docs/source/conf.py - platform/external/pytorch - Git at Google

 #
 # 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

 # import sys
 import pkgutil
 import re
 from os import path

 # 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",
     "sphinx_panels",
     "myst_parser",
 ]

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

 # Theme has bootstrap already
 panels_add_bootstrap_css = 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
     "typename",
     # torch.autograd
     "register_py_tensor_class_for_device",
     "variable",
     # torch.cuda
     "check_error",
     "cudart",
     "is_bf16_supported",
     # torch.cuda._sanitizer
     "zip_arguments",
     "zip_by_key",
     # 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",
     # torch.ao.quantization
     "default_eval_fn",
     # torch.backends
     "disable_global_flags",
     "flags_frozen",
     # torch.distributed.algorithms.ddp_comm_hooks
     "register_ddp_comm_hook",
     # torch.nn
     "factory_kwargs",
     # torch.nn.parallel
     "DistributedDataParallelCPU",
     # torch.utils
     "set_module",
     # torch.utils.model_dump
     "burn_in_info",
     "get_info_and_burn_skeleton",
     "get_inline_skeleton",
     "get_model_info",
     "get_storage_info",
     "hierarchical_pickle",
 ]

 coverage_ignore_classes = [
     # torch
     "FatalError",
     "QUInt2x4Storage",
     "Size",
     "Storage",
     "Stream",
     "Tensor",
     "finfo",
     "iinfo",
     "qscheme",
     "AggregationType",
     "AliasDb",
     "AnyType",
     "Argument",
     "ArgumentSpec",
     "AwaitType",
     "BenchmarkConfig",
     "BenchmarkExecutionStats",
     "Block",
     "BoolType",
     "BufferDict",
     "CallStack",
     "Capsule",
     "ClassType",
     "Code",
     "CompleteArgumentSpec",
     "ComplexType",
     "ConcreteModuleType",
     "ConcreteModuleTypeBuilder",
     "DeepCopyMemoTable",
     "DeserializationStorageContext",
     "DeviceObjType",
     "DictType",
     "DispatchKey",
     "DispatchKeySet",
     "EnumType",
     "ExcludeDispatchKeyGuard",
     "ExecutionPlan",
     "FileCheck",
     "FloatType",
     "FunctionSchema",
     "Gradient",
     "Graph",
     "GraphExecutorState",
     "IODescriptor",
     "InferredType",
     "IntType",
     "InterfaceType",
     "ListType",
     "LockingLogger",
     "MobileOptimizerType",
     "ModuleDict",
     "Node",
     "NoneType",
     "NoopLogger",
     "NumberType",
     "OperatorInfo",
     "OptionalType",
     "ParameterDict",
     "PyObjectType",
     "PyTorchFileReader",
     "PyTorchFileWriter",
     "RRefType",
     "ScriptClass",
     "ScriptClassFunction",
     "ScriptDict",
     "ScriptDictIterator",
     "ScriptDictKeyIterator",
     "ScriptList",
     "ScriptListIterator",
     "ScriptMethod",
     "ScriptModule",
     "ScriptModuleSerializer",
     "ScriptObject",
     "ScriptObjectProperty",
     "SerializationStorageContext",
     "StaticModule",
     "StringType",
     "SymIntType",
     "SymBoolType",
     "ThroughputBenchmark",
     "TracingState",
     "TupleType",
     "Type",
     "UnionType",
     "Use",
     "Value",
     # 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",
     "cudaStatus",
     # torch.cuda._sanitizer
     "Access",
     "AccessType",
     "Await",
     "CUDASanitizer",
     "CUDASanitizerDispatchMode",
     "CUDASanitizerErrors",
     "EventHandler",
     "SynchronizationError",
     "UnsynchronizedAccessError",
     # 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",
     "CumulativeDistributionTransform",
     "ExpTransform",
     "IndependentTransform",
     "PowerTransform",
     "ReshapeTransform",
     "SigmoidTransform",
     "SoftmaxTransform",
     "SoftplusTransform",
     "StackTransform",
     "StickBreakingTransform",
     "TanhTransform",
     "Transform",
     # torch.jit
     "CompilationUnit",
     "Error",
     "Future",
     "ScriptFunction",
     # torch.onnx
     "CheckerError",
     "ExportTypes",
     # torch.backends
     "ContextProp",
     "PropModule",
     # torch.backends.cuda
     "cuBLASModule",
     "cuFFTPlanCache",
     "cuFFTPlanCacheAttrContextProp",
     "cuFFTPlanCacheManager",
     # torch.distributed.algorithms.ddp_comm_hooks
     "DDPCommHookType",
     # torch.jit.mobile
     "LiteScriptModule",
     # torch.ao.nn.quantized.modules
     "DeQuantize",
     "Quantize",
     # torch.utils.backcompat
     "Warning",
 ]

 # 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 = "2023, PyTorch Contributors"
 author = "PyTorch 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 = "main (" + torch_version + " )"
 # The full version, including alpha/beta/rc tags.
 # TODO: verify this works as expected
 release = "main"

 # Customized html_title here.
 # Default is " ".join(project, release, "documentation") if not set
 if RELEASE:
     # Turn 1.11.0aHASH into 1.11
     # Note: the release candidates should no longer have the aHASH suffix, but in any
     # case we wish to leave only major.minor, even for rc builds.
     version = ".".join(torch_version.split(".")[:2])
     html_title = " ".join((project, version, "documentation"))
     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 = "en"

 # 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

 # Show type hints in the description
 autodoc_typehints = "description"

 # Add parameter types if the parameter is documented in the docstring
 autodoc_typehints_description_target = "documented_params"

 # Type aliases for common types
 # Sphinx type aliases only works with Postponed Evaluation of Annotations
 # (PEP 563) enabled (via `from __future__ import annotations`), which keeps the
 # type annotations in string form instead of resolving them to actual types.
 # However, PEP 563 does not work well with JIT, which uses the type information
 # to generate the code. Therefore, the following dict does not have any effect
 # until PEP 563 is supported by JIT and enabled in files.
 autodoc_type_aliases = {
     "_size_1_t": "int or tuple[int]",
     "_size_2_t": "int or tuple[int, int]",
     "_size_3_t": "int or tuple[int, int, int]",
     "_size_4_t": "int or tuple[int, int, int, int]",
     "_size_5_t": "int or tuple[int, int, int, int, int]",
     "_size_6_t": "int or tuple[int, int, int, int, int, int]",
     "_size_any_opt_t": "int or None or tuple",
     "_size_2_opt_t": "int or None or 2-tuple",
     "_size_3_opt_t": "int or None or 3-tuple",
     "_ratio_2_t": "float or tuple[float, float]",
     "_ratio_3_t": "float or tuple[float, float, float]",
     "_ratio_any_t": "float or tuple",
     "_tensor_list_t": "Tensor or tuple[Tensor]",
 }

 # 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": "GTM-T8XT4PS",
 }

 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

     if not torch.distributed.is_available():
         raise RuntimeError(
             "The coverage tool cannot run with a version "
             "of PyTorch that was built with USE_DISTRIBUTED=0 "
             "as this module's API changes."
         )

     # 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)

     output = []

     if missing:
         mods = ", ".join(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:: mod_name') "
             " or make the module private (by appending an '_' at the beginning of its name)."
         )

     # 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)


 def process_docstring(app, what_, name, obj, options, lines):
     """
     Custom process to transform docstring lines Remove "Ignore" blocks

     Args:
         app (sphinx.application.Sphinx): the Sphinx application object

         what (str):
             the type of the object which the docstring belongs to (one of
             "module", "class", "exception", "function", "method", "attribute")

         name (str): the fully qualified name of the object

         obj: the object itself

         options: the options given to the directive: an object with
             attributes inherited_members, undoc_members, show_inheritance
             and noindex that are true if the flag option of same name was
             given to the auto directive

         lines (List[str]): the lines of the docstring, see above

     References:
         https://www.sphinx-doc.org/en/1.5.1/_modules/sphinx/ext/autodoc.html
         https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html
     """
     import re

     remove_directives = [
         # Remove all xdoctest directives
         re.compile(r"\s*>>>\s*#\s*x?doctest:\s*.*"),
         re.compile(r"\s*>>>\s*#\s*x?doc:\s*.*"),
     ]
     filtered_lines = [
         line for line in lines if not any(pat.match(line) for pat in remove_directives)
     ]
     # Modify the lines inplace
     lines[:] = filtered_lines

     # make sure there is a blank line at the end
     if lines and lines[-1].strip():
         lines.append("")


 # 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)
     app.connect("autodoc-process-docstring", process_docstring)


 # 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(f'<p id="{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),
 }

 import sphinx.ext.doctest

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

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

 # 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 = fieldtype[0].astext()
                 builtin_types = ["int", "long", "float", "bool", "type"]
                 for builtin_type in builtin_types:
                     pattern = rf"(?<![\w.]){builtin_type}(?![\w.])"
                     repl = f"python:{builtin_type}"
                     typename = re.sub(pattern, repl, typename)
                 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
	#
	# 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

	# import sys
	import pkgutil
	import re
	from os import path

	# 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",
	"sphinx_panels",
	"myst_parser",
	]

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

	# Theme has bootstrap already
	panels_add_bootstrap_css = 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
	"typename",
	# torch.autograd
	"register_py_tensor_class_for_device",
	"variable",
	# torch.cuda
	"check_error",
	"cudart",
	"is_bf16_supported",
	# torch.cuda._sanitizer
	"zip_arguments",
	"zip_by_key",
	# 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",
	# torch.ao.quantization
	"default_eval_fn",
	# torch.backends
	"disable_global_flags",
	"flags_frozen",
	# torch.distributed.algorithms.ddp_comm_hooks
	"register_ddp_comm_hook",
	# torch.nn
	"factory_kwargs",
	# torch.nn.parallel
	"DistributedDataParallelCPU",
	# torch.utils
	"set_module",
	# torch.utils.model_dump
	"burn_in_info",
	"get_info_and_burn_skeleton",
	"get_inline_skeleton",
	"get_model_info",
	"get_storage_info",
	"hierarchical_pickle",
	]

	coverage_ignore_classes = [
	# torch
	"FatalError",
	"QUInt2x4Storage",
	"Size",
	"Storage",
	"Stream",
	"Tensor",
	"finfo",
	"iinfo",
	"qscheme",
	"AggregationType",
	"AliasDb",
	"AnyType",
	"Argument",
	"ArgumentSpec",
	"AwaitType",
	"BenchmarkConfig",
	"BenchmarkExecutionStats",
	"Block",
	"BoolType",
	"BufferDict",
	"CallStack",
	"Capsule",
	"ClassType",
	"Code",
	"CompleteArgumentSpec",
	"ComplexType",
	"ConcreteModuleType",
	"ConcreteModuleTypeBuilder",
	"DeepCopyMemoTable",
	"DeserializationStorageContext",
	"DeviceObjType",
	"DictType",
	"DispatchKey",
	"DispatchKeySet",
	"EnumType",
	"ExcludeDispatchKeyGuard",
	"ExecutionPlan",
	"FileCheck",
	"FloatType",
	"FunctionSchema",
	"Gradient",
	"Graph",
	"GraphExecutorState",
	"IODescriptor",
	"InferredType",
	"IntType",
	"InterfaceType",
	"ListType",
	"LockingLogger",
	"MobileOptimizerType",
	"ModuleDict",
	"Node",
	"NoneType",
	"NoopLogger",
	"NumberType",
	"OperatorInfo",
	"OptionalType",
	"ParameterDict",
	"PyObjectType",
	"PyTorchFileReader",
	"PyTorchFileWriter",
	"RRefType",
	"ScriptClass",
	"ScriptClassFunction",
	"ScriptDict",
	"ScriptDictIterator",
	"ScriptDictKeyIterator",
	"ScriptList",
	"ScriptListIterator",
	"ScriptMethod",
	"ScriptModule",
	"ScriptModuleSerializer",
	"ScriptObject",
	"ScriptObjectProperty",
	"SerializationStorageContext",
	"StaticModule",
	"StringType",
	"SymIntType",
	"SymBoolType",
	"ThroughputBenchmark",
	"TracingState",
	"TupleType",
	"Type",
	"UnionType",
	"Use",
	"Value",
	# 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",
	"cudaStatus",
	# torch.cuda._sanitizer
	"Access",
	"AccessType",
	"Await",
	"CUDASanitizer",
	"CUDASanitizerDispatchMode",
	"CUDASanitizerErrors",
	"EventHandler",
	"SynchronizationError",
	"UnsynchronizedAccessError",
	# 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",
	"CumulativeDistributionTransform",
	"ExpTransform",
	"IndependentTransform",
	"PowerTransform",
	"ReshapeTransform",
	"SigmoidTransform",
	"SoftmaxTransform",
	"SoftplusTransform",
	"StackTransform",
	"StickBreakingTransform",
	"TanhTransform",
	"Transform",
	# torch.jit
	"CompilationUnit",
	"Error",
	"Future",
	"ScriptFunction",
	# torch.onnx
	"CheckerError",
	"ExportTypes",
	# torch.backends
	"ContextProp",
	"PropModule",
	# torch.backends.cuda
	"cuBLASModule",
	"cuFFTPlanCache",
	"cuFFTPlanCacheAttrContextProp",
	"cuFFTPlanCacheManager",
	# torch.distributed.algorithms.ddp_comm_hooks
	"DDPCommHookType",
	# torch.jit.mobile
	"LiteScriptModule",
	# torch.ao.nn.quantized.modules
	"DeQuantize",
	"Quantize",
	# torch.utils.backcompat
	"Warning",
	]

	# 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 = "2023, PyTorch Contributors"
	author = "PyTorch 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 = "main (" + torch_version + " )"
	# The full version, including alpha/beta/rc tags.
	# TODO: verify this works as expected
	release = "main"

	# Customized html_title here.
	# Default is " ".join(project, release, "documentation") if not set
	if RELEASE:
	# Turn 1.11.0aHASH into 1.11
	# Note: the release candidates should no longer have the aHASH suffix, but in any
	# case we wish to leave only major.minor, even for rc builds.
	version = ".".join(torch_version.split(".")[:2])
	html_title = " ".join((project, version, "documentation"))
	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 = "en"

	# 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

	# Show type hints in the description
	autodoc_typehints = "description"

	# Add parameter types if the parameter is documented in the docstring
	autodoc_typehints_description_target = "documented_params"

	# Type aliases for common types
	# Sphinx type aliases only works with Postponed Evaluation of Annotations
	# (PEP 563) enabled (via `from __future__ import annotations`), which keeps the
	# type annotations in string form instead of resolving them to actual types.
	# However, PEP 563 does not work well with JIT, which uses the type information
	# to generate the code. Therefore, the following dict does not have any effect
	# until PEP 563 is supported by JIT and enabled in files.
	autodoc_type_aliases = {
	"_size_1_t": "int or tuple[int]",
	"_size_2_t": "int or tuple[int, int]",
	"_size_3_t": "int or tuple[int, int, int]",
	"_size_4_t": "int or tuple[int, int, int, int]",
	"_size_5_t": "int or tuple[int, int, int, int, int]",
	"_size_6_t": "int or tuple[int, int, int, int, int, int]",
	"_size_any_opt_t": "int or None or tuple",
	"_size_2_opt_t": "int or None or 2-tuple",
	"_size_3_opt_t": "int or None or 3-tuple",
	"_ratio_2_t": "float or tuple[float, float]",
	"_ratio_3_t": "float or tuple[float, float, float]",
	"_ratio_any_t": "float or tuple",
	"_tensor_list_t": "Tensor or tuple[Tensor]",
	}

	# 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": "GTM-T8XT4PS",
	}

	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

	if not torch.distributed.is_available():
	raise RuntimeError(
	"The coverage tool cannot run with a version "
	"of PyTorch that was built with USE_DISTRIBUTED=0 "
	"as this module's API changes."
	)

	# 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)

	output = []

	if missing:
	mods = ", ".join(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:: mod_name') "
	" or make the module private (by appending an '_' at the beginning of its name)."
	)

	# 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)


	def process_docstring(app, what_, name, obj, options, lines):
	"""
	Custom process to transform docstring lines Remove "Ignore" blocks

	Args:
	app (sphinx.application.Sphinx): the Sphinx application object

	what (str):
	the type of the object which the docstring belongs to (one of
	"module", "class", "exception", "function", "method", "attribute")

	name (str): the fully qualified name of the object

	obj: the object itself

	options: the options given to the directive: an object with
	attributes inherited_members, undoc_members, show_inheritance
	and noindex that are true if the flag option of same name was
	given to the auto directive

	lines (List[str]): the lines of the docstring, see above

	References:
	https://www.sphinx-doc.org/en/1.5.1/_modules/sphinx/ext/autodoc.html
	https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html
	"""
	import re

	remove_directives = [
	# Remove all xdoctest directives
	re.compile(r"\s>>>\s#\sx?doctest:\s.*"),
	re.compile(r"\s>>>\s#\sx?doc:\s.*"),
	]
	filtered_lines = [
	line for line in lines if not any(pat.match(line) for pat in remove_directives)
	]
	# Modify the lines inplace
	lines[:] = filtered_lines

	# make sure there is a blank line at the end
	if lines and lines[-1].strip():
	lines.append("")


	# 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)
	app.connect("autodoc-process-docstring", process_docstring)


	# 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(f'<p id="{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),
	}

	import sphinx.ext.doctest

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

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

	# 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 = fieldtype[0].astext()
	builtin_types = ["int", "long", "float", "bool", "type"]
	for builtin_type in builtin_types:
	pattern = rf"(?<![\w.]){builtin_type}(?![\w.])"
	repl = f"python:{builtin_type}"
	typename = re.sub(pattern, repl, typename)
	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