docs/source/conf.py

# -*- coding: utf-8 -*-
#
# PyTorch documentation build configuration file, created by
# sphinx-quickstart on Fri Dec 23 13:31:47 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
from os import path
# import sys
import pkgutil

# source code directory, relative to this file, for sphinx-autobuild
# sys.path.insert(0, os.path.abspath('../..'))

import torch

try:
    import torchvision  # noqa: F401
except ImportError:
    import warnings
    warnings.warn('unable to load "torchvision" package')

RELEASE = os.environ.get('RELEASE', False)

import pytorch_sphinx_theme

# -- General configuration ------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
#
needs_sphinx = '3.1.2'

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
    'sphinx.ext.autodoc',
    'sphinx.ext.autosummary',
    'sphinx.ext.doctest',
    'sphinx.ext.intersphinx',
    'sphinx.ext.todo',
    'sphinx.ext.coverage',
    'sphinx.ext.napoleon',
    'sphinx.ext.viewcode',
    'sphinxcontrib.katex',
    'sphinx.ext.autosectionlabel',
    'sphinx_copybutton',
]

# build the templated autosummary files
autosummary_generate = True
numpydoc_show_class_members = False

# autosectionlabel throws warnings if section names are duplicated.
# The following tells autosectionlabel to not throw a warning for
# duplicated section names that are in different documents.
autosectionlabel_prefix_document = True

# katex options
#
#

katex_prerender = True

napoleon_use_ivar = True

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

# TODO: document these and remove them from here.

coverage_ignore_functions = [
    # torch.autograd
    "register_py_tensor_class_for_device",
    "variable",
    # torch.cuda
    "check_error",
    "cudart",
    "is_bf16_supported",
    # torch.distributed.autograd
    "is_available",
    # torch.distributed.elastic.events
    "construct_and_record_rdzv_event",
    "record_rdzv_event",
    # torch.distributed.elastic.metrics
    "initialize_metrics",
    # torch.distributed.elastic.rendezvous.registry
    "get_rendezvous_handler",
    # torch.distributed.launch
    "launch",
    "main",
    "parse_args",
    # torch.distributed.rpc
    "is_available",
    # torch.distributed.run
    "config_from_args",
    "determine_local_world_size",
    "get_args_parser",
    "get_rdzv_endpoint",
    "get_use_env",
    "main",
    "parse_args",
    "parse_min_max_nnodes",
    "run",
    "run_script_path",
    # torch.distributions.constraints
    "is_dependent",
    # torch.hub
    "import_module",
    # torch.jit
    "export_opnames",
    # torch.jit.unsupported_tensor_ops
    "execWrapper",
    # torch.onnx
    "unregister_custom_op_symbolic",
]

coverage_ignore_classes = [
    # torch.cuda
    "BFloat16Storage",
    "BFloat16Tensor",
    "BoolStorage",
    "BoolTensor",
    "ByteStorage",
    "ByteTensor",
    "CharStorage",
    "CharTensor",
    "ComplexDoubleStorage",
    "ComplexFloatStorage",
    "CudaError",
    "DeferredCudaCallError",
    "DoubleStorage",
    "DoubleTensor",
    "FloatStorage",
    "FloatTensor",
    "HalfStorage",
    "HalfTensor",
    "IntStorage",
    "IntTensor",
    "LongStorage",
    "LongTensor",
    "ShortStorage",
    "ShortTensor",
    "UntypedStorage",
    "cudaStatus",
    # torch.distributed.elastic.multiprocessing.errors
    "ChildFailedError",
    "ProcessFailure",
    # torch.distributions.constraints
    "cat",
    "greater_than",
    "greater_than_eq",
    "half_open_interval",
    "independent",
    "integer_interval",
    "interval",
    "less_than",
    "multinomial",
    "stack",
    # torch.distributions.transforms
    "AffineTransform",
    "CatTransform",
    "ComposeTransform",
    "CorrCholeskyTransform",
    "ExpTransform",
    "IndependentTransform",
    "PowerTransform",
    "ReshapeTransform",
    "SigmoidTransform",
    "SoftmaxTransform",
    "StackTransform",
    "StickBreakingTransform",
    "TanhTransform",
    "Transform",
    # torch.jit
    "CompilationUnit",
    "Error",
    "Future",
    "ScriptFunction",
    # torch.onnx
    "CheckerError",
    "ExportTypes",
]

# List of modules that do not have automodule/py:module in the doc yet
# We should NOT add anything to this list, see the CI failure message
# on how to solve missing automodule issues
coverage_missing_automodule = [
    "torch",
    "torch.ao",
    "torch.ao.nn",
    "torch.ao.nn.sparse",
    "torch.ao.nn.sparse.quantized",
    "torch.ao.nn.sparse.quantized.dynamic",
    "torch.ao.ns",
    "torch.ao.ns.fx",
    "torch.ao.quantization",
    "torch.ao.quantization.fx",
    "torch.ao.quantization.fx.backend_config",
    "torch.ao.sparsity",
    "torch.ao.sparsity.experimental",
    "torch.ao.sparsity.experimental.pruner",
    "torch.ao.sparsity.scheduler",
    "torch.ao.sparsity.sparsifier",
    "torch.backends",
    "torch.backends.cuda",
    "torch.backends.cudnn",
    "torch.backends.mkl",
    "torch.backends.mkldnn",
    "torch.backends.openmp",
    "torch.backends.quantized",
    "torch.backends.xnnpack",
    "torch.contrib",
    "torch.cpu",
    "torch.cpu.amp",
    "torch.distributed.algorithms",
    "torch.distributed.algorithms.ddp_comm_hooks",
    "torch.distributed.algorithms.model_averaging",
    "torch.distributed.elastic",
    "torch.distributed.elastic.utils",
    "torch.distributed.elastic.utils.data",
    "torch.distributed.launcher",
    "torch.distributed.nn",
    "torch.distributed.nn.api",
    "torch.distributed.nn.jit",
    "torch.distributed.nn.jit.templates",
    "torch.distributed.pipeline",
    "torch.distributed.pipeline.sync",
    "torch.distributed.pipeline.sync.skip",
    "torch.fft",
    "torch.for_onnx",
    "torch.fx.experimental",
    "torch.fx.experimental.fx2trt",
    "torch.fx.experimental.fx_acc",
    "torch.fx.experimental.unification",
    "torch.fx.experimental.unification.multipledispatch",
    "torch.fx.passes",
    "torch.jit.mobile",
    "torch.nn",
    "torch.nn.backends",
    "torch.nn.intrinsic",
    "torch.nn.intrinsic.modules",
    "torch.nn.intrinsic.qat",
    "torch.nn.intrinsic.qat.modules",
    "torch.nn.intrinsic.quantized",
    "torch.nn.intrinsic.quantized.dynamic",
    "torch.nn.intrinsic.quantized.dynamic.modules",
    "torch.nn.intrinsic.quantized.modules",
    "torch.nn.modules",
    "torch.nn.parallel",
    "torch.nn.qat",
    "torch.nn.qat.modules",
    "torch.nn.qat.dynamic",
    "torch.nn.qat.dynamic.modules",
    "torch.nn.quantizable",
    "torch.nn.quantizable.modules",
    "torch.nn.quantized",
    "torch.nn.quantized.dynamic",
    "torch.nn.quantized.dynamic.modules",
    "torch.nn.quantized.modules",
    "torch.nn.utils",
    "torch.package",
    "torch.package.analyze",
    "torch.quantization",
    "torch.quantization.fx",
    "torch.sparse",
    "torch.special",
    "torch.utils",
    "torch.utils.backcompat",
    "torch.utils.benchmark.examples",
    "torch.utils.benchmark.op_fuzzers",
    "torch.utils.benchmark.utils",
    "torch.utils.benchmark.utils.valgrind_wrapper",
    "torch.utils.bottleneck",
    "torch.utils.data.communication",
    "torch.utils.data.datapipes",
    "torch.utils.data.datapipes.dataframe",
    "torch.utils.data.datapipes.iter",
    "torch.utils.data.datapipes.map",
    "torch.utils.data.datapipes.utils",
    "torch.utils.ffi",
    "torch.utils.hipify",
    "torch.utils.model_dump",
    "torch.utils.tensorboard",
]


# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = 'PyTorch'
copyright = '2019, Torch Contributors'
author = 'Torch Contributors'
torch_version = str(torch.__version__)

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
# TODO: change to [:2] at v1.0
version = 'master (' + torch_version + ' )'
# The full version, including alpha/beta/rc tags.
# TODO: verify this works as expected
release = 'master'

# Customized html_title here.
# Default is " ".join(project, release, "documentation") if not set
if RELEASE:
    # remove hash (start with 'a') from version number if any
    version_end = torch_version.find('a')
    if version_end == -1:
        html_title = " ".join((project, torch_version, "documentation"))
        version = torch_version
    else:
        html_title = " ".join((project, torch_version[:version_end], "documentation"))
        version = torch_version[:version_end]
    release = version

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = []

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'

# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True

# Disable docstring inheritance
autodoc_inherit_docstrings = False

# Disable displaying type annotations, these can be very verbose
autodoc_typehints = 'none'

# Enable overriding of function signatures in the first line of the docstring.
autodoc_docstring_signature = True

# -- katex javascript in header
#
#    def setup(app):
#    app.add_javascript("https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.js")


# -- Options for HTML output ----------------------------------------------
#
# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
#
#
#

html_theme = 'pytorch_sphinx_theme'
html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.

html_theme_options = {
    'pytorch_project': 'docs',
    'canonical_url': 'https://pytorch.org/docs/stable/',
    'collapse_navigation': False,
    'display_version': True,
    'logo_only': True,
    'analytics_id': 'UA-117752657-2',
}

html_logo = '_static/img/pytorch-logo-dark-unstable.png'
if RELEASE:
    html_logo = '_static/img/pytorch-logo-dark.svg'


# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']

html_css_files = [
    'css/jit.css',
]

from sphinx.ext.coverage import CoverageBuilder

def coverage_post_process(app, exception):
    if exception is not None:
        return

    # Only run this test for the coverage build
    if not isinstance(app.builder, CoverageBuilder):
        return

    # These are all the modules that have "automodule" in an rst file
    # These modules are the ones for which coverage is checked
    # Here, we make sure that no module is missing from that list
    modules = app.env.domaindata['py']['modules']

    # We go through all the torch submodules and make sure they are
    # properly tested
    missing = set()

    def is_not_internal(modname):
        split_name = modname.split(".")
        for name in split_name:
            if name[0] == "_":
                return False
        return True

    # The walk function does not return the top module
    if "torch" not in modules:
        missing.add("torch")

    for _, modname, ispkg in pkgutil.walk_packages(path=torch.__path__,
                                                   prefix=torch.__name__ + '.'):
        if ispkg and is_not_internal(modname):
            if modname not in modules:
                missing.add(modname)

    expected = set(coverage_missing_automodule)

    output = []

    unexpected_missing = missing - expected
    if unexpected_missing:
        mods = ", ".join(unexpected_missing)
        output.append(f"\nYou added the following module(s) to the PyTorch namespace '{mods}' "
                      "but they have no corresponding entry in a doc .rst file. You should "
                      "either make sure that the .rst file that contains the module's documentation "
                      "properly contains either '.. automodule:: mod_name' (if you do not want "
                      "the paragraph added by the automodule, you can simply use py:module) or "
                      "make the module private (by appending an '_' at the beginning of its name.")

    unexpected_not_missing = expected - missing
    if unexpected_not_missing:
        mods = ", ".join(unexpected_not_missing)
        output.append(f"\nThank you for adding the missing .rst entries for '{mods}', please update "
                      "the 'coverage_missing_automodule' in 'torch/docs/source/conf.py' to remove "
                      "the module(s) you fixed and make sure we do not regress on this in the future.")

    # The output file is hard-coded by the coverage tool
    # Our CI is setup to fail if any line is added to this file
    output_file = path.join(app.outdir, 'python.txt')

    if output:
        with open(output_file, "a") as f:
            for o in output:
                f.write(o)

# Called automatically by Sphinx, making this `conf.py` an "extension".
def setup(app):
    # NOTE: in Sphinx 1.8+ `html_css_files` is an official configuration value
    # and can be moved outside of this function (and the setup(app) function
    # can be deleted).
    html_css_files = [
        'https://cdn.jsdelivr.net/npm/katex@0.10.0-beta/dist/katex.min.css'
    ]

    # In Sphinx 1.8 it was renamed to `add_css_file`, 1.7 and prior it is
    # `add_stylesheet` (deprecated in 1.8).
    add_css = getattr(app, 'add_css_file', app.add_stylesheet)
    for css_file in html_css_files:
        add_css(css_file)

    app.connect("build-finished", coverage_post_process)

# From PyTorch 1.5, we now use autogenerated files to document classes and
# functions. This breaks older references since
# https://pytorch.org/docs/stable/torch.html#torch.flip
# moved to
# https://pytorch.org/docs/stable/generated/torch.flip.html
# which breaks older links from blog posts, stack overflow answers and more.
# To mitigate that, we add an id="torch.flip" in an appropriated place
# in torch.html by overriding the visit_reference method of html writers.
# Someday this can be removed, once the old links fade away

from sphinx.writers import html, html5

def replace(Klass):
    old_call = Klass.visit_reference

    def visit_reference(self, node):
        if 'refuri' in node and 'generated' in node.get('refuri'):
            ref = node.get('refuri')
            ref_anchor = ref.split('#')
            if len(ref_anchor) > 1:
                # Only add the id if the node href and the text match,
                # i.e. the href is "torch.flip#torch.flip" and the content is
                # "torch.flip" or "flip" since that is a signal the node refers
                # to autogenerated content
                anchor = ref_anchor[1]
                txt = node.parent.astext()
                if txt == anchor or txt == anchor.split('.')[-1]:
                    self.body.append('<p id="{}"/>'.format(ref_anchor[1]))
        return old_call(self, node)
    Klass.visit_reference = visit_reference

replace(html.HTMLTranslator)
replace(html5.HTML5Translator)

# -- Options for HTMLHelp output ------------------------------------------

# Output file base name for HTML help builder.
htmlhelp_basename = 'PyTorchdoc'


# -- Options for LaTeX output ---------------------------------------------

latex_elements = {
    # The paper size ('letterpaper' or 'a4paper').
    #
    # 'papersize': 'letterpaper',

    # The font size ('10pt', '11pt' or '12pt').
    #
    # 'pointsize': '10pt',

    # Additional stuff for the LaTeX preamble.
    #
    # 'preamble': '',

    # Latex figure (float) alignment
    #
    # 'figure_align': 'htbp',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
#  author, documentclass [howto, manual, or own class]).
latex_documents = [
    (master_doc, 'pytorch.tex', 'PyTorch Documentation',
     'Torch Contributors', 'manual'),
]


# -- Options for manual page output ---------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
    (master_doc, 'PyTorch', 'PyTorch Documentation',
     [author], 1)
]


# -- Options for Texinfo output -------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
#  dir menu entry, description, category)
texinfo_documents = [
    (master_doc, 'PyTorch', 'PyTorch Documentation',
     author, 'PyTorch', 'One line description of project.',
     'Miscellaneous'),
]


# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {
    'python': ('https://docs.python.org/3', None),
    'numpy': ('https://numpy.org/doc/stable', None),
}

# -- A patch that prevents Sphinx from cross-referencing ivar tags -------
# See http://stackoverflow.com/a/41184353/3343043

from docutils import nodes
from sphinx.util.docfields import TypedField
from sphinx import addnodes
import sphinx.ext.doctest

# Without this, doctest adds any example with a `>>>` as a test
doctest_test_doctest_blocks = ''
doctest_default_flags = sphinx.ext.doctest.doctest.ELLIPSIS
doctest_global_setup = '''
import torch
try:
    import torchvision
except ImportError:
    torchvision = None
'''


def patched_make_field(self, types, domain, items, **kw):
    # `kw` catches `env=None` needed for newer sphinx while maintaining
    #  backwards compatibility when passed along further down!

    # type: (List, unicode, Tuple) -> nodes.field
    def handle_item(fieldarg, content):
        par = nodes.paragraph()
        par += addnodes.literal_strong('', fieldarg)  # Patch: this line added
        # par.extend(self.make_xrefs(self.rolename, domain, fieldarg,
        #                           addnodes.literal_strong))
        if fieldarg in types:
            par += nodes.Text(' (')
            # NOTE: using .pop() here to prevent a single type node to be
            # inserted twice into the doctree, which leads to
            # inconsistencies later when references are resolved
            fieldtype = types.pop(fieldarg)
            if len(fieldtype) == 1 and isinstance(fieldtype[0], nodes.Text):
                typename = u''.join(n.astext() for n in fieldtype)
                typename = typename.replace('int', 'python:int')
                typename = typename.replace('long', 'python:long')
                typename = typename.replace('float', 'python:float')
                typename = typename.replace('bool', 'python:bool')
                typename = typename.replace('type', 'python:type')
                par.extend(self.make_xrefs(self.typerolename, domain, typename,
                                           addnodes.literal_emphasis, **kw))
            else:
                par += fieldtype
            par += nodes.Text(')')
        par += nodes.Text(' -- ')
        par += content
        return par

    fieldname = nodes.field_name('', self.label)
    if len(items) == 1 and self.can_collapse:
        fieldarg, content = items[0]
        bodynode = handle_item(fieldarg, content)
    else:
        bodynode = self.list_type()
        for fieldarg, content in items:
            bodynode += nodes.list_item('', handle_item(fieldarg, content))
    fieldbody = nodes.field_body('', bodynode)
    return nodes.field('', fieldname, fieldbody)

TypedField.make_field = patched_make_field

copybutton_prompt_text = r'>>> |\.\.\. '
copybutton_prompt_is_regexp = True