Source code for torch.package.package_exporter
import collections
import importlib.machinery
import io
import linecache
import pickletools
import platform
import types
from collections import OrderedDict, defaultdict
from dataclasses import dataclass
from enum import Enum
from pathlib import Path
from typing import (
Any,
BinaryIO,
Callable,
Dict,
List,
Optional,
Sequence,
Set,
Union,
cast,
DefaultDict,
)
import torch
from torch.serialization import location_tag, normalize_storage_type
from torch.types import Storage
from torch.utils.hooks import RemovableHandle
from ._digraph import DiGraph
from ._importlib import _normalize_path
from ._mangling import demangle, is_mangled
from ._package_pickler import create_pickler
from ._stdlib import is_stdlib_module
from .find_file_dependencies import find_files_source_depends_on
from .glob_group import GlobGroup, GlobPattern
from .importer import Importer, OrderedImporter, sys_importer
_gate_torchscript_serialization = True
ActionHook = Callable[["PackageExporter", str], None]
class _ModuleProviderAction(Enum):
"""Represents one of the actions that :class:`PackageExporter` can take on a module.
See :meth:`PackageExporter.extern` and friends for a description of what the actions do.
"""
INTERN = 1
EXTERN = 2
MOCK = 3
DENY = 4
# Special case: when a module is mocked, PackageExporter writes out a
# `_mock` module that implements our mocking stubs. If we re-package code,
# we may encounter a `_mock` module from the original package. If we do,
# just ignore it and write a `_mock` module once.
REPACKAGED_MOCK_MODULE = 5
# Special case: PackageImporter adds a fake module
# (`torch_package_importer`) that allows packaged code to access it. Don't
# re-export this.
SKIP = 6
class PackagingErrorReason(Enum):
"""Listing of different reasons a dependency may fail to package.
This enum is used to provide good error messages when
:class:`PackagingError` is raised.
"""
def __repr__(self):
return "<%s.%s>" % (self.__class__.__name__, self.name)
IS_EXTENSION_MODULE = (
"Module is a C extension module. torch.package supports Python modules only."
)
NO_DUNDER_FILE = "Module had no __file__ defined."
SOURCE_FILE_NOT_FOUND = (
"Module had a __file__, but we could not find it in your filesystem."
)
DEPENDENCY_RESOLUTION_FAILED = "Dependency resolution failed."
NO_ACTION = (
"Module did not match against any action pattern. Extern, mock, or intern it."
)
DENIED = "Module was denied by a pattern."
MOCKED_BUT_STILL_USED = (
"Module was mocked out, but is still being used in the package. "
"Please intern or extern the mocked modules if objects are supposed to be in "
"the package."
)
@dataclass
class _PatternInfo:
"""Holds :class:`PackageExporter`-specific info about how to execute matches against"""
# What action to take on a module that matches this pattern.
action: _ModuleProviderAction
# The value of `allow_empty` the user gave when specifying the pattern.
allow_empty: bool
# Whether this pattern has been matched during packaging.
was_matched: bool
def __init__(self, action, allow_empty):
self.action = action
self.allow_empty = allow_empty
self.was_matched = False
[docs]class EmptyMatchError(Exception):
"""This is an exception that is thrown when a mock or extern is marked as
``allow_empty=False``, and is not matched with any module during packaging.
"""
pass
[docs]class PackagingError(Exception):
"""This exception is raised when there is an issue with exporting a package.
``PackageExporter`` will attempt to gather up all the errors and present
them to you at once.
"""
def __init__(self, dependency_graph: DiGraph):
# Group errors by reason.
broken: Dict[PackagingErrorReason, List[str]] = defaultdict(list)
for module_name, attrs in dependency_graph.nodes.items():
error = attrs.get("error")
if error is None:
continue
if error == PackagingErrorReason.NO_ACTION:
assert "action" not in attrs
broken[error].append(module_name)
message = io.StringIO()
message.write("\n")
for reason, module_names in broken.items():
message.write(f"* {reason.value}\n")
for module_name in module_names:
message.write(f" {module_name}\n")
# Print additional context if it's provided.
error_context = dependency_graph.nodes[module_name].get("error_context")
if error_context is not None:
message.write(f" Context: {error_context}\n")
# Save the dependency graph so that tooling can get at it.
self.dependency_graph = dependency_graph
super().__init__(message.getvalue())
[docs]class PackageExporter:
"""Exporters allow you to write packages of code, pickled Python data, and
arbitrary binary and text resources into a self-contained package.
Imports can load this code in a hermetic way, such that code is loaded
from the package rather than the normal Python import system. This allows
for the packaging of PyTorch model code and data so that it can be run
on a server or used in the future for transfer learning.
The code contained in packages is copied file-by-file from the original
source when it is created, and the file format is a specially organized
zip file. Future users of the package can unzip the package, and edit the code
in order to perform custom modifications to it.
The importer for packages ensures that code in the module can only be loaded from
within the package, except for modules explicitly listed as external using :meth:`extern`.
The file ``extern_modules`` in the zip archive lists all the modules that a package externally depends on.
This prevents "implicit" dependencies where the package runs locally because it is importing
a locally-installed package, but then fails when the package is copied to another machine.
When source code is added to the package, the exporter can optionally scan it
for further code dependencies (``dependencies=True``). It looks for import statements,
resolves relative references to qualified module names, and performs an action specified by the user
(See: :meth:`extern`, :meth:`mock`, and :meth:`intern`).
"""
"""A importer that will be searched in order to find the modules referenced by other modules or by
pickled objects. The default module environment just uses sys_importer, which searches the Python environment.
"""
importer: Importer
[docs] def __init__(
self,
f: Union[str, Path, BinaryIO],
importer: Union[Importer, Sequence[Importer]] = sys_importer,
):
"""
Create an exporter.
Args:
f: The location to export to. Can be a ``string``/``Path`` object containing a filename
or a binary I/O object.
importer: If a single Importer is passed, use that to search for modules.
If a sequence of importers are passsed, an ``OrderedImporter`` will be constructed out of them.
"""
torch._C._log_api_usage_once("torch.package.PackageExporter")
if isinstance(f, (Path, str)):
f = str(f)
self.buffer: Optional[BinaryIO] = None
else: # is a byte buffer
self.buffer = f
self.zip_file = torch._C.PyTorchFileWriter(f)
self.zip_file.set_min_version(6)
self._written_files: Set[str] = set()
self.serialized_reduces: Dict[int, Any] = {}
# A graph tracking all the modules and pickle objects added to this
# package and the dependencies between them.
# - Each node is a module name (or a pickle name that looks like '<foo.obj.pkl>')
# - Each directed edge (u, v) means u depends on v.
# - Nodes may contain metadata that describe how to write the thing to the zipfile.
self.dependency_graph = DiGraph()
self.script_module_serializer = torch._C.ScriptModuleSerializer(self.zip_file)
self.storage_context = self.script_module_serializer.storage_context()
# These are OrderedDicts for compatibility with RemovableHandle.
# Generic OrderedDict type annotations are not present until 3.7.
# The real type signature is OrderedDict[int, Callable[[PackageExporter, str], None]]
self._extern_hooks: OrderedDict = OrderedDict()
self._mock_hooks: OrderedDict = OrderedDict()
self._intern_hooks: OrderedDict = OrderedDict()
if isinstance(importer, Importer):
self.importer = importer
else:
if not isinstance(importer, collections.abc.Sequence):
raise TypeError(
"importer arg should be an Importer or a sequence of Importers, "
f"got {type(importer)} instead."
)
self.importer = OrderedImporter(*importer)
self.patterns: Dict[GlobGroup, _PatternInfo] = {}
self._unique_id = 0
[docs] def save_source_file(
self, module_name: str, file_or_directory: str, dependencies=True
):
"""Adds the local file system ``file_or_directory`` to the source package to provide the code
for ``module_name``.
Args:
module_name (str): e.g. ``"my_package.my_subpackage"``, code will be saved to provide code for this package.
file_or_directory (str): the path to a file or directory of code. When a directory, all python files in the directory
are recursively copied using :meth:`save_source_file`. If a file is named ``"/__init__.py"`` the code is treated
as a package.
dependencies (bool, optional): If ``True``, we scan the source for dependencies.
"""
path = Path(file_or_directory)
if path.is_dir():
to_save = [] # list of tuples with arguments to save_source_string
module_path = module_name.replace(".", "/")
for filename in path.glob("**/*.py"):
relative_path = filename.relative_to(path).as_posix()
archivename = module_path + "/" + relative_path
submodule_name = None
if filename.name == "__init__.py":
submodule_name = archivename[: -len("/__init__.py")].replace(
"/", "."
)
is_package = True
else:
submodule_name = archivename[: -len(".py")].replace("/", ".")
is_package = False
# we delay the call to save_source_string so that we record all the source files
# being provided by this directory structure _before_ attempting to resolve the dependencies
# on the source. This makes sure we don't try to copy over modules that will just get
# overwritten by this directory blob
to_save.append(
(
submodule_name,
_read_file(str(filename)),
is_package,
dependencies,
)
)
for item in to_save:
self.save_source_string(*item)
else:
is_package = path.name == "__init__.py"
self.save_source_string(
module_name,
_read_file(file_or_directory),
is_package,
dependencies,
)
[docs] def get_unique_id(self) -> str:
"""Get an id. This id is guaranteed to only be handed out once for this package."""
ret = str(self._unique_id)
self._unique_id += 1
return ret
def _get_dependencies(
self, src: str, module_name: str, is_package: bool
) -> List[str]:
"""Return all modules that this source code depends on.
Dependencies are found by scanning the source code for import-like statements.
Arguments:
src: The Python source code to analyze for dependencies.
module_name: The name of the module that ``src`` corresponds to.
is_package: Whether this module should be treated as a package.
See :py:meth:`save_source_string` for more info.
Returns:
A list containing modules detected as direct dependencies in
``src``. The items in the list are guaranteed to be unique.
"""
package_name = (
module_name if is_package else module_name.rsplit(".", maxsplit=1)[0]
)
try:
dep_pairs = find_files_source_depends_on(src, package_name)
except Exception as e:
self.dependency_graph.add_node(
module_name,
error=PackagingErrorReason.DEPENDENCY_RESOLUTION_FAILED,
error_context=str(e),
)
return []
# Use a dict to get uniquing but also deterministic order
dependencies = {}
for dep_module_name, dep_module_obj in dep_pairs:
# handle the case where someone did something like `from pack import sub`
# where `sub` is a submodule. In this case we don't have to save pack, just sub.
# this ensures we don't pick up additional dependencies on pack.
# However, in the case where `sub` is not a submodule but an object, then we do have
# to save pack.
if dep_module_obj is not None:
possible_submodule = f"{dep_module_name}.{dep_module_obj}"
if self._module_exists(possible_submodule):
dependencies[possible_submodule] = True
# we don't need to save `pack`
continue
if self._module_exists(dep_module_name):
dependencies[dep_module_name] = True
return list(dependencies.keys())
[docs] def save_source_string(
self,
module_name: str,
src: str,
is_package: bool = False,
dependencies: bool = True,
):
"""Adds ``src`` as the source code for ``module_name`` in the exported package.
Args:
module_name (str): e.g. ``my_package.my_subpackage``, code will be saved to provide code for this package.
src (str): The Python source code to save for this package.
is_package (bool, optional): If ``True``, this module is treated as a package. Packages are allowed to have submodules
(e.g. ``my_package.my_subpackage.my_subsubpackage``), and resources can be saved inside them. Defaults to ``False``.
dependencies (bool, optional): If ``True``, we scan the source for dependencies.
"""
self.dependency_graph.add_node(
module_name,
source=src,
is_package=is_package,
provided=True,
action=_ModuleProviderAction.INTERN,
)
if dependencies:
deps = self._get_dependencies(src, module_name, is_package)
for dep in deps:
self.dependency_graph.add_edge(module_name, dep)
self.add_dependency(dep)
def _write_source_string(
self,
module_name: str,
src: str,
is_package: bool = False,
):
"""Write ``src`` as the source code for ``module_name`` in the zip archive.
Arguments are otherwise the same as for :meth:`save_source_string`.
"""
extension = "/__init__.py" if is_package else ".py"
filename = module_name.replace(".", "/") + extension
self._write(filename, src)
def _import_module(self, module_name: str):
try:
return self.importer.import_module(module_name)
except ModuleNotFoundError as e:
if not is_mangled(module_name):
raise
msg = (
f"Module not found: '{module_name}'. Make sure the PackageImporter that "
"created this module is present in `self.importer`"
)
raise ModuleNotFoundError(msg) from None
def _module_exists(self, module_name: str) -> bool:
try:
self._import_module(module_name)
return True
except Exception:
return False
def _get_source_of_module(self, module: types.ModuleType) -> Optional[str]:
filename = getattr(module, "__file__", None)
result = (
None
if filename is None or not filename.endswith(".py")
else linecache.getlines(filename, module.__dict__)
)
if result is None:
return None
return "".join(result)
[docs] def add_dependency(self, module_name: str, dependencies=True):
"""Given a module, add it to the dependency graph according to patterns
specified by the user.
"""
if (
module_name in self.dependency_graph
and self.dependency_graph.nodes[module_name].get("provided") is True
):
return
# Special case: PackageImporter provides a special module called
# `torch_package_importer` that allows packaged modules to reference
# their PackageImporter. We don't want to re-export this.
if module_name == "torch_package_importer":
self.dependency_graph.add_node(
module_name,
action=_ModuleProviderAction.SKIP,
provided=True,
)
return
if module_name == "_mock":
self.dependency_graph.add_node(
module_name,
action=_ModuleProviderAction.REPACKAGED_MOCK_MODULE,
provided=True,
)
return
if self._can_implicitly_extern(module_name):
self.dependency_graph.add_node(
module_name, action=_ModuleProviderAction.EXTERN, provided=True
)
return
for pattern, pattern_info in self.patterns.items():
if pattern.matches(module_name):
pattern_info.was_matched = True
self.dependency_graph.add_node(
module_name, action=pattern_info.action, provided=True
)
if pattern_info.action == _ModuleProviderAction.DENY:
# Requiring a denied module just adds an error to the graph.
self.dependency_graph.add_node(
module_name, error=PackagingErrorReason.DENIED
)
# If we are interning this module, we need to retrieve its
# dependencies and package those as well.
if pattern_info.action == _ModuleProviderAction.INTERN:
self._intern_module(module_name, dependencies)
return
# No patterns have matched. Explicitly add this as an error.
self.dependency_graph.add_node(
module_name, error=PackagingErrorReason.NO_ACTION
)
[docs] def save_module(self, module_name: str, dependencies=True):
"""Save the code for ``module`` into the package. Code for the module is resolved using the ``importers`` path to find the
module object, and then using its ``__file__`` attribute to find the source code.
Args:
module_name (str): e.g. ``my_package.my_subpackage``, code will be saved to provide code
for this package.
dependencies (bool, optional): If ``True``, we scan the source for dependencies.
"""
if not isinstance(module_name, str):
raise TypeError(
"save_module() expects a string input, did you perhaps mean to pass `__name__`?"
)
self._intern_module(module_name, dependencies)
def _intern_module(
self,
module_name: str,
dependencies: bool,
):
"""Adds the module to the dependency graph as an interned module,
along with any metadata needed to write it out to the zipfile at serialization time.
"""
module_obj = self._import_module(module_name)
# Subtle: if the import above succeeded, either:
# 1. The module name is not mangled, and this was just a regular import, or
# 2. The module name is mangled, but one of the importers was able to
# recognize the mangling and import it.
# Either way, it is now safe to demangle this name so that we don't
# serialize the mangled version to the package.
module_name = demangle(module_name)
# Find dependencies of this module and require them as well.
is_package = hasattr(module_obj, "__path__")
source = self._get_source_of_module(module_obj)
if source is None:
# Couldn't find a source! Add it to our dependency graph as broken
# and continue.
filename = getattr(module_obj, "__file__", None)
error_context = None
if filename is None:
packaging_error = PackagingErrorReason.NO_DUNDER_FILE
elif filename.endswith(tuple(importlib.machinery.EXTENSION_SUFFIXES)):
packaging_error = PackagingErrorReason.IS_EXTENSION_MODULE
else:
packaging_error = PackagingErrorReason.SOURCE_FILE_NOT_FOUND
error_context = f"filename: {filename}"
self.dependency_graph.add_node(
module_name,
action=_ModuleProviderAction.INTERN,
is_package=is_package,
error=packaging_error,
error_context=error_context,
provided=True,
)
return
self.dependency_graph.add_node(
module_name,
action=_ModuleProviderAction.INTERN,
is_package=is_package,
source=source,
provided=True,
)
if dependencies:
deps = self._get_dependencies(source, module_name, is_package)
for dep in deps:
self.dependency_graph.add_edge(module_name, dep)
self.add_dependency(dep)
[docs] def save_pickle(
self,
package: str,
resource: str,
obj: Any,
dependencies: bool = True,
pickle_protocol: int = 3,
):
"""Save a python object to the archive using pickle. Equivalent to :func:`torch.save` but saving into
the archive rather than a stand-alone file. Stanard pickle does not save the code, only the objects.
If ``dependencies`` is true, this method will also scan the pickled objects for which modules are required
to reconstruct them and save the relevant code.
To be able to save an object where ``type(obj).__name__`` is ``my_module.MyObject``,
``my_module.MyObject`` must resolve to the class of the object according to the ``importer`` order. When saving objects that
have previously been packaged, the importer's ``import_module`` method will need to be present in the ``importer`` list
for this to work.
Args:
package (str): The name of module package this resource should go in (e.g. ``"my_package.my_subpackage"``).
resource (str): A unique name for the resource, used to identify it to load.
obj (Any): The object to save, must be picklable.
dependencies (bool, optional): If ``True``, we scan the source for dependencies.
"""
assert (pickle_protocol == 4) or (
pickle_protocol == 3
), "torch.package only supports pickle protocols 3 and 4"
filename = self._filename(package, resource)
# Write the pickle data for `obj`
data_buf = io.BytesIO()
pickler = create_pickler(data_buf, self.importer, protocol=pickle_protocol)
pickler.persistent_id = self._persistent_id
pickler.dump(obj)
data_value = data_buf.getvalue()
mocked_modules = defaultdict(list)
name_in_dependency_graph = f"<{package}.{resource}>"
self.dependency_graph.add_node(
name_in_dependency_graph,
action=_ModuleProviderAction.INTERN,
provided=True,
is_pickle=True,
)
def _check_mocked_error(module: Optional[str], field: Optional[str]):
"""
checks if an object (field) comes from a mocked module and then adds
the pair to mocked_modules which contains mocked modules paired with their
list of mocked objects present in the pickle.
We also hold the invariant that the first user defined rule that applies
to the module is the one we use.
"""
assert isinstance(module, str)
assert isinstance(field, str)
if self._can_implicitly_extern(module):
return
for pattern, pattern_info in self.patterns.items():
if pattern.matches(module):
if pattern_info.action == _ModuleProviderAction.MOCK:
mocked_modules[module].append(field)
return
if dependencies:
all_dependencies = []
module = None
field = None
memo: DefaultDict[int, str] = defaultdict(None)
memo_count = 0
# pickletools.dis(data_value)
for opcode, arg, pos in pickletools.genops(data_value):
if pickle_protocol == 4:
if (
opcode.name == "SHORT_BINUNICODE"
or opcode.name == "BINUNICODE8"
):
assert isinstance(arg, str)
module = field
field = arg
memo[memo_count] = arg
elif (
opcode.name == "BINGET_LONG"
or opcode.name == "BINGET"
or opcode.name == "GET"
):
assert isinstance(arg, int)
module = field
field = memo.get(arg, None)
elif opcode.name == "MEMOIZE":
memo_count += 1
elif opcode.name == "STACK_GLOBAL":
assert isinstance(module, str)
if module not in all_dependencies:
all_dependencies.append(module)
_check_mocked_error(module, field)
elif (
pickle_protocol == 3 and opcode.name == "GLOBAL"
): # a global reference
assert isinstance(arg, str)
module, field = arg.split(" ")
if module not in all_dependencies:
all_dependencies.append(module)
_check_mocked_error(module, field)
for module_name in all_dependencies:
self.dependency_graph.add_edge(name_in_dependency_graph, module_name)
""" If an object happens to come from a mocked module, then we collect these errors and spit them
out with the other errors found by package exporter.
"""
if module in mocked_modules:
assert isinstance(module, str)
fields = mocked_modules[module]
self.dependency_graph.add_node(
module_name,
action=_ModuleProviderAction.MOCK,
error=PackagingErrorReason.MOCKED_BUT_STILL_USED,
error_context=f"Object(s) '{fields}' from module `{module_name}` was mocked out during packaging "
f"but is being used in resource - `{resource}` in package `{package}`. ",
provided=True,
)
else:
self.add_dependency(module_name)
self._write(filename, data_value)
[docs] def save_text(self, package: str, resource: str, text: str):
"""Save text data to the package.
Args:
package (str): The name of module package this resource should go it (e.g. ``"my_package.my_subpackage"``).
resource (str): A unique name for the resource, used to identify it to load.
text (str): The contents to save.
"""
return self.save_binary(package, resource, text.encode("utf-8"))
[docs] def save_binary(self, package, resource, binary: bytes):
"""Save raw bytes to the package.
Args:
package (str): The name of module package this resource should go it (e.g. ``"my_package.my_subpackage"``).
resource (str): A unique name for the resource, used to identify it to load.
binary (str): The data to save.
"""
filename = self._filename(package, resource)
self._write(filename, binary)
[docs] def register_extern_hook(self, hook: ActionHook) -> RemovableHandle:
"""Registers an extern hook on the exporter.
The hook will be called each time a module matches against an :meth:`extern` pattern.
It should have the following signature::
hook(exporter: PackageExporter, module_name: str) -> None
Hooks will be called in order of registration.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
A handle that can be used to remove the added hook by calling
``handle.remove()``.
"""
handle = RemovableHandle(self._extern_hooks)
self._extern_hooks[handle.id] = hook
return handle
[docs] def register_mock_hook(self, hook: ActionHook) -> RemovableHandle:
"""Registers a mock hook on the exporter.
The hook will be called each time a module matches against a :meth:`mock` pattern.
It should have the following signature::
hook(exporter: PackageExporter, module_name: str) -> None
Hooks will be called in order of registration.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
A handle that can be used to remove the added hook by calling
``handle.remove()``.
"""
handle = RemovableHandle(self._mock_hooks)
self._mock_hooks[handle.id] = hook
return handle
[docs] def register_intern_hook(self, hook: ActionHook) -> RemovableHandle:
"""Registers an intern hook on the exporter.
The hook will be called each time a module matches against an :meth:`intern` pattern.
It should have the following signature::
hook(exporter: PackageExporter, module_name: str) -> None
Hooks will be called in order of registration.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
A handle that can be used to remove the added hook by calling
``handle.remove()``.
"""
handle = RemovableHandle(self._intern_hooks)
self._intern_hooks[handle.id] = hook
return handle
[docs] def intern(
self,
include: "GlobPattern",
*,
exclude: "GlobPattern" = (),
allow_empty: bool = True,
):
"""Specify modules that should be packaged. A module must match some ``intern`` pattern in order to be
included in the package and have its dependencies processed recursively.
Args:
include (Union[List[str], str]): A string e.g. "my_package.my_subpackage", or list of strings
for the names of the modules to be externed. This can also be a glob-style pattern, as described in :meth:`mock`.
exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the include string.
allow_empty (bool): An optional flag that specifies whether the intern modules specified by this call
to the ``intern`` method must be matched to some module during packaging. If an ``intern`` module glob
pattern is added with ``allow_empty=False``, and :meth:`close` is called (either explicitly or via ``__exit__``)
before any modules match that pattern, an exception is thrown. If ``allow_empty=True``, no such exception is thrown.
"""
self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
_ModuleProviderAction.INTERN, allow_empty
)
[docs] def mock(
self,
include: "GlobPattern",
*,
exclude: "GlobPattern" = (),
allow_empty: bool = True,
):
"""Replace some required modules with a mock implementation. Mocked modules will return a fake
object for any attribute accessed from it. Because we copy file-by-file, the dependency resolution will sometimes
find files that are imported by model files but whose functionality is never used
(e.g. custom serialization code or training helpers).
Use this function to mock this functionality out without having to modify the original code.
Args:
include (Union[List[str], str]): A string e.g. ``"my_package.my_subpackage"``, or list of strings
for the names of the modules to be mocked out. Strings can also be a glob-style pattern
string that may match multiple modules. Any required dependencies that match this pattern
string will be mocked out automatically.
Examples :
``'torch.**'`` -- matches ``torch`` and all submodules of torch, e.g. ``'torch.nn'``
and ``'torch.nn.functional'``
``'torch.*'`` -- matches ``'torch.nn'`` or ``'torch.functional'``, but not
``'torch.nn.functional'``
exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the include string.
e.g. ``include='torch.**', exclude='torch.foo'`` will mock all torch packages except ``'torch.foo'``,
Default: is ``[]``.
allow_empty (bool): An optional flag that specifies whether the mock implementation(s) specified by this call
to the :meth:`mock` method must be matched to some module during packaging. If a mock is added with
``allow_empty=False``, and :meth:`close` is called (either explicitly or via ``__exit__``) and the mock has
not been matched to a module used by the package being exported, an exception is thrown.
If ``allow_empty=True``, no such exception is thrown.
"""
self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
_ModuleProviderAction.MOCK, allow_empty
)
[docs] def extern(
self,
include: "GlobPattern",
*,
exclude: "GlobPattern" = (),
allow_empty: bool = True,
):
"""Include ``module`` in the list of external modules the package can import.
This will prevent dependency discovery from saving
it in the package. The importer will load an external module directly from the standard import system.
Code for extern modules must also exist in the process loading the package.
Args:
include (Union[List[str], str]): A string e.g. ``"my_package.my_subpackage"``, or list of strings
for the names of the modules to be externed. This can also be a glob-style pattern, as
described in :meth:`mock`.
exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the
include string.
allow_empty (bool): An optional flag that specifies whether the extern modules specified by this call
to the ``extern`` method must be matched to some module during packaging. If an extern module glob
pattern is added with ``allow_empty=False``, and :meth:`close` is called (either explicitly or via
``__exit__``) before any modules match that pattern, an exception is thrown. If ``allow_empty=True``,
no such exception is thrown.
"""
self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
_ModuleProviderAction.EXTERN, allow_empty
)
[docs] def deny(self, include: "GlobPattern", *, exclude: "GlobPattern" = ()):
"""Blocklist modules who names match the given glob patterns from the list of modules the package can import.
If a dependency on any matching packages is found, a :class:`PackagingError` is raised.
Args:
include (Union[List[str], str]): A string e.g. ``"my_package.my_subpackage"``, or list of strings
for the names of the modules to be externed. This can also be a glob-style pattern, as described in :meth:`mock`.
exclude (Union[List[str], str]): An optional pattern that excludes some patterns that match the include string.
"""
self.patterns[GlobGroup(include, exclude=exclude)] = _PatternInfo(
_ModuleProviderAction.DENY, allow_empty=True
)
def _persistent_id(self, obj):
if torch.is_storage(obj) or isinstance(obj, torch.storage._TypedStorage):
if isinstance(obj, torch.storage._TypedStorage):
# TODO: Once we decide to break serialization FC, we can
# remove this case
untyped_storage = obj._storage
storage_type_str = obj.pickle_storage_type()
storage_type = getattr(torch, storage_type_str)
dtype = obj.dtype
storage_numel = obj.size()
elif isinstance(obj, torch._UntypedStorage):
untyped_storage = obj
storage_type = normalize_storage_type(type(storage))
dtype = torch.uint8
storage_numel = storage.nbytes()
else:
raise RuntimeError(f"storage type not recognized: {type(obj)}")
storage: Storage = cast(Storage, untyped_storage)
location = location_tag(storage)
# serialize storage if not already written
storage_present = self.storage_context.has_storage(storage)
storage_id = self.storage_context.get_or_add_storage(storage)
if not storage_present:
if storage.device.type != "cpu":
storage = storage.cpu()
num_bytes = storage.nbytes()
self.zip_file.write_record(
f".data/{storage_id}.storage", storage.data_ptr(), num_bytes
)
return ("storage", storage_type, storage_id, location, storage_numel)
if hasattr(obj, "__reduce_package__"):
if _gate_torchscript_serialization and isinstance(
obj, torch.jit.RecursiveScriptModule
):
raise Exception(
"Serializing ScriptModules directly into a package is a beta feature. "
"To use, set global "
"`torch.package.package_exporter._gate_torchscript_serialization` to `False`."
)
if self.serialized_reduces.get(id(obj)) is None:
self.serialized_reduces[id(obj)] = (
"reduce_package",
id(obj),
*obj.__reduce_package__(self),
)
return self.serialized_reduces[id(obj)]
return None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
# If __exit__ was called because an exception was raised, we do not
# attempt to finalize the package. Instead, control is returned to the
# caller to continue raising the exception.
if exc_type is not None:
# Do the bare minimum to leave the open buffer in a valid state.
self._finalize_zip()
return
self.close()
def _write(self, filename, str_or_bytes):
if filename in self._written_files:
raise AssertionError(
f"Tried to write file '{filename}', but it already exists in this archive. "
"Please file a bug."
)
self._written_files.add(filename)
if is_mangled(filename):
raise AssertionError(
f"Tried to save a torch.package'd module as '{filename}'. "
"Directly saving torch.package'd modules is not allowed."
)
if isinstance(str_or_bytes, str):
str_or_bytes = str_or_bytes.encode("utf-8")
self.zip_file.write_record(filename, str_or_bytes, len(str_or_bytes))
def _validate_dependency_graph(self):
# 1. Check the graph for any errors inserted during dependency analysis.
for module_name, attrs in self.dependency_graph.nodes.items():
if "error" in attrs:
raise PackagingError(self.dependency_graph)
# 2. Check that all patterns for which allow_empty=False have been matched at least once.
for pattern, pattern_info in self.patterns.items():
if not pattern_info.allow_empty and not pattern_info.was_matched:
raise EmptyMatchError(
f"Exporter did not match any modules to {pattern}, which was marked as allow_empty=False"
)
def _write_mock_file(self):
if "_mock.py" not in self._written_files:
mock_file = str(Path(__file__).parent / "_mock.py")
self._write_source_string("_mock", _read_file(mock_file), is_package=False)
def _execute_dependency_graph(self):
"""Takes a finalized dependency graph describing how to package all
modules and executes it, writing to the ZIP archive.
"""
self._validate_dependency_graph()
extern_modules = []
for module_name, attrs in self.dependency_graph.nodes.items():
action = attrs["action"]
if action == _ModuleProviderAction.EXTERN:
for hook in self._extern_hooks.values():
hook(self, module_name)
extern_modules.append(module_name)
elif action == _ModuleProviderAction.MOCK:
for hook in self._mock_hooks.values():
hook(self, module_name)
self._write_mock_file()
is_package = hasattr(self._import_module(module_name), "__path__")
self._write_source_string(module_name, _MOCK_IMPL, is_package)
elif action == _ModuleProviderAction.INTERN:
for hook in self._intern_hooks.values():
hook(self, module_name)
# The node in the dependency graph contains metadata that tells us
# how to intern the module.
if "provided" not in attrs:
raise AssertionError(
f"Module was marked `intern` but not provided: {module_name}"
)
if attrs.get("is_pickle") is True:
# This node came from save_pickle, we don't need to write any source for it.
continue
is_package = attrs["is_package"]
source = attrs["source"]
self._write_source_string(module_name, source, is_package)
elif action == _ModuleProviderAction.REPACKAGED_MOCK_MODULE:
self._write_mock_file()
elif action == _ModuleProviderAction.SKIP:
continue
else:
raise AssertionError(
f"Invalid action: {module_name}, {action}. Please report a bug to PyTorch."
)
extern_file_contents = "\n".join(extern_modules) + "\n"
self._write(".data/extern_modules", extern_file_contents)
def _write_python_version(self):
"""Writes the python version that the package was created with to .data/python_version"""
self._write(".data/python_version", platform.python_version())
[docs] def close(self):
"""Write the package to the filesystem. Any calls after :meth:`close` are now invalid.
It is preferable to use resource guard syntax instead::
with PackageExporter("file.zip") as e:
...
"""
self._execute_dependency_graph()
self._write_python_version()
self.script_module_serializer.write_files()
self._finalize_zip()
def _finalize_zip(self):
"""Called at the very end of packaging to leave the zipfile in a closed but valid state."""
del self.zip_file
if self.buffer:
self.buffer.flush()
def _filename(self, package, resource):
package_path = package.replace(".", "/")
resource = _normalize_path(resource)
return f"{package_path}/{resource}"
def _can_implicitly_extern(self, module_name: str):
top_level_package_name = module_name.partition(".")[0]
return top_level_package_name == "torch" or (
top_level_package_name not in _DISALLOWED_MODULES
and is_stdlib_module(top_level_package_name)
)
[docs] def dependency_graph_string(self) -> str:
"""Returns digraph string representation of dependencies in package.
Returns:
A string representation of dependencies in package.
"""
return self.dependency_graph.to_dot()
def _nodes_with_action_type(
self, action: Optional[_ModuleProviderAction]
) -> List[str]:
result = []
for name, node_dict in self.dependency_graph.nodes.items():
node_action = node_dict.get("action", None)
if node_action == action and "is_pickle" not in node_dict:
result.append(name)
result.sort()
return result
[docs] def externed_modules(self) -> List[str]:
"""Return all modules that are currently externed.
Returns:
A list containing the names of modules which will be
externed in this package.
"""
return self._nodes_with_action_type(_ModuleProviderAction.EXTERN)
[docs] def interned_modules(self) -> List[str]:
"""Return all modules that are currently interned.
Returns:
A list containing the names of modules which will be
interned in this package.
"""
return self._nodes_with_action_type(_ModuleProviderAction.INTERN)
[docs] def mocked_modules(self) -> List[str]:
"""Return all modules that are currently mocked.
Returns:
A list containing the names of modules which will be
mocked in this package.
"""
return self._nodes_with_action_type(_ModuleProviderAction.MOCK)
[docs] def denied_modules(self) -> List[str]:
"""Return all modules that are currently denied.
Returns:
A list containing the names of modules which will be
denied in this package.
"""
return self._nodes_with_action_type(_ModuleProviderAction.DENY)
[docs] def get_rdeps(self, module_name: str) -> List[str]:
"""Return a list of all modules which depend on the module ``module_name``.
Returns:
A list containing the names of modules which depend on ``module_name``.
"""
if module_name in self.dependency_graph._pred.keys():
return list(self.dependency_graph._pred[module_name].keys())
else:
return []
[docs] def all_paths(self, src: str, dst: str) -> str:
"""Return a dot representation of the subgraph
that has all paths from src to dst.
Returns:
A dot representation containing all paths from src to dst.
(https://graphviz.org/doc/info/lang.html)
"""
return self.dependency_graph.all_paths(src, dst)
# even though these are in the standard library, we do not allow them to be
# automatically externed since they offer a lot of system level access
_DISALLOWED_MODULES = ["sys", "io"]
_MOCK_IMPL = """\
from _mock import MockedObject
def __getattr__(attr: str):
return MockedObject(__name__ + '.' + attr, _suppress_err=True)
"""
def _read_file(filename: str) -> str:
with open(filename, "rb") as f:
b = f.read()
return b.decode("utf-8")
