Add Muse Glimmer AutoQuant recipe - #2269
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Signed-off-by: Jennifer Chen <jennifchen@nvidia.com>
Signed-off-by: Jennifer Chen <jennifchen@nvidia.com>
Signed-off-by: Jennifer Chen <jennifchen@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughThis change adds a Muse Glimmer mixed-precision AutoQuantize recipe and documents its calibration and export settings. It also records an Alpamayo QAD example with FSDP2 support and optional full-checkpoint export. ChangesMuse Glimmer AutoQuantize
Alpamayo QAD changelog
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🔵 Low · up to The PR adds a Muse Glimmer AutoQuant recipe, but its changelog entry still includes unrelated release-note content, which may make release documentation less accurate. The change is otherwise mergeable with owner awareness and a follow-up to clean up the changelog. Suggested reviewers: 🚥 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. (2 skipped: 2 unsupported.) Full details: Security Anti-PatternsExplanation PASS. The pull-request diff against origin/main changes only CHANGELOG.rst, one Markdown file, and one YAML recipe. It adds no modelopt or examples Python files, no dependency manifests, and no security-sensitive patterns such as torch.load(..., weights_only=False), allow_pickle=True, hardcoded trust_remote_code=True, eval/exec on external input, or # nosec comments. ✨ Finishing Touches🧪 Generate unit tests (beta)
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chadvoegele
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Looks good!
Want to add the final selected recipe to https://github.com/NVIDIA/Model-Optimizer/tree/main/modelopt_recipes/huggingface/models as well?
Signed-off-by: Jenny Chen <jennifchen@nvidia.com>
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In `@CHANGELOG.rst`:
- Line 12: Remove the changelog entries for Kimi-K3 conversion, the nemo:26.08
requirement, and the NVFP4 capability-query fix, keeping only entries related to
the Muse Glimmer AutoQuantize recipe and its documentation.
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realAsma
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Nice!! Thanks for the PR!
Signed-off-by: Jenny Chen <jennifchen@nvidia.com>
jenchen13
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This was referenced Sep 1, 2026
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### What does this PR do? Type of change: new feature (model recipe) Adds the NVFP4 PTQ recipe for **Qwen/Qwen3.8-2.4T-A95B** — the recipe used to produce [**nvidia/Qwen3.8-2.4T-A95B-NVFP4**](https://huggingface.co/nvidia/Qwen3.8-2.4T-A95B-NVFP4). Qwen/Qwen3.8-2.4T-A95B is a `qwen3_5_moe_text` MoE: 92 layers, 512 routed experts (top-10) plus a shared expert, with **hybrid attention** — gated-delta (linear-attention) layers interleaved with full-attention layers. It is transformers-native from >= 5.9 and its config ships `base_model_ep_plan`, so no ModelOpt plugin is required. The recipe applies: | component | precision | |---|---| | routed experts | NVFP4 (MSE-searched static weight scales, dynamic input scales) | | self-attention | FP8 (W8A8, all projections) | | linear-attention | FP8 (W8A8, the full gated-delta path — `conv1d` + all in/out projections) | | KV cache | FP8 (cast mode) | | everything else | BF16 — including MTP, left unquantized | Two things are documented in the file header because they affect how the recipe should be read: - **The full gated-delta path is FP8, and that was validated end-to-end.** The `conv1d` and the in/out projections (`in_proj_qkv` / `in_proj_z` / `in_proj_a` / `in_proj_b`, `out_proj`) are all FP8; only the norms stay BF16. `nn.Conv1d` is a registered ModelOpt quant module, so the recipe's broad `*linear_attn*` rules reach `linear_attn.conv1d` too — this is intentional and matches the published `nvidia/Qwen3.8-2.4T-A95B-NVFP4`, whose `hf_quant_config.json` lists `linear_attn.conv1d` as FP8 on every gated-delta layer (the interleaved full-attention layers have no `conv1d`). (An earlier revision of the file header / ptq.md wrongly stated the recurrent path is never quantized; corrected in this PR.) - **The source ships as native block-FP8** (`quant_method=fp8`, `weight_block_size [128,128]`, dynamic activations). The loader dequantizes it to BF16 before quantizers are inserted, so the calibrated scales are against BF16 weights, not against the shipped FP8. Filed under `modelopt_recipes/models/` per the split introduced in #2219 (per-`model_type` recipes vs model-hub checkpoint recipes); this one targets a published checkpoint, alongside `deepseek-ai/DeepSeek-V4-Pro-0813` and the Nemotron-3 entries. ### Usage ```bash # The recipe is consumed by the PTQ entrypoint the same way as the other # modelopt_recipes/models/ entries: python examples/hf_ptq/hf_ptq.py \ --pyt_ckpt_path <Qwen/Qwen3.8-2.4T-A95B checkpoint> \ --recipe models/Qwen/Qwen3.8-2.4T-A95B/ptq/nvfp4_experts_mse-fp8_self_attn-fp8_linear_attn-kv_fp8_cast \ --export_path <output> ``` ### Testing The exported checkpoint was evaluated against the BF16 baseline on **GPQA, AA-LCR, SciCode, IFBench and Terminal-Bench 2.1**, with no meaningful accuracy regression on any of them. The published `nvidia/Qwen3.8-2.4T-A95B-NVFP4` checkpoint is the artifact this recipe produces — its `hf_quant_config.json` is the ground truth for which modules are quantized (routed experts NVFP4; self-attention, all linear-attention projections **and** `conv1d`, and KV cache FP8). No new unit tests: this is a declarative recipe composed entirely of existing units (`base_disable_all`, `nvfp4`, `nvfp4_static`, `fp8`, `kv_fp8_cast`), all already covered. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ (new file only; no existing behaviour touched) - 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 — declarative recipe over existing, tested units - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A — consistent with the recent recipe additions (#2219, #2269, #2287), which did not add entries - Did you get Claude approval on this PR?: ❌ — not yet run ### Additional Information Model card: https://huggingface.co/nvidia/Qwen3.8-2.4T-A95B-NVFP4 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added a post-training quantization recipe for the Qwen3.8-2.4T-A95B model. * Supports MSE-searched NVFP4 quantization for routed expert layers and FP8 quantization across self-attention and gated-delta linear-attention paths. * Supports FP8 cast-mode key-value caching while retaining BF16 precision for multi-token prediction and gated-delta normalization layers. * **Documentation** * Clarified the model’s hybrid precision configuration, including FP8 treatment of the gated-delta convolution path and the scope of broad linear-attention patterns. * Documented source-checkpoint dequantization and validation behavior. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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…2312) ### What does this PR do? Type of change: new feature (model recipe) Adds an NVFP4 PTQ recipe for **[zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash)**. 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 requires `transformers >= 5.16.1`; earlier releases cannot parse the config. **`nvfp4_experts_dense_mlp-kv_fp8_cast`** applies: | component | precision | |---|---| | routed experts (layers 3–44, 288 each) | NVFP4 W4A4 | | dense MLP (layers 0–2, 9 modules) | NVFP4 W4A4 | | KV cache | FP8 (cast mode, constant amax) | | shared experts, router gate, KDA + MLA attention, vision tower, embeddings, `lm_head` | BF16 | `mlp_layer_types` marks only layers 0–2 `dense` and 3–44 `sparse`, 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 from `base_disable_all`, so only the listed globs re-enable anything. > **On scope / why only one recipe.** An earlier revision of this PR also shipped a model-specific `nvfp4_experts_only-kv_fp8_cast`. It was removed: on this model it enables the **identical** quantizer set as the general `general/ptq/nvfp4_experts_only-kv_fp8_cast` (its `*block_sparse_moe*` entries are no-ops here and `default_disabled_quantizers` is redundant in experts-only scope), so it wasn't a model-specific deviation. For plain experts-only NVFP4, use the general recipe. The genuine model-specific delta — shipped here — is the dense-MLP scope plus the vision-tower exclusion below. #### The load-bearing `*visual*` disable The vision tower reuses the language model's leaf names — `model.visual.blocks.<N>.mlp.gate_proj` and friends, across 24 blocks — so the dense-MLP patterns match **144 modules inside `model.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_quantizers` unit is deliberately not imported: for this model only its `*visual*` pattern changes anything — every other pattern either matches no module here, or matches one that `base_disable_all` already left off (`lm_head`, the `mlp.gate.` routers) and that nothing re-enables. #### Two model-specific points, documented in the file header - **`layerwise.enable=false` is required, not 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 not built, so it is neither quantized nor exported.** The config declares `num_hidden_layers: 45` (with `num_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 — alongside `moonshotai/Kimi-K3` and `mistralai/Mistral-Medium-3.5-128B`. There is no published `nvidia/GLM-5.3-Flash-NVFP4` yet; the `models/` section explicitly covers "published **(or planned)**" checkpoints. ### Usage ```bash 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 tiny `glm5_next`-like VLM MoE and asserts the enabled/disabled state per module: routed experts + dense MLP → NVFP4; vision tower, shared experts, router gate, KDA `conv1d`, MLA attention and `lm_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 router `mlp.gate`. - **`tests/unit/recipe/test_recipe_docs.py`** — all checks pass, including `test_every_model_specific_ptq_dir_is_mentioned` (the `models/zai-org/GLM-5.3-Flash/ptq/` folder appears in `ptq.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 carries `input_scale` / `weight_scale` / `weight_scale_2` on `layers.0–2.mlp.*_proj` while `visual.blocks.0.mlp.gate_proj` retains only `.weight` / `.bias`; and `kv_cache_quant_algo: FP8` survives the trailing disable. ### Before your PR is "*Ready for review*" - Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed. - Is this change backward compatible?: ✅ (one new recipe + one new unit test; `ptq.md` updated) - 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?: ✅ — `tests/unit/recipe/test_glm_5_3_recipe.py` pins the recipe's wildcard precedence - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A — consistent with the recent recipe additions (#2219, #2269, #2287), which did not add entries - Did you get Claude approval on this PR?: ❌ — not yet run ### Additional Information Source model: https://huggingface.co/zai-org/GLM-5.3-Flash <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added post-training quantization recipes for GLM-5.3-Flash. * Supports NVFP4 W4A4 quantization for routed experts, with an additional configuration covering dense MLP layers. * Enables FP8 key-value cache casting. * Uses maximum-based calibration with layerwise calibration disabled for the VLM layout. * Retains BF16 precision for shared experts, vision components, attention, embeddings, routing, language head, and MTP components. * **Documentation** * Documented the available GLM-5.3-Flash quantization configurations and precision assignments. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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