Hmm… for now, maybe something along these lines (I’m not entirely confident about the content, so please take this just as a reference…):
My direct answer would be: yes, this looks like a useful audit of channel-level quantization proxies, but I would narrow the interpretation from “weight importance in general” to “isolated, quantizer-specific channel susceptibility under this intervention.”
The one-at-a-time design is valuable because it separates a static score from the rest of a quantization pipeline. The results therefore say something concrete about whether each audited score predicts the forward-KL damage produced when one channel is quantized while everything else is held fixed.
I would not yet read that as a general verdict on the native AWQ, GPTQ, OWQ, or SpQR algorithms. Those methods use their statistics for different actions—scaling, sequential compensation, mixed precision, or weight-level outlier isolation—and this audit maps those different objects into a common static channel ranking. That is a useful benchmark, but it is not identical to asking whether each full method makes good deployment decisions.
I also see two different result regimes:
- For global continuous ranking, the loss-aware unified score appears substantially stronger.
- For fixed-budget selection within a linear, the picture is more mixed; corrected OWQ residual and simpler proxies appear competitive or stronger on some top-k measures.
That distinction may be more useful than naming one overall winner.
A practical route forward
- Lock the estimand. Describe the primary target as isolated forward-KL damage under the stated quantizer, bit-width, grouping, and data distribution.
- Add a method-to-proxy provenance table. Show the native quantity, native action, audited channel score, aggregation rule, and intentionally omitted parts.
- Separate three questions: global damage prediction, within-linear top-k selection, and native end-to-end quantization quality.
- Run two small checks first: residual-aware baselines, and a small comparison of nearby unified-score formulations.
- State the narrowest claim that survives all models and evaluation regimes.
The four clarifications I would put near the front are:
| Point |
Why it matters |
Smallest useful clarification |
| INT3 probe vs INT4 deployment |
The post calls INT3 the deployment setting, while the frozen config distinguishes an INT3 isolated probe from INT4 deployment/joint settings |
State this distinction explicitly |
| Native method vs audited proxy |
Static channel proxies omit important parts of the original algorithms |
Add a provenance table |
| Continuous ranking vs top-k selection |
Different metrics appear strongest for different uses |
Report them as separate tasks |
| Unified-score definition |
The implementation, cited framework, and measured KL target are not obviously the same quantity |
Compare variants on a few linears |
1. Measurement and sampling contract
INT3 appears to be a diagnostic probe
The forum post and README describe the measurement as the “actual deployment quantization setting (INT3, group-128).” However, the frozen config.py distinguishes:
DEPLOYMENT_BITS = 4ISOLATED_PROBE_BITS = 3
and says INT3 is used for the isolated probe for signal-to-noise reasons.
That is a defensible design. The repository also contains an INT4 rank-stability check: 2,688 channels across three v_proj strata were re-measured, with high INT3–INT4 rank agreement and about 0.89 pooled top-1% recall. At the same time, most INT4 measurements were below the selected KL floor, which explains the motivation for the amplified INT3 probe.
I would therefore describe the design as:
The main census uses an amplified INT3 isolated probe for measurement resolution, while a smaller INT4 check tests whether high-damage rankings transfer to the deployment-bit setting.
The limitation is simply that the INT4 check is currently limited to three v_proj strata.
Candidate-selected and population analyses
The repository uses:
- a candidate union built from high-scoring channels plus random controls; and
- full sweeps over selected layers.
The config correctly restricts population-correlation claims to full-sweep strata. I would keep that distinction visible in the paper:
| Analysis |
What it supports |
| Candidate union |
Tail diagnostics and selected-set comparisons |
| Full-sweep strata |
Population correlations within those strata |
| Cross-model replication |
Repetition across the tested small-model families |
Pre-registered, amended, and post-hoc
The cleanest description seems to be:
- a pre-registered core;
- documented pre-run amendments;
- labeled post-hoc robustness checks.
For example, the sequence-count amendment is recorded in the frozen config, while symmetric controls and the INT4 stability check are explicitly post-hoc. That transparency is a strength; it is just more precise than saying that every analysis choice was fixed before any result was seen.
OWQ correction and coverage
The repository’s 02b_owq_residual.py explains that the original OWQ proxy used full weight energy, while the corrected, paper-faithful variant uses quantization-residual energy multiplied by the Hessian diagonal.
Because the measured candidate union was formed using the earlier score, corrected OWQ may have top-ranked channels outside that union. The script accounts for this by computing coverage. I could not find the generated coverage summary among the public analysis files I checked; if it is not currently included, publishing that one number would make the corrected OWQ tail results easier to interpret.
KL measurement contract
It would help to place the exact KL definition in one location:
- direction;
- full vocabulary or truncation;
- temperature;
- logits/log-softmax dtype;
- shift and mask;
- token/sequence/sample aggregation;
- treatment of non-finite values;
- mean versus upper-tail statistics.
The vLLM LLM Compressor KL RFC and its full-vocabulary KL issue are useful implementation checklists. They are not evidence of a problem here; they show which details future readers may need to reproduce the measurement.
2. What the audit measures—and what it does not
I would separate four quantities that are often called “importance”:
| Quantity |
Question |
| Functional importance |
Does the trained model rely on this component? |
| Perturbation sensitivity |
How strongly does the model respond to a standardized perturbation here? |
| Realized quantization damage |
How much error does this quantizer actually create here? |
| Compensated deployment impact |
How much damage remains after the native method’s scaling or compensation? |
The isolated forward-KL probe combines at least perturbation sensitivity and realized quantization error. That is not a flaw; it means the target is conditional on the intervention.
Terms such as these may be clearer:
- isolated channel damage;
- one-at-a-time forward-KL diagnostic;
- quantizer-specific channel susceptibility.
I would reserve “general weight importance” for a claim shown to transfer across bit-widths, group sizes, quantizers, objectives, data distributions, and parameterizations.
This distinction has analogues outside quantization. Optimal Ablation for Interpretability emphasizes that an importance result is partly defined by the ablation operator. SmoothQuant shows that a function-preserving channel scaling can move quantization difficulty between weights and activations.
I did not find a previous study matching this exact combination of four method-derived scores, one-channel group-128 interventions, three open LLM families, and forward-KL measurement. There are close methodological precedents, but apparently not an exact duplicate.
3. Native methods versus audited channel proxies
The common benchmark is useful because it places different methods behind one interface. The trade-off is that the audited score is not always the native decision rule.
| Method |
Native role |
Audited object |
Main omitted context |
| AWQ |
Activation-guided equivalent scaling and scale search |
Activation magnitude by input channel |
The scaling transformation and scale search |
| GPTQ |
Sequential second-order quantization with updates |
Curvature/inverse-Hessian channel factor |
Actual residual, order, and compensation |
| OWQ |
Mixed-precision protection of weak columns |
Static column sensitivity |
Mixed-precision action and compensation |
| SpQR |
Dynamic weight-level outlier isolation |
Static channel aggregation |
Weight granularity and sequential recomputation |
| Unified |
Loss-aware static proxy |
CE input-gradient × input |
Exact mapping to the cited expression and quantization direction |
Primary references are the AWQ paper and implementation, GPTQ paper and implementation, OWQ paper and implementation, and SpQR paper and implementation.
A careful claim is:
The derived static channel proxy did or did not predict isolated forward-KL damage under the common probe.
A stronger statement such as “the native method selected the wrong weights” is not established by this comparison alone.
For GPTQ, compensation is part of the method rather than an external patch. For SpQR, converting a dynamic weight-level criterion into a static channel aggregate changes both granularity and timing. For AWQ, activation magnitude guides a scaling operation rather than merely selecting protected channels. For OWQ, the residual-aware correction is especially important because realized quantization error is part of its native sensitivity.
4. The unified score
The unified score’s strong global correlation is important. I would preserve that result while separating three questions:
- What does the cited framework define?
- What does the implementation calculate?
- Why should that quantity approximate the measured forward KL?
Activation Sensitivity as a Unifying Principle for Post-Training Quantization motivates a gradient-weighted activation statistic using a first-order loss expansion and relates activation-, Fisher-, and Hessian-style criteria.
The repository’s GradStatsAccumulator stores the linear input and uses grad_input[0]. It accumulates the squared product of the input channel and the cross-entropy gradient with respect to that input. The backward pass is produced in 01_collect_calibration.py through model(ids, labels=ids).
That is a meaningful loss-aware statistic, but it is not obviously identical to a formulation using the gradient with respect to the linear output.
The measured target is forward KL between clean and perturbed distributions. Near the clean model, local KL is usually described by a second-order Fisher/Hessian geometry; Virtual Adversarial Training is a familiar example.
This does not make the current score invalid. It means that calling it the literal “first-order linearization of the ground truth” needs an explicit bridge between:
- next-token cross-entropy;
- the input-gradient statistic;
- the actual weight perturbation;
- forward KL.
Three reasonable paths are available:
- Intentional input-gradient proxy: name it as an audit-specific variant and let the empirical result stand.
- Intended reproduction of the cited expression: compare current and paper-like variants on a few linears.
- “Analytic ceiling” used informally: replace it with “loss-aware reference proxy” or “gradient-informed comparator.”
A further reason to test the actual residual is that a highly sensitive channel may receive a tiny quantization error, while a moderately sensitive channel may receive a large or badly aligned residual. Rate Distortion for Model Compression is relevant because it separates distributional distortion from supervised loss change and explicitly includes the compressed-versus-original parameter difference.
I could not find a checked-in real-model comparison of the current input-gradient score, a paper-like output-gradient score, and a residual-direction-aware gradient score. A small comparison would show whether this is mainly a naming issue or a materially different ranking.
5. Highest-value small controls
I would start with clarification and small analyses rather than another large census.
Tier 0: no heavy rerun
- Add the provenance table.
- Correct the INT3/INT4 wording.
- Separate candidate-selected and population analyses.
- Label primary, amended, and post-hoc analyses.
- Separate global correlation from within-linear and top-k utility.
Tier 1: small experiments
Residual-aware baselines
| Baseline |
Question |
| Residual norm |
Is damage mostly explained by error magnitude? |
| Activation-weighted residual |
Does local output reconstruction explain it? |
| Curvature-weighted residual |
Does second-order/local compensation structure help? |
| Gradient–residual alignment |
Does downstream loss direction add information? |
I would avoid simply dividing KL by residual norm, because ratios can become unstable for tiny residuals. Binning, adding residual norm as a covariate, or using norm-matched perturbations is easier to interpret.
Unified-score variants
On one model and a few linears:
- current CE input-gradient version;
- paper-like output-gradient version;
- residual-direction-aware gradient version.
Existing isolated KL measurements can be reused; only the calibration-gradient pass needs to be repeated.
Held-out incremental prediction
Partial correlation is useful, but it depends on the selected controls and their collinearity. A complementary test is:
Does adding this score improve prediction on held-out linears or layers beyond activation, layer identity, and residual magnitude?
Possible outputs are held-out rank correlation, recall@k, damage-mass recall, and regret.
Tier 2: transfer and scope
A small calibration panel could compare another sample from the same corpus, another domain, another sequence length, and packed versus non-packed sequences. On the Impact of Calibration Data in Post-training Quantization and Pruning shows why same-distribution reliability and cross-distribution transfer are different questions.
A small pairwise panel—high/high, high/low, low/low, same group, different groups—would estimate how safely one-at-a-time damage extrapolates to joint sets.
Finally, a few native-action spot checks could compare the static proxy ranking with damage after scaling, compensation, or mixed-precision protection. Quantization Error Propagation is relevant background for why local and full sequential effects can differ.
6. Reading the current results by use case
The public results support different conclusions for different tasks.
Global continuous prediction
In the Qwen full-sweep summary.json, the unified score’s raw Spearman correlation is about 0.87, substantially higher than the other audited scores.
That is a strong result for pooled continuous prediction of isolated damage.
Fixed-budget selection
The same summary reports a different ordering for one top-k precision measure:
- OWQ residual: about 0.52;
- GPTQ act-order proxy: about 0.46;
- unified: about 0.41;
- AWQ: about 0.38.
The exact sampling and definition matter, but this already shows that the best global predictor need not be the best fixed-budget selector.
A useful summary would be:
The unified score is the strongest global continuous predictor, while residual-aware and simpler proxies remain competitive for within-linear top-k protection.
Control specification
The original partial analysis controls for layer, weight norm, and activation. The post-hoc symmetric_controls.json also controls for mean gradient magnitude. The unified score remains strongest, but GPTQ-, OWQ-, and SpQR-derived associations recover substantially.
I would therefore avoid saying that the other scores contain almost no information after “the shared statistic” is removed. A more stable description is:
Under the pre-specified activation-control model, several proxies retain little association with the isolated target. Under a symmetric gradient-scale control, some associations recover, while the unified score remains strongest.
Suggested metric panel
| Use case |
Primary output |
| Global ranking |
Pooled and model-stratified rank correlation |
| Within-linear ranking |
Within-linear Spearman or Kendall |
| Fixed budget |
Precision/recall@k |
| Protecting total damage |
Damage-mass recall |
| Avoiding catastrophic misses |
Worst miss and top-1/top-10 recall |
| Added information |
Held-out predictive lift |
| Decision quality |
Regret relative to the measured oracle |
A claim hierarchy
Strongly supported
- Directly auditing static channel scores is useful.
- The tested scores share substantial activation-derived structure.
- The unified loss-aware score is a strong predictor of pooled isolated forward-KL damage in the tested models.
- Global prediction and within-linear top-k selection are different evaluation problems.
- Corrected residual-aware OWQ should be treated separately from the earlier weight-energy implementation.
Supported with conditions
- Several non-unified proxies add limited information under the pre-specified activation-control model.
- INT3 preserves much of the INT4 high-damage ranking in the tested
v_proj subset.
- Damage is concentrated under this isolated probe.
Better kept as hypotheses
- Compensation hides poor native selections.
- The audited scores approximate one universal latent importance quantity.
- The unified score is a formal upper bound or analytic ceiling.
- The isolated ranking transfers directly to joint deployment quantization or downstream tasks.
Compact roadmap
If the goal is to strengthen the paper without making it much larger, I would:
- correct the INT3/INT4 wording;
- add the provenance table;
- separate global, within-linear, and top-k conclusions;
- add residual-aware baselines;
- compare a few unified-score variants on real layers;
- add held-out predictive lift alongside partial correlations;
- frame the conclusion around isolated quantizer-specific susceptibility.
If those checks preserve the main pattern, the paper becomes stronger because the result no longer depends mainly on terminology or one reference-score implementation.
If they change the ranking, that is also informative: it identifies whether activation, curvature, gradient, or realized quantization residual is carrying the predictive signal.
Overall, I think the project has a solid reusable core: measure the picks directly rather than infer their quality only from the final compressed model. The main opportunity is to make the target and comparison boundary explicit enough that readers can distinguish:
- a standardized proxy audit;
- a native-method evaluation;
- and a claim about general model importance.
Those are all worthwhile questions, but they are not the same question.
I’ll look into the concerns and work on changes to remedy them.