pplnn run mobilenet v2 model failed. (use cuda) about ppl.nn HOT 7 CLOSED

Cause we donot have 2080Ti right now, we cannot reproduce your error locally. There are two ways to skip such error I used.

You can firstly test if it still get error by using default kernel.

./pplnn-build/tools/pplnn --use-cuda --onnx-model=mobilenet_v2.onnx --kernel-type=float16 --quick-select

Or, you can rebuild ppl.nn without JIT, then test again:

cmake .. -DCMAKE_BUILD_TYPE=Release -DPPLNN_USE_CUDA=ON -DCMAKE_INSTALL_PREFIX=install -DPPLNN_ENABLE_CUDA_JIT=OFF

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--quick-select

Using --quick-select can fix the problem. But the profiling performance is poor. Running mobilenet v2 with ppl.nn using default kernel costs 0.78ms, while running with tensorrt costs 0.39ms.

Can I use kernel tuning on mobilenet v2?

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If you consider about the perfermance, you can rebuild ppl.nn without JIT. I think this way can also fix the problem you met.

cmake .. -DCMAKE_BUILD_TYPE=Release -DPPLNN_USE_CUDA=ON -DCMAKE_INSTALL_PREFIX=install -DPPLNN_ENABLE_CUDA_JIT=OFF

from ppl.nn.

If you consider about the perfermance, you can rebuild ppl.nn without JIT. I think this way can also fix the problem you met.

cmake .. -DCMAKE_BUILD_TYPE=Release -DPPLNN_USE_CUDA=ON -DCMAKE_INSTALL_PREFIX=install -DPPLNN_ENABLE_CUDA_JIT=OFF

Thanks. The performance of ppl without JIT is great.
Do ppl only support fp16 on 2080Ti? I tried to set the kernel type as fp32 but failed.

./build-no-jit/tools/pplnn  --use-cuda --onnx-model=resnet18.onnx --kernel-type=float32

[ERROR][2022-12-07 14:42:06.426][sequential_scheduler.cc:129] exec kernel[/maxpool/MaxPool] of type[:MaxPool:11] failed: unsupported
[ERROR][2022-12-07 14:42:06.426][runtime_impl.cc:337] Run() failed: unsupported
[ERROR][2022-12-07 14:42:06.426][pplnn.cc:1315] Run() failed: unsupported

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We only support fp16/int8 kernels for conv right now. So, the runtime crashed before conv.

from ppl.nn.

We only support fp16/int8 kernels for conv right now. So, the runtime crashed before conv.

OK. Thanks for your reply.

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Do you mind sent your broken model to [email protected]

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