🐛 Describe the bug
I get this error when compiling my model for the QNN back-end, my conversion script is derived from the deeplab_v3 script. Is there a solution or work around ?
I have tried with several chip set models and quantitation, the issue persists
Full Error:
/usr/lib/python3.10/copyreg.py:101: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return cls.__new__(cls, *args)
[INFO] [Qnn ExecuTorch]: Destroy Qnn device
[INFO] [Qnn ExecuTorch]: Destroy Qnn backend
Traceback (most recent call last):
File "/home/vybhav/Videos/exec_venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py", line 276, in __call__
res = fn(module)
File "/home/vybhav/Videos/exec_venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_base.py", line 46, in __call__
res = self.call(graph_module)
File "/home/vybhav/Videos/executorch/backends/qualcomm/_passes/decompose_col_im.py", line 118, in call
self._decompose_im2col(graph_module)
File "/home/vybhav/Videos/executorch/backends/qualcomm/_passes/decompose_col_im.py", line 35, in _decompose_im2col
stride == kernel_size
AssertionError: im2col can only be converted when stride == kernel_size
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/vybhav/Videos/executorch/LiteAnyStereo/las_qnn_export.py", line 139, in <module>
build_executorch_binary(
File "/home/vybhav/Videos/executorch/examples/qualcomm/utils.py", line 553, in build_executorch_binary
edge_prog_mgr = to_edge_transform_and_lower_to_qnn(
File "/home/vybhav/Videos/executorch/backends/qualcomm/utils/utils.py", line 449, in to_edge_transform_and_lower_to_qnn
return to_edge_transform_and_lower(
File "/home/vybhav/Videos/executorch/exir/program/_program.py", line 116, in wrapper
return func(*args, **kwargs)
File "/home/vybhav/Videos/executorch/exir/program/_program.py", line 1389, in to_edge_transform_and_lower
edge_manager = edge_manager.transform(transform_passes)
File "/home/vybhav/Videos/executorch/exir/program/_program.py", line 116, in wrapper
return func(*args, **kwargs)
File "/home/vybhav/Videos/executorch/exir/program/_program.py", line 1651, in transform
new_programs[name] = _transform(program, *passes_dict[name])
File "/home/vybhav/Videos/executorch/exir/program/_program.py", line 244, in _transform
return _transform_with_pass_manager(
File "/home/vybhav/Videos/executorch/exir/program/_program.py", line 266, in _transform_with_pass_manager
res = pass_manager(self.graph_module)
File "/home/vybhav/Videos/exec_venv/lib/python3.10/site-packages/torch/fx/passes/infra/pass_manager.py", line 302, in __call__
raise Exception(msg) from e # noqa: TRY002
Exception: An error occurred when running the 'DecomposeColIm' pass after the following passes: ['DecomposeMinMaxDim', 'Remove0DTensor', 'RemoveRedundancy', 'AnnotateAdaptiveAvgPool1D', 'AnnotateStack', 'AnnotateUnbind', 'DecomposeAny', 'DecomposeMaxPool3d', 'I64toI32', 'RecomposePixelUnshuffle', 'RecomposeRmsNorm', 'AnnotateQuantAttrs', 'FoldQDQ']
Cache Metrics: NoneVersions
PyTorch version: 2.12.0+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.31.10
Libc version: glibc-2.35
Python version: 3.10.12 (main, Mar 3 2026, 11:56:32) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.8.0-111-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.6.85
CUDA_MODULE_LOADING set to:
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3050 Laptop GPU
Nvidia driver version: 550.163.01
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A
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): 12
On-line CPU(s) list: 0-11
Vendor ID: GenuineIntel
Model name: 11th Gen Intel(R) Core(TM) i5-11400H @ 2.70GHz
CPU family: 6
Model: 141
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
Stepping: 1
CPU max MHz: 4500.0000
CPU min MHz: 800.0000
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 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 epb cat_l2 cdp_l2 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid movdiri movdir64b fsrm avx512_vp2intersect md_clear ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 288 KiB (6 instances)
L1i cache: 192 KiB (6 instances)
L2 cache: 7.5 MiB (6 instances)
L3 cache: 12 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-11
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks
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: 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; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
Versions of relevant libraries:
[pip3] executorch==1.2.0a0+0b0e2c5
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.85
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.29.3
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pytorch_tokenizers==1.2.0
[pip3] torch==2.12.0+cu126
[pip3] torchao==0.17.0+git02105d46c
[pip3] torchaudio==2.11.0+cu126
[pip3] torchdata==0.11.0
[pip3] torchsr==1.0.4
[pip3] torchtune==0.0.0
[pip3] torchvision==0.27.0+cu126
[pip3] triton==3.7.0
[conda] Could not collect
🐛 Describe the bug
I get this error when compiling my model for the QNN back-end, my conversion script is derived from the deeplab_v3 script. Is there a solution or work around ?
I have tried with several chip set models and quantitation, the issue persists
Full Error:
Versions