cudnn-frontend/pyproject.toml at develop · NVIDIA/cudnn-frontend · GitHub

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[project]
name = "nvidia-cudnn-frontend"
dynamic = ["version"]
description = "NVIDIA cuDNN Frontend — Python and C++ Graph API with SOTA attention (SDPA / Flash Attention), MoE grouped GEMM fusions, and FP8/MXFP8 kernels for Hopper and Blackwell GPUs."
readme = "README.md"
requires-python = ">=3.9"
license = {text = "Apache-2.0 AND MIT"}
keywords = [
    "cudnn",
    "cuda",
    "gpu",
    "nvidia",
    "deep-learning",
    "attention",
    "sdpa",
    "flash-attention",
    "transformer",
    "moe",
    "mixture-of-experts",
    "grouped-gemm",
    "fp8",
    "mxfp8",
    "blackwell",
    "hopper",
    "pytorch",
    "kernel",
    "graph-api",
]
classifiers = [
    "Development Status :: 5 - Production/Stable",
    "Intended Audience :: Developers",
    "Intended Audience :: Science/Research",
    "License :: OSI Approved :: Apache Software License",
    "License :: OSI Approved :: MIT License",
    "Operating System :: POSIX :: Linux",
    "Operating System :: Microsoft :: Windows",
    "Programming Language :: C++",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
    "Programming Language :: Python :: 3.14",
    "Environment :: GPU :: NVIDIA CUDA",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Software Development :: Libraries :: Python Modules",
]

[project.urls]
"Homepage" = "https://github.com/NVIDIA/cudnn-frontend"
"Documentation" = "https://docs.nvidia.com/deeplearning/cudnn/frontend/latest/"
"Blog" = "https://nvidia.github.io/cudnn-frontend/"
"Repository" = "https://github.com/NVIDIA/cudnn-frontend"
"Bug Tracker" = "https://github.com/NVIDIA/cudnn-frontend/issues"
"Release Notes" = "https://github.com/NVIDIA/cudnn-frontend/releases"

[project.optional-dependencies]
cutedsl = [
    # NOT pinned to the floor the FROST engines need (4.7.0): pinning it here
    # would make this package incompatible with anything holding the DSL at an
    # older version -- quack-kernels pins ==4.6.0, and vLLM and friends carry
    # their own constraints. The engines check the version at support time and
    # decline when it is too old, so an older DSL costs those engines and
    # nothing else. See CUTEDSL_MIN_VERSION in cudnn/frost/buffers.py.
    # Framework-neutral core only: the CuTeDSL APIs are type-erased, so torch (like
    # jax) is opt-in via the [dependency-groups] below (`pip install --group torch`).
    "nvidia-cutlass-dsl[cu13]>=4.5.0",
    "cuda-python",
    "apache-tvm-ffi>=0.1.11",
]

[dependency-groups]
dev = [
    "jupyter",
    "numpy<2.0.0",
    "pybind11[global]>=2.13,<3",
    "pytest",
    "pytest-xdist",
    "looseversion",
    "black==26.3.1",
    "clang-format==21.1.6",
]
# Per-framework groups for the type-erased CuTeDSL GEMM APIs (JAX is supported
# by the dense fusions: amax, swiglu, srelu, dsrelu): install with
# `pip install --group torch` / `pip install --group jax`. GPU wheels
# (torch cuXX, jax[cuda12]/[cuda13]) are left to the user.
torch = [
    "torch",
    "torch-c-dlpack-ext",
]
# The jax.jit-compatible XLA custom-call entry points (cudnn.jax, e.g.
# gemm_amax_jax_sm100) build on the CuTeDSL JAX extensions (cutlass.jax,
# shipped with nvidia-cutlass-dsl), which require jax >= 0.5.
jax = [
    "jax>=0.5",
]

[build-system]
requires = ["setuptools>=64", "cmake>=3.18", "ninja==1.11.1.1", "pybind11[global]>=2.13,<3"]
build-backend = "setuptools.build_meta"

[tool.setuptools]
packages = {find = {where = ["python", "."], include = ["cudnn*", "include"], namespaces = true}}
package-dir = {"" = "python", "include" = "include"}
include-package-data = true

[tool.setuptools.dynamic]
version = {attr = "cudnn.__version__"}

[tool.setuptools.package-data]
include = ["**/*"]