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[1/2] Reload versioned quantizer recipes in vLLM fake-quant serving - #2888
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: reloading versioned quantizer recipes in vLLM fake-quant serving. 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. Security Anti-Patterns No CRITICAL security anti-pattern is introduced. The changed ModelOpt and examples code uses yaml.safe_load/yaml.safe_dump; the existing checkpoint load usestorch.load(..., weights_only=True). …Full details: Security Anti-Patterns
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No CRITICAL security anti-pattern is introduced. The changed ModelOpt and examples code uses
yaml.safe_load/yaml.safe_dump; the existing checkpoint load usestorch.load(..., weights_only=True). The diff adds noweights_only=False,allow_pickle=True, hardcodedtrust_remote_code=True, external-inputeval()/exec(), or# nosec. The pull request changes no dependency manifests.
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Review comments at @examples/vllm_serve/vllm_ptq_utils.py:
- Line 347: Update the versioned recipe handling around
raw_recipe.get("quantizers") so an empty but valid quantizer mapping is
dispatched through the versioned path instead of falling back to load_recipe.
Preserve validation of the versioned mapping and ensure quantizers: {} produces
a configuration with quantizers disabled.
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examples/vllm_serve/vllm_ptq_utils.pyexamples/vllm_serve/vllm_reload_utils.pymodelopt/torch/quantization/nn/modules/tensor_quantizer.pytests/examples/vllm_serve/test_fakequant_example.pytests/unit/torch/quantization/test_tensor_quant_cpu.py
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- vLLM fake-quant reload accepts a versioned quant_recipe.yaml
({format: modelopt_quantizer_recipe, version: 1, quantizers: {HF name: state}})
whose entries are properties-only ModelOpt quantizer states. Megatron-Bridge's
HF export will write this format.
- TensorQuantizer.attribute_config_from_modelopt_state inverts
set_from_attribute_config, so reload restores every quantizer attribute
(e.g. use_constant_amax, unsigned, dynamic type), not a fixed subset.
The current flat recipe format still loads.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
vLLM 0.31 also registers an MoE layer's shared expert under its runner (experts._shared_experts._layer), so the state dict lists the shared expert's quantizers under both names. load_state_dict_from_path updated only the first name, and model.load_state_dict then copied the stale alias entry back over it: every shared-expert input range silently kept the one-sample reload calibration, behind a spurious "missing from every rank" warning. Give aliases the loaded value and leave them out of that warning. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Approve: the recipe-inversion helper correctly reverses set_from_attribute_config, the alias fix closes the stale-overwrite bug, and both carry focused tests.
No action needed.
No action needed:
attribute_config_from_modelopt_statemaps_disabled/_dynamicback and keeps onlyQuantizerAttributeConfigfields;_calibratoris never saved in the properties-only state, so it can't leak through.- Flat Megatron-exporter recipes now restore every config field instead of five; this is the stated intent and is backward compatible (
_disabledis still required, as before). _alias_state_dict_keysusesnamed_modules(remove_duplicate=False), so nested aliases are covered; the alias copy only runs for keys that were actually loaded.- Tests cover a YAML round-trip across five quantizer flavors, unknown-version rejection, and the alias regression; the only edit to an existing test adds one parametrize case.
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Claude review — 3 findings (CRITICAL: 0, IMPORTANT: 3, SUGGESTION: 0)
Scope: full review. 5 files changed (129+/7-); reviewed all of them — modelopt/torch/quantization/nn/modules/tensor_quantizer.py, examples/vllm_serve/vllm_ptq_utils.py, examples/vllm_serve/vllm_reload_utils.py, and both test files. Read set_from_attribute_config, _get_properties_for_modelopt_state, QuantizerAttributeConfig, QuantizerCfgEntry, set_quantizer_by_cfg, and the Megatron exporter's flat-recipe writer for the surrounding contracts.
The core idea is right and the end-to-end NLL/greedy-generation evidence in the description is convincing — inverting set_from_attribute_config is clearly better than the hardcoded five-key subset, and the shared-expert alias bug is real and well diagnosed. All three findings are about the generality of the new contracts rather than the measured paths.
Most impactful
1. The alias fix's direction is decided by module-traversal order, not by the checkpoint (vllm_reload_utils.py:681) — first_names.setdefault(module, name) makes canonical whichever name vLLM's __init__ assigned first, while the checkpoint supplies whichever name hf_to_vllm_mapper emits. When those disagree (e.g. self.experts = FusedMoE(...) before self.shared_expert = ...), key in saved_quant_dict is false for every alias, the copy loop no-ops, and the original bug returns — and worse, _validate_quantizer_ranges (which uses named_modules() with the default remove_duplicate=True, yielding the same canonical name) raises Active quantizer ranges are missing or cannot be loaded, so serving fails outright. It works on Qwen3.6-35B-A3B because that model happens to assign in the favorable order; it is a per-model/per-vLLM-version coin flip. The new test only exercises the favorable order too — swapping its two assignment lines should reproduce the failure. Suggested an order-independent, checkpoint-driven grouping in the inline comment. Same comment also notes and key not in alias_keys suppresses the missing-key warning for every aliased key unconditionally, including ones genuinely absent from all ranks.
2. A self-identified modelopt_quantizer_recipe v1 can still fall through to the PTQ-recipe parser (vllm_ptq_utils.py:340) — after the format/version check passes, quantizers: {} (or a key missing _quantizer) fails the shared truthiness gate and drops into load_recipe + assert isinstance(recipe, ModelOptPTQRecipe), so the user gets Expected PTQ recipe, but got ... for a file that correctly declared itself. This is the empty-recipe gap CodeRabbit's merge-risk note raised. Note the new version: 2 test case passes, but a version: 1, quantizers: {} case would pass for the wrong reason — it raises from the wrong parser — so assert on the message.
3. attribute_config_from_modelopt_state is not a faithful inverse for bias and rotate (tensor_quantizer.py:292) — _bias is in the properties-only state while _bias_calibrator is skipped, so the reconstructed quantizer has bias set with no calibrator; _rotate holds a RotateConfig instance, which yaml.safe_dump cannot represent at all. Neither is reachable through today's vLLM path (the Megatron exporter rejects bias is not None upstream), but this is a new public static method documented as inverting set_from_attribute_config generally, and the guard lives in another package. The new round-trip test's five parametrizations cover none of these. Suggested raising on the fields the inverse can't reproduce. Also flagged cfg.pop("enable") at the call site: safe for exporter recipes, KeyError for anything else.
Risk
Moderate. Nothing here is a silent-numerics bug, and the happy paths are well validated. Finding 1 is the one I'd want resolved before merge — it is the same class of silent-wrong-range bug this PR exists to fix, just relocated into traversal order, and its failure mode on the unfavorable ordering is a hard serving failure rather than a warning. Findings 2 and 3 are robustness of new contracts that the sibling Megatron-Bridge PR will be the first real consumer of, so they are cheapest to tighten now.
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… uses vLLM can list either name of a module registered twice first, so propagate the checkpoint's value to every name instead of assuming the first one is in the checkpoint. Also reject a versioned recipe whose quantizers mapping is empty or malformed instead of passing it to the PTQ recipe parser. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
| if quant_config["recipe_path"]: | ||
| recipe_path = Path(quant_config["recipe_path"]) | ||
| raw_recipe = yaml.safe_load(recipe_path.read_text()) if recipe_path.is_file() else None | ||
| if isinstance(raw_recipe, dict) and raw_recipe.get("format") == QUANTIZER_RECIPE_FORMAT: |
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is it possible to extend this to megatron-lm as well to keep it consistent?
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Done in 5b0c000. export_mcore_gpt_to_hf_vllm_fq (the Megatron-LM path) now writes the same versioned quant_recipe.yaml (format/version/quantizers) as the Megatron-Bridge export. The header constants live in modelopt.torch.export.plugins.vllm_fakequant_hf, shared by the exporter and the vLLM loader. The loader still accepts the older flat mapping, so existing exports keep loading.
…export too export_mcore_gpt_to_hf_vllm_fq now writes quant_recipe.yaml with the same format/version header as Megatron-Bridge's export; the vLLM loader still reads the older flat mapping. The header constants move to modelopt.torch.export.plugins.vllm_fakequant_hf so the exporter and the vllm_serve example share them. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Inline comments:
Review comments at @modelopt/torch/export/plugins/vllm_fakequant_megatron.py:
- Line 164: Update the exporter’s quantizer-recipe flow so an empty extracted
recipe removes any stale quantizer sidecars and skips writing both quantizer
state and recipe files; preserve the existing write path for non-empty recipes.
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examples/vllm_serve/vllm_ptq_utils.pymodelopt/torch/export/plugins/vllm_fakequant_hf.pymodelopt/torch/export/plugins/vllm_fakequant_megatron.pytests/gpu_megatron/torch/export/test_vllm_fakequant_megatron_export.py
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… is quantized The vLLM loader rejects an empty versioned recipe, so an export with no captured quantizer on any rank now removes quantizer_state.pth and quant_recipe.yaml instead, like Megatron-Bridge's export. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
### What does this PR do? Type of change: new feature `examples/megatron_bridge/export_quantized_megatron_to_hf.py --vllm_fakequant_export` now exports through Megatron-Bridge instead of ModelOpt's Megatron exporter: it checks the quantizers, folds the weight quantizers into BF16 weights (`mtq.fold_weight`) and calls `bridge.save_hf_pretrained`. Megatron-Bridge writes the enabled activation and KV-cache quantizers to `quantizer_state.pth` and a versioned `quant_recipe.yaml` under HF names, which `vllm_serve_fakequant.py <dir>` loads automatically. This is how the numerics team evaluates QAD checkpoints in vLLM (OMNIML-6019). What the Megatron-Bridge path adds over the old exporter: - any TP / EP / PP (`--tp_size` is new and only allowed with this flag), and every architecture Megatron-Bridge maps; - VLMs: a QAD checkpoint holds only the language model, so the vision tower is loaded from HF first; - weight-only formats (e.g. GGML IQ2) leave no quantizer enabled after folding, so no sidecars are written and stock `vllm serve` loads the export. `mtq.fold_weight` now drops every buffer of a folded weight quantizer (e.g. a pinned GGML payload), not only `_amax` / `_pre_quant_scale`; otherwise Megatron-Bridge could not save a folded IQ2 model. `SVDQuantLinear.fold_weight` reads its low-rank factors (also buffers of the weight quantizer) before folding, so SVDQuant folds keep their correction term. `mtq.fold_weight` also folds sequential weight quantizers (e.g. W4A8 INT4 + FP8 weights), which it used to skip, leaving them unfolded and enabled. `check_mcore_vllm_fakequant_quantizers` collects the existing exporter's checks (unsupported activation settings, MTP and `lm_head` quantization) so both paths reject the same inputs before anything is written. Builds on #2888 (vLLM reload of the versioned recipe, merged); #2873 added the flag on the old exporter. Needs a Megatron-Bridge with NVIDIA-NeMo/Megatron-Bridge#6565 (merged, not yet in a NeMo container); the script raises a clear error otherwise. The tests do not skip on an older Megatron-Bridge, so CI cannot silently stop covering the export. CI: the new `.github/actions/megatron-from-source` action replaces the nemo:26.08 container's Megatron-Bridge and Megatron-Core sources with Megatron-Bridge `1d9caf7324` (#6565's merge commit) and its pinned Megatron-LM in every CI job that runs in a NeMo container (today the `gpu_megatron` GPU tests and the `megatron_bridge` example tests), so the new tests run in CI. Remove it once a NeMo container includes #6565. The Pre-Requisites in `examples/megatron_bridge/README.md` show the same checkout and mounts for running locally. ### Usage ```bash torchrun --nproc_per_node 8 export_quantized_megatron_to_hf.py \ --hf_model_name_or_path Qwen/Qwen3-8B \ --megatron_path /output/qwen3_8b_nvfp4_qad/checkpoints \ --tp_size 8 --vllm_fakequant_export \ --export_unified_hf_path /tmp/Qwen3-8B-NVFP4-QAD-vllm-fq python examples/vllm_serve/vllm_serve_fakequant.py /tmp/Qwen3-8B-NVFP4-QAD-vllm-fq -tp 8 ``` ### Testing Ran in nemo:26.10.rc3 with NVIDIA-NeMo/Megatron-Bridge#6565, 2× RTX 6000 Ada, unless noted: - `tests/gpu_megatron/torch/export/test_vllm_fakequant_mbridge_export.py` (new): tiny Nemotron-H (NVFP4 + FP8 KV) and Qwen3 (W4A8, sequential weight quantizers) exported both ways match the old exporter (weights bitwise, ranges equal, recipe a superset of the old recipe's settings). - No new example tests (they are slow): `test_quantize_export.py::test_quantize_and_export` also exports each checkpoint it already quantizes with `--vllm_fakequant_export`, at EP (Qwen3-MoE, per-expert names), TP (Qwen3-VL, vision tower bit-exact) and PP (Nemotron-H). `test_qad.py` adds that export only for the Qwen3.5-VL-MoE case (packed experts), since only QAD saves a VLM's language model alone. With 2 GPUs: 6 passed in 25 min (slowest case 386 s). - `test_tensor_quant_cpu.py::test_fold_weight_folds_sequential_weight_quantizer` (new) fails without the sequential fold. - `test_tensor_quant_cpu.py::test_fold_weight_folds_svdquant_lora` (new) fails without the SVDQuant change; `test_unified_hf_export_and_check_safetensors` (incl. `nvfp4_svdquant`) passes. - Regression sweep: `tests/unit`, `tests/gpu/torch/{quantization,export}`, `tests/gpu_megatron/torch/{export,quantization}`, `tests/gpu_vllm`, `tests/examples/{megatron_bridge,vllm_serve}`: no new failures; the remaining failures also fail on main in the same container (missing optional packages, transformers/torch API drift, pytest timeouts). - In nemo:26.08 with Megatron-Bridge `1d9caf7324` installed the way the new action does it: `tests/gpu_megatron/torch/export` 139 passed; `tests/examples/megatron_bridge` (`test_quantize_export.py`, `test_qad.py`, `test_mlflow_utils.py`) 81 passed and `test_distill.py` 21 passed; `tests/gpu_megatron/torch/{prune,nas}` 62 passed; `tests/examples/vllm_serve` 120 passed, with `test_convert_ggml_to_int8` erroring as it does on main (the container's vLLM has no WNA16 kernel). - End to end, scoring 64 CNN/DailyMail test articles (first 512 tokens each) with the per-token negative log-likelihood (NLL) in each engine: | Model (NVFP4 W4A4 + FP8 KV) | Export check | Megatron vs vLLM fake-quant NLL | |---|---|---| | Qwen3-0.6B | TP1 == TP2; served output identical to the old exporter's checkpoint (8/8 greedy, Δ logprob 0.0) | – | | NVIDIA-Nemotron-Nano-9B-v2 | TP1 == TP2 == PP2 (341 tensors, 128 ranges); weights bitwise equal to the old exporter; served output identical to it (8/8) | 2.3465 vs 2.3467 (BF16: 2.2987 vs 2.2990) | | Qwen3-30B-A3B | EP2 == PP2 (18,867 tensors, 18,720 ranges); vLLM TP2 serves correctly | – | | Qwen3.6-35B-A3B (GB300, vLLM 0.31; NVFP4 MoE, FP8 attention / GatedDeltaNet, FP8 KV) | EP4 == TP2×EP2 == PP2×EP2 (1,045 tensors, 390 ranges, 410 recipe entries incl. constant-amax KV); vision and MTP weights bitwise equal to the source; text and image prompts answer correctly | each quantizer group alone within ±0.004; all together 2.2844 vs 2.3008 (HF transformers with the same quantizers: 2.2901) | On Qwen3.6 the three engines see the same 410 quantizers and ranges; with the MoE layers quantized to NVFP4, tiny kernel differences flip expert routing, which is where the 0.016 spread comes from (per-token |Δ| ≈ 0.2 with the MoE quantized vs 0.07–0.1 without). - IQ2_XXS Qwen3-0.6B: no sidecars, stock `vllm serve` loads the export; Megatron logits are bitwise identical before and after folding. - An internal quantization format with a custom KV-cache `backend` and `backend_extra_args`: both survive export and vLLM reload. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ❌ `--vllm_fakequant_export` (added in #2873, unreleased) now needs a Megatron-Bridge with the quantizer sidecars (NVIDIA-NeMo/Megatron-Bridge#6565). - 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?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ - Did you get Claude approval on this PR?: ❌ ### Additional Information NVIDIA-NeMo/Megatron-Bridge#6565 and #2888 are merged. OMNIML-6019. Known limits found while testing (unchanged by this PR): MTP and `lm_head` activation quantization cannot be reloaded by vLLM fake quant, so the export refuses them. The general PTQ recipes quantized the MTP layers of Megatron VLMs by mistake; #2911 fixed that. `vllm_serve_fakequant.py` still runs a one-sample calibration before loading the sidecars, so offline clusters need `--modelopt-quant-dataset <local jsonl>`. 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> <!-- coderabbit-ai-generated:v1 --> ## Summary by CodeRabbit * **New Features** * Export Megatron checkpoints for vLLM fake-quant serving, including LLMs and VLMs. Weight quantization is folded into BF16 weights; enabled activation and KV-cache settings are saved separately. * Fake-quant exports support tensor, expert, and pipeline parallel configurations, including expert parallelism for checkpoints built without grouped GEMM. * **Bug Fixes** * Sequential weight quantizers can now be folded into weights, preventing them from remaining active after folding. * Improved folding support for quantized weights with LoRA factors. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
What does this PR do?
Type of change: new feature, bug fix
vLLM fake-quant serving reloads a versioned
quant_recipe.yaml:Megatron-Bridge's Hugging Face export of a fake-quantized model will write this file next to
quantizer_state.pth(NVIDIA-NeMo/Megatron-Bridge#6565), sovllm_serve_fakequant.py <export_dir>picks both up automatically (OMNIML-6019).TensorQuantizer.attribute_config_from_modelopt_stateinvertsset_from_attribute_configfor a properties-only state, so the reload restores every quantizer attribute (e.g.use_constant_amax,unsigned, dynamictype) instead of a fixed subset of five. The flat recipe written by ModelOpt's Megatron exporter still loads; its_backendkeys map the same way.It also fixes reloading quantizer ranges into modules vLLM registers under two names. vLLM 0.31 registers an MoE layer's shared expert both as
mlp.shared_expertand inside its runner asmlp.experts._shared_experts._layer, so the state dict lists the shared expert's quantizers twice.load_state_dict_from_pathupdated only the first name, andmodel.load_state_dictthen copied the stale second entry back: every shared-expert input range silently kept the one-sample calibration done before loading, behind a spurious "missing from every rank" warning. The loaded range is now copied to every name of the module, whichever name the checkpoint uses and whichever comes first innamed_modules(); a range missing under all names still warns.Usage
Testing
tests/examples/vllm_serve/test_fakequant_example.py: a versioned recipe restores a constant-amax FP8 KV quantizer and leaves unlisted quantizers disabled; an unknown version, and a versioned recipe with an empty or malformedquantizersmapping, are rejected as quantizer recipes (not passed to the PTQ recipe parser); a quantizer registered under two module names keeps the loaded range in either registration order (fails without the fix).tests/examples/vllm_serveandtests/gpu_vllm(vLLM 0.29.1): all pass except failures that also fail on main in the same container (missing optional packages, pytest timeouts).vllm_serve_fakequant.py <dir>(no flags) gives greedy generations identical to the export from ModelOpt's Megatron exporter (8/8 prompts, max |Δ logprob| 0.0).backendwithbackend_extra_args: the recipe keeps both, vLLM restores them on all KV quantizers (NLL 6.491 vs 6.504 in Megatron), and serving without the backend's plugin fails withQuant backend '...' is not registeredinstead of dropping it.Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
Merge before NVIDIA-NeMo/Megatron-Bridge#6565, which starts writing the versioned recipe. Part of OMNIML-6019.
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Summary by CodeRabbit
New Features
Bug Fixes
Tests