Source code for torch.jit._serialization
"""Serialization.
This module contains functionality for serializing TorchScript modules, notably:
* torch.jit.save
* torch.jit.load
This is not intended to be imported directly; please use the exposed
functionalities in `torch.jit`.
"""
import os
import torch
from torch._utils_internal import log_torchscript_usage
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::
torch.jit.save 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
torch.jit.save(m, 'scriptmodule.pt')
# This line is equivalent to the previous
m.save("scriptmodule.pt")
# Save to io.BytesIO buffer
buffer = io.BytesIO()
torch.jit.save(m, buffer)
# Save with extra files
extra_files = {'foo.txt': b'bar'}
torch.jit.save(m, 'scriptmodule.pt', _extra_files=extra_files)
"""
log_torchscript_usage("save")
if _extra_files is None:
_extra_files = {}
if isinstance(f, (str, os.PathLike)):
m.save(f, _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, _restore_shapes=False):
r"""
Load a :class:`ScriptModule` or :class:`ScriptFunction` previously saved with :func:`torch.jit.save <torch.jit.save>`.
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 `torch.jit.save` 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.
_restore_shapes (bool): Whether or not to retrace the module on load using stored inputs
Returns:
A :class:`ScriptModule` object.
Example:
.. testcode::
import torch
import io
torch.jit.load('scriptmodule.pt')
# Load ScriptModule from io.BytesIO object
with open('scriptmodule.pt', 'rb') as f:
buffer = io.BytesIO(f.read())
# Load all tensors to the original device
torch.jit.load(buffer)
# Load all tensors onto CPU, using a device
buffer.seek(0)
torch.jit.load(buffer, map_location=torch.device('cpu'))
# Load all tensors onto CPU, using a string
buffer.seek(0)
torch.jit.load(buffer, map_location='cpu')
# Load with extra files.
extra_files = {'foo.txt': ''} # values will be replaced with data
torch.jit.load('scriptmodule.pt', _extra_files=extra_files)
print(extra_files['foo.txt'])
.. testoutput::
:hide:
...
.. testcleanup::
import os
os.remove("scriptmodule.pt")
"""
log_torchscript_usage("load")
if isinstance(f, (str, os.PathLike)):
if not os.path.exists(f): # type: ignore[type-var]
raise ValueError(f"The provided filename {f} does not exist") # type: ignore[str-bytes-safe]
if os.path.isdir(f):
raise ValueError(f"The provided filename {f} is a directory") # 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, os.PathLike)):
cpp_module = torch._C.import_ir_module(cu, os.fspath(f), map_location, _extra_files, _restore_shapes) # type: ignore[call-arg]
else:
cpp_module = torch._C.import_ir_module_from_buffer(
cu, f.read(), map_location, _extra_files, _restore_shapes
) # type: ignore[call-arg]
# 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
def jit_module_from_flatbuffer(f):
if isinstance(f, (str, os.PathLike)):
f = os.fspath(f)
return wrap_cpp_module(torch._C._load_jit_module_from_file(f))
else:
return wrap_cpp_module(torch._C._load_jit_module_from_bytes(f.read()))
def save_jit_module_to_flatbuffer(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_jit_module_from_file(filename)`` or into the Python API with
:func:`torch.jit.jit_module_from_flatbuffer<torch.jit.jit_module_from_flatbuffer>`.
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 string for file path
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
torch.jit.save_jit_module_to_flatbuffer(m, 'scriptmodule.ff')
"""
extra_files = _extra_files
if extra_files is None:
extra_files = {}
if isinstance(f, (str, os.PathLike)):
f = os.fspath(f)
torch._C._save_jit_module(m._c, f, extra_files)
else:
s = torch._C._save_jit_module_to_bytes(m._c, extra_files)
f.write(s)
def get_flatbuffer_module_info(path_or_file):
r"""Get some information regarding a model file in flatbuffer format.
Args:
path_or_file: Either str, Path or file like object (BytesIO OK).
If it's str or Path, we will read the file referenced by that
path as Bytes.
Returns:
A dict with metadata on what that file contains, currently looks like
this:
{
'bytecode_version': 4, # int
'operator_version': 4, # int
'function_names': {
'__torch__.___torch_mangle_0.Foo.forward'}, # set
'type_names': set(), # set
'opname_to_num_args': {'aten::linear': 3} # Dict[str, int]
}
"""
if isinstance(path_or_file, (str, os.PathLike)):
with open(path_or_file, "rb") as f:
all_bytes = f.read()
else:
all_bytes = path_or_file.read()
return torch._C._get_module_info_from_flatbuffer(all_bytes)
