Source code for torch.jit._serialization


This module contains functionality for serializing TorchScript modules, notably:
    * torch.jit.load

This is not intended to be imported directly; please use the exposed
functionalities in `torch.jit`.
import os
import pathlib

import torch
from torch._six import string_classes
from torch.jit._recursive import wrap_cpp_module
from torch.serialization import validate_cuda_device

[docs]def save(m, f, _extra_files=None): r""" Save an offline version of this module for use in a separate process. The saved module serializes all of the methods, submodules, parameters, and attributes of this module. It can be loaded into the C++ API using ``torch::jit::load(filename)`` or into the Python API with :func:`torch.jit.load <torch.jit.load>`. To be able to save a module, it must not make any calls to native Python functions. This means that all submodules must be subclasses of :class:`ScriptModule` as well. .. DANGER:: All modules, no matter their device, are always loaded onto the CPU during loading. This is different from :func:`torch.load`'s semantics and may change in the future. Args: m: A :class:`ScriptModule` to save. f: A file-like object (has to implement write and flush) or a string containing a file name. _extra_files: Map from filename to contents which will be stored as part of `f`. .. note:: attempts to preserve the behavior of some operators across versions. For example, dividing two integer tensors in PyTorch 1.5 performed floor division, and if the module containing that code is saved in PyTorch 1.5 and loaded in PyTorch 1.6 its division behavior will be preserved. The same module saved in PyTorch 1.6 will fail to load in PyTorch 1.5, however, since the behavior of division changed in 1.6, and 1.5 does not know how to replicate the 1.6 behavior. Example: .. testcode:: import torch import io class MyModule(torch.nn.Module): def forward(self, x): return x + 10 m = torch.jit.script(MyModule()) # Save to file, '') # This line is equivalent to the previous"") # Save to io.BytesIO buffer buffer = io.BytesIO(), buffer) # Save with extra files extra_files = {'foo.txt': b'bar'}, '', _extra_files=extra_files) """ if _extra_files is None: _extra_files = {} if isinstance(f, str) or isinstance(f, pathlib.Path):, _extra_files=_extra_files) else: ret = m.save_to_buffer(_extra_files=_extra_files) f.write(ret)
[docs]def load(f, map_location=None, _extra_files=None): r""" Load a :class:`ScriptModule` or :class:`ScriptFunction` previously saved with :func:` <>` All previously saved modules, no matter their device, are first loaded onto CPU, and then are moved to the devices they were saved from. If this fails (e.g. because the run time system doesn't have certain devices), an exception is raised. Args: f: a file-like object (has to implement read, readline, tell, and seek), or a string containing a file name map_location (string or torch.device): A simplified version of ``map_location`` in `` used to dynamically remap storages to an alternative set of devices. _extra_files (dictionary of filename to content): The extra filenames given in the map would be loaded and their content would be stored in the provided map. Returns: A :class:`ScriptModule` object. Example: .. testcode:: import torch import io torch.jit.load('') # Load ScriptModule from io.BytesIO object with open('', 'rb') as f: buffer = io.BytesIO( # Load all tensors to the original device torch.jit.load(buffer) # Load all tensors onto CPU, using a device torch.jit.load(buffer, map_location=torch.device('cpu')) # Load all tensors onto CPU, using a string torch.jit.load(buffer, map_location='cpu') # Load with extra files. extra_files = {'foo.txt': ''} # values will be replaced with data torch.jit.load('', _extra_files=extra_files) print(extra_files['foo.txt']) .. testoutput:: :hide: ... .. testcleanup:: import os os.remove("") """ if isinstance(f, string_classes): if not os.path.exists(f): # type: ignore[type-var] raise ValueError("The provided filename {} does not exist".format(f)) # type: ignore[str-bytes-safe] if os.path.isdir(f): raise ValueError("The provided filename {} is a directory".format(f)) # type: ignore[str-bytes-safe] map_location = validate_map_location(map_location) if _extra_files is None: _extra_files = {} cu = torch._C.CompilationUnit() if isinstance(f, str) or isinstance(f, pathlib.Path): cpp_module = torch._C.import_ir_module(cu, str(f), map_location, _extra_files) else: cpp_module = torch._C.import_ir_module_from_buffer( cu,, map_location, _extra_files ) # TODO: Pretty sure this approach loses ConstSequential status and such return wrap_cpp_module(cpp_module)
def validate_map_location(map_location=None): if isinstance(map_location, str): map_location = torch.device(map_location) elif not (map_location is None or isinstance(map_location, torch.device)): raise ValueError( "map_location should be either None, string or torch.device, " "but got type: " + str(type(map_location)) ) if str(map_location).startswith("cuda"): validate_cuda_device(map_location) return map_location


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