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Fix vLLM fakequant calibration for hybrid attention models - #2414
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughThe example now supports configurable vLLM versions and hybrid-model serving instructions. Calibration selects compatible block-reservation policies, assigns blocks to requests, and preserves primary errors during cleanup. Tests cover allocation and cleanup fallback behavior. ChangesvLLM calibration and serving
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix Sequence Diagram(s)sequenceDiagram
participant Calibration
participant Scheduler
participant ModelRunner
Calibration->>Scheduler: Allocate blocks and submit requests
Scheduler-->>Calibration: Return blocks to zero
Calibration->>Scheduler: Run zero-token cleanup
Scheduler->>ModelRunner: Use finish_requests if unsupported
ModelRunner-->>Calibration: Return cleanup result
Suggested reviewers: Merge Risk: ⚪ Minimal · up to The documented hybrid calibration workflow receives scratch KV-cache blocks, so no actionable merge-blocking risk remains. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
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Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
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Inline comments:
In `@examples/vllm_serve/README.md`:
- Line 25: Update the direct-install documentation around the vLLM and ModelOpt
setup to include copy-pastable pip commands, pinning vLLM to 0.28.0 and
including the ModelOpt extras required by the example; link to or reuse the
existing installation references rather than leaving the commands unconnected or
unpinned.
In `@examples/vllm_serve/vllm_ptq_utils.py`:
- Around line 214-217: Update the cleanup exception handling around
execute_model and finish_requests so cleanup failures are raised when
calibration completed without an active exception, while preserving any active
calibration exception and chaining the cleanup failure. Ensure failed cleanup
never allows the next calibration batch to proceed with stale request-scoped
state.
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2414 +/- ##
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+ Coverage 75.52% 75.85% +0.33%
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Files 542 590 +48
Lines 63778 69500 +5722
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+ Hits 48167 52721 +4554
- Misses 15611 16779 +1168
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Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
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Claude review passed — no blocking issues found. LGTM
Scope
Trigger comment was a bare /claude review, so this is a full review. 4 files changed (+202/−35): examples/vllm_serve/vllm_ptq_utils.py, examples/vllm_serve/Dockerfile, examples/vllm_serve/README.md, tests/gpu_vllm/torch/quantization/test_vllm_dynamic_modules.py. No modelopt/ files touched, so no mode-registration / config-schema / public-API surface to check. I reviewed all four, plus examples/vllm_serve/fakequant_worker.py and modelopt/torch/quantization/model_calib.py for calling context.
Note for anyone reading the CI diff: a two-dot origin/main HEAD diff on this branch also surfaces a large amount of unrelated reverse-drift from main (hf_ptq KV AutoQuantize, mlflow .experiment.json, llm_sparsity export). None of that belongs to this PR; I scoped the diff to the four files above.
Findings
CRITICAL: 0 · IMPORTANT: 0 · SUGGESTION: 4
All four are non-blocking:
- Silent fallback to empty block tables — the innermost
except ImportErrorreinstates exactly the NaN-producing behavior this PR fixes, with no warning. Both import targets are unstable vLLM internals. Also flags the shared-mutableempty_block_idsaliasing across requests (pre-existing). - Non-actionable remediation text in the block-budget error — "calibration sequence length" isn't user-configurable in this example;
--max-model-len/--gpu-memory-utilizationare the real levers. - Dropped
hasattr(finish_requests)guard degrades the error message — fail-closed is right, but anAttributeErrorbecomes the primary exception instead of the real cleanup failure. Same comment notes the cleanup step doesn't mirror theoutput is None → sample_tokens(None)handling used by the main step. sys.exc_info()[1]infinallyis broader than intended — correct for the current call path (verifiedforward_loop(model)is not called from inside anexceptinmodel_calib.py), but an explicitexcept BaseException as ... / raisestructure removes the fragility.
What I verified as correct
- Block accounting. IDs are handed out globally disjoint starting at 1 (block 0 = null block), which matches vLLM's shared
BlockPooladdress space across hybrid KV cache groups, so an ID valid in one group's tensor is valid in all. Thenext_block_id > kv_cache_config.num_blocksbound is off-by-one-correct: the last ID handed out isnext_block_id - 1 ≤ num_blocks - 1, and the reported "requires N / N available" numbers are consistent with the 1-based start. Using each group's ownkv_cache_spec.block_sizeis what makescdivcollapse to a single block forMambaSpec(whoseblock_sizeismax_model_len), so the 0.26 fallback matches the 0.28 helper's intent. - Version gating.
new_block_ids_to_zerogoes through_create_new_data_cls, which filters ondataclasses.fields, so vLLM 0.26 (no such field) drops it rather than raisingTypeError— and the redundantscheduler_fieldscheck in the allocator keepsblocks_to_zeroatNonethere.CachedRequestData.make_empty()incleanup_outputmatches what the primaryscheduler_outputalready used, so it's not a new version dependency. - Cleanup error semantics. All four branches of
_cleanup_calibration_requestsbehave as documented: cleanup success preserves an in-flight calibration error; both-fail with a calibration error re-raises the calibration error (chained throughfinish_error→execute_error); both-fail without one fails closed onfinish_error. Stalefinished_req_idsin the cleanup step are harmless — vLLM's_update_statespops with a default andremove_requestno-ops on unknown IDs — so the failure path where requests were never registered is safe. - Docs.
.[all,mlflow]are both real extras (pyproject.toml:136,142) and the linked_installation_for_Linux.rstexists.CALIB_BATCH_SIZEin the new error string is a real env var. - Tests.
SimpleNamespace/Mockare already imported in the test module;tests/gpu_vllm/is the right home since_load_example_moduletransitively importsvllm. The parametrized test plus the fallback test do overlap on thehas_calibration_error=Falsecase, but each asserts a distinct outcome (fail-closed vs. recovery), so I wouldn't collapse them.
Risk
Low. Example-only change with no library surface, gated on dataclasses.fields introspection for the newer scheduler field and falling back to prior behavior on older vLLM. The main residual risk is the reliance on vLLM private internals (_reserved_block_count, MambaSpec.mamba_cache_mode, num_speculative_blocks, vllm_config.num_lookahead_tokens) — I could not statically verify those attribute names since vLLM isn't installed in this checkout, so I'm taking the reported end-to-end 0.26.0 / 0.28.0 TP8 validation as evidence they resolve. Finding 1 is the one worth acting on, precisely because that reliance currently degrades silently.
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Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
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Inline comments:
In `@examples/vllm_serve/vllm_ptq_utils.py`:
- Around line 61-66: Update the missing vLLM warmup block-reservation helper
branch in the calibration flow to raise an error instead of warning and
returning empty block tables. Keep the failure message version-agnostic so it
applies to every unsupported vLLM configuration, including documented versions
such as 0.9.0 and 0.19.1.
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The cleanup paths are tested, but the new block allocator—the core NaN fix—has no automated regression coverage.
Needs action:
- Add focused tests for
_allocate_calibration_blockscovering vLLM 0.26/0.28 reservation branches, multiple cache groups/requests, block-zero exclusion,new_block_ids_to_zero, and insufficient capacity.
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
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The allocator coverage improved, but the version-specific reservation adapters remain untested, leaving the core compatibility boundary exposed.
Needs action:
- 💬 Author replied: the allocator is tested with a mocked policy and adapters were manually validated — still add automated tests for both
_get_calibration_block_countbranches and their version-specific arguments/semantics.
No action needed:
- ✔️ Resolved since the last review: allocation/accounting coverage, cleanup failure handling, legacy cleanup diagnostics, explicit exception preservation, documentation setup commands, and fallback warning/aliasing.
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
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The previously missing vLLM 0.26/0.28 reservation-adapter coverage is now present, and all prior critical concerns are resolved.
Needs action:
- Obtain human sign-off because this PR justifiably modifies an existing GPU test file to add focused allocator, version-adapter, capacity, and cleanup regression coverage.
No action needed:
- ✔️ Resolved since the last review: automated coverage now verifies both reservation branches, including vLLM 0.28 forwarded arguments and vLLM 0.26 attention, cross-attention, and aligned-Mamba semantics.
Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com>
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LGTM! Thanks for the findings.
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### What does this PR do? Type of change: Bug fix Fix fakequant calibration for hybrid attention/Mamba models, including NVIDIA Nemotron-3-Nano, on vLLM 0.26 and 0.28. The manual calibration scheduler path previously submitted requests with empty KV-cache block tables. Hybrid models require scheduler-compatible cache state during prefill; on current vLLM releases the empty tables caused the Mamba state to use the reserved null block and calibration activations became NaN. Request cleanup also no longer matched the vLLM 0.28 execution lifecycle, which could leave request-scoped state in the persistent batch. This PR: - Allocates non-null scratch blocks for every KV-cache group using the vLLM warmup reservation policy. - Supports both the vLLM 0.28 reservation helper and the equivalent vLLM 0.26 calculation. - Passes newly allocated blocks through `new_block_ids_to_zero` when that scheduler field is available. - Validates that the calibration batch fits in the configured cache and reports how to reduce calibration demand if it does not. - Cleans up calibration requests through a zero-token scheduler step on current vLLM, with a direct cleanup fallback for older runners. - Updates the example Dockerfile to default to vLLM 0.28.0 while retaining vLLM 0.26.0 through `VLLM_VERSION`. - Documents the validated Nemotron-3-Nano NVFP4 KV-cache workflow and clarifies that reducing `--max-num-batched-tokens` is not required. ### Usage Build the default vLLM 0.28.0 image: ```bash docker build -f examples/vllm_serve/Dockerfile \ -t vllm-modelopt:v0.28.0 . ``` Build with vLLM 0.26.0: ```bash docker build --build-arg VLLM_VERSION=0.26.0 \ -f examples/vllm_serve/Dockerfile \ -t vllm-modelopt:v0.26.0 . ``` Calibrate and serve Nemotron-3-Nano with NVFP4 KV-cache fakequant: ```bash KV_QUANT_CFG=NVFP4_KV_CFG QUANT_CALIB_SIZE=512 \ python examples/vllm_serve/vllm_serve_fakequant.py \ <nemotron3_nano_model_path> \ --trust-remote-code --enforce-eager -tp 8 \ --max-model-len 8192 --host 0.0.0.0 --port 8000 ``` ### Testing Validated on omniml-a0 with `NVIDIA-Nemotron-3-Nano-30B-A3B-BF16`, tensor parallel size 8, `NVFP4_KV_CFG`, `QUANT_CALIB_SIZE=512`, and `--max-model-len 8192`. No `--max-num-batched-tokens` override was used. - vLLM 0.28.0: - All 512 calibration samples completed. - No NaNs or cache-cleanup warnings were observed. - The server started and `/health` passed. - An OpenAI-compatible completion request returned coherent generated text. - vLLM 0.26.0: - Repeated the same 512-sample TP8 calibration with the official `vllm/vllm-openai:v0.26.0` image. - No NaNs were observed. - The server started, passed `/health`, and returned coherent generated text. - Docker: - Built and verified the updated vLLM 0.28.0 image. - Focused tests: - `tests/examples/vllm_serve/test_vllm_mlflow_utils.py`: 32 passed. - Cleanup failure, missing legacy API, and legacy fallback tests: 5 passed on both vLLM 0.26.0 and 0.28.0. - Repository hooks: - Targeted pre-commit hooks for every changed Python, Markdown, and Docker file: passed. - `git diff --check`: passed. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ — added focused coverage for fail-closed cleanup, exception chaining, and the legacy cleanup fallback; the full regression was also validated end to end. - Did you update Changelog?: N/A - Did you get Claude approval on this PR?: N/A ### Additional Information The change is quantization-format agnostic. It corrects the calibration scheduler and cache lifecycle rather than special-casing `NVFP4_KV_CFG` or using an NVFP4 cast path. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Added support for configuring the vLLM version through `VLLM_VERSION`, with vLLM 0.28.0 as the default. - Added calibration and serving guidance for hybrid attention/Mamba models, including Nemotron 3 Nano with NVFP4 KV-cache fake quantization. - **Bug Fixes** - Improved calibration block handling across supported vLLM versions. - Improved calibration cleanup to preserve original errors and provide reliable fallback behavior when standard cleanup is unavailable. - **Documentation** - Documented tested versions, direct installation commands, ModelOpt setup, serving options, and guidance to avoid NaNs during batched serving. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Kinjal Patel <kinjalpravin@nvidia.com> Signed-off-by: Chad Voegele <cvoegele@nvidia.com>
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What does this PR do?
Type of change: Bug fix
Fix fakequant calibration for hybrid attention/Mamba models, including NVIDIA Nemotron-3-Nano, on vLLM 0.26 and 0.28.
The manual calibration scheduler path previously submitted requests with empty KV-cache block tables. Hybrid models require scheduler-compatible cache state during prefill; on current vLLM releases the empty tables caused the Mamba state to use the reserved null block and calibration activations became NaN. Request cleanup also no longer matched the vLLM 0.28 execution lifecycle, which could leave request-scoped state in the persistent batch.
This PR:
new_block_ids_to_zerowhen that scheduler field is available.VLLM_VERSION.--max-num-batched-tokensis not required.Usage
Build the default vLLM 0.28.0 image:
docker build -f examples/vllm_serve/Dockerfile \ -t vllm-modelopt:v0.28.0 .Build with vLLM 0.26.0:
docker build --build-arg VLLM_VERSION=0.26.0 \ -f examples/vllm_serve/Dockerfile \ -t vllm-modelopt:v0.26.0 .Calibrate and serve Nemotron-3-Nano with NVFP4 KV-cache fakequant:
Testing
Validated on omniml-a0 with
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16, tensor parallel size 8,NVFP4_KV_CFG,QUANT_CALIB_SIZE=512, and--max-model-len 8192. No--max-num-batched-tokensoverride was used./healthpassed.vllm/vllm-openai:v0.26.0image./health, and returned coherent generated text.tests/examples/vllm_serve/test_vllm_mlflow_utils.py: 32 passed.git diff --check: passed.Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
The change is quantization-format agnostic. It corrects the calibration scheduler and cache lifecycle rather than special-casing
NVFP4_KV_CFGor using an NVFP4 cast path.Summary by CodeRabbit
New Features
VLLM_VERSION, with vLLM 0.28.0 as the default.Bug Fixes
Documentation