Source code for torch.fx.graph_module

import torch
import torch.nn as nn
import torch.overrides
from torch.nn.modules.module import _addindent
from torch.package import PackageImporter, PackageExporter
import linecache
from typing import Type, Dict, List, Any, Union, Optional, Set
from .graph import Graph, _is_from_torch, _custom_builtins, PythonCode
from torch.package import Importer, sys_importer
import copy
import itertools
import sys
import traceback
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, globals: Dict[str, Any]):
    # avoid mutating the passed in dict
    globals_copy = globals.copy()
    exec_with_source(src, globals_copy)
    forward_fn = globals_copy['forward']
    del globals_copy['forward']
    return forward_fn

def _format_import_statement(name: str, obj: Any, importer: Importer) -> str:
    if name in _custom_builtins:
        return _custom_builtins[name].import_str
    if _is_from_torch(name):
        return 'import torch'

    module_name, attr_name = importer.get_name(obj)
    return f'from {module_name} import {attr_name} as {name}'

def _format_import_block(globals: Dict[str, Any], importer: Importer):
    import_strs: Set[str] = set()
    for name, obj in globals.items():
        import_strs.add(_format_import_statement(name, obj, importer))
    return '\n'.join(import_strs)

def reduce_graph_module(body: Dict[Any, Any], import_block: str) -> torch.nn.Module:
    # BC: attribute name was changed from `code` to `_code` to facilitate
    # making `code` into a property and adding a docstring to it
    fn_src = body.get('_code') or body['code']
    forward = _forward_from_src(import_block + fn_src, {})
    return _deserialize_graph_module(forward, body, None)

def reduce_package_graph_module(importer: PackageImporter,
                                body: Dict[Any, Any],
                                generated_module_name: str) -> torch.nn.Module:
    forward = importer.import_module(generated_module_name).forward
    return _deserialize_graph_module(forward, body, importer)

def _deserialize_graph_module(forward, body: Dict[Any, Any], importer: Optional[PackageImporter]) -> 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):
            self.__dict__ = body

    # Try to retrieve the forward source in a backward-compatible way
    CodeOnlyModule.forward = forward

    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

        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)
        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

    # 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(from_obj, torch.Tensor) and not isinstance(from_obj, torch.nn.Parameter):
        to_module.register_buffer(field, from_obj)
        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[misc, valid-type] 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 class_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'): = for node in graph.nodes: if node.op in ['get_attr', 'call_module']: assert isinstance(, str) _copy_attr(root, self, elif isinstance(root, dict): targets_to_copy = [] for node in graph.nodes: if node.op in ['get_attr', 'call_module']: assert isinstance(, str) if not in root: raise RuntimeError('Node ' + str(node) + ' referenced target ' + + ' but that target was not provided in ``root``!') targets_to_copy.append( # 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, # will be assigned before Otherwise, we might assign # the user-provided ```` and wipe out the previously-assigned # ```` 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: # # 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 : Graph) -> None: """ Set the underlying ``Graph`` for this ``GraphModule``. This will internally recompile the ``GraphModule`` so that the generated ``forward()`` function corresponds to ``g`` """ assert isinstance(g, Graph), f'Expected a Graph instance, but got {type(g)}' self._graph = g g.owning_module = self 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), folder / '') 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', 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}/'))\n" model_str += f"{_addindent(self.code, 4)}\n" module_file = folder / '' module_file.write_text(model_str) init_file = folder / '' 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}")
[docs] def add_submodule(self, target: str, m: torch.nn.Module) -> bool: """ Adds the given submodule to ``self``. This installs empty Modules where none exist yet if they are subpaths of ``target``. Args: target: The fully-qualified string name of the new submodule (See example in ``nn.Module.get_submodule`` for how to specify a fully-qualified string.) m: The submodule itself; the actual object we want to install in the current Module Return: bool: Whether or not the submodule could be inserted. For this method to return True, each object in the chain denoted by ``target`` must either a) not exist yet, or b) reference an ``nn.Module`` (not a parameter or other attribute) """ *prefix, field = target.split('.') mod: torch.nn.Module = self for item in prefix: submod = getattr(mod, item, None) if submod is None: submod = torch.nn.Module() setattr(mod, item, submod) if not isinstance(submod, torch.nn.Module): return False mod = submod mod.add_module(field, m) return True
[docs] def delete_submodule(self, target: str) -> bool: """ Deletes the given submodule from ``self``. The module will not be deleted if ``target`` is not a valid target. Args: target: The fully-qualified string name of the new submodule (See example in ``nn.Module.get_submodule`` for how to specify a fully-qualified string.) Returns: bool: Whether or not the target string referenced a submodule we want to delete. A return value of ``False`` means that the ``target`` was not a valid reference to a submodule. """ atoms = target.split(".") path, target_submod = atoms[:-1], atoms[-1] mod: torch.nn.Module = self # Get the parent module for item in path: if not hasattr(mod, item): return False mod = getattr(mod, item) if not isinstance(mod, torch.nn.Module): return False if not hasattr(mod, target_submod): return False if not isinstance(getattr(mod, target_submod), torch.nn.Module): return False delattr(mod, target_submod) return True
[docs] def delete_all_unused_submodules(self) -> None: """ Deletes all unused submodules from ``self``. A Module is considered "used" if any one of the following is true: 1. It has children that are used 2. Its forward is called directly via a ``call_module`` node 3. It has a non-Module attribute that is used from a ``get_attr`` node This method can be called to clean up an ``nn.Module`` without manually calling ``delete_submodule`` on each unused submodule. """ used: List[str] = [] for node in self.graph.nodes: if node.op == "call_module" or node.op == "get_attr": # A list of strings representing the different parts # of the path. For exmaple, `` gives us # ["foo", "bar", "baz"] fullpath =".") # If we're looking at multiple parts of a path, join # join them with a dot. Otherwise, return that single # element without doing anything to it. def join_fn(x: str, y: str) -> str: return '.'.join([x, y] if y else [x]) # Progressively collect all the names of intermediate # modules. For example, if we have the target # ``, we'll add `foo`, ``, and # `` to the list. for path in itertools.accumulate(fullpath, join_fn): used.append(path) to_delete = [name for name, _ in self.named_modules() if name not in used] for name in to_delete: self.delete_submodule(name)
@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) -> PythonCode: """ 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. """ if self._graph._pytree_info is not None: self._in_spec = self._graph._pytree_info.in_spec self._out_spec = self._graph._pytree_info.out_spec python_code = self._graph.python_code(root_module='self') self._code = python_code.src cls = type(self) cls.forward = _forward_from_src(self._code, python_code.globals) cls_call = cls.__call__ # Previously, if an error occurred when valid # symbolically-traced code was run with an invalid input, the # user would see the source of the error as coming from # `File "<eval_with_key_N">`, where N is some number. We use # this function to generate a more informative error message. We # return the traceback itself, a message explaining that the # error occurred in a traced Module's generated forward # function, and five lines of context surrounding the faulty # line def generate_error_message(frame_summary: traceback.FrameSummary) -> str: # auxiliary variables (for readability) err_lineno = frame_summary.lineno err_line_len = len(frame_summary.line) all_src_lines = _eval_cache[frame_summary.filename] # constituent substrings of the error message tb_repr = traceback.format_exc() custom_msg = ("Call using an FX-traced Module, " f"line {err_lineno} of the traced Module's " "generated forward function:") before_err = "".join(all_src_lines[err_lineno - 2 : err_lineno]) marker = "~" * err_line_len + "~~~ <--- HERE" err_and_after_err = "\n".join(all_src_lines[err_lineno : err_lineno + 2]) # joined message return "\n".join([tb_repr, custom_msg, before_err, marker, err_and_after_err]) def wrapped_call(self, *args, **kwargs): try: return cls_call(self, *args, **kwargs) except Exception as e: assert e.__traceback__ topmost_framesummary: traceback.FrameSummary = \ traceback.StackSummary.extract(traceback.walk_tb(e.__traceback__))[-1] # type: ignore[arg-type] if "eval_with_key" in topmost_framesummary.filename: print(generate_error_message(topmost_framesummary), file=sys.stderr) raise e.with_traceback(None) cls.__call__ = wrapped_call return python_code
def __reduce_package__(self, exporter: PackageExporter): generated_module_name = f'fx-generated._{exporter.get_unique_id()}' python_code = self.recompile() import_block = _format_import_block(python_code.globals, exporter.importer) module_code = import_block + self.code exporter.save_source_string(generated_module_name, module_code) dict_without_graph = self.__dict__.copy() del dict_without_graph['_graph'] return (reduce_package_graph_module, (dict_without_graph, generated_module_name)) 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() python_code = self.recompile() import_block = _format_import_block(python_code.globals, sys_importer) del dict_without_graph['_graph'] return (reduce_graph_module, (dict_without_graph, import_block)) # 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 # orig_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' # = patched_cat


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