Add the NVFP4 experts-only PTQ recipe for zai-org/GLM-5.3-Flash - #2312
Add the NVFP4 experts-only PTQ recipe for zai-org/GLM-5.3-Flash#2312shengliangxu wants to merge 2 commits into
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Included review availability: Your plan provides up to 12 included reviews per hour; 9 remain after this review. 📝 WalkthroughWalkthroughAdds a GLM-5.3-Flash PTQ recipe. The recipe applies NVFP4 to routed expert weights and inputs, enables FP8 KV-cache casting, uses max calibration, and leaves other components in BF16. ChangesGLM-5.3-Flash PTQ
Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: 🔵 Low · up to The recipe may perform an unnecessary full VLM calibration forward even though its enabled activation quantizers do not need calibration statistics, increasing calibration time and resource use without changing the intended quantized result. It is mergeable with explicit owner awareness or follow-up to remove or justify that overhead. 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
Full details: Docstring CoverageExplanation No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0 files. (1 skipped: 1 unsupported.) Full details: Security Anti-PatternsExplanation PASS: The pull request changes only one YAML recipe and ✨ Finishing Touches🧪 Generate unit tests (beta)
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In
`@modelopt_recipes/models/zai-org/GLM-5.3-Flash/ptq/nvfp4_experts_only-kv_fp8_cast.yaml`:
- Line 53: Update the MaxCalibConfig configuration containing method: max to set
skip_forward_without_activation_calib to true, preventing max_calibrate from
running an unnecessary forward loop when no activation calibration is required.
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modelopt_recipes/models/zai-org/GLM-5.3-Flash/ptq/nvfp4_experts_only-kv_fp8_cast.yaml
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GLM-5.3-Flash is a `glm5_next` VLM MoE: 45 decoder layers, 288 routed experts, and hybrid attention -- KDA (linear-attention) layers interleaved with NoPE sparse-MLA layers. It needs transformers >= 5.16.1; earlier releases cannot parse the config. Routed experts -> NVFP4 W4A4 (both weight and input quantizers), KV cache -> FP8 cast, everything else BF16: dense MLP, shared experts, router gate, both attention families, the vision tower, embeddings and lm_head. The quant_cfg is deliberately minimal -- four entries. `base_disable_all` sets every quantizer to `enable: false`, and the only entries after it re-enable the routed experts and the KV cache, so nothing else needs listing: - The `*block_sparse_moe*` globs from the general experts-only recipe are dropped. They target Mixtral-style naming and match nothing here (0 keys in the exported index against 146,016 `.experts.` keys). - The shared `default_disabled_quantizers` unit is not imported. Verified against all 38,321 modules of an exported checkpoint: no module matched by `*.experts.*` is also matched by any of its patterns, so it is a no-op for this model. (`*.experts.*` needs a literal `.experts.`, so `mlp.shared_experts.*` -- 129 modules -- does not match and the shared experts stay BF16.) Both simplifications are behaviour-preserving; the resulting quantized module set is identical to the general recipe's on this architecture. Two model facts are documented in the header because they are easy to get wrong: - `layerwise.enable=false` is required rather than incidental. This is a VLM, so the decoder layers nest under `model.language_model.layers` and layerwise_calibrate cannot locate them. - The MTP head is inlined as decoder layer 45 and is NOT quantized. The config declares `num_hidden_layers: 45`, so transformers builds layers 0-44 and never instantiates layer 45; no quantizer attaches to it and it stays BF16. ptq.md gains a matching entry under "Checkpoint mirrors", required by tests/unit/recipe/test_recipe_docs.py::test_every_model_specific_ptq_dir_is_mentioned, which asserts every `models/<org>/<model_id>/ptq/` folder appears in the doc. The entry states plainly that this one does not mix precisions across component types the way its neighbours do -- it is the general scheme pinned to a planned checkpoint, capturing the two model facts above. All five checks in that test file pass. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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The entry ran to 17 lines against 4-9 for its neighbours in the same section, and most of the extra length was detail that belongs in the recipe header rather than the index. - Drop "planned" -- it is the NVFP4 config for this checkpoint, no qualifier needed. - Drop the `default_disabled_quantizers` explanation (glob matching, the `mlp.shared_experts.*` note, the `base_disable_all` walkthrough). The recipe header already covers why the quant_cfg is minimal; the index does not need it. - Collapse the full BF16 component list to "everything else", keeping only the MTP block at decoder layer 45, which is the non-obvious part. - Replace the laboured "unlike the other entries here it does not mix precisions" contrast with "carries the experts-only scope rather than a mixed-precision map". What a reader scanning the index needs is kept: the model and its hybrid attention, what is quantized, that MTP stays BF16, and the `layerwise.enable=false` requirement with its reason. tests/unit/recipe/test_recipe_docs.py still passes -- `zai-org/GLM-5.3-Flash` remains present, as test_every_model_specific_ptq_dir_is_mentioned requires. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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What does this PR do?
Type of change: new feature (model recipe)
Adds the NVFP4 experts-only PTQ recipe for zai-org/GLM-5.3-Flash.
GLM-5.3-Flash is a
glm5_nextVLM MoE — 45 decoder layers, 288 routed experts, and hybridattention: KDA (linear-attention) layers interleaved with NoPE sparse-MLA layers. It requires
transformers >= 5.16.1; earlier releases cannot parse the config.The recipe applies:
lm_head, and the inlined MTP layerbase_disable_allturns every quantizer off first, so only the two expert globs re-enableanything.
Two model-specific points are documented in the file header because they are easy to get
wrong:
layerwise.enable=falseis required, not incidental. This is a VLM, so the decoderlayers nest under
model.language_model.layersandlayerwise_calibratecannot locatethem.
num_hidden_layers: 45, so transformers builds layers 0–44 and never instantiates layer45; no quantizer attaches to it and it stays BF16.
Filed under
modelopt_recipes/models/per the split introduced in #2219, keyed by the sourcehub model — alongside
moonshotai/Kimi-K3andmistralai/Mistral-Medium-3.5-128B.The
ptq.mdentry states plainly that, unlike its neighbours in that section, this recipedoes not mix precisions across component types — it is the general scheme pinned to a
planned checkpoint, capturing the two model facts above. There is no published
nvidia/GLM-5.3-Flash-NVFP4yet; the section explicitly covers "published (or planned)"checkpoints.
Usage
python examples/hf_ptq/hf_ptq.py \ --pyt_ckpt_path <zai-org/GLM-5.3-Flash checkpoint> \ --recipe models/zai-org/GLM-5.3-Flash/ptq/nvfp4_experts_only-kv_fp8_cast \ --export_path <output>Testing
tests/unit/recipe/test_recipe_docs.py— all five checks pass, includingtest_every_model_specific_ptq_dir_is_mentioned, which requires everymodels/<org>/<model_id>/ptq/folder to appear inptq.md. The matchingptq.mdentry isincluded in this PR.
The
quantizeblock produces the same quantized module set as the generalnvfp4_experts_only-kv_fp8_castrecipe on this architecture, which was used to produce acheckpoint evaluated end to end against the BF16 baseline with no meaningful accuracy
regression.
Two simplifications versus that general recipe, both verified behaviour-preserving:
*block_sparse_moe*globs dropped — Mixtral-style naming; 0 keys in the exportedindex against 146,016
.experts.keys.default_disabled_quantizersnot imported — checked against all 38,321 modules ofan exported checkpoint: no module matched by
*.experts.*is also matched by any of itspatterns, so it is a no-op here. (
*.experts.*requires a literal.experts., so the 129mlp.shared_experts.*modules do not match and shared experts stay BF16.)The result is a four-entry
quant_cfg:base_disable_all, the two expert globs, andkv_fp8_cast.No new unit tests: declarative recipe over existing, tested units.
Before your PR is "Ready for review"
CONTRIBUTING.md: N/AAdditional Information
Source model: https://huggingface.co/zai-org/GLM-5.3-Flash
Summary by CodeRabbit
New Features
Documentation