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Weight the IQ2 encoders by the square root of GPTQ's Hessian diagonal - #2686
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…Hessian diagonal The IQ2_XXS, IQ2_XS and IQ2_S encoders take an optional per-input-column importance w and search for the codes minimising sum w (|x| - s q)^2 instead of the unweighted squared error: the local-scale choice, the grid entry and the even-parity sign flip all use the weighted terms. This is how llama.cpp uses an imatrix. The CUDA kernel gets it through a kWeighted template flag, so the unweighted kernel compiles as before, and uniform importance reproduces the unweighted payload exactly. The super-block scale predictor is unchanged. GGMLGPTQHelper passes each GGML block its slice of the square root of the Hessian diagonal, E[x_j^2] (llama.cpp's imatrix statistic), floored at 1e-4 of its mean, for any format whose record declares a weighted search. GPTQ already collects that Hessian, so the built-in IQ2 recipes get the weighting with no extra calibration. On Qwen3.8-27B IQ2_XXS the raw diagonal raised wikitext-2 KL from 0.298 to 0.319 while its root lowered it to 0.287. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Included review availability: This review used your included allowance. Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughIQ2_S, IQ2_XS, and IQ2_XXS accept optional per-column importance weights in Torch and CUDA encoding. GPTQ can derive weights from the Hessian diagonal for supported formats. The change adds an opt-in GPTQ setting and enables it in the built-in IQ2 presets. ChangesWeighted IQ2 Quantization
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~25 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant GPTQ as GGMLGPTQHelper
participant Format as GGMLFormat
participant Quantizer as IQ2 quantizer
participant Packer as IQ2 CUDA packer
participant Encoder as IQ2 encoding kernel
GPTQ->>Format: Quantize group with column slice
Format->>Quantizer: Pass group and Hessian-derived importance
Quantizer->>Packer: Pass blocks and prepared importance
Packer->>Encoder: Validate and encode with weighted scoring
Merge Risk: ⚪ Minimal · up to No issue identified here requires a change before merging. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
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🧪 Generate unit tests (beta)
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Review comments at @modelopt/torch/quantization/ggml/gptq.py:
- Around line 99-108: Update `_blockwise_update` so `importance` remains `None`
when the Hessian diagonal mean is zero or negative; only compute the clamped
square-root weights for a positive mean. This lets weighted IQ2 quantization use
unweighted error when no positive calibration importance is available.
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CHANGELOG.rstmodelopt/torch/kernels/quantization/ggml/common.cuhmodelopt/torch/kernels/quantization/ggml/ggml.cppmodelopt/torch/kernels/quantization/ggml/iq2_family.cuhmodelopt/torch/kernels/quantization/ggml/iq2_s.cumodelopt/torch/kernels/quantization/ggml/iq2_xs.cumodelopt/torch/kernels/quantization/ggml/iq2_xxs.cumodelopt/torch/quantization/ggml/common.pymodelopt/torch/quantization/ggml/gptq.pymodelopt/torch/quantization/ggml/iq2_s.pymodelopt/torch/quantization/ggml/iq2_xs.pymodelopt/torch/quantization/ggml/iq2_xxs.pytests/gpu/torch/quantization/test_iq_formats_cuda.pytests/unit/torch/quantization/test_ggml_gptq.pytests/unit/torch/quantization/test_iq_formats.py
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Weighting the IQ2 search by GPTQ's Hessian diagonal is now opted into with a GPTQ config field, importance_weighted (default False), instead of following from the format alone. The right weighting depends on the algorithm that supplies the statistic -- the square root of diag(H) under GPTQ, while an imatrix-only calibration without error compensation would want the raw E[x^2] -- so the switch belongs with the algorithm. Formats without a weighted search ignore it, so a recipe can set it once. The built-in iq2_xxs, iq2_xs and iq2_s presets set it, so their output does not change. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Requesting changes: the weighted encoders look correct and are well tested, but an MoE expert that gets no calibration tokens ends up with an all-zero importance, and its IQ2 codes come out degenerate.
Needs action:
- Fix
GGMLGPTQHelper._blockwise_updateinggml/gptq.py. Whendiag(H)is all zero, or its mean is not positive, fall back toimportance=None. Todayclamp_min(1e-4 * 0)leaves every weight at 0, so every candidate scores zero error and the search picks entry 0 at the smallest local scale. See the inline comment. - Add a unit test in
test_ggml_gptq.pyfor that case. Give a weighted format a zero Hessian and assert it reproduces the unweighted payload.
No action needed:
- About 300 core-logic lines, under the size budget. No new files and no licensing changes.
- The design question is covered. This adds an optional argument to the existing encoders and GPTQ helper rather than a new system, and the PR body explains why the switch sits on the algorithm and why it uses the square root.
- I checked the weighted parity-flip math (
dot − 2·min w|x|q), theblock % blocks_per_rowcolumn mapping in the CUDA kernel, and the per-group slicing ofimportance[columns]. - The test edits are justified: the
_round_to_tenthsand_round_in_groups_of_4helpers only gained the newcolumnsparameter. - Minor:
check_importancerepeats the finite and non-negative check that the Python side already does, which adds a host sync. Optional.
| importance = None | ||
| if self.importance_weighted and ggml_format.weighted: | ||
| diagonal = self.hessian.diagonal().to(self.weight.device, torch.float32) | ||
| importance = diagonal.clamp_min(_IMPORTANCE_FLOOR * diagonal.mean()).sqrt() |
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An MoE expert that receives no calibration tokens keeps self.hessian at zero (update_hessian skips empty batches). Then diagonal.mean() is 0, the floor is 0, and importance is all zeros. In the weighted search every candidate then scores exactly zero error, so each vector gets the lowest local scale and grid entry 0. That is far worse than the unweighted encode the expert gets today, which GPTQ's identity h_inv fallback keeps close to RTN. The built-in IQ2 recipes target MoE weights, so this will happen in practice. Suggested fix: if not diagonal.mean() > 0: importance = None, plus a test.
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Fixed in 340e12b: _blockwise_update in ggml/gptq.py now weights the search only when the Hessian diagonal has a positive mean, so a module no calibration token reached keeps the plain IQ2 search. The new test_a_module_calibration_never_reached_gets_the_plain_search in test_ggml_gptq.py leaves one layer out of the calibration loop and checks its codes match the unweighted encode exactly; it failed on every value before the fix.
The optional item is in 3b2c5c5: the CUDA packer no longer repeats the finite and non-negative check (the Python encoders already do it), so the extra host sync per encode is gone. It keeps its dtype, shape and device checks.
An MoE expert that no calibration token is routed to keeps a zero Hessian. Its importance was then all zeros, so every candidate code scored zero error and each 8-value group took grid entry 0 at the smallest local scale -- far worse than the plain encode GPTQ's identity fallback gives such a module today. Weight the search only when the Hessian diagonal has a positive mean. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
The Python encoders already reject a non-finite or negative importance before calling the CUDA packer, so the packer's own value check only added a host sync to every encode, which GPTQ runs once per 256-column group. The packer keeps its dtype, shape and device checks. Also list the packers' optional importance argument in the extension docstring. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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Approving: the zero-Hessian blocker from the last review is fixed, it has a regression test, and nothing new came up on re-review.
No action needed.
No action needed:
- ✔️ Resolved since the last review:
- The idle-expert fallback:
_blockwise_updatenow passesimportance=Noneunlessdiag(H).mean() > 0. - Its regression test:
test_a_module_calibration_never_reached_gets_the_plain_searchchecks the codes match the unweighted encode exactly. - The optional CUDA host-sync item:
check_importanceno longer repeats the finite and non-negative checks.
- The idle-expert fallback:
- New since the last review: the
importance_weightedGPTQ option. It defaults toFalseand is threaded throughGPTQCalibConfig,gptq()andGPTQHelper.__init__.GGMLGPTQHelperis the only subclass ofGPTQHelper, so the new keyword breaks nothing. - Tests cover all three settings: weighted on an IQ2 format, flag off, and an unweighted format (
iq1_s). Each format with a weighted search ignores the flag when it isn't set. - Size is about 320 core-logic lines, within the budget. No licensing changes.
- Design: this adds an optional argument to the existing encoders and GPTQ helper rather than a new system. The PR body explains why the switch sits on the algorithm and why it uses the square root.
Complex PR: 20 files changed (≥ 20); spans 7 directories (≥ 5); 1 existing test file modified or removed. Looping in a human for approval.
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Resolve the import conflicts in the IQ2 encoders (iq2_s, iq2_xs, iq2_xxs): main removed the IQFormat compatibility alias in favour of GGMLFormat, and this branch added the importance helpers to the same import list. Keep GGMLFormat together with chunk_importance, importance_blocks and iq2_tile_terms. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
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…#2723) ### What does this PR do? Type of change: new feature #2686 let the IQ2 encoders weight their codebook search by a per-input-column importance, which GPTQ fills with the square root of its Hessian diagonal. This PR does the same for **IQ1_S and IQ1_M**. **Weighted encoders.** `quantize_iq1_s` and `quantize_iq1_m` take the same optional `importance` `w` as the IQ2 encoders. Each 8-value group is approximated by `s·(q + δ)`, with `q` from the ternary grid and `δ = ±1/8`. The search now minimises `Σ w·(x − s·(q + δ))²` instead of the plain squared error. - The weighting covers each group's grid entry, its shift `δ`, and the local scale it shares with neighbouring groups (4 groups in IQ1_S, 2 in IQ1_M). - The super-block scale predictors stay unweighted, as in IQ2. - The CUDA kernel gets the weighting through the same `kWeighted` template flag as IQ2, so the unweighted kernel compiles as before. - With uniform importance, the payload is identical to the unweighted one. - Both format records now declare `weighted=True`, so GPTQ's `importance_weighted` option reaches them. **The built-in `general/ptq/iq1_s` and `iq1_m` recipes now set `importance_weighted: true`**, as the IQ2 recipes do in #2686. ### Usage The built-in IQ1 recipes turn it on: ```bash python examples/hf_ptq/hf_ptq.py --pyt_ckpt_path <model> --recipe general/ptq/iq1_s --export_path <dir> ``` Calling the encoder directly, for example with an imatrix from elsewhere: ```python from modelopt.torch.quantization.ggml.iq1_s import quantize_iq1_s packed, shape = quantize_iq1_s(weight, importance=imatrix) # imatrix: [in_features], finite, >= 0 ``` ### Testing **Qwen3.8-27B, MLP-only IQ1 with GPTQ**, using the default `general/ptq/iq1_*` recipes. The setup is the same as #2686: - Calibration: `hf_ptq.py` defaults (`cnn_nemotron_v2_mix`, 1024 × 512). - Scoring: KL divergence from BF16 on wikitext-2 test, 100 × 512-token chunks, scored as llama.cpp's `llama-perplexity --kl-divergence` does. - All runs use this branch, before its rebase onto the merged #2686. The only difference between them is the importance passed to the search. Mean KL divergence for each format and each choice of importance: | Format | Unweighted | `diag(H)` | **`sqrt(diag(H))` (this PR)** | sqrt − unweighted, paired (chunks where sqrt is lower) | |---|---|---|---|---| | IQ1_S | 0.926 | 0.941 | **0.847** | −0.079 ± 0.007 (95/100) | | IQ1_M | 0.560 | 0.587 | **0.544** | −0.016 ± 0.004 (66/100) | | Format | Perplexity (BF16: 6.746): unweighted / `diag(H)` / sqrt | Same top token: unweighted / `diag(H)` / sqrt | |---|---|---| | IQ1_S | 15.44 / 15.65 / **14.20** | 60.7 / 59.2 / **61.1** % | | IQ1_M | 10.81 / 11.06 / **10.62** | 69.5 / 67.8 / 69.4 % | - **IQ1 follows the same pattern as all three IQ2 formats.** - The square root beats the raw diagonal on both formats: KL divergence is lower by 0.094 (IQ1_S) and 0.042 (IQ1_M), in 95 and 91 of the 100 chunks. - The raw diagonal is worse than no weighting on both. - So the fixed square root from #2686 holds for all five IQ formats. - **The gain is largest on IQ1_S:** 8.5% lower KL divergence and 1.2 lower perplexity. - **Same-top-token agreement** improves on IQ1_S and is unchanged within noise on IQ1_M (−0.13 ± 0.23 points). - **Paired differences** compare both arms on the same tokens. The standard error is over the 100 chunks. - **Every unweighted arm ran with uniform importance.** That gives byte-identical codes: the tests check it, and so did a run on Qwen3.8-27B's layer-10 MLP weights (0 of 348,160 blocks differ for either format). GPTQ's per-layer relative error (the `Relative MSE error` it logs), weighted ÷ unweighted, geometric mean over 64 layers: | Format | Importance | `gate_proj` | `up_proj` | `down_proj` | |---|---|---|---|---| | IQ1_S | `diag(H)` | 0.98 (42/64 layers lower) | 1.00 (24/64) | 0.93 (41/64) | | IQ1_S | `sqrt(diag(H))` | 0.96 (63/64) | 0.99 (60/64) | 0.91 (62/64) | | IQ1_M | `diag(H)` | 0.99 (41/64) | 1.01 (23/64) | 0.92 (42/64) | | IQ1_M | `sqrt(diag(H))` | 0.96 (61/64) | 0.98 (59/64) | 0.90 (63/64) | **Unit tests:** 137 passed in `test_iq_formats.py` and `test_ggml_gptq.py`, with and without a GPU visible, plus the recipe tests. The weighted tests are driven by the format registry, so they now run for IQ1_S and IQ1_M too: - uniform importance reproduces the unweighted payload exactly; - importance lowers the importance-weighted error below 0.75 × that of the unweighted search; - invalid importance is rejected. The GPTQ test now covers IQ1_S as a weighted format and uses Q8_0 as the unweighted one. **GPU tests:** 96 passed. - The weighted CUDA packers match the PyTorch weighted encoders, with at most 1 block in 1,000 differing. - With uniform importance, the CUDA packers reproduce the unweighted payload. - They reject an importance that doesn't tile the blocks. `pre-commit` passes. ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: ✅ The API is compatible, since `importance` is optional. The codes GPTQ picks for the built-in IQ1 recipes change, which is the point of this PR. - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: ✅ - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ This extends #2686's entry to the IQ1 formats and recipes. - Did you get Claude approval on this PR?: ❌ Not yet. ### Additional Information Follows #2665 (block-GPTQ for the GGML formats) and #2686 (weighted IQ2 encoders). 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * IQ1_S and IQ1_M quantization now support optional per-column importance weights, helping prioritize values that matter most during codebook search. * GPTQ calibration presets for IQ1_S and IQ1_M now enable importance-weighted quantization by default. * Importance-weighted calibration is supported across the built-in IQ1 and IQ2 recipes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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What does this PR do?
Type of change: new feature
This is the second step of the IQ GPTQ + imatrix plan, after #2665: the IQ2 search now weights each input column by how much the layer's outputs depend on it.
Weighted encoders.
quantize_iq2_xxs,quantize_iq2_xsandquantize_iq2_stake an optional per-input-columnimportancew. They then choose codes that minimiseΣ w·(|x| − s·q)²instead of the plain squared error, which is how llama.cpp uses an imatrix.kWeightedtemplate flag. The unweighted kernel compiles as before.importancemust be finite and non-negative, with one entry per input column; anything else raises.GGMLFormat.weightedfield marks the formats that accept it.GPTQ feeds them, when the recipe asks. A new GPTQ option,
importance_weighted(defaultfalse), turns it on.GGMLGPTQHelperthen gives each GGML block of a weighted format its slice ofsqrt(diag(H)). Herediag(H) = E[x_j²]is llama.cpp's imatrix statistic, floored at 1e-4 of its mean. GPTQ already accumulatesH, so this needs no extra calibration pass. The built-ingeneral/ptq/iq2_*presets set the option, so their output is the weighted one. Formats without a weighted search (IQ1_*, NVFP4, INT4) ignore it, so one recipe can set it for mixed formats.Why the switch is on the algorithm, not the format. The right weight depends on what supplies the statistic. Under GPTQ, later columns absorb part of each block's error, and the square root measured best. A future imatrix-only calibration, with no error compensation, would want the raw
E[x²]and would be its ownmethod. The encoder'simportance=argument andGGMLFormat.weightedare shared by both.Why the square root. The raw diagonal is heavy-tailed, so a few outlier columns dominate the search. It made KL divergence worse than no weighting on IQ2_XXS and IQ2_XS (table below). The square root keeps the column ranking with half the log-range, and it beats both on all three formats.
IQ1_S and IQ1_M stay unweighted for now. Their encoders are the next step.
Usage
The built-in IQ2 recipes turn it on:
In a custom recipe:
Calling the encoder directly, for example with an imatrix from elsewhere:
Testing
Qwen3.8-27B, MLP-only IQ2 with GPTQ, using the default
general/ptq/iq2_*recipes.hf_ptq.pydefaults (cnn_nemotron_v2_mix, 1024 × 512).llama-perplexity --kl-divergencedoes. This is the same setup as Support GPTQ for the GGML formats #2665, with the Report KL divergence from the unquantized model in hf_ptq #2662 code run locally.Mean KL divergence for each format and each choice of importance:
diag(H)sqrt(diag(H))(this PR)diag(H)/ sqrtdiag(H)/ sqrtGPTQ's per-layer relative error (the
Relative MSE errorit logs), weighted ÷ unweighted:gate_projup_projdown_projdiag(H)sqrt(diag(H))diag(H)sqrt(diag(H))diag(H)sqrt(diag(H))These are geometric means over the 64 layers. With layerwise calibration, each run's later layers see different inputs, so read these as indicative.
The raw diagonal barely lowers GPTQ's own error on
gate_projandup_proj, while the square root lowers it on every projection. So the raw diagonal is a worse stand-in for GPTQ's per-block objective, not a case of over-fitting the calibration data.The plan came from a prototype that used llama.cpp's own quantizers on the UD-IQ1_S layout. In it, Hessian-diagonal weighting stacked with block-GPTQ (wikitext-2 KL divergence):
diag(H)Unit tests, on CPU. They pass with and without a GPU visible, 122 passed.
tests/unit/torch/quantization/test_iq_formats.py, for each weighted format:tests/unit/torch/quantization/test_ggml_gptq.py:sqrt(diag(H))column-sliced per block;iq1_s) gets nothing.GPU tests, 92 passed:
tests/gpu/torch/quantization/test_iq_formats_cuda.py,test_ggml_gptq_cuda.py,test_gptq.py,test_iq1_s_cuda.pyandtest_iq2_xs_cuda.py.pre-commitpasses.Before your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices (e.g. avoiding hardcoded
trust_remote_code=True,torch.load(..., weights_only=False),pickle, etc.).importanceis optional. The codes GPTQ picks for the IQ2 recipes change, which is the point of this PR.CONTRIBUTING.md: N/AAdditional Information
Follows #2665 (block-GPTQ for the GGML formats). Next: weighted IQ1_S / IQ1_M encoders.
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