[Model] Gemma4: robust quantized MoE expert weight loading#39406
[Model] Gemma4: robust quantized MoE expert weight loading#39406yueshen2016 wants to merge 1 commit intovllm-project:mainfrom
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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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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.
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…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>
## 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>
Summary
ignore_suffixesto gracefully skip unrecognized quantization scale parameters (e.g.weight_scale,input_scale) instead of raisingKeyErrorin the fallthrough pathreturn_success=Truetoweight_loadercalls soFusedMoEcan signal rejection and the loader can try the next expert mapping or skip gracefullyis_expert_weightto skip expert keys not mapped to the current rank (distributed/EP scenarios)elseblock withname not in params_dictcheckThese changes match the robust loading pattern used by Qwen3 MoE (
qwen3_moe.pylines 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_iteratorandexpert_params_mapping(trailing dot/underscore pattern +re.subremap) correctly handle these, but the loading loop lacks robustness: if a mapped scale name doesn't exist inparams_dict, the code falls through to direct load and crashes withKeyError.Test plan
google/gemma-4-26B-A4B-itwith ModelOpt NVFP4 (hf_ptq.py --qformat nvfp4_mlp_only)vllm serve <path> --quantization modelopt— should load without errors🤖 Generated with Claude Code