Add NVFP4 PTQ recipe for zai-org/GLM-5.3-Flash (experts + dense MLP) - #2312
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughAdds two GLM-5.3-Flash PTQ recipes. The recipes apply NVFP4 to routed experts, with one also covering dense-MLP projections. Both enable FP8 KV-cache casting, use max calibration, preserve specified components in BF16, and include wildcard-precedence tests. ChangesGLM-5.3-Flash PTQ
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~25 minutes Merge Risk: 🔵 Low · up to The new GLM-5.3-Flash recipes add NVFP4 expert quantization and FP8 KV-cache casting, but the experts-only recipe may perform an unnecessary full vision-language calibration pass, increasing recipe runtime and resource use. This is a bounded follow-up risk. 🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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`@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>
Widens nvfp4_experts_only-kv_fp8_cast to cover the dense MLP as well. mlp_layer_types marks only layers 0-2 dense, so this adds 9 modules (mlp.gate_proj / up_proj / down_proj) on top of the routed experts; layers 3-44 carry mlp.experts.<i>.* instead. The vision tower reuses those leaf names across its 24 blocks, so the dense-MLP patterns also match 144 modules under model.visual.*. A trailing '*visual*' disable keeps them BF16, and it has to stay last because entries apply in order. That single rule is used instead of importing the shared default_disabled_quantizers unit: of that unit's patterns only '*visual*' changes anything for this model (the rest match no module here, or match modules base_disable_all already left off), and both spellings leave the same 74322 quantizers enabled. Also record in the experts-only recipe why it needs no such rule -- the vision tower is a dense ViT with no 'experts' in any module name, so '*.experts.*' cannot reach it. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Adds a CPU-only recipe test (in the style of test_minimax_m3_recipe.py) that
applies each GLM-5.3-Flash recipe to a tiny glm5_next-like VLM MoE and asserts the
enabled/disabled state per module, covering the non-obvious wildcard matches these
recipes depend on:
- routed experts -> NVFP4 W4A4 (both recipes)
- dense MLP (layers 0-2) -> NVFP4 only in the experts+dense_mlp recipe
- vision tower -> BF16 in both; for experts+dense_mlp this is the
load-bearing case, since the vision MLP reuses gate_proj/up_proj/down_proj and
only the trailing *visual* disable (which must stay last) keeps it off
- shared experts / router gate / KDA conv1d / MLA attention / lm_head -> BF16
(verifies *.experts.* needs a literal .experts. and *mlp.gate_proj* != mlp.gate)
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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The recipes are well tested, but the MTP handling and model-specific rationale are currently inaccurate.
Needs action:
- Clarify or fix MTP handling: an uninstantiated layer 45 is omitted, not retained in BF16; ensure the planned checkpoint preserves it or correct every recipe/doc claim.
- Remove the duplicate experts-only recipe, or document a concrete checkpoint-specific reason it belongs beside the equivalent general recipe and reconcile
ptq.md’s “Why special” claim.
Address review on the GLM-5.3-Flash recipes: - Remove nvfp4_experts_only-kv_fp8_cast: on this model it is functionally identical to the general general/ptq/nvfp4_experts_only-kv_fp8_cast (its *block_sparse_moe* entries are no-ops and default_disabled_quantizers is redundant in experts-only scope), so it does not represent a model-specific deviation. Keep only nvfp4_experts_dense_mlp-kv_fp8_cast, whose dense-MLP scope plus the load-bearing trailing *visual* disable is a genuine deviation. ptq.md now points users to the general recipe for plain experts-only NVFP4. - Correct the MTP note: the nextn (MTP) layer is not built by the HF class at num_hidden_layers=45, so it is neither quantized nor carried into the exported checkpoint -- it is omitted, not 'inlined as layer 45 and kept BF16'. - Simplify the recipe test to the single remaining recipe. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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The duplicate recipe is removed, but the prior MTP/documentation concern remains partially unresolved in the recipe metadata and checkpoint-mirror rationale.
Needs action:
- 💬 Author replied that MTP wording was fixed — update the stale
metadata.descriptionthat still says an inlined MTP layer stays BF16. - Reconcile
ptq.md’s blanket “unlike any general recipe” claim with the documented general experts-only NVFP4 plus FP8-KV recipe.
No action needed:
- ✔️ Resolved since the last review: the duplicate experts-only recipe was removed, and the calibration-forward suggestion was correctly rebutted.
…r GLM Follow-up to the review: - The recipe metadata.description still said the inlined MTP layer stays BF16; correct it to match the header/ptq.md -- the MTP layer is not built at num_hidden_layers=45, so it is neither quantized nor exported. - Generalize the checkpoint-mirror 'Why special' paragraph so it no longer claims every entry is a per-component FP8/NVFP4 map hardcoding a published layout: note GLM-5.3-Flash is the exception whose deviation is a model-specific scope (a wildcard scheme plus the vision-tower exclusion and the VLM-required layerwise.enable=false). Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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The code concerns are resolved, but the PR metadata still advertises the removed recipe and obsolete MTP behavior.
Needs action:
- Update the PR title and description to describe only
nvfp4_experts_dense_mlp-kv_fp8_cast; remove the invalid experts-only usage and state that MTP is neither built nor exported.
No action needed:
- ✔️ Resolved since the last review: the duplicate experts-only recipe was removed, and the YAML metadata plus
ptq.mdnow consistently describe MTP and the model-specific scope.
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….3-flash-recipe Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> # Conflicts: # modelopt_recipes/ptq.md
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All prior correctness and documentation concerns are resolved, and the focused wildcard-precedence test covers the model-specific quantization scope.
No action needed:
- ✔️ Resolved since the last review: duplicate experts-only recipe removal, accurate MTP handling, consistent PR/YAML/docs scope, and calibration-forward clarification.
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What does this PR do?
Type of change: new feature (model recipe)
Adds an NVFP4 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 hybrid attention: KDA (linear-attention) layers interleaved with NoPE sparse-MLA layers. It requirestransformers >= 5.16.1; earlier releases cannot parse the config.nvfp4_experts_dense_mlp-kv_fp8_castapplies:lm_headmlp_layer_typesmarks only layers 0–2denseand 3–44sparse, so the dense-MLP scope adds just 9 modules (mlp.gate_proj/mlp.up_proj/mlp.down_proj) on top of the routed experts. The recipe starts frombase_disable_all, so only the listed globs re-enable anything.The load-bearing
*visual*disableThe vision tower reuses the language model's leaf names —
model.visual.blocks.<N>.mlp.gate_projand friends, across 24 blocks — so the dense-MLP patterns match 144 modules insidemodel.visual.*. Entries apply in order, so a trailing{quantizer_name: '*visual*', enable: false}is what keeps them BF16, and it has to stay last. (*.experts.*needs a literal.experts., so it never reaches the vision tower.)The shared
default_disabled_quantizersunit is deliberately not imported: for this model only its*visual*pattern changes anything — every other pattern either matches no module here, or matches one thatbase_disable_allalready left off (lm_head, themlp.gate.routers) and that nothing re-enables.Two model-specific points, documented in the file header
layerwise.enable=falseis required, not incidental. This is a VLM, so the decoder layers nest undermodel.language_model.layersandlayerwise_calibratecannot locate them.num_hidden_layers: 45(withnum_nextn_predict_layers: 1), so the HF model class instantiates decoder layers 0–44 only and never constructs the MTP layer.Filed under
modelopt_recipes/models/per the split introduced in #2219, keyed by the source hub model — alongsidemoonshotai/Kimi-K3andmistralai/Mistral-Medium-3.5-128B. There is no publishednvidia/GLM-5.3-Flash-NVFP4yet; themodels/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_dense_mlp-kv_fp8_cast \ --export_path <output>Testing
tests/unit/recipe/test_glm_5_3_recipe.py(new) — applies the recipe to a tinyglm5_next-like VLM MoE and asserts the enabled/disabled state per module: routed experts + dense MLP → NVFP4; vision tower, shared experts, router gate, KDAconv1d, MLA attention andlm_head→ BF16. This pins the wildcard precedence — in particular that the trailing*visual*disable keeps the vision tower BF16 even though it reuses the dense-MLP leaf names, and that*mlp.gate_proj*doesn't catch the routermlp.gate.tests/unit/recipe/test_recipe_docs.py— all checks pass, includingtest_every_model_specific_ptq_dir_is_mentioned(themodels/zai-org/GLM-5.3-Flash/ptq/folder appears inptq.md).The recipe's scope was also checked against the model's actual module names: the dense-MLP patterns match 144 modules under
model.visual.*, which the trailing disable returns to BF16; an exported checkpoint carriesinput_scale/weight_scale/weight_scale_2onlayers.0–2.mlp.*_projwhilevisual.blocks.0.mlp.gate_projretains only.weight/.bias; andkv_cache_quant_algo: FP8survives the trailing disable.Before your PR is "Ready for review"
ptq.mdupdated)CONTRIBUTING.md: N/Atests/unit/recipe/test_glm_5_3_recipe.pypins the recipe's wildcard precedenceAdditional Information
Source model: https://huggingface.co/zai-org/GLM-5.3-Flash
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