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Improve common error messages #8273

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GregoryComer opened this issue Feb 6, 2025 · 1 comment
Open

Improve common error messages #8273

GregoryComer opened this issue Feb 6, 2025 · 1 comment
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module: user experience Issues related to reducing friction for users triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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@GregoryComer
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GregoryComer commented Feb 6, 2025

🚀 The feature, motivation and pitch

This is a tracking task for improving clarity on common error messages seen by users, based on early adopter experiences and filed issues.

Feel free to add any additional error messages that we can improve in the comments.

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cc @mergennachin @byjlw

@GregoryComer GregoryComer added the module: user experience Issues related to reducing friction for users label Feb 6, 2025
@GregoryComer GregoryComer self-assigned this Feb 6, 2025
@github-project-automation github-project-automation bot moved this to To triage in ExecuTorch DevX Feb 6, 2025
@GregoryComer GregoryComer moved this from To triage to In progress in ExecuTorch DevX Feb 6, 2025
@mergennachin mergennachin added this to the 0.6.0 milestone Feb 6, 2025
@digantdesai digantdesai added the triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module label Feb 6, 2025
@mergennachin mergennachin moved this from In progress to Ready in ExecuTorch DevX Feb 6, 2025
@swolchok
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swolchok commented Feb 7, 2025

The ET_LOG_AND_RETURN errors in kernels/portable/cpu/util could be systematically improved:

  • the ones containing tensors_have_same_size_at_dims should report the shape using the newly added tensor_shape_to_c_string
  • all of them should be reporting the relevant values that caused the check to fail; often the text of the check is not maximally helpful for debugging because it contains references to variables, which of course are not printed

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module: user experience Issues related to reducing friction for users triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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