Add the NVFP4 PTQ recipe for Qwen/Qwen3.8-2.4T-A95B - #2302
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This is 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 needed. The recipe applies: routed experts NVFP4 (MSE-searched static weight scales, dynamic input scales) self-attention FP8 (W8A8, all projections) linear-attention FP8 (W8A8, the gated-delta projections) KV cache FP8 (cast mode) everything else BF16 -- including MTP, which is left unquantized Quantizing the gated-delta projections is the part worth calling out. The conv1d and the norms carry no Linear quantizer, so the recurrent state path itself is never quantized -- only the projections around it are. That was validated rather than assumed: 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. One loading detail is documented in the header because it affects what the scales mean: the source checkpoint ships as native block-FP8 (`quant_method=fp8`, `weight_block_size [128, 128]`, dynamic activations), and 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. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughAdds a Qwen3.8-2.4T-A95B PTQ recipe and updates its precision documentation. Routed experts use NVFP4. Attention and gated-delta paths use FP8. KV caching uses FP8 cast mode. Other specified components remain BF16. ChangesQwen PTQ recipe
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Merge Risk: ⚪ Minimal · up to This change adds a mixed-precision Qwen PTQ recipe and accompanying documentation. No concrete unresolved production-impact risk is established by the available evidence. Suggested reviewers: 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Add a checkpoint-mirror entry for models/Qwen/Qwen3.8-2.4T-A95B/ptq/nvfp4_experts_mse-fp8_self_attn-fp8_linear_attn-kv_fp8_cast to modelopt_recipes/ptq.md so the new recipe is documented in the PTQ recipe guide, satisfying tests/unit/recipe/test_recipe_docs.py::test_every_model_specific_ptq_dir_is_mentioned. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Small, well-documented checkpoint-mirror recipe that fits the models/<org>/<model_id>/ptq/ tier and satisfies the ptq.md doc test (the Qwen/Qwen3.8-2.4T-A95B identifier is present). Only the project's standard Apache-2.0/NVIDIA header is added, so no licensing hold.
One likely correctness bug: the broad '*linear_attn*weight_quantizer' / '*linear_attn*input_quantizer' rules also match linear_attn.conv1d.*_quantizer — nn.Conv1d is in QuantModuleRegistry (modelopt/torch/quantization/nn/modules/quant_conv.py), which is exactly why configs/ptq/units/default_disabled_quantizers.yaml and huggingface/qwen3_5/ptq/*.quant_cfg.yaml explicitly keep *linear_attn.conv1d* disabled (the latter enumerates in_proj_qkv / in_proj_z / out_proj individually rather than wildcarding linear_attn). As written, this recipe FP8-quantizes the gated-delta conv1d, which contradicts both the file header and the new ptq.md paragraph ("the recurrent state path itself is never quantized"). Either the recipe or the documentation is wrong; please fix the patterns (or add an explicit *linear_attn.conv1d*: enable: false) and reword the doc.
Secondary points: enabling FP8 on in_proj_a / in_proj_b deliberately diverges from the shared default_disabled_quantizers and the qwen3_5/qwen3_5_moe family recipes, which is worth an in-file note; and this repo already has a convention of a tiny CPU recipe test for wildcard-precedence-sensitive model recipes (tests/unit/recipe/test_kimi_k3_recipe.py, test_minimax_m3_recipe.py, test_qwen_vision_recipe.py) — such a test would have caught the conv1d issue.
Re-review of the new author reply only; code is unchanged at The reply ("this recipe targets qwen3.8-2.4t-a95b only ... intentional to remove lines that do not apply to this model instance") doesn't resolve the conv1d point, because the omitted Whichever way it was actually run, the recipe and its documentation currently disagree: the YAML header (line 33) and
A quick check of the exported checkpoint's
So either the recipe FP8-quantizes the conv1d (in which case the header at line 33 and the |
The recipe's broad '*linear_attn*' wildcards FP8-quantize linear_attn.conv1d (nn.Conv1d is a registered ModelOpt quant module, matched via fnmatch), which is correct: the published nvidia/Qwen3.8-2.4T-A95B-NVFP4 checkpoint's hf_quant_config.json lists linear_attn.conv1d as FP8 on every layer. The recipe header and ptq.md paragraph wrongly claimed the gated-delta recurrent/conv1d path is never quantized. Correct both to state conv1d is FP8 (only the norms stay BF16), matching the mirrored checkpoint. Recipe behavior is unchanged. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> (cherry picked from commit fe9880adb3d6ca42a9a16c3066afaa2e490f3342)
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Inline comments:
In `@modelopt_recipes/ptq.md`:
- Line 419: Update the checkpoint description in modelopt_recipes/ptq.md to
state that FP8 linear_attn.conv1d entries appear on every gated-delta layer,
replacing the broader “on every layer” wording while preserving the surrounding
claim.
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modelopt_recipes/models/Qwen/Qwen3.8-2.4T-A95B/ptq/nvfp4_experts_mse-fp8_self_attn-fp8_linear_attn-kv_fp8_cast.yamlmodelopt_recipes/ptq.md
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Qwen3.8-2.4T-A95B has hybrid attention (full_attention_interval=4): conv1d only exists on gated-delta (linear-attention) layers, not the interleaved full-attention layers. Correct 'FP8 on every layer' to 'every gated-delta layer' in the recipe header and ptq.md. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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The conv1d contradiction is resolved — the YAML header and ptq.md now state the full gated-delta path (including linear_attn.conv1d) is intentionally FP8 — but the recipe still ships with no test pinning its wildcard precedence.
Needs action:
- Add a tiny CPU recipe test in the style of
tests/unit/recipe/test_minimax_m3_recipe.pyasserting enabled/disabled state formlp.experts.*,self_attn.*,linear_attn.in_proj_*,linear_attn.conv1dandmtp.*— this recipe deliberately diverges fromdefault_disabled_quantizersonconv1d/in_proj_a/in_proj_b, so nothing currently guards that. - Scope the "lists
linear_attn.conv1das FP8 on every layer" claim to gated-delta layers in both the YAML header andmodelopt_recipes/ptq.md(~line 419);conv1donly exists on those layers.
No action needed:
- 💬 Author documented the conv1d/
in_proj_a/in_proj_bdivergence plus end-to-end eval in the file header — that closes the prior recipe-vs-docs mismatch. - Header matches
LICENSE_HEADERverbatim; no licensing hold.
Adds a CPU-only recipe test (in the style of test_minimax_m3_recipe.py) that
quantizes a tiny qwen3_5_moe_text-like model with the checkpoint-mirror recipe
and asserts the intended enabled/disabled state per module:
- routed experts -> NVFP4 (static weights, dynamic block-16 inputs)
- self-attention q/k/v/o -> FP8 W8A8
- linear-attention proj + conv1d -> FP8 W8A8 (regression guard: nn.Conv1d is
a registered quant module, so the broad *linear_attn* rules must reach it)
- MTP block -> BF16 (trailing *mtp* disable beats the enables)
- router gate / shared expert / lm_head -> BF16
This locks in the wildcard precedence the recipe depends on, so the conv1d
placement (which the released hf_quant_config.json confirms is FP8) can't
silently drift from the recipe again.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
Call out in the file header that FP8-quantizing linear_attn.conv1d, in_proj_a and in_proj_b deliberately diverges from default_disabled_quantizers and the qwen3_5/qwen3_5_moe family recipes (which disable all three), so a future reader doesn't revert it to the family default. It reproduces the published checkpoint and was validated end-to-end. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
State it plainly: conv1d/in_proj_a/in_proj_b are FP8 here, whereas the shared default_disabled_quantizers and the qwen3_5/qwen3_5_moe family recipes leave them disabled; this recipe enables them to match the published checkpoint. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Approving: both open items from the last review are now closed — the recipe/doc conv1d story is consistent and scoped to gated-delta layers, and tests/unit/recipe/test_qwen3_8_recipe.py pins the wildcard precedence.
No action needed.
- ✔️ Resolved since the last review: the missing wildcard-precedence test (new CPU test asserts NVFP4 experts, FP8
self_attn/linear_attnincl.conv1d, disabledmtp/gate/shared-expert/lm_head) and the "on every layer" claim, now "on every gated-delta layer" in both the YAML header andmodelopt_recipes/ptq.md. - New test follows
tests/unit/recipe/test_minimax_m3_recipe.py; per-expert quantizer names resolve via_normalize_fused_experts_quantizer_name, so the*mlp.experts.*patterns do match. - File header matches
LICENSE_HEADERverbatim; no licensing hold.
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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.
Qwen/Qwen3.8-2.4T-A95B is a
qwen3_5_moe_textMoE: 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:
conv1d+ all in/out projections)Two things are documented in the file header because they affect how the recipe should be
read:
conv1dandthe 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.Conv1dis a registered ModelOpt quant module, sothe recipe's broad
*linear_attn*rules reachlinear_attn.conv1dtoo — this is intentionaland matches the published
nvidia/Qwen3.8-2.4T-A95B-NVFP4, whosehf_quant_config.jsonlistslinear_attn.conv1das FP8 on every gated-delta layer (the interleaved full-attention layershave no
conv1d). (An earlier revision of the file header / ptq.md wrongly stated therecurrent path is never quantized; corrected in this PR.)
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_typerecipes vs model-hub checkpoint recipes); this one targets a published checkpoint, alongside
deepseek-ai/DeepSeek-V4-Pro-0813and the Nemotron-3 entries.Usage
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-NVFP4checkpoint is the artifact this recipe produces —its
hf_quant_config.jsonis the ground truth for which modules are quantized (routed expertsNVFP4; 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"
CONTRIBUTING.md: N/AAdditional Information
Model card: https://huggingface.co/nvidia/Qwen3.8-2.4T-A95B-NVFP4
Summary by CodeRabbit
New Features
Documentation