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Speed up CPU unit tests to stay under the 20 min CI limit - #2823
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- Fix _source_checkpoint returning "" for from_config models, which made exports glob the working directory (export unit tests 718s -> 15s) - Share two persistent gloo workers across multi-rank unit tests - Move spawn/subprocess-heavy tests to tests/gpu, drop a redundant subprocess test, shrink the auto_quantize matrix (60 -> 9 cases) - Mark slow AutoNAS and ONNX-entropy cases manual - Tighten test guidance in AGENTS.md and CONTRIBUTING.md Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely summarizes the primary objective: reducing CPU unit-test time to remain within the 20-minute CI limit. 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 PASS. The PR changes only one production Python file under modeloptand no example Python files. Its change only adds anor Nonefallback in_source_checkpoint; it adds no `torch.load(..., weigh…
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Changes requested: the speedups are sensible, but marking both entropy regressions manual removes their numerical contract checks from every CI job.
Needs action:
- Move the INT8/FP8 entropy cases in
test_calibrated_quantization.pytotests/gpu/onnx, or schedule them in CI; preserve the existing scale and graph assertions (see inline comment).
No action needed:
- The
_source_checkpointfix correctly normalizes empty provenance toNone, with a targeted regression. - Design review: this addresses repeated interpreter startup and the CPU timeout. It reuses the existing
DistributedWorkerPool; the body explains choosing persistent ranks over repeated spawns. Batched spawns and installedpytest-xdistare alternatives, not new dependencies needed here. - Test moves preserve assertions; the reduced AutoQuant matrix and deprecated-warning deduplication have visible rationale. AutoNAS exclusions explicitly cite inactive maintenance, unlike the still-active entropy contract.
- Tests were inspected, not executed.
nox's unit session forwards extra args to pytest; the scheduled run passes --run-manual. Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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Inline comments:
Review comments at @AGENTS.md:
- Around line 47-48: Update the test-placement guidance in AGENTS.md (lines
47–48) and CONTRIBUTING.md (lines 193–195) to add a narrow exception for shared
session-scoped CPU worker fixtures that start persistent Gloo workers and reuse
them across tests; keep the existing placement rules unchanged for other tests.
Review comments at @tests/gpu/torch/utils/test_hf_checkpoint_utils_dist.py:
- Line 42: Update the test’s mocked `snapshot_download` recording to append one
entry per call instead of overwriting the rank’s marker. After the existing
filename assertion, verify that the selected downloader’s marker contains
exactly one recorded `org/model` call.
Review comments at @tests/unit/conftest.py:
- Line 45: Move the process-spawning cpu_dist_workers fixture and its consumers
out of the unit test tree into the appropriate tests/gpu* location, preserving
their existing behavior.
Review comments at @tests/unit/torch/export/test_fsdp2_parallel_export.py:
- Line 179: Update _two_rank_streaming_export to cancel the faulthandler
traceback timer in a finally block, ensuring cancellation runs whether export
succeeds or raises before the worker is reused.
- Line 179: Update DistributedWorkerPool.run to check worker liveness while
waiting for results; if a worker exits before returning its result, fail
immediately and identify the affected rank instead of waiting for the queue
timeout.
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📒 Files selected for processing: 19
.github/workflows/unit_tests.ymlAGENTS.mdCONTRIBUTING.mdmodelopt/torch/export/unified_export_hf.pynoxfile.pytests/gpu/torch/models/test_modeling_ptq_registration.pytests/gpu/torch/utils/test_hf_checkpoint_utils_dist.pytests/unit/conftest.pytests/unit/onnx/quantization/test_calibrated_quantization.pytests/unit/recipe/test_presets.pytests/unit/torch/export/test_fsdp2_parallel_export.pytests/unit/torch/export/test_unified_export_hf.pytests/unit/torch/nas/test_full_algorithms_vision.pytests/unit/torch/nas/test_nas.pytests/unit/torch/quantization/test_autoquant.pytests/unit/torch/quantization/test_autoquant_shapley.pytests/unit/torch/quantization/test_dist.pytests/unit/torch/quantization/test_quantize_cpu.pytests/unit/torch/utils/test_hf_checkpoint_utils.py
💤 Files with no reviewable changes: 2
- tests/unit/torch/utils/test_hf_checkpoint_utils.py
- tests/unit/recipe/test_presets.py
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2823 +/- ##
==========================================
+ Coverage 69.33% 78.61% +9.27%
==========================================
Files 654 655 +1
Lines 72665 72777 +112
==========================================
+ Hits 50385 57213 +6828
+ Misses 22280 15564 -6716
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…wnload, document cpu_dist_workers Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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…eline-config Pick up NVIDIA#2823, which keeps the Unit tests linux job under its 20-minute limit. CHANGELOG.rst and unified_export_hf.py, changed on both sides, merged cleanly. Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com>
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### What does this PR do? Type of change: Bug fix (CI / tests) Follow-up to #2823. Its auto-merge landed before the last two commits were pushed, so main is missing them and every push-triggered `Unit tests / linux` run now fails. - **noxfile.py:** with `--run-manual`, the `manual` unit tests run in a second pytest process (no coverage). `test_transformers_load_with_multi_thread` is documented as flaky and leaves BF16 default-dtype state, which broke later tests when `--run-manual` shared the process with the regular suite (`BFloat16 != float` in `test_accelerate`, `test_fused_experts`, `test_auto_quantize`). - **tests/gpu/.../test_hf_dflash.py:** fixes the nightly 2-GPU failure (3 nights in a row). With 4 layers and 2 draft layers `target_layer_ids == [1, 1]`, so the test's own precondition (target layers straddle the shard split) could never hold; 8 layers gives `[1, 5]`. - **tests/_test_utils/.../distributed/utils.py, tests/gpu_megatron/conftest.py, gpu_tests.yml:** stop one hung test from timing out the rest of the `gpu_megatron` nightly job (cancelled at the 60-min limit on 5 of the last 8 nights, with 18-28 pytest timeouts of 120 s each). When a job hangs or a failed rank leaves its peers blocked in a collective, the shared worker pool stayed stuck and every later test in the module waited out its own timeout. `DistributedWorkerPool` now prints the workers' stacks and kills them (job interrupted, or peers not done 30 s after a rank failed) and marks itself broken; the gpu_megatron fixtures respawn a fresh pool; job timeout 60 -> 90 min (a healthy run takes ~47 min). ### Testing - Reproduced the dtype pollution locally with `pytest tests/unit --run-manual` (1 failure); the `manual` tests alone pass (17 in 87s) and the regular suite passes without `--run-manual`. - DFlash test: fails with `AssertionError: [1, 1]` before, 14 speculative GPU tests pass on 2 GPUs after. - gpu_megatron: reproduced the cascade in the `nemo:26.08` CI image on 2 GPUs (RTX 6000 Ada); with the change `test_megatron.py` takes 5m42s (127 passed, 3 failed) instead of cascading into timeouts, and the 2 new pool tests pass. The first hang (nightly: `test_fp8_real_quantize` on the RTX Pro 6000 runner) did not reproduce, so it can still occur; it now costs ~2 min instead of ~30 and its worker stacks go to the log. The 3 failures here are `test_te_grouped_vs_sequential_default_loss` x2 and `test_auto_quantize_moe_ep` on 48 GB cards (likely memory pressure, not confirmed). - Not yet run end to end: the new nox flow in CI (will dispatch `Unit tests` on this branch). - Is this change backward compatible?: ✅ - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: N/A - Did you write any new necessary tests?: N/A - Did you update Changelog?: N/A - Did you get Claude approval on this PR?: ❌ 🤖 Generated with [Claude Code](https://claude.com/claude-code) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Tests** * Improved reliability of distributed test runs by detecting failed or unresponsive workers, ending stalled jobs, and replacing unusable worker pools. * Added a way to run manual unit tests separately when requested, while keeping the standard test run’s coverage settings intact. * Expanded GPU test coverage for sharded hidden states across devices and generation output placement. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Sonnet 5.5 <noreply@anthropic.com>
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The unit workflow's `linux` job gates every GPU and example workflow: their pr-gate jobs wait for it (poseidon/wait-for-status-checks, `^linux$`). That wait is a median 13.8 min, 23% of a PR's GPU run, and the job itself takes a median 14.8 min. The tests are CPU-bound and run serially, so use the runner's four vCPUs. - unit_tests.yml: `PYTEST_ADDOPTS=-n 4 --dist loadfile` on the `linux` job and the multi-version matrix. One worker per vCPU (`-n auto` counts physical cores), whole files per worker, and `OMP_NUM_THREADS=1`: with torch's default threads per worker the runner is oversubscribed and the run is slower than serial (1,481 s and 41 failures in my local runs). - 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 xdist workers collected different test ids. Use a tuple. - test_hf_dflash2.py: test_overfits_a_single_batch needs more than the default 60 s call timeout when one thread runs it (about 53 s with torch's default threads). Give it 300 s. pytest-xdist is already in the dev-test extra on main (#2771); uv.lock is refreshed by the weekly bump_uv_lock workflow. Testing: the full tests/unit suite, on 4 CPUs with coverage as in CI, on the base before the unit-test cleanup of #2823. Serial 825 s; this configuration 332-344 s in three repeated runs (4,724 passed, 0 failed). The other matrix entries (Python 3.10, 3.13, 3.14, torch 2.8, transformers min) take 4.5-5.3 min. Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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#2769 #2770 #2719 #2796 #2801 #2794 #2787 #2682 #2793 #2791 #2792 #2766 #2823 #2777 #2773 #2813 #2832 #2795 #2684 #2822 #2838 #2807 #2772 #2837 #2820 (#2843) ## Cherry-picked PRs - #2647 - #2700 - #2718 - #2578 - #2580 - #2711 - #2731 - #2726 - #2697 - #2769 - #2770 - #2719 - #2796 - #2801 - #2794 - #2787 - #2682 - #2793 - #2791 - #2792 - #2766 - #2823 - #2777 - #2773 - #2813 - #2832 - #2795 - #2684 - #2822 - #2838 - #2807 - #2772 - #2837 - #2820 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Expanded quantized model export support for MXFP4, MXFP8, FP8, and additional block-quantization formats. * Added quantization support for GPT-OSS and Step-family models, plus export support for native FP8 experts. * Added chat JSONL data preparation for Megatron workflows and an option to freeze the student MTP head during distillation. * Added model recipes for Kimi, Gemma, DiffusionGemma, and GLM variants. * **Bug Fixes** * Improved ONNX quantization and export compatibility, distributed calibration consistency, and handling of packed attention caches. * Improved safe model loading and low-precision quantization accuracy. * **Documentation** * Updated the 0.48.0 changelog and guidance for data preparation and quantization recipes. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Also includes a partial backport of #2656 (only the `_validate_fold_weight` hook and the `mtq.fold_weight` pre-check), which #2820 depends on. --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> Signed-off-by: Rohan Joshi <rohjoshi@nvidia.com> Signed-off-by: Juhi Mittal <juhim@nvidia.com> Signed-off-by: Fridah-nv <201670829+Fridah-nv@users.noreply.github.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Signed-off-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com> Signed-off-by: weimingc <17592131+meenchen@users.noreply.github.com> Signed-off-by: Yue <yueshen@nvidia.com> Signed-off-by: joe-nv <222375587+joe-nv@users.noreply.github.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com> Signed-off-by: Ajinkya Rasane <131806219+ajrasane@users.noreply.github.com> Signed-off-by: ColinM-sys <cmcdonough@50words.com> Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Signed-off-by: Meng Xin <164952785+mxinO@users.noreply.github.com> Signed-off-by: gcunhase <4861122+gcunhase@users.noreply.github.com> Signed-off-by: Chad Voegele <cvoegele@nvidia.com> Co-authored-by: Shengliang Xu <106840466+shengliangxu@users.noreply.github.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com> Co-authored-by: Juhi Mittal <39641197+juhi10071998@users.noreply.github.com> Co-authored-by: Frida Hou <201670829+Fridah-nv@users.noreply.github.com> Co-authored-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com> Co-authored-by: yingguo-trt <244492186+yingguo-trt@users.noreply.github.com> Co-authored-by: Wei-Ming Chen <17592131+meenchen@users.noreply.github.com> Co-authored-by: yueshen2016 <39203804+yueshen2016@users.noreply.github.com> Co-authored-by: joe <222375587+joe-nv@users.noreply.github.com> Co-authored-by: Ajinkya Rasane <131806219+ajrasane@users.noreply.github.com> Co-authored-by: Colin McDonough <cmcdonough@50words.com> Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: mxinO <164952785+mxinO@users.noreply.github.com> Co-authored-by: Gwena Cunha <4861122+gcunhase@users.noreply.github.com> Co-authored-by: Chad Voegele <chadvoegele@users.noreply.github.com>
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…#2845) ### What does this PR do? Type of change: CI (unit-test runtime) The unit `linux` job gates every GPU and example workflow (their pr-gate jobs wait for it), and its tests are CPU-bound but run serially. This runs them under pytest-xdist on the runner's four vCPUs. The `linux` job and the multi-version matrix get `PYTEST_ADDOPTS=-n 4 --dist loadfile` and `OMP_NUM_THREADS=1`: one worker per vCPU (`-n auto` counts physical cores), whole files per worker, and one thread per worker, because with torch's default threads the runner is oversubscribed and slower than serial (1,481 s and 41 failures in my local runs). What else changes so the suite passes under xdist: - `noxfile.py`: the second pytest process that runs the `manual` tests clears `PYTEST_ADDOPTS`, so it stays serial. Those tests leak global state into each other, and a worker would run them in some order. - `tests/_test_utils/torch/quantization/onnx_export.py`: `TEST_MODELS` was a set of classes, whose iteration order differs between processes, so workers collected different test ids. It is a tuple now. - `test_hf_dflash2.py::test_overfits_a_single_batch` takes about 30 s on one thread on main (53 s before #2836 halved its loop to 30 steps; that version hit the 60 s default call timeout in the nightly on main on 2026-10-20). Its tiny model is built in bf16, and a CPU without native bf16 support emulates bf16 matmuls (about 0.9 s per step, 0.1 s in float32). The model is float32 now, which takes the test to about 4 s on one thread, so it needs no timeout override; the loop and both assertions are unchanged. - `test_full_algorithms_vision.py::test_search_constraints` assigned `ConstraintsFunc._get_true_latency` and never restored it. Serial runs never noticed, because `test_evaluate_constraints.py` sorts first. Under xdist a worker can get the files in the other order, and the Python 3.10 and 3.14 legs failed two tests after it. It uses `monkeypatch` now. - `tests/conftest.py`: the unit per-test cap is 120 s on every platform. `test_dist.py::test_data_parallel` takes 8-15 s serial and 25-35 s with four workers sharing four vCPUs, and went over 60 s on the slowest runner. A hung test is still reported, after 2 min instead of 1. Why: on current main the `linux` job has a median of 12.9 min (27 runs of other PRs since #2823 merged), and a pull request's GPU run waits a median 14.5 min for it (125 runs of 33 PRs). ### Time improvement (measured in CI) Unit `linux` job: serial on current main (27 runs of other PRs since #2823 merged) against the runs of this series with `-n 4`. | | before (serial) | with this PR | |---|---|---| | pytest | 630 s median (355-709 s) | 479 s in this PR's own run; 294 s median over the 11 runs of the series (166-479 s) | | whole job | 12.9 min median (8.8-14.8) | 10.9 min in this PR's own run; 7.0 min median over the 11 runs (5.0-10.9) | | unit workflow: `linux`, then the multi-version and Windows legs | 24.1 min median (12 runs) | 17.2 min median (the 7 runs that ran every leg) | - The runs of #2846 and above also carry the sysmon core of #2846 (about 22% off the unit suite on its own, measured locally), so the 294 s median is xdist plus sysmon; this PR's own run is the xdist-only number. It was the slowest of the 11 (166-479 s for nearly the same tests: runner variance), so it understates the gain. Locally on 4 CPUs with coverage as in CI, the base took 825 s serial and 332-344 s with this configuration (3 runs). - The GPU, Example and Regression workflows hold their jobs until the unit `linux` check is done (`pr-gate / wait-checks`, at least 5 min). That wait is a median 14.5 min (IQR 12.9-15.8) on 125 GPU runs of 33 PRs since #2823; in this PR's own GPU run it was 9.7 min (its `linux` job took 10.9 min). With a 7 min `linux` job instead of 12.9 min, a GPU run requested right after a push starts about 6 min earlier. That figure is derived from the job times: the later runs of the series were requested after their unit check had finished (the wait sat at its 5.2 min floor in 7 of the 9 GPU runs), so they cannot show it. Method: every PR of the series has its own CI run, so "before" is the run of the PR(s) below that lack the change and "after" the runs that carry it. Times come from the job logs (`--durations`, or the gap between consecutive result lines). Where runner speed matters, the tests that no PR of the series touches scale each run, and the raw and the scaled medians are both shown. One test varies by 15-30% from run to run on its own, so smaller differences are not evidence. ### Usage Nothing user-facing. Locally: `OMP_NUM_THREADS=1 pytest tests/unit -n 4 --dist loadfile`. ### Testing - Full `tests/unit` on 4 CPUs with coverage as in CI, on the base before #2823: serial 825 s, this configuration 332-344 s in three repeated runs (4,724 passed, 0 failed). - This PR's own CI run: the `linux` job took 10.9 min (479 s of pytest) against the 12.9 min serial median; see the time section above for the later runs of the series. - `tests/unit` without `puzzletron` (it needs `hydra`, absent from my local environment) in reversed and in shuffled file order shows no other order dependence; `test_search_constraints` followed by `test_evaluate_constraints.py` in one process failed before the fix and passes after it. - `test_hf_dflash2.py` (33 tests) on one CPU thread: 13.5 s, of which `test_overfits_a_single_batch` takes about 4 s (30 s with the bf16 model on main; the next slowest test takes about 1.3 s). - Pre-commit (all hooks) passes. ### Before your PR is "*Ready for review*" Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md) and your commits are signed (`git commit -s -S`). Make sure you read and follow the [Security Best Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors) (e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(..., weights_only=False)`, `pickle`, etc.). - Is this change backward compatible?: N/A (CI and tests) - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: no new dependency (`pytest-xdist` is already in the `dev-test` extra on main) - Did you write any new necessary tests?: N/A (two existing tests fixed or sped up so they pass under xdist) - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A (CI and tests) - Did you get Claude approval on this PR?: N/A (optional, not run) ### Additional Information Slice 1 of 11 of the GPU-CI speed-up work, split out of the reference draft #2730 (not for merge; it holds the whole series with its measurements). Merge in order; each PR stands on its own and passes CI without the later ones. 1. #2845 [OMNIML-6178] [1/11] Run the unit tests in parallel with pytest-xdist **(this PR)** 2. #2846 [OMNIML-6178] [2/11] Use the sysmon coverage core in CI 3. #2847 [OMNIML-6179] [3/11] Run manual-marked tests on nightly and on-demand GPU runs 4. #2848 [OMNIML-6181] [4/11] Give gpu_megatron's slow cold starts more time 5. #2849 [OMNIML-6179] [5/11] Make the ONNX tests cheaper: CPU-only ones to the unit lane, small models 6. #2850 [OMNIML-6179] [6/11] Use local tokenizers and calibration text in GPU tests, shrink puzzletron data 7. #2851 [OMNIML-6180] [7/11] Trim the llm_eval, llm_qat, EAGLE and megatron_bridge example tests 8. #2852 [OMNIML-6180] [8/11] Trim the DFlash regression test 9. #2853 [OMNIML-6180] [9/11] Trim the torch_onnx example lane 10. #2854 [OMNIML-6181] [10/11] Make the distributed worker pool fail fast when a worker dies 11. #2855 [OMNIML-6181] [11/11] Share one Megatron worker pool across the gpu_megatron modules <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Tests** * Improved consistency and performance of automated test runs across platforms and Python configurations through controlled parallel execution. * Preserved predictable model-check ordering and isolated temporary test changes. * Increased unit-test time limits to better support process-spawning and slower runs. * Improved test reliability by explicitly controlling numeric precision and ensuring serial execution for manual CPU tests. * No end-user functionality or product behavior changed. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@nvidia.com>
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What does this PR do?
Type of change: Bug fix, new tests (test cleanup)
The
Unit tests / linuxjob (serial,timeout-minutes: 20) was taking 13–19.5 min on main, and two recent main runs were cancelled at the limit. This cuts CPU unit-test time without dropping meaningful coverage._source_checkpointreturned""for models built withfrom_config, soPath("")resolved to the cwd and every export globbed the whole working tree (in CI, including the nox venv).tests/unit/torch/exportwent 718s → 15s locally. Regression test added.cpu_dist_workersfixture (two persistent gloo ranks) replaces a ~20s-on-CI spawn per test for autoquant (2), shapley, quantize indexer,test_dist, FSDP2 two-rank export.tests/gpu: the 6ensure_local_checkpointspawn tests andtest_modeling_ptq_registration(8 interpreters).test_presetssubprocess test (theFutureWarningtest beside it covers it);test_auto_quantizematrix 60 → 9 cases (every model, format list and method still appears).manual(run on push/nightly): slow AutoNAS-only cases and the two ONNX entropy-calibration cases (FastNAS representatives stay enabled).Testing
Clean CI-like venv (py3.12, torch 2.14 CPU, CPU-only): full
tests/uniton the working tree was 1181s with local-only failures before, 217s and 4791 passed after (CI runners are slower, so expect roughly a 4–6 min saving, not these exact numbers). On this branch: affected unit dirs 3276 passed; movedtests/gputests run on CPU with gloo, 7 passed. New_source_checkpointtest fails without the fix. Not run: the moved tests on a GPU runner.The
linuxjob now passes--run-manualon every non-PR event (push, nightly, dispatch) (noxunitsession forwards extra args to pytest); all 17manualunit tests pass locally (87s). Caveat: pinned ONNX entropy scales are no longer checked per-PR, only after merge.CONTRIBUTING.md: N/A🤖 Generated with Claude Code
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