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[Model] Gemma4: robust quantized MoE expert weight loading#39406

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yueshen2016:fix/gemma4-quantized-moe-loading
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[Model] Gemma4: robust quantized MoE expert weight loading#39406
yueshen2016 wants to merge 1 commit intovllm-project:mainfrom
yueshen2016:fix/gemma4-quantized-moe-loading

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Summary

  • Add ignore_suffixes to gracefully skip unrecognized quantization scale parameters (e.g. weight_scale, input_scale) instead of raising KeyError in the fallthrough path
  • Add return_success=True to weight_loader calls so FusedMoE can signal rejection and the loader can try the next expert mapping or skip gracefully
  • Track is_expert_weight to skip expert keys not mapped to the current rank (distributed/EP scenarios)
  • Guard against missing params in the fallthrough else block with name not in params_dict check

These changes match the robust loading pattern used by Qwen3 MoE (qwen3_moe.py lines 607-668) and ensure reliable loading of quantized (ModelOpt NVFP4/FP8, GPTQ) Gemma4 MoE checkpoints where per-expert scale tensors may not map to local parameters.

Context: When quantizing Gemma4 26B-A4B with ModelOpt, the exported checkpoint contains per-expert scale keys like experts.0.gate_proj.input_scale. The current _weight_iterator and expert_params_mapping (trailing dot/underscore pattern + re.sub remap) correctly handle these, but the loading loop lacks robustness: if a mapped scale name doesn't exist in params_dict, the code falls through to direct load and crashes with KeyError.

Test plan

  • Quantize google/gemma-4-26B-A4B-it with ModelOpt NVFP4 (hf_ptq.py --qformat nvfp4_mlp_only)
  • Serve with vllm serve <path> --quantization modelopt — should load without errors
  • Verify text generation produces coherent output
  • Verify unquantized Gemma4 checkpoint loading still works (regression test)

🤖 Generated with Claude Code

Add robustness improvements to Gemma4's MoE expert weight loading,
matching the pattern used by Qwen3 MoE (qwen3_moe.py). This ensures
reliable loading of quantized (ModelOpt/GPTQ) Gemma4 MoE checkpoints
where per-expert scale tensors (input_scale, weight_scale,
weight_scale_2) may not map to local parameters.

Changes:
- Add ignore_suffixes to gracefully skip unrecognized scale parameters
  instead of raising KeyError
- Add return_success=True to weight_loader calls so FusedMoE can signal
  rejection and the loader can try the next expert mapping
- Track is_expert_weight to skip expert keys not mapped to the current
  rank (distributed/EP scenarios)
- Guard against missing params in the fallthrough else block

Signed-off-by: Yue Shen <yueshen2016@users.noreply.github.com>
Signed-off-by: James Shen <yueshen@nvidia.com>
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Code Review

This pull request refactors the load_weights method in gemma4.py to enhance the handling of various weight types, particularly for expert weights and quantization parameters. It introduces an ignore_suffixes tuple to manage skipping specific parameter suffixes during weight loading. The review feedback points out a redundancy where an explicit check for .bias can be removed, as it is already covered by the ignore_suffixes mechanism.

Comment on lines 1391 to 1392
if name.endswith(".bias") and name not in params_dict:
continue
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high

This check for .bias is redundant. The ignore_suffixes tuple, defined on line 1286, already includes ".bias". Therefore, the check on lines 1389-1390 fully covers this case. You can remove these lines to avoid code duplication.

Edwardf0t1 added a commit to NVIDIA/Model-Optimizer that referenced this pull request Apr 16, 2026
…els (#1236)

### What does this PR do?

Type of change: Skills update

Add a debug loop guide for deploying unsupported models to the
deployment skill. When deploying models not in the validated support
matrix (e.g., newly quantized VLMs or models with new architectures like
Devstral/ministral3), the inference framework (vLLM, SGLang, TRT-LLM)
often fails during model init or weight loading.

This PR adds:
- `references/unsupported-models.md` — a 5-step iterative debug
workflow: **run → read error → diagnose → patch framework source →
re-run**
- A short pointer in `SKILL.md` under "Unsupported Models" (keeps
SKILL.md concise, matching the PTQ skill's pattern)

The guide covers five common error categories with real-world examples:
- **Weight key mismatches** (e.g.,
[vllm#39406](vllm-project/vllm#39406))
- **Quantized/unquantized layer confusion** (e.g.,
[sglang#18937](sgl-project/sglang#18937))
- **Missing architecture support** (e.g., `ministral3` not handled in
vLLM's `mistral3.py`)
- **Transformers version mismatches**
- **Kernel-level issues** (escalate to framework team)

Motivated by deploying a Devstral-Small-2-24B NVFP4 checkpoint on vLLM,
where vLLM's `mistral3.py` didn't handle `ministral3` as a text backbone
model type.

### Testing

Validated end-to-end: NVFP4 quantization of Devstral-Small-2-24B → vLLM
deployment on B100 GPUs with the debug loop (3 iterations to get the
server running).

### Before your PR is "*Ready for review*"

- Is this change backward compatible?: N/A (documentation only)
- 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?: N/A (skill documentation)
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **Documentation**
* Added a deployment guide for unsupported models with an iterative "run
→ read error → diagnose → patch → re-run" troubleshooting workflow,
common failure categories, escalation criteria, and practical
remediation tips.
* Added post-quantization validation guidance and a lightweight script
to verify which layers are quantized vs excluded, plus recommendations
for addressing unexpected layers and MoE/VLM naming gaps.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com>
yueshen2016 added a commit to NVIDIA/Model-Optimizer that referenced this pull request May 5, 2026
## Summary
- Register `Gemma4TextExperts` with `_QuantQwen35MoeExperts` plugin to
unfuse fused 3D expert tensors into per-expert `nn.Linear` layers for
quantization
- Add structural `is_moe()` detection for modules with `router` +
`experts` attributes (Gemma4 has no dedicated `SparseMoeBlock` class —
the decoder layer directly owns `router` and `experts`)
- Add `Gemma4TextDecoderLayer` to `get_expert_linear_names()` returning
`["gate_proj", "down_proj", "up_proj"]`
- Add `"*.experts.*"` pattern to `NVFP4_MLP_ONLY_CFG` and
`NVFP4_EXPERTS_ONLY_CFG` to match Gemma4's expert path
(`model.layers.X.experts.*`, not nested under `mlp`)

**Context:** Gemma4 MoE models (e.g. `google/gemma-4-26B-A4B-it`) store
expert weights as fused 3D `nn.Parameter` tensors (`gate_up_proj`,
`down_proj`) instead of `nn.ModuleList` of `nn.Linear`. Since ModelOpt's
quantizer only discovers `nn.Linear` modules, it silently skips the
expert weights — the bulk of the model remains unquantized.

**Companion vLLM PR:** vllm-project/vllm#39406
(robust quantized MoE weight loading for Gemma4)

## Test plan
- [x] `hf_ptq.py --pyt_ckpt_path google/gemma-4-26B-A4B-it --qformat
nvfp4_mlp_only` — 35k+ quantizers inserted, 17GB output (vs 49GB BF16)
- [x] `vllm serve <path> --quantization modelopt` — loads and serves
successfully
- [x] Text generation: correct ("The capital of France is **Paris**.")
- [x] Vision: correct (describes image content accurately)

🤖 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**
* Support quantizing models with separate base/full components (handles
heads present only on the full model)
* Enhanced Mixture-of-Experts detection and explicit support for Gemma4
expert layer layouts
* Extended NVFP4 selective quantization presets and recipes to include
expert-layer patterns and enable FP8 for expert modules

* **Bug Fixes**
* Improved loss/logit handling and clearer errors for unsupported
quantization methods
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: James Shen <yueshen@nvidia.com>
Signed-off-by: Yue Shen <yueshen@nvidia.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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