Blame for submodules/pytorch3d/setup.py

#!/usr/bin/env python
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

import glob
import os
import runpy
import sys
import warnings
from typing import List, Optional

import torch
from setuptools import find_packages, setup
from torch.utils.cpp_extension import CppExtension, CUDA_HOME, CUDAExtension, ROCM_HOME


def get_existing_ccbin(nvcc_args: List[str]) -> Optional[str]:
    """
    Given a list of nvcc arguments, return the compiler if specified.

    Note from CUDA doc: Single value options and list options must have
    arguments, which must follow the name of the option itself by either
    one of more spaces or an equals character.
    """
    last_arg = None
    for arg in reversed(nvcc_args):
        if arg == "-ccbin":
            return last_arg
        if arg.startswith("-ccbin="):
            return arg[7:]
        last_arg = arg
    return None


def get_extensions():
    no_extension = os.getenv("PYTORCH3D_NO_EXTENSION", "0") == "1"
    if no_extension:
        msg = "SKIPPING EXTENSION BUILD. PYTORCH3D WILL NOT WORK!"
        print(msg, file=sys.stderr)
        warnings.warn(msg)
        return []

    this_dir = os.path.dirname(os.path.abspath(__file__))
    extensions_dir = os.path.join(this_dir, "pytorch3d", "csrc")
    sources = glob.glob(os.path.join(extensions_dir, "**", "*.cpp"), recursive=True)
    source_cuda = glob.glob(os.path.join(extensions_dir, "**", "*.cu"), recursive=True)
    extension = CppExtension

    extra_compile_args = {"cxx": ["-std=c++17"]}
    define_macros = []
    include_dirs = [extensions_dir]

    # ROCm/HIP support. When PyTorch is built with HIP, the cpp_extension
    # BuildExtension auto-hipifies .cu sources and swaps nvcc -> hipcc.
    is_rocm = torch.version.hip is not None

    force_cuda = os.getenv("FORCE_CUDA", "0") == "1"
    force_no_cuda = os.getenv("PYTORCH3D_FORCE_NO_CUDA", "0") == "1"
    gpu_home_available = CUDA_HOME is not None or (is_rocm and ROCM_HOME is not None)
    if (
        not force_no_cuda and torch.cuda.is_available() and gpu_home_available
    ) or force_cuda:
        extension = CUDAExtension

        sources += source_cuda
        define_macros += [("WITH_CUDA", None)]
        # Thrust is only used for its tuple objects.
        # With CUDA 11.0 we can't use the cudatoolkit's version of cub.
        # We take the risk that CUB and Thrust are incompatible, because
        # we aren't using parts of Thrust which actually use CUB.
        define_macros += [("THRUST_IGNORE_CUB_VERSION_CHECK", None)]
        nvcc_args = [
            "-DCUDA_HAS_FP16=1",
            "-D__CUDA_NO_HALF_OPERATORS__",
            "-D__CUDA_NO_HALF_CONVERSIONS__",
            "-D__CUDA_NO_HALF2_OPERATORS__",
        ]
        if os.name != "nt":
            nvcc_args.append("-std=c++17")

        if not is_rocm:
            # CUDA 13.0+ compatibility flags for pulsar.
            # Starting with CUDA 13, __global__ function visibility changed.
            # See: https://developer.nvidia.com/blog/
            #      cuda-c-compiler-updates-impacting-elf-visibility-and-linkage/
            cuda_version = torch.version.cuda
            if cuda_version is not None:
                major = int(cuda_version.split(".")[0])
                if major >= 13:
                    nvcc_args.extend(
                        [
                            "--device-entity-has-hidden-visibility=false",
                            "-static-global-template-stub=false",
                        ]
                    )

            # NVIDIA CUB. On ROCm, hipcub from the ROCm toolchain is used and
            # no external CUB_HOME is required.
            cub_home = os.environ.get("CUB_HOME", None)
            if cub_home is None:
                prefix = os.environ.get("CONDA_PREFIX", None)
                if prefix is not None and os.path.isdir(prefix + "/include/cub"):
                    cub_home = prefix + "/include"

            if cub_home is None:
                warnings.warn(
                    "The environment variable `CUB_HOME` was not found. "
                    "NVIDIA CUB is required for compilation and can be downloaded "
                    "from `https://github.com/NVIDIA/cub/releases`. You can unpack "
                    "it to a location of your choice and set the environment variable "
                    "`CUB_HOME` to the folder containing the `CMakeListst.txt` file."
                )
            else:
                include_dirs.append(os.path.realpath(cub_home).replace("\\ ", " "))
        nvcc_flags_env = os.getenv("NVCC_FLAGS", "")
        if nvcc_flags_env != "":
            nvcc_args.extend(nvcc_flags_env.split(" "))

        # This is needed for pytorch 1.6 and earlier. See e.g.
        # https://github.com/facebookresearch/pytorch3d/issues/436
        # It is harmless after https://github.com/pytorch/pytorch/pull/47404 .
        # But it can be problematic in torch 1.7.0 and 1.7.1
        # On ROCm the host compiler is selected by hipcc itself; -ccbin is
        # an nvcc-only flag.
        if not is_rocm and torch.__version__[:4] != "1.7.":
            CC = os.environ.get("CC", None)
            if CC is not None:
                existing_CC = get_existing_ccbin(nvcc_args)
                if existing_CC is None:
                    CC_arg = "-ccbin={}".format(CC)
                    nvcc_args.append(CC_arg)
                elif existing_CC != CC:
                    msg = f"Inconsistent ccbins: {CC} and {existing_CC}"
                    raise ValueError(msg)

        extra_compile_args["nvcc"] = nvcc_args

    sources = [os.path.join(extensions_dir, s) for s in sources]

    ext_modules = [
        extension(
            "pytorch3d._C",
            sources,
            include_dirs=include_dirs,
            define_macros=define_macros,
            extra_compile_args=extra_compile_args,
        )
    ]

    return ext_modules


# Retrieve __version__ from the package.
__version__ = runpy.run_path("pytorch3d/__init__.py")["__version__"]


if os.getenv("PYTORCH3D_NO_NINJA", "0") == "1":

    class BuildExtension(torch.utils.cpp_extension.BuildExtension):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, use_ninja=False, **kwargs)

else:
    BuildExtension = torch.utils.cpp_extension.BuildExtension

trainer = "pytorch3d.implicitron_trainer"

setup(
    name="pytorch3d",
    version=__version__,
    author="FAIR",
    url="https://github.com/facebookresearch/pytorch3d",
    description="PyTorch3D is FAIR's library of reusable components "
    "for deep Learning with 3D data.",
    packages=find_packages(
        exclude=("configs", "tests", "tests.*", "docs.*", "projects.*")
    )
    + [trainer],
    package_dir={trainer: "projects/implicitron_trainer"},
    install_requires=["iopath"],
    extras_require={
        "all": ["matplotlib", "tqdm>4.29.0", "imageio", "ipywidgets"],
        "dev": ["flake8", "usort"],
        "implicitron": [
            "hydra-core>=1.1",
            "visdom",
            "lpips",
            "tqdm>4.29.0",
            "matplotlib",
            "accelerate",
            "sqlalchemy>=2.0",
        ],
    },
    entry_points={
        "console_scripts": [
            f"pytorch3d_implicitron_runner={trainer}.experiment:experiment",
            f"pytorch3d_implicitron_visualizer={trainer}.visualize_reconstruction:main",
        ]
    },
    ext_modules=get_extensions(),
    cmdclass={"build_ext": BuildExtension},
    package_data={
        "": ["*.json"],
    },
)