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Summary

  • Update CLI tool to support input list assignment
    (e.g., input1:=input1.raw input2:=input2.raw)
  • Update CLI tool output structure
    origin: outputs/output_0_0.pt outputs/outupt_1_0.pt
    new: outputs/Result_0/output_0.pt output/Result_1/output_0.pt

Motivation

This PR facilitates QC CI/QA teams to validate the QNN output with goldens.

Test plan

python ${EXECUTORCH_ROOT}/backends/qualcomm/tests/test_qnn_delegate.py -k TestUtilsScript.test_cli_with_input_list_assignment --model <soc_model> --build_folder <build_folder> --host <host> --device <device>

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pytorch-bot bot commented Dec 1, 2025

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/16012

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❌ 1 New Failure, 6 Pending

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@meta-cla meta-cla bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Dec 1, 2025
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Thanks! Can you rebase to trigger CI?

from torchao.quantization import pt2e
from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e

INPUT_ORDER = "input_order"
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On a side note, it seems this cli tool is for QA and not necessarily for community users, can we document it?

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Done, thanks.

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github-actions bot commented Dec 2, 2025

This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

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@cccclai cccclai merged commit 3b1aeda into pytorch:main Dec 2, 2025
139 of 141 checks passed
AdrianLundell pushed a commit to AdrianLundell/executorch that referenced this pull request Dec 2, 2025
### Summary
- Update CLI tool to support input list assignment
  (e.g., input1:=input1.raw input2:=input2.raw)
- Update CLI tool output structure
  origin: outputs/output_0_0.pt outputs/outupt_1_0.pt
  new: outputs/Result_0/output_0.pt output/Result_1/output_0.pt

### Motivation
This PR facilitates QC CI/QA teams to validate the QNN output with
goldens.

### Test plan
```
python ${EXECUTORCH_ROOT}/backends/qualcomm/tests/test_qnn_delegate.py -k TestUtilsScript.test_cli_with_input_list_assignment --model <soc_model> --build_folder <build_folder> --host <host> --device <device>
```
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2 participants