[Bug]: DiffusionGemma fails to start with RuntimeError in _compiled_sample_step during torch.compile (matmul shape mismatch)

[knoka0812]

Your current environment

(torch) root@gltbjaudgggwsmky-make-84bbd6bc9d-5w755:/data/coding# python collect_env.py

Collecting environment information...

    System Info

==============================
OS : Ubuntu 24.04.4 LTS (x86_64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version : Could not collect
CMake version : version 3.28.3
Libc version : glibc-2.39

==============================
PyTorch Info

PyTorch version : 2.11.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
XPU used to build PyTorch : N/A

==============================
Python Environment

Python version : 3.12.13 | packaged by Anaconda, Inc. | (main, Mar 19 2026, 20:20:58) [GCC 14.3.0] (64-bit runtime)
Python platform : Linux-5.19.0-50-generic-x86_64-with-glibc2.39

==============================
CUDA / GPU Info

Is CUDA available : True
CUDA runtime version : 13.0.48
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA A100-PCIE-40GB
GPU 1: NVIDIA A100-PCIE-40GB

Nvidia driver version : 590.48.01
cuDNN version : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.19.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.19.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.19.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.19.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.19.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.19.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.19.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.19.0
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True

==============================
CPU Info

Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
Stepping: 7
CPU(s) scaling MHz: 78%
CPU max MHz: 4000.0000
CPU min MHz: 1200.0000
BogoMIPS: 6000.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 1.5 MiB (48 instances)
L1i cache: 1.5 MiB (48 instances)
L2 cache: 48 MiB (48 instances)
L3 cache: 71.5 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-23,48-71
NUMA node1 CPU(s): 24-47,72-95
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Mitigation; Enhanced IBRS
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled

==============================
Versions of relevant libraries

[pip3] flashinfer-python==0.6.12
[pip3] numpy==2.3.5
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cccl==13.3.3.4.1
[pip3] nvidia-cuda-crt==13.3.73
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvcc==13.2.78
[pip3] nvidia-cuda-nvrtc==13.3.33
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime==13.3.29
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cuda-tileiras==13.2.78
[pip3] nvidia-cudnn-cu12==9.19.0.56
[pip3] nvidia-cudnn-frontend==1.25.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.5.2
[pip3] nvidia-cutlass-dsl-libs-base==4.5.2
[pip3] nvidia-cutlass-dsl-libs-cu13==4.5.2
[pip3] nvidia-ml-py==13.590.48
[pip3] nvidia-nccl-cu12==2.28.9
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] nvidia-nvvm==13.2.78
[pip3] pyzmq==27.1.0
[pip3] tokenspeed-triton==3.7.10.post20260531
[pip3] torch==2.11.0+cu128
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.11.0+cu128
[pip3] torchvision==0.26.0+cu128
[pip3] transformers==5.12.1
[pip3] triton==3.6.0
[conda] flashinfer-python 0.6.12 pypi_0 pypi
[conda] numpy 2.3.5 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi
[conda] nvidia-cuda-cccl 13.3.3.4.1 pypi_0 pypi
[conda] nvidia-cuda-crt 13.3.73 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cuda-nvcc 13.2.78 pypi_0 pypi
[conda] nvidia-cuda-nvrtc 13.3.33 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-cuda-runtime 13.3.29 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cuda-tileiras 13.2.78 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.19.0.56 pypi_0 pypi
[conda] nvidia-cudnn-frontend 1.25.0 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.13.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi
[conda] nvidia-cutlass-dsl 4.5.2 pypi_0 pypi
[conda] nvidia-cutlass-dsl-libs-base 4.5.2 pypi_0 pypi
[conda] nvidia-cutlass-dsl-libs-cu13 4.5.2 pypi_0 pypi
[conda] nvidia-ml-py 13.590.48 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.28.9 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-nvshmem-cu12 3.4.5 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-nvvm 13.2.78 pypi_0 pypi
[conda] pyzmq 27.1.0 pypi_0 pypi
[conda] tokenspeed-triton 3.7.10.post20260531 pypi_0 pypi
[conda] torch 2.11.0+cu128 pypi_0 pypi
[conda] torch-c-dlpack-ext 0.1.5 pypi_0 pypi
[conda] torchaudio 2.11.0+cu128 pypi_0 pypi
[conda] torchvision 0.26.0+cu128 pypi_0 pypi
[conda] transformers 5.12.1 pypi_0 pypi
[conda] triton 3.6.0 pypi_0 pypi

==============================
vLLM Info

ROCM Version : Could not collect
vLLM Version : 0.24.0
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled
GPU Topology:
GPU0 GPU1 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X SYS PIX 0-23,48-71 0 N/A
GPU1 SYS X SYS 24-47,72-95 1 N/A
NIC0 PIX SYS X

Legend:

X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks

NIC Legend:

NIC0: mlx5_bond_0

==============================
Environment Variables

NVIDIA_VISIBLE_DEVICES=void
LD_LIBRARY_PATH=/usr/local/cuda-13.0/lib64:
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib/x86_64-linux-gnu
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root

(torch) root@gltbjaudgggwsmky-make-84bbd6bc9d-5w755:/data/coding#
(torch) root@gltbjaudgggwsmky-make-84bbd6bc9d-5w755:/data/coding#
(torch) root@gltbjaudgggwsmky-make-84bbd6bc9d-5w755:/data/coding#

🐛 Describe the bug

Environment

• OS: Ubuntu (cloud/K8s GPU instance)
• GPU: 2 x NVIDIA A100-PCIE-40GB
• CUDA: 13.0 (toolkit manually installed, driver >= 580)
• Python: 3.12
• vLLM version: (latest from main / please fill in vllm --version)
• PyTorch version: 2.11.0+cu130
• Model: diffusiongemma-26B-A4B

Reproduction command

vllm serve diffusiongemma-26B-A4B \
  --host 0.0.0.0 \
  --port 30041 \
  --max-model-len 262144 \
  --max-num-seqs 10 \
  --gpu-memory-utilization 0.9 \
  --mm-processor-kwargs '{"max_soft_tokens":1120}' \
  --diffusion-config '{"canvas_length":256,"max_denoising_steps":16}' \
  --hf_overrides '{"diffusion_sampler":"entropy_bound","diffusion_entropy_bound":0.1,"diffusion_confidence_threshold":0.0}' \
  --limit-mm-per-prompt '{"image":7}' \
  --no-enable-prefix-caching \
  --enable-auto-tool-choice \
  --tool-call-parser gemma4 \
  --reasoning-parser gemma4 \
  --served-model-name gemma4 \
  --tensor-parallel-size 2

Error

Model weights load successfully, but EngineCore crashes during warmup / torch.compile of _compiled_sample_step:

RuntimeError: a and b must have same reduction dim, but got [s23*((s88//s23)), s3] X [131072, 2816].

Full worker trace points to:

File ".../vllm/model_executor/models/diffusion_gemma.py", line 630, in _compiled_sample_step
    soft_embeds = torch.matmul(probs.to(embed_weight.dtype), embed_weight) * normalizer

Fake-tensor shape mismatch occurs when Dynamo attempts to compile this step with symbolic dimensions.

Logs (key excerpt)

(Worker_TP1 pid=29147) ERROR [multiproc_executor.py:1000] WorkerProc hit an exception.
...
torch._dynamo.exc.TorchRuntimeError: RuntimeError when making fake tensor call
  Explanation: Dynamo failed to run FX node with fake tensors:
    call_function <built-in method matmul of type object ...>
    (*(FakeTensor(..., size=(s23, (s88//s23), s3), dtype=torch.bfloat16),
       Parameter(FakeTensor(..., size=(131072, 2816), dtype=torch.bfloat16))),
     **{}):
    got RuntimeError('a and b must have same reduction dim, but got [s23*((s88//s23)), s3] X [131072, 2816].')
  Hint: Your code may result in an error when running in eager.
        Please double check that your code doesn't contain a similar error when actually running eager/uncompiled.
        You can do this by removing the `torch.compile` call, or by using `torch.compiler.set_stance("force_eager")`.

Expected behavior

vLLM server should finish warmup and expose the OpenAI-compatible API on port 30041.

What I've already tried

1. Installed CUDA 13.0 toolkit and set LD_LIBRARY_PATH=/usr/local/cuda-13.0/lib64
2. Confirmed model weights load successfully (weights loading takes ~13s)
3. The crash happens after model loading, during kernel warmup / torch.compile in _compiled_sample_step

Possible cause

The @torch.compile-decorated _compiled_sample_step in diffusion_gemma.py may not correctly handle dynamic shapes (symbolic s23, s88, s3) on the torch.matmul between probs and embed_weight. The reduction dimension in probs appears reshaped as [s23*((s88//s23)), s3] while embed_weight expects [131072, 2816], leading to a mismatch during fake-tensor tracing.

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[secrett2633]

Thanks for the report. The traceback seems to point to the old DiffusionGemma self-conditioning matmul path:

soft_embeds = torch.matmul(probs.to(embed_weight.dtype), embed_weight) * normalizer

This path was changed in #46177. Current main slices probs to the local vocab shard before the matmul.

I tested the current main on an A100 with a large DiffusionGemma-like shape, and the _compiled_sample_step compile path passed. When I tested the previous matmul implementation under the same A100 setup, it reproduced the same class of Dynamo fake-tensor mismatch.

Could you try reproducing this with the latest main or a version that includes #46177?

[knoka0812]

Thanks for the report. The traceback seems to point to the old DiffusionGemma self-conditioning matmul path:

soft_embeds = torch.matmul(probs.to(embed_weight.dtype), embed_weight) * normalizer
This path was changed in #46177. Current slices to the local vocab shard before the matmul.main``probs

I tested the current on an A100 with a large DiffusionGemma-like shape, 和 the compile path passed. When I tested the previous matmul implementation under the same A100 setup, it reproduced the same class of Dynamo fake-tensor mismatch.main``_compiled_sample_step

Could you try reproducing this with the latest or a version that includes #46177?main

Thanks for confirming! I upgraded to the vLLM main branch that includes #46177 (commit g3406e8f83) and verified the issue is resolved.

My setup:

2 × NVIDIA A100-PCIE-40GB
Tensor Parallelism = 2
dtype = bfloat16
After the upgrade, the original RuntimeError: a and b must have same reduction dim disappeared and the model completes warmup and starts serving normally.

[InfoSage05]

Hi @knoka0812, thank you for the detailed report.

The crash you are seeing was caused by the self-conditioning matmul slicing embed_weight inside the torch.compile-decorated _compiled_sample_step. When Dynamo traces that path with symbolic dimensions, the reduction dim of probs and the sliced embed_weight can be seen as mismatched, producing the RuntimeError you observed.

I have opened #47278 which moves the embedding shard slicing out of the compiled region:

• DiffusionSampler now pre-slices embed_weight once at construction time.
• The compiled step receives local_embed_weight of the exact shard shape and performs torch.matmul(local_probs, local_embed_weight) without any slicing inside the graph.
• An init-time guard raises a clear error if the weight is smaller than the expected shard size.

This should prevent the fake-tensor shape mismatch during warmup. If you are able to test the PR branch with your diffusiongemma-26B-A4B TP=2 setup, please let us know whether the server now finishes warmup successfully.

[knoka0812]

Hi @knoka0812, thank you for the detailed report.

The crash you are seeing was caused by the self-conditioning matmul slicing embed_weight inside the torch.compile-decorated _compiled_sample_step. When Dynamo traces that path with symbolic dimensions, the reduction dim of probs and the sliced embed_weight can be seen as mismatched, producing the RuntimeError you observed.

I have opened #47278 which moves the embedding shard slicing out of the compiled region:

• DiffusionSampler now pre-slices embed_weight once at construction time.
• The compiled step receives local_embed_weight of the exact shard shape and performs torch.matmul(local_probs, local_embed_weight) without any slicing inside the graph.
• An init-time guard raises a clear error if the weight is smaller than the expected shard size.

This should prevent the fake-tensor shape mismatch during warmup. If you are able to test the PR branch with your diffusiongemma-26B-A4B TP=2 setup, please let us know whether the server now finishes warmup successfully.

Tested PR #47278 on my setup:

Model: diffusiongemma-26B-A4B
Config: TP=2, max-model-len=65536, gpu-memory-utilization=0.75
Version: vllm-0.23.1rc1.dev623+g33f916d45
Result: warmup completed successfully. The previous shape mismatch error did not reappear.

Confirmed that moving the embedding shard slicing out of the compiled region prevents the Dynamo fake-tensor shape mismatch at its root. Thanks for the fix!

[knoka0812]

你好，感谢你的详细报告。

你看到的崩溃是由火炬内部的自调节 matmul 切割embed_weight引起的。编译装饰_compiled_sample_step。当Dynamo用符号维度追踪该路径时，概率的缩减和切片embed_weight会显得不匹配，产生你观察到的运行时错误。

我打开了 #47278，它将嵌入分片切片从编译区域移出：

• DiffusionSampler 现在在构建时预先切片embed_weight一次。
• 编译后的步骤会获得精确的分片形状local_embed_weight，并在图中不进行切片的情况下执行torch.matmul（local_probs， local_embed_weight）。
• 如果权重低于预期的分片大小，初始时间守卫会显示明显错误。

这应该能防止热身时出现假张量形状不匹配。如果你能用你的diffusiongemma-26B-A4B TP=2设置测试PR分支，请告诉我们服务器是否成功完成预热。

DiffusionGemma-26B-A4B + vLLM TP=2 测试报告

测试环境

项目	配置
GPU	2 × NVIDIA A100-PCIE-40GB
vLLM 版本	PR #47278 (vllm-0.23.1rc1.dev623+g33f916d45) / main (g3406e8f83)
Python	3.12 + conda torch 环境
Tensor Parallelism	2
dtype	bfloat16

---

1. Bug 修复验证

PR #46177（已验证）

vLLM 0.24.0 下 TP=2 启动 DiffusionGemma 报错：

RuntimeError: a and b must have same reduction dim,
but got [s23*((s88//s23)), s3] X [131072, 2816]

升级到包含 #46177 的 main 分支后，warmup 通过，不再报错。

PR #47278（已验证）

PR #47278 进一步将 embedding shard 切片移出 torch.compile 区域，从源头避免 Dynamo fake-tensor 形状不匹配。

测试结果：warmup 完全正常，服务成功启动，API 调用正常。

---

2. 性能测试结果

单请求速度

任务类型	tokens	TTFT	吞吐	说明
HTML 代码生成	2048	0.55 s	~414 tok/s	简单重复结构，速度最快
HTML 代码生成 (32k max)	2534	0.54 s	~434 tok/s	模型自然 stop
Python 算法 (LRU Cache)	1539	0.66 s	~376 tok/s	复杂逻辑，速度适中
英文创意写作	380	—	~187 tok/s	创意任务，速度较低
中文创意写作	395	—	~201 tok/s	生成质量差

并发稳定性

• 3 轮连续测试吞吐波动在 ±15% 范围内
• TTFT 不稳定（0.5–1.9s），受 torch.compile cache 命中和 CUDA Graph 状态影响

---

3. 生成质量问题

英文场景

英文创意写作基本正常，输出连贯可读，偶有轻微重复（the the、of of），不影响阅读。

中文场景

中文创意写作严重退化，表现为：

• 大量重复字词：年轻人年轻人年轻人年轻人年轻人
• 标点符号混乱：，，，，，，，，，，，，，，，
• 语义断裂：缝缝缝。"。"。"。"
• 完全不可读

此问题在 PR #47278 和 main 分支上均存在，是模型本身的中文能力不足问题吗？