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ganler avatar ganler commented on July 29, 2024 1

image I entered the tvm environment to reinstall the required configuration for nnsmith and ran the command "nnsmith. model_exec model. type=onnx"\

Backend. type=onnxruntime\

Model. path=nnsmith_ Output/model.onnx\

Cmp. with='{type: tvm, optmax: true, target: CPU}' 'conducted differential testing, but encountered an error as shown in the above figure. Can someone help me identify the cause.

Well, this is a TVM problem and nnsmith cannot help here.

More specifically, the pre-built TVM binary is compiled using a GLibC version which is newer than what your OS has right now. You can either recompile TVM locally to use your local glibc or install a newer glibc in conda.

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Zeus1116 avatar Zeus1116 commented on July 29, 2024 1

image I entered the tvm environment to reinstall the required configuration for nnsmith and ran the command "nnsmith. model_exec model. type=onnx"
Backend. type=onnxruntime
Model. path=nnsmith_ Output/model.onnx
Cmp. with='{type: tvm, optmax: true, target: CPU}' 'conducted differential testing, but encountered an error as shown in the above figure. Can someone help me identify the cause.

Well, this is a TVM problem and nnsmith cannot help here.

More specifically, the pre-built TVM binary is compiled using a GLibC version which is newer than what your OS has right now. You can either recompile TVM locally to use your local glibc or install a newer glibc in conda.

I understand. Thanks for your help.

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Zeus1116 avatar Zeus1116 commented on July 29, 2024

image
I entered the tvm environment to reinstall the required configuration for nnsmith and ran the command "nnsmith. model_exec model. type=onnx"\

Backend. type=onnxruntime\

Model. path=nnsmith_ Output/model.onnx\

Cmp. with='{type: tvm, optmax: true, target: CPU}' 'conducted differential testing, but encountered an error as shown in the above figure. Can someone help me identify the cause.

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ganler avatar ganler commented on July 29, 2024

Thanks for reporting the issue. Looks like you are right, onnx updated their API in 1.15.0 to load_from_string. Please bear a bit by downgrading the onnx versions for now before I brought up a fix.

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