Hi @naisy, I previously updated the PyTorch 1.9 patch to include the NEON patch (after finding it was causing runtime computation errors)
That is the path from which I built it on my Jetson. This is the corresponding location in the PyTorch 1.9 source code:
This is the Context.cpp file, and I have build source and set variables.
And why I still have the problems?
terminate called after throwing an instance of 'c10::Error' what(): quantized engine QNNPACK is not supported Exception raised from setQEngine at /media/nvidia/NVME/pytorch/pytorch-v1.9.0/aten/src/ATen/Context.cpp:181 (most recent call first): frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0xa0 (0x7f445c7300 in /home/yuantian/.local/lib/python3.6/site-packages/torch/lib/libc10.so) frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xb4 (0x7f445c36b4 in /home/yuantian/.local/lib/python3.6/site-packages/torch/lib/libc10.so) frame #2: at::Context::setQEngine(c10::QEngine) + 0x138 (0x7f5aeb0940 in /home/yuantian/.local/lib/python3.6/site-packages/torch/lib/libtorch_cpu.so) frame #3: THPModule_setQEngine(_object*, _object*) + 0x94 (0x7f5fa12364 in /home/yuantian/.local/lib/python3.6/site-packages/torch/lib/libtorch_python.so) <omitting python frames> frame #5: python3() [0x52ba70] frame #7: python3() [0x529978] frame #9: python3() [0x5f4d34] frame #11: python3() [0x5a7228] frame #12: python3() [0x582308] frame #16: python3() [0x529978] frame #17: python3() [0x52b8f4] frame #19: python3() [0x52b108] frame #24: __libc_start_main + 0xe0 (0x7f78952720 in /lib/aarch64-linux-gnu/libc.so.6) frame #25: python3() [0x420e94] Aborted (core dumped)
Thanks for the reply. I tried out your suggestion and used the latest updated patch from @dusty_nv (https://github.com/pytorch/pytorch/blob/d69c22dd61a2f006dcfe1e3ea8468a3ecaf931aa/aten/src/ATen/Context.cpp#L181) to build PyTorch. It did successfully resolved my ISSUE 1. Now, it takes about 10 sec to parse the tensor from CPU to GPU.
However, the ISSUE 2 which is the error of the torch.solve function still appears. Should I just ignore the error or does the error means anything in this case?
Thank you and I do appreciate your help very much!
Hi @ziimiin, I haven’t built MAGMA before, but are you able to test run MAGMA independently of PyTorch?
Maybe there is some setting you need to compile MAGMA with to enable the correct GPU architectures for Jetson? (sm53, sm62, sm72)
EDIT: It appears they need to be added in the MAGMA makefile: