Add imatrix calibration for the GGML formats and use it for fused MoE experts

[cjluo-nv]

What does this PR do?

Type of change: new feature

Adds imatrix calibration (algorithm: {method: imatrix}) for the GGML formats, and uses it in the built-in IQ1 and IQ2 recipes for fused MoE experts, which GPTQ does not reach.

What imatrix does. It encodes each weight once. The codebook search is weighted by the square root of the inputs' per-column second moment E[x_j²], which is llama.cpp's importance-matrix statistic.

• Collection: after max_calibrate, one forward with weight fake-quant disabled collects the statistic for every weight whose quantizer backend registered an imatrix encoder.
• Encoding: the GGML encoder encodes each weight with its statistic, writes the decoded weight back, and pins the payload, so later forwards and export use exactly those codes.
• Coverage: dense quantized linears and HF fused-MoE experts (_QuantFusedExperts). Each expert gets its own statistic, over the tokens routed to it.
• Idle experts: a weight no calibration token reaches gets the plain search.
• Single pass by default: one forward collects every statistic, as llama.cpp does. layerwise: {enable: true} instead collects each layer's statistics after the earlier layers are quantized.
• Checked up front: a weight the encoder can't take, such as an IQ weight whose input width isn't a multiple of 256, is refused before any forward runs, including max calibration's for enabled activation quantizers, and before any weight or amax changes, naming its module.
• Bounded memory: the input hook squares 1,024 rows at a time. For one 4,096-token batch into a 17,408-wide projection, the calibration forward's peak is 204 MiB above resident, against 689 MiB when squaring the whole input at once.
• Single rank: the statistics aren't summed across data-parallel ranks yet, so imatrix warns when run on several ranks, as local_hessian does.

How it composes with GPTQ. A weight that already holds a pinned payload is left alone. So [{method: gptq}, {method: imatrix}] runs GPTQ on the dense linears and imatrix on the experts GPTQ skips.

• The built-in general/ptq/iq1_s, iq1_m, iq2_xxs, iq2_xs and iq2_s recipes now use this list. GPTQ stays layerwise, and imatrix runs in a single pass after it, so its statistics already see GPTQ's codes in the dense linears.
• New experts-only recipes for every IQ format: general/ptq/iq1_s_experts_only, iq1_m_experts_only, iq2_xxs_experts_only, iq2_xs_experts_only and iq2_s_experts_only put the format on the MoE routed experts only (the scope of nvfp4_experts_only), calibrated with imatrix alone. Shared experts, dense layers and attention stay in BF16.
• On a dense model, the imatrix pass finds nothing left to encode, so the codes are unchanged.
• IQ1_S and IQ1_M have a weighted search since Weight the IQ1 encoders by the square root of GPTQ's Hessian diagonal #2723, so their recipes get the step too. The preset test requires it for every weighted format. The MoE measurements below are IQ2_XXS.

Why the square root. It's the same exponent GPTQ uses (#2686). Without error compensation, the raw E[x²] is the exact diagonal of the output error. But it is heavy-tailed, and noisy for an expert that sees about 3% of the calibration tokens. The square root measured better on the dense MLP and on the routed experts (tables below). The one exception was quantizing the shared expert alone (a dense MLP that sees every token), where raw was slightly better: 0.081 vs 0.087 KL divergence, against 0.137 for the plain encode.

Plumbing.

• Registry: imatrix plugs in through register_imatrix_encoder, a backend registry next to register_gptq_helper.
• Shared input hooks: the hooks local-Hessian calibration used for dense linears and fused experts are now shared as _register_weight_input_hooks. Their marker is renamed to _WeightInputHook, so Nemotron-H's MTP head also runs its forward during imatrix.
• Shared floor: GPTQ's floor on the Hessian diagonal is shared as floored_second_moments.
• hf_ptq: --moe_calib_experts_ratio now applies to every algorithm in a list.

Usage

The built-in IQ1 and IQ2 recipes use it with no change:

python examples/hf_ptq/hf_ptq.py --pyt_ckpt_path <moe model> --recipe general/ptq/iq2_xxs --export_path <dir>

As a standalone method (one encode per weight, no Hessian), or after GPTQ in a custom recipe:

algorithm:
  - method: gptq
    block_size: 256
    perc_damp: 0.3
    importance_weighted: true
    layerwise: {enable: true}
  - method: imatrix          # encodes the fused MoE experts GPTQ leaves, in a single pass

Testing

Unless noted, runs use hf_ptq.py defaults for calibration (cnn_nemotron_v2_mix, 1024 × 512). They are scored by KL divergence from BF16 on wikitext-2 test, 100 × 512-token chunks, scored as llama.cpp's llama-perplexity --kl-divergence does; the scoring code is #2662, run locally. Paired differences compare two arms on the same tokens, with the standard error over the 100 chunks. The exponent was set through an eval-only switch; the final recipes match the tested ones.

Qwen3.5-35B-A3B (256 experts, top-8), iq2_xxs_experts_only: IQ2_XXS on the routed experts only, shared expert BF16

Calibration	Mean KL divergence	Perplexity (BF16: 6.398)	Same top token
plain encode (max)	0.1847	7.515	82.15 %
imatrix (this PR's recipe)	0.1801	7.485	82.29 %

imatrix vs plain: −0.0046 ± 0.0011 KL divergence (z = −4.2), lower in 68/100 chunks. This is the scope GPTQ cannot reach at all, so it isolates what imatrix adds.

Single pass vs layerwise (Qwen3.5-35B-A3B, IQ2_XXS)

Scope	Plain / GPTQ only	imatrix, single pass (the recipes)	imatrix, layerwise
experts only	0.1847	0.1801	0.1778
MLP + experts, after GPTQ	0.2193	0.2135	0.2122

Layerwise minus single pass is −0.0023 ± 0.0006 (z = −3.8) experts-only and −0.0013 ± 0.0006 (z = −2.1) after GPTQ. On the two larger models below, the two tie. The recipes use the single pass: the layerwise gain is small here and absent on the larger models, and the single pass keeps no cache of each layer's inputs (layerwise ran out of memory at 1024 samples on Qwen3.8-Flash-Next). For a model partly offloaded to CPU, layerwise is the faster choice, because each layer moves to the GPU once instead of on every batch.

Larger MoE models, experts only (IQ2_XXS)

Run on 8×H100 nodes, with the same KL-divergence setup, on the BF16 checkpoints of Qwen3.8-Flash-Next (180B, 512 experts) and GLM-5.3-Flash (321B, 288 experts).

• Calibration uses 256 samples instead of 1024, to fit the layerwise arms in memory and within the cluster's 4-hour job limit. On Qwen3.8-Flash-Next, the single pass at 1024 samples gives 0.2857, the same as at 256 within noise (+0.0008 ± 0.0012).
• Qwen3.8-Flash-Next: the experts' down_proj has 640 inputs, not a multiple of 256, so only gate_up_proj is quantized and down_proj stays BF16 in every arm.

Model	Plain encode	imatrix, single pass (the recipes)	imatrix, layerwise
Qwen3.8-Flash-Next (BF16 perplexity 3.28)	0.2958 (perplexity 3.61)	0.2865 (3.60), −0.0093 ± 0.0026, lower in 64/100	0.2864 (3.59), −0.0094 ± 0.0026, lower in 65/100
GLM-5.3-Flash (BF16 perplexity 3.23)	0.7415 (6.15)	0.7265 (6.10), −0.0150 ± 0.0039, lower in 61/100	0.7269 (6.10), −0.0146 ± 0.0037, lower in 71/100

Mean KL divergence, with paired differences against the plain encode.

• imatrix beats the plain encode on both models.
• Layerwise and the single pass tie: −0.0001 ± 0.0014 on Qwen3.8-Flash-Next and +0.0004 ± 0.0027 on GLM-5.3-Flash.

Qwen3.5-35B-A3B (256 experts, top-8), IQ2_XXS on the MLP: routed experts and shared expert

Calibration	Mean KL divergence	Perplexity (BF16: 6.398)	Same top token
plain encode (max)	0.3115	8.50	77.9 %
imatrix only, raw E[x²]	0.2522	7.99	79.6 %
imatrix only, square root	0.2467	7.92	80.0 %
GPTQ only (experts get the plain encode; the recipe before this PR)	0.2193	7.75	80.7 %
GPTQ, then imatrix on the experts, raw	0.2255	7.81	80.3 %
GPTQ, then imatrix on the experts, square root, layerwise	0.2122	7.71	81.0 %
GPTQ, then imatrix on the experts, square root, single pass (this PR)	0.2135	7.71	81.0 %

The imatrix rows other than the last ran layerwise.

• vs GPTQ only: −0.0058 ± 0.0014 KL divergence (z = −4.2), lower in 73/100 chunks.
• Square root vs raw, both layerwise: −0.0133 ± 0.0012, lower in 89/100.
• Raw weighting on the experts is worse than no weighting (+0.0062 ± 0.0016 vs GPTQ only), which is why the square root is the fixed choice.

Qwen3.8-27B (dense), MLP only, imatrix alone vs GPTQ

Calibration	IQ2_XXS	IQ1_S
plain encode	0.450	1.469
imatrix, raw	0.362	1.219
imatrix, square root	0.347	1.137
GPTQ, unweighted	0.307	0.926
GPTQ, square root	0.287	0.847

• imatrix alone closes 53–63% of the gap from the plain encode to GPTQ, with no Hessian or inverse.
• The square root beats raw: −0.015 for IQ2_XXS and −0.082 for IQ1_S, the latter lower in 90/100 chunks.
• The imatrix rows ran layerwise; the IQ1_S rows use Weight the IQ1 encoders by the square root of GPTQ's Hessian diagonal #2723's weighted IQ1 encoders.

Unit tests (tests/unit/torch/quantization/test_ggml_imatrix.py, CPU):

• imatrix hands the IQ2 search the floored square root of each linear's input E[x²], and pins the codes; the forward then uses them;
• its output error beats the plain encode;
• [gptq, imatrix] keeps GPTQ's payload byte for byte;
• on a tiny Llama, layerwise imatrix gives layer 1 statistics that see layer 0's quantized MLP, while layer 0's match the single pass;
• a module calibration never reaches gets the plain search;
• a width the encoder can't take is refused before any forward runs, with and without an enabled activation quantizer, leaving every weight and amax unchanged;
• running on several ranks warns that the statistics aren't synced;
• on a fused-experts MoE block with a shared expert, quantized with the iq2_xxs_experts_only recipe: each expert's gate_up_proj and down_proj get the statistic of the tokens routed to that expert, an idle expert gets the plain search but still pinned codes, and the shared expert stays in BF16.

Other tests updated or added:

• test_presets.py now expects GPTQ followed by imatrix for every weighted format, and checks that each experts-only recipe quantizes only the routed experts, with imatrix.
• modelopt_recipes/ptq.md lists the new recipes and the IQ recipes' new algorithm.
• test_example_utils.py covers --moe_calib_experts_ratio on algorithm lists.
• Related suites, in the TensorRT-LLM 1.3.0rc22 container: 1,611 unit tests (36 files plus test_example_utils.py) and 164 GPU tests pass. These cover local-Hessian (shared hooks), Nemotron-H MTP, fused experts, GGML GPTQ and backend, config validation, and recipes.
• Folding: after Make folding exact for GPTQ'd GGML weights, so the vLLM fake-quant export evaluates them #2710, imatrix-pinned BF16 weights, dense and fused-expert slices, still hold exactly the pinned codes after mtq.fold_weight and the vLLM fake-quant export's fold.
• Not run: multi-GPU, data-parallel or FSDP2 calibration. The CPU-offload path ran end to end on GLM-5.3-Flash.

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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.).

• Is this change backward compatible?: ✅ A new method, nothing removed. The built-in IQ1 and IQ2 recipes now encode fused MoE experts with imatrix; dense models get the same codes as before.
• 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?: ✅
• Did you get Claude approval on this PR?: ❌ Not yet.

Additional Information

Follows #2665 (GPTQ for GGML), #2686 (weighted IQ2 search) and #2723 (weighted IQ1 search).

🤖 Generated with Claude Code

Summary by CodeRabbit

• New Features
  • Added imatrix calibration for GGML-compatible quantization, using input statistics to guide quantization and separate statistics for fused MoE experts.
  • Added expert-only IQ1 and IQ2 recipes for routed MoE experts.
• Improvements
  • Updated existing IQ recipes to apply imatrix quantization to fused MoE experts not processed by GPTQ.
  • Imatrix encoding preserves weights already quantized by an earlier method.

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📝 Walkthrough

This change adds imatrix calibration for weighted GGML formats using per-column input second moments. It adds shared statistic-capture hooks and a GGML encoder, updates IQ recipes to apply imatrix after GPTQ, and adds routed-expert-only IQ recipes.

Changes: Imatrix calibration and IQ recipes

Layer / File(s)	Summary
Calibration contracts and statistic capture modelopt/torch/quantization/config.py, modelopt/torch/quantization/utils/calib_utils.py, modelopt/torch/quantization/mode.py, modelopt/torch/quantization/model_calib.py, modelopt/torch/models/nemotron_h/mtp.py, examples/hf_ptq/example_utils.py, tests/examples/hf_ptq/test_example_utils.py	Adds the imatrix configuration, encoder registry, calibration mode, and shared input hooks for collecting per-column second moments. The HF PTQ helper applies moe_calib_experts_ratio to each dictionary entry in an algorithm list.
GGML imatrix encoding and validation modelopt/torch/quantization/ggml/*, tests/unit/torch/quantization/test_ggml_imatrix.py	Adds weighted GGML encoding with pinned packed codes and reuses floored second moments in GPTQ. Tests cover GPTQ code preservation, routed-expert statistics, idle experts, and calibration behavior.
IQ recipe integration and validation CHANGELOG.rst, modelopt_recipes/configs/ptq/presets/model/iq*.yaml, modelopt_recipes/general/ptq/iq*.yaml, modelopt_recipes/ptq.md, tests/unit/recipe/test_presets.py	Updates IQ presets to run imatrix after GPTQ, adds experts-only recipes, updates the recipe catalog, and tests algorithm ordering and GGML format settings.

Priority: ➖ Normal

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

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant imatrix_calibrate
  participant Model
  participant GGMLImatrixEncoder
  participant Quantizer
  imatrix_calibrate->>Model: Run forward pass with weight quantizers disabled
  Model->>imatrix_calibrate: Provide inputs for second-moment collection
  imatrix_calibrate->>GGMLImatrixEncoder: Encode selected weights using second moments
  GGMLImatrixEncoder->>Quantizer: Pin packed GGML codes

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Experts-only quantization can also quantize nested shared experts, contrary to the recipes’ intended BF16 behavior. Restrict the selector or explicitly exclude those weights before merging.

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❌ Patch coverage is 95.20000% with 6 lines in your changes missing coverage. Please review.
✅ Project coverage is 77.68%. Comparing base (2541953) to head (7a53b58).
⚠️ Report is 30 commits behind head on main.

Files with missing lines	Patch %	Lines
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modelopt/torch/quantization/ggml/imatrix.py	96.00%	1 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #2735      +/-   ##
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Changes requested: imatrix's preflight validation runs after max calibration, so invalid weights can trigger a calibration forward before the promised refusal.

Needs action:

• Move target discovery and weight validation before max_calibrate in model_calib.py; see inline comment.
• Add an invalid-width regression with enabled activation calibration in test_ggml_imatrix.py, asserting the forward never runs and calibration state remains unchanged.

No action needed:

• Design: fused experts need importance-weighted encoding without a full Hessian. Compared with extending GPTQ, reusing local-Hessian collection, and direct Torch hooks/backend dispatch, the body reasonably justifies the separate method and shared hooks.
• Existing preset assertions were updated for the intentional algorithm-list change without losing packing-contract coverage.
• New headers match LICENSE_HEADER. Core logic is under budget; no split required.
• Tests were inspected, not executed.

[meenchen]

Bot comment.

Preflight validation needs to precede this max_calibrate call. skip_forward_without_activation_calib=True only skips the forward when no activation quantizer needs statistics. With enabled activation calibration, max runs forward_loop(model) and updates calibration state before the invalid IQ width is rejected below. The new refusal test disables all activation quantizers, so it misses this path. Discover/validate targets first (using the weight-materialization context where needed), then run max calibration, and add an enabled-activation invalid-width test asserting no forward or calibration-state changes.

[cjluo-nv]

Addressed in ad8fe16: target discovery, the width check and the multi-rank warning now run before max_calibrate, so an unsupported width is refused before any forward. The refusal test also runs with an enabled input quantizer, asserting no forward and no amax change. With no imatrix targets, max still calibrates the activations.

[meenchen]

Bot review (gpt-6.1-sol) — DM the bot to share feedback.

Changes requested: the nested shared-expert exclusion remains unresolved in all five IQ experts-only recipes.

Needs action:

• Restrict routed-expert selectors or explicitly disable shared experts in modelopt_recipes/configs/ptq/presets/model/iq*_experts_only.yaml; see inline comment.
• Add a parametrized regression in tests/unit/recipe/test_presets.py verifying nested block_sparse_moe.shared_expert quantizers remain disabled for all five formats.

No action needed:

• ✔️ Resolved since the last review: preflight precedes max calibration, enabled-activation refusal coverage is added, and rank-local statistics produce a warning without all-reduce.
• Design remains justified against extending GPTQ, local-Hessian collection, and direct Torch hooks/backend dispatch: shared hooks avoid duplication without requiring a full Hessian.
• Existing packing-contract test edits match the intentional algorithm-list change and preserve coverage. New headers match LICENSE_HEADER; core logic is under budget.
• Tests were inspected, not executed.

[meenchen]

Bot comment.

The previous shared-expert correctness concern is still present. *block_sparse_moe*weight_quantizer matches, for example, model.layers.0.block_sparse_moe.shared_expert.down_proj.weight_quantizer, and default_disabled_quantizers has no shared-expert exclusion. This enables IQ quantization on a shared expert despite the experts-only contract. Restrict the selector to routed experts or add shared-expert exclusions after the enabling entries in all five iq*_experts_only.yaml presets (including IQ2_XXS). Add a parametrized nested-shared-expert regression: the current preset test only checks selector strings, and the imatrix fixture places its shared expert under mlp, so neither exercises this match.

[cjluo-nv]

Keeping the selectors as they are, by design: the IQ experts-only presets use exactly nvfp4_experts_only's selectors (*.experts.*weight_quantizer from the experts_nvfp4 unit and *block_sparse_moe*weight_quantizer from block_sparse_moe_nvfp4), so both experts-only scopes stay aligned. No model in transformers 4.57 or 5.18 nests a shared expert under block_sparse_moe. A model that does would need the same handling for both formats, in a model-specific recipe, as DeepSeek-V4-Pro-0813 and minimax_m3_vl already do by disabling *shared_experts*.

[meenchen]

Looks good overall, left one general question.

[meenchen]

every calibration pass routes tokens to the same experts. Is routing the same set of tokens to the same experts important? i.e. token routing stays the same across gptq and imatrix

[cjluo-nv]

No, the passes don't need the same routing. Each expert weight is encoded by one pass only: GPTQ takes the 2-D linears, and imatrix skips any weight that already holds pinned codes, so it only encodes the fused experts GPTQ can't reach (imatrix_calibrate). Routing in one pass never feeds the other's weights. It can't match exactly anyway, since imatrix's forward runs after GPTQ has changed the earlier layers. The comment overstated it; 7a53b58 rewords it to say each algorithm runs its own calibration, so each gets the ratio.

One related detail: the ratio widens routing only in the max-calibration forward each algorithm starts with (_QuantSparseSequentialMoe.forward keys on the experts' _if_calib, plugins/huggingface.py:726). GPTQ's Hessian forward and imatrix's statistics forward both see the router's own top-k, so imatrix weights each expert by the tokens actually routed to it.