Source code for torch.fx.graph_module
import torch
import torch.nn as nn
import torch.overrides
from torch.nn.modules.module import _addindent
import linecache
from typing import Type, Dict, List, Any, Union, Optional
from .graph import Graph
import copy
import sys
import traceback
import math
from pathlib import Path
import os
import warnings
# normal exec loses the source code, however we can patch
# the linecache module to still recover it.
# using exec_with_source will add it to our local cache
# and then tools like TorchScript will be able to get source info.
_next_id = 0
def exec_with_source(src: str, globals: Dict[str, Any]):
global _next_id
key = f'<eval_with_key_{_next_id}>'
_next_id += 1
_eval_cache[key] = [line + '\n' for line in src.splitlines()]
exec(compile(src, key, 'exec'), globals)
# patch linecache so that any code we exec using exec_with_source
# works with inspect
_eval_cache : Dict[str, List[str]] = {}
_orig_getlines = linecache.getlines
def patched_getline(*args, **kwargs):
if args[0] in _eval_cache:
return _eval_cache[args[0]]
return _orig_getlines(*args, **kwargs)
linecache.getlines = patched_getline
def _forward_from_src(src : str):
# If you add more globals here, remember to add their names to fx.graph._shadows_builtin_name!
gbls: Dict[str, Any] = {'inf': math.inf, 'nan': math.nan, 'NoneType' : type(None)}
exec_with_source(src, gbls)
return gbls['forward']
def deserialize_graphmodule(body : dict) -> torch.nn.Module:
"""
Deserialize a GraphModule given the dictionary of the original module,
using the code to reconstruct the graph. We delete the actual graph before
saving the dictionary so that changes to the in-memory graph format do not
get serialized.
"""
# We create a dummy class here because symbolic_trace pulls the forward()
# function off of the class, rather than the instance
class CodeOnlyModule(torch.nn.Module):
def __init__(self, body):
super().__init__()
self.__dict__ = body
try:
CodeOnlyModule.forward = _forward_from_src(body['_code'])
except KeyError:
# BC: attribute name was changed from `code` to `_code` to facilitate
# making `code` into a property and adding a docstring to it
CodeOnlyModule.forward = _forward_from_src(body['code'])
from .symbolic_trace import Tracer
# we shouldn't trace into any of the submodules, they were not
# because they were not traced in the original GraphModule
class KeepModules(Tracer):
def is_leaf_module(self, _: torch.nn.Module, __: str) -> bool:
return True
com = CodeOnlyModule(body)
return GraphModule(com, KeepModules().trace(com))
# copy an attribute value with qualified name 'target' from 'from_module' to 'to_module'
# This installs empty Modules where none exist yet if they are subpaths of target
def _copy_attr(from_module: torch.nn.Module, to_module: torch.nn.Module, target: str):
*prefix, field = target.split('.')
for item in prefix:
f = getattr(from_module, item)
t = getattr(to_module, item, None)
if f is t:
# we have already installed one of its parents
# (e.g. target = root.linear.weight, but we have already installed root.linear)
# once we install a parent, we no longer need to copy the children
# since all the needed properties will already be present
return
if t is None:
t = torch.nn.Module()
setattr(to_module, item, t)
from_module, to_module = f, t
orig = getattr(from_module, field)
# If it is a tensor and not a parameter attribute of a module, it should be a named buffer.
# So, we register it as a named buffer in the target module.
if isinstance(orig, torch.Tensor) and not isinstance(orig, torch.nn.Parameter):
to_module.register_buffer(field, orig)
else:
setattr(to_module, field, orig)
# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module
# This installs empty Modules where none exist yet if they are subpaths of target
def _assign_attr(from_obj: Any, to_module: torch.nn.Module, target: str):
*prefix, field = target.split('.')
for item in prefix:
t = getattr(to_module, item, None)
if t is None:
t = torch.nn.Module()
setattr(to_module, item, t)
to_module = t
setattr(to_module, field, from_obj)
[docs]class GraphModule(torch.nn.Module):
"""
GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a
``graph`` attribute, as well as ``code`` and ``forward`` attributes generated
from that ``graph``.
.. warning::
When ``graph`` is reassigned, ``code`` and ``forward`` will be automatically
regenerated. However, if you edit the contents of the ``graph`` without reassigning
the ``graph`` attribute itself, you must call ``recompile()`` to update the generated
code.
"""
def __new__(cls: 'Type[GraphModule]', *args, **kwargs):
# each instance of a graph module needs its own forward method
# so create a new singleton class for each instance.
# it is a subclass of the user-defined class, the only difference
# is an extra layer to install the forward method
class GraphModuleImpl(cls): # type: ignore
pass
return super().__new__(GraphModuleImpl)
[docs] def __init__(self, root: Union[torch.nn.Module, Dict[str, Any]], graph: Graph, class_name: str = 'GraphModule'):
"""
Construct a GraphModule.
Args:
root (Union[torch.nn.Module, Dict[str, Any]):
``root`` can either be an nn.Module instance or a Dict mapping strings to any attribute type.
In the case that ``root`` is a Module, any references to Module-based objects (via qualified
name) in the Graph's Nodes' ``target`` field will be copied over from the respective place
within ``root``'s Module hierarchy into the GraphModule's module hierarchy.
In the case that ``root`` is a dict, the qualified name found in a Node's ``target`` will be
looked up directly in the dict's keys. The object mapped to by the Dict will be copied
over into the appropriate place within the GraphModule's module hierarchy.
graph (Graph): ``graph`` contains the nodes this GraphModule should use for code generation
name (str): ``name`` denotes the name of this GraphModule for debugging purposes. If it's unset, all
error messages will report as originating from ``GraphModule``. It may be helpful to set this
to ``root``'s original name or a name that makes sense within the context of your transform.
"""
super().__init__()
self.__class__.__name__ = class_name
if isinstance(root, torch.nn.Module):
if hasattr(root, 'training'):
self.training = root.training
for node in graph.nodes:
if node.op in ['get_attr', 'call_module']:
assert isinstance(node.target, str)
_copy_attr(root, self, node.target)
elif isinstance(root, dict):
targets_to_copy = []
for node in graph.nodes:
if node.op in ['get_attr', 'call_module']:
assert isinstance(node.target, str)
if node.target not in root:
raise RuntimeError('Node ' + str(node) + ' referenced target ' + node.target +
' but that target was not provided in ``root``!')
targets_to_copy.append(node.target)
# Sort targets in ascending order of the # of atoms.
# This will ensure that less deeply nested attributes are assigned
# before more deeply nested attributes. For example, foo.bar
# will be assigned before foo.bar.baz. Otherwise, we might assign
# the user-provided ``foo.bar`` and wipe out the previously-assigned
# ``foo.bar.baz``
targets_to_copy.sort(key=lambda t: t.count('.'))
for target_to_copy in targets_to_copy:
_assign_attr(root[target_to_copy], self, target_to_copy)
else:
raise RuntimeError('Unsupported type ' + str(root) + ' passed for root!')
self.graph = graph
# TorchScript breaks trying to compile the graph setter because of the
# continued string literal. Issue here: https://github.com/pytorch/pytorch/issues/44842
#
# Shouldn't be an issue since these methods shouldn't be used in TorchScript anyway
__jit_unused_properties__ = ['graph']
@property
def graph(self) -> Graph:
"""
Return the ``Graph`` underlying this ``GraphModule``
"""
return self._graph
@graph.setter
def graph(self, g) -> None:
"""
Set the underlying ``Graph`` for this ``GraphModule``. This will internally
recompile the ``GraphModule`` so that the generated ``forward()`` function
corresponds to ``g``
"""
self._graph = g
self.recompile()
[docs] def to_folder(self, folder: Union[str, os.PathLike], module_name : str = "FxModule"):
"""Dumps out module to ``folder`` with ``module_name`` so that it can be
imported with ``from <folder> import <module_name>``
Args:
folder (Union[str, os.PathLike]): The folder to write the code out to
module_name (str): Top-level name to use for the ``Module`` while
writing out the code
"""
folder = Path(folder)
Path(folder).mkdir(exist_ok=True)
torch.save(self.state_dict(), folder / 'state_dict.pt')
tab = " " * 4
model_str = f"""
import torch
from torch.nn import *
class {module_name}(torch.nn.Module):
def __init__(self):
super().__init__()
"""
def _gen_model_repr(module_name: str, module: torch.nn.Module) -> Optional[str]:
safe_reprs = [nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]
if type(module) in safe_reprs:
return f"{module.__repr__()}"
else:
return None
blobified_modules = []
for module_name, module in self.named_children():
module_str = _gen_model_repr(module_name, module)
if module_str is None:
module_file = folder / f'{module_name}.pt'
torch.save(module, module_file)
blobified_modules.append(module_name)
module_repr = module.__repr__().replace('\r', ' ').replace('\n', ' ')
module_str = f"torch.load(r'{module_file}') # {module_repr}"
model_str += f"{tab*2}self.{module_name} = {module_str}\n"
for buffer_name, buffer in self._buffers.items():
model_str += f"{tab*2}self.register_buffer('{buffer_name}', torch.empty({list(buffer.shape)}))\n"
for param_name, param in self._parameters.items():
model_str += f"{tab*2}self.{param_name} = torch.nn.Parameter(torch.empty({list(buffer.shape)}))\n"
model_str += f"{tab*2}self.load_state_dict(torch.load(r'{folder}/state_dict.pt'))\n"
model_str += f"{_addindent(self.code, 4)}\n"
module_file = folder / 'module.py'
module_file.write_text(model_str)
init_file = folder / '__init__.py'
init_file.write_text('from .module import *')
if len(blobified_modules) > 0:
warnings.warn("Was not able to save the following children modules as reprs -"
f"saved as pickled files instead: {blobified_modules}")
@property
def code(self) -> str:
"""
Return the Python code generated from the ``Graph`` underlying this
``GraphModule``.
"""
if not hasattr(self, '_code'):
raise RuntimeError('Code has not been generated! Please report a bug to PyTorch')
return self._code
[docs] def recompile(self) -> None:
"""
Recompile this GraphModule from its ``graph`` attribute. This should be
called after editing the contained ``graph``, otherwise the generated
code of this ``GraphModule`` will be out of date.
"""
self._code = self._graph.python_code(root_module='self')
cls = type(self)
cls.forward = _forward_from_src(self._code)
cls_call = cls.__call__
def print_full_traceback(exctype, value, tb):
traceback.print_exception(exctype, value, tb)
def wrapped_call(self, *args, **kwargs):
old_excepthook = sys.excepthook
try:
sys.excepthook = print_full_traceback
return cls_call(self, *args, **kwargs)
finally:
sys.excepthook = old_excepthook
cls.__call__ = wrapped_call
def __reduce__(self):
"""
Serialization of GraphModule. We serialize only the generated code, not
the underlying ``Graph``. This is because ``Graph`` does not have on-disk
backward-compatibility guarantees, whereas Python source code does.
On the deserialization side, we symbolically trace through the generated
code to regenerate the underlying ``Graph``
"""
dict_without_graph = self.__dict__.copy()
del dict_without_graph['_graph']
return (deserialize_graphmodule, (dict_without_graph,))
# because __reduce__ is defined for serialization,
# we need to define deepcopy otherwise it will call __reduce__
# and cause symbolic tracing to occur every time we try to copy the object
def __deepcopy__(self, memo):
fake_mod = torch.nn.Module()
fake_mod.__dict__ = copy.deepcopy(self.__dict__)
return GraphModule(fake_mod, self.graph)
def __copy__(self):
return GraphModule(self, self.graph)
def __str__(self) -> str:
orig_str = super().__str__()
return '\n'.join([orig_str, self._code])
# workarounds for issues in __torch_function__
# WAR for __torch_function__ not handling tensor lists,
# fix is in https://github.com/pytorch/pytorch/pull/34725
# orig_cat = torch.cat
# def patched_cat(*args, **kwargs):
# tensors = args[0]
# for t in tensors:
# if isinstance(t, Proxy):
# return t.__torch_function__(patched_cat, (), args, kwargs)
# return orig_cat(*args, **kwargs)
# patched_cat.__module__ = 'torch'
# patched_cat.__name__ = 'cat'
# torch.cat = patched_cat
