[GG] Bound EXL3 Trellis prefill arena capacity - #210
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📝 WalkthroughWalkthroughEXL3 prefill execution now supports configurable, scheduler-bounded capacity for uniform and mixed-Trellis runtimes. Oversized batches are dispatched in slices, with routing and output preservation covered by expanded tests. ChangesEXL3 prefill capacity
Estimated code review effort: 4 (Complex) | ~45 minutes Sequence Diagram(s)sequenceDiagram
participant EXL3Runtime
participant TrellisRuntime
participant OutputBuffer
EXL3Runtime->>EXL3Runtime: Read configured prefill capacity
EXL3Runtime->>TrellisRuntime: Dispatch capacity-sized prefill slice
TrellisRuntime->>OutputBuffer: Return slice result
EXL3Runtime->>OutputBuffer: Copy slice result into final output
Possibly related PRs
Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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🧹 Nitpick comments (1)
vllm/model_executor/layers/quantization/exl3.py (1)
1837-1845: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick winExtract the duplicated prefill-capacity validation into a shared helper.
The "resolve
VLLM_EXL3_PREFILL_CAPACITY, reject if it exceedsmax_num_batched_tokens" block is duplicated verbatim between the mixed-bitrate (_mixed_rank_sliced_runtime) and uniform (_rank_sliced_runtime) planners. A future edit to the message or threshold logic in one place could silently diverge from the other.♻️ Proposed extraction
def _resolve_prefill_capacity(max_batched_tokens: int) -> int: """Resolve VLLM_EXL3_PREFILL_CAPACITY within the scheduler bound. Args: max_batched_tokens: The scheduler's max_num_batched_tokens for this layer's runtime. Returns: The validated prefill capacity (defaults to max_batched_tokens). Raises: ValueError: If the configured capacity exceeds max_batched_tokens. """ prefill_capacity = _positive_env_int( "VLLM_EXL3_PREFILL_CAPACITY", max_batched_tokens ) if prefill_capacity > max_batched_tokens: raise ValueError( "VLLM_EXL3_PREFILL_CAPACITY cannot exceed " "max_num_batched_tokens: " f"capacity={prefill_capacity}, max={max_batched_tokens}" ) return prefill_capacityThen in both call sites:
- prefill_capacity = _positive_env_int( - "VLLM_EXL3_PREFILL_CAPACITY", max_batched_tokens - ) - if prefill_capacity > max_batched_tokens: - raise ValueError( - "VLLM_EXL3_PREFILL_CAPACITY cannot exceed " - "max_num_batched_tokens: " - f"capacity={prefill_capacity}, max={max_batched_tokens}" - ) + prefill_capacity = _resolve_prefill_capacity(max_batched_tokens)Also applies to: 2031-2043
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the rest with a brief reason, keep changes minimal, and validate. In `@vllm/model_executor/layers/quantization/exl3.py` around lines 1837 - 1845, Extract the duplicated VLLM_EXL3_PREFILL_CAPACITY resolution and max_batched_tokens validation from _mixed_rank_sliced_runtime and _rank_sliced_runtime into a shared _resolve_prefill_capacity helper. Have the helper return the validated capacity while preserving the existing default, threshold, and ValueError message, then replace both call sites with calls to it.
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Nitpick comments:
In `@vllm/model_executor/layers/quantization/exl3.py`:
- Around line 1837-1845: Extract the duplicated VLLM_EXL3_PREFILL_CAPACITY
resolution and max_batched_tokens validation from _mixed_rank_sliced_runtime and
_rank_sliced_runtime into a shared _resolve_prefill_capacity helper. Have the
helper return the validated capacity while preserving the existing default,
threshold, and ValueError message, then replace both call sites with calls to
it.
ℹ️ Review info
⚙️ Run configuration
Configuration used: Organization UI
Review profile: CHILL
Plan: Pro Plus
Run ID: 37e103c5-7525-4626-bb07-470f6ae7f85a
📒 Files selected for processing (3)
tests/quantization/test_exl3_prefill_plan.pyvllm/envs.pyvllm/model_executor/layers/quantization/exl3.py
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Addressed CodeRabbit's duplication nit in |
Co-authored-by: OpenAI Codex <noreply@openai.com>
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Final branch-history correction: PR #210 is now cleanly based on current |
Rental-machine validation — PASS focused; full-model sweep BLOCKEDTested PR head: Host: Vast.ai 46335896, Ubuntu 24.04.4 / kernel 7.0.0-28, 2x EPYC 9455, 1.5 TiB RAM. Commands and results
Unset/blank capacity, exact one-shot capacity, slicing/tail preservation, invalid-value rejection, and cache-key coverage all passed. The full GLM Report and durable logs: field-review result No unreported source modifications were present; the tested worktrees were clean. Repair-campaign update — full-model capacity claim verifiedRe-fetched head: Full GLM-5.2 gate:
Thus the field claim is real and tunable. Capacity 1,024 is the best measured balance on this host: 480.1 MiB/rank returned and 64,000 extra KV tokens for ~2% short-prefill cost, with decode TPOT flat. Capacity 512 is valid but is a capacity-first tradeoff. One independent appliance issue was exposed: cold startup reaches ready before several first-prefill Triton/CuTeDSL shapes compile. A dedicated prime was excluded from medians; steady repeats had no new prefill JIT. This is not attributed to #210. Commands were the turnkey one-process full gate followed by
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| Capacity | Target arena | Draft arena | Available KV | Result |
|---|---|---|---|---|
| unset (=3,072) | 759.8 MiB | 1,054.2 MiB | 0.82 GiB | FAIL KV validation; estimated max 110,080 |
| 1,024 | 279.7 MiB | 414.1 MiB | 1.91 GiB / 257,024 tokens | PASS; 126,327-token retrieval exact |
The explicit capacity returned 1,120.2 MiB/rank across target+draft. This is a direct controlled observation, not an estimate: unset capacity deterministically could not serve one 131,072-token request, while capacity 1,024 booted, captured graphs, returned arithmetic 391, passed every measured request, and retrieved Kyoto: AJY-4896 at 50% depth from 126,327 tokenizer-measured tokens with no degeneration.
Steady MTP3 sample: median PP 1,783.0 tok/s; median TPOT 15.967 ms (62.63 tok/s); MAL 3.111; draft acceptance 70.37%; no request errors, preemptions, OOMs, or Xids. A separate prime absorbed the known cold first-shape JIT gap; no further JIT occurred during the measured sample or 126K retrieval.
Exact logs and JSON: field ledger at a9becf0.
Capacity 2,048 was also healthy (519.7 MiB target + 734.1 MiB draft, 183,552 KV), but improved median 3,072-token PP by only 0.29% over capacity 1,024 while surrendering 73,472 KV tokens; TPOT was unchanged. Capacity 1,024 therefore remains the measured balanced choice rather than promoting noise-sized throughput.
Matched MTP3 versus MTP5 workload check
The same exact #210 integration and capacity-1,024 shape was repeated with six identical 3,072-token prompt bodies, a reusable compile cache, GMU 0.92, TP4/DCP4, and dynamic NVFP4 KV. Both depths exposed 257,024 KV tokens. A separate prime absorbed all post-ready JIT before measurement.
| Draft depth | Median PP | Draft acceptance | MAL | Mean TPOT | Median TPOT | p95 TPOT |
|---|---|---|---|---|---|---|
| MTP3 | 1,767.3 tok/s | 84.10% | 3.523 | 16.962 ms | 15.059 ms | 23.647 ms |
| MTP5 | 1,775.5 tok/s | 39.84% | 2.992 | 25.104 ms | 13.663 ms | 48.709 ms |
MTP5's attractive median concealed two 48.4–48.8 ms requests; its mean was 48% worse and p95 106% worse than MTP3. Prefill differed by only 0.46%. MTP3 remains the balanced candidate for this workload. Both modes completed every request; the MTP3 process-group teardown released all four GPUs cleanly.
Request-level JSON and complete logs: evidence at 3539ab8.
Exact r14-base composition — PASS
The deployable image boundary was reconstructed from the published r14 lock, not inferred from the moving development branch. Replaying blackwell-llm-docker@2464cc03 produced the exact published vLLM tree 749050edab1b6664937c52fa1b0be360be632c1e; merged #211 and both exact #210 commits then cherry-picked without conflict.
Candidate 5a7ac1481e1fc24b0bbc35efe160b2ff34797bee (tree 46f6f5bf387ee2c07712808cfe64f722fa2b99f3) passed:
- Ruff on every changed runtime/test file;
- [GG] Bound EXL3 Trellis prefill arena capacity #210/[GG] fix(spec-decode): centralize probabilistic draft sampling #211 CPU union: 16/16;
- [GG] fix(spec-decode): centralize probabilistic draft sampling #211 GPU union on physical GPU 0: 11/11 cold and 11/11 warm.
The unchanged r14 native binaries were temporarily linked for source-worktree import, with hashes recorded, and removed afterward; the exact source worktree was clean. This closes the source-version gap between the focused/current-dev evidence and the image that will receive the final combined GLM gate.
Exact logs and reconstruction contract: r14 ledger at 4d2aa18.
2 participants
Summary
Add an opt-in bound for EXL3 Trellis prefill arena capacity and execute larger eager prefills as contiguous slices through the same caller-owned arena.
Completes the caller-side capacity work from local-inference-lab/sparkinfer#93. SparkInfer PR #100 supplies the separate cheap sizing query and reachable-block planner correction.
Value
The current 3072-token GLM-5.2 prefill plan reserves about 1.05 GiB/rank. A 512-row opt-in plan prices near 254 MiB/rank, potentially returning roughly 800 MiB/rank to KV capacity. This PR exposes that tradeoff without changing the shipped default; throughput/TTFT selection remains an operator measurement decision.
Change
VLLM_EXL3_PREFILL_CAPACITYfor homogeneous and mixed rank-sliced EXL3 Trellis.max_num_batched_tokens; existing one-shot planning and dispatch remain unchanged.Dependency base
The feature was developed against prerequisite PR #190 head
46c36c0f89a44fade81947c1bf84faede14755ad. After #190 squash-merged, this branch was rewritten cleanly onto currentdev/gilded-gnosis: two commits ahead, zero behind, exactly the intended three-file delta.Verification
CPU-isolated against the pulled r12 image dependencies:
Suite:
tests/quantization/test_exl3_prefill_plan.py.Changed-file Ruff lint passes. Regressions cover opt-in slicing, exact capacity boundaries and short tails, route/output preservation, default one-shot dispatch, mixed-mode slicing, invalid/above-bound values, parity fallback, and capture rejection.
CodeRabbit's only finding was the duplicated homogeneous/mixed capacity validation; both now call one shared resolver without semantic change.
No live-GPU or throughput result is claimed. All four GPUs remain occupied by the unchanged production service; production was not rebuilt or restarted.