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[OMNIML-6180] Run the small example lanes of a job together - #2924
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The title clearly and concisely describes the main change: grouping small example lanes within a job. |
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2924 +/- ##
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- Coverage 79.49% 79.09% -0.40%
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Every example had its own job, and each job spent a container start (2.6-4.2 min),
checkout and base install before its first test, while llm_distill, llm_sparsity,
specdec_bench and gpt-oss run only 1-3 min of tests each.
The runner now accepts several examples ("llm_eval gpt-oss") and runs them one
after the other, each in its own step (new composite action run-example: its
requirements, its tests, its coverage upload under the same examples-<name> flag
as before). A failing example does not stop the next one, as when each had its
own job; the job fails at the end. Requirements are installed right before each
example's tests, so an example that upgrades packages does not change the
environment of the ones before it. The allow-failure list (repo variable) is now
read per example inside the runner instead of once per job by the caller.
Grouped: llm_distill, llm_sparsity and specdec_bench in the torch lane; llm_eval
and gpt-oss in the trtllm lane (gpt-oss upgrades datasets and trl, so it runs
last). Kept apart: llm_qat, speculative_decoding and hf_ptq (long jobs), and
torch_trt, whose requirements downgrade torch 2.13 to 2.12.1 and torchvision to
0.27, which torch_onnx must not run on. Lane gating, images, extras, timeouts and
the required-check aggregation are unchanged.
Testing: actionlint clean; PyYAML comparison of the old and new jobs (13 -> 10
example jobs, every example still run once); stub test of the new shell step;
pre-commit on the changed files. Not run on GitHub runners.
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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- Line 58: Update the examples array count validation to require at least one
and at most three names, and perform this check before emitting e0 so an empty
example list is rejected before any action runs.
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Skip the Codecov upload after an allowed failure, which continue-on-error leaves green, so its partial coverage is not reported under the example's flag. Require 1 to 3 names in a job's example list (an empty list made the first action run all of tests/examples) and pass the list through the environment instead of expanding it into the script. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Claude review — PR #2924
Scope. The trigger comment was a bare /claude review, so this is a full review. All 3 changed files were opened (.github/actions/run-example/action.yml, .github/workflows/_example_tests_runner.yml, .github/workflows/example_tests.yml), plus the surrounding context the change depends on but does not show: the base version of the runner workflow, the whole of example_tests.yml (lane filters and example-pr-required-check), pyproject.toml's pytest/coverage config, .github/actions/cache-extensions and save-extensions, and the grouped examples' tests under tests/examples/ to check their GPU-count gating and subprocess handling. No modelopt/ or examples/ source is touched, so the usual algorithm / mode-state / export categories do not apply.
Findings
| Severity | Count |
|---|---|
| CRITICAL | 0 |
| IMPORTANT | 1 |
| SUGGESTION | 3 |
IMPORTANT — the allow-failure gate is the one mechanism that changed implementation, and it is the one path CI never ran (run-example/action.yml:30). It moved from a continue-on-error on a workflow job step to one on a composite-action step — a different runner code path, with no precedent anywhere else in .github/actions/, and not part of the documented runs.steps[*] metadata for composite actions. The description states the failure path has not been exercised on GitHub. The exposure lands exactly on the only example currently in ALLOW_FAILURE_EXAMPLE_TESTS: diffusers is a single-example job, so example0's outer continue-on-error is false and there is no fallback — if the inner one is not honoured, a known-broken lane starts blocking every PR that touches examples/diffusers. Either move the exit-code handling into the shell (runner-independent, sketch in the comment) or force an allowed failure once and confirm both directions before merging.
The three SUGGESTIONs: the Flag allowed failure condition dropped its allow-list check and now relies on composite-scoped success() for truthfulness; the cap of 3 is encoded in four places where raising only the validation bound silently stops running the 4th example; and the torch lane's timeout_minutes: 30 was not scaled for the 3-example group.
What I checked and found correct
Recording these so they do not get re-litigated:
- Failure propagation across the group. The
continue-on-error: ${{ steps.examples.outputs.eN != '' }}ladder is right for all three group sizes: the last example always has itfalseand fails the job at its own step, earlier ones are caught by the finalexample0 || example1check. Acontinue-on-errorfailure does not flipsuccess(), so that final step does run after an earlier hard failure, and it is correctly skipped (job already red) when the last example is the one that failed. Skipped steps reportoutcome == 'skipped', not'failure', so single- and two-example jobs do not trip it. - Per-example coverage attribution — the thing most likely to break when three pytest sessions share a workspace. It holds:
addoptshas no--cov-append, so pytest-cov'sCentral.start()callsCoverage.erase(), and with[tool.coverage.run] parallel = truethat erase also globs away the previous example's.coverage.*subprocess files. Each example'scoverage.xmltherefore contains only its own lines, and theexamples-<name>flags keep their old meaning and carryforward behaviour. PIP_CONSTRAINTpersisted to$GITHUB_ENVis necessary, not incidental: the example requirements install moved out of the step that didexport PIP_CONSTRAINT, so without this line thetorch<2.14pin would have been lost for the tensorrt-image lanes. It is set viadocker exec --env, so it overrides the container's bakedPIP_CONSTRAINT: "", and the affected lanes (diffusers,onnx) are ungrouped, so behaviour matches the base exactly.- The grouping ordering rationale survives the extension cache.
cache-extensionsstores only/root/.cache/torch_extensions, not site-packages, so a group's cache entry cannot smugglegpt-oss's upgradeddatasets/trlinto the next run'sllm_eval. Excludingtorch_trtfrom thetorch_onnxjob is the right call for the same reason — its torch downgrade would have put two torch versions' builds under one key. - Cache key is byte-identical to the old one for single-example jobs (
IFS=+over a one-element array), so only the two new groups pay a cold build once. - Equivalences.
allow_failure_examplesreaches the same verdict as the old per-jobcontains(format(...))for the whole-lane callers (megatron_bridge,diffusers). The*"diffusers"*wildcard for the apex uninstall still works against a space-separated list.secrets: inheritis present on every caller, sosecrets.CODECOV_TOKENresolves.pr-gate's lane filters are directory-based andexample-pr-required-checkaggregates on job ids, so neither notices the matrix reshuffle — the renamed per-matrix check names are not required checks. - Validation and shell hygiene. An empty
examplefails at the first step, before the container does any work;(( ... ))returns 1 on a zero result so the||guard fires;shell: bashbrings-e -o pipefail, which also makes a typo'd example name fail atfindrather than install nothing silently. - Cross-example GPU/process leakage was my other worry about sharing one container, and
tests/_test_utils/examples/run_command.pylargely closes it — it kills the command's process group on timeout and warns about descendants holding the pipe open, and each example's pytest process exits before the next starts, so GPU memory is released. Not raised as a finding.
Risk assessment
Low, with one caveat. The structural work is careful and unusually well evidenced — the ordering constraints, the cache-key continuity and the install-late design are all reasoned out in the description and hold up against the code. The residual risk is concentrated in the single untested behaviour above, and it is asymmetric: it is invisible while every example passes (as in both of this PR's runs) and only shows up the first time an allow-listed example fails, which is precisely when you least want CI semantics to change. Worth closing that before merge; everything else here is non-blocking.
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Approving: the grouped-job logic is correct, every example still runs exactly once, and the PR's own CI exercised the new step path on all lanes.
No action needed.
No action needed:
- I traced the failure paths by hand. Steps 1 and 2 use
continue-on-error, so a failing example doesn't stop the next one. The finalexit 1step then catches those failures, and step 3 or a single-example job fails at its own step. An allowed failure is markedcontinue-on-errorinside the composite action, so it never trips the final step. - Megatron and diffusers now go through the same per-example allow-list match as the other lanes (
megatron_bridge,diffusers), so their behaviour is unchanged. PIP_CONSTRAINTis now written toGITHUB_ENV, so the per-example requirement installs on the tensorrt images still get thetorch<2.14cap.- The failure and allowed-failure paths have not run on GitHub yet, as the PR body says. Worth a look the first time an example in a grouped job breaks. Also worth checking that the second example's coverage starts clean, since pytest-cov should erase data left by the first.
- Expect a small rebase against #2905, which also edits
_example_tests_runner.yml.
Restore the allow-list check in the condition of the allowed-failure warning, and derive the example loop bound from the validated cap, so the validation and the outputs cannot drift apart. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
A failed step ends the job at once, whatever continue-on-error the caller puts on the action's step (checked on a hosted runner), so a failing example skipped the examples after it. The action now reports `failed` and the last step of the job fails it after the remaining examples have run. The allow-failure verdict moves into the action. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Review comments at @.github/actions/run-example/action.yml:
- Line 38: Handle the requirements installation step’s failure separately from
the test step’s continue-on-error setting: record the installation failure
through Verdict as a blocking example failure, then allow the runner to proceed
to the next example. Ensure the failure-reporting step runs after an
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Claude review — [OMNIML-6180] Run the small example lanes of a job together
Scope: all 3 changed files reviewed in full (.github/actions/run-example/action.yml, .github/workflows/_example_tests_runner.yml, .github/workflows/example_tests.yml). No modelopt/ or examples/ source changes. Nothing opened speculatively beyond the surrounding context of the runner workflow, the lane definitions, pyproject.toml's coverage config and the examples//tests/examples/ trees used to check the grouped names.
Findings: CRITICAL: 0 · IMPORTANT: 0 · SUGGESTION: 3 — all three on action.yml, none blocking.
- The install step is not failure-tolerant — a broken
requirements.txtin an earlier example aborts the job and skips the later ones, which the one-job-per-example layout did not, and which the action's own description says it avoids. It also bypasses the allow-failure list for an allowed example whose install rather than tests break. This is the one I'd most want addressed, since example requirement resolution is a common way these lanes break. - Reliance on
continue-on-errorinside a composite action — undocumented forruns.steps[*]; works today per run 39822372305, but a silent reversion would reintroduce the e332c2e bug with no signal. Capturing pytest's exit status in the script removes the dependency. - Whitespace-sensitive allow-list match — pre-existing, fails safe, one-line fix now that the variable reaches a shell.
What I checked and found correct
- Allow-failure refactor is semantically equivalent, in both directions. The
megatronanddiffuserslanes dropped their hardcodedcontains(..., ',megatron_bridge,')/',diffusers,'in favour of the per-example match, and theirexampleinputs are exactlymegatron_bridgeanddiffusers, so neither widens nor narrows. The boundary-anchored",$LIST," == *",$EXAMPLE,"*test has no substring false positive (torch_trtvstorch_onnx,torch_trt), and an empty list yields,,, which matches nothing. The oldformat(',{0},', matrix.example)could never have matched a space-separated group entry, so the rename was genuinely required, not cosmetic._example_tests_runner.ymlhas exactly one caller, so the input rename breaks nothing. - No path by which a blocking failure goes green.
steps.run_tests.outcome(notconclusion) is the right field undercontinue-on-error. A skippedExample 2/Example 3leavesoutputs.failedempty, and the||chain still catchesexample0. If any step outside the tolerated one fails, the job is already red and the finalexit 1step being skipped doesn't mask it.Save extensionskeeps itsalways(). - Coverage does not cross-contaminate between examples sharing a container.
[tool.coverage.run] parallel = truemade me check this: pytest-cov without--cov-appendcallserase()at session start, which removes stale.coverage.*parallel files, so a hard crash in example 1 can't fold its subprocess data into example 2'sexamples-<name>flag. Flags themselves are unchanged, so Codecov's per-example carryforward is preserved as the description claims. PIP_CONSTRAINTplumbing is equivalent. The per-example installs moved out of the step that didexport PIP_CONSTRAINT, and the newGITHUB_ENVwrite restores the constraint for them. It only fires for*/tensorrt:*images (diffusers,onnx) — not thetensorrt-llm/releaseimage — so it affects no currently grouped lane, but it is correct for future grouping. The*"diffusers"*wildcard in the apex-uninstall test still matches a group containingdiffusers.- Timeouts cover the merged jobs. torch group: 30 min against a measured 519 s. trtllm group: default 60 min against a cold-cache 1,271 s. I checked
tests/examples/{llm_distill,llm_sparsity,specdec_bench,llm_eval,gpt-oss}for GPU-count gating and found none — these run tiny fixture models, so unlikemegatron_bridge(11 min PR → 58 min nightly) the 2-GPU nightly shouldn't inflate them. The grouped jobs stay well under the lane's longest job, so wall time holds. - All five grouped example directories and their
requirements.txtexist. Worth confirming because the composite step'sshell: bashaddspipefail, which the old install step (defaultbash -e, no pipefail) lacked: a missingexamples/<name>would now makefindfail the pipeline rather than silently install nothing. That's a strict improvement here, not a break. - Cache key.
IFS=+join gives the unchanged key for a single example, and the per-group key correctly avoids the torch_trt/torch_onnx problem the description calls out (cached extensions built against a torch the next example downgrades). - No prompt-injection or review-steering text in the diff.
max=3validation rejects 0 and 4+ before any example runs, and the comments on both sides tie the cap to the threeExample Nsteps.
Residual risk worth a line in the description, not a change
Grouped examples now share one container, which the old layout never did. Two consequences the PR's green run can't rule out for future changes: a stray GPU-holding subprocess from an earlier example (several of these tests launch example scripts as subprocesses; specdec_bench even has a test_shutdown.py) can OOM a later one, and HF/model caches plus pip downloads accumulate on one disk. A nvidia-smi or df -h line between examples would cost nothing and make the eventual "it passes alone but fails in the group" report diagnosable. Separately, "Re-run failed jobs" now re-runs all three examples of a group rather than the one that failed — a real but modest ergonomic cost against the ~900 runner-seconds saved.
Risk: low. CI-only, well-reasoned, and unusually well-evidenced — the failure paths were exercised on a throwaway branch across six scenarios rather than argued about, the merge candidates were chosen against actual install logs, and the measured numbers in the description are reported honestly including the cold-cache first run. The findings above are refinements, not blockers.
### What does this PR do? Type of change: CI (test runtime, tests only) PR runs of the `hf_ptq` example lane use 4 pytest-xdist workers on the runner's one GPU (#2771). Small-model tests share the GPU, and tests that need it alone take an exclusive lock (`tests/examples/hf_ptq/conftest.py`). While an exclusive test runs, the other three workers can only run tests that need no GPU. Four tests were exclusive: `test_ptq_t5`, `test_ptq_mixtral` and the two `test_qwen_vl` cases. Their calls took 25 s, 114 s, 114 s and 82 s, 335 s in total, one after the other, in a lane whose pytest time is 547 s (job 852 s; run 39662683232, job 119443637479, on a 1-GPU PR runner, 238 passed). T5 (t5-small) and Mixtral (`TinyLlama-1.1B-Chat-v1.0-x2-MoE`, a 2B two-expert MoE) do not need the GPU alone. The reason they did is the calibration batch size: without one, `hf_ptq.py` runs a forward pass sized to fill all free GPU memory. The TinyLlama tests already pass a batch size equal to the calibration size and share the GPU. This PR gives T5 and Mixtral the same treatment through one helper (`_run_sharing_the_gpu`), used by all three tests. Test ids are unchanged. The two Qwen3-VL tests keep the exclusive lock: the TRT-LLM multimodal deploy reserves 60% of the free GPU memory for its KV cache (60 GiB on a 96 GiB GPU, measured in #2771). Measured in CI in two pairs of runs of the `hf_ptq` job (table below): pytest -77 s (-14%) on equally fast runners, and -24 s (-4%) after the rebase, where this PR's runner was about 10% slower than the baseline's (roughly -70 s scaled by that). The simulation had predicted -92 s. ### Time improvement (measured in CI) Seconds, from the `--durations` lines and the result-line timestamps of the `hf_ptq` job on a 1-GPU runner (`COVERAGE_CORE=sysmon`), in two pairs of runs. - Pair 1: before = the run of PR #2868 (run 39662683232), which has the unchanged test file and 238 tests; after = this PR's first run (run 39686597553). - Pair 2, after the rebase onto main f983619: before = the `hf_ptq` job of #2924's run (run 39737194943; main's unchanged test file, started within 10 s of the other); after = this PR's run (run 39737199847). Both have 241 tests: the three extra ones (`test_convert_ggml_to_int8.py`) came with main, take no GPU lock and cost both runs alike. | | before 1 | after 1 | change 1 | before 2 | after 2 | change 2 | |---|---|---|---|---|---|---| | `test_ptq_t5` call (exclusive, now shared) | 25.3 | 27.4 | +2.2 | 43.9 | 57.9 | +14.0 | | `test_ptq_mixtral` call (exclusive, now shared) | 114.0 | 122.4 | +8.4 | 114.3 | 133.3 | +19.0 | | `test_qwen_vl[fp8]` call (exclusive, unchanged) | 114.0 | 111.7 | -2.2 | 113.9 | 124.1 | +10.2 | | `test_qwen_vl[nvfp4]` call (exclusive, unchanged) | 81.6 | 81.0 | -0.6 | 80.9 | 89.5 | +8.6 | | the 15 `test_ptq_llama` calls, summed (they now run next to T5 and Mixtral) | 516.8 | 573.7 | +56.9 | 511.3 | 582.0 | +70.7 | | the exclusive tests' calls, summed (time the GPU is held alone) | 334.8 | 192.7 | -142.1 | 353.0 | 213.6 | -139.3 | | `setup` durations pytest lists for the LLM and VLM tests, summed (waiting for the GPU lock) | 644.5 | 351.1 | -293.4 | 852.2 | 337.2 | -515.0 | | first to last test result | 532.0 | 455.3 | -76.7 | 561.6 | 532.8 | -28.8 | | **pytest** | **547.5** | **470.3** | **-77.2 (-14%)** | **576.9** | **552.5** | **-24.3 (-4%)** | | test step (`Run tests`; `Example 1: hf_ptq` in #2924's run) | 560 | 483 | -77 | 606 | 568 | -38 | | job | 852 | 788 | -64 | 883 | 877 | -6 | The job also includes the container start and the dependency install, which this PR does not change (237 and 26 s before, 251 and 28 s after in pair 1; 243 and 17 s, 255 and 29 s in pair 2). In pair 1 the unchanged exclusive tests (Qwen3-VL) took the same time in both runs within 2%, so the runners were equally fast. In pair 2 the runner of the "after" run was about 10% slower (the same two tests took 9% and 11% longer), which hides part of the gain: scaled by that slowdown, the pytest saving is roughly 70 s instead of 24 s. In both pairs the mechanism shows the same way: the GPU is held alone for about 140 s less, and the workers wait 293 s and 515 s less in total for the lock. T5 and Mixtral take longer (+2 to +19 s each) because they now run next to the TinyLlama workers, but they no longer block them. Mixtral's call stays under the 300 s per-test cap in both runs, and no test failed or ran out of memory. The two Qwen3-VL tests still hold the GPU alone for about 200 s, which bounds what is left to save. ### Usage Nothing user-facing. ### Testing - Collection gives the same 21 node ids for `test_llm_ptq.py` and `test_vlm_ptq.py` as main. `--setup-plan` shows `shared_gpu` for t5, mixtral and llama and `exclusive_gpu` for qwen_vl. - `tests/examples/hf_ptq/test_gpu_lock.py` (the lock itself) passes: 3 tests. - Scheduling simulation on CPU, with the real lock and `-n 4`: a plugin (not part of this PR) fakes the GPU facts and replaces `run_hf_ptq_command` with a sleep of the measured call time of each test (from the 1-GPU run above, scaled by 0.1). On main the exclusive tests never overlap another test, and the first-to-last-test span is 486 s at real scale. On this branch they still never overlap, t5 and mixtral run alongside the llama cases, and the span is 394 s (-92 s). Every fake run of t5, mixtral and llama received `calib_batch_size=16` (4 for the AutoQuantize case, as before), and qwen_vl received none. - Pre-commit passes. Not run on a GPU locally; the real lane ran in this PR's own CI (below). What this PR's own GPU run confirmed, and what is left: - Confirmed by this PR's own `trtllm (hf_ptq)` runs on the 1-GPU runner (before and after the rebase): T5 and Mixtral stay within the GPU's memory next to the TinyLlama workers (no failure or out-of-memory) and Mixtral's call takes 122 s and 133 s, under the 300 s per-test cap. Peak GPU memory was not logged; to measure it, a cluster agent (lyris, hecate or ptyche) can run `pytest tests/examples/hf_ptq/test_llm_ptq.py -n 4` at this SHA in the TRT-LLM `release:1.3.0rc20` container on one GPU while logging `nvidia-smi --query-gpu=memory.used`, and compare with main. - The nightly is unaffected except that T5 and Mixtral now use a fixed calibration batch of 16 instead of the memory probe. ### 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 change their GPU lock; the lock has its own 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?: ✅ (`/claude review` approved 456b2ed, no findings) ### Additional Information Left out on purpose, each needs a real GPU run or the owners' call: - **Qwen3-VL deploys sharing the GPU (up to 196 s of the remaining serial chain).** The deploy is TRT-LLM's `quickstart_multimodal.py` from the container (`/app/tensorrt_llm/examples/llm-api/`), called from `huggingface_example.sh` with only `--model_dir` and `--modality image`, so it uses the script's default KV fraction. Capping it needs a flag whose name for `release:1.3.0rc20` I could not check here (the image pins rc20 because newer releases break the script), plus a proof that a smaller KV cache does not OOM or slow the deploy while three other workers run. `KV_CACHE_FREE_GPU_MEMORY_FRACTION` in `parser.sh` is a different knob (the lm_eval path, default 0.8). - **xdist on the 2-GPU nightly (it runs serially, 1,260-1,620 s before the sysmon core).** The lock treats the machine as one GPU, so it would allow it, but `modelopt.deploy.llm.LLM` sets `tp = torch.cuda.device_count()`, so on 2 GPUs every deploy test runs with tensor parallel 2 and several would overlap on both GPUs. That needs a 2-GPU run to trust. - Dropping or moving to nightly the nvfp4 Qwen3-VL case (82 s), or the six IQ/q8_0 recipe cases (about 50 s of wall time): coverage trade-offs for the owners. - The comment in `_example_tests_runner.yml` ("small-model tests share it and the rest run alone on it") is now slightly stale (only the Qwen3-VL tests run alone). Editing the workflow would make every example lane run on this PR, so I left it for the next workflow change. Related: #2771 (the GPU lock and `-n 4`). <!-- This is an auto-generated comment: release notes by coderabbit.ai --> <!-- coderabbit-ai-generated:v1 --> ## Summary by CodeRabbit * **Tests** * Updated post-training quantization test runs for T5, Mixtral, and TinyLlama to share GPU resources and apply calibration batch-size defaults consistently. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
The requirements install ran in a step of its own, so a broken requirements.txt failed the job at once, skipped the later examples of its group and bypassed the allow-failure list. The install and the tests now run in one step that records the exit status instead of failing, and the verdict reads it, so nothing depends on continue-on-error inside the action. The allow-failure match ignores whitespace, so "diffusers, llm_qat" works. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
### What does this PR do? Type of change: CI (test runtime, tests only) On pull requests the `llm_qat` example lane runs one training backend per test family: the `fsdp2` cases of `test_qwen3_qat_nvfp4` and `test_qwen3_qad_nvfp4`. The `deepspeed` case of both and the `ddp` case of `test_qwen3_qat_nvfp4` keep their ids and run in the scheduled CI (nightly and on demand) as before. Why: PR runners have one GPU (`linux-amd64-gpu-rtxpro6000-latest-1`; the nightly has two), so a backend case on a PR exercises the backend wrapper and ModelOpt's patches but not sharding. Each of the three cases is an `accelerate launch` of `train.py` and takes 99-109 s on a normal-speed runner, 146-157 s on a slow one. `fsdp2` is the one that stays because it is the documented default and has the most ModelOpt-specific code (`_patch_fsdp2_post_backward`, `_patch_accelerate_for_fsdp2_fix`, the sharded save in `transformers_trainer.py`, and the FSDP2-only path of distillation, which rejects FSDP1). In the CI history since 10-06 all four failures of this lane are on `fsdp2` or QLoRA cases (three at the 300 s per-test cap on slow runners, one real error in `test_qwen3_qad_nvfp4[fsdp2]`); none is on `deepspeed` or `ddp`. `test_qwen3_qlora_nvfp4` still trains on `ddp` on PRs. The skip is a `pytest.mark.skipif` on the three parameters, using the same `GITHUB_REF` check as `tests/examples/torch_onnx/test_torch_quant_to_onnx.py` (`refs/heads/pull-request/`). The example workflow does not pass `--run-manual` (only the unit workflow does), so the `manual` marker is not an option here. No workflow file changes. ### Time improvement (measured in CI, per run of the lane on 1 GPU, `COVERAGE_CORE=sysmon`) Per-test times from the result-line gaps of the lane's job logs on 2026-10-21: | run | runner | pytest | QAT deepspeed | QAT ddp | QAD deepspeed | skipped on PRs | after | |---|---|---|---|---|---|---|---| | e5220c3 (20:36 UTC) | normal | 680 s | 99 s | 100 s | 109 s | 308 s | about 372 s | | 7607678 (19:07 UTC) | normal | 687 s | 105 s | 101 s | 109 s | 314 s | about 373 s | | d1dad23 (20:57 UTC) | slow | 984 s | 151 s | 147 s | 157 s | 454 s | about 530 s | So about 310 s of pytest (45%) per PR run on a normal runner and about 450 s on a slow one; the lane's job goes from about 900 to about 600 s (normal) and from about 1,320 to about 900 s (slow). The remaining cases on a PR are `fsdp2` QAT (100 s), `fsdp2` QAD (106 s), QLoRA (145 s) and the pre-tokenize test (18 s). Nightly and local runs are unchanged. Measured in this PR's own run (run 39686596776, 1-GPU runner): pytest 464 s, 13 passed and 4 skipped (the three nightly-only cases with the reason "Only fsdp2 runs on PRs; runs in the scheduled CI", plus the existing LoRA skip); `fsdp2` QAT 152 s, `fsdp2` QAD 103 s, QLoRA 178 s, pre-tokenize 28 s. This runner was slower than the normal-speed runners of the table (the four unchanged cases took 461 s together against 369 s on a normal runner, 1.25x), so at this speed the three skipped cases would have cost about 385 s (308 s at normal speed): the saving is about 45% of the lane's pytest time, as projected. The job as a whole took 1,260 s only because this run's container initialization took 655 s; the `Run tests` step was 492 s. After the rebase onto main f983619, this PR's run (run 39737195847) took 594 s for the job and 342 s for pytest (13 passed, 4 skipped), against 1,144 s and 761 s (16 passed, 1 skipped) for the unchanged `llm_qat` job of #2924's run on the same main (run 39737194943, started within 14 s of it): -550 s for the job and -419 s (-55%) for pytest. The three skipped cases took 121 + 105 + 92 = 318 s in the unchanged job; the rest of that gap is runner speed (the unchanged QLoRA test took 135 s against 108 s, `fsdp2` QAT 128 s against 83 s). ### Usage Nothing user-facing. On a `pull-request/N` ref (what copy-pr-bot runs for a PR) the three cases are reported as skipped with "Only fsdp2 runs on PRs; runs in the scheduled CI". ### Testing - On a CPU host with `run_example_command` replaced by a recorder (scratch plugin, not part of the PR), `pytest tests/examples/llm_qat/test_llm_qat.py -k "qat_nvfp4 or qad_nvfp4"`: - `GITHUB_REF=refs/heads/pull-request/2900`: `test_qwen3_qat_nvfp4[fsdp2]` and `test_qwen3_qad_nvfp4[fsdp2]` run; `test_qwen3_qat_nvfp4[deepspeed]`, `[ddp]` and `test_qwen3_qad_nvfp4[deepspeed]` are skipped with the reason (2 passed, 4 skipped, the 4th being the existing LoRA skip). - `GITHUB_REF=refs/heads/main` (the nightly) and an unset `GITHUB_REF`: all five run (5 passed, 1 skipped, the same existing skip). - Pre-commit over the range against main passes. No workflow file changed, so no actionlint run. - Run on a GPU runner: this PR's Example run shows the two `fsdp2` cases, QLoRA and the pre-tokenize test passing and the three nightly-only cases skipped (numbers above). Still to confirm: the next nightly runs all five. ### Coverage that PRs lose - DeepSpeed ZeRO-3 training for QAT and QAD: `make_deepspeed_compatible`, the ZeRO-3 state-dict loading patch (`_load_params_and_buffers_into_zero3_model`), the parameter gathering in distillation (`deepspeed.zero.GatheredParameters`) and the AWQ/SmoothQuant guard for DeepSpeed. Both DeepSpeed cases still run nightly, on two GPUs, which is the only place sharding is exercised anyway. - Full-parameter `ddp` QAT. The DDP wrapper itself still runs on PRs through the QLoRA test. - `fsdp2` QAT and QAD, QLoRA, the pre-tokenize test and the unit-style tests of the lane are unchanged. Open question for the code owners: keep `fsdp2` for both families (this PR), or spread the backends so every one stays on PRs once (`fsdp2` for QAT, `deepspeed` for QAD), which keeps DeepSpeed's distillation path on PRs but moves `fsdp2` distillation to the nightly. A regression in a skipped backend would surface the next night rather than on the PR. ### 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 cases are skipped on PRs; ids 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?: ✅ (`/claude review` approved 992db89; its one suggestion, a shared PR-detection helper, is postponed) ### Additional Information Part of the GPU-CI cost reduction under OMNIML-6177. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> <!-- coderabbit-ai-generated:v1 --> ## Summary by CodeRabbit * **Tests** * Some quantization-aware training checks now run only in scheduled CI, including DeepSpeed cases and the NVFP4 DDP case. * DDP is no longer included among the QAD backend test cases. * QLoRA test coverage remains unchanged. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
Docker pull time vs. what grouping savesI looked at the job step timings of the last 7 nightlies and the last 150 PR runs of
The pull alone is about 26% of example-job runner time (nightly 14.3k of 54.1k s; PR 42.6k of 129.8k s). Fixed cost per job (PR medians): pull 170–260 s (onnx about 70 s), then about 40 s more before the tests (about 30 s of it the base Saved by this PR (its 4 PR runs so far):
That is about 800 runner-seconds per full PR run (about 10%), and about 75% of it is the pull. If the pull were quick, only about 65 s per removed job would remain (about 200 s per full run, 2–3%), which wouldn't justify sharing a container and an environment between examples. Grouping also means fewer GPU runners needed at once, which may shorten queue times; these numbers don't measure that. So the bigger lever is the pull itself: about 3,000 s per full PR run across all 13 example jobs, not only the 3 that grouping removes. A registry mirror or pull-through cache near the runners, runner images with the CI images preloaded, or slimmer images would cut it. If the pull gets fast, it would be worth going back to one example per job. Until then, this PR is a reasonable stopgap, and it targets the right lanes (the torch and trtllm images are the slow pulls; onnx at about 70 s is correctly left alone). Risks of running several examples in one job
Checked and fine: only |
Examples of one job share the GPU and the environment. Log the GPU state before each example and the packages its requirements changed, so a failure caused by an earlier example can be traced. The apex uninstall for diffusers moves from the job-wide install step, whose substring check would also remove apex for any example grouped with diffusers, into the action, where it applies to that example only. The grouping comment now says an example that changes torch must not be grouped. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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Thanks, this is a useful analysis, and I agree with the economics: the pull is the main fixed cost, grouping is a stopgap, and going back to one example per job is only a matrix edit once pulls are fast. On the pull itself, our shared-cache investigation (#2905) found it is extraction-bound, not download-bound: layers arrive in 28-135 s but pulls end at 66-298 s, all five images are gzip, and the Docker daemon's registry mirror covers Docker Hub only (nvcr.io pulls go direct). So a mirror or pull-through cache would not help; zstd-compressed images (the platform docs claim -31 to -73% init time, unverified on these runners), pre-pulled runner images or slimmer images would. That is a platform ask rather than a repo change. For the risks, ba8aebb covers 1, 2, 3 and 5; 4 was already in the earlier push:
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kevalmorabia97
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LGTM, thanks for addressing the earlier points. Checked on the green run of ba8aebb: the per-example pip freeze diffs and GPU-state groups show up as intended (gpt-oss's datasets 3.1→5.1, huggingface_hub, pyarrow and trl upgrades confirm it belongs last; every example started on an idle GPU), and the apex uninstall now matches diffusers exactly. Agreed on the pull being extraction-bound, so that's a platform ask rather than a mirror.
One non-blocking nit inline.
| # that pins or upgrades packages (e.g. gpt-oss upgrades datasets, torch_trt downgrades torch) must not | ||
| # change the environment of the examples that run before it. | ||
| packages_before="${RUNNER_TEMP:-/tmp}/packages-before-${{ inputs.example }}.txt" | ||
| python3 -m pip freeze > "$packages_before" || true |
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Nit (non-blocking): on the pytorch image every pip freeze prints ~10 lines of git config ... exit code 128 / fatal: not a git repository: /opt/pytorch/ao to stderr (twice per example; this one lands outside any log group). The diff itself is right. Dropping stderr removes the noise:
| python3 -m pip freeze > "$packages_before" || true | |
| python3 -m pip freeze 2>/dev/null > "$packages_before" || true |
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Applied in f1d797a: the stderr of pip freeze goes to /dev/null here, thanks.
| find examples/${{ inputs.example }} -name "requirements.txt" | while read req_file; do python3 -m pip install -r "$req_file" || exit 1; done || status=$? | ||
| echo "::endgroup::" | ||
| echo "::group::Packages changed by the requirements of ${{ inputs.example }} (< before, > after)" | ||
| { python3 -m pip freeze | diff "$packages_before" - || true; } | grep "^[<>]" || echo "none" |
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Same here:
| { python3 -m pip freeze | diff "$packages_before" - || true; } | grep "^[<>]" || echo "none" | |
| { python3 -m pip freeze 2>/dev/null | diff "$packages_before" - || true; } | grep "^[<>]" || echo "none" |
On the pytorch image every `pip freeze` prints about ten lines of `git config ... exit code 128` and `fatal: not a git repository: /opt/pytorch/ao` to stderr, twice per example, one of them outside any log group. The freeze is only logged (`|| true`) and the diff is unaffected, so stderr goes to /dev/null (review suggestion). Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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… skips (#2982) ### What does this PR do? Type of change: CI (test selection) Step 1 of 2 of the follow-up to a review note on #2925 ("make it generic like unit and gpu tests: `pytest.mark.nightly` for nightly ones and `pytest.mark.manual` for the ones we may want to skip in nightly CI too"). Test selection by tier was done three different ways: the unit and GPU workflows pass `--run-manual` off pull requests, so `manual` means "also nightly" there; the example workflow passes nothing, so `manual` means "never in CI" in `tests/examples` (the two eagle cases in `test_eagle.py` never run); and the `llm_qat` and `torch_onnx` example tests each decided "PR or not" themselves from `GITHUB_REF` (two copies of the same line). There was no way to say "nightly only" in a test. This PR adds the `nightly` marker next to `manual` and `release` and uses it for the two example tests: - `pyproject.toml` registers the marker; `tests/conftest.py` gets `--run-nightly` and one entry in the existing marker/flag table. - The unit and GPU workflows pass `--run-nightly` with `--run-manual` off pull requests; the example action passes it when `github.ref` is not a `pull-request/N` ref (the same test `hf_ptq` uses for `-n 4`). - `test_llm_qat.py`: the three backend cases that only run nightly are `pytest.mark.nightly` params; the `GITHUB_REF` check and its `os` import are gone. - `test_torch_quant_to_onnx.py`: the three swinv2 cases that only run nightly are `pytest.mark.nightly` params of one flat `(model_key, qformat)` parametrization with the same ids and order as before; the in-test `pytest.skip` on `GITHUB_REF` is gone. - `CONTRIBUTING.md` names the two marks. Behavior: pull-request runs and nightly runs select exactly the same tests as before. Local runs of those two example files now skip the six nightly cases unless `--run-nightly` is given (they used to run everything because `GITHUB_REF` is unset); that is what `manual` already does for its tests. Step 2 (separate PR, needs the maintainers' agreement) makes the meaning uniform: the unit and GPU tests that are marked `manual` today but run nightly become `nightly`, the nightly workflows stop passing `--run-manual`, and `manual` means "never in CI" everywhere. It will be guarded by a before/after comparison of the nightly selection. ### Usage ```bash pytest tests/examples/llm_qat --run-nightly # also run the nightly-marked cases ``` ```python @pytest.mark.nightly # skipped on PRs, runs in the scheduled and on-demand CI pytest.param("deepspeed", marks=pytest.mark.nightly) # one case of a parametrization ``` ### Testing - Collection of `tests/examples/{llm_qat,torch_onnx,speculative_decoding}` (156 tests) on main (67e7ac3) and on this branch under three setups, comparing which tests are not skipped by a mark: a PR run (`GITHUB_REF=refs/heads/pull-request/7`, no flags): 147 selected before and after, identical; a nightly run (`GITHUB_REF=refs/heads/main`, `--run-nightly` after): 153 before and after, identical; a local run (no `GITHUB_REF`, no flags): 153 before, 147 after, the difference being exactly the six nightly cases (`test_qwen3_qat_nvfp4[ddp]`, `test_qwen3_qat_nvfp4[deepspeed]`, `test_qwen3_qad_nvfp4[deepspeed]`, `test_torch_onnx[swinv2_tiny-int8]`, `[swinv2_tiny-mxfp8]`, `[swinv2_tiny-auto]`). All 156 test ids are unchanged. The old `torch_onnx` skip happened inside the test, so for the "before" PR run those three ids were removed by hand. - `--strict-markers` collection passes with the new marker; `--run-nightly` appears in `pytest --help`; a CPU unit file runs with `--run-nightly --run-manual`. - YAML of the three changed workflow and action files parses; pre-commit passes. - This PR's own CI, PR side: `llm_qat` 13 passed / 4 skipped, `torch_onnx` 20 / 6, `speculative_decoding` 109 / 4, identical to the last run before the change (#2924's head). Not run: the nightly side (`--run-nightly` off `pull-request/*` refs); the first nightly after the merge shows it (the last one before: `llm_qat` 16 passed / 1 skipped, `torch_onnx` 23 / 3). - No test was added: the marker reuses the existing flag-gated skip loop in `tests/conftest.py`, and the example lanes exercise it. ### 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?: yes for CI; local runs of the six cases need `--run-nightly` (see Behavior) - 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 (selection mechanism, covered by the example lanes) - 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 (not run yet) ### Additional Information Jira: [OMNIML-6180]. Follow-up (step 2): re-mark the unit and GPU `manual` tests that run nightly as `nightly`; 24 marks in 10 files today (unit 17 in 4 files, gpu 6 in 5, gpu_megatron 1 in 1); the 2 in `tests/examples` (the eagle cases that never run in CI) stay `manual`. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
What does this PR do?
Type of change: CI (job layout of the example tests)
Every example of the example workflow has its own job, and a job spends a container start (156-252 s), the checkout and the base install before its first test. Several examples run only 48-156 s of tests: llm_distill 102 s, llm_sparsity 156 s, specdec_bench 48 s and gpt-oss 54 s (1-GPU PR run, 2026-10-21). This PR lets the runner take several examples and run them one after the other in one job.
_example_tests_runner.yml):examplelists 1 to 3 names separated by spaces (an empty list fails before any example runs). Each example gets its own step (Example 1: llm_eval,Example 2: gpt-oss) through a new composite action,.github/actions/run-example, that installs the example's requirements, runs its tests with coverage and uploads the coverage under the sameexamples-<name>flag as before (Codecov's per-example carryforward is unchanged), except after a failed run, allowed or not, whose partial coverage is no longer uploaded. A failing example (its requirements install or its tests) does not fail its own step: the action records the exit status and reportsfailed(a failing step inside a composite action ends the job at once, whatevercontinue-on-errorthe calling step has, as a hosted-runner check showed, so the action uses none), so the next examples still run, as when each had its own job withfail-fast: false, and the last step of the job fails it. A job with one example fails in that last step too, with anExample failederror annotation from the example's step; its cache key is unchanged.ALLOW_FAILURE_EXAMPLE_TESTS(nowdiffusers) is passed to the runner asallow_failure_examplesand read per example inside the action (itsVerdictstep: a warning for an allowed failure, an error annotation andfailed=trueotherwise; whitespace in the variable is ignored, sodiffusers, llm_qatworks); the callers lose their per-jobcontains(format(...))expressions.example_tests.yml): the torch lane runsspecdec_bench llm_sparsity llm_distillin one job (llm_distill last: it pins trl and torchao), the trtllm lane runsllm_eval gpt-ossin one job (gpt-oss last: it upgrades datasets 3.1.0 to 5.1.0 and trl). Both pairs share an image, extras ([hf,dev-test]) and timeout, and their requirement files do not conflict (read, and compared with the install logs of the separate jobs: the three torch examples ended on the same transformers 5.18.0, peft 0.21.2 and accelerate 1.15.0, and in the trtllm pair only gpt-oss changes anything, datasets and trl, which is why it goes last).torch-tensorrt<2.13), so either the torch_onnx tests would run on a different torch than they do, or the extension cache of the job would hold builds from two torch versions. Its install step takes 204 s and includes that torch swap, so grouping would save little anyway.Lane gating (
pr-gate), images, extras, timeouts and theexample-pr-required-checkaggregation are unchanged: the jobs keep their ids (torch,trtllm, ...), only their matrix entries and display names change (for exampletorch (specdec_bench llm_sparsity llm_distill) / run-test).Time improvement (estimated, then measured in this PR's run)
From the job step timings of the 1-GPU PR run of 2026-10-21 (container init, checkout, install, tests, post):
13 example jobs become 10: about 660 runner-seconds less per full PR run (about 8% of the roughly 7,800 job-seconds of a full run; the nightly saves the same container starts on 2-GPU runners). The grouped jobs keep their tests and installs; what disappears is the container start (156-252 s), checkout, caches and the base install of the second and third job. The merged jobs (about 606 and 726 s) are shorter than the workflow's longest job, so the wall time does not change. Measured in this PR's own run (run 39686590762, 1-GPU runners, every lane green):
specdec_bench llm_sparsity llm_distill) took 519 s (container 182 s, install 29 s, the three examples 54 + 149 + 91 s). The three separate jobs of a run of the same day took 273 + 342 + 948 = 1,563 s, but the 948 included an unusually slow 649 s container start (about 190 s is typical), so about 1,104 s typical: the merge saves about 585 s (estimate: 396 s).llm_eval gpt-oss) took 1,271 s because its cache key is new and the extension cache was cold (install 204 s instead of about 30 s, llm_eval 553 s instead of about 260 s). A re-run of that job with the cache saved took 600 s (container 220 s, install 20 s, llm_eval 285 s, gpt-oss 61 s), against 572 + 351 = 923 s for the two separate jobs of a run of the same day: about 323 s saved (estimate: 270 s). After this PR lands, the first run of each grouped job on main pays the cold build once.Usage
Nothing user-facing. To group another small example, list its name in the matrix entry of its lane (
example: ["a b", c]), put the example that pins or upgrades packages last, and keep at most 3 names per job.Testing
actionlint1.7.7: clean on all workflows (it caught that expressions have noreplace(), so the cache key is built in a shell step; for a single example it is identical to the old key).example_tests.yml: every example is still run exactly once, images, extras and timeouts per lane are equal,pr-gateandexample-pr-required-checkare equal, 13 jobs become 10.example_tests.yml, which every lane's change filter includes, so its own CI ran all lanes (all required checks green on 3048746, ab0a36f and e332c2e, and, with the install change below, on 5d6dfe5, the head of this PR; numbers above): the two grouped jobs and the single-example jobs through the new step path. Those runs built the extension cache cold under the new group keys (the 1,271 s first run of the trtllm group) and ran the per-example Codecov upload steps: every example uploaded its coverage (oneUpload coverage to Codecovstep per example in each job log). No example failed in them, so the failure paths were run separately (next bullet)._example_tests_runner.yml, with a passing and a failing fake example in six scenarios (an allowed or a blocking failure in a one-example job, as the first and as the last of two). First run (run 39821769250, this PR before e332c2e, withcontinue-on-erroron the example steps): an allowed failure left the job green with the warning and no coverage upload for the failed example, but a blocking failure in the first example skipped the second, because a failing step inside a composite action ends the job at once whatevercontinue-on-errorthe calling step has. The action therefore no longer fails on test failures: it reportsfailed, and the last step of the job fails it. Second run (run 39822372305, e332c2e): all six scenarios behave as designed. Third run (run 39828420032, this version, which also records the install's exit status and dropscontinue-on-errorfrom the action): eleven scenarios, the six above plus a requirements install that cannot resolve (blocking as the first of two, and allowed), an allow list writtenfake_pass, fake_fail, the same split over several lines, and an entry that only shares a prefix with the example. All behave as designed: allowed failures, including a failing install, leave the job green with the warning and no coverage upload for the failed example; blocking failures add anExample failederror annotation, still run the remaining examples (the passing one uploads its coverage, and an install failure in the first example does not stop the second) and fail the job in the final step; the prefix does not match.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.).allow_failureinput becameallow_failure_examples, and its only caller isexample_tests.yml)CONTRIBUTING.md: no new dependency/claude reviewapproved e332c2e, with three non-blocking suggestions)Additional Information
Part of the GPU-CI cost work under OMNIML-6177 (task OMNIML-6180, example lanes). It edits
_example_tests_runner.yml, as the open cache PR #2905 does (this PR touches the cache key line, #2905 the cache steps, so expect a small conflict); whichever lands second rebases. The repo variableALLOW_FAILURE_EXAMPLE_TESTSneeds no change.Summary by CodeRabbit