docs: add AutoQuantize mixed-precision search blog - #1979
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughAdded a comprehensive AutoQuantize announcement. Registered it on the documentation index with a filter button and an announcement card dated August 24, 2026. ChangesAutoQuantize documentation
Estimated code review effort: 1 (Trivial) | ~5 minutes Merge Risk: ⚪ Minimal · up to This documentation-only change adds the AutoQuantize article, figure, landing-page card, and filter without introducing an actionable merge-blocking risk; it is merge-ready after normal checks and review. Suggested reviewers: 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
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| A mixed-precision assignment is only useful if the runtime can execute it. So AutoQuantize performs a deployment-aware search — runtime coupling constraints are folded into the search rather than patched up afterwards, meaning the searched model is deployable out of the box in vLLM, SGLang, TensorRT-LLM, and similar inference runtimes. Any restriction of the form "this group of operators takes one joint format decision" becomes a merged knapsack item with aggregated sensitivity and cost. | ||
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| **1) Joint quantization for fused linear layers.** Inference runtimes often fuse linear operators, which imposes a shared quantization format across the fused group. This constraint is applied within each layer: that layer's Q, K, and V projections are fused and must share one format, so the fused QKV projection becomes a single decision variable. The naive score would just sum the three per-projection sensitivities — but that treats their Hessians as independent, when the three outputs actually interact through the attention operation. Instead, AutoQuantize quantizes all three projections jointly with format :math:`f` and measures the sensitivity at the attention output, so the metric naturally captures how the projections' quantization errors combine through attention: |
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Looks like there's no score rule to redirect QKV to an attention output, we only have that for MoEs: https://github.com/NVIDIA/Model-Optimizer/blob/main/modelopt/torch/quantization/algorithms.py#L1027
In my understanding the QKV will actually get a summed score
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Yes, @meenchen had recently implemented that and found that it was better. So I am keeping that here instead of making it confusing.
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I see, is it this PR:#1878? We should consider merging it if it works better :)
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@Fridah-nv agreed! I need to dig up the PR. I am removing this QKV specifics from the blog since I found it did not show much improvements. see https://docs.google.com/presentation/d/1Mxu8YpvhzSP70Ca4jomLh23FOZ_vkRUVhSW6w5wHJ5g/edit?slide=id.g3f7967c23e8_0_0#slide=id.g3f7967c23e8_0_0
@meenchen once you get a chance, could please create the PR for QKV scoring at the self attention output and update this blog if you find enough evidence for QKV scoring at self attention output is better than summing QKV scores?
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High-priority documentation correctness findings are inline.
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/claude review |
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Claude review passed — no blocking issues found. LGTM
Scope: Documentation-only PR (3 files: new docs/source/announcements/autoquantize.rst, one PNG asset, and a landing-page card/filter in docs/source/index.rst). Reviewed the RST blog and index changes; the binary PNG was not opened.
What I verified:
- The
mtq.auto_quantize(...)code example matches the real public API: signature/argument names (constraints,quantization_formats,data_loader,forward_step,loss_func,num_calib_steps,num_score_steps), themodel, search_state = ...return-value unpacking, and theNVFP4_DEFAULT_CFG/FP8_DEFAULT_CFGconfig constants all exist inmodelopt/torch/quantization. - The sensitivity-score derivation (Taylor → OBS → diagonal-Fisher/squared-gradient approximation) is internally consistent and correctly presented.
- The landing-page card in
index.rstis consistent with the RST (title, date, tags, author order all match); newest-first ordering is preserved.
Findings (1 SUGGESTION, non-blocking):
- The benchmark model name
Qwen3.6-35B-A3B(Table 1 timing paragraph) does not correspond to a real Qwen3 release — the MoE model with 3B active params isQwen3-30B-A3B. Since this is customer-facing published content, please confirm the exact model used and correct the identifier before publishing. (Inline comment posted.)
Risk: Low — docs-only, no code/API/state changes.
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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/claude review |
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Claude review — 2 IMPORTANT, 4 SUGGESTION
Scope: Full review of all 3 changed files (docs-only, +187/-0): the new docs/source/announcements/autoquantize.rst, the landing-page card/filter in docs/source/index.rst, and the PNG asset (binary, not opened).
Because this is a technical blog describing a shipped algorithm, I traced every behavioral claim against the implementation in modelopt/torch/quantization/ rather than reviewing prose in isolation.
Findings by severity — CRITICAL: 0 · IMPORTANT: 2 · SUGGESTION: 4
IMPORTANT — both in the MoE layer constraints section
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gate_projis missing from the grouped sparse-expert projections (line 90). The rule atalgorithms.py:642-643groupsgate_proj|up_proj|down_projfor Qwen3-like MoE; only the NemotronH.mixer.expertsvariant isup_proj+down_proj. The blog documents the NemotronH shape, while the results figure and Table 1 timing are both on Qwen3 models — so the one case a reader will map onto their own model is the case the sentence gets wrong. -
'so each is searched independently' over-claims for shared experts (line 96). Shared experts do escape the all-experts restriction, but their
gate_proj/up_projremain coupled by the generic gate/up fusion rule atalgorithms.py:647. Onlydown_projand the latent projections are truly independent. This section is specifically about which decisions are coupled, so the imprecision lands on the exact property being documented.
Both are small wording fixes, but this is customer-facing published content about coupling constraints, so they are worth getting right before it goes live.
SUGGESTION
- The 'fused linear layers' section covers only QKV, though
quant_grouping_rulesalso couples gate/up, Mixtralw1/w2/w3, MCorelinear_fc1/fc2, and the Qwen3.5/3.6in_proj_qkvz/in_proj_bapairs. - Lines 15 and 158 use the pre-rename
NVIDIA/TensorRT-Model-OptimizerURL; all 31 other GitHub self-references underdocs/source/useNVIDIA/Model-Optimizer. Redirects work, so cosmetic. ~51xvs Table 1's ~14 h / ~16 min = ~52.5x.index.rstcard omitsmodeloptfrom its visible tag spans; both existing cards render all theirdata-tags.
Retracting a prior false positive
An earlier Claude review on this PR flagged Qwen3.6-35B-A3B as not a real model. That was wrong — please disregard it. The model appears in this repo's own support matrix (docs/source/deployment/3_unified_hf.rst:125, 'Qwen 3.6 35B-A3B / NVFP4') and has a dedicated recipe tree at modelopt_recipes/huggingface/qwen3_6_moe/. No change needed there.
What I verified as correct
- API example matches the real signature (
model_quant.py:272-288): argument names, the(model, state_dict)tuple return, andnum_calib_steps=512/num_score_steps=128are even the actual library defaults.NVFP4_DEFAULT_CFG/FP8_DEFAULT_CFGand theconstraints={'effective_bits': ...}key all check out. - The score formula matches the code exactly. The blog's sum of
g^2 * (Y - Y_Q)^2over the feature dim is precisely_get_auto_quantize_score(algorithms.py:1432-1434):(grad.float() * output_diff.float()).square().sum(), including the fp32 upcast. The Taylor expansion signs are right (the-g^T (Y - Y_Q)convention is consistent withdelta = Y_Q - Y), and the proportionality correctly absorbs the dropped1/2. - The complexity claims and the headline speedup framing hold. The KL path (
algorithms.py:1836-1858) really does a fullforward_stepper (hparam, recipe) inside the batch loop, givingO(N_layers^2 * N_formats); the gradient path replays candidates locally in a forward hook plus one backward per batch, givingO(N_layers) * O(N_formats). - QKV claims are accurate:
algorithms.py:639groups the three projections, and the absence of a q/k/vscore_module_ruleconfirms sensitivity is measured at each projection's own output. The MoE-block-output scoring claim matchesscore_module_rulesatalgorithms.py:1476-1477. - Effective-bits cost model matches the documented precedence (explicit
effective_bits, elsenum_bits) atalgorithms.py:209-249, and the knapsack constraint matchestotal_cost / total_weight_size * 16. - Docs plumbing:
:orphan:matches both sibling announcements, card ordering is correctly newest-first (08-13, 07-15, 07-13), the filter button'sdata-tagmatches the card'sdata-tags, and theexamples/hf_ptq#autoquantizeanchor resolves to a real## AutoQuantizeheading.
Risk: Low. Docs-only — no code, public API, mode registration, modelopt_state, or export path is touched, and codecov confirms zero coverable lines changed. The two IMPORTANT items are accuracy-of-published-content issues, not runtime risk. Once the two MoE sentences are corrected, this is good to publish.
Signed-off-by: realAsma <akuriparambi@nvidia.com>
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Signed-off-by: realAsma <akuriparambi@nvidia.com>
Signed-off-by: realAsma <akuriparambi@nvidia.com>
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Claude review — 2 IMPORTANT, 2 SUGGESTION
Scope: Full review of all 3 changed files (docs-only, +186/-0): the new docs/source/announcements/autoquantize.rst, the landing-page card/filter in docs/source/index.rst, and the PNG asset (binary, not opened). As in the prior round, I traced behavioral claims against modelopt/torch/quantization/ rather than reviewing prose in isolation, and re-checked the landing-page plumbing (_static/announcements.js) since the card changed.
Findings by severity — CRITICAL: 0 · IMPORTANT: 2 · SUGGESTION: 2
IMPORTANT — both are card-vs-article inconsistencies in index.rst, and both look introduced by the latest commit rather than deliberate
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Date mismatch, which also mis-sorts the card. The article says
:Date: August 24, 2026(autoquantize.rst:7); the card saysdata-date="2026-07-15"/July 15, 2026._static/announcements.js:3-5re-sorts cards bydata-datedescending, so this post renders third — below the2026-08-13release card — while the article claims to be the newest. Pick the intended date, sync all three spots (RST:Date:,data-date, visible meta), and if Aug 24 is right, move the<article>block above the2026-08-13card so source order matches rendered order. -
The article lost its named author credits. The card credits eight people;
autoquantize.rst:6now reads:Author: Model Optimizer Team. The PR description explicitly says the blog credits the named authors, and a prior review round verified the two matched — so the named list was there and has since been replaced. Both sibling announcements keep card meta and RST:Author:identical, so this is the only card that disagrees with its article. Worth fixing carefully since it is real-contributor credit on published content. (Note:Shengliang Xuis on the card but not in the PR description's list — worth confirming the intended set and order.)
SUGGESTION
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Line 81 is in the wrong place. The new "same grouping mechanism covers other supported fused layouts" sentence was inserted between line 79's colon-terminated lead-in and the QKV formula on line 85, so the page renders a dangling colon, an unrelated paragraph, then an orphaned equation — and line 87's explanation refers back to that formula. The sentence's content is accurate against
quant_grouping_rules; only its position is wrong. Suggested placement is inline. -
"the Qwen3 models benchmarked here" (line 89) is loose. Figure 1 sweeps Qwen3.5-2B/9B (dense — the repo separates
modelopt_recipes/huggingface/qwen3_5/fromqwen3_5_moe/), while only Table 1's Qwen3.6-35B-A3B is MoE. Naming that model directly avoids pointing MoE-grouping behavior at models it cannot apply to.
Prior-round findings — all six are resolved
gate_projnow included in the grouped sparse-expert projections (line 89) ✅- shared-expert coupling now stated precisely: gate/up share a format,
down_projsearched separately (line 95) — matches the generic gate/up rule with nodown_projrule ✅ - fused-layout coverage generalized beyond QKV (line 81, modulo the placement issue above) ✅
- pre-rename
TensorRT-Model-OptimizerURLs nowNVIDIA/Model-Optimizer✅ ~51x→~52×, consistent with Table 1's ~14 h / ~16 min ✅- card now renders all four
data-tagsas visible spans ✅
Re-verified as correct this round
quant_grouping_rules(algorithms.py:638-655) backs the QKV, MoE-expert, and shared-expert claims exactly as written, including the.mlp.experts.<N>.gate/up/down group key.- The
mtq.auto_quantize(...)example still matches the public signature — argument names, the(model, search_state)tuple return,NVFP4_DEFAULT_CFG/FP8_DEFAULT_CFG, and theconstraints/effective_bitskey. - Score formula, Taylor/diagonal-Fisher derivation, complexity claims, and the effective-bits cost model are unchanged from the prior round and still check out.
- Model identifiers are real:
Qwen3.6-35B-A3Band the Qwen3.5 families both appear in this repo's recipes and support matrix. (An early review on this PR wrongly flaggedQwen3.6-35B-A3B; that was already retracted — no action needed.) - Docs plumbing:
:orphan:matches both siblings, the filter button'sdata-tag="autoquantize"matches the card'sdata-tags, and thehf_ptq#autoquantizeanchor resolves.
Risk: Low. Docs-only — no code, public API, mode registration, modelopt_state, or export path touched; codecov confirms zero coverable lines changed. Both IMPORTANT items are metadata consistency issues in customer-facing published content, not runtime risk, and all four findings are small edits. Once the date and author fields agree between the card and the article, this is good to publish.
Signed-off-by: realAsma <akuriparambi@nvidia.com>
Signed-off-by: realAsma <akuriparambi@nvidia.com>
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Inline comments:
In `@docs/source/announcements/autoquantize.rst`:
- Line 106: Update the AutoQuantize gradient-scoring complexity description in
the paragraph contrasting direct sensitivity measurement so it accounts for
replaying every candidate format at each scored module, plus the single backward
pass. Replace the current O(N_layers) claim with complexity consistent with
Table 1: O(N_layers) × O(N_formats), while preserving the surrounding comparison
and speedup statement.
In `@docs/source/index.rst`:
- Around line 33-34: Align the announcement-card date and displayed date in the
landing page with the canonical date declared by the AutoQuantize announcement,
then reorder the card chronologically relative to the August 13, 2026 entry if
needed. Update only the AutoQuantize card in the landing-page announcements
markup and preserve its existing metadata.
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The content LGTM, left minor comments, thanks!
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Thanks @realAsma , looks really good and comprehensive! |
Signed-off-by: realAsma <akuriparambi@nvidia.com>
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/claude review |
| AutoQuantize gradient is fast! | ||
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| Direct sensitivity measurement evaluates the full model for every layer-format pair. For instance, KL-divergence-based mixed-precision assignment algorithms, including AutoQuantize KL-divergence scoring, quantize one layer at a time and compare the output distributions of the quantized and unquantized models. Because each layer requires a full-model pass, scoring scales as :math:`O(N_{\mathrm{layers}}^2)`. In contrast, for each scoring batch, AutoQuantize gradient scoring uses one backward pass and locally replays every candidate format at each scored module. Hence, its scoring work scales as :math:`O(N_{\mathrm{layers}} \times N_{\mathrm{formats}})`, resulting in a ~52× speedup on Qwen3.6-35B-A3B (Table 1). |
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[SUGGESTION] The prose complexity for KL-divergence scoring contradicts Table 1 four lines below.
This sentence says scoring scales as :math:O(N_{\mathrm{layers}}^2), but Table 1 lists KL divergence as :math:O(N_{\mathrm{layers}}^2 \times N_{\mathrm{formats}}). The table is the correct one: the paragraph opens with "evaluates the full model for every layer-format pair", so there are :math:N_{\mathrm{layers}} \times N_{\mathrm{formats}} full-model passes, each costing :math:O(N_{\mathrm{layers}}).
Why it matters: the whole point of this section is the gradient-vs-KL asymptotic contrast. Dropping :math:N_{\mathrm{formats}} from the KL side understates the gap and makes the two numbers in the same section disagree, which a reader will notice immediately.
Fix — carry the format factor through in the prose so it matches the table:
Because each layer-format pair requires a full-model pass, scoring scales as
:math:`O(N_{\mathrm{layers}}^2 \times N_{\mathrm{formats}})`.|
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| *ModelOpt AutoQuantize supports both sensitivity scoring methods — gradient (the default) and KL divergence. Measured on 4× NVIDIA RTX 6000 Ada GPUs with 128 samples at sequence length 512. Times cover sensitivity scoring only — not the end-to-end AutoQuantize run, which also includes calibration time for each format.* | ||
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| **Memory.** By default, AutoQuantize uses activation recomputation for gradient scoring. This is memory efficient because it avoids retaining all intermediate tensors from the forward pass. As shown in Table 1, the resulting peak memory overhead over a forward-only pass is small. |
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[SUGGESTION] "By default" overstates where activation recomputation actually kicks in.
In AutoQuantizeGradientSearcher.estimate_sensitivity_scores (modelopt/torch/quantization/algorithms.py:1681-1690), the gradient-checkpointing context is only used when a registered _custom_support predicate matches the model; otherwise it falls back to nullcontext():
grad_checkpointing_ctxt = None
for is_supported_checker, ctxt_candidate, grad_enabled_candidate in self._custom_support:
if is_supported_checker(self.model):
grad_checkpointing_ctxt = ctxt_candidate
...
with grad_checkpointing_ctxt(self.model) if grad_checkpointing_ctxt else nullcontext():register_custom_support is called in exactly two places — plugins/huggingface.py:1861 and plugins/megatron.py:986. So recomputation applies to HF and Megatron-Core models, not to arbitrary nn.Module models.
Why it matters: this paragraph is the blog's only statement about AutoQuantize's memory profile, and Table 1's 29 GB number is what readers will budget against. Someone running AutoQuantize on a custom (non-HF, non-MCore) model gets full activation retention and a substantially different peak, with nothing in the blog to explain why.
Fix — qualify the scope in one clause:
**Memory.** For Hugging Face and Megatron-Core models, AutoQuantize enables activation
recomputation for gradient scoring by default. This is memory efficient because it avoids
retaining all intermediate tensors from the forward pass.|
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| AutoQuantize turns mixed-precision quantization from trial and error into a principled search: gradient-based sensitivity scoring in a single sweep, optimization with an ILP solver under your cost budget, and selected runtime coupling constraints incorporated into the assignment. Sweep the bit budget to find your model's accuracy-vs-compression sweet spot, then follow the documented export and deployment workflow for the target model, formats, and runtime. | ||
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| .. _references: |
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[SUGGESTION] Use a namespaced label instead of the generic references.
Sphinx _labels live in a single global namespace across the whole doc set. references is the most likely name any future announcement will reach for, and the second one to use it produces a duplicate label warning — which matters here because the announcements landing page is meant to accumulate pages over time, and this repo builds docs fail-on-warning.
The sibling announcement already follows the namespaced convention: dspark-vs-domino.rst defines dspark-domino-references and cross-references it as :ref:`dspark-domino-references` . This label is also currently unreferenced from anywhere in the file, so renaming it costs nothing.
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| .. _autoquantize-references: |
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Claude review passed — no blocking issues found. LGTM
Scope. Full review (trigger had no scoping instructions). 3 files changed, +180/-0 — all reviewed: docs/source/announcements/autoquantize.rst (new, 173 lines), docs/source/index.rst (+7), and the new PNG asset (binary, not opened). Since this PR's entire value is the accuracy of its technical claims, I spent the budget verifying the blog against the actual AutoQuantize implementation rather than on prose review — CodeRabbit already covers style/typos and reported no actionable comments.
Findings — CRITICAL: 0 · IMPORTANT: 0 · SUGGESTION: 3
- Line 106 — KL-divergence complexity in the prose (
O(N_layers^2)) contradicts Table 1 four lines below (O(N_layers^2 x N_formats)). The table is right; the prose drops the format factor it just introduced, understating the gradient-vs-KL gap that is the whole point of the section. - Line 128 — 'By default, AutoQuantize uses activation recomputation' is narrower in the code:
register_custom_supportis called only from the HF and Megatron-Core plugins, and everything else falls back tonullcontext(). Worth qualifying, since Table 1's 29 GB is the number readers will budget against. - Line 165 — generic
.. _references:label sits in Sphinx's global namespace;dspark-vs-domino.rstalready uses the namespaceddspark-domino-references. Collision risk as announcements accumulate, and the label is currently unreferenced so renaming is free.
Technical claims verified against modelopt/. Everything substantive checked out — this is unusually well-grounded for a blog post:
- Sensitivity score
S ∝ Σ g² (Y − Y^Q)²matches_get_auto_quantize_score(algorithms.py:1432,(grad * diff).square().sum()). Taylor-expansion signs in the first equation are also correct. - ILP claim ('one binary variable for every candidate format in each search decision… selects exactly one format per decision') matches
searcher.py:344-370—pulp.LpBinaryvars with one-hot plus budget constraints. Genuinely an ILP, not an LP relaxation. - Effective-bits constraint
Σ N_params × bits ≤ N_total × b̄is exactly the code'stotal_cost ≤ total_weight_size × (effective_bits/16). - NVFP4 = 4.5 is correct and not merely the paper value:
modelopt_recipes/configs/numerics/nvfp4.yamlsetseffective_bits: 4.5explicitly, and its comment confirms thenum_bitsheuristic would under-count it as 4.0. The blog's 'format-provided overhead when an explicit effective-bits value is available; otherwise…num_bits' precisely describesestimate_quant_compression. - 'Embeddings, norms, and other parameters outside the search are not included' matches
_get_total_weight_size, which sums only_is_auto_quantize_moduleweights. - Grouping claims match
quant_grouping_rules(algorithms.py:640-657): QKV grouped, gate+up grouped for dense MLP with down_proj separate, all routed MoE experts collapsed into one decision, shared experts left as their own decision. - 'Sensitivity measured jointly at the MoE block output' matches
AutoQuantizeGradientSearcher.score_module_rules(algorithms.py:1475-1479), which redirects expert projections to the.mlpoutput. - API snippet matches
auto_quantize's signature and its documented(model, state_dict)return;quantization_formatsaccepts config dicts, somtq.NVFP4_DEFAULT_CFGis valid. - ~52x speedup is consistent with Table 1 (14 h / 16 min ≈ 52.5x).
- Landing-page wiring: the new card's
data-tagsincludesautoquantizeandannouncements.jsfilters via exactsplit(' ').includes(activeTag), so the new button resolves correctly.:orphan:plus raw-HTML card linking matches both existing announcements; card date and page date agree (2026-08-24).
Note on the PR description. It says the change 'credits the authors in this order: Asma Beevi K T, Wei Ming, Frida Hou, Juhi Mittal, Jenny Chen, Ajinkya Rasane, Meng Xin,' but the committed file and the landing-page card both attribute to Model Optimizer Team. If that was the deliberate outcome of the earlier review feedback, the description is just stale — worth reconciling so the final PR record isn't misleading.
Risk: low. Documentation-only; no library code, mode/state, config schema, or export surface touched. All three findings are non-blocking precision improvements.
What does this PR do?
Type of change: documentation.
Adds the AutoQuantize technical blog to the announcements system introduced by #1971.
This is a stacked PR targeting the branch for #1971. After #1971 merges, this PR can be retargeted to main.
Usage
N/A; documentation only.
Testing
Before your PR is "Ready for review"
Additional Information
Depends on #1971.
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