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graph.py
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graph.py
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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'}
def _is_magic(x: str) -> bool:
return x.startswith('__') and x.endswith('__')
def snake_case(s: str) -> str:
return ''.join(['_' + i.lower() if i.isupper() else i for i in s]).lstrip('_')
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')
class Graph:
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):
return _node_list(self)
def graph_copy(self, g : 'Graph', val_map : Dict[Node, Node]) -> Optional[Argument]:
"""
Append all nodes from graph `g` to this graph. `val_map` should be a dictionary
that maps nodes in `g` to nodes in `self. `val_map` will be populated with more
items by this function. Returns the equivalent output value of `g` with
Nodes switched to refer to nodes in `self`.
"""
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
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:
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
sanitized_name = self._register_name_used(name) if name is not None else self._name(target)
n = Node(self, sanitized_name, op, target, args, kwargs, type_expr)
self._insert(n)
self._len += 1
return n
def erase_node(self, to_erase : Node):
"""
Erases the node `to_erase` from the `Graph`. Throws an exception if
there are still users of that node in 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
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)
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
def placeholder(self, name: str, type_expr: Optional[Any] = None) -> Node:
return self.create_node('placeholder', name, type_expr=type_expr)
def get_attr(self, name: str, type_expr: Optional[Any] = None) -> Node:
return self.create_node('get_attr', name, type_expr=type_expr)
def call_module(self,
module_name: str,
args: Optional[Tuple[Argument, ...]] = None,
kwargs: Optional[Dict[str, Argument]] = None,
type_expr: Optional[Any] = None) -> Node:
return self.create_node('call_module', module_name, args, kwargs, type_expr=type_expr)
def call_method(self,
method_name: str,
args: Optional[Tuple[Argument, ...]] = None,
kwargs: Optional[Dict[str, Argument]] = None,
type_expr: Optional[Any] = None) -> Node:
return self.create_node('call_method', method_name, args, kwargs, type_expr=type_expr)
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:
return self.create_node('call_function', the_function, args, kwargs, type_expr=type_expr)
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:
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 = map_arg(node.args, arg_transform)
kwargs = map_arg(node.kwargs, arg_transform)
assert isinstance(args, tuple)
assert isinstance(kwargs, dict)
if node.op == "placeholder":
# Placeholder names are user-visible, so they should be copied as-is without normalizing them.
name = node.name
else:
sanitized_name = node.name
if '_' in node.name:
base, maybe_idx = node.name.rsplit('_', 1)
if base != '':
try:
int(maybe_idx)
sanitized_name = base
except ValueError:
pass
name = self._name(sanitized_name)
return self.create_node(node.op, node.target, args, kwargs, name, node.type)
def output(self, result: Argument, type_expr: Optional[Any] = None):
return self.create_node(op='output', target='output', args=(result,), type_expr=type_expr)
def _name(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 = op.replace('.', '_')
# delete all characters that are illegal in a Python identifier
op = re.sub('[^0-9a-zA-Z_]+', '_', op)
op = snake_case(op)
if op[0].isdigit():
op = f'_{op}'
return self._register_name_used(op)
def _register_name_used(self, op : str) -> str:
"""
Even if a user provides us with a name, we must register that that
name is used to prevent duplication of names from further nodes as
well as ensure that the name provided does not shadow a builtin.
"""
if op not in self._used_names:
self._used_names[op] = 0
# Avoid shadowing PyTorch and Python builtins.
if not hasattr(torch, op) and \
not hasattr(torch.nn.functional, op) and \
not hasattr(torch.nn, op) and \
not _shadows_builtin_name(op):
return op
i = self._used_names[op] = self._used_names[op] + 1
return f'{op}_{i}'
def python_code(self, root_module: str) -> str:
free_vars: List[str] = []
modules_used : Set[str] = set()
body: List[str] = []
maybe_return_annotation : 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
for node in self.nodes:
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')
continue
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)})\n')
continue
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))}\n')
continue
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])}\n')
continue
body.append(f'{node.name} = {qualified_name}({_format_args(node.args, node.kwargs)})\n')
continue
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)})\n')
continue
elif node.op == 'get_attr':
assert isinstance(node.target, str)
body.append(f'{node.name} = {_format_target(root_module, node.target)}\n')
continue
elif node.op == 'output':
if node.type is not None:
maybe_return_annotation = f" -> {type_repr(node.type)}"
body.append(f'return {node.args[0]}')
continue
raise NotImplementedError(f'node: {node.op} {node.target}')
# 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)
code = ''.join(body)
code = '\n'.join(' ' + line for line in code.split('\n')) + '\n'
fn_code = f"""\
{import_block}
def forward(self, {', '.join(free_vars)}){maybe_return_annotation}:
{code}
"""
return fn_code
def __str__(self) -> str:
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 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={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
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 `target`s exist in `root`
"""
# 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)