import torch from torch.serialization import normalize_storage_type, location_tag import io import pickletools from .find_file_dependencies import find_files_source_depends_on from ._custom_import_pickler import create_custom_import_pickler, import_module_from_importers from ._importlib import _normalize_path from ._mangling import is_mangled import types import importlib from typing import List, Any, Callable, Dict, Tuple, Union, Iterable, BinaryIO, Optional from distutils.sysconfig import get_python_lib from pathlib import Path import linecache import sys from urllib.parse import quote import re 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 :method:`extern_module`. 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. Dependencies ------------ When source code is added to the package, the exporter optionally can scan it for further code dependencies (`dependencies=True`). It looks for import statements, resolves relative references to qualified module names, and calls :method:`require_module` on each it finds, recursively resolving dependencies. """ importers: List[Callable[[str], Any]] """ A list of functions that will be called in order to find the module assocated with module names referenced by other modules or by pickled objects. Initialized to `[importlib.import_module]` by default. When pickling code or objects that was loaded from an imported packaged, that `importer.import_module` should be put into the importer list. When a name conflict occurs between importers, the first importer in the list takes precedence, and only objects that refer to this first importers class can be saved """ def __init__(self, f: Union[str, Path, BinaryIO], verbose: bool = True): """ 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. verbose: Print information about dependency resolution to stdout. Useful for tracking down why certain files get included. """ 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.serialized_storages : Dict[str, Any] = {} self.external : List[str] = [] self.provided : Dict[str, bool] = {} self.verbose = verbose self.importers = [importlib.import_module] self.patterns : List[Tuple[Any, Callable[[str], None]]] = [] # 'any' is 're.Pattern' but breaks old mypy self.debug_deps : List[Tuple[str, str]] = [] 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 (see :ref:`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 if filename.is_dir(): self.provided[archivename] = True else: 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 self.provided[submodule_name] = True # 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, str(filename))) 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, file_or_directory) def save_source_string(self, module_name: str, src: str, is_package: bool = False, dependencies: bool = True, orig_file_name: str = None): """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 (see :ref:`Dependencies`). orig_file_name (str, optional): If present, used in logging to identifying where the source came from. Defaults to None. """ self.provided[module_name] = True extension = '/__init__.py' if is_package else '.py' filename = module_name.replace('.', '/') + extension self._write(filename, src) if dependencies: package = module_name if is_package else module_name.rsplit('.', maxsplit=1)[0] dep_pairs = find_files_source_depends_on(src, package) dep_list = {} 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): dep_list[possible_submodule] = True # we don't need to save `pack` continue if self._module_exists(dep_module_name): dep_list[dep_module_name] = True for dep in dep_list.keys(): self.debug_deps.append((module_name, dep)) if self.verbose: dep_str = ''.join(f' {dep}\n' for dep in dep_list.keys()) file_info = f'(from file {orig_file_name}) ' if orig_file_name is not None else '' print(f"{module_name} {file_info}depends on:\n{dep_str}\n") for dep in dep_list.keys(): self.require_module_if_not_provided(dep) def _import_module(self, module_name: str): try: return import_module_from_importers(module_name, self.importers) except ModuleNotFoundError as e: if not is_mangled(module_name): raise msg = (f"Module not found: '{module_name}'. Modules imported " "from a torch.package cannot be re-exported directly.") 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 _write_dep_graph(self, failing_module=None): edges = '\n'.join(f'"{f}" -> "{t}";' for f, t in self.debug_deps) failing = '' if failing_module is None else f'"{failing_module}" [color=red];' template = f"""\ digraph G {{ rankdir = LR; node [shape=box]; {failing} {edges} }} """ arg = quote(template, safe='') return f'https://dreampuf.github.io/GraphvizOnline/#{arg}' def _get_source_of_module(self, module: types.ModuleType) -> 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: extra = '' if self.verbose: extra = f' See the dependency graph for more info: \n{self._write_dep_graph(module.__name__)}' raise ValueError(f'cannot save source for module "{module.__name__}" because ' f'its source file "{filename}" could not be found.{extra}') return ''.join(result) def require_module_if_not_provided(self, module_name: str, dependencies=True): if self._module_is_already_provided(module_name): return self.require_module(module_name, dependencies) def require_module(self, module_name: str, dependencies=True): """This is called by dependencies resolution when it finds that something in the package depends on the module and it is not already present. It then decides how to provide that module. The default resolution rules will mark the module as extern if it is part of the standard library, and call `save_module` otherwise. Clients can subclass this object and override this method to provide other behavior, such as automatically mocking out a whole class of modules""" root_name = module_name.split('.', maxsplit=1)[0] if self._can_implicitly_extern(root_name): if self.verbose: print(f'implicitly adding {root_name} to external modules ' f'since it is part of the standard library and is a dependency.') self.save_extern_module(root_name) return for pattern, action in self.patterns: if pattern.matches(module_name): action(module_name) return self.save_module(module_name, dependencies) def save_module(self, module_name: str, dependencies=True): """Save the code for `module_name` 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 (see :ref:`Dependencies`). """ module = self._import_module(module_name) source = self._get_source_of_module(module) self.save_source_string(module_name, source, hasattr(module, '__path__'), dependencies, module.__file__) def save_pickle(self, package: str, resource: str, obj: Any, dependencies: bool = True): """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 it (e.g. "my_package.my_subpackage") resource (str): A unique name for the resource, used to indentify it to load. obj (Any): The object to save, must be picklable. dependencies (bool, optional): If True, we scan the source for dependencies (see :ref:`Dependencies`). """ filename = self._filename(package, resource) # Write the pickle data for `obj` data_buf = io.BytesIO() pickler = create_custom_import_pickler(data_buf, self.importers) pickler.persistent_id = self._persistent_id pickler.dump(obj) data_value = data_buf.getvalue() if dependencies: all_dependencies = [] for opcode, arg, pos in pickletools.genops(data_value): if opcode.name == 'GLOBAL': # a global reference assert isinstance(arg, str) module, field = arg.split(' ') if module not in all_dependencies: all_dependencies.append(module) for dep in all_dependencies: self.debug_deps.append((package + '.' + resource, dep)) if self.verbose: dep_string = ''.join(f' {dep}\n' for dep in all_dependencies) print(f"{resource} depends on:\n{dep_string}\n") for module_name in all_dependencies: self.require_module_if_not_provided(module_name) self._write(filename, data_value) 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 indentify it to load. text (str): The contents to save """ return self.save_binary(package, resource, text.encode('utf-8')) 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 indentify it to load. binary (str): The data to save. """ filename = self._filename(package, resource) self._write(filename, binary) def mock(self, include: 'GlobPattern', *, exclude: 'GlobPattern' = ()): """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: [] """ self.patterns.append((_GlobGroup(include, exclude), self.save_mock_module)) def extern(self, include: 'GlobPattern', *, exclude: 'GlobPattern' = ()): """Include `module` in the list of external modules the package can import. This will prevent dependency discover 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. """ self.patterns.append((_GlobGroup(include, exclude), self.save_extern_module)) def save_extern_module(self, module_name: str): """Add `module_name` to the list of external modules, regardless of whether it is required by other modules. Prefer using `extern` to only mark modules extern if they are actually required by the packaged code. """ if module_name not in self.external: self.external.append(module_name) def save_mock_module(self, module_name: str): """Add `module_name` to the package, implemented it with a mocked out version that can be imported but does not include any implementations. Prefer using `mock` to only include this module if it is required by other modules. """ if '_mock' not in self.provided: self.save_source_file('_mock', str(Path(__file__).parent / '_mock.py'), dependencies=False) is_package = hasattr(self._import_module(module_name), '__path__') self.save_source_string(module_name, _MOCK_IMPL, is_package, dependencies=False) def _module_is_already_provided(self, qualified_name: str) -> bool: for mod in self.external: if qualified_name == mod or qualified_name.startswith(mod + '.'): return True return qualified_name in self.provided def _persistent_id(self, obj): # FIXME: the docs say that persistent_id should only return a string # but torch store returns tuples. This works only in the binary protocol # see # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537 if torch.is_storage(obj): storage_type = normalize_storage_type(type(obj)) obj_key = str(obj._cdata) location = location_tag(obj) self.serialized_storages[obj_key] = obj return ('storage', storage_type, obj_key, location, obj.size()) return None def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def _write(self, filename, str_or_bytes): if is_mangled(filename): raise RuntimeError(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 close(self): """Write the package to the filesystem. Any calls after close are now invalid. It is preferable to use resource guard syntax instead: with PackageExporter("file.zip") as e: ... """ if self.verbose: print(f"Dependency graph for exported package: \n{self._write_dep_graph()}") # Write each tensor to a file named tensor/the_tensor_key in the zip archive for key in sorted(self.serialized_storages.keys()): name = 'data/{}'.format(key) storage = self.serialized_storages[key] # location information is saved in python, but to actually # get the data from non cpu tensors we need to move them over first if storage.device.type != 'cpu': storage = storage.cpu() num_bytes = storage.size() * storage.element_size() self.zip_file.write_record(name, storage.data_ptr(), num_bytes) contents = ('\n'.join(self.external) + '\n') self._write('extern_modules', contents) 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): return module_name == 'torch' or (module_name not in _DISALLOWED_MODULES and _is_builtin_or_stdlib_module(self._import_module(module_name))) # 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'] def _is_builtin_or_stdlib_module(module: types.ModuleType) -> bool: if module.__name__ in sys.builtin_module_names: return True filename = getattr(module, '__file__', None) if filename is None: return False standard_lib = get_python_lib(standard_lib=True) # this is often a subdirectory of standard_lib so we have to check # that the file is in the standard_lib directory but not in this one installed_libs = get_python_lib(standard_lib=False) in_standard_lib = filename.startswith(standard_lib + '/') in_installed_libs = filename.startswith(installed_libs + '/') return in_standard_lib and not in_installed_libs _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') GlobPattern = Union[str, Iterable[str]] class _GlobGroup: def __init__(self, include: 'GlobPattern', exclude: 'GlobPattern'): self._dbg = f'_GlobGroup(include={include}, exclude={exclude})' self.include = _GlobGroup._glob_list(include) self.exclude = _GlobGroup._glob_list(exclude) def __str__(self): return self._dbg def matches(self, candidate: str) -> bool: candidate = '.' + candidate return any(p.fullmatch(candidate) for p in self.include) and all(not p.fullmatch(candidate) for p in self.exclude) @staticmethod def _glob_list(elems: 'GlobPattern'): if isinstance(elems, str): return [_GlobGroup._glob_to_re(elems)] else: return [_GlobGroup._glob_to_re(e) for e in elems] @staticmethod def _glob_to_re(pattern: str): # to avoid corner cases for the first component, we prefix the candidate string # with '.' so `import torch` will regex against `.torch` def component_to_re(component): if '**' in component: if component == '**': return '(\\.[^.]+)*' else: raise ValueError('** can only appear as an entire path segment') else: return '\\.' + '[^.]*'.join(re.escape(x) for x in component.split('*')) result = ''.join(component_to_re(c) for c in pattern.split('.')) return re.compile(result)