test(e2e): strengthen perf CLI coverage#1149
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July 21, 2026 13:50
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Jul 21, 2026
The perf ONNX suite shared one generated float MatMul across CPU/GPU/NPU tests, which is not representative of NPU (QNN/VitisAI) execution. Add an npu_model_arg fixture that defaults to model_arg, override it in TestPerfONNXDirect to point at tests/assets/model.onnx, and route the NPU tests (memory, monitor, ep-device, op-tracing) through it. CPU/GPU tests keep the lightweight generated model. Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
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Pull request overview
This PR strengthens end-to-end coverage of the winml perf CLI by expanding argument-level tests and tightening expectations for monitored GPU/NPU runs, while introducing a real ONNX asset to better represent NPU execution.
Changes:
- Require monitored GPU/NPU runs to report positive utilization in
hw_monitor. - Add CPU E2E tests for
--batch-sizeand--input-data. - Add QNN NPU E2E coverage for
--op-tracing basicand validation of the generated op-trace report.
Reviewed changes
Copilot reviewed 2 out of 3 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
tests/e2e/test_perf_e2e.py |
Adds new perf CLI E2E test coverage (batch size, input data, op tracing), introduces an NPU-specific model fixture, and strengthens monitoring assertions. |
tests/assets/model.onnx.data |
Adds external data sidecar needed by the repo-shipped ONNX model used in NPU E2E tests. |
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Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
Restore the model_arg parameter on TestPerfONNXDirect.npu_model_arg so it matches the _PerfBenchmarkSuite base fixture signature, resolving the CodeQL signature-mismatch alert.
…-cli into hualxie/perf_e2e
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Summary
--batch-sizeand--input-data--op-tracing basicand its trace reportConvert hf-tiny-model-private/tiny-random-ResNetForImageClassification via FTK's amd npu resnet recipe