setup.py

# ------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# ------------------------------------------------------------------------
# pylint: disable=C0103

import datetime
import json
import logging
import platform
import shlex
import subprocess
import sys
from glob import glob, iglob
from os import environ, getcwd, path, popen, remove
from pathlib import Path
from shutil import copyfile

from packaging.tags import sys_tags
from setuptools import Extension, setup
from setuptools.command.build_ext import build_ext as _build_ext
from setuptools.command.install import install as InstallCommandBase

nightly_build = False
package_name = "onnxruntime"
wheel_name_suffix = None
logger = logging.getLogger()


def parse_arg_remove_boolean(argv, arg_name):
    arg_value = False
    if arg_name in sys.argv:
        arg_value = True
        argv.remove(arg_name)

    return arg_value


def parse_arg_remove_string(argv, arg_name_equal):
    arg_value = None
    for arg in sys.argv[1:]:
        if arg.startswith(arg_name_equal):
            arg_value = arg[len(arg_name_equal) :]
            sys.argv.remove(arg)
            break

    return arg_value


# Any combination of the following arguments can be applied

if parse_arg_remove_boolean(sys.argv, "--nightly_build"):
    nightly_build = True

wheel_name_suffix = parse_arg_remove_string(sys.argv, "--wheel_name_suffix=")

cuda_version = None
cuda_major_version = None
is_migraphx = False
is_openvino = False
is_qnn = False
qnn_version = None
# The following arguments are mutually exclusive
if wheel_name_suffix == "gpu":
    cuda_version = parse_arg_remove_string(sys.argv, "--cuda_version=")
    if cuda_version:
        cuda_major_version = cuda_version.split(".")[0]
elif parse_arg_remove_boolean(sys.argv, "--use_migraphx"):
    is_migraphx = True
    package_name = "onnxruntime-migraphx"
elif parse_arg_remove_boolean(sys.argv, "--use_openvino"):
    is_openvino = True
    package_name = "onnxruntime-openvino"
elif parse_arg_remove_boolean(sys.argv, "--use_dnnl"):
    package_name = "onnxruntime-dnnl"
elif parse_arg_remove_boolean(sys.argv, "--use_tvm"):
    package_name = "onnxruntime-tvm"
elif parse_arg_remove_boolean(sys.argv, "--use_vitisai"):
    package_name = "onnxruntime-vitisai"
elif parse_arg_remove_boolean(sys.argv, "--use_acl"):
    package_name = "onnxruntime-acl"
elif parse_arg_remove_boolean(sys.argv, "--use_cann"):
    package_name = "onnxruntime-cann"
elif parse_arg_remove_boolean(sys.argv, "--use_azure"):
    # keep the same name since AzureEP will release with CpuEP by default.
    pass
elif parse_arg_remove_boolean(sys.argv, "--use_qnn"):
    is_qnn = True
    package_name = "onnxruntime-qnn"
    qnn_version = parse_arg_remove_string(sys.argv, "--qnn_version=")
elif parse_arg_remove_boolean(sys.argv, "--use_webgpu"):
    package_name = "onnxruntime-webgpu"


# PEP 513 defined manylinux1_x86_64 and manylinux1_i686
# PEP 571 defined manylinux2010_x86_64 and manylinux2010_i686
# PEP 599 defines the following platform tags:
# manylinux2014_x86_64
# manylinux2014_i686
# manylinux2014_aarch64
# manylinux2014_armv7l
# manylinux2014_ppc64
# manylinux2014_ppc64le
# manylinux2014_s390x
manylinux_tags = [
    "manylinux1_x86_64",
    "manylinux1_i686",
    "manylinux2010_x86_64",
    "manylinux2010_i686",
    "manylinux2014_x86_64",
    "manylinux2014_i686",
    "manylinux2014_aarch64",
    "manylinux2014_armv7l",
    "manylinux2014_ppc64",
    "manylinux2014_ppc64le",
    "manylinux2014_s390x",
    "manylinux_2_28_x86_64",
    "manylinux_2_28_aarch64",
    "manylinux_2_34_x86_64",
    "manylinux_2_34_aarch64",
]
is_manylinux = environ.get("AUDITWHEEL_PLAT", None) in manylinux_tags


class build_ext(_build_ext):  # noqa: N801
    def build_extension(self, ext):
        dest_file = self.get_ext_fullpath(ext.name)
        logger.info("copying %s -> %s", ext.sources[0], dest_file)
        copyfile(ext.sources[0], dest_file)


try:
    from wheel.bdist_wheel import bdist_wheel as _bdist_wheel

    class bdist_wheel(_bdist_wheel):  # noqa: N801
        """Helper functions to create wheel package"""

        if is_openvino and is_manylinux:

            def get_tag(self):
                _, _, plat = _bdist_wheel.get_tag(self)
                if platform.system() == "Linux":
                    # Get the right platform tag by querying the linker version
                    glibc_major, glibc_minor = popen("ldd --version | head -1").read().split()[-1].split(".")
                    """# See https://github.com/mayeut/pep600_compliance/blob/master/
                    pep600_compliance/tools/manylinux-policy.json"""
                    if glibc_major == "2" and glibc_minor == "17":
                        plat = "manylinux_2_17_x86_64.manylinux2014_x86_64"
                    else:  # For manylinux2014 and above, no alias is required
                        plat = f"manylinux_{glibc_major}_{glibc_minor}_x86_64"
                tags = next(sys_tags())
                return (tags.interpreter, tags.abi, plat)

        def finalize_options(self):
            _bdist_wheel.finalize_options(self)
            if not is_manylinux:
                self.root_is_pure = False

        def _rewrite_ld_preload(self, to_preload):
            with open("onnxruntime/capi/_ld_preload.py", "a") as f:
                if len(to_preload) > 0:
                    f.write("from ctypes import CDLL, RTLD_GLOBAL\n")
                    f.writelines(
                        '_{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split(".")[0], library)
                        for library in to_preload
                    )

        def _rewrite_ld_preload_cuda(self, to_preload):
            with open("onnxruntime/capi/_ld_preload.py", "a") as f:
                if len(to_preload) > 0:
                    # Generate a cascade loop so each version is tried independently;
                    # the first one that loads successfully wins and the rest are skipped.
                    # This avoids the single-try-block pitfall where a missing newer version
                    # (e.g. libcudart.so.13 on a CUDA 12 machine) would short-circuit to
                    # ORT_CUDA_UNAVAILABLE without ever trying the older version.
                    f.write("import os\n")
                    f.write("from ctypes import CDLL, RTLD_GLOBAL\n")
                    f.write("_libcudart = None\n")
                    f.write(f"for _cudart_lib in {json.dumps(to_preload)}:\n")
                    f.write("    try:\n")
                    f.write("        _libcudart = CDLL(_cudart_lib, mode=RTLD_GLOBAL)\n")
                    f.write("        break\n")
                    f.write("    except OSError:\n")
                    f.write("        pass\n")
                    f.write("if _libcudart is None:\n")
                    f.write('    os.environ["ORT_CUDA_UNAVAILABLE"] = "1"\n')

        def _rewrite_ld_preload_tensorrt(self, to_preload):
            with open("onnxruntime/capi/_ld_preload.py", "a", encoding="ascii") as f:
                if len(to_preload) > 0:
                    f.write("from ctypes import CDLL, RTLD_GLOBAL\n")
                    f.write("try:\n")
                    f.writelines(
                        '    _{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split(".")[0], library)
                        for library in to_preload
                    )
                    f.write("except OSError:\n")
                    f.write("    import os\n")
                    f.write('    os.environ["ORT_TENSORRT_UNAVAILABLE"] = "1"\n')

        def _rewrite_ld_preload_nv_tensorrt_rtx(self, to_preload):
            with open("onnxruntime/capi/_ld_preload.py", "a", encoding="ascii") as f:
                if len(to_preload) > 0:
                    f.write("from ctypes import CDLL, RTLD_GLOBAL\n")
                    f.write("try:\n")
                    f.writelines(
                        '    _{} = CDLL("{}", mode=RTLD_GLOBAL)\n'.format(library.split(".")[0], library)
                        for library in to_preload
                    )
                    f.write("except OSError:\n")
                    f.write("    import os\n")
                    f.write('    os.environ["ORT_NV_TENSORRT_RTX_UNAVAILABLE"] = "1"\n')

        def run(self):
            if is_manylinux:
                source = "onnxruntime/capi/onnxruntime_pybind11_state.so"
                dest = "onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so"
                logger.info("copying %s -> %s", source, dest)
                copyfile(source, dest)

                to_preload = []
                to_preload_cuda = []
                to_preload_tensorrt = []
                to_preload_nv_tensorrt_rtx = []
                to_preload_cann = []

                cuda_dependencies = [
                    "libcuda.so.1",
                    "libcublas.so.11",
                    "libcublas.so.12",
                    "libcublas.so.13",
                    "libcublasLt.so.11",
                    "libcublasLt.so.12",
                    "libcublasLt.so.13",
                    "libcudart.so.11.0",
                    "libcudart.so.12",
                    "libcudart.so.13",
                    "libcudnn.so.8",
                    "libcudnn.so.9",
                    "libcufft.so.10",
                    "libcufft.so.11",
                    "libcufft.so.12",
                    "libcurand.so.10",
                    "libnvJitLink.so.12",
                    "libnvJitLink.so.13",
                    "libnvrtc.so.11.2",  # A symlink to libnvrtc.so.11.8.89
                    "libnvrtc.so.12",
                    "libnvrtc.so.13",
                    "libnvrtc-builtins.so.11",
                    "libnvrtc-builtins.so.12",
                    "libnvrtc-builtins.so.13",
                ]

                migraphx_dependencies = [
                    "libamd_comgr.so.2",
                    "libamdhip64.so.5",
                    "libamdhip64.so.6",
                    "libdrm.so.2",
                    "libdrm_amdgpu.so.1",
                    "libelf.so.1",
                    "libhipfft.so.0",
                    "libhiprtc.so.5",
                    "libhiprtc.so.6",
                    "libhsa-runtime64.so.1",
                    "libMIOpen.so.1",
                    "libnuma.so.1",
                    "librccl.so.1",
                    "libhipblas.so.2",
                    "librocblas.so.3",
                    "librocblas.so.4",
                    "librocfft.so.0",
                    "libroctx64.so.4",
                    "librocm_smi64.so.5",
                    "librocm_smi64.so.6",
                    "libroctracer64.so.4",
                    "libtinfo.so.6",
                    "libmigraphx_c.so.3",
                    "libmigraphx.so.2",
                    "libmigraphx_onnx.so.2",
                    "libmigraphx_tf.so.2",
                ]

                tensorrt_dependencies = ["libnvinfer.so.10", "libnvinfer_plugin.so.10", "libnvonnxparser.so.10"]

                cann_dependencies = ["libascendcl.so", "libacl_op_compiler.so", "libfmk_onnx_parser.so"]

                qnn_dependencies = ["libcdsprpc.so"]

                dest = "onnxruntime/capi/libonnxruntime_providers_openvino.so"
                if path.isfile(dest):
                    subprocess.run(
                        ["patchelf", "--set-rpath", "$ORIGIN", dest, "--force-rpath"],
                        check=True,
                        stdout=subprocess.PIPE,
                        text=True,
                    )

                self._rewrite_ld_preload(to_preload)
                self._rewrite_ld_preload_cuda(to_preload_cuda)
                self._rewrite_ld_preload_tensorrt(to_preload_tensorrt)
                self._rewrite_ld_preload_tensorrt(to_preload_nv_tensorrt_rtx)
                self._rewrite_ld_preload(to_preload_cann)
            elif platform.system() == "Linux":
                # Non-manylinux Linux builds: preload libcudart so that undefined CUDA symbols
                # in onnxruntime_pybind11_state.so can be resolved at import time.
                if cuda_major_version:
                    # Use the exact version this wheel was built against.
                    cudart_libs = [f"libcudart.so.{cuda_major_version}"]
                    self._rewrite_ld_preload_cuda(cudart_libs)

            # qnn links libc++ rather than libstdc++ for its x86_64 dependencies which we currently do not
            # support for many_linux. This is not the case for other platforms.
            qnn_run_audit = environ.get("AUDITWHEEL_ARCH", "x86_64") != "x86_64"

            _bdist_wheel.run(self)
            if is_manylinux and not disable_auditwheel_repair and not is_openvino and (not is_qnn or qnn_run_audit):
                assert self.dist_dir is not None
                file = glob(path.join(self.dist_dir, "*linux*.whl"))[0]
                logger.info("repairing %s for manylinux1", file)
                auditwheel_cmd = ["auditwheel", "-v", "repair", "-w", self.dist_dir, file]
                for i in (
                    cuda_dependencies
                    + migraphx_dependencies
                    + tensorrt_dependencies
                    + cann_dependencies
                    + qnn_dependencies
                ):
                    auditwheel_cmd += ["--exclude", i]
                logger.info("Running %s", " ".join([shlex.quote(arg) for arg in auditwheel_cmd]))
                try:
                    subprocess.run(auditwheel_cmd, check=True, stdout=subprocess.PIPE)
                finally:
                    logger.info("removing %s", file)
                    remove(file)

except ImportError as error:
    print("Error importing dependencies:")
    print(error)
    bdist_wheel = None


class InstallCommand(InstallCommandBase):
    def finalize_options(self):
        ret = InstallCommandBase.finalize_options(self)
        self.install_lib = self.install_platlib
        return ret


providers_cuda = "onnxruntime_providers_cuda"
providers_tensorrt_or_migraphx = "onnxruntime_providers_" + ("migraphx" if is_migraphx else "tensorrt")
providers_nv_tensorrt_rtx = "onnxruntime_providers_nv_tensorrt_rtx"
providers_openvino = "onnxruntime_providers_openvino"
providers_cann = "onnxruntime_providers_cann"
providers_qnn = "onnxruntime_providers_qnn"

if platform.system() == "Linux":
    providers_cuda = "lib" + providers_cuda + ".so"
    providers_tensorrt_or_migraphx = "lib" + providers_tensorrt_or_migraphx + ".so"
    providers_nv_tensorrt_rtx = "lib" + providers_nv_tensorrt_rtx + ".so"
    providers_openvino = "lib" + providers_openvino + ".so"
    providers_cann = "lib" + providers_cann + ".so"
    providers_qnn = "lib" + providers_qnn + ".so"
elif platform.system() == "Windows":
    providers_cuda = providers_cuda + ".dll"
    providers_tensorrt_or_migraphx = providers_tensorrt_or_migraphx + ".dll"
    providers_nv_tensorrt_rtx = providers_nv_tensorrt_rtx + ".dll"
    providers_openvino = providers_openvino + ".dll"
    providers_cann = providers_cann + ".dll"
    providers_qnn = providers_qnn + ".dll"

# Additional binaries
dl_libs = []
libs = []

if platform.system() == "Linux" or platform.system() == "AIX":
    libs = [
        "onnxruntime_pybind11_state.so",
        "libdnnl.so.2",
        "libmklml_intel.so",
        "libmklml_gnu.so",
        "libiomp5.so",
        "mimalloc.so",
        "libonnxruntime.so*",
    ]
    dl_libs = ["libonnxruntime_providers_shared.so"]
    dl_libs.append(providers_cuda)
    dl_libs.append(providers_tensorrt_or_migraphx)
    dl_libs.append(providers_cann)
    dl_libs.append(providers_qnn)
    dl_libs.append("libonnxruntime.so*")
    # DNNL, TensorRT, OpenVINO, and QNN EPs are built as shared libs
    libs.extend(["libonnxruntime_providers_shared.so"])
    libs.extend(["libonnxruntime_providers_dnnl.so"])
    libs.extend(["libonnxruntime_providers_openvino.so"])
    libs.extend(["libonnxruntime_providers_vitisai.so"])
    libs.append(providers_cuda)
    libs.append(providers_nv_tensorrt_rtx)
    libs.append(providers_tensorrt_or_migraphx)
    libs.append(providers_cann)
    libs.append(providers_qnn)
    # QNN
    qnn_deps = [
        "libQnnCpu.so",
        "libQnnGpu.so",
        "libQnnHtp.so",
        "libQnnHtpPrepare.so",
        "libQnnHtpV68Skel.so",
        "libQnnHtpV68Stub.so",
        "libQnnIr.so",
        "libQnnSaver.so",
        "libQnnSystem.so",
        "libHtpPrepare.so",
    ]
    dl_libs.extend(qnn_deps)
    libs.extend(qnn_deps)
    # NV TensorRT RTX
    nv_tensorrt_rtx_deps = ["libtensorrt_rtx.so", "libtensorrt_onnxparser_rtx.so"]
    dl_libs.extend(nv_tensorrt_rtx_deps)
    libs.extend(nv_tensorrt_rtx_deps)
    if nightly_build:
        libs.extend(["libonnxruntime_pywrapper.so"])
elif platform.system() == "Darwin":
    libs = [
        "onnxruntime_pybind11_state.so",
        "libdnnl.2.dylib",
        "mimalloc.so",
        "libonnxruntime*.dylib",
    ]  # TODO add libmklml and libiomp5 later.
    # DNNL & TensorRT EPs are built as shared libs
    libs.extend(["libonnxruntime_providers_shared.dylib"])
    libs.extend(["libonnxruntime_providers_dnnl.dylib"])
    libs.extend(["libonnxruntime_providers_tensorrt.dylib"])
    libs.extend(["libonnxruntime_providers_cuda.dylib"])
    libs.extend(["libonnxruntime_providers_vitisai.dylib"])
    if nightly_build:
        libs.extend(["libonnxruntime_pywrapper.dylib"])
else:
    libs = [
        "onnxruntime_pybind11_state.pyd",
        "dnnl.dll",
        "mklml.dll",
        "libiomp5md.dll",
        providers_cuda,
        providers_tensorrt_or_migraphx,
        providers_nv_tensorrt_rtx,
        providers_cann,
        "onnxruntime.dll",
    ]
    # DNNL, TensorRT, OpenVINO, and QNN EPs are built as shared libs
    libs.extend(["onnxruntime_providers_shared.dll"])
    libs.extend(["onnxruntime_providers_dnnl.dll"])
    libs.extend(["onnxruntime_providers_tensorrt.dll"])
    libs.extend(["onnxruntime_providers_nv_tensorrt_rtx.dll"])
    libs.extend(["onnxruntime_providers_openvino.dll"])
    libs.extend(["onnxruntime_providers_cuda.dll"])
    libs.extend(["onnxruntime_providers_migraphx.dll"])
    libs.extend(["onnxruntime_providers_vitisai.dll"])
    libs.extend(["onnxruntime_providers_qnn.dll"])
    # DirectML Libs
    libs.extend(["DirectML.dll"])
    # WebGPU/Dawn Libs
    libs.extend(["dxcompiler.dll", "dxil.dll"])
    # QNN V68/V73/V81 dependencies
    qnn_deps = [
        "QnnCpu.dll",
        "QnnGpu.dll",
        "QnnHtp.dll",
        "QnnIr.dll",
        "QnnSaver.dll",
        "QnnSystem.dll",
        "QnnHtpPrepare.dll",
        "QnnHtpV81Stub.dll",
        "libQnnHtpV81Skel.so",
        "libqnnhtpv81.cat",
        "QnnHtpV73Stub.dll",
        "libQnnHtpV73Skel.so",
        "libqnnhtpv73.cat",
        "QnnHtpV68Stub.dll",
        "libQnnHtpV68Skel.so",
    ]
    libs.extend(qnn_deps)
    if nightly_build:
        libs.extend(["onnxruntime_pywrapper.dll"])
    migraphx_deps = [
        "amd_comgr0602.dll",
        "amd_comgr0604.dll",
        "amd_comgr0700.dll",
        "hiprtc0602.dll",
        "hiprtc0604.dll",
        "hiprtc0700.dll",
        "hiprtc-builtins0602.dll",
        "hiprtc-builtins0604.dll",
        "hiprtc-builtins0700.dll",
        "migraphx-hiprtc-driver.exe",
        "migraphx.dll",
        "migraphx_c.dll",
        "migraphx_cpu.dll",
        "migraphx_device.dll",
        "migraphx_gpu.dll",
        "migraphx_onnx.dll",
        "migraphx_tf.dll",
    ]
    libs.extend(migraphx_deps)
    # NV TensorRT RTX Libs
    nv_tensorrt_rtx_deps = ["tensorrt_onnxparser_rtx_*.dll", "tensorrt_rtx_*.dll"]
    libs.extend(nv_tensorrt_rtx_deps)

if is_manylinux:
    if is_openvino:
        ov_libs = [
            "libopenvino_intel_cpu_plugin.so",
            "libopenvino_intel_gpu_plugin.so",
            "libopenvino_auto_plugin.so",
            "libopenvino_hetero_plugin.so",
            "libtbb.so.2",
            "libtbbmalloc.so.2",
            "libopenvino.so",
            "libopenvino_c.so",
            "libopenvino_onnx_frontend.so",
        ]
        for x in ov_libs:
            y = "onnxruntime/capi/" + x
            subprocess.run(
                ["patchelf", "--set-rpath", "$ORIGIN", y, "--force-rpath"],
                check=True,
                stdout=subprocess.PIPE,
                text=True,
            )
            dl_libs.append(x)
        dl_libs.append(providers_openvino)
        dl_libs.append("plugins.xml")
        dl_libs.append("usb-ma2x8x.mvcmd")
    data = ["capi/libonnxruntime_pywrapper.so"] if nightly_build else []
    data += [path.join("capi", x) for x in dl_libs if glob(path.join("onnxruntime", "capi", x))]
    ext_modules = [
        Extension(
            "onnxruntime.capi.onnxruntime_pybind11_state",
            ["onnxruntime/capi/onnxruntime_pybind11_state_manylinux1.so"],
        ),
    ]
else:
    data = [path.join("capi", x) for x in libs if glob(path.join("onnxruntime", "capi", x))]
    ext_modules = []

# Additional examples
examples_names = ["mul_1.onnx", "logreg_iris.onnx", "sigmoid.onnx"]
examples = [path.join("datasets", x) for x in examples_names]

# Extra files such as EULA and ThirdPartyNotices (and Qualcomm License, only for QNN release packages)(and TensorRT RTX License and Acknowledgements for TRT RTX EP)
extra = [
    "LICENSE",
    "ThirdPartyNotices.txt",
    "Privacy.md",
    "Qualcomm_LICENSE.pdf",
    "TRT_RTX_LICENSE.txt",
    "TRT_RTX_Acknowledgements.txt",
]

# Description
readme_file = "docs/python/ReadMeOV.rst" if is_openvino else "docs/python/README.rst"
README = path.join(getcwd(), readme_file)
if not path.exists(README):
    this = path.dirname(__file__)
    README = path.join(this, readme_file)

if not path.exists(README):
    raise FileNotFoundError("Unable to find 'README.rst'")
with open(README, encoding="utf-8") as fdesc:
    long_description = fdesc.read()

# Include files in onnxruntime/external if --enable_external_custom_op_schemas build.sh command
# line option is specified.
# If the options is not specified this following condition fails as onnxruntime/external folder is not created in the
# build flow under the build binary directory.
if path.isdir(path.join("onnxruntime", "external")):
    # Gather all files under onnxruntime/external directory.
    extra.extend(
        [
            str(Path(*Path(x).parts[1:]))
            for x in list(iglob(path.join(path.join("onnxruntime", "external"), "**/*.*"), recursive=True))
        ]
    )

packages = [
    "onnxruntime",
    "onnxruntime.backend",
    "onnxruntime.capi",
    "onnxruntime.datasets",
    "onnxruntime.tools",
    "onnxruntime.tools.mobile_helpers",
    "onnxruntime.tools.ort_format_model",
    "onnxruntime.tools.ort_format_model.ort_flatbuffers_py",
    "onnxruntime.tools.ort_format_model.ort_flatbuffers_py.fbs",
    "onnxruntime.tools.qdq_helpers",
    "onnxruntime.tools.qnn",
    "onnxruntime.quantization",
    "onnxruntime.quantization.operators",
    "onnxruntime.quantization.CalTableFlatBuffers",
    "onnxruntime.quantization.fusions",
    "onnxruntime.quantization.execution_providers.qnn",
    "onnxruntime.quantization.neural_compressor",
    "onnxruntime.transformers",
    "onnxruntime.transformers.models.bart",
    "onnxruntime.transformers.models.bert",
    "onnxruntime.transformers.models.gpt2",
    "onnxruntime.transformers.models.llama",
    "onnxruntime.transformers.models.longformer",
    "onnxruntime.transformers.models.phi2",
    "onnxruntime.transformers.models.sam2",
    "onnxruntime.transformers.models.stable_diffusion",
    "onnxruntime.transformers.models.t5",
    "onnxruntime.transformers.models.whisper",
]

package_data = {"onnxruntime.tools.mobile_helpers": ["*.md", "*.config"]}
data_files = []

requirements_file = "requirements.txt"

local_version = None
enable_training = parse_arg_remove_boolean(sys.argv, "--enable_training")
enable_training_apis = parse_arg_remove_boolean(sys.argv, "--enable_training_apis")
disable_auditwheel_repair = parse_arg_remove_boolean(sys.argv, "--disable_auditwheel_repair")
default_training_package_device = parse_arg_remove_boolean(sys.argv, "--default_training_package_device")

classifiers = [
    "Development Status :: 5 - Production/Stable",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Operating System :: POSIX :: Linux",
    "Operating System :: Microsoft :: Windows",
    "Operating System :: MacOS",
    "Topic :: Scientific/Engineering",
    "Topic :: Scientific/Engineering :: Mathematics",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
    "Topic :: Software Development",
    "Topic :: Software Development :: Libraries",
    "Topic :: Software Development :: Libraries :: Python Modules",
    "Programming Language :: Python",
    "Programming Language :: Python :: 3 :: Only",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
    "Programming Language :: Python :: 3.14",
]

if enable_training or enable_training_apis:
    packages.append("onnxruntime.training")
    if enable_training:
        packages.extend(
            [
                "onnxruntime.training.amp",
                "onnxruntime.training.experimental",
                "onnxruntime.training.experimental.gradient_graph",
                "onnxruntime.training.optim",
                "onnxruntime.training.ortmodule",
                "onnxruntime.training.ortmodule.experimental",
                "onnxruntime.training.ortmodule.experimental.json_config",
                "onnxruntime.training.ortmodule.experimental.hierarchical_ortmodule",
                "onnxruntime.training.ortmodule.torch_cpp_extensions",
                "onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.aten_op_executor",
                "onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.torch_interop_utils",
                "onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.torch_gpu_allocator",
                "onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.fused_ops",
                "onnxruntime.training.ortmodule.graph_optimizers",
                "onnxruntime.training.ortmodule.experimental.pipe",
                "onnxruntime.training.ort_triton",
                "onnxruntime.training.ort_triton.kernel",
                "onnxruntime.training.utils",
                "onnxruntime.training.utils.data",
                "onnxruntime.training.utils.hooks",
                "onnxruntime.training.api",
                "onnxruntime.training.onnxblock",
                "onnxruntime.training.onnxblock.loss",
                "onnxruntime.training.onnxblock.optim",
            ]
        )

        package_data["onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.aten_op_executor"] = ["*.cc"]
        package_data["onnxruntime.training.ortmodule.torch_cpp_extensions.cpu.torch_interop_utils"] = ["*.cc", "*.h"]
        package_data["onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.torch_gpu_allocator"] = ["*.cc"]
        package_data["onnxruntime.training.ortmodule.torch_cpp_extensions.cuda.fused_ops"] = [
            "*.cpp",
            "*.cu",
            "*.cuh",
            "*.h",
        ]

    requirements_file = "requirements-training.txt"
    # with training, we want to follow this naming convention:
    # stable:
    # onnxruntime-training-1.7.0+cu111-cp36-cp36m-linux_x86_64.whl
    # nightly:
    # onnxruntime-training-1.7.0.dev20210408+cu111-cp36-cp36m-linux_x86_64.whl
    # this is needed immediately by pytorch/ort so that the user is able to
    # install an onnxruntime training package with matching torch cuda version.
    if not is_openvino:
        # To support the package consisting of both openvino and training modules part of it
        package_name = "onnxruntime-training"

        disable_local_version = environ.get("ORT_DISABLE_PYTHON_PACKAGE_LOCAL_VERSION", "0")
        disable_local_version = (
            disable_local_version == "1"
            or disable_local_version.lower() == "true"
            or disable_local_version.lower() == "yes"
        )
        # local version should be disabled for internal feeds.
        if not disable_local_version:
            # we want put default training packages to pypi. pypi does not accept package with a local version.
            if not default_training_package_device or nightly_build:
                if cuda_version:
                    # removing '.' to make Cuda version number in the same form as Pytorch.
                    local_version = "+cu" + cuda_version.replace(".", "")
                else:
                    # cpu version for documentation
                    local_version = "+cpu"
        else:
            if not cuda_version:
                # Training CPU package for ADO feeds is called onnxruntime-training-cpu
                package_name = "onnxruntime-training-cpu"

if package_name == "onnxruntime-tvm":
    packages += ["onnxruntime.providers.tvm"]

package_data["onnxruntime"] = data + examples + extra + ["py.typed"]

version_number = ""
with open("VERSION_NUMBER") as f:
    version_number = f.readline().strip()
if nightly_build:
    # https://docs.microsoft.com/en-us/azure/devops/pipelines/build/variables
    build_suffix = environ.get("BUILD_BUILDNUMBER")
    if build_suffix is None:
        # The following line is only for local testing
        build_suffix = str(datetime.datetime.now().date().strftime("%Y%m%d"))
    else:
        build_suffix = build_suffix.replace(".", "")

    if len(build_suffix) > 8 and len(build_suffix) < 12:
        # we want to format the build_suffix to avoid (the 12th run on 20210630 vs the first run on 20210701):
        # 2021063012 > 202107011
        # in above 2021063012 is treated as the latest which is incorrect.
        # we want to convert the format to:
        # 20210630012 < 20210701001
        # where the first 8 digits are date. the last 3 digits are run count.
        # as long as there are less than 1000 runs per day, we will not have the problem.
        # to test this code locally, run:
        # NIGHTLY_BUILD=1 BUILD_BUILDNUMBER=202107011 python tools/ci_build/build.py --config RelWithDebInfo \
        #   --enable_training --use_cuda --cuda_home /usr/local/cuda --cudnn_home /usr/lib/x86_64-linux-gnu/ \
        #   --nccl_home /usr/lib/x86_64-linux-gnu/ --build_dir build/Linux --build --build_wheel --skip_tests \
        #   --cuda_version 11.1
        def check_date_format(date_str):
            try:
                datetime.datetime.strptime(date_str, "%Y%m%d")
                return True
            except Exception:
                return False

        def reformat_run_count(count_str):
            try:
                count = int(count_str)
                if count >= 0 and count < 1000:
                    return f"{count:03}"
                elif count >= 1000:
                    raise RuntimeError(f"Too many builds for the same day: {count}")
                return ""
            except Exception:
                return ""

        build_suffix_is_date_format = check_date_format(build_suffix[:8])
        build_suffix_run_count = reformat_run_count(build_suffix[8:])
        if build_suffix_is_date_format and build_suffix_run_count:
            build_suffix = build_suffix[:8] + build_suffix_run_count
    elif len(build_suffix) >= 12:
        raise RuntimeError(f'Incorrect build suffix: "{build_suffix}"')

    if enable_training:
        from packaging import version
        from packaging.version import Version

        # with training package, we need to bump up version minor number so that
        # nightly releases take precedence over the latest release when --pre is used during pip install.
        # eventually this shall be the behavior of all onnxruntime releases.
        # alternatively we may bump up version number right after every release.
        ort_version = version.parse(version_number)
        if isinstance(ort_version, Version):
            # TODO: this is the last time we have to do this!!!
            # We shall bump up release number right after release cut.
            if ort_version.major == 1 and ort_version.minor == 8 and ort_version.micro == 0:
                version_number = f"{ort_version.major}.{ort_version.minor + 1}.{ort_version.micro}"

    version_number = version_number + ".dev" + build_suffix

if local_version:
    version_number = version_number + local_version

if wheel_name_suffix:
    if not (enable_training and wheel_name_suffix == "gpu"):
        # for training packages, local version is used to indicate device types
        package_name = f"{package_name}-{wheel_name_suffix}"

cmd_classes = {}
if bdist_wheel is not None:
    cmd_classes["bdist_wheel"] = bdist_wheel
cmd_classes["install"] = InstallCommand
cmd_classes["build_ext"] = build_ext

requirements_path = path.join(getcwd(), requirements_file)
if not path.exists(requirements_path):
    this = path.dirname(__file__)
    requirements_path = path.join(this, requirements_file)
if not path.exists(requirements_path):
    raise FileNotFoundError("Unable to find " + requirements_file)
with open(requirements_path) as f:
    install_requires = f.read().splitlines()

# Adding CUDA Runtime as dependency for NV TensorRT RTX python wheel
if package_name == "onnxruntime-trt-rtx":
    major = cuda_major_version or "12"  # Default to CUDA 12
    # CUDA 13 dropped the "-cuNN" suffix from the CUDA Runtime package name.
    runtime_pkg = "nvidia-cuda-runtime" if int(major) >= 13 else f"nvidia-cuda-runtime-cu{major}"
    install_requires.append(f"{runtime_pkg}~={major}.0")


def save_build_and_package_info(package_name, version_number, cuda_version, qnn_version):
    sys.path.append(path.join(path.dirname(__file__), "onnxruntime", "python"))
    from onnxruntime_collect_build_info import find_cudart_versions  # noqa: PLC0415

    version_path = path.join("onnxruntime", "capi", "build_and_package_info.py")
    with open(version_path, "w") as f:
        f.write(f"package_name = '{package_name}'\n")
        f.write(f"__version__ = '{version_number}'\n")

        if cuda_version:
            f.write(f"cuda_version = '{cuda_version}'\n")

            # The cudart version used in building training packages in Linux.
            # It is possible to parse version.json at cuda_home in build.py, then pass in the parameter directly.
            if enable_training and platform.system().lower() == "linux":
                cudart_versions = find_cudart_versions(build_env=True)
                if cudart_versions and len(cudart_versions) == 1:
                    f.write(f"cudart_version = {cudart_versions[0]}\n")
                else:
                    print(
                        "Error getting cudart version. ",
                        (
                            "did not find any cudart library"
                            if not cudart_versions or len(cudart_versions) == 0
                            else "found multiple cudart libraries"
                        ),
                    )
        elif qnn_version:
            f.write(f"qnn_version = '{qnn_version}'\n")


save_build_and_package_info(package_name, version_number, cuda_version, qnn_version)

# sympy is optional - only needed for symbolic shape inference
# ml_dtypes is optional - needed for quantization utilities
extras_require = {
    "symbolic": ["sympy"],
    "quantization": ["ml_dtypes"],
}
if package_name == "onnxruntime-gpu" and cuda_major_version:
    # Determine cufft version: CUDA 13 uses cufft 12, CUDA 12 uses cufft 11
    cufft_version = "12.0" if cuda_major_version == "13" else "11.0"

    # Starting with CUDA 13, NVIDIA renamed the per-component CUDA Toolkit packages by
    # dropping the "-cuNN" suffix (e.g. "nvidia-cuda-runtime-cu12" -> "nvidia-cuda-runtime").
    # cuDNN keeps the suffixed package name ("nvidia-cudnn-cu13").
    cuda_pkg_suffix = "" if int(cuda_major_version) >= 13 else f"-cu{cuda_major_version}"
    extras_require.update(
        {
            "cuda": [
                f"nvidia-cuda-nvrtc{cuda_pkg_suffix}~={cuda_major_version}.0",
                f"nvidia-cuda-runtime{cuda_pkg_suffix}~={cuda_major_version}.0",
                f"nvidia-cufft{cuda_pkg_suffix}~={cufft_version}",
                f"nvidia-curand{cuda_pkg_suffix}~=10.0",
            ],
            "cudnn": [
                f"nvidia-cudnn-cu{cuda_major_version}~=9.0",
            ],
        }
    )

setup(
    name=package_name,
    version=version_number,
    description="ONNX Runtime is a runtime accelerator for Machine Learning models",
    long_description=long_description,
    author="Microsoft Corporation",
    author_email="onnxruntime@microsoft.com",
    cmdclass=cmd_classes,
    license="MIT License",
    license_files=["LICENSE", "ThirdPartyNotices.txt"],
    packages=packages,
    ext_modules=ext_modules,
    package_data=package_data,
    url="https://onnxruntime.ai",
    download_url="https://github.com/microsoft/onnxruntime/tags",
    data_files=data_files,
    install_requires=install_requires,
    extras_require=extras_require,
    python_requires=">=3.11",
    keywords="onnx machine learning",
    entry_points={
        "console_scripts": [
            "onnxruntime_test = onnxruntime.tools.onnxruntime_test:main",
        ]
    },
    classifiers=classifiers,
)