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[OMNIML-6178] [2/11] Use the sysmon coverage core in CI - #2846
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The title clearly and concisely identifies the main change: using the sysmon coverage core in CI. |
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The pull request changes only three GitHub Actions workflow files. It adds COVERAGE_CORE: sysmon and comments; it does not modify Python files under modelopt or examples, nor pyproject.toml or… |
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[OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models
#2849
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2846 +/- ##
==========================================
+ Coverage 76.90% 78.98% +2.08%
==========================================
Files 658 658
Lines 74029 74029
==========================================
+ Hits 56933 58475 +1542
+ Misses 17096 15554 -1542
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Python 3.12 defaults to coverage's C tracer, which slows every line of Python that runs under it. Every lane starts coverage in each spawned worker and subprocess through COVERAGE_PROCESS_START, so the import-heavy start-ups pay for it: the 76 s Megatron worker-pool start, the 22 s hf_ptq processes of the unified-export tests, the puzzletron spawns, the vLLM engine core. COVERAGE_CORE=sysmon records the same lines through sys.monitoring at a fraction of the overhead. Measured (the first three here, the last by #2727 on its hf_ptq example lane): - serial tests/unit run on 4 CPUs: 572 s without coverage, 825 s with the C tracer, 640 s with sysmon; - `import modelopt.torch.quantization.extensions` with this repo's coverage config: 17.1 s native, 23.6 s with the C tracer, 18.3 s with sysmon, and the child process of a spawn writes its data file too, with the same 362 files and 13,821 executed lines; - one TinyLlama fp8 plus TRT-LLM deploy test: 64.8 s without coverage, 103.3 s with the C tracer, 70.5 s with sysmon, identical covered lines. Applies to the unit `linux` job (Python 3.12), gpu_tests.yml and regression_tests.yml. The example runner is left to #2727, which makes the same change there. sysmon needs Python 3.12 or newer; on an older interpreter coverage warns and keeps its default core, so a lane that turns out to run an older Python is slower, not broken. The gpu and gpu_megatron logs show 3.12. Testing: actionlint is clean on all workflows. Not yet run on a real CI runner; the first run should be compared against the previous coverage.xml. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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…#2845) ### What does this PR do? Type of change: CI (unit-test runtime) The unit `linux` job gates every GPU and example workflow (their pr-gate jobs wait for it), and its tests are CPU-bound but run serially. This runs them under pytest-xdist on the runner's four vCPUs. The `linux` job and the multi-version matrix get `PYTEST_ADDOPTS=-n 4 --dist loadfile` and `OMP_NUM_THREADS=1`: one worker per vCPU (`-n auto` counts physical cores), whole files per worker, and one thread per worker, because with torch's default threads the runner is oversubscribed and slower than serial (1,481 s and 41 failures in my local runs). What else changes so the suite passes under xdist: - `noxfile.py`: the second pytest process that runs the `manual` tests clears `PYTEST_ADDOPTS`, so it stays serial. Those tests leak global state into each other, and a worker would run them in some order. - `tests/_test_utils/torch/quantization/onnx_export.py`: `TEST_MODELS` was a set of classes, whose iteration order differs between processes, so workers collected different test ids. It is a tuple now. - `test_hf_dflash2.py::test_overfits_a_single_batch` takes about 30 s on one thread on main (53 s before #2836 halved its loop to 30 steps; that version hit the 60 s default call timeout in the nightly on main on 2026-10-20). Its tiny model is built in bf16, and a CPU without native bf16 support emulates bf16 matmuls (about 0.9 s per step, 0.1 s in float32). The model is float32 now, which takes the test to about 4 s on one thread, so it needs no timeout override; the loop and both assertions are unchanged. - `test_full_algorithms_vision.py::test_search_constraints` assigned `ConstraintsFunc._get_true_latency` and never restored it. Serial runs never noticed, because `test_evaluate_constraints.py` sorts first. Under xdist a worker can get the files in the other order, and the Python 3.10 and 3.14 legs failed two tests after it. It uses `monkeypatch` now. - `tests/conftest.py`: the unit per-test cap is 120 s on every platform. `test_dist.py::test_data_parallel` takes 8-15 s serial and 25-35 s with four workers sharing four vCPUs, and went over 60 s on the slowest runner. A hung test is still reported, after 2 min instead of 1. Why: on current main the `linux` job has a median of 12.9 min (27 runs of other PRs since #2823 merged), and a pull request's GPU run waits a median 14.5 min for it (125 runs of 33 PRs). ### Time improvement (measured in CI) Unit `linux` job: serial on current main (27 runs of other PRs since #2823 merged) against the runs of this series with `-n 4`. | | before (serial) | with this PR | |---|---|---| | pytest | 630 s median (355-709 s) | 479 s in this PR's own run; 294 s median over the 11 runs of the series (166-479 s) | | whole job | 12.9 min median (8.8-14.8) | 10.9 min in this PR's own run; 7.0 min median over the 11 runs (5.0-10.9) | | unit workflow: `linux`, then the multi-version and Windows legs | 24.1 min median (12 runs) | 17.2 min median (the 7 runs that ran every leg) | - The runs of #2846 and above also carry the sysmon core of #2846 (about 22% off the unit suite on its own, measured locally), so the 294 s median is xdist plus sysmon; this PR's own run is the xdist-only number. It was the slowest of the 11 (166-479 s for nearly the same tests: runner variance), so it understates the gain. Locally on 4 CPUs with coverage as in CI, the base took 825 s serial and 332-344 s with this configuration (3 runs). - The GPU, Example and Regression workflows hold their jobs until the unit `linux` check is done (`pr-gate / wait-checks`, at least 5 min). That wait is a median 14.5 min (IQR 12.9-15.8) on 125 GPU runs of 33 PRs since #2823; in this PR's own GPU run it was 9.7 min (its `linux` job took 10.9 min). With a 7 min `linux` job instead of 12.9 min, a GPU run requested right after a push starts about 6 min earlier. That figure is derived from the job times: the later runs of the series were requested after their unit check had finished (the wait sat at its 5.2 min floor in 7 of the 9 GPU runs), so they cannot show it. Method: every PR of the series has its own CI run, so "before" is the run of the PR(s) below that lack the change and "after" the runs that carry it. Times come from the job logs (`--durations`, or the gap between consecutive result lines). Where runner speed matters, the tests that no PR of the series touches scale each run, and the raw and the scaled medians are both shown. One test varies by 15-30% from run to run on its own, so smaller differences are not evidence. ### Usage Nothing user-facing. Locally: `OMP_NUM_THREADS=1 pytest tests/unit -n 4 --dist loadfile`. ### Testing - Full `tests/unit` on 4 CPUs with coverage as in CI, on the base before #2823: serial 825 s, this configuration 332-344 s in three repeated runs (4,724 passed, 0 failed). - This PR's own CI run: the `linux` job took 10.9 min (479 s of pytest) against the 12.9 min serial median; see the time section above for the later runs of the series. - `tests/unit` without `puzzletron` (it needs `hydra`, absent from my local environment) in reversed and in shuffled file order shows no other order dependence; `test_search_constraints` followed by `test_evaluate_constraints.py` in one process failed before the fix and passes after it. - `test_hf_dflash2.py` (33 tests) on one CPU thread: 13.5 s, of which `test_overfits_a_single_batch` takes about 4 s (30 s with the bf16 model on main; the next slowest test takes about 1.3 s). - Pre-commit (all hooks) passes. ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: N/A (CI and tests) - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: no new dependency (`pytest-xdist` is already in the `dev-test` extra on main) - Did you write any new necessary tests?: N/A (two existing tests fixed or sped up so they pass under xdist) - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A (CI and tests) - Did you get Claude approval on this PR?: N/A (optional, not run) ### Additional Information Slice 1 of 11 of the GPU-CI speed-up work, split out of the reference draft #2730 (not for merge; it holds the whole series with its measurements). Merge in order; each PR stands on its own and passes CI without the later ones. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist **(this PR)** 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI 3. #2847 [OMNIML-6179] [3/11] Run manual-marked tests on nightly and on-demand GPU runs 4. #2848 [OMNIML-6181] [4/11] Give gpu_megatron's slow cold starts more time 5. #2849 [OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models 6. #2850 [OMNIML-6179] [6/11] Use local tokenizers and calibration text in GPU tests, shrink puzzletron data 7. #2851 [OMNIML-6180] [7/11] Trim the llm_eval, llm_qat, EAGLE and megatron_bridge example tests 8. #2852 [OMNIML-6180] [8/11] Trim the DFlash regression test 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Tests** * Improved consistency and performance of automated test runs across platforms and Python configurations through controlled parallel execution. * Preserved predictable model-check ordering and isolated temporary test changes. * Increased unit-test time limits to better support process-spawning and slower runs. * Improved test reliability by explicitly controlling numeric precision and ensuring serial execution for manual CPU tests. * No end-user functionality or product behavior changed. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Approving: this is an 8-line CI env change that switches coverage to the sysmon core on lanes that run Python 3.12, and it doesn't change what coverage records.
No action needed.
No action needed:
pyproject.tomlpinscoverage[toml]~=7.14.0and setsbranch = false. So the older limitation ofsysmon(no branch coverage, which made it fall back to the C tracer) doesn't apply here.- The unit
linuxjob runsunit-3.12. On older Pythons, coverage warns and falls back to its default core, so nothing breaks. - The
multi-version,windowsandpartial-installjobs don't setCOVERAGE_CORE, so they are unaffected. - The example-runner lane is left to #2727, as the PR description says.
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What does this PR do?
Type of change: CI (test runtime)
Sets
COVERAGE_CORE=sysmonon the unitlinuxjob,gpu_tests.ymlandregression_tests.yml. Python 3.12 defaults to coverage's C tracer, which slows every line of Python run under it, and every lane starts coverage in each spawned worker and subprocess throughCOVERAGE_PROCESS_START, so import-heavy start-ups pay for it (the 76 s Megatron pool start, the 22 s hf_ptq processes of the unified-export tests, the puzzletron spawns, the vLLM engine core). sysmon records the same lines throughsys.monitoring. The example runner is left to #2727, which makes the same change there. On a Python older than 3.12 coverage warns and keeps its default core, so a lane that turned out to run an older Python would be slower, not broken (the gpu and gpu_megatron logs show 3.12).Time improvement (measured in CI)
pytest time of each lane in the run of #2845 (the base of this PR) and in this PR's run, one run each:
linux(xdist)Workflow wall time: GPU tests 59.2 -> 39.6 min, Regression 21.3 -> 15.2 min.
coverage.xml-only report change was dropped from this PR. The runs above are of its earlier head, which also had it. It showed no CI effect: the pytest summary line came 1-4 s after the last test in the [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist #2845 runs, which still wrote the html report, and 0-3 s in this PR's runs (median 1.1 s over the 82 lane runs of the series), so the table is the sysmon core's. It would only have saved developers' local runs (about 25 s per pytest session).Method: every PR of the series has its own CI run, so "before" is the run of the PR(s) below that lack the change and "after" the runs that carry it. Times come from the job logs (
--durations, or the gap between consecutive result lines). Where runner speed matters, the tests that no PR of the series touches scale each run, and the raw and the scaled medians are both shown. One test varies by 15-30% from run to run on its own, so smaller differences are not evidence.Usage
Nothing user-facing.
Testing
Measured on this tree (tests/unit on 4 CPUs serial: 572 s without coverage, 825 s with the C tracer, 640 s with sysmon;
import modelopt.torch.quantization.extensions: 17.1 s native, 23.6 s with the C tracer, 18.3 s with sysmon, with the same 362 files and 13,821 executed lines in the spawned child's data file; one TinyLlama fp8 plus TRT-LLM deploy test: 64.8 s without coverage, 103.3 s with the C tracer, 70.5 s with sysmon, identical covered lines). Pre-commit and actionlint pass. The CI runs of the series are the check on real runners (time section above).Before your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices (e.g. avoiding hardcoded
trust_remote_code=True,torch.load(..., weights_only=False),pickle, etc.).CONTRIBUTING.md: no new dependencyAdditional Information
Slice 2 of 11 of the GPU-CI speed-up work, split out of the reference draft #2730 (not for merge; it holds the whole series with its measurements). Merge in order; each PR stands on its own and passes CI without the later ones.
Summary by CodeRabbit