CGO-26-AE/pyproject.toml at main · Deep-Learning-Profiling-Tools/CGO-26-AE · GitHub

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[project]
name = "cgo-26-ae"
version = "0.1.0"
description = "CGO 2026 Artifact for Proton"
readme = "README.md"
requires-python = "==3.13.1"
dependencies = [
    "bitsandbytes",
    "brokenaxes",
    "datasets==4.3.0",
    "llnl-hatchet",
    "matplotlib",
    "pytest",
    "triton",
    "torch",
    # "triton_kernels",
    "torchvision",
    "unsloth",
    "unsloth-zoo",
]

[project.optional-dependencies]
cuda = [
    "torch==2.9.0+cu128",
    "torchvision"
]
rocm = [
    "torch",
    "torchvision",
    "bitsandbytes"
]

[tool.uv]
override-dependencies = [
    "xformers; sys_platform == 'never'",
    "pytorch-triton-rocm; sys_platform == 'never'",
]

conflicts = [
    [
        {extra = "cuda"},
        {extra = "rocm"}
    ],
]

[tool.uv.sources]
triton = { path = "./submodules/triton", editable=true}
unsloth = { path = "./submodules/unsloth", editable=true }
unsloth-zoo = { path = "./submodules/unsloth-zoo", editable=true }
# we simply have copies of triton_kernels for each figure so that we can instrument them differently
# triton_kernels = { path = "./submodules/triton/python/triton_kernels", editable=true}
torch = [
    { index = "pytorch", extra = "cuda"},
    { url =  "https://download.pytorch.org/whl/nightly/rocm7.0/torch-2.10.0.dev20251116%2Brocm7.0-cp313-cp313-manylinux_2_28_x86_64.whl", extra = "rocm"},
]
torchvision = [
    { index = "pytorch", extra = "cuda" },
    { url = "https://download.pytorch.org/whl/nightly/rocm7.0/torchvision-0.25.0.dev20251117%2Brocm7.0-cp313-cp313-manylinux_2_28_x86_64.whl", extra = "rocm"}
]
bitsandbytes = [
    { url = "https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_main/bitsandbytes-1.33.7.preview-py3-none-manylinux_2_24_x86_64.whl", extra = "rocm"}
]

[[tool.uv.index]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu128"
explicit = true