Files
h-guo18andClaude Opus 5 23355eda90 fix(deps): declare httpx, unbreaking partial-install (torch) for every PR (#2547)
## Summary

`partial-install (torch)` has been failing on **every** PR since
2026-09-24 — including PRs whose branches predate the breakage — and
because it is a *collection* error rather than a test failure, it aborts
the entire run:

```
ImportError while importing test module '.../tests/unit/torch/speculative/plugins/test_hf_streaming_dataset.py'
E   ModuleNotFoundError: No module named 'httpx'
collected 2243 items / 1 error / 45 skipped
!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!
```

`unit-pr-required-check` aggregates it, so nothing currently merges on a
fresh run.

## What happened

**No code changed.**
`modelopt/torch/speculative/plugins/hf_streaming_dataset.py` has
imported `httpx` at module scope since #1509 (2026-06-02), and `httpx`
has never appeared in `pyproject.toml`. It arrived only transitively:
`dev-test` → `timm` → `huggingface_hub` → `httpx`.

**huggingface_hub 2.0.0**, published **2026-09-24T12:01:21Z**, replaced
`httpx<1,>=0.23.0` with the separate **`httpx2<3,>=2.0.0`**
distribution. Different package name, so `httpx` stopped being installed
and the chain disappeared.

The boundary is exact — every run *created* before that timestamp
passes, every one after fails:

| PR | run created | result |
|---|---|---|
| #2536 / #2535 | 09-23 22:02 | pass |
| #2500 | 09-23 23:45 | pass — **merged 09-24 20:01 on this stale-green
result** |
| *hub 1.33.0 (still requires httpx)* | *09-24 09:49* | |
| **hub 2.0.0 published** | **09-24 12:01** | ← |
| #2539 | 09-24 16:57 | fail |
| #2544 | 09-24 18:32 | fail |
| #2216 | 09-25 11:58 | fail |

#2500 merging afterwards is not a counterexample: GitHub does not re-run
checks at merge time, so it merged on a result from ~20 hours earlier.
That is also why this went unnoticed.

## The changes

### 1. Declare `httpx` in the `hf` extra

`httpx` is not incidental to streaming — it is the only transport:

- every fetch is HTTP: `POST /v1/completions` to the vLLM serve plus
`GET /meta` and `/desc` against the connector's sidecar, all through
`httpx.Client`;
- there is no non-HTTP path — the base `StreamingDataset._fetch` is an
abstract seam and `EagleVllmStreamingDataset._fetch` is its only
implementation;
- no other HTTP library appears in the module (`requests` / `urllib` /
`aiohttp`: zero hits, and `requests` is not declared either);
- even the retry predicate is built from it: `_TRANSIENT_FETCH_ERRORS =
(httpx.HTTPError, OSError)`.

It belongs in `hf` rather than in the core `dependencies`: the same
module needs `transformers.trainer_pt_utils` at module scope, so one
extra already gates the whole file, and a core install has no use for an
HTTP client. The bound matches the 0.x API the code uses — `httpx` has
no 1.0 release, and 2.x is a different distribution.

This is the part that stops it recurring. `[hf]` currently gets `httpx`
only because `datasets` happens to require it — the same accident with a
different supplier, one release away from repeating.

### 2. Acquire `httpx` in the test through the existing skip guard

The test file already intends to skip where the extra is absent — it has
`pytest.importorskip("transformers")` and a comment explaining why, and
`transformers` is absent in this job too. It broke only because `import
httpx` sat **five lines above** that guard, where a missing module ends
collection instead of skipping one file.

## Verification

- With everything installed: **18 passed**, no behaviour change.
- The import-order property is checked with an AST walk over the
module's top-level statements: no `hf`-extra-only import precedes the
first `importorskip` (which is now line 41).
- A faithful local reproduction was attempted and abandoned honestly:
hiding `httpx` locally also breaks `huggingface_hub` 1.28, which
`modelopt.torch.opt.plugins.huggingface` imports, so the local failure
is not the CI one. CI is the oracle for that half — this PR's own
`partial-install (torch)` run is the check that matters.

## Scope

Two files, five lines of declaration and four of test import order.
Deliberately not folded into any feature PR: it blocks the whole repo,
and burying a repo-wide fix inside unrelated work is how these stay
invisible.

🤖 Generated with [Claude Code](https://claude.com/claude-code)


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

* **Chores**
* Optional Hugging Face installations now include `httpx`, supporting
features that require HTTP communication without requiring it for all
installations.
* **Tests**
* Hugging Face streaming dataset tests now skip when `httpx` is
unavailable, allowing the remaining test suite to be collected and run
without it.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-09-26 04:27:24 +05:30

386 lines
14 KiB
TOML

####################################################################################################
############################### BUILD CONFIGURATION ##############################################
####################################################################################################
[build-system]
requires = ["setuptools>=80", "setuptools-scm>=8,<10"]
build-backend = "setuptools.build_meta"
[tool.setuptools_scm]
fallback_version = "0.0.0"
####################################################################################################
############################### PROJECT METADATA #################################################
####################################################################################################
[project]
name = "nvidia-modelopt"
dynamic = ["version"]
description = "Nvidia Model Optimizer: A unified library of SOTA model optimization techniques like quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed."
readme = { text = "Checkout https://github.com/nvidia/Model-Optimizer for more information.", content-type = "text/markdown" }
license = "Apache-2.0"
license-files = ["LICENSE_HEADER"]
requires-python = ">=3.10,<3.15"
authors = [{ name = "NVIDIA Corporation" }]
classifiers = [
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
]
dependencies = [
# Common
"ninja", # for faster building of C++ / CUDA extensions
"numpy",
"nvidia-ml-py>=12",
"packaging",
"setuptools>=80", # torch.utils.cpp_extension imports setuptools at load time
"torch>=2.8",
"tqdm",
# modelopt.torch
"PyYAML>=6.0",
"omegaconf>=2.3.0",
"pulp<4.0", # breaking changes in upcoming 4.0 release
"pydantic>=2.0",
"regex",
"rich",
"safetensors",
"scipy",
]
[project.optional-dependencies]
onnx = [
"cppimport",
"cupy-cuda12x; platform_machine != 'aarch64' and platform_machine != 'ARM64' and platform_system != 'Darwin'",
"lief",
"ml_dtypes",
"onnx-graphsurgeon>=0.6.1",
"onnx~=1.21.0",
"onnxconverter-common~=1.16.0",
# Python 3.10 support for ModelOpt ONNX is deprecated and will be removed in a future release.
# Native Windows ARM64 does not support ModelOpt ONNX on Python 3.10.
# ORT for Windows x64
"onnxruntime-gpu==1.22.0; platform_system == 'Windows' and platform_machine == 'AMD64'",
# ORT host and standalone TensorRT-RTX ABI EP for native Windows ARM64.
"onnxruntime~=1.24.2; python_version > '3.10' and platform_system == 'Windows' and platform_machine == 'ARM64'",
"onnxruntime-ep-nv-tensorrt-rtx-cu13==0.4.0; python_version > '3.10' and platform_system == 'Windows' and platform_machine == 'ARM64'",
# ORT with Python <= 3.10 on supported platforms
"onnxruntime~=1.22.0; python_version <= '3.10' and (platform_machine == 'aarch64' or platform_system == 'Darwin')",
"onnxruntime-gpu~=1.22.0; python_version <= '3.10' and platform_machine != 'aarch64' and platform_system != 'Darwin' and platform_system != 'Windows'",
# ORT with Python > 3.10
"onnxruntime~=1.24.2; python_version > '3.10' and (platform_machine == 'aarch64' or platform_system == 'Darwin')",
"onnxruntime-gpu~=1.24.2; python_version > '3.10' and platform_machine != 'aarch64' and platform_system != 'Darwin' and platform_system != 'Windows'",
"onnxscript",
"onnxslim>=0.1.76",
"polygraphy>=0.53.4",
]
hf = [
"accelerate>=1.0.0",
"datasets>=3.0.0",
"deepspeed>=0.9.6; platform_system != 'Darwin' and platform_system != 'Windows'",
"diffusers>=0.32.2",
"httpx>=0.23.0,<1",
"huggingface_hub>=0.24.0",
"nltk",
"peft>=0.17.0",
"sentencepiece>=0.2.1", # Also implicitly used in test_unified_export_megatron, test_vllm_fakequant_megatron_export
"tiktoken",
"transformers>=4.57,<5.15", # Should match modelopt/torch/__init__.py and noxfile.py
"wonderwords",
]
puzzletron = [ # Dependedencies for modelopt.torch.puzzletron subpackage
"fire",
"hydra-core~=1.3.4",
"immutabledict",
"lru-dict",
"pandas",
"typeguard",
]
dev-lint = [
"bandit[toml]==1.7.9", # security/compliance checks
"mypy==2.1.0",
"pre-commit==4.6.0",
"ruff==0.15.20",
]
# Docs are only built on Python >=3.12 (sphinx 9.x requires it); markers keep `uv lock` from resolving these for 3.10/3.11, which we don't care about for building docs.
dev-docs = [
"autodoc_pydantic~=2.2.0; python_version >= '3.12'",
"sphinx~=9.1.0; python_version >= '3.12'",
"sphinx-argparse~=0.6.0; python_version >= '3.12'",
"sphinx-autobuild==2025.8.25; python_version >= '3.12'",
"sphinx-copybutton~=0.5.2; python_version >= '3.12'",
"sphinx-inline-tabs==2025.12.21.14; python_version >= '3.12'",
"shibuya~=2026.7.0; python_version >= '3.12'",
"sphinx-togglebutton~=0.4.0; python_version >= '3.12'",
]
dev-test = [
"coverage[toml]~=7.14.0", # a1_coverage.pth for subprocess tracking requires this
"nox",
"pytest~=9.1.0",
"pytest-cov~=7.1.0",
"pytest-instafail==0.5.0",
"pytest-timeout~=2.4.0",
"tomli; python_version < '3.11'",
"uv",
# test-specific dependencies
"psutil", # tools/resource_monitor.py sidecar (tests/unit/tools)
"timm",
"torchprofile==0.0.4", # optional dependency for modelopt.torch
"torchvision",
"torch-geometric",
]
# Compound extras via self-references
mlflow = [ # Dependencies for modelopt.torch.utils.mlflow run tracking
"mlflow-skinny>=2.9", # client only; the tracking server's own deps are not needed here
]
# mlflow is deliberately not in `all`: it is opt-in tracking for the examples, and the
# full client would land in the documented `nvidia-modelopt[all]` install path.
all = ["nvidia-modelopt[onnx,hf,puzzletron]"]
dev = ["nvidia-modelopt[all,mlflow,dev-docs,dev-lint,dev-test]"]
[project.urls]
Homepage = "https://github.com/NVIDIA/Model-Optimizer"
[tool.setuptools.packages.find]
include = ["modelopt*"]
[tool.setuptools.package-data]
modelopt = ["**/*.cpp", "**/*.cu", "**/*.cuh", "**/*.h"]
modelopt_recipes = ["**/*.yml", "**/*.yaml"]
[tool.setuptools.exclude-package-data]
# The recipe library keeps two backward-compat symlinks: the top-level
# ``huggingface`` -> ``model_type`` rename alias, and the nested
# ``model_type/models`` -> ``../models`` alias. The recursive package-data glob above
# follows both, so drop the aliased copies here to avoid shipping every recipe twice;
# setuptools' exclude glob is non-recursive, hence the explicit per-depth entries.
# MANIFEST.in prunes the symlink dir entries themselves (build_py can't copy a
# symlink-to-dir). Old huggingface/... --recipe paths keep working via the loader
# alias in modelopt/recipe/loader.py.
modelopt_recipes = [
"huggingface",
"model_type/models",
# Architecture recipes duplicated via ``huggingface`` -> ``model_type``.
"huggingface/*/*/*.yaml", "huggingface/*/*/*.yml",
# Checkpoint mirrors duplicated via ``huggingface/models`` and
# ``model_type/models`` (both resolve to the real top-level ``models/`` tier).
"huggingface/models/*/*/*/*.yaml", "huggingface/models/*/*/*/*.yml",
"huggingface/models/*/*/*/*/*.yaml", "huggingface/models/*/*/*/*/*.yml",
"model_type/models/*/*/*/*.yaml", "model_type/models/*/*/*/*.yml",
"model_type/models/*/*/*/*/*.yaml", "model_type/models/*/*/*/*/*.yml",
]
[tool.uv]
managed = true
# override-dependencies = ["torch; sys_platform == 'never'"]
####################################################################################################
############################### LINTING, FORMATTING CONFIGURATION ################################
####################################################################################################
[tool.ruff]
target-version = "py310"
line-length = 100 # Line length limit for code
fix = true
[tool.ruff.format]
# Like Black, respect magic trailing commas.
skip-magic-trailing-comma = false
docstring-code-format = true
# Set the line length limit used when formatting code snippets in docstrings.
docstring-code-line-length = "dynamic"
[tool.ruff.lint]
# See available rules at https://docs.astral.sh/ruff/rules/
# Flake8 is equivalent to pycodestyle + pyflakes + mccabe.
select = [
"C4", # Flake8 comprehensions
"D", # pydocstyle
"E", # pycodestyle errors
"F", # pyflakes
"FURB", # refurb
"I", # isort
"ISC", # flake8-implicit-str-concat
"N", # pep8 naming
"PERF", # Perflint
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"PLE", # pylint errors
"PLR", # pylint refactor
"PT", # flake8-pytest-style
"RUF", # ruff
"SIM", # flake8-simplify
"TC", # flake8-type-checking
"UP", # pyupgrade
"W", # pycodestyle warnings
]
extend-ignore = [
"D105",
"D417",
"N812",
"PLR0402",
"PLR0912",
"PLR0913",
"PLR0915",
"PLR2004",
"PLR0911",
"PT011",
"PT018",
"PT028",
"PT030",
"RUF002",
"RUF012",
"RUF059",
"SIM102",
"SIM108",
"SIM115",
]
[tool.ruff.lint.per-file-ignores]
"__init__.py" = ["F401", "F403"]
"examples/*" = ["D"]
"noxfile.py" = ["D", "E501"]
"tests/*" = ["B017", "D", "E402", "PT012"]
"plugins/modelopt/skills/*/tests/test_*.py" = ["D", "E402"] # Skill test scripts: docstring (D) + sys.path import-order (E402) exemptions
"*/_[a-zA-Z]*" = ["D"] # Private packages (_abc/*.py) or modules (_xyz.py)
"*.ipynb" = ["D", "E501"] # Ignore missing docstrings or line length for Jupyter notebooks
"modelopt/torch/kernels/*" = ["N803", "N806", "E731"] # triton style
"modelopt/torch/puzzletron/*" = [
"C4",
"D",
"E",
"F",
"N",
"PERF",
"RUF",
"SIM",
"UP",
] # TODO: Disabled for now, will enable once newer puzzletron code with ruff fixes is migrated
[tool.ruff.lint.pycodestyle]
max-line-length = 120 # Line length limit for comments and docstrings
[tool.ruff.lint.pydocstyle]
convention = "google"
[tool.ruff.lint.isort]
known-first-party = ["modelopt"]
known-third-party = ["vllm"]
split-on-trailing-comma = false
[tool.mypy]
files = "."
python_version = "3.10"
install_types = true
non_interactive = true
show_error_codes = true
disable_error_code = [
"assignment",
"operator",
"has-type",
"var-annotated",
"override",
]
enable_error_code = [
"ignore-without-code", # require `# type: ignore[code]` instead of bare `# type: ignore`
"deprecated", # use of `@deprecated`-marked APIs
# TODO: enable in a follow-up (each surfaces many errors needing case-by-case fixes):
# "possibly-undefined", "redundant-expr", "truthy-bool"
]
explicit_package_bases = true
namespace_packages = true
# strict checks
strict = true
disallow_subclassing_any = false
disallow_untyped_decorators = false
disallow_any_generics = false
disallow_untyped_calls = false
disallow_incomplete_defs = false
disallow_untyped_defs = false
warn_return_any = false
[[tool.mypy.overrides]]
module = ["tests.*"]
ignore_errors = true
[[tool.mypy.overrides]]
module = ["examples.*"]
disable_error_code = ["attr-defined"]
# Vendored verbatim from NVIDIA-NeMo/Automodel (Apache-2.0); kept faithful to upstream rather
# than annotated to modelopt's strict mypy (e.g. worker-global ``X | None`` initialized lazily).
[[tool.mypy.overrides]]
module = ["examples.diffusers.fastgen.preprocess.*"]
ignore_errors = true
[tool.bandit]
exclude_dirs = [".github/", "examples/", "noxfile.py", "tests/"]
# Do not change `skips`. It should be consistent with NVIDIA's Wheel-CI-CD bandit.yml config.
# Use of `# nosec BXXX` requires special approval
skips = [
"B101", # assert_used
"B110", # try_except_pass
"B112", # try_except_continue
"B303", # MD2, MD4, MD5, or SHA1
"B311", # random
]
####################################################################################################
############################### TESTING CONFIGURATION ############################################
####################################################################################################
[tool.pytest.ini_options]
# Default additional options
# Show a short test summary info for all except passed tests with -ra flag
# print execution time for 50 slowest tests and generate coverage reports
addopts = "-v -ra --instafail --cov-report=term-missing --cov-report=html --cov-report=xml:coverage.xml --cov-config=pyproject.toml --durations=50 --strict-markers"
pythonpath = ["tests/"]
# Apply per-test timeouts (see tests/conftest.py) to the test call only, not fixture setup/teardown
timeout_func_only = true
markers = [
"integration: Tests that require external services or other non-hermetic dependencies",
"manual: Only run when --run-manual is given",
"release: Regression tests that should be run before every release",
]
[tool.coverage.run]
branch = false
parallel = true
concurrency = ["multiprocessing", "thread"]
source_pkgs = ["modelopt", "modelopt_recipes"]
[tool.coverage.report]
skip_covered = true
ignore_errors = true
exclude_lines = [
"pragma: no cover",
# Don't complain about missing debug or verbose code
"def __repr__",
"if verbose",
# Don't complain if tests don't hit defensive exception handling code
"raise AssertionError",
"raise NotImplementedError",
"raise RuntimeError",
"raise ValueError",
"raise KeyError",
"raise AttributeError",
"except ImportError",
# Don't complain if non-runnable code isn't run
"if __name__ == \"__main__\":",
"if TYPE_CHECKING:",
# Don't complain about abstract methods, they aren't run
"@(abc\\.)?abstractmethod",
]