Source code for torch.fx.graph
from .node import Node, Argument, Target, map_arg
from typing import Callable, Any, List, Dict, Optional, Tuple, Set
import builtins
import torch
import types
import keyword
import re
def _shadows_builtin_name(name: str) -> bool:
return name in builtins.__dict__ or name in keyword.kwlist or name in {'inf', 'nan', 'NoneType'}
def _is_magic(x: str) -> bool:
return x.startswith('__') and x.endswith('__')
def _snake_case(s: str) -> str:
"""
Transforms the given string ``s`` to a Python-style variable name
Examples:
``mod.snake_case`` -> ``mod.snake_case``
``mod.pascalCase``-> ``mod.pascal_case``
``mod.ALL_CAPS`` -> ``mod.all_caps``
"""
chars = []
prev_lower = False
for c in s:
if prev_lower and c.isupper():
chars.append('_')
chars.append(c.lower())
prev_lower = c.islower()
return ''.join(chars)
def get_qualified_name(func: Callable[..., Any]) -> str:
# things like getattr just appear in builtins
if getattr(builtins, func.__name__, None) is func:
return func.__name__
name = func.__name__
module = _find_module_of_method(func)
module = module.replace('torch._ops', 'torch.ops') # WAR for bug in how torch.ops assigns module
return f'{module}.{name}'
# this is fixed on master, WAR for 1.5
def _find_module_of_method(orig_method: Callable[..., Any]) -> str:
name = orig_method.__name__
module = orig_method.__module__
if module is not None:
return module
for guess in [torch, torch.nn.functional]:
if getattr(guess, name, None) is orig_method:
return guess.__name__
raise RuntimeError(f'cannot find module for {orig_method}')
def _format_args(args: Tuple[Argument, ...], kwargs: Dict[str, Argument]) -> str:
args_s = ', '.join(repr(a) for a in args)
kwargs_s = ', '.join(f'{k} = {repr(v)}' for k, v in kwargs.items())
if args_s and kwargs_s:
return f'{args_s}, {kwargs_s}'
return args_s or kwargs_s
def _format_target(base: str, target: str) -> str:
elems = target.split('.')
r = base
for e in elems:
if not e.isidentifier():
r = f'getattr({r}, "{e}")'
else:
r = f'{r}.{e}'
return r
# Borrowed from CPython typing module
# https://github.com/python/cpython/blob/f90dc36c15d7fee0efaf6d39e97be0bdf2683e93/Lib/typing.py#L156
def _type_repr(obj):
"""Return the repr() of an object, special-casing types (internal helper).
If obj is a type, we return a shorter version than the default
type.__repr__, based on the module and qualified name, which is
typically enough to uniquely identify a type. For everything
else, we fall back on repr(obj).
"""
# HACK: In Python 3.6, type aliases from ``typing`` are instances of ``type``, but in
# later Python versions, type aliases are not instances of ``type``!! We want
# all type aliases to fall through to ``repr``, so if we have a type that is
# in the module typing, don't go down this path.
if isinstance(obj, type) and obj.__module__ != 'typing':
if obj.__module__ == 'builtins':
return obj.__qualname__
return f'{obj.__module__}.{obj.__qualname__}'
if obj is ...:
return('...')
if isinstance(obj, types.FunctionType):
return obj.__name__
return repr(obj)
class _InsertPoint:
def __init__(self, graph, new_insert):
self.graph = graph
self.orig_insert, graph._insert = graph._insert, new_insert
def __enter__(self):
pass
def __exit__(self, type, value, tb):
self.graph._insert = self.orig_insert
class _node_list:
def __init__(self, graph: 'Graph', direction: str = '_next'):
assert direction in ['_next', '_prev']
self.graph = graph
self.direction = direction
def __len__(self):
return self.graph._len
def __iter__(self):
root, direction = self.graph._root, self.direction
cur = getattr(root, direction)
while cur is not root:
if not cur._erased:
yield cur
cur = getattr(cur, direction)
def __reversed__(self):
return _node_list(self.graph, '_next' if self.direction == '_prev' else '_prev')
[docs]class Graph:
"""
``Graph`` is the main data structure used in the FX Intermediate Representation.
It consists of a series of ``Node`` s, each representing callsites (or other
syntactic constructs). The list of ``Node`` s, taken together, constitute a
valid Python function.
For example, the following code
.. code-block:: python
import torch
import torch.fx
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.param = torch.nn.Parameter(torch.rand(3, 4))
self.linear = torch.nn.Linear(4, 5)
def forward(self, x):
return torch.topk(torch.sum(self.linear(x + self.linear.weight).relu(), dim=-1), 3)
m = MyModule()
gm = torch.fx.symbolic_trace(m)
Will produce the following Graph::
print(gm.graph)
.. code-block:: text
graph(x):
%linear_weight : [#users=1] = self.linear.weight
%add_1 : [#users=1] = call_function[target=operator.add](args = (%x, %linear_weight), kwargs = {})
%linear_1 : [#users=1] = call_module[target=linear](args = (%add_1,), kwargs = {})
%relu_1 : [#users=1] = call_method[target=relu](args = (%linear_1,), kwargs = {})
%sum_1 : [#users=1] = call_function[target=torch.sum](args = (%relu_1,), kwargs = {dim: -1})
%topk_1 : [#users=1] = call_function[target=torch.topk](args = (%sum_1, 3), kwargs = {})
return topk_1
For the semantics of operations represented in the ``Graph``, please see :class:`Node`.
"""
[docs] def __init__(self):
"""
Construct an empty Graph.
"""
self._root : Node = Node(self, '', 'root', '', (), {})
self._used_names : Dict[str, int] = {} # base name -> number
self._insert = self._root.prepend
self._len = 0
@property
def nodes(self) -> _node_list:
"""
Get the list of Nodes that constitute this Graph.
Note that this ``Node`` list representation is a doubly-linked list. Mutations
during iteration (e.g. delete a Node, add a Node) are safe.
Returns:
A doubly-linked list of Nodes. Note that ``reversed`` can be called on
this list to switch iteration order.
"""
return _node_list(self)
[docs] def graph_copy(self, g : 'Graph', val_map : Dict[Node, Node]) -> 'Optional[Argument]':
"""
Copy all nodes from a given graph into ``self``.
Args:
g (Graph): The source graph from which to copy Nodes.
val_map (Dict[Node, Node]): a dictionary that will be populated with a mapping
from nodes in ``g`` to nodes in ``self``. Note that ``val_map`` can be passed
in with values in it already to override copying of certain values.
Returns:
The value in ``self`` that is now equivalent to the output value in ``g``,
if ``g`` had an ``output`` node. ``None`` otherwise.
"""
for node in g.nodes:
if node in val_map:
continue
if node.op == 'output':
rv = map_arg(node.args[0], lambda n: val_map[n])
return rv
val_map[node] = self.node_copy(node, lambda n : val_map[n])
return None
def __deepcopy__(self, memo=None) -> 'Graph':
"""
Explicitly implement __deepcopy__ to prevent excessive recursion depth
from the default implementation. This uses graph_copy to copy the nodes
in an iterative way, rather than recursive. It also populates the
memoization table to prevent unnecessary copies (e.g. references to
nodes or other parts of the Graph from a custom GraphModule implementation
"""
memo = memo if memo else {}
g = Graph()
output_val = g.graph_copy(self, val_map=memo)
g.output(output_val)
return g
[docs] def create_node(self, op: str, target: 'Target',
args: Optional[Tuple['Argument', ...]] = None,
kwargs: Optional[Dict[str, 'Argument']] = None,
name: Optional[str] = None,
type_expr: Optional[Any] = None) -> Node:
"""
Create a ``Node`` and add it to the ``Graph`` at the current insert-point.
Note that the current insert-point can be set via :meth:`Graph.inserting_before`
and :meth:`Graph.inserting_after`.
Args:
op (str): the opcode for this Node. One of 'call_function', 'call_method', 'get_attr',
'call_module', 'placeholder', or 'output'. The semantics of these opcodes are
described in the ``Graph`` docstring.
args (Optional[Tuple[Argument, ...]]): is a tuple of arguments to this node.
kwargs (Optional[Dict[str, Argument]]): the kwargs of this Node
name (Optional[str]): an optional string name for the ``Node``.
This will influence the name of the value assigned to in the
Python generated code.
type_expr (Optional[Any]): an optional type annotation representing the
Python type the output of this node will have.
Returns:
The newly-created and inserted node.
"""
assert op in ('call_function', 'call_method', 'get_attr', 'call_module', 'placeholder', 'output')
args = () if args is None else args
kwargs = {} if kwargs is None else kwargs
assert isinstance(args, tuple), "args must be a tuple"
assert isinstance(kwargs, dict), "kwargs must be a dict"
unique_name = self._create_unique_name(name if name is not None else self._target_to_str(target))
n = Node(self, unique_name, op, target, args, kwargs, type_expr)
self._insert(n)
self._len += 1
return n
[docs] def erase_node(self, to_erase : Node) -> None:
"""
Erases a ``Node`` from the ``Graph``. Throws an exception if
there are still users of that node in the ``Graph``.
Args:
to_erase (Node): The ``Node`` to erase from the ``Graph``.
"""
if len(to_erase.users) > 0:
raise RuntimeError(f'Tried to erase Node {to_erase} but it still had {len(to_erase.users)} '
f'users in the graph: {to_erase.users}!')
to_erase._remove_from_list()
to_erase._erased = True # iterators may retain handles to erased nodes
self._len -= 1
# Null out this Node's argument nodes so that the Nodes referred to
# can update their ``users`` accordingly
new_args = map_arg(to_erase.args, lambda n: None)
assert isinstance(new_args, tuple)
to_erase.args = new_args
new_kwargs = map_arg(to_erase.kwargs, lambda n: None)
assert isinstance(new_kwargs, dict)
to_erase.kwargs = new_kwargs
[docs] def inserting_before(self, n: Optional[Node] = None):
"""Set the point at which create_node and companion methods will insert into the graph.
When used within a 'with' statement, this will temporary set the insert point and
then restore it when the with statement exits::
with g.inserting_before(n):
... # inserting before node n
... # insert point restored to what it was previously
g.inserting_before(n) # set the insert point permanently
Args:
n (Optional[Node]): The node before which to insert. If None this will insert before
the beginning of the entire graph.
Returns:
A resource manager that will restore the insert point on ``__exit__``.
"""
if n is None:
return self.inserting_after(self._root)
assert n.graph == self, "Node to insert before is not in graph."
return _InsertPoint(self, n.prepend)
[docs] def inserting_after(self, n: Optional[Node] = None):
"""Set the point at which create_node and companion methods will insert into the graph.
When used within a 'with' statement, this will temporary set the insert point and
then restore it when the with statement exits::
with g.inserting_after(n):
... # inserting after node n
... # insert point restored to what it was previously
g.inserting_after(n) # set the insert point permanently
Args:
n (Optional[Node]): The node before which to insert. If None this will insert after
the beginning of the entire graph.
Returns:
A resource manager that will restore the insert point on ``__exit__``.
"""
if n is None:
return self.inserting_before(self._root)
assert n.graph == self, "Node to insert after is not in graph."
return _InsertPoint(self, n.append)
# sugar for create_node when you know the op
[docs] def placeholder(self, name: str, type_expr: Optional[Any] = None) -> Node:
"""
Insert a ``placeholder`` node into the Graph. A ``placeholder`` represents
a function input.
Args:
name (str): A name for the input value. This corresponds to the name
of the positional argument to the function this ``Graph`` represents.
type_expr (Optional[Any]): an optional type annotation representing the
Python type the output of this node will have. This is needed in some
cases for proper code generation (e.g. when the function is used
subsequently in TorchScript compilation).
.. note::
The same insertion point and type expression rules apply for this method
as ``Graph.create_node``.
"""
return self.create_node('placeholder', name, type_expr=type_expr)
[docs] def get_attr(self, qualified_name: str, type_expr: Optional[Any] = None) -> Node:
"""
Insert a ``get_attr`` node into the Graph. A ``get_attr`` ``Node`` represents the
fetch of an attribute from the ``Module`` hierarchy.
Args:
qualified_name (str): the fully-qualified name of the attribute to be retrieved.
For example, if the traced Module has a submodule named ``foo``, which has a
submodule named ``bar``, which has an attribute named ``baz``, the qualified
name ``foo.bar.baz`` should be passed as ``qualified_name``.
type_expr (Optional[Any]): an optional type annotation representing the
Python type the output of this node will have.
Returns:
The newly-created and inserted ``get_attr`` node.
.. note::
The same insertion point and type expression rules apply for this method
as ``Graph.create_node``.
"""
return self.create_node('get_attr', qualified_name, type_expr=type_expr)
[docs] def call_module(self,
module_name: str,
args: Optional[Tuple['Argument', ...]] = None,
kwargs: Optional[Dict[str, 'Argument']] = None,
type_expr: Optional[Any] = None) -> Node:
"""
Insert a ``call_module`` ``Node`` into the ``Graph``. A ``call_module`` node
represents a call to the forward() function of a ``Module`` in the ``Module``
hierarchy.
Args:
module_name (str): The qualified name of the ``Module`` in the ``Module``
hierarchy to be called. For example, if the traced ``Module`` has a
submodule named ``foo``, which has a submodule named ``bar``, the
qualified name ``foo.bar`` should be passed as ``module_name`` to
call that module.
args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed
to the called method. Note that this should *not* include a ``self`` argument.
kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed
to the called method
type_expr (Optional[Any]): an optional type annotation representing the
Python type the output of this node will have.
Returns:
The newly-created and inserted ``call_module`` node.
.. note::
The same insertion point and type expression rules apply for this method
as :meth:`Graph.create_node`.
"""
return self.create_node('call_module', module_name, args, kwargs, type_expr=type_expr)
[docs] def call_method(self,
method_name: str,
args: Optional[Tuple['Argument', ...]] = None,
kwargs: Optional[Dict[str, 'Argument']] = None,
type_expr: Optional[Any] = None) -> Node:
"""
Insert a ``call_method`` ``Node`` into the ``Graph``. A ``call_method`` node
represents a call to a given method on the 0th element of ``args``.
Args:
method_name (str): The name of the method to apply to the self argument.
For example, if args[0] is a ``Node`` representing a ``Tensor``,
then to call ``relu()`` on that ``Tensor``, pass ``relu`` to ``method_name``.
args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed
to the called method. Note that this *should* include a ``self`` argument.
kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed
to the called method
type_expr (Optional[Any]): an optional type annotation representing the
Python type the output of this node will have.
Returns:
The newly created and inserted ``call_method`` node.
.. note::
The same insertion point and type expression rules apply for this method
as :meth:`Graph.create_node`.
"""
return self.create_node('call_method', method_name, args, kwargs, type_expr=type_expr)
[docs] def call_function(self,
the_function: Callable[..., Any],
args: Optional[Tuple['Argument', ...]] = None,
kwargs: Optional[Dict[str, 'Argument']] = None,
type_expr: Optional[Any] = None) -> Node:
"""
Insert a ``call_function`` ``Node`` into the ``Graph``. A ``call_function`` node
represents a call to a Python callable, specified by ``the_function``. ``the_function``
can be
Args:
the_function (Callable[..., Any]): The function to be called. Can be any PyTorch
operator, Python function, or member of the ``builtins`` or ``operator``
namespaces.
args (Optional[Tuple[Argument, ...]]): The positional arguments to be passed
to the called function.
kwargs (Optional[Dict[str, Argument]]): The keyword arguments to be passed
to the called function
type_expr (Optional[Any]): an optional type annotation representing the
Python type the output of this node will have.
Returns
The newly created and inserted ``call_function`` node.
.. note::
The same insertion point and type expression rules apply for this method
as :meth:`Graph.create_node`.
"""
return self.create_node('call_function', the_function, args, kwargs, type_expr=type_expr)
[docs] def node_copy(self, node: Node, arg_transform: Callable[[Node], 'Argument'] = lambda x: x) -> Node:
"""
Copy a node from one graph into another. ``arg_transform`` needs to transform arguments from
the graph of node to the graph of self. Example::
# Copying all the nodes in `g` into `new_graph`
g : torch.fx.Graph = ...
new_graph = torch.fx.graph()
value_remap = {}
for node in g.nodes:
value_remap[node] = new_graph.node_copy(node, lambda n : value_remap[n])
Args:
node (Node): The node to copy into ``self``.
arg_transform (Callable[[Node], Argument]): A function that transforms
``Node`` arguments in node's ``args`` and ``kwargs`` into the
equivalent argument in ``self``. In the simplest case, this should
retrieve a value out of a table mapping Nodes in the original
graph to ``self``.
"""
args = map_arg(node.args, arg_transform)
kwargs = map_arg(node.kwargs, arg_transform)
assert isinstance(args, tuple)
assert isinstance(kwargs, dict)
return self.create_node(node.op, node.target, args, kwargs, node.name, node.type)
[docs] def output(self, result: 'Argument', type_expr: Optional[Any] = None):
"""
Insert an ``output`` ``Node`` into the ``Graph``. An ``output`` node represents
a ``return`` statement in Python code. ``result`` is the value that should
be returned.
Args:
result (Argument): The value to be returned.
type_expr (Optional[Any]): an optional type annotation representing the
Python type the output of this node will have.
.. note::
The same insertion point and type expression rules apply for this method
as ``Graph.create_node``.
"""
return self.create_node(op='output', target='output', args=(result,), type_expr=type_expr)
def _target_to_str(self, target : Target) -> str:
if callable(target):
op = target.__name__
else:
assert isinstance(target, str)
op = target
if _is_magic(op):
op = op[2:-2]
op = _snake_case(op)
return op
def _create_unique_name(self, candidate : str) -> str:
# delete all characters that are illegal in a Python identifier
candidate = re.sub('[^0-9a-zA-Z_]+', '_', candidate)
if candidate[0].isdigit():
candidate = f'_{candidate}'
def illegal_shadowing_name(name : str) -> bool:
return hasattr(torch, name) or \
hasattr(torch.nn.functional, name) or \
hasattr(torch.nn, name) or \
_shadows_builtin_name(name)
while candidate in self._used_names or illegal_shadowing_name(candidate):
match = re.match(r"(.*)_(\d+)$", candidate)
if match is None:
candidate = candidate + '_1'
else:
base, num = match.group(1, 2)
candidate = f'{base}_{int(num) + 1}'
self._used_names.setdefault(candidate)
return candidate
[docs] def python_code(self, root_module: str) -> str:
"""
Turn this ``Graph`` into valid Python code.
Args:
root_module (str): The name of the root module on which to look-up
qualified name targets. This is usually 'self'.
Returns:
The string source code generated from this ``Graph``.
"""
free_vars: List[str] = []
modules_used : Set[str] = set()
body: List[str] = []
# Wrap string in list to pass by reference
maybe_return_annotation : List[str] = ['']
def register_modules_used(qualified_name : str):
if '.' in qualified_name:
module_name = qualified_name.split('.', maxsplit=1)[0]
modules_used.add(module_name)
def type_repr(o : Any):
typename = _type_repr(o)
if all(x.isidentifier() for x in typename.split('.')):
register_modules_used(typename)
else:
# this is a constructor type, e.g. typing.List[torch.Tensor]
modules_used.add(o.__module__)
for sub_type in o.__args__:
# make sure we have torch.Tensor
type_repr(sub_type)
return typename
# Run through reverse nodes and record the first instance of a use
# of a given node. This represents the *last* use of the node in the
# execution order of the program, which we will use to free unused
# values
node_to_last_use : Dict[Node, Node] = {}
user_to_last_uses : Dict[Node, List[Node]] = {}
def register_last_uses(n : Node, user : Node):
if n not in node_to_last_use:
node_to_last_use[n] = user
user_to_last_uses.setdefault(user, []).append(n)
for node in reversed(self.nodes):
map_arg(node.args, lambda n: register_last_uses(n, node))
map_arg(node.kwargs, lambda n: register_last_uses(n, node))
def delete_unused_values(user : Node):
"""
Delete values after their last use. This ensures that values that are
not used in the remainder of the code are freed and the memory usage
of the code is optimal.
"""
if user.op == 'placeholder':
return
if user.op == 'output':
body.append('\n')
return
nodes_to_delete = user_to_last_uses.get(user, [])
if len(nodes_to_delete):
to_delete_str = ' = '.join([n.name for n in nodes_to_delete] + ['None'])
body.append(f'; {to_delete_str}\n')
else:
body.append('\n')
def emit_node(node : Node):
if node.op == 'placeholder':
assert isinstance(node.target, str)
maybe_type_annotation = '' if node.type is None else f' : {type_repr(node.type)}'
maybe_default_arg = '' if not node.args else f' = {repr(node.args[0])}'
free_vars.append(f'{node.target}{maybe_type_annotation}{maybe_default_arg}')
raw_name = node.target.replace('*', '')
if raw_name != node.name:
body.append(f'{node.name} = {raw_name}\n')
return
elif node.op == 'call_method':
assert isinstance(node.target, str)
body.append(
f'{node.name} = {_format_target(repr(node.args[0]), node.target)}'
f'({_format_args(node.args[1:], node.kwargs)})')
return
elif node.op == 'call_function':
assert callable(node.target)
# pretty print operators
if node.target.__module__ == '_operator' and node.target.__name__ in magic_methods:
assert isinstance(node.args, tuple)
body.append(f'{node.name} = {magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}')
return
qualified_name = get_qualified_name(node.target)
register_modules_used(qualified_name)
if qualified_name == 'getattr' and \
isinstance(node.args, tuple) and \
isinstance(node.args[1], str) and \
node.args[1].isidentifier():
# pretty print attribute access
body.append(f'{node.name} = {_format_target(repr(node.args[0]), node.args[1])}')
return
body.append(f'{node.name} = {qualified_name}({_format_args(node.args, node.kwargs)})')
return
elif node.op == 'call_module':
assert isinstance(node.target, str)
body.append(f'{node.name} = {_format_target(root_module, node.target)}({_format_args(node.args, node.kwargs)})')
return
elif node.op == 'get_attr':
assert isinstance(node.target, str)
body.append(f'{node.name} = {_format_target(root_module, node.target)}')
return
elif node.op == 'output':
if node.type is not None:
maybe_return_annotation[0] = f" -> {type_repr(node.type)}"
body.append(f'return {repr(node.args[0])}')
return
raise NotImplementedError(f'node: {node.op} {node.target}')
for node in self.nodes:
# NOTE: emit_node does not emit a string with newline. It depends
# on delete_unused_values to append one
emit_node(node)
delete_unused_values(node)
# repr() for inf and nan floating point values aren't parseable by
# python as literals. Explicitly import the names from the ``math`` module.
import_strs = [f'import {name}' for name in sorted(modules_used)]
import_block = '\n'.join(import_strs)
if len(body) == 0:
# If the Graph has no non-placeholder nodes, no lines for the body
# have been emitted. To continue to have valid Python code, emit a
# single pass statement
body.append('pass\n')
code = ''.join(body)
code = '\n'.join(' ' + line for line in code.split('\n'))
fn_code = f"""\
{import_block}
def forward(self, {', '.join(free_vars)}){maybe_return_annotation[0]}:
{code}"""
return fn_code
def __str__(self) -> str:
"""
Print a human-readable (not machine-readable) string representation
of this Graph
"""
placeholder_names : List[str] = []
# This is a one-element array just so ``format_node`` can modify the closed
# over value
maybe_return_typename : List[str] = ['']
def format_arg(arg) -> str:
if isinstance(arg, list):
items = ', '.join(format_arg(a) for a in arg)
return f'[{items}]'
elif isinstance(arg, tuple):
items = ', '.join(format_arg(a) for a in arg)
maybe_comma = ',' if len(arg) == 1 else ''
return f'({items}{maybe_comma})'
elif isinstance(arg, dict):
items_str = ', '.join(f'{k}: {format_arg(v)}' for k, v in arg.items())
return f'{{{items_str}}}'
if isinstance(arg, Node):
return '%' + str(arg)
else:
return str(arg)
def pretty_print_target(target):
"""
Make target printouts more user-friendly.
1) builtins will be printed as `builtins.xyz`
2) operators will be printed as `operator.xyz`
3) other callables will be printed with qualfied name, e.g. torch.add
"""
if isinstance(target, str):
return target
if hasattr(target, '__module__'):
if not hasattr(target, '__name__'):
# Just to be defensive, if we don't have `__name__`, get the
# qualname. Not sure if this happens for any members of `operator`
# or `builtins`. This fallback path is not as good, since e.g.
# things in `operator` have `_operator` as their __module__.
return get_qualified_name(target)
if target.__module__ == 'builtins':
return f'builtins.{target.__name__}'
elif target.__module__ == '_operator':
return f'operator.{target.__name__}'
return get_qualified_name(target)
def format_node(n : Node) -> Optional[str]:
if n.op == 'placeholder':
assert isinstance(n.target, str)
arg_str = n.target
arg_str += arg_str + f': {_type_repr(n.type)}' if n.type is not None else ''
placeholder_names.append(arg_str)
return None
elif n.op == 'get_attr':
maybe_typename = f'{_type_repr(n.type)} ' if n.type is not None else ''
return f'%{n.name} : {maybe_typename}[#users={len(n.users)}] = self.{n.target}'
elif n.op == 'output':
if n.type is not None:
maybe_return_typename[0] = f' -> {_type_repr(n.type)}'
return f'return {n.args[0]}'
else:
maybe_typename = f'{_type_repr(n.type)} ' if n.type is not None else ''
return f'%{n.name} : {maybe_typename}[#users={len(n.users)}] = {n.op}[target={pretty_print_target(n.target)}](' \
f'args = {format_arg(n.args)}, kwargs = {format_arg(n.kwargs)})'
node_strs = [format_node(node) for node in self.nodes]
param_str = ', '.join(placeholder_names)
s = f'graph({param_str}){maybe_return_typename[0]}:'
for node_str in node_strs:
if node_str:
s += '\n ' + node_str
return s
[docs] def print_tabular(self):
"""
Prints the intermediate representation of the graph in tabular
format.
"""
try:
from tabulate import tabulate
except ImportError:
print("`print_tabular` relies on the library `tabulate`, "
"which could not be found on this machine. Run `pip "
"install tabulate` to install the library.")
node_specs = [[n.op, n.name, n.target, n.args, n.kwargs]
for n in self.nodes]
print(tabulate(node_specs,
headers=['opcode', 'name', 'target', 'args', 'kwargs']))
[docs] def lint(self, root : Optional[torch.nn.Module] = None):
"""
Runs various checks on this Graph to make sure it is well-formed. In
particular:
- Checks Nodes have correct ownership (owned by this graph)
- Checks Nodes appear in topological order
- If ``root`` is provided, checks that targets exist in ``root``
Args:
root (Optional[torch.nn.Module]): The root module with which to check
for targets. This is equivalent to the ``root`` argument that is
passed when constructing a ``GraphModule``.
"""
# Check topo order
def check_arg(arg : Node, n : Optional[Node] = None) -> None:
context_str = f' of Node \'{n}\' ' if n else ' '
if arg.graph is not self:
raise RuntimeError(f'Argument \'{arg}\'{context_str}does not belong to this Graph, '
f'but was used as an argument! If you are copying nodes from another graph, make '
f'sure to use ``arg_transform`` on node_copy() to remap values\n{self}')
if arg not in seen_values:
raise RuntimeError(f'Argument \'{arg}\'{context_str}was used before it has been '
f'defined! Please check that Nodes in the graph are topologically ordered\n{self}')
seen_names : Set[str] = set()
seen_values : Set[Node] = set()
for node in self.nodes:
if node.op not in ['placeholder', 'call_method', 'call_module', 'call_function', 'get_attr', 'output']:
raise RuntimeError(f'Node {node} had unknown opcode {node.op}!')
if node.graph is not self:
raise RuntimeError(f'Node \'{node}\' does not belong to this Graph!')
map_arg(node.args, lambda arg: check_arg(arg, node))
map_arg(node.kwargs, lambda arg: check_arg(arg, node))
seen_values.add(node)
if node.name in seen_names:
raise RuntimeError(f'Node redefined name {node.name}!')
seen_names.add(node.name)
# Check targets are legit
if root:
for node in self.nodes:
if node.op in ['get_attr', 'call_module']:
assert isinstance(node.target, str)
target_atoms = node.target.split('.')
m_itr = root
for i, atom in enumerate(target_atoms):
m_itr = getattr(m_itr, atom, None)
if m_itr is None:
seen_qualname = '.'.join(target_atoms[:i])
raise RuntimeError(f'Node {node} target {node.target} references nonexistent attribute '
f'{atom} of {seen_qualname}')
reflectable_magic_methods = {
'add': '{} + {}',
'sub': '{} - {}',
'mul': '{} * {}',
'floordiv': '{} // {}',
'truediv': '{} / {}',
'div': '{} / {}',
'mod': '{} % {}',
'pow': '{} ** {}',
'lshift': '{} << {}',
'rshift': '{} >> {}',
'and': '{} & {}',
'or': '{} | {}',
'xor': '{} ^ {}',
'getitem': '{}[{}]'
}
magic_methods = dict({
'eq': '{} == {}',
'ne': '{} != {}',
'lt': '{} < {}',
'gt': '{} > {}',
'le': '{} <= {}',
'ge': '{} >= {}',
'pos': '+{}',
'neg': '-{}',
'invert': '~{}'}, **reflectable_magic_methods)
