Errors during forward pass with Vulkan on Android

[Fredrik00]

🐛 Describe the bug

I have exported a PARSeq model (https://github.com/baudm/parseq) using executorch and lowered it using to_edge_transform_and_lower in 3 different variations.

1. partitioner=[XnnpackPartitioner()]
2. partitioner=[VulkanPartitioner()]
3. partitioner=[VulkanPartitioner(), XnnpackPartitioner()]

When executing these models on an Android device (Pixel 8 Pro) using executorch-android-vulkan 1.0.0, only the first model version successfully performs a forward pass.

For version 2 (Vulkan only) I get the following error:

com.facebook.jni.CppException: Exception raised from get_shader_info at /pytorch/executorch/backends/vulkan/runtime/api/ShaderRegistry.cpp:54: (it != listings_.end()) is false! Could not find ShaderInfo with name concat_1_texture3d_int32
	at org.pytorch.executorch.Module.executeNative(Native Method)
	at org.pytorch.executorch.Module.execute(Module.java:145)
	at org.pytorch.executorch.Module.forward(Module.java:128)

For version 3 (Vulkan + XNNPack) I get a different error:

com.facebook.jni.CppException: Exception raised from get_uniform_data at /pytorch/executorch/../executorch/backends/vulkan/runtime/api/containers/Tensor.h:679: (sizes_.size() <= 4) is false! 
	at org.pytorch.executorch.Module.executeNative(Native Method)
	at org.pytorch.executorch.Module.execute(Module.java:145)
	at org.pytorch.executorch.Module.forward(Module.java:128)

Is this indicative of a bug with the Vulkan lowering, or is it likely to be model incompatibility?
I would have expected any operations incompatible with Vulkan to fallback to XNNPack, considering the XNNPack model is running fine.
I only see info logs for operators being skipped during export, but otherwise no warnings/errors.

Versions

PyTorch version: 2.9.0+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.3 LTS (x86_64)
GCC version: (Ubuntu 12.4.0-2ubuntu1~24.04) 12.4.0
Clang version: 18.1.3 (1ubuntu1)
CMake version: version 4.1.0
Libc version: glibc-2.39

Python version: 3.12.11 | packaged by conda-forge | (main, Jun 4 2025, 14:45:31) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-85-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 13.0.88
CUDA_MODULE_LOADING set to:
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 4090

Nvidia driver version: 580.95.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.14.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.14.0
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
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): 24
On-line CPU(s) list: 0-23
Vendor ID: GenuineIntel
Model name: 12th Gen Intel(R) Core(TM) i9-12900K
CPU family: 6
Model: 151
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
Stepping: 2
CPU(s) scaling MHz: 76%
CPU max MHz: 5200,0000
CPU min MHz: 800,0000
BogoMIPS: 6374,40
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 tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 640 KiB (16 instances)
L1i cache: 768 KiB (16 instances)
L2 cache: 14 MiB (10 instances)
L3 cache: 30 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-23
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
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 / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] executorch==1.0.0
[pip3] numpy==2.1.2
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.3.83
[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-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] onnx==1.19.0
[pip3] onnx_graphsurgeon==0.5.8
[pip3] onnx-ir==0.1.9
[pip3] onnxruntime-gpu==1.21.0
[pip3] onnxscript==0.5.2
[pip3] pytorch-lightning==2.5.5
[pip3] torch==2.9.0
[pip3] torchao==0.14.0
[pip3] torchmetrics==1.8.2
[pip3] torchvision==0.24.0
[pip3] triton==3.5.0
[pip3] vit-pytorch==1.12.4
[conda] executorch 1.0.0 pypi_0 pypi
[conda] numpy 2.1.2 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.3.83 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-nccl-cu12 2.27.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi
[conda] pytorch-lightning 2.5.5 pypi_0 pypi
[conda] torch 2.9.0 pypi_0 pypi
[conda] torchao 0.14.0 pypi_0 pypi
[conda] torchmetrics 1.8.2 pypi_0 pypi
[conda] torchvision 0.24.0 pypi_0 pypi
[conda] triton 3.5.0 pypi_0 pypi
[conda] vit-pytorch 1.12.4 pypi_0 pypi

cc @SS-JIA @manuelcandales @digantdesai @cbilgin

[SS-JIA]

Hello @Fredrik00, apologies for the delayed response. Would you be able to provide a script that can be used to export the model? If so, I can reproduce and investigate the issue.

Your results are indicative of a gap in the Vulkan lowering proces. It is likely that the partitioner logic needs to be updated to prevent unsupported op cases from being lowered to Vulkan.