[Quantization] Add compressed-tensors NVFP4 MoE Support #19990

dsikka · 2025-06-23T16:22:00Z

Note:

CMake update is required to cuda_archs_loose_intersection to fix this PR which currently prevents building cutlass_moe_fp4 on b200

Purpose

Add MoE support for nvfp4 compressed-tensors models
When running on a device < sm100, fall-back to use marlin with nvfp4a16/ignoring activations

Test Plan

Testing nm-testing/Qwen3-30B-A3B-NVFP4 e2e with tp>=1

import numpy
import torch

from vllm import LLM, SamplingParams


prompts = [
    "The Swiss Alps are", 
    "Brad Marchand is",
    "The Boston Bruins are"
]


sampling_params = SamplingParams(temperature=0.80, top_p=0.95, max_tokens=40, min_tokens=10)
llm = LLM("nm-testing/Qwen3-30B-A3B-NVFP4", tensor_parallel_size=2)

Generations:

 a mountain range in Switzerland, and the Alps are the highest and most extensive mountain range in Europe. Given these facts, what is the answer to the question "Which is the highest and most extensive mountain


 the president of the board of directors of the National Association of Public Hospitals and Health Systems (NAPHHS), a position he has held since 2010. He is also the vice president


 a professional ice hockey team based in Boston, Massachusetts. They are members of the Atlantic Division of the Eastern Conference of the National Hockey League (NHL). They were founded in 1917

github-actions · 2025-06-23T16:22:08Z

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Summary of Changes

Hello @dsikka, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances vLLM's quantization capabilities by adding comprehensive support for NVFP4 quantization specifically for Mixture-of-Experts (MoE) models. It introduces a new quantization method that handles the unique structure of NVFP4 weights and integrates with optimized Marlin kernels to ensure high-performance inference for these quantized models.

Highlights

NVFP4 MoE Support: Introduced CompressedTensorsW4A4MoeMethod to enable support for NVFP4 (NVIDIA FP4) quantized Mixture-of-Experts (MoE) models, allowing for more efficient inference with these models.
Marlin Kernel Integration: Leveraged highly optimized Marlin kernels for the efficient execution of NVFP4 MoE operations, ensuring performance benefits for quantized models.
Quantized Weight Handling: Implemented the loading and processing of NVFP4 quantized weights, including packed 4-bit weights (torch.uint8), FP8 block scales (torch.float8_e4m3fn), and FP32 global scales, which are crucial for the new quantization scheme.
Dynamic Method Selection: Updated the quantization method selection logic to correctly identify and instantiate the new NVFP4 MoE method based on the detected quantization configuration.

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Code Review

The pull request adds support for MoE models with nvfp4 compressed tensors. The changes include modifications to the weight loader in fused_moe/layer.py and the addition of a new class CompressedTensorsW4A4MoeMethod in compressed_tensors_moe.py to handle the new quantization method. There are several blocks of code that are commented out, and a breakpoint that should be removed.

vllm/model_executor/layers/fused_moe/layer.py

dsikka · 2025-06-24T18:21:22Z

@mgoin

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

mgoin · 2025-06-25T18:25:52Z

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

+        assert activation == "silu", "Only SiLU activation is supported."
+        assert not apply_router_weight_on_input, (
+            "Router weight on input is not "
+            "supported for ModelOptNvFp4FusedMoE.")


These should still be enforced for fused_marlin_moe. Also update the mention of ModelOptNvFp4FusedMoE

Should they have been enforced in the ModelOpt integration?

vllm/vllm/model_executor/layers/quantization/modelopt.py

Line 682 in 27c065d

return torch.ops.vllm.fused_marlin_moe(

Yes, definitely there as well

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

mergify · 2025-06-25T19:16:14Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @dsikka.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: Dipika Sikka <[email protected]>

Signed-off-by: Dipika <[email protected]>

… move utils Signed-off-by: Dipika <[email protected]>

Signed-off-by: Dipika <[email protected]>

Signed-off-by: Dipika Sikka <[email protected]>

dsikka · 2025-06-26T14:01:23Z

@mgoin I would like to add a test but I am mindful of the test time for MoEs being added to the quantization tests - Maybe deepseek-ai/DeepSeek-V2-Lite? Do we have MoE tests beyond the weight loading tests. I guess lm-eval tests? I can add it in a follow-up

Signed-off-by: Dipika Sikka <[email protected]>

mgoin

Thanks! We can followup with the automated testing

gemini-code-assist bot reviewed Jun 23, 2025

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mergify bot added the ci/build label Jun 24, 2025

dsikka marked this pull request as ready for review June 25, 2025 13:25

dsikka requested review from mgoin, robertgshaw2-redhat and tlrmchlsmth as code owners June 25, 2025 13:25

mgoin reviewed Jun 25, 2025

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mergify bot added the needs-rebase label Jun 25, 2025

dsikka added 7 commits June 26, 2025 13:47

initial comit

d02387b

fix all parameters

eb69b46

Signed-off-by: Dipika Sikka <[email protected]>

enable cutlass

186727b

Signed-off-by: Dipika Sikka <[email protected]>

update

94d5405

Signed-off-by: Dipika <[email protected]>

temporarily enable sm100 build; fix condition for when to use marlin;…

8e90691

… move utils Signed-off-by: Dipika <[email protected]>

remove if condition

2bae08e

Signed-off-by: Dipika <[email protected]>

Update CMakeLists.txt

f75fe6d

dsikka force-pushed the moe_ct_fp4 branch from e158452 to f75fe6d Compare June 26, 2025 13:49

mergify bot removed the needs-rebase label Jun 26, 2025

PR comments

766e2b4

Signed-off-by: Dipika Sikka <[email protected]>

dsikka requested a review from mgoin June 26, 2025 13:58

format

7f6b39b

Signed-off-by: Dipika Sikka <[email protected]>

mgoin approved these changes Jun 26, 2025

View reviewed changes

mgoin enabled auto-merge (squash) June 26, 2025 17:12

github-actions bot added the ready ONLY add when PR is ready to merge/full CI is needed label Jun 26, 2025

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[Quantization] Add compressed-tensors NVFP4 MoE Support #19990

[Quantization] Add compressed-tensors NVFP4 MoE Support #19990

dsikka commented Jun 23, 2025 •

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[Quantization] Add compressed-tensors NVFP4 MoE Support #19990

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Test Plan

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dsikka commented Jun 24, 2025

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dsikka commented Jun 23, 2025 •

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