Open Alihamdy2496 opened 4 weeks ago
🐛 Describe the bug
I have a chebyshev spectral graph convolutional operator
the custom layer code is the following
import torch from torch_geometric.nn import MessagePassing, ChebConv, knn_graph class SphericalChebConv(MessagePassing): def __init__(self, in_channels: int, out_channels: int, K: int, aggr: str): super().__init__() self.conv1 = ChebConv(in_channels, out_channels, K=K, aggr=aggr) def forward(self, x: torch.Tensor, position: torch.Tensor, knn: int): edges = knn_graph(position.squeeze(0), k=knn, flow= 'target_to_source') x = self.conv1(x.transpose(2,1), edges) return x.transpose(2,1) chebconv_ = torch.jit.script(SphericalChebConv(in_channels=256, out_channels=256*2, K=2, aggr='add')) print(chebconv_) torch.jit.save(chebconv_, "SphericalChebConv.pt")
the results I get when I save the model
{ "name": "RuntimeError", "message": "strides() called on an undefined Tensor", "stack": "--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Cell In[1], line 20 18 chebconv_ = torch.jit.script(SphericalChebConv(in_channels=256, out_channels=256*2, K=2, aggr='add')) 19 print(chebconv_) ---> 20 torch.jit.save(chebconv_, \"torchScript_sphereGlue.pt\") File /usr/local/lib/python3.10/dist-packages/torch/jit/_serialization.py:80, in save(m, f, _extra_files) 78 _extra_files = {} 79 if isinstance(f, (str, pathlib.Path)): ---> 80 m.save(f, _extra_files=_extra_files) 81 else: 82 ret = m.save_to_buffer(_extra_files=_extra_files) File /usr/local/lib/python3.10/dist-packages/torch/jit/_script.py:740, in RecursiveScriptModule.save(self, f, **kwargs) 732 def save(self, f, **kwargs): 733 r\"\"\" 734 save(f, _extra_files={}) 735 (...) 738 DO NOT confuse these two functions when it comes to the 'f' parameter functionality. 739 \"\"\" --> 740 return self._c.save(str(f), **kwargs) RuntimeError: strides() called on an undefined Tensor" }
however, the model is exported and works just fine
RecursiveScriptModule( original_name=SphericalChebConv (conv1): RecursiveScriptModule( original_name=ChebConv (aggr_module): RecursiveScriptModule(original_name=SumAggregation) (lins): RecursiveScriptModule( original_name=ModuleList (0): RecursiveScriptModule(original_name=Linear) (1): RecursiveScriptModule(original_name=Linear) ) ) )
% Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 24366 100 24366 0 0 16797 0 0:00:01 0:00:01 --:--:-- 16804 Collecting environment information... PyTorch version: 2.1.2+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.22.1 Libc version: glibc-2.35 Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.8.0-47-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.2.140 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3050 Ti Laptop GPU Nvidia driver version: 535.183.01 cuDNN version: Could not collect 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): 16 On-line CPU(s) list: 0-15 Vendor ID: GenuineIntel Model name: 12th Gen Intel(R) Core(TM) i7-12650H CPU family: 6 Model: 154 Thread(s) per core: 2 Core(s) per socket: 10 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 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 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 rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 416 KiB (10 instances) L1i cache: 448 KiB (10 instances) L2 cache: 9.5 MiB (7 instances) L3 cache: 24 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 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] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.1.3.1 [pip3] nvidia-cuda-cupti-cu12==12.1.105 [pip3] nvidia-cuda-nvrtc-cu12==12.1.105 [pip3] nvidia-cuda-runtime-cu12==12.1.105 [pip3] nvidia-cudnn-cu12==8.9.2.26 [pip3] nvidia-cufft-cu12==11.0.2.54 [pip3] nvidia-curand-cu12==10.3.2.106 [pip3] nvidia-cusolver-cu12==11.4.5.107 [pip3] nvidia-cusparse-cu12==12.1.0.106 [pip3] nvidia-nccl-cu12==2.18.1 [pip3] nvidia-nvjitlink-cu12==12.6.77 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] torch==2.1.2 [pip3] torch-cluster==1.6.3+pt21cu121 [pip3] torch-geometric==2.6.1 [pip3] torch-scatter==2.1.2+pt21cu121 [pip3] torch-sparse==0.6.18+pt21cu121 [pip3] torchvision==0.16.2 [pip3] triton==2.1.0 [pip3] tritonclient==2.46.0 [conda] Could not collect
🐛 Describe the bug
I have a chebyshev spectral graph convolutional operator
the custom layer code is the following
the results I get when I save the model
however, the model is exported and works just fine
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