Execution of method forward failed with status 0x14 #4462
Description
🐛 Describe the bug
Description:
I encountered an issue while using the ExecuTorch runtime library, which was built from source with the following parameters:
- ANDROID_NDK: 26.2.11394342
- ANDROID_ABI: arm64-v8a
I am trying to export a simple model with the following Python code:
from torch.export import export, ExportedProgram
from executorch.backends.vulkan.partitioner.vulkan_partitioner import VulkanPartitioner
from executorch.exir import EdgeProgramManager, ExecutorchProgramManager, to_edge
from executorch.exir.backend.backend_api import to_backend
import executorch.backends.vulkan.serialization.vulkan_graph_schema as vk_graph_schema
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
def forward(self, x):
return torch.rand(1, 96, 60, 60)
model = SimpleModel().eval()
sample_inputs = (torch.rand(1, 3, 960, 960),)
exported_program: ExportedProgram = export(model, sample_inputs)
edge: EdgeProgramManager = to_edge(exported_program)
# Lower the model to Vulkan backend
edge = edge.to_backend(VulkanPartitioner())
exec_prog = edge.to_executorch()
with open("simple_model.pte", "wb") as file:
exec_prog.write_to_file(file)
And the following Java code for loading and running the model on Android:
try {
mModule =
Module.load(MainActivity.assetFilePath(getApplicationContext(), "simple_model.pte"));
} catch (IOException e) {
Log.e("ImageSegmentation", "Error reading assets", e);
finish();
}
final Tensor inputTensor = Tensor.fromBlob(new float[1*3*960*960], new long[] {1, 3, 960, 960});
mModule.forward(EValue.from(inputTensor));
However, I am encountering the following error:
FATAL EXCEPTION: Thread-2
Process: com.example.executorchdemo, PID: 19256
java.lang.Exception: Execution of method forward failed with status 0x14
at org.pytorch.executorch.NativePeer.forward(Native Method)
at org.pytorch.executorch.Module.forward(Module.java:56)
at com.example.executorchdemo.MainActivity.run(MainActivity.java:251)
Interestingly, if the SimpleModel returns torch.ones(1, 96, 60, 60) instead of torch.rand(1, 96, 60, 60), the error does not occur.
Any guidance or suggestions to resolve this issue would be greatly appreciated.
Versions
Collecting environment information...
PyTorch version: 2.4.0+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.30.1
Libc version: glibc-2.35
Python version: 3.10.0 | packaged by conda-forge | (default, Nov 20 2021, 02:24:10) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3070 Ti Laptop GPU
Nvidia driver version: 550.90.07
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0
/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0
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: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 20
On-line CPU(s) list: 0-19
Vendor ID: GenuineIntel
Model name: 12th Gen Intel(R) Core(TM) i7-12700H
CPU family: 6
Model: 154
Thread(s) per core: 2
Core(s) per socket: 14
Socket(s): 1
Stepping: 3
CPU max MHz: 4700.0000
CPU min MHz: 400.0000
BogoMIPS: 5376.00
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 tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb 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 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 rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 544 KiB (14 instances)
L1i cache: 704 KiB (14 instances)
L2 cache: 11.5 MiB (8 instances)
L3 cache: 24 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-19
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 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==0.3.0a0+e480f7f
[pip3] numpy==2.0.1
[pip3] torch==2.4.0+cpu
[pip3] torchaudio==2.4.0+cpu
[pip3] torchsr==1.0.4
[pip3] torchvision==0.19.0+cpu
[conda] executorch 0.3.0a0+e480f7f pypi_0 pypi
[conda] numpy 2.0.1 pypi_0 pypi
[conda] torch 2.4.0+cpu pypi_0 pypi
[conda] torchaudio 2.4.0+cpu pypi_0 pypi
[conda] torchsr 1.0.4 pypi_0 pypi
[conda] torchvision 0.19.0+cpu pypi_0 pypi