[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 #28971

xyang16 · 2025-11-18T23:49:06Z

Purpose

This PR is to support FusedMoE LoRA Triton kernel for mxfp4 model.

Add UnfusedOAITritonExperts
Inject lora module in activation: Since matmul_ogs can fuse activation, set fused_activation to None to unfuse activation in the first matmul_ogs.
Inject lora module in moe_sum: This need to unfuse sum in second matmul_ogs. Grouped reduction does scatter + accumulate, it is essentially equal to: y[dst_indx // n_expts_act, :] += x[src_indx, :], so that scatter sum across multiple experts, and collapse M * topk to M rows. Therefore, we need to set routing_data.n_expts_act to 1, so it doesn't sum across multiple experts, in order unfuse moe_sum in the second matmul_ogs.
Added test_modular_oai_triton_moe.py

Test Plan

pytest -s -v tests/kernels/moe/test_modular_oai_triton_moe.py

Test Result

Tests passed.

Benchmark

Baseline (marlin):

VLLM_MXFP4_USE_MARLIN=1 vllm serve openai/gpt-oss-20b \
  --tensor-parallel-size 1 \
  --max-num-seqs 16 \
  --compilation-config '{"max_cudagraph_capture_size": 128, "compile_sizes": [1, 2, 4, 8, 16]}' \
  --enable-lora \
  --max-loras 6 \
  --lora-modules lora1=/opt/dlami/nvme/models/gpt-oss-20b-lora-gsm8k \
  --max-lora-rank 64

vllm bench serve \
  --model openai/gpt-oss-20b \
  --lora-modules lora1 \
  --dataset-name sharegpt \
  --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
  --max-concurrency 16 \
  --num-prompts 1000 \
  --num-warmups 60 \
  --ignore-eos

============ Serving Benchmark Result ============
Successful requests:                     1000      
Failed requests:                         0         
Maximum request concurrency:             16        
Benchmark duration (s):                  142.34    
Total input tokens:                      215312    
Total generated tokens:                  199033    
Request throughput (req/s):              7.03      
Output token throughput (tok/s):         1398.29   
Peak output token throughput (tok/s):    1677.00   
Peak concurrent requests:                29.00     
Total Token throughput (tok/s):          2910.95   
---------------Time to First Token----------------
Mean TTFT (ms):                          43.24     
Median TTFT (ms):                        26.77     
P99 TTFT (ms):                           140.09    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          11.19     
Median TPOT (ms):                        10.93     
P99 TPOT (ms):                           17.44     
---------------Inter-token Latency----------------
Mean ITL (ms):                           11.12     
Median ITL (ms):                         9.73      
P99 ITL (ms):                            56.55     
==================================================

PR (triton):

Install triton_kernels

pip install "git+https://github.com/triton-lang/triton.git@0a2e3a391cbb9e13d29bf12a2a0005e358102d74#subdirectory=python/triton_kernels"

vllm serve openai/gpt-oss-20b \
  --tensor-parallel-size 1 \
  --max-num-seqs 16 \
  --compilation-config '{"max_cudagraph_capture_size": 128, "compile_sizes": [1, 2, 4, 8, 16]}' \
  --enable-lora \
  --max-loras 6 \
  --lora-modules lora1=/opt/dlami/nvme/models/gpt-oss-20b-lora-gsm8k \
  --max-lora-rank 64

vllm bench serve \
  --model openai/gpt-oss-20b \
  --lora-modules lora1 \
  --dataset-name sharegpt \
  --dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
  --max-concurrency 16 \
  --num-prompts 1000 \
  --num-warmups 60 \
  --ignore-eos

============ Serving Benchmark Result ============
Successful requests:                     1000      
Failed requests:                         0         
Maximum request concurrency:             16        
Benchmark duration (s):                  125.59    
Total input tokens:                      215312    
Total generated tokens:                  199033    
Request throughput (req/s):              7.96      
Output token throughput (tok/s):         1584.77   
Peak output token throughput (tok/s):    2171.00   
Peak concurrent requests:                29.00     
Total Token throughput (tok/s):          3299.15   
---------------Time to First Token----------------
Mean TTFT (ms):                          52.14     
Median TTFT (ms):                        19.03     
P99 TTFT (ms):                           129.60    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          9.82      
Median TPOT (ms):                        9.28      
P99 TPOT (ms):                           21.31     
---------------Inter-token Latency----------------
Mean ITL (ms):                           9.72      
Median ITL (ms):                         7.32      
P99 ITL (ms):                            90.10     
==================================================

Accuracy Testing

20b deepep triton

VLLM_ALL2ALL_BACKEND="deepep_high_throughput" vllm serve openai/gpt-oss-20b \
  --tensor-parallel-size 1 \
  --data-parallel-size 2 \
  --enable-expert-parallel \
  --no-enable-prefix-caching

OPENAI_API_KEY=EMPTY python3 -m gpt_oss.evals --model openai/gpt-oss-20b --eval gpqa --n-threads 200 --reasoning-effort low

Writing report to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251125_122239.html
{'chars': np.float64(55.219065656565654), 'chars:std': np.float64(220.291583893894), 'score': np.float64(0.5707070707070707), 'score:std': np.float64(0.4949752621616814)}
Writing results to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251125_122239.json
Writing all results to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251125_122239_allresults.json
[{'eval_name': 'gpqa', 'model_name': 'openai__gpt-oss-20b-low_temp1.0_20251125_122239', 'metric': 0.5707070707070707}]

20b lora triton

vllm serve openai/gpt-oss-20b \
  --tensor-parallel-size 1 \
  --compilation-config '{"cudagraph_mode": "PIECEWISE"}' \
  --max-num-seqs 16 \
  --enable-lora \
  --max-loras 6 \
  --lora-modules lora1=/opt/dlami/nvme/models/gpt-oss-20b-lora/checkpoint-13 \
  --max-lora-rank 64

OPENAI_API_KEY=EMPTY python3 -m gpt_oss.evals --model lora1 --eval gpqa --n-threads 200 --reasoning-effort low

Writing report to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251126_124509.html
{'chars': np.float64(62.859848484848484), 'chars:std': np.float64(230.21663448129942), 'score': np.float64(0.577020202020202), 'score:std': np.float64(0.4940322747359399)}
Writing results to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251126_124509.json
Writing all results to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251126_124509_allresults.json
[{'eval_name': 'gpqa', 'model_name': 'openai__gpt-oss-20b-low_temp1.0_20251126_124509', 'metric': 0.577020202020202}]

20b lora marlin

VLLM_MXFP4_USE_MARLIN=1 vllm serve openai/gpt-oss-20b \
  --tensor-parallel-size 1 \
  --compilation-config '{"cudagraph_mode": "PIECEWISE"}' \
  --max-num-seqs 16 \
  --enable-lora \
  --max-loras 6 \
  --lora-modules lora1=/opt/dlami/nvme/models/gpt-oss-20b-lora/checkpoint-13 \
  --max-lora-rank 64

OPENAI_API_KEY=EMPTY python3 -m gpt_oss.evals --model lora1 --eval gpqa --n-threads 200 --reasoning-effort low

Writing report to /tmp/gpqa_lora1-low_temp1.0_20251126_125725.html
{'chars': np.float64(25.56691919191919), 'chars:std': np.float64(165.94325370775), 'score': np.float64(0.5669191919191919), 'score:std': np.float64(0.4955015860245882)}
Writing results to /tmp/gpqa_lora1-low_temp1.0_20251126_125725.json
Writing all results to /tmp/gpqa_lora1-low_temp1.0_20251126_125725_allresults.json
[{'eval_name': 'gpqa', 'model_name': 'lora1-low_temp1.0_20251126_125725', 'metric': 0.5669191919191919}]

Essential Elements of an Effective PR Description Checklist

The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
(Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

gemini-code-assist

Code Review

This pull request introduces support for FusedMoE LoRA with Triton kernels for mxfp4 models, which provides a significant performance improvement as shown in the benchmarks. The changes are well-structured, adding the necessary logic to select the Triton backend and adapting the kernels for this new path. However, I've identified a critical issue where attributes in OAITritonExperts are used without being initialized, which could lead to a runtime error in non-LoRA use cases. Please address this to ensure the stability of the implementation.

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

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vllm/model_executor/layers/quantization/mxfp4.py

robertgshaw2-redhat · 2025-11-19T01:33:56Z

nice speedup!

varun-sundar-rabindranath · 2025-11-19T03:10:01Z

vllm/lora/layers/fused_moe.py

    modular_triton_fused_moe,
    try_get_optimal_moe_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe_modular_method import (


@xyang16 can you add a unit test for gpt-oss lora + triton_kernels. The test can be predicated on has_triton_kernels like in https://github.com/vllm-project/vllm/blob/main/tests/kernels/moe/test_gpt_oss_triton_kernels.py

Added test_modular_oai_triton_moe.py. Thanks!

varun-sundar-rabindranath

The changes to triton_kernel_fused_experts are invasive and it is a bit confusing reason about the fused_act=True, fuse_sum=True and fuse_act=False,fused_sum=False cases as the assumptions and expectations from matmul_ogs is different in both cases.

The main difference between the non-LoRA and the LoRA case,

For the non-LoRA case, no assumptions about the sizes of matmul_ogs output tensors are made. The only requirement here is that the second matmul_ogs must return a tensor of size [M, K]. For the LoRA case, we expect the outputs to be of a specific shape - This pattern is similar to TritonExperts
For the non-LoRA case, there are no requirements on the gather_indx and scatter_indx sizes. The LoRA case requires the tensors in these objects to be a specific shape.

For these reasons, I think it will be better to create a separate implementation of BaseOAITritonExperts class for the LoRA case, naming it something like UnfusedOAITritonExperts. Apart of being easier to assert for expectations, with this we can create correct and adequate workspace shapes for both workspace13 and workspace2 and reuse them properly in the implementation. Please refer to TritonExperts I think the implementation here would be very similar and all the logic could be contained within the apply function, thus not disturbing the existing triton_kernel_fused_experts function. something like,

def apply():
        routing_data, gather_indx, scatter_indx = self._make_routing_data(
            topk_ids, topk_weights, local_num_experts
        )
        matmul_ogs(..., 
                             y = intermediate_cache1)
        activation(intermediate_cache2, intermediate_cache1)
       matmul_ogs(...,
                            y = intermediate_cache3)

vllm/lora/ops/triton_ops/fused_moe_lora_op.py

varun-sundar-rabindranath · 2025-11-19T03:34:25Z

vllm/model_executor/layers/quantization/mxfp4.py


-    if with_lora_support:
-        return get_mxfp4_backend_with_lora()
-


I think we should maintain get_mxfp4_backend_with_lora() and return the appropriate backend from within that function. This is because, there is no guarantee that the logic below will choose a LoRA compatible backend.

Added back get_mxfp4_backend_with_lora. Thanks for review!

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

varun-sundar-rabindranath · 2025-11-19T04:01:25Z

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

        precision_config=quant_config.w1_precision,
        gammas=gammas if apply_router_weight_on_input else None,
        fused_activation=act,
    )


@xyang16 To guarantee that matmul_ogs will return an output shaped [M * topk, N], I think it is better to pass in the output tensor ourselves using the argument y. Note that matmul_ogs actually checks if the output is of expected size here https://github.com/triton-lang/triton/blob/c3c476f357f1e9768ea4e45aa5c17528449ab9ef/python/triton_kernels/triton_kernels/matmul_ogs.py#L180 . That way it is guaranteed that matmul_ogs will respect the contract.

Same for the second matmul_ogs also.

Raised a separate PR #29219 for this

varun-sundar-rabindranath · 2025-11-19T04:05:24Z

vllm/model_executor/layers/fused_moe/prepare_finalize.py

        apply_router_weight_on_input: bool,
        weight_and_reduce_impl: mk.TopKWeightAndReduce,
-    ) -> None:
+    ) -> torch.Tensor:


This violates the base class contract at

vllm/vllm/model_executor/layers/fused_moe/modular_kernel.py

Line 251 in 814843e

def finalize(

Changed back. Thanks.

vllm/model_executor/layers/fused_moe/topk_weight_and_reduce.py

mergify · 2025-11-19T17:19:21Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @xyang16.

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

Signed-off-by: Xin Yang <[email protected]>

xyang16 · 2025-11-26T20:13:17Z

@xyang16 Could you please resolve conflicts?

@jeejeelee I have resolved the conflicts. Thanks a lot!

xyang16 · 2025-11-26T21:10:52Z

Thank you for the work @xyang16. It generally looks good to me - just left some minor comments. Can you add some gpt-oss eval numbers for OAITritonExperts and UnfusedOAITritonExperts please.

I have added gpt-oss eval numbers. Please take a look. Thanks!

I see the vllm serve commands have --compilation-config '{"max_cudagraph_capture_size": 16, "compile_sizes": [1, 2, 4, 8, 16]}' - can you confirm that it passes without the --compilation-config arguments ? Thanks.

@varun-sundar-rabindranath I have rerun the gpt-oss eval numbers, but I have to set --compilation-config '{"cudagraph_mode": "PIECEWISE"}'. Otherwise I will get !!! in output. See the details in #29539.

Fixing the !!! issue is beyond the scope of this PR. So I have to set PIECEWISE mode. Thanks!

20b lora triton

vllm serve openai/gpt-oss-20b \
  --tensor-parallel-size 1 \
  --compilation-config '{"cudagraph_mode": "PIECEWISE"}' \
  --max-num-seqs 16 \
  --enable-lora \
  --max-loras 6 \
  --lora-modules lora1=/opt/dlami/nvme/models/gpt-oss-20b-lora/checkpoint-13 \
  --max-lora-rank 64

Writing report to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251126_124509.html
{'chars': np.float64(62.859848484848484), 'chars:std': np.float64(230.21663448129942), 'score': np.float64(0.577020202020202), 'score:std': np.float64(0.4940322747359399)}
Writing results to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251126_124509.json
Writing all results to /tmp/gpqa_openai__gpt-oss-20b-low_temp1.0_20251126_124509_allresults.json
[{'eval_name': 'gpqa', 'model_name': 'openai__gpt-oss-20b-low_temp1.0_20251126_124509', 'metric': 0.577020202020202}]

20b lora marlin

Writing report to /tmp/gpqa_lora1-low_temp1.0_20251126_125725.html
{'chars': np.float64(25.56691919191919), 'chars:std': np.float64(165.94325370775), 'score': np.float64(0.5669191919191919), 'score:std': np.float64(0.4955015860245882)}
Writing results to /tmp/gpqa_lora1-low_temp1.0_20251126_125725.json
Writing all results to /tmp/gpqa_lora1-low_temp1.0_20251126_125725_allresults.json
[{'eval_name': 'gpqa', 'model_name': 'lora1-low_temp1.0_20251126_125725', 'metric': 0.5669191919191919}]

Signed-off-by: Xin Yang <[email protected]>

jeejeelee · 2025-11-27T16:43:12Z

@xyang16 All tests pass correctly, but I realized that the GPT-OSS LoRA test does not select the Triton backend. Could you verify whether the Triton backend can pass correctly?

xyang16 · 2025-11-28T01:01:31Z

@xyang16 All tests pass correctly, but I realized that the GPT-OSS LoRA test does not select the Triton backend. Could you verify whether the Triton backend can pass correctly?

@jeejeelee Thanks a lot! Triton backend can pass with slight modification of test_gpt_oss_lora_tp2 like below. Because FULL_AND_PIECEWISE cudagraph mode is causing some !!! in output, you can see the details in #29539. But fixing that problem is beyond the scope of this PR.

        compilation_config=vllm.config.CompilationConfig(  # Avoid OOM
            cudagraph_mode=CUDAGraphMode.PIECEWISE,
            cudagraph_specialize_lora=False,
        ),

jeejeelee · 2025-11-28T02:33:56Z

@xyang16 Any plan to fix this issue, if we have ,I think we can land this PR now

xyang16 · 2025-11-28T02:40:44Z

@xyang16 Any plan to fix this issue, if we have ,I think we can land this PR now

@jeejeelee Yes, I think the issue should be fixed soon. I talked about this issue with @varun-sundar-rabindranath and he suggested me to create an issue for this, and see what other people think how to fix it. Maybe quickest is to not make FULL_AND_PIECEWISE default (FULL_AND_PIECEWISE was made default recently starting from 0.11.0).

jeejeelee

Thank you for contribution

…oject#28971)" This reverts commit 745a3ba. Signed-off-by: Huamin Li <[email protected]>

DarkLight1337 · 2025-11-28T23:26:51Z

Reverted by #29697, please redo

#29697) Signed-off-by: Huamin Li <[email protected]>

) Signed-off-by: Xin Yang <[email protected]> Co-authored-by: Jee Jee Li <[email protected]>

…oject#28971)" (vllm-project#29697) Signed-off-by: Huamin Li <[email protected]>

) Signed-off-by: Xin Yang <[email protected]> Co-authored-by: Jee Jee Li <[email protected]> Signed-off-by: Hashem Hashemi <[email protected]>

…oject#28971)" (vllm-project#29697) Signed-off-by: Huamin Li <[email protected]> Signed-off-by: Hashem Hashemi <[email protected]>

xyang16 requested review from jeejeelee, mgoin, pavanimajety, robertgshaw2-redhat, tlrmchlsmth and yewentao256 as code owners November 18, 2025 23:49

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xyang16 force-pushed the fused_moe_lora_triton branch 3 times, most recently from 8acf2f1 to 8f24eec Compare November 19, 2025 00:59

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xyang16 changed the title ~~Support FusedMoE LoRA Triton kernel for mxfp4 model~~ [LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 model Nov 19, 2025

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xyang16 and others added 3 commits November 26, 2025 13:15

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Merge branch 'main' into fused_moe_lora_triton

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Merge branch 'main' into fused_moe_lora_triton

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This was referenced Nov 28, 2025

Fix LoRA compatibility with quantized MoE models #29376

Open

Add INT4 + LoRA support with tensor materialization #28793

Closed

Add INT4 compressed-tensors + LoRA support (including MoE) #28791

Closed

hl475 added a commit to hl475/vllm that referenced this pull request Nov 28, 2025

Revert "[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 (vllm-pr…

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…oject#28971)" This reverts commit 745a3ba. Signed-off-by: Huamin Li <[email protected]>

hl475 mentioned this pull request Nov 28, 2025

Revert "[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 (#28971)" #29697

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Revert "[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 (#28971)" (

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#29697) Signed-off-by: Huamin Li <[email protected]>

xyang16 mentioned this pull request Nov 29, 2025

[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 #29708

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[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 (vllm-project#28971

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kitaekatt pushed a commit to kitaekatt/vllm that referenced this pull request Dec 1, 2025

Revert "[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 (vllm-pr…

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…oject#28971)" (vllm-project#29697) Signed-off-by: Huamin Li <[email protected]>

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[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 #28971

[LoRA] Support FusedMoE LoRA Triton kernel for mxfp4 #28971

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robertgshaw2-redhat commented Nov 19, 2025

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