.circleci/cimodel/data/binary_build_data.py

"""
This module models the tree of configuration variants
for "smoketest" builds.

Each subclass of ConfigNode represents a layer of the configuration hierarchy.
These tree nodes encapsulate the logic for whether a branch of the hierarchy
should be "pruned".
"""

from collections import OrderedDict

from cimodel.lib.conf_tree import ConfigNode
import cimodel.data.dimensions as dimensions


LINKING_DIMENSIONS = [
    "shared",
    "static",
]


DEPS_INCLUSION_DIMENSIONS = [
    "with-deps",
    "without-deps",
]


def get_processor_arch_name(gpu_version):
    return "cpu" if not gpu_version else (
        "cu" + gpu_version.strip("cuda") if gpu_version.startswith("cuda") else gpu_version
    )

CONFIG_TREE_DATA = OrderedDict(
)

# GCC config variants:
#
# All the nightlies (except libtorch with new gcc ABI) are built with devtoolset7,
# which can only build with old gcc ABI. It is better than devtoolset3
# because it understands avx512, which is needed for good fbgemm performance.
#
# Libtorch with new gcc ABI is built with gcc 5.4 on Ubuntu 16.04.
LINUX_GCC_CONFIG_VARIANTS = OrderedDict(
    manywheel=['devtoolset7'],
    conda=['devtoolset7'],
    libtorch=[
        "devtoolset7",
        "gcc5.4_cxx11-abi",
    ],
)

WINDOWS_LIBTORCH_CONFIG_VARIANTS = [
    "debug",
    "release",
]


class TopLevelNode(ConfigNode):
    def __init__(self, node_name, config_tree_data, smoke):
        super(TopLevelNode, self).__init__(None, node_name)

        self.config_tree_data = config_tree_data
        self.props["smoke"] = smoke

    def get_children(self):
        return [OSConfigNode(self, x, c, p) for (x, (c, p)) in self.config_tree_data.items()]


class OSConfigNode(ConfigNode):
    def __init__(self, parent, os_name, gpu_versions, py_tree):
        super(OSConfigNode, self).__init__(parent, os_name)

        self.py_tree = py_tree
        self.props["os_name"] = os_name
        self.props["gpu_versions"] = gpu_versions

    def get_children(self):
        return [PackageFormatConfigNode(self, k, v) for k, v in self.py_tree.items()]


class PackageFormatConfigNode(ConfigNode):
    def __init__(self, parent, package_format, python_versions):
        super(PackageFormatConfigNode, self).__init__(parent, package_format)

        self.props["python_versions"] = python_versions
        self.props["package_format"] = package_format


    def get_children(self):
        if self.find_prop("os_name") == "linux":
            return [LinuxGccConfigNode(self, v) for v in LINUX_GCC_CONFIG_VARIANTS[self.find_prop("package_format")]]
        elif self.find_prop("os_name") == "windows" and self.find_prop("package_format") == "libtorch":
            return [WindowsLibtorchConfigNode(self, v) for v in WINDOWS_LIBTORCH_CONFIG_VARIANTS]
        else:
            return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]


class LinuxGccConfigNode(ConfigNode):
    def __init__(self, parent, gcc_config_variant):
        super(LinuxGccConfigNode, self).__init__(parent, "GCC_CONFIG_VARIANT=" + str(gcc_config_variant))

        self.props["gcc_config_variant"] = gcc_config_variant

    def get_children(self):
        gpu_versions = self.find_prop("gpu_versions")

        # XXX devtoolset7 on CUDA 9.0 is temporarily disabled
        # see https://github.com/pytorch/pytorch/issues/20066
        if self.find_prop("gcc_config_variant") == 'devtoolset7':
            gpu_versions = filter(lambda x: x != "cuda_90", gpu_versions)

        # XXX disabling conda rocm build since docker images are not there
        if self.find_prop("package_format") == 'conda':
            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)

        # XXX libtorch rocm build  is temporarily disabled
        if self.find_prop("package_format") == 'libtorch':
            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)

        return [ArchConfigNode(self, v) for v in gpu_versions]


class WindowsLibtorchConfigNode(ConfigNode):
    def __init__(self, parent, libtorch_config_variant):
        super(WindowsLibtorchConfigNode, self).__init__(parent, "LIBTORCH_CONFIG_VARIANT=" + str(libtorch_config_variant))

        self.props["libtorch_config_variant"] = libtorch_config_variant

    def get_children(self):
        return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]


class ArchConfigNode(ConfigNode):
    def __init__(self, parent, gpu):
        super(ArchConfigNode, self).__init__(parent, get_processor_arch_name(gpu))

        self.props["gpu"] = gpu

    def get_children(self):
        return [PyVersionConfigNode(self, v) for v in self.find_prop("python_versions")]


class PyVersionConfigNode(ConfigNode):
    def __init__(self, parent, pyver):
        super(PyVersionConfigNode, self).__init__(parent, pyver)

        self.props["pyver"] = pyver

    def get_children(self):
        package_format = self.find_prop("package_format")
        os_name = self.find_prop("os_name")

        has_libtorch_variants = package_format == "libtorch" and os_name == "linux"
        linking_variants = LINKING_DIMENSIONS if has_libtorch_variants else []

        return [LinkingVariantConfigNode(self, v) for v in linking_variants]


class LinkingVariantConfigNode(ConfigNode):
    def __init__(self, parent, linking_variant):
        super(LinkingVariantConfigNode, self).__init__(parent, linking_variant)

    def get_children(self):
        return [DependencyInclusionConfigNode(self, v) for v in DEPS_INCLUSION_DIMENSIONS]


class DependencyInclusionConfigNode(ConfigNode):
    def __init__(self, parent, deps_variant):
        super(DependencyInclusionConfigNode, self).__init__(parent, deps_variant)

        self.props["libtorch_variant"] = "-".join([self.parent.get_label(), self.get_label()])