Files
Keval Morabia 51cc5dbade Make torch 2.14 the unit-test default and constrain it for the tensorrt example images (#2309)
### What does this PR do?

Type of change: Bug fix (CI) + test coverage

**Fixes `onnx (torch_onnx)` and `onnx (diffusers)`**, which have failed
on every branch since
`torch 2.14.0` was published to PyPI today (2026-09-02 13:42 UTC), and
**adds torch 2.14 to the unit
test 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:

```
RuntimeError: CUDNN_BACKEND_TENSOR_DESCRIPTOR cudnnFinalize failed
  ptrDesc->finalize() cudnn_status: CUDNN_STATUS_SUBLIBRARY_LOADING_FAILED
```

`nvcr.io/nvidia/tensorrt:26.05-py3` ships cuDNN **9.22** and has no
preinstalled torch, so pip
resolved the newest one — and torch 2.14 pins
`nvidia-cudnn-cu13==9.24.0.43`. Loading 9.24
sublibraries against the image's 9.22 `libcudnn.so.9` is exactly what
that status reports.

| | last good run (08:55) | first failing run (13:34) |
|---|---|---|
| `torch` | 2.13.0 | **2.14.0** |
| `nvidia-cudnn-cu13` | 9.20.0.48 | **9.24.0.43** |
| image cuDNN | 9.22.0.52 | 9.22.0.52 |

### Why only these two jobs

- The **nemo** and **pytorch** images have a preinstalled torch that
already satisfies `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-py3` has no preinstalled torch**, so pip takes the
newest from PyPI.
- **`onnx (torch_trt)`** shares that image but passes throughout,
because `torch-tensorrt<2.13`
  already holds torch below 2.14.

### The changes

1. **Constrain torch only where the incompatibility is.**
`PIP_CONSTRAINT=torch<2.14` in the example
runner, applied when the job's image is a `tensorrt` one. It also covers
the
`examples/*/requirements.txt` loop in the same shell, which matters
because `nemo_automodel`
pulls torch in too. Not pinned in `pyproject.toml`: torch 2.14 is fine
anywhere its own bundled
cuDNN is the one loaded, so that would constrain users to work around
one pinned image.
2. **Test torch 2.14.** `torch_214` added to `TORCH_VERSIONS`
(`torchvision~=0.29.0`) and promoted to
the 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

- `actionlint` and YAML/TOML parse clean; pre-commit clean.
- Verified by this PR's own jobs: `onnx (torch_onnx)` and `onnx
(diffusers)` reproduce the failure on
`main` right now, and the new `unit-3.12(torch_214, tf_latest)` job is
the first run of ModelOpt
  against torch 2.14.

### Before your PR is "*Ready for review*"

- Is this change backward compatible?: ✅ — CI-only; no source or package
metadata change
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A — no new
dependency
- Did you write any new necessary tests?: ✅ — torch 2.14 added to the
unit test matrix
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A — internal CI, not user-facing
- Did you get Claude approval on this PR?: ❌ — not yet requested

---------

Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
2026-09-03 01:11:16 +05:30

222 lines
9.5 KiB
Python

# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Nox session definitions for testing, linting, docs, and wheel builds.
Usage:
python -m pip install nox uv # install nox and uv (once)
nox -l # list all sessions
nox -s gpu_megatron # run a GPU session (inside container)
nox -s "unit-3.12(torch_211, tf_latest)" # run a specific unit test combination
nox -s "unit-3.12(torch_211, tf_latest)" -R # force-recreate venv (e.g. after dep changes)
COVERAGE_PROCESS_START=pyproject.toml nox -s "unit-3.12(torch_211, tf_latest)" # with coverage
"""
import glob
import os
import shutil
import nox
nox.options.default_venv_backend = "uv" if shutil.which("uv") else "virtualenv"
nox.options.envdir = "/tmp/.nox"
nox.options.reuse_existing_virtualenvs = True
TORCH_VERSIONS = {
"torch_28": "torchvision~=0.23.0",
"torch_29": "torchvision~=0.24.0",
"torch_210": "torchvision~=0.25.0",
"torch_211": "torchvision~=0.26.0",
"torch_212": "torchvision~=0.27.0",
"torch_213": "torchvision~=0.28.0",
"torch_214": "torchvision~=0.29.0",
}
# Extra install pins applied per transformers matrix entry (installed after the base
# ``.[all,dev-test]`` install to constrain that env).
TRANSFORMERS_VERSIONS = {
"tf_latest": ("transformers~=5.14.0",),
# transformers 4.57 caps ``huggingface_hub<1.0``, but ``diffusers>=0.40`` requires
# ``huggingface_hub>=1.23``. Bound diffusers to a hub<1.0-compatible release so this env
# stays internally consistent; otherwise diffusers' pipeline import fails and diffusers
# models silently misroute to the LLM path on export.
"tf_min": ("transformers~=4.57.0", "diffusers<0.40"),
}
def _cov_args():
"""Return --cov when COVERAGE_PROCESS_START is set (CI only)."""
return ["--cov"] if os.environ.get("COVERAGE_PROCESS_START") else []
# ─── CPU unit tests ───────────────────────────────────────────────────────────
_CPU_ONLY_ENV = {"CUDA_VISIBLE_DEVICES": ""}
@nox.session(python=["3.10", "3.11", "3.12", "3.13", "3.14"])
@nox.parametrize("tf_ver", [nox.param(k, id=k) for k in TRANSFORMERS_VERSIONS])
@nox.parametrize("torch_ver", [nox.param(k, id=k) for k in TORCH_VERSIONS])
def unit(session, torch_ver, tf_ver):
"""Unit tests — parametrized over torch and transformers versions."""
session.install(TORCH_VERSIONS[torch_ver], "-e", ".[all,dev-test]")
tf_pins = TRANSFORMERS_VERSIONS[tf_ver]
if tf_pins:
session.install(*tf_pins)
session.run("python", "-m", "pytest", "tests/unit", *_cov_args(), env=_CPU_ONLY_ENV)
@nox.session(python="3.12")
@nox.parametrize("subset", ["onnx", "torch", "torch_deploy"])
def partial_unit(session, subset):
"""Unit tests with partial installs."""
if subset == "onnx":
session.install("torchvision~=0.26.0", ".[onnx,dev-test]")
session.run("python", "-m", "pytest", "tests/unit/onnx", env=_CPU_ONLY_ENV)
elif subset == "torch":
session.install("megatron-core", ".[dev-test]")
session.run(
"python",
"-m",
"pytest",
"tests/unit/torch",
"--ignore=tests/unit/torch/deploy",
"--ignore=tests/unit/torch/puzzletron",
env=_CPU_ONLY_ENV,
)
else: # torch_deploy
session.install(".[onnx,dev-test]")
session.run("python", "-m", "pytest", "tests/unit/torch/deploy", env=_CPU_ONLY_ENV)
# ─── GPU sessions (run inside containers — no new venv) ──────────────────────
# `venv_backend="none"` skips creating a new venv so the session runs directly in the container's
# existing Python environment (e.g. /opt/venv in NeMo) instead of an isolated one.
# Use `python -m pip/pytest` to ensure the container's active venv Python is used,
# not a stale PATH entry (e.g. NeMo container has pip → /usr/local/bin/pip but python → /opt/venv/bin/python).
# Container: nvcr.io/nvidia/pytorch:26.01-py3 or later
@nox.session(venv_backend="none")
def gpu(session):
# tests/gpu/_extensions/test_onnx_extensions.py fails for newer containers
# until https://github.com/tbenthompson/cppimport/pull/98
session.run(
"python",
"-m",
"pip",
"install",
"--no-build-isolation",
"git+https://github.com/Dao-AILab/fast-hadamard-transform.git",
)
session.run("python", "-m", "pip", "install", "-e", ".[all,dev-test]")
session.run("python", "-m", "pip", "uninstall", "-y", "cupy-cuda12x")
session.run("python", "-m", "pip", "install", "cupy-cuda13x")
session.run(
"python",
"-m",
"pip",
"install",
"--no-build-isolation",
# Install the latest *released* sdists (built against the container torch)
"mamba_ssm",
"causal-conv1d",
)
session.run("python", "-m", "pytest", "tests/gpu", *_cov_args())
# Container: nvcr.io/nvidia/nemo:26.08 or later
@nox.session(venv_backend="none")
def gpu_megatron(session):
# NeMo containers have transformers 5.x but a system-wide installed trtllm which does not support it causing import errors
session.run("pip", "uninstall", "-y", "tensorrt_llm")
# Pre-installed nvidia-modelopt shadows the editable install
session.run("pip", "uninstall", "-y", "nvidia-modelopt")
session.run("python", "-m", "pip", "install", "-e", ".[hf,dev-test]")
session.run("python", "-m", "pytest", "tests/gpu_megatron", *_cov_args())
# Container: nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc10 or later
@nox.session(venv_backend="none")
def gpu_trtllm(session):
session.run("python", "-m", "pip", "install", "-e", ".[hf,dev-test]")
session.run("python", "-m", "pytest", "tests/gpu_trtllm", *_cov_args())
# Container: docker.io/vllm/vllm-openai (the published image ships vLLM + CUDA + torch).
# Pin must stay in sync with examples/vllm_serve/Dockerfile.
@nox.session(venv_backend="none")
def gpu_vllm(session):
session.run("python3", "-m", "pip", "install", "-e", ".[hf,puzzletron,dev-test]")
session.run("python3", "-m", "pytest", "tests/gpu_vllm", *_cov_args())
# Container: nvcr.io/nvidia/pytorch:26.01-py3 or later
@nox.session(venv_backend="none")
def regression(session):
session.run("python", "-m", "pip", "install", "-e", ".[hf,dev-test]")
session.run("python", "-m", "pytest", "tests/regression", *_cov_args())
# ─── Code quality ─────────────────────────────────────────────────────────────
@nox.session
def pre_commit_all(session):
session.install("-e", ".[all,dev-lint]")
session.run("pre-commit", "run", "--all-files", "--show-diff-on-failure")
@nox.session
def pre_commit_diff(session):
session.install("-e", ".[all,dev-lint]")
session.run("pre-commit", "run", "--from-ref", "origin/main", "--to-ref", "HEAD")
# ─── Docs ─────────────────────────────────────────────────────────────────────
@nox.session
def docs(session):
session.install("-e", ".[all,dev-docs]")
shutil.rmtree("docs/build", ignore_errors=True)
shutil.rmtree("docs/source/reference/generated", ignore_errors=True)
with session.chdir("docs"):
session.run(
"sphinx-build",
"-d",
"/tmp/doctrees",
"source",
"build/html",
"--fail-on-warning",
"--show-traceback",
"--keep-going",
)
@nox.session
def docs_debug(session):
session.install("-e", ".[all,dev-docs]")
shutil.rmtree("docs/build", ignore_errors=True)
shutil.rmtree("docs/source/reference/generated", ignore_errors=True)
with session.chdir("docs"):
session.run("sphinx-autobuild", "source", "build/html", "--host", "0.0.0.0")
# ─── Wheel build ──────────────────────────────────────────────────────────────
@nox.session
def build_wheel(session):
shutil.rmtree("build", ignore_errors=True)
session.install("twine")
session.run("pip", "wheel", "--no-deps", "--wheel-dir=dist", ".")
wheels = glob.glob("dist/*.whl")
session.run("twine", "check", *wheels)
(modelopt_wheel,) = glob.glob("dist/nvidia_modelopt-*.whl")
session.install(modelopt_wheel, "-f", "dist")
with session.chdir("dist"):
session.run("python", "-c", "import modelopt; print(modelopt.__version__)")