gate sees only spec types and the expert count, and Inkling's convolution state is a SlidingWindowSpec group (window 4), so Inkling TP4 and gpt-oss without EP look the same to it. Requiring EP for MoE would send Inkling TP4, the configuration this PR fixes, back to synthetic KV. For gpt-oss this costs an untimed prefill per execution (about the sum of total_kv_read_tokens per sweep, documented) and no accuracy.
3. Seed-regime contract. Decode points of a gate-rejected configuration are no longer stamped kvwarm_fake_fallback, so their rows read skip:<reason>, and kvwarm.points_gate_skipped counts them. fake_fallback now means an eligible configuration whose point fell back. The giant off-by-batch correction now keys on "warm-up on and not real KV", so its behavior is unchanged. Under attention data parallelism the reason is per rank (skip:<own reason> on the rank that failed its check, skip:peer_ineligible on the others), while the points themselves are identical. AISimulate's collector derives its own regime, so accepting skip:dense_model_content_insensitive rows there is a separate change.
4. Cost. DYN_BENCH_KV_WARMUP now says the untimed prefill per sweep is about the sum of total_kv_read_tokens over the decode executions. I did not reuse stages across identical coordinates: a resumed native request decodes forward, so a reused stage would start at ctx + k instead of the point's coordinate.

Validation is in the PR description: the scheduler module passes 320/0 on vLLM 0.30.0, and the GB300 TP4 Inkling live check again gives 15/15 real_kv rows with all 18 native stages succeeding.

[Arsene12358]

@tianhaox I merged main into this branch (vLLM 0.31.0 and #15110, which rewrote the same scheduler code). One integration fix was needed: native decode points now record #15110's prompt evidence, which they had stored as unavailable; everything else composed as is. Revalidated on vLLM 0.31.0: the scheduler tests pass (410 on GB200), and the GB300 Inkling TP4 live check again gives 15/15 real_kv rows with all 18 native stages succeeding, now with recorded prompt evidence on every row. CI is green, and the details are in the updated description. Could you take another look when you have time? The GLM-5.3-Flash validation you offered would still help for the Mamba/KDA path.

[tianhaox]

Re-reviewed at 5d654ec319. The 10-02 update addresses all four points from my first pass (Mamba/KDA admitted to native, dense attention-only back on synthetic KV, skip:<reason> rows with points_gate_skipped, and the cost bound documented), and I agree with keeping TP-only MoE with a finite-window layout on native since the gate cannot tell Inkling's convolution group from any other SlidingWindowSpec. The 10-10 commits (prompt evidence on native resume, the #15110/vLLM 0.31.0 test adjustments, evidence docs) look right: the stage prompts are populated in _kvwarm_start_stage, read in _kvwarm_resume_native, then cleared.

Approving. Three non-blocking nits, fine to take as follow-ups:

1. environment-variables.mdx says the native strategy "covers Inkling's convolution cache and hybrid models such as GLM-5.3-Flash", but the Mamba/KDA layouts have no GPU run yet (as the PR description states). Consider "is designed to cover" or similar until that run exists.
2. _bench_make_steady_step calls self.kv_cache_manager.take_kv_cache_block_copies() directly in the native branch, while _kvwarm_take_cow_copies guards the same method with getattr. Pinned vLLM has it, so this is consistency only.
3. The V1 model runner resume path (resumed_req_ids + full all_token_ids) is unit-tested only; the live check used V2. Worth noting in the follow-up list.

On the GLM-5.3-Flash tep4/dep4 validation against the #14614 ground truth: I will run it as a follow-up and report in a separate issue if anything diverges. It should not block this PR.

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Review assisted by Claude Code.