[OMNIML-6178] [2/11] Use the sysmon coverage core in CI by shengliangxu · Pull Request #2846 · NVIDIA/Model-Optimizer

shengliangxu · 2026-10-17T01:41:26Z

What does this PR do?

Type of change: CI (test runtime)

Sets COVERAGE_CORE=sysmon on the unit linux job, gpu_tests.yml and regression_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 through COVERAGE_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 through sys.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:

lane	#2845	this PR	change
gpu	2,093 s	1,289 s	-38%
gpu_megatron	2,240 s	1,212 s	-46%
gpu_vllm v0.20.0	204 s	170 s	-17%
gpu_vllm v0.30.0	426 s	328 s	-23%
regression	366 s	268 s	-27%
unit `linux` (xdist)	479 s	305 s	-36%

Workflow wall time: GPU tests 59.2 -> 39.6 min, Regression 21.3 -> 15.2 min.

How much of that is this PR. Two single runs of the same code differ by 15-30% on their own: the eight example lanes that run over 100 s, which this PR does not touch, moved by -4% to -31% (llm_sparsity +38%) between the same two runs. The deterministic part is the Megatron worker-pool start, the setup of the first test of each gpu_megatron module: 75-77 s in each of 14 modules before (all setups of 12 s or more add up to 1,365 s) and 22-27 s after (575 s), which is 790 s of the lane's 1,028 s. Locally the unit suite takes 825 s with the C tracer, 640 s with sysmon and 572 s without coverage (4 CPUs, serial).
A 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.).

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
Did you write any new necessary tests?: N/A (CI configuration)
Did you update Changelog?: N/A (CI and tests)
Did you get Claude approval on this PR?: N/A (optional, not run)

Additional 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). Slices 5, 8 and 10 (#2849, #2852, #2854) target main and merge independently; the other open slices form a stack and merge bottom-up. Each PR stands on its own and passes CI without the later ones.

[OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged)
[OMNIML-6178] [2/11] Use the sysmon coverage core in CI #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (this PR)
[OMNIML-6179] [3/11] Run manual-marked tests on nightly and on-demand GPU runs #2847 [OMNIML-6179] [3/11] Run manual-marked tests on nightly and on-demand GPU runs
[OMNIML-6181] [4/11] Give gpu_megatron's slow cold starts more time #2848 [OMNIML-6181] [4/11] Give gpu_megatron's slow cold starts more time
[OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models #2849 [OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models (targets main)
[OMNIML-6179] [6/11] Use local tokenizers and calibration text in GPU tests, shrink puzzletron data #2850 [OMNIML-6179] [6/11] Use local tokenizers and calibration text in GPU tests, shrink puzzletron data
[OMNIML-6180] [7/11] Trim the llm_eval, llm_qat, EAGLE and megatron_bridge example tests #2851 [OMNIML-6180] [7/11] Trim the llm_eval, llm_qat, EAGLE and megatron_bridge example tests
[OMNIML-6180] [8/11] Trim the DFlash regression test #2852 [OMNIML-6180] [8/11] Trim the DFlash regression test (targets main)
[OMNIML-6180] [9/11] Trim the torch_onnx example lane #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane
[OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies (targets main)
[OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules

Summary by CodeRabbit

Tests
- Updated coverage collection for GPU, regression, and Linux unit-test runs to use Python’s monitoring-based coverage core on supported Python 3.12+ environments.
- This configuration reduces overhead during coverage-enabled test execution, particularly for import-heavy worker processes.
- No changes to application functionality or public interfaces are included.

copy-pr-bot · 2026-10-17T01:41:30Z

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coderabbitai · 2026-10-17T01:41:35Z

📝 Walkthrough

The Linux unit-test, regression-test, and GPU-test workflows set COVERAGE_CORE to sysmon. Comments document sys.monitoring and its Python 3.12+ requirement.

Changes: Coverage core in CI tests

Layer / File(s)	Summary
Set coverage core in test workflows `.github/workflows/gpu_tests.yml`, `.github/workflows/regression_tests.yml`, `.github/workflows/unit_tests.yml`	The three workflows set `COVERAGE_CORE` to `sysmon`. Comments document `sys.monitoring`, tracing overhead, and the Python 3.12+ requirement.

Priority: ⬇️ Low

Estimated code review effort: 1 (Trivial) | ~5 minutes

Change: Other

Merge Risk: 🟡 Moderate · up to 32dbd

The new setting can prevent coverage-enabled CI jobs from starting their tests, so the configuration should be corrected before merging.

Pre-merge checks: 6

Check name	Status	Explanation
Title check		The title clearly and concisely identifies the main change: using the sysmon coverage core in CI.
Docstring Coverage		No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Security Anti-Patterns		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…
Description Check		Check skipped - CodeRabbit’s high-level summary is enabled.

Full details: Docstring Coverage

Explanation

No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0 files. (1 skipped: 1 unsupported.)

Full details: Security Anti-Patterns

Explanation

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 requirements.txt. None of the listed security anti-patterns is introduced.

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codecov · 2026-10-17T01:49:55Z

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 78.98%. Comparing base (25f99e6) to head (0f26cc3).
⚠️ Report is 5 commits behind head on main.

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

Flag	Coverage Δ
gpu	`59.43% <ø> (+8.40%)`	⬆️
regression	`15.10% <ø> (-0.02%)`	⬇️
unit	`60.46% <ø> (-0.01%)`	⬇️

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shengliangxu · 2026-10-17T02:37:52Z

/ok to test 1c8d165

shengliangxu · 2026-10-17T07:03:13Z

/ok to test 73d351b

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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>

…#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  ## 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.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

cjluo-nv

Bot review (bedrock-claude-opus-5-5) — DM the bot to share feedback.

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.toml pins coverage[toml]~=7.14.0 and sets branch = false. So the older limitation of sysmon (no branch coverage, which made it fall back to the C tracer) doesn't apply here.
The unit linux job runs unit-3.12. On older Pythons, coverage warns and falls back to its default core, so nothing breaks.
The multi-version, windows and partial-install jobs don't set COVERAGE_CORE, so they are unaffected.
The example-runner lane is left to #2727, as the PR description says.

kevalmorabia97

nice

github-actions · 2026-10-21T07:58:01Z

PR Preview Action v1.8.1
Preview removed because the pull request was closed.
2026-10-21 07:57 UTC

### What does this PR do? Type of change: CI (test runtime, tests only) **The DFlash regression trains 500 steps and resumes for 20.** `test_dflash_training` trained 3 epochs (1,500 steps) and `test_dflash_resume` then a fourth epoch (500 more). On the 1-GPU PR runner the recorded durations solve to about 33 s of start-up plus 55-73 ms per step, so the two tests spend roughly 115-140 s and 60-70 s. The loss assertion only needs a final loss below 3.0, and the L40 baseline is already 1.78 at step 500. The resume still goes through the whole restore-and-train path, and it now reads `trainer_state.json` and checks that `global_step` is 520 and that a log entry follows step 500 (it had no assertion besides the exit status). The step count is fixed with `max_steps` rather than `num_train_epochs`, because the regression job runs on 2 GPUs off pull requests, where `accelerate launch` starts one process per visible GPU and an epoch is 250 steps. Measured: 81 s per PR regression job. Risk: the 1.78 baseline comes from a 1,500-step run, and with `max_steps=500` the linear schedule decays to zero at step 500, so the final loss can differ from it; the bound leaves 1.2 of margin. ### Time improvement (measured in CI) | lane | | before | after | change | |---|---|---|---|---| | regression | `test_dflash_training` | 118 s | 62 s | -56 s | | regression | `test_dflash_resume` | 56 s | 31 s | -25 s | | regression | **lane pytest** | 268 s | 170 s | **-98 s (-37%)** | DFlash: before = #2846, #2848 and #2850 (#2849 ran on a slower runner), after = #2852, #2854 and #2855 (median; the lane took 168 and 170 s on the normal-speed runners and 243 s on the slow one). On its own, on main (2026-10-21): this PR's regression job against #2854's, which has the same base and lacks this change. The unchanged DFlash tests took the same time within about 3% (equally fast runners). `test_dflash_training` takes 144 s before and 83 s after, `test_dflash_resume` 73 s and 47 s, and the lane's pytest time goes from 374 s to 291 s (-83 s). The lane passes (5 passed), so the loss stays below 3.0 after 500 steps and the resume ends at `global_step` 520. A vLLM change that sized the KV pool of the Qwen3-MoE and DeepSeek engines down was dropped from this PR. The runs above are of its earlier head, which had it: the engines it touched booted 5-6 s per vLLM version faster than the ones it left alone, in line with the 1-3 s per boot expected and below the boot-to-boot noise (the unchanged Llama engine took 84-99 s on v0.30.0), so it saved nothing measurable. 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 I read the transformers 4.57.1 sources (`max_steps` overrides `num_train_epochs`; with the steps save strategy a checkpoint is saved at step 500 and again at the end) and the accelerate 1.10.0 launcher defaults. Not run on a GPU here: this PR's regression job is the check, in particular that the final DFlash loss stays below 3.0 after 500 steps and that the resume ends at `global_step` 520. Pre-commit 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 - Did you write any new necessary tests?: N/A (existing tests made cheaper; the DFlash resume test now also asserts on its result) - 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 8 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). Slices 5, 8 and 10 (#2849, #2852, #2854) target main and merge independently; the other open slices form a stack and merge bottom-up. Each PR stands on its own and passes CI without the later ones. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 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 (targets main) 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 (targets main) **(this PR)** 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 (targets main) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules  ## Summary by CodeRabbit * **Tests** * Updated regression coverage to use fixed training limits of 500 steps, with resumed runs extending to step 520. * Improved checkpoint and resume validation to confirm the final training step and expected post-resume progress logs. * Updated loss expectations to reflect the result at the 500-step training milestone.  Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

… a worker dies (#2854) ### What does this PR do? Type of change: CI (test reliability); changes `tests/_test_utils` only A worker that exits without reporting (an illegal memory access aborts the process, or an OOM kill) was only noticed when the result timeout ran out: 600 s for a job, 120 s for the start. Until then the parent waits on a queue nobody writes to, and the other workers wait in the job, typically in a collective, for their dead peer. A start that fails, for example on a bad backend, also waited out the whole ready timeout and left the workers it had spawned behind. `DistributedWorkerPool` now checks the workers' liveness once a second while it waits. A dead worker is noticed within a second and fails the run, or the start, naming its rank and exit code (about 4 s after the death, which includes the 3 s the pool already waits for the other workers' stack dumps). If another rank had already reported an exception, its traceback is kept in that error. The pool is marked `broken` and the other workers are killed with their stacks printed, the way a hung job or a failed rank already ends in main (`get_live_pool` then hands out a new pool). A start that fails kills the workers it had spawned. `ready_timeout` is a constructor argument (120 s by default, as before), so a test can reach the start timeout without waiting for it. This builds on the hang handling of #2832 (`broken`, `get_live_pool`, the stack dump) and does not change it. ### Time improvement (measured locally, not in CI) No lane time changes in a passing run, and no worker died in this series' CI runs, so CI cannot show it. What changes is how long a dead worker costs. CPU, gloo, two ranks, the pool test's own job (rank 0 exits with code 13, rank 1 keeps waiting), seconds from `pool.run(...)` (or the constructor) to the exception, on main and on this PR: | scenario | before | after | |---|---|---| | a rank dies mid-job, its peer is stuck in a wait, `job_timeout` 30 s (as in the test) | 33.1 s, `queue.Empty` | 4.0 s, `Worker process(es) died (rank, exit code): [(0, 13)]` | | the same with the default `job_timeout` of 600 s | 603.1 s, `queue.Empty` | 4.0 s | | the same, the peer waits in `dist.barrier()` | 33.3 s (the 30 s `failure_grace`) | 4.3 s | | the start fails (`backend="not-a-backend"`), default `ready_timeout` of 120 s | 120.1 s, `queue.Empty` | 5.0 s | | rank 0 dies at the start, rank 1 hangs in the rendezvous | 120.0 s, one live worker left behind | 5.0 s, no worker left | The after time is the 1 s liveness poll plus the 3 s the pool waits for the other workers' stack dumps before it kills them. At the production timeouts (120 s for the start, 600 s for a job) a dead worker therefore costs 4-5 s instead of 2-10 minutes of a CI job: the test fails at once and the next one gets a fresh pool from `get_live_pool`, instead of waiting for a queue nobody writes to. Cost: the four new cases add about 27 s to the `gpu` lane: the pool test module takes 43 s in this PR's run and 17 s in #2849's run, which has the same base (main) and lacks them (both from the job logs). Method: a small script (not part of this PR) builds the pool the way the repo's test does and times each scenario with `time.monotonic()` around the call, once per commit, on a CPU host. ### Usage ```python pool = DistributedWorkerPool(world_size=2, backend="nccl", ready_timeout=300) # new argument, default 120 ``` ### Testing `tests/gpu/torch/utils/test_distributed_worker_pool.py` runs the pool with gloo on CPU (it extends main's three tests; the new cases take 24 s together): - a rank that exits with code 13 while its peer waits fails the run in about 5 s and kills the peer. The test sets `job_timeout = 30`, so a regression fails there with `queue.Empty` instead of at the test's own cap. With the liveness check disabled it fails exactly that way. - the same test with a second job: rank 0 raises an exception and rank 1 exits with code 13 two seconds later, after rank 0 has reported. The error carries the exit code and rank 0's traceback. Without the change it carries only the exit code, and the case fails. - a start that fails raises and leaves no worker, both when every worker dies (a bad backend) and when the wait times out while they are still starting (`ready_timeout=0.01`). With `_abort()` removed from the failed-start path, the second case fails ("workers left behind"); the first passes either way, since all its workers die on their own, so it covers the liveness wiring only. Main's three tests (as reworked by #2836) still pass, and the three new cases fail against main's unmodified pool. Pre-commit passes. This PR is rebased onto #2836, which also edited the pool (reporting a failed rank before its teardown, retiring a pool after any failed job); the changes are in different places. The default start timeout stays 120 s here; #2848 raises it to 300 s, so whichever of the two merges second resolves that one line. ### 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 - Did you write any new necessary tests?: ✅ 2 test functions (4 cases) in `tests/gpu/torch/utils/test_distributed_worker_pool.py`, run on CPU with gloo; each fails without its fix - 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 10 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). Slices 5, 8 and 10 (#2849, #2852, #2854) target main and merge independently; the other open slices form a stack and merge bottom-up. Each PR stands on its own and passes CI without the later ones. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 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 (targets main) 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 (targets main) 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 (targets main) **(this PR)** 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules  ## Summary by CodeRabbit * **Bug Fixes** * Distributed worker pools now detect workers that exit during startup or job execution and report failures instead of waiting for the full job timeout. * Worker startup supports a configurable readiness timeout with prompt timeout handling. * Failed or timed-out initialization now cleans up spawned worker processes, preventing orphaned processes and leaving the pool in a consistent state. * Job result retrieval now responds promptly when a worker fails.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…e unit lane, small models (#2849) ### What does this PR do? Type of change: CI (test runtime, tests only) Four changes to the ONNX tests of the `gpu` job: - **Three CPU-only files move to the unit lane** (`tests/gpu/onnx/quantization/` to `tests/unit/onnx/quantization/`, content unchanged): `test_dla_qdq_rules.py`, `test_dla_scalar_constants.py` and `test_quantization_initializer_names.py` (added with #2678, 68 cases). They quantize with `calibration_eps=["cpu"]` and run the result on the CPUExecutionProvider, so they need no GPU, but they cost 26.8 s per `gpu` job (1.5 + 5.3 + 20.0 s in the CI logs); the unit job runs them in a few seconds on its xdist workers. - **`test_concat_elim` calibrates a 32-channel 16x16 model on the CPU** instead of the (1, 128, 240, 64) three-conv model on `[cpu, cuda:0, trt]`: the two tests took about 22 s each on the base (15 s together in the series' CI runs with the sysmon core of #2846), and their assertions only look at where the pass places Q/DQ nodes. The convs stay above the 16-channel limit of `graph_selection.py`, and both modes produce the same 8 Q and 8 DQ nodes as with the default size. `build_conv_concat_model` takes optional `channels`, `height` and `width` (defaults: the old values; with a seeded RNG the default model serializes to the same bytes). - **The autotune benchmark tests build a 1x32x32x32 model** instead of the 64x32x224x224 one (103 M random floats and about 1.2 GB copied per iteration; the five tests took 31 s, and none looks at the latency beyond "a positive float"). The two trtexec tests also pass `--duration=0`, because trtexec measures for at least 3 s whatever the iteration count. `_create_simple_conv_onnx_model` takes optional `batch_size` and `spatial_size` with the old defaults, so the unit autotuner tests that share it build the same model (their 161 tests pass). - **The autotune workflow test passes `--duration=0` to trtexec**: `test_export_quantized_model[True]` took 44 s, mostly inside trtexec's 3 s minimum measurement per scheme. It still runs the 100 requested iterations per measurement; the model and `timing_runs` keep their size, because the test asserts the best scheme carries Q/DQ nodes. ### Time improvement (measured in CI) gpu lane, time per file from the per-test timestamps; before = the runs of #2846-#2848, after = this PR's run and the 4 runs above it (median): | | before | after | change | |---|---|---|---| | three CPU-only files (68 cases), moved to the unit lane | 7.4 s | 0 | -7.4 s | | `test_concat_elim.py` | 15.2 s | 1.2 s | -14.0 s | | `autotune/test_benchmark.py` (36 cases) | 37.1 s | 21.0 s | -16.0 s | | `autotune/test_workflow.py` | 44.4 s | 28.5 s | -15.9 s | | **sum** | 104 s | 51 s | **-53 s per gpu job** (-59 s after speed scaling) | The whole `tests/gpu/onnx` directory takes 110-120 s before and 58 s in the three later runs on normal-speed runners (81 s and 74 s in #2849 and #2850, which ran on 1.3x slower runners). The 68 moved cases cost the unit job 7-13 s of CPU, spread over four xdist workers (below the unit job's run-to-run spread). My first estimate of the gpu saving (97-107 s) came from base runs without sysmon, where these files were slower. On its own, on main as of 07:06 UTC on 2026-10-21 (before #2846 added the sysmon core): the `gpu` lane's `tests/gpu/onnx` directory takes 145 s in #2854's run, which has the same base and lacks this change, and 66 s in this PR's run (-79 s). The runners were equally fast (median ratio 0.99 over the 17 untouched files above 20 s). `test_concat_elim.py` goes from 35 s to 2.7 s, `autotune/test_benchmark.py` from 39 s to 24 s and `autotune/test_workflow.py` from 46 s to 28 s, and the three moved files cost 14 s there. Times are the gaps between consecutive result lines in the job logs. 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 - The three moved files: 68 passed in 7.1 s at the old location and in 13.3 s from the new one (this includes the unit conftest's session setup) on Python 3.12 with onnx 1.21.0 and onnxruntime 1.24.4, and 68 passed in 15.3 s on Python 3.10.19 with onnxruntime 1.22.0, the oldest runtime `pyproject` allows. The whole `tests/unit/onnx` directory passes (756 passed, 12 xfailed). - `test_concat_elim`: the two tests pass in 1.3 s on CPU; quantizing the default and the small model in both modes gives Q=8, DQ=8. - The shared helper changes: the unit autotune tests (161) pass. - Not run: the TensorRT tests, because there is no TensorRT here; this PR's `gpu` job is the check. Pre-commit 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 - Did you write any new necessary tests?: N/A (existing tests moved or made smaller; the 68 moved cases are unchanged) - 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 5 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). Slices 1, 2, 8 and 10 (#2845, #2846, #2852, #2854) have merged. Slice 5 (#2849) targets main and merges independently; the other open slices form a stack and merge bottom-up. Each PR stands on its own and passes CI without the later ones. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (merged) 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 (targets main) **(this PR)** 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 (merged) 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 (merged) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules   ## Summary by CodeRabbit * **Tests** * Added configurable dimensions to ONNX test models and smaller inputs for selected scenarios. * Streamlined GPU benchmark timing while retaining configured measurement runs. * Updated INT8 and FP8 concat-elimination coverage to use CPU calibration and smaller model shapes. * Reorganized DLA and quantization checks as unit tests instead of GPU tests.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

### What does this PR do? Type of change: CI (test runtime); the only non-test file is `examples/torch_onnx/torch_quant_to_onnx.py` Three cuts to the torch_onnx example lane: - **No DataLoader workers in the calibration loaders.** `load_calibration_data` builds its DataLoaders over lists of tensors that are already on the GPU, so the four workers load nothing, and since the script forced the `spawn` start method (CUDA cannot be used in forked workers), every loader pass spawned four processes that re-imported torch, timm and modelopt and received the dataset over CUDA IPC: about 14 s per pass and about 300 s per torch_onnx CI job. Both loaders use `num_workers=0`, and the module-level `mp.set_start_method("spawn", force=True)` is dropped, which also stops importing the script from changing the start method of the importing process (two tests import it inside the pytest process). Nothing else in the script uses multiprocessing. The `--evaluate` path keeps the CPU-only DataLoader workers of `examples/onnx_ptq/evaluation.py`, now under the default start method, exactly as in the `onnx_ptq` example that calls the same function. - **swinv2 int8, mxfp8 and auto are skipped on pull requests.** `swinv2_tiny` runs all five formats on top of `swin_tiny`, which already covers every format through the same exporter and TensorRT path; swinv2 only adds its cosine attention and post-norm blocks. On PRs (`GITHUB_REF` under `refs/heads/pull-request/`) fp8 and nvfp4 still run and exercise those blocks; scheduled and local runs keep every combination, and test ids are unchanged. `swinv2_tiny[auto]` alone averages 121 s over 304 CI runs; the three skipped cases cost about 110 s of every PR run. - **`build_trt_engine` runs trtexec for one iteration.** It let trtexec run its default benchmark after the build (200 ms warm-up, then at least 3 s and 10 iterations) and discarded the output. `--warmUp=0 --duration=0 --iterations=1` still executes the engine once and removes about 3 s per engine, roughly 40 s per lane run. `--trt_build` users see no difference besides it returning sooner. ### Time improvement (measured in CI) torch_onnx lane; before = #2846, #2848 and #2850 (875-880 s; #2849 ran on a slower runner: 1,060 s), after = #2853, #2854 and #2855 (452-463 s). | | before | after | change | |---|---|---|---| | **lane pytest** | 877 s | 458 s | **-419 s (-48%)** | | of which: the three swinv2 cases skipped on PRs | 143 s | 0 | -143 s | | of which: the cases that still run (no DataLoader workers, trtexec runs once) | | | -277 s | | whole job | 18.6 min | 12.2 min | -6.4 min | This lane repeats to within 1% (875-880 s before, 452-463 s after), so it is the cleanest measurement of the series. For example `swin_tiny` with auto quantization goes from 66 s to 24 s and `vit_tiny` with auto from 60 s to 17 s. 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 No API change. The skip is keyed on `GITHUB_REF`, so a local run keeps every combination. ### Testing `py_compile`, ruff 0.15.20 and `pytest --collect-only` on CPU (24 tests, ids unchanged). With the example command stubbed, `GITHUB_REF=refs/heads/pull-request/123` skips exactly swinv2 int8, mxfp8 and auto, and any other ref skips none; the two in-process mapping tests that import the script pass on CPU, and `build_trt_engine` passes the three flags to a fake trtexec. Not run: the lane itself (it needs a GPU, TensorRT and model downloads) and the flags against a real trtexec, so this PR's torch_onnx job is the check. Pre-commit passes. #2685 (open) also edits this example; the changes touch different lines. ### 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 - Did you write any new necessary tests?: N/A (existing tests; the PR-only skip keeps test ids) - 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 9 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). Slices 1, 2, 5, 8 and 10 (#2845, #2846, #2849, #2852, #2854) have merged. The remaining slices (#2847, #2848, #2850, #2851, #2853, #2855) are independent PRs on main: each applies to main on its own, and merging all six onto main in different orders gives the same tree. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (merged) 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 (merged) 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 (merged) 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane **(this PR)** 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies (merged) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules  ## Summary by CodeRabbit * **Updates** * Quantization examples now use zero-worker calibration loading with device-resident samples. * TensorRT example builds perform a single inference iteration without warmup or timing. * **Tests** * Pull-request validation skips selected SwinV2 Tiny formats, while scheduled validation continues to cover them. * Pull-request detection uses the repository’s CI reference environment. * **Impact** * These changes affect example workflows and validation coverage only; no application user-facing functionality is changed.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

… tests, shrink puzzletron data (#2850) ### What does this PR do? Type of change: CI (test runtime and reliability, tests only) Less data and fewer Hub round trips in four GPU-lane tests: - **Six `preprocess_data` tests use the tiny local tokenizer** (`tests/gpu_megatron`). They tokenized with `gpt2` from the Hub, and `megatron_preprocess_data` loads the tokenizer once for the vocabulary size and again in every worker, so each test made several round trips; none depends on gpt2's vocabulary. They use the checked-in byte-level `tiny_tokenizer_path` fixture that the blend test already uses. Three of the six read only local data, so the tokenizer download was their only network access (the jsonl `max_tokens` test is among the lane's 120 s timeouts, 3 failures). Measured on a CPU host with a cold Hub cache, the 11 tests of the module take 42.8 and 44.0 s before and 35.0 and 36.1 s after (two runs each; the nine tests of the earlier base took 38.5 s and 24.4 s, and 37 s and 23 s in CI). The Qwen3 tests keep their real tokenizer (chat template and EOS handling). - **The puzzletron test data shrinks, and a duplicate NAS test is folded into another.** `save_dummy_dataset` wrote 2500 copies of one conversation into each split (1738 blocks of 2048 tokens), while the pruning scores read 100 blocks and the later steps 2 each; 400 conversations (278 blocks) take the three dataloader builds plus the save from 2.8 s to 0.36 s per test, and the validation loss moved by at most 0.02 on random-init toy Llamas (the scoring tolerance is 0.15). `calc_subblock_stats` ran 12 passes where only the entry for `batch_size` 96 is read: `batch_sizes: [96]` runs 4. `test_nas_convert_ffn_pruning` ran the same `mtn.convert` on the same toy Llama and config as `test_nas_search` (21.6 s with its own 2-rank spawn); its two assertions move into `test_nas_search`. - **The unified HF export test calibrates on local text, with no previews or batch probing.** Each of its 11 params starts a fresh `hf_ptq` process (22 s per param). It now reads a local jsonl of 64 random-word texts (new helper `write_calib_jsonl`, shared through the session-wide `calib_jsonl` fixture in `tests/gpu/conftest.py`, which fails the session if a test writes into its directory) instead of `cnn_dailymail` from the Hub, passes `--batch_size 64` (the whole calibration set in one batch instead of probing for the largest batch) and `--skip_generate`. The test only checks the structure of the exported safetensors. - **The GPTQ end-to-end test reads 8 texts from the same local jsonl** instead of streaming `cnn_dailymail` for each of its three params (and a test that could fail on a Hub hiccup). ### Time improvement (measured in CI) Time of the changed tests; before = the runs of #2846-#2849, after = #2850, #2852, #2854 and #2855 (median). #2849 and #2850 ran on runners 1.26x and 1.29x slower for every test, so the change after speed scaling is in brackets. | lane | tests | before | after | change | |---|---|---|---|---| | gpu | `tests/gpu/torch/puzzletron` (18 tests, 17 after the folded NAS test) | 290 s | 202 s | -88 s (-70 s, -26%) | | gpu | unified HF export, 11 params | 228 s | 153 s | -75 s (-67 s, -30%) | | gpu | GPTQ end-to-end, 3 params | 7.2 s | 1.1 s | -6 s | | gpu_megatron | `preprocess_data` module | 37 s | 23 s | -14 s (-10 s, -27%) | So about 170 s (144 s after scaling) off the gpu job and 14 s off the gpu_megatron job. This PR's own run was on a slow runner (puzzletron 310 s); the three later runs agree with each other (199-203 s). My first estimates were 50 s, 38 s and 6 s for the first three rows. 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 - `preprocess_data`: ran the module on CPU next to a copy of the `tiny_tokenizer_path` fixture with the Hub reachable: 11 passed before and after (main has dropped the HF xfail marks), with identical test ids. - `calib_jsonl`: with the real fixture and the repo's `get_dataset_dataloader` on a CPU host, the 8 samples read from the shared 64-text file equal those of the old 8-text file, and two reads leave the directory unmodified (the fixture's teardown check passes). `--setup-plan` shows the unified export test using the conftest fixture; the GPTQ and export tests themselves need a GPU. - puzzletron: timed the dataloader builds, the subblock-stats passes and the loss sensitivity on CPU with the repo's own helpers; collected `tests/gpu/torch/puzzletron` (16 tests, one fewer than before). The Nemotron-3 toy keeps its 128 experts, because shrinking it changes `EXPECTED_TEACHER_MEMORY_MIB` and `EXPECTED_TEACHER_NUM_PARAMS`, which need a GPU runner with `mamba_ssm` to regenerate. - unified export: no GPU here, so I ran the test on a CPU host through a shim that lets `hf_ptq` pass its GPU guard and passes `--device cpu`. 8 of the 11 params pass with the new command line, scale-equality assertions included (fp8, nvfp4, int4_awq, w4a8_awq_beta, int8_weight_only, w4a16_nvfp4, the Qwen3 MoE nvfp4 and the GPT-OSS fp8); the other three need CUDA or Triton. GPTQ: the flow completes on the jsonl for the FP8 and INT4 blockwise configs (NVFP4 needs CUDA). - Not run: the GPU tests themselves; this PR's gpu and gpu_megatron jobs are the check. Pre-commit 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 - Did you write any new necessary tests?: ✅ one small helper, `write_calib_jsonl` (`tests/_test_utils/torch/calib_data.py`), and one session fixture, `calib_jsonl` (`tests/gpu/conftest.py`), used by two tests; no new test functions - 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 6 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). Slices 1, 2, 5, 8, 9 and 10 (#2845, #2846, #2849, #2852, #2853, #2854) have merged. The remaining slices (#2847, #2848, #2850, #2851, #2855) are independent PRs on main: each applies to main on its own, and merging all of them onto main in different orders gives the same tree. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (merged) 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 (merged) 6. #2850 [OMNIML-6179] [6/11] Use local tokenizers and calibration text in GPU tests, shrink puzzletron data **(this PR)** 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 (merged) 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane (merged) 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies (merged) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules   ## Summary by CodeRabbit * **Tests** * Updated model export and quantization checks to use locally generated calibration data. * Standardized preprocessing tests on a small local tokenizer. * Restricted model analysis configurations to a batch size of 96. * Reduced sample dataset sizes for pruning-related checks. * Strengthened conversion verification by checking generated pruning outputs. * Removed the FFN pruning conversion test while retaining attention-pruning coverage.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…ridge example tests (#2851) ### What does this PR do? Type of change: CI (test runtime, tests only) Five cuts to the example lanes, each with its reasoning: - **`test_lm_eval_hf` evaluates one MMLU subtask.** It ran `--tasks mmlu --limit 0.1`: the 57-subtask group, about 5.6k loglikelihood requests, on a tiny Qwen3 whose `max_position_embeddings` is 32, so its runtime is dominated by loading one HF dataset config per subtask (over 331 CI runs: min 198.9 s, median 212.5 s). It runs `mmlu_abstract_algebra` (100 test documents, 5-shot) through the same `lm_eval_hf.py` path and the same accuracy gate (exactly one task, lower bound 0.1 unchanged; the label distribution of that split is 22/26/31/21, so even a model that always predicts one letter scores at least 0.21). - **The llm_qat QAT and QAD tests quantize once per module.** `test_qwen3_qat_nvfp4` (3 backends), `test_qwen3_qad_nvfp4` (2) and `test_qwen3_qlora_nvfp4` ran 6 `quantize.py` and 12 cold tokenized-dataset builds per module (104-121 s each, qlora 169 s). A module-scoped fixture runs `quantize.py` once (guarded by `assert_unmodified_tree`, like the other shared tiny-model fixtures) and all quantize and train calls share one dataset cache; the qlora test keeps its own PTQ because it passes `--compress True`. - **The 1-GPU EAGLE3 compile smoke test skips the Triton GEMM autotune** (`TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_BACKENDS=ATEN`, the mechanism `test_diffusers.py` already uses). `test_llama_eagle3[1-False]` takes 175.5 s against 40.6 s for `[1-True]` (the same flow without compile), about 135 s of it `torch.compile` benchmarking Triton GEMM templates for a draft model that is one tiny decoder layer. The mode stays `max-autotune`, and the shortcut applies only when the run has one GPU: `[2-False]` and any run on more GPUs (the nightly) keep the full autotune. - **The duplicate tiny-llama offline EAGLE3 training test is dropped** (`test_offline_eagle3_training[tinyllama]`, 34.9 s per job, almost all launcher start-up). `test_offline_eagle_training` already runs that mode on the same model with hidden states from `compute_hidden_states_hf.py`, and the FakeBaseModel case keeps the synthetic path. The one thing the dropped case covered that stage 2 did not, the collator truncating dumps, moves there (`training_seq_len=16`, so 28-token dumps are truncated), and the collator is also unit tested on CPU. - **`megatron_bridge` tests that use no GPU skip the Megatron state reset.** The conftest resets Megatron's global state around every test (`destroy_model_parallel`, `gc.collect()`, `empty_cache`), about 1.3 s per test; 77 tests in the directory are pure Python (the mlflow wiring and checkpoint-path tests) and leave nothing to reset (the 82 tests that took under 3 s each add up to 118 s in the series' runs before this PR and to 0.3 s after it). The reset now runs only for tests that request `num_gpus`; the environment restore stays for every test. The job runs at 68-75 minutes against its 75-minute nightly limit. ### Time improvement (measured in CI) Example lanes, pytest time of the changed tests and of the lane; before = #2846 (plus #2848 where its runner was as fast), after = #2851, #2854 and #2855 (median). #2849 and #2850 ran on runners 1.3-1.5x slower for every test and are left out of the before column. | lane | | before | after | change | |---|---|---|---|---| | llm_eval | `test_lm_eval_hf` | 130 s | 19 s | -111 s | | llm_eval | **lane** | 332 s | 212 s | **-120 s (-36%)** | | llm_qat | six QAT, QAD and QLoRA tests (call time) | 563 s | 428 s | -135 s (the shared quantize adds 20 s to the first test's setup) | | llm_qat | **lane** | 591 s | 475 s | **-116 s (-20%)** | | speculative_decoding | `test_llama_eagle3[1-False]` | 131 s | 87 s | -44 s | | speculative_decoding | dropped `test_offline_eagle3_training[tinyllama]` | 20 s | - | -20 s | | speculative_decoding | **lane** | 407 s | 341 s | **-66 s (-16%)** | | megatron | the 82 megatron_bridge tests that use no GPU (the state reset) | 118 s | 0.3 s | -118 s | | megatron | **lane** | 583 s | 460 s | **-123 s (-21%)** | - A `mmlu_limit=1` change for `test_qwen3_eval_fp8` was dropped from this PR. The runs above are of its earlier head, which had it: the test took 175-246 s in all 7 runs, before and after (187 s against 176 s on comparable runners), so the CI history it was based on (mean 723.7 s, 2026-09-25 to 10-09) is not reproduced by the current lane and the change saved nothing measurable. The llm_eval lane figure above is `test_lm_eval_hf`'s. - That is 425 s of lane time per PR run. The Example workflow ends with its slowest lane, torch_onnx (18.1-18.8 min of job time) until #2853, so the gain reaches the workflow's wall time only with that PR. 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 - llm_eval: ran the test's command on CPU with the tiny Qwen3 it builds: old configuration 223.4 s, `mmlu/acc` 0.2343; new 22.4-24.0 s, `mmlu_abstract_algebra/acc` 0.27, gate passes. - llm_qat: with `run_example_command` faked, `quantize.py` runs 6 -> 2 times and cold dataset builds go 12 -> 2; real `quantize.py` and `train.py` (QAT, then QAD from the checkpoint) ran on CPU with the int4 and fp8 recipes (NVFP4 needs CUDA) and left the checkpoint unmodified. - EAGLE: stage 1 and 2 of the offline flow ran on CPU with `training_seq_len` 64 and 16 (the collator pads 28 -> 64 and truncates 28 -> 16); with `run_example_command` faked, only `[1-False]` on one GPU gets the autotune variable (that case on 2 GPUs and every other case keep the full autotune) and the only removed test id is `test_offline_eagle3_training[tinyllama]`. - megatron_bridge: a throwaway pytest module (not in this PR) against the patched conftest counts the resets: plain tests trigger none, tests that request `num_gpus` trigger one before and one after; it fails against the old conftest. - Not run: the example lanes themselves (they need GPUs, TRT-LLM and model downloads), so the accuracy with CUDA bf16 numerics, the savings and the autotune variable are checked by this PR's example jobs. Pre-commit passes. - `trust_remote_code=True` in `test_offline_eagle3_training` is not new: it is the line on main (the Kimi and MiniMax repos it loads need remote code), and this PR only renames its variable after dropping the tinyllama case. ### 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 - Did you write any new necessary tests?: N/A (existing tests made cheaper; one duplicate test dropped, see the description) - 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 7 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). Slices 1, 2, 5, 8, 9 and 10 (#2845, #2846, #2849, #2852, #2853, #2854) have merged. The remaining slices (#2847, #2848, #2850, #2851, #2855) are independent PRs on main: each applies to main on its own, and merging all of them onto main in different orders gives the same tree. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (merged) 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 (merged) 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 **(this PR)** 8. #2852 [OMNIML-6180] [8/11] Trim the DFlash regression test (merged) 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane (merged) 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies (merged) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules   ## Summary by CodeRabbit * **Tests** * Focused language-model evaluation coverage on the abstract algebra task while retaining five-shot evaluation. * Streamlined quantization-aware training scenarios by reusing cached datasets and checkpoints, reducing repeated setup. * Improved Megatron test isolation by applying state cleanup only where needed. * Refined EAGLE test configurations for single-GPU runs, remote model coverage, and offline training with shorter sequence lengths.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…2848) ### What does this PR do? Type of change: CI (test reliability) `DistributedWorkerPool` waited 120 s for its workers to import the Megatron/TE/modelopt stack and report ready. The start takes 75-77 s in 14 modules of the PR run I read and has a p90 of 94-98 s, so slow hosts time out and the whole module errors: 15 jobs (903 job-minutes) in two weeks, 15 of the 63 failed gpu_megatron PR jobs in that period. It waits 300 s. An earlier version of this PR also gave `test_mamba_search_space` more time. Main's #2836 has since set its cap to 150 s, and that holds: in 58 gpu_megatron jobs from the last six days the test took 64-132 s (median 73 s, none above 150 s), so nothing is left to change there. ### Time improvement (measured in CI) None in a passing run: this PR raises one limit that only failing runs reach. - In the 9 gpu_megatron runs of this series, the worker-pool start took 75-77 s before #2846 and 18-27 s after it, against the old 120 s ready timeout. No run came near the limit, so the series cannot show the failures this PR prevents (the sysmon core of #2846 also takes most starts out of reach of the old ready timeout). - What it prevents, from the CI history of the two weeks before the series: the ready timeout failed 15 jobs (903 job-minutes), 15 of the 63 failed gpu_megatron PR jobs. Each failed job costs the author a re-run of the lane, 20-45 min of job time in this series. 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 Pre-commit passes. Not run on a GPU here: the effect is on cold-cache runs, so this PR's gpu_megatron job can only show that nothing regresses. ### 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 - Did you write any new necessary tests?: N/A (a timeout in the shared test utilities) - 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 4 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). Slices 1, 2, 5, 8, 9 and 10 (#2845, #2846, #2849, #2852, #2853, #2854) have merged. The remaining slices (#2847, #2848, #2850, #2851, #2855) are independent PRs on main: each applies to main on its own, and merging all of them onto main in different orders gives the same tree. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (merged) 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 **(this PR)** 5. #2849 [OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models (merged) 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 (merged) 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane (merged) 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies (merged) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules   ## Summary by CodeRabbit * **Bug Fixes** * Increased the wait time for distributed worker initialization to improve reliability during startup.  Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

…egatron modules (#2855) ### What does this PR do? Type of change: CI (test runtime); changes `tests/gpu_megatron` and `tests/_test_utils` only Each of the 15 gpu_megatron modules that use `dist_workers` started its own worker pool: about 19 of the lane's 36 minutes. Fourteen logged module starts took 75-77 s each (the GDN module 179 s: 76 s pool plus 103 s Triton JIT), plus 3.6-10 s of shutdown, and the start has a p90 of 94-98 s. The 76 s is not distributed init (an in-process init takes 0.1 s): the spawned child unpickles `megatron_worker_teardown` by reference, which imports the heavy `tests/gpu_megatron/conftest.py` (transformers, megatron.core, all of modelopt and its plugins, TE) before it reaches `init_process_group`. The same import chain takes pytest itself 89 s to start here against 17 s in the gpu lane, whose session-scoped pool, with a light teardown function, starts in 1.7 s. Module scope was chosen when a spawn cost about 10 s ("avoids cross-test state contamination"); the git history shows no contamination incident, and the cost has grown 7.6x. `_pool_cache` is now session-scoped, so the pools are keyed by world size and shared by every module. The `dist_workers` fixtures stay function-scoped and get their pool from `get_live_pool`, which replaces a pool that a hang killed. What isolation the module scope gave is kept explicitly: - `megatron_worker_teardown`, after every job: synchronizes CUDA first (an asynchronous CUDA error then surfaces in the test that caused it), destroys Megatron's model-parallel state as before, and now also the process groups it leaves behind (about 20 NCCL groups per dispatch, each with a communicator and a watchdog thread; `test_megatron.py` alone dispatches about 110 times). A pool that lives through the whole session needs it more than a per-module pool did. - `_reset_shared_pools`, an autouse module-scoped fixture: when a module starts, it resets the process-global torch settings that helpers like `set_seed` change (cudnn and TF32 flags, deterministic algorithms, RNG) to what a fresh worker starts with (`reset_worker_state`; the workers record those settings right after they start), in the pools that already exist. It is not part of `_get_pool`, which runs once per test and would add a job to each. - `get_live_pool` and the pool's abort handling: a job that times out, is interrupted, loses a worker **or fails on any rank** retires the pool, and the next test gets a fresh one, for the cost of one pool start. `DistributedWorkerPool.run` has retired a pool after a plain failed job since #2836, which matters more with a session-wide pool: a CUDA error such as the known Blackwell illegal memory access is sticky, the workers stay poisoned, and every later test would fail with it. `_reset_shared_pools` no longer lets a failed reset error the module's tests (they may not use a pool at all). Measured saving on top of #2846's sysmon core: 216 s of the gpu_megatron lane (1,038 s to 822 s; see the time section). Without sysmon a pool start costs 76 s, and the saving would be about 17 min (an estimate; the series was not run that way). One pool start instead of 15 also means fewer chances to hit the ready timeout. ### Time improvement (measured in CI) gpu_megatron lane, pytest time: | main (#2845) | + #2846 | slices 1-10 (the earlier base) | + this PR | |---|---|---|---| | 2,240 s | 1,212 s | 1,038 s (1,029 s in #2852) | **822 s** | This PR on its own: 1,038 s -> 822 s, **-216 s (-21%)**, against the two earlier runs (#2852: 1,029 s, #2854: 1,038 s). This PR's run was on a slightly slower runner (the heavy `call` phases of the tests it does not change took 6% longer than in #2854), so the gain is not runner luck. The setups of 12 s or more, which are the pool starts and the module-level compiles, fall from 16 (489 s) to 3 (246 s): the pool starts once instead of in each of 14 modules, and what remains is the extension build (124 s), the GDN module's Triton JIT (105 s) and one 18 s pool start. With sysmon (#2846) a pool start takes 18-27 s instead of 75-77 s, so each avoided start saves less than the 76 s this PR was designed around. Without #2846 the saving would be 14 starts of 76 s plus the 3.6-10 s shutdowns, about 17 min; that is an estimate, because the series was never run without sysmon on this PR. This is one CI run of the final head: the two runs before it failed on the sticky CUDA error and on the missing `gc.collect()` described below. 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 (test fixtures). ### Testing - `reset_worker_state` has a gloo test next to the other pool tests (it runs on CPU): flags and seed changed in a job are back at their spawn-time values afterwards; it fails when the helper forgets one of the settings. - The Megatron lane needs GPUs, so this PR's gpu_megatron job is the check: 822 s of pytest on the final head, against 1,038 s for the earlier base, #2854's run (381 passed, 129 skipped, as before). - The first CI run of this PR failed in gpu_megatron: the Blackwell illegal-memory-access flake hit `test_lora_then_quantize` and poisoned the shared pool, and every later module errored (3 failed, 180 errors), because the pool was reused after a failed job and `_reset_shared_pools` ran a job in it at each module start. The second commit retired a pool after any failed job and made a failed reset harmless; with it the lane ended with 1 failure instead of 183, in `test_forward_with_one_lora[lora_config1]` (the same CUDA error). Re-running the job on that commit failed at the same test again, so it was not a random flake: the teardown of this PR, unlike the draft's, did not collect garbage after destroying the groups, and the draft passed the lane three times. The third commit adds that `gc.collect()`; the lane then passes. The cause is not established (the finished test's Megatron and TE modules can still reference the destroyed groups, and their finalizers then run at an arbitrary point of the next job). #2836 has since landed the retirement in `run()` for its own reasons (with its own gloo test), so the second commit now only makes a failed reset harmless; main's teardown has neither the process-group loop nor the collection, and a session-wide pool needs both. - Pre-commit 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 - Did you write any new necessary tests?: ✅ 1 test function in `tests/gpu/torch/utils/test_distributed_worker_pool.py` (gloo, CPU) for the new helper; the session pool itself is exercised by the whole gpu_megatron lane - 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 11 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). Slices 1, 2, 4, 5, 6, 7, 8, 9 and 10 (#2845, #2846, #2848, #2849, #2850, #2851, #2852, #2853, #2854) have merged. The two remaining slices (#2847, #2855) are independent PRs on main: each applies to main on its own. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (merged) 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 (merged) 5. #2849 [OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models (merged) 6. #2850 [OMNIML-6179] [6/11] Use local tokenizers and calibration text in GPU tests, shrink puzzletron data (merged) 7. #2851 [OMNIML-6180] [7/11] Trim the llm_eval, llm_qat, EAGLE and megatron_bridge example tests (merged) 8. #2852 [OMNIML-6180] [8/11] Trim the DFlash regression test (merged) 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane (merged) 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies (merged) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules **(this PR)**   ## Summary by CodeRabbit * **Tests** * Distributed GPU test workers now restore their initial PyTorch settings and random seed between test modules. * Worker teardown now synchronizes CUDA, removes non-default process groups, and cleans up Python and GPU resources. * Worker pools are shared across test modules and shut down at the end of the test session for more consistent test execution.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

… GPU runs (#2847) ### What does this PR do? Type of change: CI (test runtime) Tests marked `manual` collect only when pytest gets `--run-manual`. The unit workflow passes it for every run that is not a pull request, but the GPU workflow never did, so a `manual` GPU test never ran in CI. This sets `PYTEST_ADDOPTS=--run-manual` in the "Run gpu tests" step for every run that is not a pull-request run (the nightly schedule and manual dispatches), which lets slow or network-heavy checks leave the pull-request jobs without leaving CI. Pull-request runs are unchanged by the flag itself: copy-pr-bot runs them as `push` events, the workflow's only `push` trigger, and the expression is `github.event_name != 'push' && '--run-manual' || ''`. An earlier version of this PR used the `release` marker; review feedback pointed out that `release` is the QA team's marker for long real-model runs in their own pipeline, while `manual` is what the unit tests already use for nightly and on-demand runs. **Side effect:** `--run-manual` also runs the existing `manual`-marked tests of `tests/gpu` on those runs, which no GPU run executed before: 12 NAS vision-model cases (`test_search_space_with_vision_models.py`), 6 puzzletron cases (about 40 s each by their marker) and the TRT client benchmark models (they need TensorRT, so I could not count them on a CPU host). The unit lane already runs its `manual` tests nightly the same way. A `workflow_dispatch` run of the GPU workflow on this branch (run 39668011982: 2-GPU runners and `--run-manual`, the nightly's setup) passed all four GPU jobs: `gpu` in 28 min (1,866 passed, 40 skipped; cap 60 min) and `gpu_megatron` in 27 min (403 passed, 87 skipped; cap 75 min). In `gpu` the six puzzletron params took 174 s (26-32 s each), the 14 NAS vision results 29 s in total and the Nemotron v3 test 20 s, about 4 min together; the TRT client tests are skipped in that container (`libnvinfer.so.10` is missing), so they add nothing. Two tests use it: - `test_megatron_generate_and_mmlu[tp]` builds a 28-layer Qwen3-0.6B Megatron model, downloads 1.2-1.5 GB of real weights into a fresh container cache and scores about 1,400 MMLU prompts: a 21-39 s call in every PR gpu_megatron job (21 s with the sysmon core of #2846, 39 s without it), and the only test in the lane that downloads real weights. - `test_get_dataset_dataloader_nemotron_v3_chat_template` streams the seven gated Nemotron post-training v3 datasets from the Hub (a 17-24 s call that needs an HF token and the network, and a source of Hub flakes). The combo expansion and sample distribution it goes through are covered by CPU unit tests that record the per-dataset calls. **Trade-off:** these two checks no longer run on pull requests. The real-weight accuracy check (0.36 < MMLU < 0.39) and the gated-data smoke test run every night and on demand, a day after a change instead of on the PR. ### Time improvement (measured in CI) Time of the module that holds the skipped test, from the per-test timestamps; before = the run of #2846, after = this PR's run and the runs above it that carry the change (median): | lane | skipped test (call time before) | module before | module after | change | |---|---|---|---|---| | gpu_megatron | `test_megatron_generate_and_mmlu[tp]` (21 s) | 64 s | 41 s (39 s in this PR's run) | -22 s (-35% after speed scaling) | | gpu | `test_get_dataset_dataloader_nemotron_v3_chat_template` (17 s) | 32 s | 13.5 s (13 s in this PR's run) | -19 s (-62%) | So 22 s off the gpu_megatron job and 19 s off the gpu job of every PR run. The lane totals cannot show a change this small (gpu_megatron 1,212 -> 1,206 s, gpu 1,289 -> 1,351 s: their run-to-run spread is several times larger). The price is that the two checks run only on the nightly and on demand. 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 `pytest --run-manual` runs the manual-marked tests locally. ### Testing On a CPU host the Nemotron v3 test is skipped with "Only run when --run-manual is given" without the flag and runs with it, and `-m manual` selects it. The expression `github.event_name != 'push'` is false for the `push` events that run pull requests and true for the nightly and manual dispatches. Pre-commit and actionlint pass. Run on GPU runners with the flag (dispatch run 39668011982, the nightly's setup): the Nemotron v3 test passed in 20 s and the four Megatron MMLU cases in 145 s (tp 66 s, cp 39 s, pp 32 s, dp 8 s). This PR's own GPU run shows both skipped on a pull request. ### 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 - Did you write any new necessary tests?: N/A (marks two existing tests; no new tests) - 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 3 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). Slices 1, 2, 4, 5, 6, 7, 8, 9 and 10 (#2845, #2846, #2848, #2849, #2850, #2851, #2852, #2853, #2854) have merged. The two remaining slices (#2847, #2855) are independent PRs on main: each applies to main on its own. The times above are from the series' CI runs before 2026-10-21, when all slices formed one linear stack; a PR number in a method note names that PR's run from then. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist (merged) 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI (merged) 3. #2847 [OMNIML-6179] [3/11] Run manual-marked tests on nightly and on-demand GPU runs **(this PR)** 4. #2848 [OMNIML-6181] [4/11] Give gpu_megatron's slow cold starts more time (merged) 5. #2849 [OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models (merged) 6. #2850 [OMNIML-6179] [6/11] Use local tokenizers and calibration text in GPU tests, shrink puzzletron data (merged) 7. #2851 [OMNIML-6180] [7/11] Trim the llm_eval, llm_qat, EAGLE and megatron_bridge example tests (merged) 8. #2852 [OMNIML-6180] [8/11] Trim the DFlash regression test (merged) 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane (merged) 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies (merged) 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules   ## Summary by CodeRabbit - **Tests** - GPU test coverage now includes selected manual tests during scheduled and on-demand workflow runs. - Tests requiring gated datasets or model downloads are excluded from standard push-based runs to keep routine checks focused and efficient. - **Chores** - Updated GPU test execution settings to support explicitly enabled manual test runs.  --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>

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[OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist #2845

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shengliangxu changed the title ~~shengliangx/ci 2 coverage~~ [2/11] Cut the coverage overhead in CI: coverage.xml only, sysmon core Oct 17, 2026

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[OMNIML-6182] [1/2] Run the required checks in GitHub's merge queue #2858

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shengliangxu changed the title ~~[2/11] Cut the coverage overhead in CI: coverage.xml only, sysmon core~~ [2/12] Cut the coverage overhead in CI: coverage.xml only, sysmon core Oct 17, 2026

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[OMNIML-6183] [2/2] Run the GPU, example and regression tests in the merge queue only #2860

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[OMNIML-6259] Speed up CI JIT compiles: Triton cache and arch-limited CUDA wheels #2905

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[OMNIML-6178] [2/11] Use the sysmon coverage core in CI - #2846

[OMNIML-6178] [2/11] Use the sysmon coverage core in CI#2846
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