feat(quantization): register NVFP4 four-over-six as a real calibration algorithm by Fridah-nv · Pull Request #2465 · NVIDIA/Model-Optimizer

Fridah-nv · 2026-09-18T00:10:18Z

What does this PR do?

Type of change: new feature

4/6 is the one NVFP4 feature in the repo that is not a nameable unit. It is two coordinated things living in two unrelated config keys:

a numerics flag in quant_cfg — block_sizes: {four_over_six: true}, whose entire effect is that fp8_max_for_normalization() returns E4M3_MAX_46 = 256.0 instead of 448.0, giving the per-block FP8 scales 1.75x more headroom;
a magic MSE stanza in algorithm — {method: mse, fp8_scale_sweep: false, start_multiplier: 1.0, stop_multiplier: 1.5, step_size: 0.5}. linspace(1.0, 1.5, 2) is exactly [1.0, 1.5], and 1.5 == 6/4, so MSE's per-block pick between those two multipliers is the M=6/M=4 choice, folded into _amax.

Nothing connected the two halves. No calibration code read block_sizes["four_over_six"] anywhere — the flag has four semantic readers, all on the fake-quant/export numerics path. So both directions failed silently:

flag without the multipliers → you pay the 256 normalization and no M=6/M=4 selection ever happens;
multipliers without the flag → the selection happens but the scales are normalized by 448, so the M=4 blocks are encoded wrongly.

Neither raised. The flag was also accepted on any quantizer — INT8, FP8, type: dynamic — where it is simply inert.

The cost showed up as copy-paste: the magic stanza appeared verbatim in five shipped files, including two released model recipes and one auto_quantize candidate format (configs/ptq/presets/model/nvfp4_four_over_six, Nemotron-3-Ultra-550B nvfp4-4o6, Nemotron-3.5-Lightning-30B w4a16_nvfp4_4o6, Muse-Glimmer-30B w4a16_nvfp4_4o6_mixed ×2).

Usage

# modelopt_recipes/.../ptq/nvfp4-4o6.yaml
algorithm: four_over_six          # was: a 5-line mse stanza with two magic numbers
quant_cfg:
  - quantizer_name: '*mlp.experts*weight_quantizer'
    cfg: {$import: nvfp4_four_over_six}     # still carries four_over_six: true

import modelopt.torch.quantization as mtq

mtq.quantize(model, mtq.NVFP4_FOUR_OVER_SIX_CFG, forward_loop)   # now algorithm="four_over_six"

# Writing it by hand still works -- those are legitimate general MSE parameters:
mtq.quantize(model, {"quant_cfg": [...], "algorithm": {"method": "mse", "start_multiplier": 1.0,
                                                       "stop_multiplier": 1.5, "step_size": 0.5}}, loop)

What changed

four_over_six is a registered calibrate mode — FourOverSixCalibConfig + FourOverSixCalibrateModeDescriptor + four_over_six_calibrate, which delegates to the existing mse_calibrate. No new search code.

The config deliberately does not expose start_multiplier / stop_multiplier / step_size / fp8_scale_sweep. The grid is derived from the format — FOUR_OVER_SIX_MULTIPLIER = E2M1_MAX / 4.0 — which is the difference between the named algorithm and writing the equivalent MseCalibConfig by hand: the multipliers cannot be retyped, so they cannot drift. mse stays fully general and undeprecated.

Three config-time rules replace the silence, all hard errors, all before any calibration runs:

four_over_six: true requires static NVFP4 with E4M3 scales (validate_block_sizes) — the config-level spelling of TensorQuantizer.is_nvfp4_static, including its treatment of an absent type as static.
The flag requires a weight-scale search in the algorithm chain — four_over_six, mse, or local_hessian (QuantizeConfig model validator). This rejects algorithm: max + the flag.
algorithm: four_over_six requires the flag on some enabled entry.

Rules 2–3 destructure all four algorithm shapes and descend into the bundled weight-scale algorithms of nvfp4_act_headroom and lsq, including the method each falls back to when its field is unset (lsq → mse, nvfp4_act_headroom → max), because those run a full weight-scale search one level down. They are presence-based over quant_cfg entries that are not explicitly disabled: quant_cfg is last-wins layered, so without a model we cannot resolve which entry owns a given quantizer. Conservative, and it covers all five shipped recipes.

mtq.compress refuses up front. The rule now lives in TensorQuantizer._is_real_quantize_support() — the predicate that already existed to answer "can this format be real-quantized" and until now had a single caller. compress_convert screens the whole model against that predicate before packing anything, so the failure is immediate and complete instead of partway through pack_real_quantize_weight with one layer named, and every future unsupported format gets the same treatment for free. The screen runs under the same SequentialQuantizer.convert_to_single_quantizer view and the same weight/enable/fake_quant gate as the packing loop, so it neither misses a sequential quantizer nor rejects a layer that would have been skipped anyway.

Plumbing: four_over_six added to _AUTO_QUANTIZE_SUPPORTED_ALGORITHMS (required — the migrated Muse Glimmer recipe raises without it), to _run_weight_scale_calibration's dispatch and the _ScaleCalibConfig union (so it is usable as lsq.scale_algorithm / nvfp4_act_headroom.weight_scale_algorithm, like mse and local_hessian). get_auto_quantize_config now emits algorithm="four_over_six" rather than "max" when any surviving entry carries the flag — max there was never merely a downgrade but wrong, since the flag normalizes the FP8 scales by 256 on the assumption that something will pick M=4.

Also fixed stale comments found while editing: presets/model/nvfp4_four_over_six.yaml and units/w4a4_nvfp4_nvfp4_four_over_six.yaml both described the weights as "dynamic NVFP4" when the numerics file is type: static, and the calibrate docstring's algorithm list was missing six of the twelve registered algorithms.

The recipe migration is bit-identical

This is the acceptance gate for renaming three released recipes: the name has to be a name, not a behaviour change. four_over_six_calibrate calls mse_calibrate with exactly the stanza's parameters, so the calibrated amax is unchanged.

Pinned two ways:

CPU (TestFourOverSixIsTheLegacyStanzaOnCPU) — calibrates a seeded toy model twice, once with the legacy stanza and once with algorithm: "four_over_six", and asserts torch.equal on every weight quantizer's amax. Static NVFP4 fake-quant is Triton-only, so the kernel is stubbed with a deterministic pure-torch stand-in; both arms use the same stand-in, and the test asserts the amax is per-block (numel > 1) so the comparison says something about 4/6. A companion test asserts a different grid gives a different answer, so it cannot pass vacuously.
GPU (TestFourOverSixIsTheLegacyStanzaOnCUDA, tests/gpu/.../test_nvfp4_static_quantizer_cuda.py) — the same comparison against the real Triton kernel, plus a guard that the M=4 candidate actually wins somewhere on that fixture.

The three released recipes keep calibrating their FP8 quantizers with the same [1.0, 1.5] grid they do today. That is arguably wrong — the grid has nothing to do with those quantizers — but narrowing it needs per-scope calibration (#2292) and re-validation, so it is deliberately out of scope here.

Review found problems; all are fixed

Two follow-up passes (/simplify, /code-review) plus PR review found these. Every one was reproduced before being fixed, and each has a regression test. Recording them because almost all were in the validators rather than the calibration, and they share one root cause: the rules were written against the shape of the shipped YAMLs rather than against what the runtime actually accepts.

#	Problem	Fix
1	Widening `_ScaleCalibConfig` made `weight_scale_algorithm: {method: four_over_six}` representable, but rule 2 read only top-level method names — so `nvfp4_act_headroom` and `lsq` were false-rejected with the flag set, and the mirror case (nested `four_over_six`, no flag) passed silently	destructuring descends into both bundled-scale fields
2	An absent `block_sizes["type"]` is static at runtime (`is_static_block_quant` tests `!= "dynamic"`), but rule 1 compared against the literal `"static"`	`block_type in (None, "static")`
3	`lsq` leaves `scale_algorithm` unset and `_run_weight_scale_calibration` substitutes mse, so `algorithm="lsq"` does search — but rule 2 rejected the default	bundled-scale lookup keyed by host algorithm, carrying each one's fallback
4	`get_auto_quantize_config` hardcoded `algorithm="max"` while preserving the flag, so exporting a 4/6 search — which the shipped Muse-Glimmer recipe does — produced a config rule 2 rejects, at the end of an expensive search	emits `four_over_six` when any surviving entry carries the flag
5	The compress screen claimed to mirror `_compress_and_update_module_weight` but omitted its weight guards and could not see `SequentialQuantizer` weight quantizers — over-rejecting meta/1-byte weights and missing the sequential case it exists to catch	screens under the same `convert_to_single_quantizer` view with the same gate
6	A `mode="after"` validator is also a checkpoint-load gate. `load_state_dict` reconstructs the stored config through `QuantizeConfig(stored)`, so a checkpoint carrying the flag with `algorithm: "max"` — what pre-PR `get_auto_quantize_config` emitted — no longer loaded at all**, with an error unactionable at restore time	rules share one `four_over_six_config_problems()`; `QuantizeConfig` warns, `mtq.quantize` raises next to `_check_weight_quantization_took_effect`
7	The compress screen was still stricter than the packing loop: that loop skips the root module, and `TensorQuantizer.forward` early-returns on an empty tensor and `_if_quant=False`	screen skips the root module and checks `numel()` / `_if_quant`
8	The generic screen appended a Four-Over-Six note unconditionally, so an INT4 or custom-backend offender read as though 4/6 were the problem	note emitted only when an offender carries the flag
9	`get_auto_quantize_config` choosing `four_over_six` silently moves a mixed result's non-4/6 layers off `max` — newly introduced here, unlike the released recipes	the warning now says so

The cleanup pass also removed a dead public constant, collapsed the numerics check to one tuple comparison, made the algorithm destructurer private, folded the duplicated compress refusal text into one place (the two copies had already drifted on "MSE calibration" vs "calibration"), and deleted two tests that asserted nothing the others did.

Testing

tests/unit/torch/quantization/ (excl. plugins, which does not collect in my env — transformers too old for HybridCache, pre-existing) + tests/unit/recipe/: 1308 passed, 1 skipped.
tests/unit/torch/quantization/test_autoquant.py: 110 passed (covers the get_auto_quantize_config change).
test_nvfp4_four_over_six.py grew from 12 to 42 tests: mode registration, the derived grid, the bit-identity pair, one test per validation rule plus the negatives (legacy stanza and plain mse still validate), the bundled-scale-algorithm matrix, and the compress cases.
pre-commit run clean on all changed files. The check-modelopt-recipes hook fails in my environment on a read-only ~/.cache/uv; run directly against this tree it exits 0.
Pre-existing failures confirmed against clean main and unrelated: 4 in tests/unit/torch/export/test_quant_aware_conversion.py.

Not run: the GPU test (TestFourOverSixIsTheLegacyStanzaOnCUDA). No GPU in my environment — it collects but has not executed, and it is the one check tying the CPU stub to the real Triton kernel. This is the one outstanding item; it needs a run before merge.

Before your PR is "Ready for review"

Is this change backward compatible?: ✅ — the recipe migration is bit-identical, the hand-written stanza still works undeprecated, and mse is unchanged. One deliberate exception: a config with only half of 4/6 now raises instead of calibrating silently. Such a config was already producing a wrongly scaled checkpoint. Note also that checkpoints from the migrated recipes record the four_over_six_calibrate mode, which an older ModelOpt cannot restore — inherent to adding any algorithm, same as local_hessian and gptq.
If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A — no copied code, no new dependencies.
Did you write any new necessary tests?: ✅ — 30 new CPU tests and 2 GPU tests.
Did you update Changelog?: ✅ — one entry under 0.48.0 New Features → Quantization.
Did you get Claude approval on this PR?: ✅ — reviewed; every finding either fixed (threads resolved) or answered with a pushback left open for a CODEOWNER.

Additional Information

Outstanding: the GPU bit-identity test still needs a run on real hardware.

Two review findings were pushed back on rather than fixed, and are left open:

Restricting the 4/6 search to flagged quantizers. This would break bit-identity with the stanza three released recipes used — Nemotron Ultra's FP8 weight quantizers were searched by it — which is this PR's acceptance gate for renaming them.
A model-aware effective-config check, and rejecting ["four_over_six", "max"]. Both observations are correct, but ["mse", "max"] and ["local_hessian", "max"] discard their searches identically and are not rejected either, so nothing makes 4/6 special. That is stage-dependency analysis, i.e. feat(quantization): scoped calibration pipelines via algo_cfg [prototype] #2292.

Related: #2292 (scoped calibration pipelines). This PR does not depend on it. That prototype's AlgoCapabilities declares writes_whole_module / optimizes / consumes / produces / conflicts_with / honors_write_mask — no notion of a required quantizer attribute — so it would not have caught the 4/6 coordination bug either. If it lands, four_over_six composes with it for free, and the generalization worth making there is turning rules 1–3 into a declared requires_attributes on the capability model.

Known limits of the coordination check. It is presence-based, so algorithm: ["mse", "max"] is accepted even though the trailing max re-runs max calibration and discards the M=4/M=6 choice. Catching that needs sequencing analysis rather than presence checking — that is #2292's job, and I would rather name it than half-solve it.

Follow-ups this surfaced, none of them blockers:

mse with fp8_scale_sweep: true silently skips every non-static-NVFP4 weight quantizer (_uses_modelopt_fp8_weight_scales returns None with no warning). Same class of bug as 4/6's — the second instance of "half the calibration config quietly does nothing" — and the general fix is the Phase-2 contract in the calibration design rather than another bespoke rule here.
kv_cache_auto_quant._algorithm_method duplicates the destructurer this PR generalized, minus the list case, so a candidate written as algorithm: ["max", "awq_lite"] reads as "no calibration" and bypasses the KV-only guard. Repointing it is a behaviour fix that deserves its own test, so it is not in this PR.
_FOUR_OVER_SIX_CAPABLE_ALGORITHMS, _BUNDLED_SCALE_ALGORITHMS, _AUTO_QUANTIZE_SUPPORTED_ALGORITHMS and _run_weight_scale_calibration's dispatch are four hand-maintained name lists that every new algorithm must be added to, all derivable from CalibrateModeRegistry. config.py cannot import mode.py, so deriving them needs the declaration to live on the config classes (the _mutates_weights shape) — worth doing, but as its own change.
mtq.compress(model, {"pattern": False}) — the shape its own docstring advertises — fails pydantic validation, because compress() passes the bare dict where a {"compress": {...}} is expected. Pre-existing.

🤖 Generated with Claude Code

Summary by CodeRabbit

New Features
- Added the named NVFP4 Four-Over-Six calibration algorithm.
- Added selection between M=4 and M=6 dynamic ranges during weight calibration.
- Added Four-Over-Six support to auto-quantization and calibration options.
Bug Fixes
- Added validation for incomplete or incompatible Four-Over-Six configurations.
- Unsupported Four-Over-Six real-quantization formats are now rejected with guidance to quantize or export instead.
Documentation
- Updated calibration guidance and NVFP4 recipes to use the Four-Over-Six algorithm.

…ithm 4/6 was the one NVFP4 feature with no name. It is two coordinated things in two unrelated config keys: the `four_over_six` block_sizes flag (which only makes `fp8_max_for_normalization` return 256 instead of 448) and an MSE stanza whose multipliers happen to be `[1.0, 1.5]` -- 1.5 == 6/4, so MSE's per-block pick between them *is* the M=6/M=4 choice. Nothing connected the two: no calibration code reads the flag, and the stanza was copy-pasted verbatim across five shipped files, two of them released model recipes and one an auto_quantize candidate. Both halves failed silently on their own. The flag without a search pays for headroom nothing uses; the search without the flag normalizes the M=4 blocks by 448 and encodes them wrongly. The flag was also accepted, and inert, on INT8, FP8 and dynamic-block quantizers. Register `four_over_six` as a calibrate mode. It delegates to `mse_calibrate` with the grid derived from the format (`FOUR_OVER_SIX_MULTIPLIER = E2M1_MAX / 4`) rather than exposing the multipliers, so they cannot be retyped or drift, and it is bit-identical to the stanza it replaces -- pinned by a CPU test that stubs the Triton-only static NVFP4 kernel and by a GPU test against the real one. The five shipped copies now say `algorithm: four_over_six`. Three config-time rules replace the silence: the flag requires static NVFP4 with E4M3 scales, the flag requires a weight-scale search in the algorithm chain, and the algorithm requires the flag. `mtq.compress` also refuses up front, naming every quantizer that would be packed, instead of dying partway through `pack_real_quantize_weight` with one layer named. Writing the stanza by hand still works: those are legitimate general MSE parameters, and `four_over_six` is the named, validated shorthand -- not a replacement for `mse`. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

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📝 Walkthrough

Walkthrough

The pull request adds named NVFP4 four_over_six calibration. It validates configuration consistency, defers enforcement to quantization, blocks unsupported real quantization, updates recipes, and adds unit and CUDA coverage.

Changes

NVFP4 Four-Over-Six calibration

Layer / File(s)	Summary
Calibration contract and implementation `modelopt/torch/quantization/config.py`, `modelopt/torch/quantization/model_calib.py`, `modelopt/torch/quantization/mode.py`, `modelopt/torch/quantization/utils/numeric_utils.py`, `modelopt/torch/quantization/model_quant.py`	Defines `FourOverSixCalibConfig`, validates compatible NVFP4 settings, and calibrates with the `1.0` and `1.5` multipliers. Quantization raises configuration errors before calibration.
Runtime detection and compression validation `modelopt/torch/quantization/algorithms.py`, `modelopt/torch/quantization/nn/modules/tensor_quantizer.py`, `modelopt/torch/quantization/compress.py`	Detects enabled Four-Over-Six configurations and rejects unsupported real quantization before packing.
Recipe and documentation migration `CHANGELOG.rst`, `docs/source/guides/10_recipes.rst`, `modelopt_recipes/...`	Replaces inline MSE multiplier sweeps with the named `four_over_six` algorithm in recipes and documentation.
Calibration and compatibility tests `tests/unit/torch/quantization/test_nvfp4_four_over_six.py`, `tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py`	Covers legacy-equivalent results, configuration warnings and enforcement, checkpoint restoration, auto-quantization, compression, and CUDA behavior.

Priority: ⬇️ Low

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant QuantizeConfig
  participant quantize
  participant FourOverSixCalibrateModeDescriptor
  participant four_over_six_calibrate
  participant mse_calibrate
  QuantizeConfig->>quantize: provide Four-Over-Six configuration
  quantize->>FourOverSixCalibrateModeDescriptor: select calibration mode
  FourOverSixCalibrateModeDescriptor->>four_over_six_calibrate: dispatch calibration
  four_over_six_calibrate->>mse_calibrate: evaluate 1.0 and 1.5 multipliers

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codecov · 2026-09-18T00:22:50Z

Codecov Report

❌ Patch coverage is 95.65217% with 5 lines in your changes missing coverage. Please review.
✅ Project coverage is 77.84%. Comparing base (b163567) to head (81d79c5).
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Files with missing lines	Patch %	Lines
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Additional details and impacted files

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##             main    #2465      +/-   ##
==========================================
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…tests Cleanup pass over the previous commit. No change to what 4/6 calibrates. One real defect, introduced by that commit: widening `_ScaleCalibConfig` to accept `FourOverSixCalibConfig` made `weight_scale_algorithm: {method: four_over_six}` representable, but the coordination validator only read top-level method names. So `nvfp4_act_headroom` and `lsq` -- which run their weight-scale search one level down -- were false-rejected whenever the 4/6 flag was set, and the mirror case (nested `four_over_six`, no flag) passed silently. `_algorithm_methods` now descends into both bundled-scale-algorithm fields; five regression tests cover it. Altitude: `four_over_six` moves into `TensorQuantizer._is_real_quantize_support()`, the predicate that already exists to answer "can this format be real-quantized" and until now had a single caller. `mtq.compress`'s up-front scan is then generic over that predicate rather than 4/6-shaped, so every future unsupported format gets the same whole-model error for free, and the refusal text exists once instead of twice (the two copies had already drifted on "MSE calibration" vs "calibration"). Reuse: `_as_exmy` defers to the config loader's `_parse_exmy` instead of restating its ExMy regex, so the YAML and Python paths cannot disagree about the grammar. A new `TensorQuantizer.is_four_over_six` property replaces the hand-spelled `block_sizes.get("four_over_six")` at its quantizer-level call sites. Simplification: drop `FOUR_OVER_SIX_M`, which was public API holding the literal 4.0 for one division; collapse the three-problem accumulation in the numerics validator to one tuple comparison that always reports all three values; make `algorithm_methods` private; delete two tests that asserted nothing the others did and rebuild the grid test so it is driven by what `four_over_six_calibrate` passes rather than by literals. The CPU and CUDA bit-identity classes now have distinguishing names, and the CUDA one uses a fixture instead of repeating setup. Left alone deliberately: the `isinstance(level, QuantizerAttributeConfig)` guard is redundant at runtime but the pre-commit mypy env has no pydantic stubs and widens `cfg` -- now commented rather than removed. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

All four are in the validators the previous commits added, not in what 4/6 calibrates. Each was reproduced before fixing. - An absent ``block_sizes["type"]`` is static at runtime (``is_static_block_quant`` tests ``!= "dynamic"``), but the numerics check compared against the literal ``"static"`` and rejected the equivalent Python-API config. Its docstring claimed to be the config-level spelling of ``is_nvfp4_static``; now it is. - ``lsq`` leaves ``scale_algorithm`` unset by default and ``_run_weight_scale_calibration`` substitutes mse, so ``algorithm="lsq"`` does search weight scales -- but the coordination check only saw the nested field when it was written out, and rejected the default. The bundled-scale lookup is now keyed by host algorithm and carries each one's fallback, so ``lsq`` defaults to mse and ``nvfp4_act_headroom`` to max, which is what their call sites do. A bare method string now reaches that lookup too. - ``get_auto_quantize_config`` hardcoded ``algorithm="max"`` while preserving the 4/6 flag in the entries it emits, so exporting a 4/6 search -- the shipped Muse-Glimmer recipe does exactly this -- produced a config the new validator rejects, at the end of an expensive search. It now emits ``four_over_six`` when any surviving entry carries the flag. ``max`` there was never a downgrade but simply wrong: the flag normalizes the FP8 scales by 256 on the assumption that something will pick M=4. - The up-front ``mtq.compress`` screen claimed to mirror ``_compress_and_update_module_weight`` but omitted its weight guards and could not see ``SequentialQuantizer`` weight quantizers -- so it both over-rejected meta/1-byte weights and missed the sequential case it exists to catch. It now screens under the same ``convert_to_single_quantizer`` view with the same gate. Also trims the CHANGELOG entry to the one-or-two sentences CONTRIBUTING asks for, and adds ``four_over_six`` to the two bundled-scale field descriptions that still enumerated only max/mse/local_hessian. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Comment-only pass over the three preceding commits. No behaviour change; 1308 unit tests and the recipe validator still pass. The explanatory prose had accumulated past what it was earning: the same "M=4 is amax x 6/4" paragraph appeared in four docstrings and two error messages, several comments restated the line below them, and the recipe YAML comments re-explained what `algorithm: four_over_six` now says by its name. Source comments go from ~30 lines to 16. What is left is the non-obvious why: that "max" is wrong rather than merely weaker for a 4/6 entry; that the compress screen deliberately mirrors another function's gate; that an absent block_sizes `type` counts as static; that a bare method string must reach the bundled-default lookup; that a single step over [1.0, 6/4] yields exactly two candidates; and that one `isinstance` exists for the mypy hook rather than for the runtime. Docstrings keep their first line and drop the restatements. `FourOverSixCalibConfig` stays the one full explanation, since it is what renders in the API docs and the other sites now point at it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Fridah-nv · 2026-09-18T18:45:44Z

/claude review

claude

Claude review — NVFP4 4/6 as a named calibrate mode

Scope: full review (trigger comment carried no scoping instructions). 18 changed files; reviewed all 8 touched modelopt/torch/quantization/ files plus the 5 recipe YAMLs and the docs/CHANGELOG entries. Read the test names but did not audit test bodies line by line. (Note: a two-dot origin/main..HEAD diff here also picks up 7 unrelated commits of base drift — diffusers/ONNX/iq* churn, nvfp4_fp8_conv.yaml deletion; I reviewed only the files in this PR's own file list.)

Findings

CRITICAL: 0 · IMPORTANT: 1 · SUGGESTION: 1

[IMPORTANT Compatibility] config.py:1727 — _validate_four_over_six_coordination also runs on the restore path (ModeloptStateManager.load_state_dict/modes_with_states → get_config_class → QuantizeConfig(**stored_config)), so a checkpoint saved before this PR with four_over_six: true + algorithm: "max" — precisely what pre-PR get_auto_quantize_config() emitted for the shipped Muse-Glimmer 4/6 recipe, i.e. this PR's own review finding #4 — now fails to load at all, with an error about calibration intent that is unactionable at restore time and no migration or bypass. Suggested fix: enforce at the mtq.quantize/calibrate boundary (next to _check_weight_quantization_took_effect) rather than in the pydantic validator, or warn instead of raise when reconstructing stored state.
[SUGGESTION] algorithms.py:2174 — emitting algorithm="four_over_six" is correct, but mse_calibrate searches all weight quantizers, so a mixed AutoQuantize result now calibrates its non-4/6 layers with the [1.0, 1.5] grid instead of max. Worth saying so in the warning that follows.

What I checked and found sound

Bit-identity of the migration. four_over_six_calibrate forwards step_size=0.5, start=1.0, stop=1.5, fp8_scale_sweep=False, distributed_sync, shared_states — every parameter the five removed YAML stanzas set, and MseCalibConfig's own defaults for distributed_sync/shared_states match. MseCalibConfig's documented candidate count ceil((stop-start)/step)+1 gives exactly 2, and FOUR_OVER_SIX_MULTIPLIER - 1.0 is exact in binary, so the grid is [1.0, 1.5] with no FP drift. _mse_quant_func evaluates candidates through the quantizer's own fake-quant, so the 256-normalization is applied consistently while scoring M=4 — the two halves are genuinely coupled at evaluation time, not just at config time.
Mode/state. FourOverSixCalibrateModeDescriptor follows MseCalibrateModeDescriptor exactly (config class + _calib_func, _mutates_weights=False matching MseCalibConfig); the method literal registers the mode name; _run_weight_scale_calibration's dispatch, _ScaleCalibConfig, and _AUTO_QUANTIZE_SUPPORTED_ALGORITHMS are all updated consistently, so four_over_six works as lsq.scale_algorithm / nvfp4_act_headroom.weight_scale_algorithm.
Rule 1 vs. runtime. block_type in (None, "static") does match is_static_block_quant's != "dynamic" for the cases the schema permits, and _as_exmy handles both the YAML-parsed tuple and the "e2m1" string. Field ordering holds (num_bits at :366 precedes block_sizes at :522, so info.data["num_bits"] is populated). Requiring num_bits in the same entry is right, not over-strict: set_quantizer_by_cfg applies a non-None cfg via set_quantizer_attributes_full, which fully replaces attributes, so a block_sizes-only overlay entry never inherited num_bits anyway. The shipped configs/numerics/nvfp4_four_over_six.yaml (e2m1 / static / e4m3) validates.
Rule 2's algorithm set. Excluding awq_clip/awq_full is correct, not merely conservative: their clip search only reduces amax, so it cannot reach the 1.5x point that encodes M=4. _BUNDLED_SCALE_ALGORITHMS' fallbacks match the call sites (lsq → {"method": "mse"} in _run_weight_scale_calibration; nvfp4_act_headroom → weight_scale_algorithm or {"method": "max"}), and _algorithm_methods handles str/dict/config-object/list uniformly. cfg is coerced to QuantizerAttributeConfig by QuantizerCfgEntry, so the isinstance flag scan does fire.
Compress screen. _reject_unsupported_real_quant_formats faithfully mirrors _compress_and_update_module_weight's gate (None/meta weight, element_size() > 1, is_enabled, not _fake_quant) under the same convert_to_single_quantizer view, and moving the refusal into _is_real_quantize_support() keeps the two callers in agreement. It does skip GroupedQuantizer weight quantizers, but so does the packing loop it mirrors (_fake_quant is not in _delegated_properties), so that is pre-existing, not a new hole.

Minor, not worth a thread

modelopt_recipes/configs/numerics/nvfp4_four_over_six.yaml (untouched by this PR) still says the choice is "made by the calibration algorithm (MSE), configured in the preset" — stale in the same way as the two sibling comments this PR did fix.
_has_four_over_six in algorithms.py and the flag scan inside _validate_four_over_six_coordination are the same predicate written twice (dicts vs. pydantic objects) and can drift; a shared helper in config.py would cover both.

Risk assessment

Low-to-moderate. The calibration change is a faithful rename with a bit-identity test on both CPU and GPU, and the new hard errors replace two genuinely silent mis-encodings — good trade. The residual risk is entirely in where the new validation runs: a mode="after" validator on QuantizeConfig is also a checkpoint-load gate, and finding 1 is the case where that bites an already-saved artifact. Also worth honoring the PR's own note that the GPU bit-identity test has not been executed — that is the only check tying the CPU stub to the real Triton kernel, so it should run before this leaves draft.

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Inline comments:
In `@modelopt/torch/quantization/config.py`:
- Around line 1745-1766: Move the Four-Over-Six coordination validation from the
raw configuration scan into the model-aware flow after set_quantizer_by_cfg
applies entries and before calibration begins. Inspect only effective enabled
static NVFP4 weight quantizers, accounting for later entries replacing earlier
attributes, and require the final calibration algorithm that writes weight
scales to support Four-Over-Six rather than accepting any capable stage in the
algorithm list.

In `@modelopt/torch/quantization/model_calib.py`:
- Around line 810-818: Update the four_over_six_calibrate path to pass an
eligibility predicate into the mse_calibrate weight search, allowing the [1.0,
1.5] sweep only when weight_quantizer.is_four_over_six is true. Keep unflagged
weight quantizers on the ordinary MSE calibration path and preserve existing
behavior for flagged quantizers.

In `@tests/unit/torch/quantization/test_nvfp4_four_over_six.py`:
- Around line 176-179: Move the listed modelopt imports from test methods and
helpers to module scope at the top of the test file, including the symbols from
mode, config, model_calib, calib, tensor_quantizer, and algorithms. Keep an
import local only if it has a genuine circular-import or optional-dependency
requirement, and add a brief comment naming that reason.

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CHANGELOG.rst
docs/source/guides/10_recipes.rst
modelopt/torch/quantization/algorithms.py
modelopt/torch/quantization/compress.py
modelopt/torch/quantization/config.py
modelopt/torch/quantization/mode.py
modelopt/torch/quantization/model_calib.py
modelopt/torch/quantization/model_quant.py
modelopt/torch/quantization/nn/modules/tensor_quantizer.py
modelopt/torch/quantization/utils/numeric_utils.py
modelopt_recipes/configs/ptq/presets/model/nvfp4_four_over_six.yaml
modelopt_recipes/configs/ptq/units/w4a4_nvfp4_nvfp4_four_over_six.yaml
modelopt_recipes/models/meta-models/Muse-Glimmer-30B/auto_quantize/w4a16_nvfp4_4o6_mixed.yaml
modelopt_recipes/models/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16/ptq/nvfp4-4o6.yaml
modelopt_recipes/models/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16/ptq/w4a16_nvfp4_4o6.yaml
modelopt_recipes/ptq.md
tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py
tests/unit/torch/quantization/test_nvfp4_four_over_six.py

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cjluo-nv

Bot review (claude-opus-5) — DM the bot to share feedback.

Solid, well-tested work that follows the existing CalibrateModeRegistry pattern, but it is still a draft whose GPU acceptance test has never been executed, and the compress-side change reaches beyond 4/6.

Needs action:

Run TestFourOverSixIsTheLegacyStanzaOnCUDA in tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py and take the PR out of draft — it is the only check that the real Triton kernel agrees with the CPU stub.
Narrow _reject_unsupported_real_quant_formats in compress.py: it now hard-fails every unsupported weight format with a 4/6-specific parenthetical, and its gate is stricter than pack_real_quantize_weight (see inline).
Confirm that restoring an existing checkpoint whose stored QuantizeConfig pairs four_over_six: true with max/awq/null still loads — the new model validator runs on the restore path; note it in CHANGELOG.rst if it does not.
Add to the PR body why an algorithm: {$import: ...} snippet (already used by configs/ptq/presets/model/nvfp4_awq_clip.yaml) could not have removed the five-way copy-paste, i.e. name the coordination validation as the part YAML cannot do.

No action needed:

Tests are only added, none weakened or deleted; recipe migration is covered by a bit-identity pair plus a non-vacuity guard.

Review on #2465 found that `_validate_four_over_six_coordination` also runs on the restore path: `ModeloptStateManager.load_state_dict` reconstructs the stored quantize-mode config through `QuantizeConfig(**stored)`, so a `mode="after"` validator is also a checkpoint-load gate. Reproduced -- a checkpoint carrying `four_over_six: true` with `algorithm: "max"`, exactly what pre-PR `get_auto_quantize_config` emitted for the shipped Muse-Glimmer 4/6 recipe, no longer loaded at all. Restore has no calibration to fix, so the error was unactionable there and left the checkpoint stranded. The rules now live in one `four_over_six_config_problems()` that both callers share. `QuantizeConfig` warns; `mtq.quantize` raises, next to `_check_weight_quantization_took_effect`, which is already the "fail before calibration" guard. Enforcement therefore still happens before any calibration runs -- the point of having the check at all -- while loading an existing artifact only warns. The numerics rule moves out of `validate_block_sizes` into the same function for the same reason. Also from that review: `get_auto_quantize_config`'s warning now says that choosing `four_over_six` runs the two-point search on the non-4/6 layers too, which for a mixed search result is newly introduced rather than preserved; `_has_four_over_six` reuses the shared predicate instead of a second copy; and the numerics YAML comment that still credited MSE is corrected. Not taken: the suggestion to restrict the search to flagged quantizers. That would break bit-identity with the stanza the five shipped recipes used, which is this PR's whole acceptance gate -- those recipes have FP8 weight quantizers that the stanza did search. Scoping it needs #2292, as the PR body already records. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Three points from review, each verified against the code it cites. `_compress_and_update_module_weight` skips the root module (`if name != ""`), and `TensorQuantizer.forward` early-returns for an empty tensor and when `_if_quant` is False, so such a quantizer never reaches `_real_quantize`. The screen matched none of those and could therefore reject a layer packing would have skipped. It now does, which is what its docstring already claimed. The screen is generic but its message appended a Four-Over-Six note unconditionally, so an INT4 or custom-backend offender read as though 4/6 were the problem. The note is now emitted only when an offender actually carries the flag, collected inside the `convert_to_single_quantizer` view where the quantizer is still the single one that was screened. `is_nvfp4_static` also requires `_fake_quant`, which the config-time rule deliberately omits -- compress flips it later, so it is runtime state rather than a property of the config. The comment claiming plain equivalence now says so. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Per the repo's test path instructions, none of these are optional dependencies or circular-import workarounds, so an import error should surface at collection time rather than mid-test. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com>

Fridah-nv · 2026-09-19T00:09:33Z

/claude review

cjluo-nv

Bot review (claude-opus-5) — DM the bot to share feedback.

Nudge: all prior code concerns are resolved, but the one acceptance-gate test (TestFourOverSixIsTheLegacyStanzaOnCUDA) still has not been run, so a human with hardware has to close it out.

Needs action:

Run TestFourOverSixIsTheLegacyStanzaOnCUDA in tests/gpu/torch/quantization/test_nvfp4_static_quantizer_cuda.py and take the PR out of draft — it is the only check that the real Triton kernel agrees with the CPU stub used for the bit-identity proof.
Confirm the intentional gap that four_over_six_config_problems is enforced only in mtq.quantize, not on the auto_quantize/calibrate paths, so a flagged candidate format with algorithm: max still calibrates silently there.

No action needed:

✔️ Resolved since the last review: the checkpoint-restore regression (validator now warns, mtq.quantize raises, with two round-trip tests), the compress screen's over-strict gate (root module, numel(), _if_quant now match pack_real_quantize_weight), the 4/6-specific message on non-4/6 offenders, the is_nvfp4_static comment, the mixed-AutoQuantize warning, and the in-function test imports.
Tests were only added, none weakened or deleted.

claude · 2026-09-19T00:19:41Z

+
+    Pairs with the ``four_over_six: true`` flag in a weight quantizer's ``block_sizes``,
+    which normalizes the per-block FP8 scales by 256 instead of 448 to leave headroom for
+    the M=4 blocks. :class:`QuantizeConfig` rejects a config that has one half without the


[SUGGESTION] This public docstring is stale relative to the final design of the coordination check.

QuantizeConfig no longer rejects a half-configured 4/6 config — _warn_on_four_over_six_mismatch (config.py:1783) only warnings.warns, precisely so the restore path can still reconstruct a stored config. The rejection lives in mtq.quantize (model_quant.py:336). Since FourOverSixCalibConfig is rendered in the published API docs, a user reading this will expect a ValidationError from QuantizeConfig(**cfg) and get a warning instead.

Suggested change

the M=4 blocks. :class:`QuantizeConfig` rejects a config that has one half without the

the M=4 blocks. :func:`mtq.quantize` rejects a config that has one half without the

(and the following line becomes other; constructing :class:QuantizeConfig directly only warns, so an existing… — adjust the wrap as you prefer.)

claude · 2026-09-19T00:19:53Z

+        num_bits, block_sizes = cfg.num_bits, cfg.block_sizes or {}
+    # TensorQuantizer.is_nvfp4_static minus its `_fake_quant` term, which is a runtime
+    # state compress flips rather than a property of the config. An absent `type` is static
+    # there too, since is_static_block_quant tests != "dynamic".
+    scale_bits, block_type = block_sizes.get("scale_bits"), block_sizes.get("type")
+    is_static = block_type in (None, "static")
+    if (_as_exmy(num_bits), is_static, _as_exmy(scale_bits)) == ((2, 1), True, (4, 3)):


[SUGGESTION] _as_exmy makes rule 1 strictly more permissive than the runtime predicate the comment says it mirrors, so it blesses one config where the flag really is inert.

QuantizerAttributeConfig.num_bits is int | tuple[int, int] | str with no exmy-normalizing field validator — only the YAML path normalizes (config_loader._parse_exmy_num_bits). So a hand-written Python config keeps num_bits="e2m1" / block_sizes["scale_bits"]="e4m3" verbatim, and _as_exmy maps both back to (2, 1) / (4, 3) → this returns None (no problem). But TensorQuantizer.is_nvfp4_static (tensor_quantizer.py:576-581) compares self._num_bits == (2, 1) and self._block_sizes.get("scale_bits") == (4, 3) against the unnormalized strings, so it is False, the static-NVFP4 fake-quant path is never taken, and four_over_six stays inert — exactly the silent half-configuration this PR exists to reject.

Not blocking: a string num_bits is documented as "current used only for custom backends" and such a quantizer is broken for bigger reasons than the flag, and no shipped recipe uses that spelling. But since the comment above claims this is "the config-level spelling of TensorQuantizer.is_nvfp4_static", the two should agree. Either drop the string branch from _as_exmy (making rule 1 reject the string spelling, matching the runtime), or normalize num_bits/scale_bits on QuantizerAttributeConfig so the runtime predicate sees tuples too — the latter fixes the root cause.

claude · 2026-09-19T00:20:04Z

+    def test_flag_accepts_the_exmy_string_spelling(self):
+        """Recipe YAML arrives as tuples, but the Python API keeps whatever was written."""
+        cfg = QuantizerAttributeConfig(
+            num_bits="e2m1",
+            block_sizes={
+                -1: BLOCK_SIZE,
+                "type": "static",
+                "scale_bits": "e4m3",
+                "four_over_six": True,
+            },
+        )
+        assert cfg.block_sizes["four_over_six"]


[SUGGESTION] This test doesn't exercise what its name and docstring claim, so the _as_exmy string branch it was written for is uncovered.

Every other test in TestFourOverSixCoordination calls self._problems(...); this one only asserts cfg.block_sizes["four_over_six"] is truthy — i.e. that pydantic stored the key you just passed. It would pass identically with _as_exmy's string branch deleted, with num_bits="int8", or with the whole numerics rule removed.

If the intent is "rule 1 accepts the exmy string spelling," assert that:

def test_flag_accepts_the_exmy_string_spelling(self): """Recipe YAML arrives as tuples, but the Python API keeps whatever was written.""" assert not self._problems( _weight_only_quant_cfg( { "num_bits": "e2m1", "block_sizes": { -1: BLOCK_SIZE, "type": "static", "scale_bits": "e4m3", "four_over_six": True, }, } ), "four_over_six", )

Note that writing it this way pins the divergence from TensorQuantizer.is_nvfp4_static flagged on config.py:1549-1555 — so whichever way you resolve that, this test is where the decision should be recorded.

claude · 2026-09-19T00:20:13Z

-    start_multiplier: 1.0   # M=6 (keep amax)
-    stop_multiplier: 1.5    # M=4 (amax x 6/4)
-    step_size: 0.5          # candidates [1.0, 1.5]
+  # Weight-only: the dynamic NVFP4 activation quantizers are max-calibrated.


[SUGGESTION] This replaces a correct comment with an incorrect one: the activation quantizers here are type: dynamic (configs/numerics/nvfp4.yaml), so they compute their per-block scale from the tensor at runtime and have no calibrated amax — calling them "max-calibrated" is self-contradictory with "dynamic" in the same sentence, and misleads a reader into thinking the calibration pass produces their scales.

The old wording ("dynamic activation quantizers are not MSE-calibrated") was accurate; the only thing that needed updating was "MSE" → the algorithm name. Same issue in configs/ptq/presets/model/nvfp4_four_over_six.yaml:25 ("# Weight-only: the activation quantizers are max-calibrated.") — note that file's own header comment two lines up correctly says "activations are dynamic".

Suggested change

# Weight-only: the dynamic NVFP4 activation quantizers are max-calibrated.

# Weight-only: the dynamic NVFP4 activation quantizers are not calibrated.

claude · 2026-09-19T00:20:23Z

+    block_sizes = cfg.get("block_sizes") if isinstance(cfg, Mapping) else cfg.block_sizes
+    return bool(block_sizes and block_sizes.get("four_over_six"))
+
+
+def _four_over_six_numerics_problem(cfg: Any) -> str | None:
+    """Describe why the 4/6 flag is inert on this quantizer's numerics, or None if it isn't.
+
+    Its whole effect -- normalizing the per-block FP8 scales by 256 instead of 448 -- is


[SUGGESTION] has_four_over_six (and four_over_six_config_problems below) become part of the mtq.* public namespace as a side effect.

modelopt/torch/quantization/__init__.py:23 does from .config import *, and config.py declares no __all__, so every non-underscore module-level name here is re-exported — this PR therefore adds mtq.has_four_over_six and mtq.four_over_six_config_problems to the public surface, undocumented and untested as public API. Both have exactly one caller each (algorithms.py and model_quant.py), both import by explicit name, so prefixing with _ costs nothing and keeps them internal — consistent with _algorithm_methods / _four_over_six_numerics_problem right next to them, and with _has_four_over_six in algorithms.py (the near-identical name for a different-shaped input is itself a bit of a trap).

claude

Claude review — NVFP4 4/6 as a named calibrate mode (round 2)

Scope: full review; the trigger comment carried no scoping instructions. 19 changed files. Reviewed all 8 touched modelopt/torch/quantization/ files, all 6 recipe YAMLs, both test files, and the CHANGELOG.rst / ptq.md entries. (As last round: a two-dot origin/main..HEAD diff also surfaces ~12 commits of base drift — ggml/IQ export, ONNX graph_utils, diffusers — so I scoped every diff to this PR's own file list.)

This round focused on what the previous pass changed: the four_over_six_config_problems split (warn in the validator, raise in mtq.quantize), the compress screen, and the get_auto_quantize_config algorithm selection.

Findings

CRITICAL: 0 · IMPORTANT: 0 · SUGGESTION: 5

The prior round's one IMPORTANT (the mode="after" validator doubling as a checkpoint-load gate) is resolved as described: QuantizeConfig._warn_on_four_over_six_mismatch warns, mtq.quantize raises next to _check_weight_quantization_took_effect, and test_a_checkpoint_saved_with_the_old_pairing_still_restores pins the restore path with the flag + algorithm: "max" pairing that pre-PR get_auto_quantize_config emitted.

Suggestions posted inline, none blocking:

config.py:1027 — FourOverSixCalibConfig's docstring still says "QuantizeConfig rejects a config that has one half without the other." It warns; mtq.quantize rejects. This one is rendered in the published API docs.
config.py:1549-1555 — _as_exmy makes rule 1 more permissive than TensorQuantizer.is_nvfp4_static, the predicate the comment says it mirrors. num_bits is int | tuple | str with no exmy field validator (only the YAML loader normalizes), so a Python-API num_bits="e2m1" passes rule 1 while the runtime comparison against (2, 1) is False and the flag stays inert. Narrow — that spelling is documented as custom-backend-only and no shipped recipe uses it — but it is the one spelling where the coordination check blesses a silently-inert config.
test_nvfp4_four_over_six.py:328-339 — test_flag_accepts_the_exmy_string_spelling never calls _problems; it only asserts pydantic stored the key you passed, so it would pass with the _as_exmy string branch deleted. That is where finding 2 should be pinned.
nvfp4-4o6.yaml:24 (and presets/model/nvfp4_four_over_six.yaml:25) — the new comments call the type: dynamic activation quantizers "max-calibrated", replacing an accurate comment with an inaccurate one.
config.py:1536-1543 — has_four_over_six / four_over_six_config_problems land in mtq.* via from .config import * (config.py has no __all__). One caller each, both imported by name; underscore-prefixing keeps them internal.

What I checked and found correct

Bit-identity. four_over_six_calibrate forwards step_size=1.5-1.0, start=1.0, stop=1.5, fp8_scale_sweep=False compatibly with mse_calibrate's positional signature; E2M1_MAX / 4.0 and 1.5 - 1.0 are both exact in binary. The CPU/GPU pair plus the two anti-vacuity guards are the right shape for the acceptance gate.
Mode/state. _get_mode_name(FourOverSixCalibConfig().method) → four_over_six_calibrate; registration and _mutates_weights = False match MseCalibConfig; the round-trip test covers save/restore.
The algorithm destructurer. Verified _BUNDLED_SCALE_ALGORITHMS' fallbacks against the call sites rather than the docstrings: lsq → _run_weight_scale_calibration(..., scale_algorithm) with None → {"method": "mse"} (model_calib.py:2382), nvfp4_act_headroom → weight_scale_algorithm or {"method": "max"} (model_calib.py:640). Both match. All four algorithm shapes plus the bare-string-into-bundled-default case destructure correctly. _ScaleCalibConfig's new member is discriminated by its Literal method, and _serialize_weight_scale_algorithm / _serialize_scale_algorithm are generic, so nested four_over_six serializes without a new case.
Compress screen. Compared gate-for-gate against _compress_and_update_module_weight (base_qtensor.py:210-228): the screen's extra numel() == 0 / _if_quant conditions are exactly the two places TensorQuantizer.forward early-returns (lines 1148, 1208), so it cannot over-reject; running under the same convert_to_single_quantizer view closes the SequentialQuantizer blind spot. pack_real_quantize_weight has no caller outside compress.py, so narrowing _is_real_quantize_support() for 4/6 cannot reach an export path, and a restored compressed model is skipped by the element_size() <= 1 gate.
AutoQuantize. _cfg_to_dict's model_dump(exclude_defaults=True) preserves block_sizes, so _has_four_over_six sees the flag; four_over_six is in _AUTO_QUANTIZE_SUPPORTED_ALGORITHMS; the emitted config satisfies rule 3 by construction. Traced the migrated Muse-Glimmer recipe: the 4/6 candidate carries the flag and the FP8 candidate carries algorithm: max with no flag, so no candidate trips either rule.
Recipes. All five call sites of the old stanza are migrated; w4a4_nvfp4_nvfp4_four_over_six puts the flag only on *weight_quantizer (inputs import plain dynamic nvfp4), so rule 1 has nothing to reject. The CHANGELOG entry is one sentence under 0.48.0 → New Features → Quantization, per CONTRIBUTING.

Risk

Low. The behaviour change is confined to configs that were already producing wrongly scaled checkpoints, the recipe migration is pinned bit-identical, and mse is untouched. The genuine outstanding item is the one the author already named: TestFourOverSixIsTheLegacyStanzaOnCUDA has not been run, and it is the only check tying the CPU stub to the real Triton kernel — worth a GPU run before merge.

No blocking issues found. LGTM.

Fridah-nv and others added 3 commits September 18, 2026 17:18

Fridah-nv marked this pull request as ready for review September 18, 2026 18:45

Fridah-nv requested review from a team as code owners September 18, 2026 18:45

Fridah-nv requested a review from realAsma September 18, 2026 18:45

Fridah-nv changed the title ~~feat(quantization): make NVFP4 four-over-six a real calibration algorithm~~ feat(quantization): register NVFP4 four-over-six as a real calibration algorithm Sep 18, 2026

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Fridah-nv requested review from cjluo-nv, jinhangchoi, meenchen and mxinO and removed request for realAsma September 18, 2026 23:20

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Fridah-nv and others added 3 commits September 18, 2026 23:35

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cjluo-nv reviewed Sep 19, 2026

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	the M=4 blocks. :class:`QuantizeConfig` rejects a config that has one half without the
	the M=4 blocks. :func:`mtq.quantize` rejects a config that has one half without the

	# Weight-only: the dynamic NVFP4 activation quantizers are max-calibrated.
	# Weight-only: the dynamic NVFP4 activation quantizers are not calibrated.

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Claude review — NVFP4 4/6 as a named calibrate mode

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Claude review — NVFP4 4/6 as a named calibrate mode (round 2)

Findings

What I checked and found correct

Risk

Fridah-nv commented Sep 18, 2026 •

edited

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