Source code for torch.fx.proxy
import dis
import torch
import inspect
import operator
from .graph import magic_methods, reflectable_magic_methods, Graph
from typing import Tuple, Dict, Optional, Iterable, Any, Iterator
from .node import Target, Node, Argument, base_types, map_aggregate
class TracerBase:
graph: Graph
def create_node(self, kind : str, target : Target,
args : Tuple[Argument, ...], kwargs : Dict[str, Argument], name : Optional[str] = None,
type_expr : Optional[Any] = None) -> Node:
"""
Inserts a graph node given target, args, kwargs, and name.
This method can be overridden to do extra checking, validation, or
modification of values used in node creation. For example, one might
want to disallow in-place operations from being recorded.
"""
return self.graph.create_node(kind, target, args, kwargs, name, type_expr)
def proxy(self, node: Node) -> 'Proxy':
return Proxy(node, self)
def create_proxy(self, kind: str, target: Target, args: Tuple[Any, ...], kwargs: Dict[str, Any],
name: Optional[str] = None, type_expr : Optional[Any] = None):
'''
Create a Node from the given arguments, then return the Node
wrapped in a Proxy object.
If kind = 'placeholder', then we're creating a Node that
represents the parameter of a function. If we need to encode
a default parameter, we use the ``args`` tuple. ``args`` is
otherwise empty for ``placeholder`` Nodes.
'''
args_ = self.create_arg(args)
kwargs_ = self.create_arg(kwargs)
assert isinstance(args_, tuple)
assert isinstance(kwargs_, dict)
return self.proxy(self.create_node(kind, target, args_, kwargs_, name, type_expr))
def create_arg(self, a: Any) -> Argument:
"""
A method that lowers the objects seen as arguments during symbolic evaluation
into Argument types that can be stored in IR.
Can be override to support more trace-specific types.
"""
# aggregates
if isinstance(a, tuple) and hasattr(a, '_fields'):
# NamedTuple constructors don't seem to like getting a generator
# expression as an argument to their constructor, so build this
# intermediate tuple and unpack it into the NamedTuple constructor
args = tuple(self.create_arg(elem) for elem in a)
return type(a)(*args) # type: ignore
elif isinstance(a, (tuple, list)):
return type(a)(self.create_arg(elem) for elem in a)
elif isinstance(a, dict):
r = {}
for k, v in a.items():
# Check for invalid dict keys. We do not want a Proxy to appear
# anywhere within the key. Since keys can be collection types,
# we iterate through the key with map_aggregate
k = self.create_arg(k)
def no_node(arg):
if isinstance(arg, Node):
raise RuntimeError("Keys for dictionaries used as an argument cannot contain a "
"Node. Got key: {k}")
map_aggregate(k, no_node)
r[k] = self.create_arg(v)
return r
elif isinstance(a, slice):
return slice(self.create_arg(a.start), self.create_arg(a.stop), self.create_arg(a.step))
if isinstance(a, Proxy):
# base case: we unwrap the Proxy object
return a.node
elif isinstance(a, base_types) or a is None or a is ...:
return a
raise NotImplementedError(f"argument of type: {type(a)}")
def to_bool(self, obj: 'Proxy') -> bool:
"""Called when a proxy object is being converted to a boolean, such as
when used in control flow. Normally we don't know what to do because
we don't know the value of the proxy, but a custom tracer can attach more
information to the graph node using create_node and can choose to return a value.
"""
raise TraceError('symbolically traced variables cannot be used as inputs to control flow')
def iter(self, obj: 'Proxy') -> Iterator:
"""Called when a proxy object is being iterated over, such as
when used in control flow. Normally we don't know what to do because
we don't know the value of the proxy, but a custom tracer can attach more
information to the graph node using create_node and can choose to return an iterator.
"""
raise TraceError('Proxy object cannot be iterated. '
'This can be attempted when used in a for loop or as a *args or **kwargs function argument.')
def keys(self, obj: 'Proxy') -> Any:
"""Called when a proxy object is has the keys() method called.
This is what happens when ** is called on a proxy. This should return an
iterator it ** is suppose to work in your custom tracer.
"""
return Attribute(obj, 'keys')()
# used in Proxy object when just appending to the graph while not tracing.
class GraphAppendingTracer(TracerBase):
def __init__(self, graph: Graph):
super().__init__()
self.graph = graph
class TraceError(ValueError):
pass
[docs]class Proxy:
"""
``Proxy`` objects are ``Node`` wrappers that flow through the
program during symbolic tracing and record all the operations
(``torch`` function calls, method calls, operators) that they touch
into the growing FX Graph.
If you're doing graph transforms, you can wrap your own ``Proxy``
method around a raw ``Node`` so that you can use the overloaded
operators to add additional things to a ``Graph``.
"""
def __init__(self, node: Node, tracer: 'Optional[TracerBase]' = None):
if tracer is None:
# This allows you to create a Proxy object around a raw Node
tracer = GraphAppendingTracer(node.graph)
self.tracer = tracer
self.node = node
def __repr__(self) -> str:
return f'Proxy({self.node.name})'
def __getattr__(self, k) -> 'Attribute':
# note: not added to the graph yet, if this is a method call
# we peephole optimize to the method invocation
return Attribute(self, k)
def __call__(self, *args, **kwargs) -> 'Proxy':
return self.tracer.create_proxy('call_method', '__call__', (self,) + args, kwargs)
def __iter__(self) -> Iterable['Proxy']:
frame = inspect.currentframe()
assert frame is not None
calling_frame = frame.f_back
assert calling_frame is not None
inst = list(dis.get_instructions(calling_frame.f_code))[calling_frame.f_lasti // 2]
if inst.opname == 'UNPACK_SEQUENCE':
return (self[i] for i in range(inst.argval)) # type: ignore
return self.tracer.iter(self)
def __bool__(self) -> bool:
return self.tracer.to_bool(self)
def keys(self):
return self.tracer.keys(self)
def __len__(self):
raise RuntimeError("'len' is not supported in symbolic tracing by default. If you want "
"this call to be recorded, please call torch.fx.wrap('len') at "
"module scope")
def __torch_function__(self, orig_method, types, args=None, kwargs=None):
args = args if args else ()
kwargs = kwargs if kwargs else {}
if torch.overrides.is_tensor_method_or_property(orig_method):
return self.tracer.create_proxy('call_method', orig_method.__name__, args, kwargs)
else:
return self.tracer.create_proxy('call_function', orig_method, args, kwargs,
name=self.tracer.graph._target_to_str(orig_method.__name__))
class Attribute(Proxy):
def __init__(self, root: Proxy, attr: str):
self.root = root
self.attr = attr
self.tracer = root.tracer
self._node: Optional[Node] = None
@property
def node(self):
# the node for attributes is added lazily, since most will just be method calls
# which do not rely on the getitem call
if self._node is None:
self._node = self.tracer.create_proxy('call_function', getattr, (self.root, self.attr), {}).node
return self._node
def __call__(self, *args, **kwargs):
return self.tracer.create_proxy('call_method', self.attr, (self.root,) + args, kwargs)
for method in magic_methods:
def scope(method):
def impl(*args, **kwargs):
tracer = args[0].tracer
target = getattr(operator, method)
return tracer.create_proxy('call_function', target, args, kwargs)
impl.__name__ = method
as_magic = f'__{method}__'
setattr(Proxy, as_magic, impl)
scope(method)
def _define_reflectable(orig_method_name):
method_name = f'__r{orig_method_name}__'
def impl(self, rhs):
target = getattr(operator, orig_method_name)
return self.tracer.create_proxy('call_function', target, (rhs, self), {})
impl.__name__ = method_name
impl.__qualname__ = method_name
setattr(Proxy, method_name, impl)
for orig_method_name in reflectable_magic_methods:
_define_reflectable(orig_method_name)
