[Quantization] Enable shared expert fusion compatibility with online shared_expert quantization (showcase: along Quark MXFP4 routed experts) - #55686
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Signed-off-by: Felix Marty <Felix.Marty@amd.com>
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Included review availability: Your plan provides up to 10 included reviews per hour; 9 remain after this review. WalkthroughThe change adds online MXFP4 shared-expert loading for fused MoE layers. It updates quantization method resolution to use layer classes, registers shared-expert projections, adds loader implementations, and adds ROCm integration coverage. ChangesOnline shared-expert quantization
Estimated code review effort: 4 (Complex) | ~45 minutes Sequence Diagram(s)sequenceDiagram
participant RoutedExperts
participant OnlineQuantizationConfig
participant OnlineMxfp4SharedExpertLoader
participant FusedMoEStorage
RoutedExperts->>OnlineQuantizationConfig: Resolve shared-expert quantization method
OnlineQuantizationConfig-->>RoutedExperts: Return quantization metadata
RoutedExperts->>OnlineMxfp4SharedExpertLoader: Load floating-point shared-expert weight
OnlineMxfp4SharedExpertLoader->>FusedMoEStorage: Store quantized weight and scales
Merge Risk: 🟡 Moderate · up to This change adds online MXFP4 shared-expert loading for fused MoE models, but supported fused shared-expert checkpoints may fail to load or route full-precision weights into quantized storage under naming variations. Resolve these loader-path issues and the outstanding lint violations before merge. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
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Inline comments:
In `@tests/quantization/test_online_shared_expert_fusion.py`:
- Around line 75-78: Parenthesize the adjacent expected log string literals in
the test assertion around the quantization message, preserving the exact string
content while satisfying the repository’s ISC004 Ruff check.
In `@vllm/model_executor/layers/fused_moe/routed_experts.py`:
- Around line 934-937: The shared-expert quantization prefix in RoutedExperts
uses a hardcoded singular name that can disagree with
resolve_layer_fused_shared_expert registrations. Pass the resolved shared-expert
name or prefix into RoutedExperts and reuse it when constructing
shared_expert_projection_prefix, preserving matching behavior for both targeted
quantization and should_ignore_layer lookups.
In `@vllm/model_executor/layers/quantization/online/moe_shared_expert.py`:
- Line 156: Update the shared-expert weight-loading flow around
OnlineMxfp4SharedExpertLoader and RoutedExperts.load_weights to split the fused
w1 gate/up tensor along dimension 0 into two [I, H] halves, then dispatch both
halves using the w1 and w3 weight identifiers so _load receives the expected
shape and loads both projections.
In `@vllm/model_executor/layers/quantization/quark/quark_moe.py`:
- Line 1244: The shared-expert loader property in QuarkOCP_MX_MoEMethod must
only return OnlineMxfp4SharedExpertLoader when self.weight_dtype equals "mxfp4";
for mxfp6_e3m2 and mxfp6_e2m3, return super().shared_expert_online_loader
instead. Preserve the existing loader behavior for mxfp4 and avoid introducing
an FP6 codec.
In `@vllm/model_executor/layers/quantization/utils/config_utils.py`:
- Around line 128-129: Update both returned tuples in the relevant configuration
utility to wrap their adjacent string concatenations in parentheses, resolving
Ruff ISC004 while preserving the existing messages and tuple contents.
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tests/quantization/test_online.pytests/quantization/test_online_shared_expert_fusion.pytests/quantization/test_quantization_config_args.pytests/quantization/utils.pyvllm/model_executor/layers/fused_moe/fused_moe_method_base.pyvllm/model_executor/layers/fused_moe/routed_experts.pyvllm/model_executor/layers/fused_moe/utils.pyvllm/model_executor/layers/quantization/base_config.pyvllm/model_executor/layers/quantization/inc/schemes/inc_mxfp4_moe.pyvllm/model_executor/layers/quantization/online/base.pyvllm/model_executor/layers/quantization/online/fp8.pyvllm/model_executor/layers/quantization/online/moe_shared_expert.pyvllm/model_executor/layers/quantization/quark/quark_moe.pyvllm/model_executor/layers/quantization/utils/config_utils.py
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Signed-off-by: Felix Marty <Felix.Marty@amd.com>
Signed-off-by: Felix Marty <Felix.Marty@amd.com>
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I tested this on
I ran three configurations:
Arms 1 and 2 are the same checkpoint on the same image and differ only by this PR, so that pair isolates the change. Arm 3 is a different checkpoint that already ships MXFP4 shared experts and therefore fuses without this PR; it is the result arm 2 should be able to reach. AccuracyGSM8K: 5-shot, greedy, thinking disabled, 1319 examples.
Both differences are within noise for these sample sizes. ThroughputFixed request shapes, identical seeds in every arm.
Summary
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Signed-off-by: Felix Marty <Felix.Marty@amd.com>
fxmarty-amd
changed the title
shared_expert quantizationshared_expert quantization (showcase: along Quark MXFP4 routed experts)
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Sep 9, 2026
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Sep 10, 2026
| weights onto meta device and materializes them just-in-time.""" | ||
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| uses_meta_device: bool = True | ||
| activation_quant_key: QuantKey | None |
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Needed for
assert issubclass(shared_method_cls, OnlineLinearBase)
shared_activation_key = shared_method_cls.activation_quant_keyin config_utils.py
BowenBao
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Sep 10, 2026
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LGTM, thanks! Got some comment and suggest DSV4.1 flash as a usecase but not blocking.
| if self.is_fused_shared_expert_enabled | ||
| else None, | ||
| fuse_shared_experts=self.is_fused_shared_expert_enabled, | ||
| shared_expert_prefix=f"{prefix}.shared_experts", |
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should this be applied to more models?
| for projection_name in projection_names | ||
| ] | ||
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| # TODO: Extend with use at your own risk. |
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is this a todo or note?
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| # TODO: Extend with use at your own risk. | ||
| if isinstance(quant_config, QuarkConfig): |
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could we extend for standard dsv4, kimi k3 etc?
| raise AssertionError("Unimplemented expert weight codecs cannot load weights.") | ||
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| class OnlineMxfp4SharedExpertLoader(OnlineSharedExpertLoader): |
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IIUC, the main issue was that pre-quantized FSE does not instantiate shared-expert class, but instead load quantized shared-expert weights into FMoE.experts weights. This class enables the online version of it.
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cc @tjtanaa , @dllehr-amd , @AndreasKaratzas |
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Disclosure
AI assistance was used. The changes were reviewed and tested manually.
Purpose
This PR enables compatibility of online
shared_expertquantization with fused shared expert optimization. As an example, it implements it for forQuarkOCP_MX_MoEMethod(andINCMxfp4MoEMethodto showcase extensibility, possibly to be removed).The main idea is that given
vllm/vllm/model_executor/models/qwen3_next.py
Lines 168 to 180 in f27ae25
self.shared_expert = Qwen3NextMLPis not defined when usingVLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1, we use weight loading in theRoutedExpertsto capture the shared expert weight loading, quantize it there, and copy the quantized shared expert into the correct quantized fused expert slot.The original
quant_methodis preserved (e.g.QuarkOCP_MX_MoEMethod).This PR does NOT implement
experts+shared_expertjoint online quantization + fusion.expertsis expected to be already quantized in this PR (loaded throughQuarkOCP_MX_MoEMethod/INCMxfp4MoEMethod)Main design goal is to be as much as possible quant method agnostic (currently: based on weight quant key, NOT quant method). See compatibility of
OnlineMxfp4SharedExpertLoaderwithQuarkOCP_MX_MoEMethodandINCMxfp4MoEMethodwith very minimal change there.We also preserve quant config compatibility checks for fused shared expert, that could be extended to accommodate new precisions e.g. #53161
Test Plan
Testing:
pytest tests/quantization/test_online_shared_expert_fusion.py -s -vvvvvTest Result
test_online_shared_expert_fusion.pypassing7 passed, 14 warnings in 10.31sDisplayed to user:
(EngineCore pid=27900) INFO 08-17 10:29:41 [base_loader.py:109] Quantized 80 layers of types: mlp.shared_expert.down_proj: 40 (from linear: QuantSpec(weight=QuantKey(dtype=torch.uint8, scale=ScaleDesc(dtype=torch.uint8, static=True, group_shape=GroupShape(row=1, col=32)), scale2=None, symmetric=True), activation=None)); mlp.shared_expert.gate_up_proj: 40 (from linear: QuantSpec(weight=QuantKey(dtype=torch.uint8, scale=ScaleDesc(dtype=torch.uint8, static=True, group_shape=GroupShape(row=1, col=32)), scale2=None, symmetric=True), activation=None))