Source code for torch.fx.graph
from collections import defaultdict
from .node import Node, Argument, Target, map_arg, _type_repr, _get_qualified_name
import torch.utils._pytree as pytree
from . import _pytree as fx_pytree
from ._compatibility import compatibility
import contextlib
from typing import TYPE_CHECKING, Callable, Any, List, Dict, NamedTuple, Optional, Tuple, Set, FrozenSet, Type
from dataclasses import dataclass
from contextlib import contextmanager
import copy
import torch
import keyword
import re
import builtins
import math
import warnings
import inspect
__all__ = ["PythonCode", "CodeGen", "Graph"]
if TYPE_CHECKING:
from .graph_module import GraphModule # noqa: F401
from ._symbolic_trace import Tracer # noqa: F401
# Mapping of builtins to their `typing` equivalent.
_origin_type_map = {
list: List,
dict: Dict,
set: Set,
frozenset: FrozenSet,
tuple: Tuple,
}
# Signature for functions thattransforms the body (`list[str]`) of the
# generated code
TransformCodeFunc = Callable[[List[str]], List[str]]
class _CustomBuiltin(NamedTuple):
"""Additional objs that we add to every graph's globals.
The repr() for some standard library objects is not valid Python code without
an import. For common objects of this sort, we bundle them in the globals of
every FX graph.
"""
# How to import this object from the standard library.
import_str: str
# The actual object, produced from that import string.
obj: Any
_custom_builtins: Dict[str, _CustomBuiltin] = {}
def _register_custom_builtin(name: str, import_str: str, obj: Any):
_custom_builtins[name] = _CustomBuiltin(import_str, obj)
_register_custom_builtin('inf', 'from math import inf', math.inf)
_register_custom_builtin('nan', 'from math import nan', math.nan)
_register_custom_builtin('NoneType', 'NoneType = type(None)', type(None))
_register_custom_builtin('torch', 'import torch', torch)
_register_custom_builtin('device', 'from torch import device', torch.device)
_register_custom_builtin('fx_pytree', 'import torch.fx._pytree as fx_pytree', fx_pytree)
_register_custom_builtin('pytree', 'import torch.utils._pytree as pytree', pytree)
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 _is_from_torch(obj: Any) -> bool:
module_name = getattr(obj, '__module__', None)
if module_name is not None:
base_module = module_name.partition('.')[0]
return (
base_module == 'torch' and
not module_name.startswith("torch._dynamo.") and
not module_name.startswith("torch._inductor.")
)
name = getattr(obj, '__name__', None)
# exclude torch because torch.torch.torch.torch works. idk mang
if name is not None and name != 'torch':
for guess in [torch, torch.nn.functional]:
if getattr(guess, name, None) is obj:
return True
return False
class _Namespace:
"""A context for associating names uniquely with objects.
The following invariants are enforced:
- Each object gets a single name.
- Each name is unique within a given namespace.
- Names generated do not shadow builtins, unless the object is indeed that builtin.
"""
def __init__(self):
self._obj_to_name: Dict[Any, str] = {}
self._unassociated_names = set()
self._used_names: Set[str] = set()
self._base_count: Dict[str, int] = defaultdict(int)
self._illegal_char_regex = re.compile('[^0-9a-zA-Z_]+')
self._name_suffix_regex = re.compile(r"(.*)_(\d+)$")
def create_name(self, candidate: str, obj: Optional[Any]) -> str:
"""Create a unique name.
Arguments:
candidate: used as the basis for the unique name, relevant to the user.
obj: If not None, an object that will be associated with the unique name.
"""
if obj is not None and obj in self._obj_to_name:
return self._obj_to_name[obj]
# delete all characters that are illegal in a Python identifier
candidate = self._illegal_char_regex.sub('_', candidate)
if not candidate:
candidate = '_unnamed'
if candidate[0].isdigit():
candidate = f'_{candidate}'
match = self._name_suffix_regex.match(candidate)
if match is None:
base = candidate
num = None
else:
base, num_str = match.group(1, 2)
num = int(num_str)
candidate = base if num is None else f'{base}_{num}'
if not num:
num = self._base_count[base]
while candidate in self._used_names or self._is_illegal_name(candidate, obj):
num += 1
candidate = f'{base}_{num}'
self._used_names.add(candidate)
self._base_count[base] = num
if obj is None:
self._unassociated_names.add(candidate)
else:
self._obj_to_name[obj] = candidate
return candidate
def associate_name_with_obj(self, name: str, obj: Any):
"""Associate a unique name with an object.
Neither `name` nor `obj` should be associated already.
"""
assert obj not in self._obj_to_name
assert name in self._unassociated_names
self._obj_to_name[obj] = name
self._unassociated_names.remove(name)
def _is_illegal_name(self, name: str, obj: Any) -> bool:
# 1. keywords are never allowed as names.
if name in keyword.kwlist:
return True
# 2. Can't shadow a builtin name, unless you *are* that builtin.
if name in builtins.__dict__:
return obj is not builtins.__dict__[name]
# 3. Can't shadow our custom builtins either
if name in _custom_builtins:
return obj is not _custom_builtins[name].obj
return False
dtype_abbrs = {
torch.bfloat16: 'bf16',
torch.float64: 'f64',
torch.float32: 'f32',
torch.float16: 'f16',
torch.complex32: 'c32',
torch.complex64: 'c64',
torch.complex128: 'c128',
torch.int8: 'i8',
torch.int16: 'i16',
torch.int32: 'i32',
torch.int64: 'i64',
torch.bool: 'b8',
torch.uint8: 'u8',
}
@compatibility(is_backward_compatible=True)
@dataclass
class PythonCode:
"""
Represents all the information necessary to exec or save a graph as Python code.
"""
# Python source code for the forward function definition.
src: str
# Values in global scope during exection of `src_def`.
globals: Dict[str, Any]
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
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 _PyTreeInfo(NamedTuple):
"""
Contains extra info stored when we're using Pytrees
"""
orig_args: List[str]
in_spec: pytree.TreeSpec
out_spec: Optional[pytree.TreeSpec]
@compatibility(is_backward_compatible=False)
class CodeGen:
def __init__(self):
self._body_transformer: Optional[TransformCodeFunc] = None
def gen_fn_def(self, free_vars: List[str], maybe_return_annotation: str) -> str:
"""
Given the free variables and a return annotation, generates the beginning of the FX function.
By default, `gen_fn_def(['a', 'b'], '') == 'def forward(a, b):'`
"""
# If the original function didn't have self as its first argument, we
# would have added it.
if len(free_vars) == 0 or free_vars[0] != 'self':
free_vars.insert(0, 'self')
return f"def forward({', '.join(free_vars)}){maybe_return_annotation}:"
def generate_output(self, output_args: Argument) -> str:
"""
Given the output arguments, generates the return statement of the FX function.
Note: The returned statement should not be indented.
"""
return f'return {repr(output_args)}'
def process_inputs(self, *args: Any) -> Any:
"""
Transforms the inputs so that the graph can take them as arguments, as
non-default codegen may result in the inputs to the function being
different from the inputs to the graph.
If the graph was directly runnable, this invariant should hold true
`f.graph.process_outputs(f.graph(*f.graph.process_inputs(*inputs))) == f(*inputs)`
"""
return args
def process_outputs(self, outputs: Any) -> Any:
"""
Transforms the outputs of the graph to be identical to the codegen.
See ``process_inputs`` for more details.
"""
return outputs
def additional_globals(self) -> List[Tuple[str, Any]]:
"""
If your codegen uses extra global values, add tuples of (identifier,reference to the value) here.
For example, return ['List', typing.List] if you need ``List`` in the global context.
"""
return []
def _gen_python_code(self, nodes, root_module: str, namespace: _Namespace, *, verbose: bool = False) -> PythonCode:
free_vars: List[str] = []
body: List[str] = []
globals_: Dict[str, Any] = {}
wrapped_fns: Dict[str, None] = {}
# Wrap string in list to pass by reference
maybe_return_annotation : List[str] = ['']
def add_global(name_hint: str, obj: Any):
"""Add an obj to be tracked as a global.
We call this for names that reference objects external to the
Graph, like functions or types.
Returns: the global name that should be used to reference 'obj' in generated source.
"""
if _is_from_torch(obj) and obj != torch.device: # to support registering torch.device
# HACK: workaround for how torch custom ops are registered. We
# can't import them like normal modules so they must retain their
# fully qualified name.
return _get_qualified_name(obj)
# normalize the name hint to get a proper identifier
global_name = namespace.create_name(name_hint, obj)
if global_name in globals_:
assert globals_[global_name] is obj
return global_name
globals_[global_name] = obj
return global_name
# Pre-fill the globals table with registered builtins.
for name, (_, obj) in _custom_builtins.items():
add_global(name, obj)
def type_repr(o : Any):
if o == ():
# Empty tuple is used for empty tuple type annotation Tuple[()]
return '()'
typename = _type_repr(o)
if hasattr(o, '__origin__'):
# This is a generic type, e.g. typing.List[torch.Tensor]
origin_type = _origin_type_map.get(o.__origin__, o.__origin__)
origin_typename = add_global(_type_repr(origin_type), origin_type)
if hasattr(o, '__args__'):
# Assign global names for each of the inner type variables.
args = [type_repr(arg) for arg in o.__args__]
if len(args) == 0:
# Bare type, such as `typing.Tuple` with no subscript
# This code-path used in Python < 3.9
return origin_typename
return f'{origin_typename}[{",".join(args)}]'
else:
# Bare type, such as `typing.Tuple` with no subscript
# This code-path used in Python 3.9+
return origin_typename
# Common case: this is a regular module name like 'foo.bar.baz'
return add_global(typename, o)
def _format_args(args: Tuple[Argument, ...], kwargs: Dict[str, Argument]) -> str:
def _get_repr(arg):
# Handle NamedTuples (if it has `_fields`) via add_global.
if isinstance(arg, tuple) and hasattr(arg, '_fields'):
qualified_name = _get_qualified_name(type(arg))
global_name = add_global(qualified_name, type(arg))
return f"{global_name}{repr(tuple(arg))}"
return repr(arg)
args_s = ', '.join(_get_repr(a) for a in args)
kwargs_s = ', '.join(f'{k} = {_get_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
# 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(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([repr(n) for n in nodes_to_delete] + ['None'])
body.append(f'; {to_delete_str}\n')
else:
body.append('\n')
prev_stacktrace = None
def append_stacktrace_summary(node : Node):
"""
Append a summary of the stacktrace to the generated code. This is
useful for debugging.
"""
nonlocal prev_stacktrace
pattern = re.compile(r"^File \"(.+)\", line (\d+), in (.+)$")
if node.op not in {'placeholder', 'output'}:
if node.stack_trace:
if node.stack_trace != prev_stacktrace:
prev_stacktrace = node.stack_trace
lines = node.stack_trace.strip().split('\n')
idx = 0
while idx < len(lines):
line = lines[idx].strip()
if line.startswith('File '):
break
idx += 1
summary_lines = []
if idx + 1 < len(lines):
matches = pattern.match(lines[idx].strip())
if matches:
file = matches.group(1)
lineno = matches.group(2)
lineage = f'File: {file}:{lineno}'
summary_lines.append(lineage)
code = f"code: {lines[idx + 1].strip()}"
summary_lines.append(code)
summary_str = ', '.join(summary_lines)
body.append(f'\n# {summary_str}\n')
elif prev_stacktrace != "":
prev_stacktrace = ""
body.append('\n# No stacktrace found for following nodes\n')
def stringify_shape(shape : torch.Size) -> str:
return f"[{', '.join(str(x) for x in shape)}]"
def emit_node(node : Node):
maybe_type_annotation = '' if node.type is None else f' : {type_repr(node.type)}'
if verbose:
# override annotation with more detailed information
from torch._subclasses.fake_tensor import FakeTensor
from torch.fx.experimental.proxy_tensor import py_sym_types
from torch.fx.passes.shape_prop import TensorMetadata
meta_val = node.meta.get('val', node.meta.get('tensor_meta', None))
if isinstance(meta_val, FakeTensor):
maybe_type_annotation = f': {dtype_abbrs[meta_val.dtype]}{stringify_shape(meta_val.shape)}'
elif isinstance(meta_val, py_sym_types):
maybe_type_annotation = f': Sym({meta_val})'
elif isinstance(meta_val, TensorMetadata):
maybe_type_annotation = f': {dtype_abbrs[meta_val.dtype]}{stringify_shape(meta_val.shape)}'
if node.op == 'placeholder':
assert isinstance(node.target, str)
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 != repr(node):
body.append(f'{repr(node)} = {raw_name}\n')
return
elif node.op == 'call_method':
assert isinstance(node.target, str)
body.append(
f'{repr(node)}{maybe_type_annotation} = {_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 getattr(node.target, "__module__", "") == '_operator' and node.target.__name__ in magic_methods:
assert isinstance(node.args, tuple)
body.append(f'{repr(node)}{maybe_type_annotation} = '
f'{magic_methods[node.target.__name__].format(*(repr(a) for a in node.args))}')
return
# pretty print inplace operators; required for jit.script to work properly
# not currently supported in normal FX graphs, but generated by torchdynamo
if getattr(node.target, "__module__", "") == '_operator' and node.target.__name__ in inplace_methods:
body.append(f'{inplace_methods[node.target.__name__].format(*(repr(a) for a in node.args))}; '
f'{repr(node)}{maybe_type_annotation} = {repr(node.args[0])}')
return
qualified_name = _get_qualified_name(node.target)
global_name = add_global(qualified_name, node.target)
# special case for getattr: node.args could be 2-argument or 3-argument
# 2-argument: attribute access; 3-argument: fall through to attrib function call with default value
if global_name == 'getattr' and \
isinstance(node.args, tuple) and \
isinstance(node.args[1], str) and \
node.args[1].isidentifier() and \
len(node.args) == 2:
body.append(f'{repr(node)}{maybe_type_annotation} = {_format_target(repr(node.args[0]), node.args[1])}')
return
body.append(f'{repr(node)}{maybe_type_annotation} = {global_name}({_format_args(node.args, node.kwargs)})')
if node.meta.get('is_wrapped', False):
wrapped_fns.setdefault(global_name)
return
elif node.op == 'call_module':
assert isinstance(node.target, str)
body.append(f'{repr(node)}{maybe_type_annotation} = '
f'{_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'{repr(node)}{maybe_type_annotation} = {_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(self.generate_output(node.args[0]))
return
raise NotImplementedError(f'node: {node.op} {node.target}')
for node in nodes:
# NOTE: emit_node does not emit a string with newline. It depends
# on delete_unused_values to append one
if verbose:
append_stacktrace_summary(node)
emit_node(node)
delete_unused_values(node)
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')
if len(wrapped_fns) > 0:
wrap_name = add_global('wrap', torch.fx.wrap)
wrap_stmts = '\n'.join([f'{wrap_name}("{name}")' for name in wrapped_fns])
else:
wrap_stmts = ''
if self._body_transformer:
body = self._body_transformer(body)
for name, value in self.additional_globals():
add_global(name, value)
prologue = self.gen_fn_def(free_vars, maybe_return_annotation[0])
code = ''.join(body).lstrip('\n')
code = '\n'.join(' ' + line for line in code.split('\n'))
fn_code = f"""
{wrap_stmts}
{prologue}
{code}"""
return PythonCode(fn_code, globals_)
# Ideally, we'd like to refactor all of the pytree logic into this codegen
# class. Unfortunately, there are 3 areas we currently need extra logic in FX.
# 1. In the initial symbolic trace, the pytree logic is tied up with `concrete_args`.
# 2. In the FX graph, we need to access 2 attributes - in_spec and out_spec.
# Since we can't access .graph within the FX forward, we need to copy the attribute to the module.
# 3. We currently can't register the pytree imports with `add_global` - not sure why.
class _PyTreeCodeGen(CodeGen):
def __init__(self, pytree_info: _PyTreeInfo):
super().__init__()
self.pytree_info: _PyTreeInfo = pytree_info
def process_inputs(self, *inputs: Any) -> Any:
flat_args, _ = pytree.tree_flatten(inputs)
return flat_args
def process_outputs(self, out: Any) -> Any:
if self.pytree_info is None:
return out
if not isinstance(out, list):
out = [out]
assert(self.pytree_info.out_spec is not None)
return pytree.tree_unflatten(out, self.pytree_info.out_spec)
def gen_fn_def(self, free_vars, maybe_return_annotation):
# Given a user function/model:
# myargs = [myargs0, myargs1]
# mykwargs = {'mykwargs0': ..., 'mykwargs1': ...}
# def forward(self, mypos, *myargs, mykey=None, **mykwargs):
#
# The generated code flattens all keywords into positional arguments for `forward()`
# e.g forward(self, mypos, myargs0, myargs1, mykey, mykwargs0, mykwargs1):
#
# Within `forward`, `tree_flatten_spec``still parses args and kwargs separately
# e.g. tree_flatten_spec(([mypos, myargs0, myargs1],
# {'mykey':mykey, 'mykwargs0':mykwargs0, 'mykwargs1':mykwargs1}),
# self._in_spec)
#
# If the user function/model does not have keywords, the dict is suppressed from tree_flatten_spec
# e.g. tree_flatten_spec([mypos, myargs0, myargs1]), self._in_spec)
if self.pytree_info is None:
return super().gen_fn_def(free_vars, maybe_return_annotation)
fn_args = self.pytree_info.orig_args
has_orig_self = (fn_args[0] == 'self') if len(fn_args) > 0 else False
if has_orig_self:
free_vars.insert(0, 'self')
fn_definition = super().gen_fn_def(fn_args[:], maybe_return_annotation)
if len(free_vars) > 0: # pytree has placeholders in it
# when kwargs is present, in_spec is tuple(args, kwargs)
has_args_kwargs_tuple = self.pytree_info.in_spec.type == tuple and \
len(self.pytree_info.in_spec.children_specs) == 2 and \
self.pytree_info.in_spec.children_specs[0].type == tuple and \
self.pytree_info.in_spec.children_specs[1].type == dict
fn_kwargs = '{}'
fn_signature = f"[{', '.join(fn_args)}], self._in_spec"
if has_args_kwargs_tuple:
count_args = len(self.pytree_info.in_spec.children_specs[0].children_specs)
fn_args = self.pytree_info.orig_args[:count_args]
fn_kwargs = '{' + ', '.join(f"'{k}':{v}" for k, v in zip(
self.pytree_info.in_spec.children_specs[1].context,
self.pytree_info.orig_args[count_args:])) + '}'
fn_signature = f"([{', '.join(fn_args)}], {fn_kwargs}), self._in_spec"
fn_definition += f"""
{', '.join(free_vars)}, = fx_pytree.tree_flatten_spec({fn_signature})"""
return fn_definition
def generate_output(self, output_args):
if self.pytree_info:
return f'return pytree.tree_unflatten({repr(output_args)}, self._out_spec)'
else:
return super().generate_output(output_args)
[docs]@compatibility(is_backward_compatible=True)
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] @compatibility(is_backward_compatible=True)
def __init__(self, owning_module: Optional["GraphModule"] = None, tracer_cls: Optional[Type["Tracer"]] = None,
tracer_extras: Optional[Dict[str, Any]] = None):
"""
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
self._graph_namespace = _Namespace()
self._owning_module = owning_module
self._tracer_cls = tracer_cls
self._tracer_extras = tracer_extras
self._codegen = CodeGen()
@property
def owning_module(self):
return self._owning_module
@owning_module.setter
def owning_module(self, mod: Optional["GraphModule"]):
self._owning_module = mod
@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] @compatibility(is_backward_compatible=True)
def graph_copy(self, g : 'Graph', val_map : Dict[Node, Node], return_output_node=False) -> '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 if not return_output_node else (rv, node)
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(tracer_cls=self._tracer_cls)
output_vals = g.graph_copy(self, val_map=memo, return_output_node=True)
g._codegen = copy.deepcopy(self._codegen)
assert isinstance(output_vals, tuple)
output_val, old_output_val = output_vals
g.output(output_val, type_expr=getattr(old_output_val, 'type', None))
return g
[docs] @compatibility(is_backward_compatible=True)
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"
candidate = name if name is not None else self._target_to_str(target)
name = self._graph_namespace.create_name(candidate, None)
n = Node(self, name, op, target, args, kwargs, type_expr)
self._graph_namespace.associate_name_with_obj(name, n)
self._insert(n)
self._len += 1
return n
[docs] @compatibility(is_backward_compatible=False)
def process_inputs(self, *args):
"""
Processes args so that they can be passed to the FX graph.
"""
return self._codegen.process_inputs(*args)
[docs] @compatibility(is_backward_compatible=False)
def process_outputs(self, out):
return self._codegen.process_outputs(out)
[docs] @compatibility(is_backward_compatible=True)
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] @compatibility(is_backward_compatible=True)
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] @compatibility(is_backward_compatible=True)
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)
[docs] @compatibility(is_backward_compatible=True)
def placeholder(self, name: str, type_expr: Optional[Any] = None,
default_value : Any = inspect.Signature.empty) -> 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).
default_value (Any): The default value this function argument should take
on. NOTE: to allow for `None` as a default value, `inspect.Signature.empty`
should be passed as this argument to specify that the parameter does _not_
have a default value.
.. note::
The same insertion point and type expression rules apply for this method
as ``Graph.create_node``.
"""
args = () if default_value is inspect.Signature.empty else (default_value,)
return self.create_node('placeholder', name, args=args, type_expr=type_expr)
[docs] @compatibility(is_backward_compatible=True)
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``.
"""
def _get_attr_reference_exists(mod: torch.nn.Module, qualified_name: str) -> bool:
module_path, _, name = qualified_name.rpartition(".")
try:
submod: torch.nn.Module = mod.get_submodule(module_path)
except AttributeError:
warnings.warn(f"Failed to fetch module {module_path}!")
return False
if not hasattr(submod, name):
return False
res = getattr(submod, name)
if (not isinstance(res, torch.nn.Module)
and not isinstance(res, torch.nn.Parameter)
and name not in submod._buffers):
return False
return True
if (self.owning_module and
not _get_attr_reference_exists(self.owning_module, qualified_name)):
warnings.warn("Attempted to insert a get_attr Node with no "
"underlying reference in the owning "
"GraphModule! Call "
"GraphModule.add_submodule to add the "
"necessary submodule, "
"GraphModule.add_parameter to add the "
"necessary Parameter, or "
"nn.Module.register_buffer to add the "
"necessary buffer", stacklevel=2)
return self.create_node('get_attr', qualified_name, type_expr=type_expr)
[docs] @compatibility(is_backward_compatible=True)
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`.
"""
if (self.owning_module and
self.owning_module.get_submodule(module_name) is None):
warnings.warn("Attempted to insert a call_module Node with "
"no underlying reference in the owning "
"GraphModule! Call "
"GraphModule.add_submodule to add the "
"necessary submodule")
return self.create_node('call_module', module_name, args, kwargs, type_expr=type_expr)
[docs] @compatibility(is_backward_compatible=True)
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] @compatibility(is_backward_compatible=True)
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``.
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] @compatibility(is_backward_compatible=True)
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)
result_node = self.create_node(node.op, node.target, args, kwargs, node.name, node.type)
result_node.meta = copy.copy(node.meta)
return result_node
[docs] @compatibility(is_backward_compatible=True)
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
[docs] @compatibility(is_backward_compatible=True)
def python_code(self, root_module: str, *, verbose: bool = False) -> PythonCode:
"""
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:
A PythonCode object, consisting of two fields:
src: the Python source code representing the object
globals: a dictionary of global names in `src` -> the objects that they reference.
"""
# NOTE: [Graph Namespaces]
#
# There are two types of symbols in generated Python source code:
# locals and globals.
# Locals are locally defined by the output of a node in the Graph.
# Globals are references to external objects, like functions or types.
#
# When generating Python code, we need to make sure to name things
# appropriately. In particular:
# - All names should be unique, to avoid weird shadowing bugs.
# - These names need to be consistent, e.g. a object should always be
# referenced by the same name.
#
# To do this, we create a new namespace just for this source. All names
# that get printed must come from this namespace.
#
# Why can't we re-use node.name? Because it was generated within the
# namespace `self._graph_namespace`. In order to provide uniqueness
# over both locals (node.name) *and* globals, we create a completely
# new namespace to put all identifiers in.
namespace = _Namespace()
# Override Node's repr to generate a valid name within our namespace.
# Since repr() is designed to produce a valid Python expression, it
# makes sense to re-use it. This way, it's easy to print something like
# Tuple[Node, Node] by simply calling repr() on it. Node's __repr__ is
# implemented cooperatively to allow this.
def node_repr(n: Node):
return namespace.create_name(n.name, n)
@contextmanager
def override_node_repr(graph: Graph):
orig_repr_fns = {}
for node in graph.nodes:
orig_repr_fns[node] = node._repr_fn
node._repr_fn = node_repr
try:
yield None
finally:
# restore the original repr functions
for node in graph.nodes:
node._repr_fn = orig_repr_fns[node]
with override_node_repr(self):
return self._python_code(root_module, namespace, verbose=verbose)
def _python_code(self, root_module: str, namespace: _Namespace, *, verbose: bool = False) -> PythonCode:
return self._codegen._gen_python_code(self.nodes, root_module, namespace, verbose=verbose)
def __str__(self) -> str:
"""
Return 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] = ['']
node_strs = [node.format_node(placeholder_names) 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] @compatibility(is_backward_compatible=True)
def print_tabular(self):
"""
Prints the intermediate representation of the graph in tabular
format. Note that this API requires the ``tabulate`` module to be
installed.
"""
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] @compatibility(is_backward_compatible=True)
def lint(self):
"""
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 this Graph has an owning GraphModule, checks that targets
exist in that 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 self.owning_module:
for node in self.nodes:
if node.op == 'call_function':
if not callable(node.target):
raise ValueError(f'Node {node} target {node.target} has type {torch.typename(node.target)} but '
'a Callable is expected')
else:
if not isinstance(node.target, str):
raise ValueError(f'Node {node} target {node.target} has type {torch.typename(node.target)} but '
'a str is expected')
if node.op in ['get_attr', 'call_module']:
target_atoms = node.target.split('.')
m_itr = self.owning_module
for i, atom in enumerate(target_atoms):
new_m_itr = getattr(m_itr, atom, None)
seen_qualname = '.'.join(target_atoms[:i])
if new_m_itr is None:
raise RuntimeError(f'Node {node} target {node.target} references nonexistent attribute '
f'{atom} of {seen_qualname}')
if (node.op == "call_module"
and not isinstance(new_m_itr, torch.nn.Module)):
raise RuntimeError(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
'not reference an nn.Module')
elif (node.op == "get_attr"
and not isinstance(new_m_itr, torch.nn.Module)
and not isinstance(new_m_itr, torch.nn.Parameter)
and atom not in m_itr._buffers):
warnings.warn(f'Node {node} target {node.target} {atom} of {seen_qualname} does '
'not reference an nn.Module, nn.Parameter, or buffer, which is '
'what \'get_attr\' Nodes typically target')
else:
m_itr = new_m_itr
[docs] @compatibility(is_backward_compatible=True)
def eliminate_dead_code(self):
"""
Remove all dead code from the graph, based on each node's number of
users, and whether the nodes have any side effects. The graph must be
topologically sorted before calling.
Returns:
bool: Whether the graph was changed as a result of the pass.
Example:
Before dead code is eliminated, `a` from `a = x + 1` below has no users
and thus can be eliminated from the graph without having an effect.
.. code-block:: python
def forward(self, x):
a = x + 1
return x + self.attr_1
After dead code is eliminated, `a = x + 1` has been removed, and the rest
of `forward` remains.
.. code-block:: python
def forward(self, x):
return x + self.attr_1
.. warning::
Dead code elimination has some heuristics to avoid removing
side-effectful nodes (see Node.is_impure) but in general coverage
is very bad, so you should assume that this method is not sound
to call unless you know that your FX graph consists entirely
of functional operations.
"""
# Lint the graph first to make sure its topologically sorted, otherwise
# DCE below will not behave as expected.
self.lint()
# Reverse iterate so that when we remove a node, any nodes used as an
# input to that node have an updated user count that no longer reflects
# the removed node.
changed = False
for node in reversed(self.nodes):
if not node.is_impure() and len(node.users) == 0:
self.erase_node(node)
changed = True
return changed
[docs] @compatibility(is_backward_compatible=False)
def set_codegen(self, codegen: CodeGen):
self._codegen = codegen
[docs] @compatibility(is_backward_compatible=False)
def on_generate_code(
self,
make_transformer: Callable[[Optional[TransformCodeFunc]], TransformCodeFunc]
):
"""Register a transformer function when python code is generated
Args:
make_transformer (Callable[[Optional[TransformCodeFunc]], TransformCodeFunc]):
a function that returns a code transformer to be registered.
This function is called by `on_generate_code` to obtain the
code transformer.
This function is also given as its input the currently
registered code transformer (or None if nothing is registered),
in case it is not desirable to overwrite it. This is useful to
chain code transformers together.
Returns:
a context manager that when used in a `with` statement, to automatically
restore the previously registered code transformer.
Example:
.. code-block:: python
gm: fx.GraphModule = ...
# This is a code transformer we want to register. This code
# transformer prepends a pdb import and trace statement at the very
# beginning of the generated torch.fx code to allow for manual
# debugging with the PDB library.
def insert_pdb(body):
return ["import pdb; pdb.set_trace()\\n", *body]
# Registers `insert_pdb`, and overwrites the current registered
# code transformer (given by `_` to the lambda):
gm.graph.on_generate_code(
lambda _: insert_pdb
)
# Or alternatively, registers a code transformer which first
# runs `body` through existing registered transformer, then
# through `insert_pdb`:
gm.graph.on_generate_code(
lambda current_trans: (
lambda body: insert_pdb(
current_trans(body) if current_trans
else body
)
)
)
gm.recompile()
gm(*inputs) # drops into pdb
This function can also be used as a context manager, with the benefit to
automatically restores the previously registered code transformer:
.. code-block:: python
# ... continue from previous example
with gm.graph.on_generate_code(lambda _: insert_pdb):
# do more stuff with `gm`...
gm.recompile()
gm(*inputs) # drops into pdb
# now previous code transformer is restored (but `gm`'s code with pdb
# remains - that means you can run `gm` with pdb here too, until you
# run next `recompile()`).
"""
on_gen_code_old = self._codegen._body_transformer
self._codegen._body_transformer = make_transformer(on_gen_code_old)
@contextlib.contextmanager
def on_generate_code_context_manager():
try:
yield
finally:
self._codegen._body_transformer = on_gen_code_old
return on_generate_code_context_manager()
reflectable_magic_methods = {
'add': '{} + {}',
'sub': '{} - {}',
'mul': '{} * {}',
'floordiv': '{} // {}',
'truediv': '{} / {}',
'div': '{} / {}',
'mod': '{} % {}',
'pow': '{} ** {}',
'lshift': '{} << {}',
'rshift': '{} >> {}',
'and_': '{} & {}',
'or_': '{} | {}',
'xor': '{} ^ {}',
'getitem': '{}[{}]',
'matmul': '{} @ {}',
}
magic_methods = dict({
'eq': '{} == {}',
'ne': '{} != {}',
'lt': '{} < {}',
'gt': '{} > {}',
'le': '{} <= {}',
'ge': '{} >= {}',
'pos': '+{}',
'neg': '-{}',
'invert': '~{}'}, **reflectable_magic_methods)
inplace_methods = {
'iadd': '{} += {}',
'iand': '{} &= {}',
'ifloordiv': '{} //= {}',
'ilshift': '{} <<= {}',
'imod': '{} %= {}',
'imul': '{} *= {}',
'imatmul': '{} @= {}',
'ior': '{} |= {}',
'ipow': '{} **= {}',
'irshift': '{} >>= {}',
'isub': '{} -= {}',
'itruediv': '{} /= {}',
'ixor': '{} ^= {}',
'setitem': '{}[{}] = {}',
}
