Make torch 2.14 the unit-test default and constrain it for the tensorrt example images - #2309
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📝 WalkthroughWalkthroughThe project now requires Torch versions from 2.8 up to, but not including, 2.14. Comments document CI failures related to cuDNN and ChangesTorch dependency constraint
Estimated code review effort: 1 (Trivial) | ~2 minutes Merge Risk: 🟡 Moderate · up to The PR globally changes the supported Torch range to Suggested reviewers: 🚥 Pre-merge checks | ✅ 6✅ Passed checks (6 passed)
Full details: Docstring CoverageExplanation 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-PatternsExplanation PASS: The PR changes only Full details: Title checkExplanation The title identifies the TensorRT example-image Torch constraint, which is a main part of the change. However, it incorrectly states that Torch 2.14 becomes the unit-test default; the change constrains Torch to versions below 2.14. ✨ Finishing Touches🧪 Generate unit tests (beta)
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2309 +/- ##
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+ Misses 13075 13065 -10
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Inline comments:
In @.github/workflows/_example_tests_runner.yml:
- Line 67: Update the shell condition in the workflow step to read docker_image
through an environment variable rather than interpolating inputs.docker_image
directly into Bash source; preserve the existing TensorRT image pattern check
and pass the reusable workflow input into the step environment.
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In `@pyproject.toml`:
- Line 44: Restore the global Torch requirement to torch>=2.8 without the <2.14
cap, then scope the cap through PIP_CONSTRAINT only in the trtllm and onnx
TensorRT jobs within _example_tests_runner.yml. Keep unit sessions and other
example-test jobs unconstrained by the TensorRT-specific upper bound.
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…rrt images torch 2.14.0 was published to PyPI today and broke the onnx and diffusers example jobs: nvcr.io/nvidia/tensorrt:26.05-py3 ships cuDNN 9.22 and has no preinstalled torch, so pip resolved 2.14, whose bundled cuDNN is 9.24. Every test failed with CUDNN_SUBLIBRARY_LOADING_FAILED. Constrain torch there, where the incompatibility actually is, rather than in pyproject where it would reach users for whom torch 2.14 is fine. Nothing exercised 2.14 before this: the nemo and pytorch images have a preinstalled torch that already satisfies torch>=2.8, so pip never resolves a new one, and torch_trt is held below 2.14 by torch-tensorrt<2.13. Promote it to the unit-test default across the supported Python versions and demote 2.13 to the back-compat row, so the next torch release is caught by unit tests rather than by an example job. Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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pre_commit_all installs .[all,dev-lint] with a fresh resolve, so it picks up torch 2.14. In that env torchvision::nms fails to register, which makes modelopt's transformers plugin unavailable, so ModelOptHFTrainer is never exported and the ARGUMENTS.md hook dies on an unguarded import of it. Sets UV_CONSTRAINT as well as PIP_CONSTRAINT: nox builds the session venv with uv, and uv ignores PIP_CONSTRAINT. Verified with `uv pip compile` -- torch resolves to 2.14.0 with PIP_CONSTRAINT alone and to 2.13.0 with UV_CONSTRAINT. The plugin itself is fine on torch 2.14 -- reproduced locally in a venv with .[all,dev-lint] on torch 2.14.0 and transformers 5.14.1, where it imports cleanly because torchvision is not pulled in at all. The failure needs a mismatched torchvision present, so this is an environment constraint rather than a source fix. Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
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### What does this PR do? Type of change: bug fix Cherry picks for 0.47 release Merge order: #2287, #2219, #2276, #2298, #2296, #2309, #2318, #2332, #2320, #2180, #2358, #2300, #2334. ### Usage ```python # Add a code snippet demonstrating how to use this ``` ### Testing <!-- Mention how have you tested your change if applicable. --> ### 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 <!--- If ❌, explain why. --> - If you copied code from any other sources or added a new PIP dependency, did you follow guidance in `CONTRIBUTING.md`: ✅ / ❌ / N/A <!--- Mandatory --> - Did you write any new necessary tests?: ✅ / ❌ / N/A <!--- Mandatory for new features or examples. --> - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: ✅ / ❌ / N/A <!--- Very short summary of changes only for new features, backward breaking changes, deprecations, or fixes for critical bugs present in previous releases. --> - Did you get Claude approval on this PR?: ✅ / ❌ / N/A <!--- Run `/claude review`. NVIDIA org members can self-trigger for complex changes; orthogonal to CodeRabbit. --> ### Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added PETR, VoVNet, and FAR3D ONNX post-training quantization and TensorRT evaluation workflows. * Added Qwen3.5-VL export support, expanded multimodal checkpoint loading, and new model-specific quantization recipes. * Added configurable MoE expert layouts and KV-cache scaling controls. * **Bug Fixes** * Improved ONNX Autotune precision selection and fallback behavior. * Fixed checkpoint validation, VLM calibration, expert exports, and KV-cache configuration. * **Documentation** * Clarified recipe locations, model export workflows, and Autotune behavior. * **Breaking Changes** * FAR3D decoder quantization and several deprecated quantization options were removed. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Signed-off-by: Chad Voegele <cvoegele@nvidia.com> Signed-off-by: Shengliang Xu <shengliangx@nvidia.com> Signed-off-by: Jennifer Chen <jennifchen@nvidia.com> Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com> Signed-off-by: realAsma <akuriparambi@nvidia.com> Signed-off-by: Chenjie Luo <chenjiel@nvidia.com> Co-authored-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com> Co-authored-by: Claude Opus 5 <noreply@anthropic.com> Co-authored-by: Shengliang Xu <106840466+shengliangxu@users.noreply.github.com> Co-authored-by: Jenny Chen <jennifchen@nvidia.com> Co-authored-by: Ajinkya Rasane <131806219+ajrasane@users.noreply.github.com> Co-authored-by: Codex <codex@openai.com> Co-authored-by: realAsma <86726418+realAsma@users.noreply.github.com> Co-authored-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com>
What does this PR do?
Type of change: Bug fix (CI) + test coverage
Fixes
onnx (torch_onnx)andonnx (diffusers), which have failed on every branch sincetorch 2.14.0was published to PyPI today (2026-09-02 13:42 UTC), and adds torch 2.14 to the unittest matrix as the new default so the next torch release is caught there rather than in an example job.
Root cause
Every test in those two jobs failed with:
nvcr.io/nvidia/tensorrt:26.05-py3ships cuDNN 9.22 and has no preinstalled torch, so pipresolved the newest one — and torch 2.14 pins
nvidia-cudnn-cu13==9.24.0.43. Loading 9.24sublibraries against the image's 9.22
libcudnn.so.9is exactly what that status reports.torchnvidia-cudnn-cu13Why only these two jobs
torch>=2.8,so pip never resolves a new one — confirmed from the megatron job log, where torch does not appear
in
Successfully installed.tensorrt:26.05-py3has no preinstalled torch, so pip takes the newest from PyPI.onnx (torch_trt)shares that image but passes throughout, becausetorch-tensorrt<2.13already holds torch below 2.14.
The changes
PIP_CONSTRAINT=torch<2.14in the examplerunner, applied when the job's image is a
tensorrtone. It also covers theexamples/*/requirements.txtloop in the same shell, which matters becausenemo_automodelpulls torch in too. Not pinned in
pyproject.toml: torch 2.14 is fine anywhere its own bundledcuDNN is the one loaded, so that would constrain users to work around one pinned image.
torch_214added toTORCH_VERSIONS(torchvision~=0.29.0) and promoted tothe unit-test default across the supported Python versions, with 2.13 demoted to the back-compat
row.
release.yml's basic unit test moves to the same default (it was still on 2.12).Nothing exercised 2.14 before — which is why a torch release reached us through an example
job instead of a unit test.
Testing
actionlintand YAML/TOML parse clean; pre-commit clean.onnx (torch_onnx)andonnx (diffusers)reproduce the failure onmainright now, and the newunit-3.12(torch_214, tf_latest)job is the first run of ModelOptagainst torch 2.14.
Before your PR is "Ready for review"
CONTRIBUTING.md: N/A — no new dependency