[jthomson04]

Why and what changed

#519 threaded the native MTP slot lifecycle through the scheduler pass that vLLM and TRT-LLM share, and runs without MTP pay for it on every request of every pass. On main, the allocator's no-growth decode step alone (allocate_lease_with_slots self plus bind_lease_owned_capacity) is 3.7–4.9% of native replay on dense aggregated runs, and each per-pass ScheduledWork entry grew from 56 to 96 bytes.

This change takes three things off that path:

• VllmKvManager::allocate_lease returns before the slot path when the target needs no new block, which is 125 of a request's 128 calls at 64-token blocks. This restores the early return feat: support vLLM G2 host offload with MTP #519 removed. The target is not clamped to the lease's entries, so the result matches the slot path's count == 0 branch: the watermark advances and pending write dependencies are reported again.
• ScheduledWork stores a native_mtp flag instead of Option<MtpWork>. Later stages read only that and the valid drafts, which draft_tokens already holds.
• The native MTP depth is resolved once at construction instead of for every scheduled and every ready request.

Simulated outputs are unchanged.

Review map

• Risk level: low
• Start with: VllmKvManager::allocate_lease in crates/core/src/engine/kv_manager/vllm_backend.rs, then speculative_bursts in crates/core/src/engine/scheduler/vllm/core.rs, the one MTP read site whose shape changed.
• Public or serialized contract changed: no

Evidence

Native replay wall time, median of 5 paired rounds:

Workload	Base	Head	Change	Rounds
dense-vllm	2,051 ms	1,971 ms	-4.0% (-4.4%…-3.0%)	5/5
dense-trtllm	2,332 ms	2,237 ms	-4.1% (-5.2%…-2.6%)	5/5
vllm-c256-fixed	7,402 ms	7,097 ms	-2.9% (-5.3%…-1.5%)	5/5
pd-vllm	1,991 ms	2,006 ms	+0.9% (+0.4%…+1.4%)	5/5
mla-dp8	1,538 ms	1,506 ms	-1.7% (-2.1%…-1.5%)	5/5
mtp-vllm	1,665 ms	1,651 ms	-0.3% (-1.4%…+0.2%)	4/5

• Final patch, 2 paired rounds at replay-dominated size: dense-vllm-8x 14,579 ms to 13,841 ms (-5.1%), pd-vllm-4x 7,245 ms to 6,948 ms (-4.1%). The table above was measured before the MTP depth moved from a per-pass local to a constructor field.
• P/D: decode ranks take the same no-growth path on every decode step, and at 4x size they show a gain close to the aggregated runs. The 2 s pd-vllm row is inside the noise band.
• Noise: band ±1.0% (in-run A/A on dense-vllm ±0.6%).
• Profile: on the same configs at 4x and 8x size, bind_lease_owned_capacity plus allocate_lease_with_slots self are 4.6–4.7% of native replay on main and 0.2% with the fix; the inlined check adds 0.7–1.7% to schedule_request self.
• Tests: cargo test -p aisim-core (3,007 lib tests and every integration binary pass), cargo fmt --check, cargo clippy -p aisim-core --all-targets (nothing on changed lines).
• Negative or boundary cases: fenced_multiblock_allocation_keeps_owned_suffix_across_smaller_target now also checks that a no-growth retry reports the unauthorized write dependency again; a compile-time assertion in test builds keeps ScheduledWork at seven words.
• Host and build: Tyche ptyche0242 (aarch64 Neoverse-V2), exclusive node bound to one NUMA node; release wheels, rustc 1.99.0; base 912e91755a23. The final-patch run used ptyche0271.
• Repro: aisim-e2e run --site tyche --arm base=912e91755a23a79724426a0301f3ab157086be2f --arm non-mtp-pass=912e91755a23a79724426a0301f3ab157086be2f:non-mtp-pass.patch --workload dense-vllm=dense-vllm.yaml --workload dense-trtllm=dense-trtllm.yaml --workload vllm-c256-fixed=vllm-c256-fixed.yaml --workload pd-vllm=pd-vllm.yaml --workload mla-dp8=mla-dp8.yaml --workload mtp-vllm=mtp-vllm.yaml --rounds 5 --control aa. Final-patch run: base against this PR's diff (git diff 912e91755a23 7fae0e13a), with --workload dense-vllm-8x=… --workload pd-vllm-4x=… --rounds 2 --control none; those configs are dense-vllm and pd-vllm with traffic.stop.requests set to 524,288 and 163,840.

Modeling or data provenance

No model, data or formula change. The harness hashes each run's prediction.json with host-time fields removed. The hash is equal across the two arms on every workload above, including the native-MTP run and both final-patch workloads.

Tracking

• Related PRs or issues: feat: support vLLM G2 host offload with MTP #519 introduced the cost.

Workloads

Workloads

dense-vllm: Qwen3-32B, vLLM 0.24.0 aggregated TP4 on b200_sxm, op_level timing, 64-token blocks; synthetic 1024 in/128 out, concurrency 64, 65,536 requests (CI gate dense-vllm); non-MTP decode pass

aisimulate predict --config config.yaml

traffic:
  source: {type: synthetic, input_tokens: 1024, output_tokens: 128}
  load: {type: concurrency, concurrency: 64}
  stop: {requests: 65536}
engine:
  mode: aggregated
  model: Qwen/Qwen3-32B
  hardware: b200_sxm
  backend: vllm
  backend_version: "0.24.0"
  estimation_mode: op_level
  database_mode: SILICON
  fallback_policy: deny
  context_length: max
  enable_shared_layer: true
  workers:
    aggregated:
      parallelism: {replicas: 1, tensor: 4, pipeline: 1, attention_data: 1, moe_tensor: 1, moe_expert: 1}
      scheduler: {max_batched_tokens: 8192, max_sequences: 256}
      kv_cache: {block_size: 64, prefix_caching: true, capacity: {type: fixed, blocks: 16384}}
      timing: {type: default}

dense-trtllm: Qwen3-32B, TRT-LLM 1.3.0rc20 aggregated TP4 on b200_sxm, op_level timing, 32-token blocks; synthetic 1024 in/128 out, concurrency 64, 65,536 requests (CI gate dense-trtllm); same shared core

aisimulate predict --config config.yaml

traffic:
  source: {type: synthetic, input_tokens: 1024, output_tokens: 128}
  load: {type: concurrency, concurrency: 64}
  stop: {requests: 65536}
engine:
  mode: aggregated
  model: Qwen/Qwen3-32B
  hardware: b200_sxm
  backend: trtllm
  backend_version: "1.3.0rc20"
  estimation_mode: op_level
  database_mode: SILICON
  fallback_policy: deny
  context_length: max
  enable_shared_layer: true
  workers:
    aggregated:
      parallelism: {replicas: 1, tensor: 4, pipeline: 1, attention_data: 1, moe_tensor: 1, moe_expert: 1}
      scheduler: {max_batched_tokens: 8192, max_sequences: 256}
      kv_cache: {block_size: 32, prefix_caching: true, capacity: {type: fixed, blocks: 32768}}
      timing: {type: default}

vllm-c256-fixed: Qwen3-32B, vLLM 0.24.0 aggregated TP4, fixed 1 ms timing (no perf model), 64-token blocks; synthetic 1024 in/128 out, concurrency 256, 262,144 requests; isolates the scheduler pass

aisimulate predict --config config.yaml

traffic:
  source: {type: synthetic, input_tokens: 1024, output_tokens: 128}
  load: {type: concurrency, concurrency: 256}
  stop: {requests: 262144}
engine:
  mode: aggregated
  model: Qwen/Qwen3-32B
  hardware: b200_sxm
  backend: vllm
  backend_version: "0.24.0"
  context_length: max
  workers:
    aggregated:
      parallelism: {replicas: 1, tensor: 4, pipeline: 1, attention_data: 1, moe_tensor: 1, moe_expert: 1}
      scheduler: {max_batched_tokens: 8192, max_sequences: 256}
      kv_cache: {block_size: 64, prefix_caching: true, capacity: {type: fixed, blocks: 16384}}
      timing: {type: fixed, prefill_ms: 1, decode_ms: 1}

pd-vllm: Qwen3-32B, vLLM 0.24.0 P/D (1 prefill + 2 decode workers, TP4) on b200_sxm, op_level timing; synthetic 1024 in/128 out, concurrency 64, 40,960 requests (CI gate pd-vllm); P/D roles

aisimulate predict --config config.yaml

traffic:
  source: {type: synthetic, input_tokens: 1024, output_tokens: 128}
  load: {type: concurrency, concurrency: 64}
  stop: {requests: 40960}
engine:
  mode: disaggregated
  model: Qwen/Qwen3-32B
  hardware: b200_sxm
  backend: vllm
  backend_version: "0.24.0"
  estimation_mode: op_level
  database_mode: SILICON
  fallback_policy: deny
  context_length: max
  enable_shared_layer: true
  kv_transfer: {bandwidth_gb_per_second: 400.0, timing_mode: destination_missing}
  workers:
    prefill:
      parallelism: {replicas: 1, tensor: 4, pipeline: 1, attention_data: 1, moe_tensor: 1, moe_expert: 1}
      scheduler: {max_batched_tokens: 8192, max_sequences: 16}
      kv_cache: {block_size: 64, prefix_caching: true, capacity: {type: fixed, blocks: 16384}}
      timing: {type: default}
    decode:
      parallelism: {replicas: 2, tensor: 4, pipeline: 1, attention_data: 1, moe_tensor: 1, moe_expert: 1}
      scheduler: {max_batched_tokens: 8192, max_sequences: 256}
      kv_cache: {block_size: 64, prefix_caching: true, capacity: {type: fixed, blocks: 16384}}
      timing: {type: default}

mla-dp8: DeepSeek-V3.2, vLLM 0.24.0 aggregated, 2 replicas x 8 attention-DP ranks (TP1, EP8) on b200_sxm, op_level timing; synthetic 1024 in/128 out, concurrency 128, 32,768 requests; multi-rank passes

aisimulate predict --config config.yaml

traffic:
  source: {type: synthetic, input_tokens: 1024, output_tokens: 128}
  load: {type: concurrency, concurrency: 128}
  stop: {requests: 32768}
engine:
  mode: aggregated
  model: deepseek-ai/DeepSeek-V3.2
  hardware: b200_sxm
  backend: vllm
  backend_version: "0.24.0"
  estimation_mode: op_level
  database_mode: SILICON
  fallback_policy: deny
  context_length: max
  enable_shared_layer: true
  workers:
    aggregated:
      parallelism: {replicas: 2, tensor: 1, pipeline: 1, attention_data: 8, moe_tensor: 1, moe_expert: 8}
      scheduler: {max_batched_tokens: 8192, max_sequences: 256}
      kv_cache: {block_size: 64, prefix_caching: true, capacity: {type: fixed, blocks: 16384}}
      timing: {type: default}

mtp-vllm: Qwen3-32B, vLLM 0.24.0 aggregated TP4 on b200_sxm, op_level timing, native MTP (nextn 2, 1.5 accepted); synthetic 1024 in/128 out, concurrency 64, 65,536 requests; the MTP path the fix edits

aisimulate predict --config config.yaml

traffic:
  source: {type: synthetic, input_tokens: 1024, output_tokens: 128}
  load: {type: concurrency, concurrency: 64}
  stop: {requests: 65536}
engine:
  mode: aggregated
  model: Qwen/Qwen3-32B
  hardware: b200_sxm
  backend: vllm
  backend_version: "0.24.0"
  estimation_mode: op_level
  database_mode: SILICON
  fallback_policy: deny
  context_length: max
  nextn: 2
  nextn_accepted: 1.5
  enable_shared_layer: true
  workers:
    aggregated:
      parallelism: {replicas: 1, tensor: 4, pipeline: 1, attention_data: 1, moe_tensor: 1, moe_expert: 1}
      scheduler: {max_batched_tokens: 8192, max_sequences: 256}
      kv_cache: {block_size: 64, prefix_caching: true, capacity: {type: fixed, blocks: 16384}}
      timing: {type: default}

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Summary

Risk: Medium. Human review should focus on:

1. The allocate_lease no-growth path, including watermark updates and pending write dependencies.
2. Native-MTP depth selection and its effects on scheduling and speculative burst limits.
3. The test-only assumption that ScheduledWork occupies seven machine words.

Changed behavior

• VllmKvManager::allocate_lease now skips pool allocation when the rounded-up target fits within the prior allocation. It keeps allocated_tokens monotonic, returns zero newly acquired blocks, and reports pending capacity-write dependencies.
• The changed test verifies that a smaller-target retry repeats unauthorized dependencies, leaves the watermark at eight tokens, and returns no dependencies after authorization.
• VllmCore resolves native-MTP depth during initialization when the worker and KV-manager conditions allow it. ScheduledWork stores a native_mtp flag instead of Option<MtpWork>.
• Native-MTP work uses draft_tokens + 1 as its speculative burst limit. Non-native work retains the policy-derived limit.
• A test-only compile-time assertion requires ScheduledWork to occupy seven usize values. No exported or public entity changes are reported.

Evidence and gaps

• The supplied diff confirms the allocation fast path, dependency retry test, stored MTP-depth field, ScheduledWork change, burst-limit logic, and size assertion.
• The objectives report unchanged simulated predictions and performance results for several workloads. They also report cargo test -p aisim-core, cargo fmt --check, and cargo clippy -p aisim-core --all-targets. Completed results on the current head are not available.
• No current review findings were supplied. Review severity counts are not available. The bot’s completed-review notice is not approval.

Technical quality and merge readiness
The diff contains targeted checks for allocation dependencies and the ScheduledWork size assumption. Human review should verify the no-growth path across relevant lease states and confirm native and non-native burst behavior. Merge readiness is not established because current-head CI, build, and test results are not available.

The scheduler stores native MTP depth during initialization and uses it in scheduling and speculative-work paths. The KV manager skips pool allocation when a target is already covered and returns pending capacity-write dependencies.

Changes: Native MTP scheduling

Layer / File(s)	Summary
Stored native MTP depth crates/core/src/engine/scheduler/vllm/core.rs	Initialization resolves native MTP depth for eligible workers. Scheduling, prefill reservation, decode readiness, and speculative emission use the stored depth.
Speculative-work tracking crates/core/src/engine/scheduler/vllm/core.rs	ScheduledWork stores a native_mtp flag instead of the optional MTP plan. Rollback and burst planning use the flag. Native burst limits use draft_tokens + 1. A test checks the record size.

Covered KV allocation retries

Layer / File(s)	Summary
Covered allocation retries crates/core/src/engine/kv_manager/vllm_backend.rs	When the target block count is already covered, allocate_lease skips pool allocation, returns zero new blocks, and reports pending write dependencies. A test checks that a smaller-target retry repeats pending dependencies before authorization.

Estimated code review effort: 3 (Moderate) | ~20 minutes

Possibly related PRs

• ai-dynamo/aisimulate#519: Adds native speculative-slot allocation and dependency fencing in the vLLM KV manager and scheduler.

Merge Risk: Minimal · up to cad5a

No actionable merge-blocking risk is established for this change. It is mergeable after normal checks.

Pre-merge checks: 7 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Modeling And Data Evidence		The PR changes allocation and speculative burst selection (vllm_backend.rs:663–675, core.rs:3645–3652). The description reports matching prediction hashes, but does not provide prediction values o…	Provide reproducible before-and-after prediction evidence for the changed paths: an explained golden diff, an independent or held-out comparison, or a machine-readable output summary with anomaly checks.

Passed checks: 7 passed

Check name	Status	Explanation
Title check		The title identifies the behavioral change: removing native-MTP plumbing from the non-MTP vLLM scheduler pass.
Description check		The description covers the change, review map, compatibility contract, test and performance evidence, boundary cases, data provenance, and related work. The feature-support matrix and compatibility or…
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.
Cross-Layer Contract		The diff changes internal scheduler and allocator paths, not public inputs, schemas, defaults, or serialized outputs. The no-growth path has retry and dependency coverage in `vllm_backend.rs:2414-2449…
Compatibility Boundaries		The PR changes only internal scheduler and KV allocation code. It does not change public surfaces, schemas, versions, bindings, defaults, dependencies, or artifact configuration. The no-growth path pr…
Review Evidence		The description reports test and replay commands, results, boundary validation, and prediction-hash parity. It identifies the Tyche host and does not present fake-runner or production results as hoste…

Full details: Description check

Explanation

The description covers the change, review map, compatibility contract, test and performance evidence, boundary cases, data provenance, and related work. The feature-support matrix and compatibility or rollback concern fields are not explicit, but the description is mostly complete.

Full details: Cross-Layer Contract

Explanation

The diff changes internal scheduler and allocator paths, not public inputs, schemas, defaults, or serialized outputs. The no-growth path has retry and dependency coverage in vllm_backend.rs:2414-2449, and the existing block lifecycle test covers same-block growth at lines 2222-2242. Native MTP burst limits have coverage in native_mtp_tests.rs:427-446. The PR also reports matching prediction hashes across the replay workloads.

Full details: Compatibility Boundaries

Explanation

The PR changes only internal scheduler and KV allocation code. It does not change public surfaces, schemas, versions, bindings, defaults, dependencies, or artifact configuration. The no-growth path preserves the watermark and pending dependencies (vllm_backend.rs:663-675); native MTP drafts retain the previous burst limit (core.rs:3157-3159, 3336-3342, 3645-3652).

Full details: Review Evidence

Explanation

The description reports test and replay commands, results, boundary validation, and prediction-hash parity. It identifies the Tyche host and does not present fake-runner or production results as hosted CI evidence. The smaller-target retry checks are present in crates/core/src/engine/kv_manager/vllm_backend.rs:2413-2448. The PR does not cite inactive nested workflows as hosted CI evidence.

Full details: Modeling And Data Evidence

Explanation

The PR changes allocation and speculative burst selection (vllm_backend.rs:663–675, core.rs:3645–3652). The description reports matching prediction hashes, but does not provide prediction values or an explained golden diff. The timing table is not prediction-output evidence. The added test covers one allocation dependency case, not the measured outputs. The prior failure remains unresolved.

Resolution

Provide reproducible before-and-after prediction evidence for the changed paths: an explained golden diff, an independent or held-out comparison, or a machine-readable output summary with anomaly checks.

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[jthomson04]

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Multi-repo context ai-dynamo/dynamo, ai-dynamo/aiconfigurator

ai-dynamo/dynamo

• The published schema supports non-native ngram speculation with 1–5 speculative tokens and explicit acceptance rates. Preserve its policy-derived burst behavior. [::ai-dynamo/dynamo::deploy/operator/internal/aisimulate/schemas/latest.json:2112-2149]
• Dynamo request-trace replay uses AISimulate’s vLLM backend and derives block size from trace records, making it a relevant integration case for KV accounting. [::ai-dynamo/dynamo::benchmarks/request_trace/README.md:47-70] [::ai-dynamo/dynamo::benchmarks/request_trace/README.md:78-92]

ai-dynamo/aiconfigurator

• The compatibility reference models MTP depth separately from average accepted drafts: each step yields 1 + nextn_accepted on average. [::ai-dynamo/aiconfigurator::docs/cli_user_guide.md:962-978]
• Its vLLM release-contract test generates MTP with num_speculative_tokens: 2. [::ai-dynamo/aiconfigurator::tests/golden/generator/test_release_1_2_0_backend_contracts.py:305-319]

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Failed pre-merge checks:
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• crates/core/src/engine/scheduler/vllm/core.rs

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/core.rs

Source excerpt: The root `CODEOWNERS` is generated from `.github/codeowners/areas.yaml` - never hand-edit it; CI fails on drift.

CodeRabbit inference engine (AGENTS.md)

Files:

• crates/core/src/engine/kv_manager/vllm_backend.rs
• crates/core/src/engine/scheduler/vllm/core.rs

Source excerpt: Python describes and orchestrates work.

CodeRabbit inference engine (REVIEW.md)

Files:

• crates/core/src/engine/kv_manager/vllm_backend.rs
• crates/core/src/engine/scheduler/vllm/core.rs

Multi-repo context ai-dynamo/dynamo, ai-dynamo/aiconfigurator

ai-dynamo/dynamo

• GLM-5.3-Flash recipes combine MTP7 with prefix caching, in both aggregated and disaggregated deployments. These are useful fallback-parity cases for the new native-MTP eligibility check. [::ai-dynamo/dynamo::docs/fern/pages/recipes/model-recipes/glm-5-3-flash.mdx:453-454]

ai-dynamo/aiconfigurator

• The compatibility model scales verification by nextn + 1, but in mixed batches applies that multiplier only to decode tokens, not context tokens. This provides a reference for reviewing burst and prefill accounting. [::ai-dynamo/aiconfigurator::aic-core/src/aiconfigurator_core/sdk/backends/base_backend.py:1738-1758]

[jasonqinzhou]

APPROVE · 9.3/10 · high confidence

What this PR does

This PR reduces host CPU and per-pass memory cost in the shared vLLM/TRT-LLM scheduler by restoring the no-growth KV-allocation shortcut, compacting scheduled MTP state, and computing native-MTP eligibility once per worker.

The write-fence retry regression protects the subtle dependency behavior an allocator shortcut could lose, and the paired workload measurements distinguish host execution cost from simulated outputs.

Why this score

9.3/10; APPROVE as a code-review recommendation. The shortcut preserves allocation watermarks and pending write dependencies, and the compact MTP state preserves native and policy-derived speculative burst limits. All 295 targeted local tests passed, and exact-head Full CI and replay-consumer tests passed. No actionable findings or comments are recommended. Applicable CODEOWNER approval remains a separate merge gate.