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android / platform / external / pytorch / 6de28e92d2c8b90823a2a29d06759040f64a5a28 / . / torch / fx / _symbolic_trace.py
blob: ca9ac78279fcabaee30737cd775c15db8a7def4c [file] [log] [blame]
import builtins
import copy
import functools
import inspect
import math
import os
import warnings
import collections
from itertools import chain
from types import CodeType, FunctionType, ModuleType
from typing import (
Any,
Callable,
Dict,
List,
NamedTuple,
Optional,
Set,
Tuple,
Type,
Union,
)
import torch
import torch.utils._pytree as pytree
from torch._C import ScriptObject # type: ignore[attr-defined]
from ._compatibility import compatibility
from .graph import _PyTreeCodeGen, _PyTreeInfo, Graph
from .graph_module import GraphModule
from .node import Argument, base_types, map_aggregate
from .proxy import ParameterProxy, Proxy, TracerBase, Scope, ScopeContextManager
HAS_VARSTUFF = inspect.CO_VARARGS | inspect.CO_VARKEYWORDS
# These need to run in global scope to handle nested calls correctly
_orig_module_call: Callable = torch.nn.Module.__call__
_orig_module_getattr: Callable = torch.nn.Module.__getattr__
_proxyable_classes: Dict[Type, None] = {}
_is_fx_tracing_flag = False
def is_fx_tracing():
return _is_fx_tracing_flag
@compatibility(is_backward_compatible=True)
class ProxyableClassMeta(type):
"""
ProxyableClassMeta allows you to make construction of a given Python class
symbolically traceable. For example::
import torch
import torch.fx
class TensorPair(metaclass=torch.fx.ProxyableClassMeta):
def __init__(self, left, right):
self.left, self.right = left, right
def add(self, other):
l = self.left + other.left
r = self.right + other.right
return TensorPair(l, r)
def mul(self, other):
l = self.left * other.left
r = self.right * other.right
return TensorPair(l, r)
def use_tensor_pair_ctor(x : TensorPair, y : torch.Tensor):
s = x.add(TensorPair(y, y))
return s.mul(x)
x = TensorPair(torch.randn(5, 3), torch.randn(5, 3))
y = torch.randn(5, 3)
ref_out = use_tensor_pair_ctor(x, y)
traced = torch.fx.symbolic_trace(use_tensor_pair_ctor)
print(traced.code)
'''
def forward(self, x : __main___TensorPair, y : torch.Tensor):
tensor_pair = __main___TensorPair(y, y); y = None
add = x.add(tensor_pair); tensor_pair = None
mul = add.mul(x); add = x = None
return mul
'''
From this example, we can see that construction of a class (``TensorPair``)
defined with ``ProxyableClassMeta`` as metaclass can be recorded in symbolic
tracing.
"""
def __init__(cls, name, bases, attrs):
_proxyable_classes.setdefault(cls)
super().__init__(name, bases, attrs)
def __call__(cls, *args, **kwargs):
instance = cls.__new__(cls) # type: ignore[call-overload]
if not is_fx_tracing():
cls.__init__(instance, *args, **kwargs) # type: ignore[misc]
return instance
found_proxies = []
def check_proxy(a):
if isinstance(a, Proxy):
found_proxies.append(a)
map_aggregate(args, check_proxy)
map_aggregate(kwargs, check_proxy)
if len(found_proxies) != 0:
tracer = found_proxies[0].tracer
return tracer.create_proxy("call_function", cls, args, kwargs)
else:
cls.__init__(instance, *args, **kwargs) # type: ignore[misc]
return instance
def _patch_function(fn: FunctionType, nargs: int) -> FunctionType:
co = fn.__code__
co_flags = co.co_flags & ~HAS_VARSTUFF
co_args: tuple
if hasattr(co, "co_qualname"):
# Python-3.11+ code signature
co_args = (
nargs,
0,
0,
co.co_nlocals,
co.co_stacksize,
co_flags,
co.co_code,
co.co_consts,
co.co_names,
co.co_varnames,
co.co_filename,
co.co_name,
co.co_qualname, # type: ignore[attr-defined]
co.co_firstlineno,
co.co_lnotab,
co.co_exceptiontable, # type: ignore[attr-defined]
co.co_freevars,
co.co_cellvars,
)
elif hasattr(co, "co_posonlyargcount"):
co_args = (
nargs,
0,
0,
co.co_nlocals,
co.co_stacksize,
co_flags,
co.co_code,
co.co_consts,
co.co_names,
co.co_varnames,
co.co_filename,
co.co_name,
co.co_firstlineno,
co.co_lnotab,
co.co_freevars,
co.co_cellvars,
)
else:
co_args = (
nargs,
0,
co.co_nlocals,
co.co_stacksize,
co_flags,
co.co_code,
co.co_consts,
co.co_names,
co.co_varnames,
co.co_filename,
co.co_name,
co.co_firstlineno,
co.co_lnotab,
co.co_freevars,
co.co_cellvars,
)
new_code = CodeType(*co_args) # type: ignore[arg-type]
return FunctionType(
new_code, fn.__globals__, fn.__name__, fn.__defaults__, fn.__closure__
)
# we need to insert placeholder nodes for *args and **kwargs
# we can't call this function normally, otherwise it would try to unpack them
# instead, let's make python think that args and kwargs are normal variables
@compatibility(is_backward_compatible=False)
class PHBase:
"""
Object representing an input placeholder to `concrete_args`
"""
def __repr__(self):
return "PH"
PH = PHBase()
@compatibility(is_backward_compatible=False)
class PHWithMeta(PHBase):
"""
Object representing an input placeholder to `concrete_args`
"""
def __init__(self, ph_key: Optional[str] = None):
super().__init__()
# Provide a hey for user to identify placeholder node during analysis
self.ph_key = ph_key
def _transfer_attrs(fr, to):
for attr_name in dir(fr):
attr_val = getattr(fr, attr_name)
if (
not callable(attr_val)
and not attr_name.startswith("__")
and not hasattr(to, attr_name)
):
setattr(to, attr_name, attr_val)
@compatibility(is_backward_compatible=True)
class Tracer(TracerBase):
# Reference: https://github.com/pytorch/pytorch/issues/54354
# The first line of this docstring overrides the one Sphinx generates for the
# documentation. We need it so that Sphinx doesn't leak `math`s path from the
# build environment (e.g. `<module 'math' from '/leaked/path').
"""Tracer(autowrap_modules=(math,), autowrap_functions=())
``Tracer`` is the class that implements the symbolic tracing functionality
of ``torch.fx.symbolic_trace``. A call to ``symbolic_trace(m)`` is equivalent
to ``Tracer().trace(m)``.
Tracer can be subclassed to override various behaviors of the tracing
process. The different behaviors that can be overridden are described
in the docstrings of the methods on this class.
"""
# Not checking BC on this API because the default value for `autowrap_modules`
# includes the local filepath to the `math` module, which would jitter
# across machines.
@compatibility(is_backward_compatible=True)
def __init__(
self,
autowrap_modules: Tuple[ModuleType] = (math,),
autowrap_functions: Tuple[Callable, ...] = (),
param_shapes_constant: bool = False,
) -> None:
# This method's signature is overridden by the first line of this class'
# docstring. If this method's signature is modified, the signature that
# overrides it also should be modified accordingly.
"""
Construct a Tracer object.
Args:
autowrap_modules (Tuple[ModuleType]): defaults to `(math, )`,
Python modules whose functions should be wrapped automatically
without needing to use fx.wrap(). Backward-compatibility for
this parameter is guaranteed.
autowrap_functions (Tuple[Callable, ...]): defaults to `()`,
Python functions that should be wrapped automatically without
needing to use fx.wrap(). Backward compatibility for this
parameter is guaranteed.
param_shapes_constant (bool): When this flag is set, calls to shape,
size and a few other shape like attributes of a module's parameter
will be evaluated directly, rather than returning a new Proxy value
for an attribute access. Backward compatibility for this parameter
is guaranteed.
"""
super().__init__()
# Functions we will eagerly wrap when we see them while tracing
# this captures both `math.sqrt()` and `from math import sqrt` automatically
self._autowrap_function_ids: Set[int] = {
id(value)
for name, value in chain(*[m.__dict__.items() for m in autowrap_modules])
if not name.startswith("_") and callable(value)
}
self._autowrap_function_ids.update({id(f) for f in autowrap_functions})
# Python modules to apply autowrap to at the start, in addition to
# modules we see while tracing
self._autowrap_search: List[ModuleType] = list(autowrap_modules)
self.param_shapes_constant = param_shapes_constant
self.submodule_paths: Optional[Dict[torch.nn.Module, str]] = None
self.root_module_name: str = ""
# Maps the containing module's name to the operator name
self.scope = Scope("", None)
# Records the module call stack
self.module_stack = collections.OrderedDict()
# Mapping of node name to module scope
self.node_name_to_scope: Dict[str, Tuple[str, type]] = {}
@compatibility(is_backward_compatible=True)
def create_arg(self, a: Any) -> "Argument":
"""
A method to specify the behavior of tracing when preparing values to
be used as arguments to nodes in the ``Graph``.
By default, the behavior includes:
#. Iterate through collection types (e.g. tuple, list, dict) and recursively
call ``create_args`` on the elements.
#. Given a Proxy object, return a reference to the underlying IR ``Node``
#. Given a non-Proxy Tensor object, emit IR for various cases:
* For a Parameter, emit a ``get_attr`` node referring to that Parameter
* For a non-Parameter Tensor, store the Tensor away in a special
attribute referring to that attribute.
This method can be overridden to support more types.
Args:
a (Any): The value to be emitted as an ``Argument`` in the ``Graph``.
Returns:
The value ``a`` converted into the appropriate ``Argument``
"""
# The base tracer is used to construct Graphs when there is no associated
# module hierarchy, so it can never create parameter references.
# The default tracer adds the ability to refer to parameters when
# tracing modules.
if isinstance(a, torch.nn.Parameter):
for n, p in self.root.named_parameters():
if a is p:
return self.create_node("get_attr", n, (), {})
raise NameError("parameter is not a member of this module")
elif isinstance(a, torch.Tensor):
for n_, p_ in self.root.named_buffers():
if a is p_:
return self.create_node("get_attr", n_, (), {})
elif isinstance(a, torch.nn.Module):
for n_, p_ in self.root.named_modules():
if a is p_:
return self.create_node("get_attr", n_, (), {})
# For NamedTuple instances that appear literally as args, we emit
# a node to construct the NamedTuple and use that Node as the argument.
if isinstance(a, tuple) and hasattr(a, "_fields"):
args = tuple(self.create_arg(elem) for elem in a)
return self.create_node("call_function", a.__class__, args, {})
# Tensors do not have a reliable string repr() from which they can be
# constructed (and we probably don't want to rely on that, either), so
# for any constant Tensor values we encounter, first search for if they
# are an attribute of some module in the module hierarchy. If so, emit
# a get_attr to retrieve that tensor. Otherwise, we'll store away the
# tensor value into a special attribute on the Module s.t. we can
# retrieve it with a get_attr.
if isinstance(a, (torch.Tensor, ScriptObject)):
qualname: Optional[str] = self.tensor_attrs.get(a)
# Tensor was not found in the Module hierarchy, stow it away in a
# special attribute and set the qualname to refer to that
if not qualname:
i = 0
while True:
qualname = f"_tensor_constant{i}"
if not hasattr(self.root, qualname):
break
i += 1
self.tensor_attrs[a] = qualname
setattr(self.root, qualname, a)
return self.create_node("get_attr", qualname, (), {})
if type(a) in _proxyable_classes:
# This is an instance of a proxyable class for which we did not
# witness its construction. Intern this as a constant attribute
# TODO: binary search
i = 0
while True:
qualname = f"_{a.__class__.__name__}_constant_{i}"
if not hasattr(self.root, qualname):
break
i += 1
setattr(self.root, qualname, a)
return self.create_node("get_attr", qualname, (), {})
return super().create_arg(a)
@compatibility(is_backward_compatible=True)
def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
"""
A method to specify whether a given ``nn.Module`` is a "leaf" module.
Leaf modules are the atomic units that appear in
the IR, referenced by ``call_module`` calls. By default,
Modules in the PyTorch standard library namespace (torch.nn)
are leaf modules. All other modules are traced through and
their constituent ops are recorded, unless specified otherwise
via this parameter.
Args:
m (Module): The module being queried about
module_qualified_name (str): The path to root of this module. For example,
if you have a module hierarchy where submodule ``foo`` contains
submodule ``bar``, which contains submodule ``baz``, that module will
appear with the qualified name ``foo.bar.baz`` here.
"""
return (
(m.__module__.startswith("torch.nn") or m.__module__.startswith("torch.ao.nn"))
and not isinstance(m, torch.nn.Sequential)
)
@compatibility(is_backward_compatible=True)
def path_of_module(self, mod: torch.nn.Module) -> str:
"""
Helper method to find the qualified name of ``mod`` in the Module hierarchy
of ``root``. For example, if ``root`` has a submodule named ``foo``, which has
a submodule named ``bar``, passing ``bar`` into this function will return
the string "foo.bar".
Args:
mod (str): The ``Module`` to retrieve the qualified name for.
"""
# Prefer the O(1) algorithm
if self.submodule_paths:
path = self.submodule_paths.get(mod)
if path is None:
raise NameError("module is not installed as a submodule")
assert isinstance(path, str)
return path
# O(N^2) fallback in the case that we didn't store the submodule
# paths.
else:
for n, p in self.root.named_modules():
if mod is p:
return n
raise NameError("module is not installed as a submodule")
@compatibility(is_backward_compatible=True)
def call_module(
self,
m: torch.nn.Module,
forward: Callable[..., Any],
args: Tuple[Any, ...],
kwargs: Dict[str, Any],
) -> Any:
"""
Method that specifies the behavior of this ``Tracer`` when it encounters
a call to an ``nn.Module`` instance.
By default, the behavior is to check if the called module is a leaf module
via ``is_leaf_module``. If it is, emit a ``call_module`` node referring to
``m`` in the ``Graph``. Otherwise, call the ``Module`` normally, tracing through
the operations in its ``forward`` function.
This method can be overridden to--for example--create nested traced
GraphModules, or any other behavior you would want while tracing across
``Module`` boundaries.
Args:
m (Module): The module for which a call is being emitted
forward (Callable): The forward() method of the ``Module`` to be invoked
args (Tuple): args of the module callsite
kwargs (Dict): kwargs of the module callsite
Return:
The return value from the Module call. In the case that a ``call_module``
node was emitted, this is a ``Proxy`` value. Otherwise, it is whatever
value was returned from the ``Module`` invocation.
"""
module_qualified_name = self.path_of_module(m)
with ScopeContextManager(self.scope, Scope(module_qualified_name, type(m))) as _scope:
# module_stack is an ordered dict so writing then deleting the
# entry is equivalent to push/pop on a list
self.module_stack[_scope.module_path] = (module_qualified_name, _scope.module_type)
if not self.is_leaf_module(m, module_qualified_name):
ret_val = forward(*args, **kwargs)
else:
ret_val = self.create_proxy("call_module", module_qualified_name, args, kwargs)
key, _ = self.module_stack.popitem(last=True)
assert key == _scope.module_path, f" Unexpected key {key}"
return ret_val
@compatibility(is_backward_compatible=False)
def getattr(self, attr: str, attr_val: Any, parameter_proxy_cache: Dict[str, Any]):
"""
Method that specifies the behavior of this ``Tracer`` when we call getattr
on a call to an ``nn.Module`` instance.
By default, the behavior is to return a proxy value for the attribute. It
also stores the proxy value in the ``parameter_proxy_cache``, so that future
calls will reuse the proxy rather than creating a new one.
This method can be overridden to --for example-- not return proxies when
querying parameters.
Args:
attr (str): The name of the attribute being queried
attr_val (Any): The value of the attribute
parameter_proxy_cache (Dict[str, Any]): A cache of attr names to proxies
Return:
The return value from the getattr call.
"""
def maybe_get_proxy_for_attr(
attr_val, collection_to_search, parameter_proxy_cache
):
for n, p in collection_to_search:
if attr_val is p:
if n not in parameter_proxy_cache:
kwargs = {}
if (
"proxy_factory_fn"
in inspect.signature(self.create_proxy).parameters
):
kwargs["proxy_factory_fn"] = (
None
if not self.param_shapes_constant
else lambda node: ParameterProxy(
self, node, n, attr_val
)
)
val_proxy = self.create_proxy("get_attr", n, (), {}, **kwargs) # type: ignore[arg-type]
parameter_proxy_cache[n] = val_proxy
return parameter_proxy_cache[n]
return None
if isinstance(attr_val, torch.nn.Parameter):
maybe_parameter_proxy = maybe_get_proxy_for_attr(
attr_val, self.root.named_parameters(), parameter_proxy_cache
)
if maybe_parameter_proxy is not None:
return maybe_parameter_proxy
if self.proxy_buffer_attributes and isinstance(attr_val, torch.Tensor):
maybe_buffer_proxy = maybe_get_proxy_for_attr(
attr_val, self.root.named_buffers(), parameter_proxy_cache
)
if maybe_buffer_proxy is not None:
return maybe_buffer_proxy
return attr_val
# This method will be refactored
@compatibility(is_backward_compatible=False)
def create_args_for_root(self, root_fn, is_module, concrete_args=None):
"""
Create ``placeholder`` nodes corresponding to the signature of the ``root``
Module. This method introspects root's signature and emits those
nodes accordingly, also supporting ``*args`` and ``**kwargs``.
"""
# In some cases, a function or method has been decorated with a wrapper
# defined via ``functools.wraps``. In this case, the outer code object
# will likely not contain the actual parameters we care about, so unwrap
# the function to get to the innermost callable.
fn_for_analysis = inspect.unwrap(root_fn)
co = fn_for_analysis.__code__
total_args = co.co_argcount + co.co_kwonlyargcount
orig_args = list(co.co_varnames)
names_iter = iter(co.co_varnames)
args: List[Any] = []
skip_arg_idx = 0
if is_module:
if total_args == 0:
raise RuntimeError(
"``self`` argument cannot be part of *args expansion!"
)
skip_arg_idx = 1
next(names_iter) # skip self
args.append(self.root)
sig = inspect.signature(fn_for_analysis)
def proxy_placeholder(name: str):
if concrete_args is not None and name in concrete_args:
cnt = 0
def replace_ph(x):
nonlocal cnt
cnt += 1
param = sig.parameters[name]
default = (
()
if param.default is inspect.Parameter.empty
else (param.default,)
)
out = self.create_proxy(
"placeholder", f"{name}_{str(cnt)}", default, {}
)
if isinstance(x, PHBase):
if x != PH:
# Transfer attrs in the case where you're using a placeholder other
# than the singleton PH (PH has no attributes to transfer).
# Proxies were created out of the placeholders.
# Transfer any metadata (put on the placeholders in the form of
# attributes set by the user) from the placeholder to the
# underlying nodes (the proxy is unwrapped by the user, but
# the metadata should hold).
_transfer_attrs(fr=x, to=out.node)
return out
# Union[int, bool] == bool in Python <= 3.6
if (
type(x) == bool
or type(x) in base_types
and type(x) != torch.Tensor
):
torch._assert(
out == x,
f"{name} has been specialized to have value {x} but got another value",
)
elif type(x) == type(None):
args = (
out,
f"{name} has been specialized to have value None but got another value",
)
self.create_proxy("call_function", _assert_is_none, args, {})
else:
warnings.warn(
f"Was not able to add assertion to guarantee correct input {name} to "
f"specialized function. It is up to the user to make sure that your inputs match the "
f"inputs you specialized the function with."
)
return x
return pytree.tree_map(replace_ph, concrete_args[name])
if name[0] == "*":
default = ()
else:
param = sig.parameters[name]
default = () if param.default is inspect.Parameter.empty else (param.default,) # type: ignore[assignment]
return self.create_proxy(
"placeholder",
name,
default,
{},
type_expr=fn_for_analysis.__annotations__.get(name, None)
)
# This covers the very specific case where we are passing in flat
# concrete_args as a tuple, but our traced fn takes (*args, **kwargs).
# In this case, just take the concrete_args and pass them through.
name_idx = 0
if isinstance(concrete_args, tuple) and \
len(concrete_args) > 0 and \
(co.co_flags & HAS_VARSTUFF) and \
total_args == 1:
for concrete_arg in concrete_args:
out = self.create_proxy("placeholder", f"input_{name_idx}", (), {})
if isinstance(concrete_arg, PHBase):
if concrete_arg != PH:
# Transfer attrs in the case where you're using a placeholder other
# than the singleton PH (PH has no attributes to transfer).
# Proxies were created out of the placeholders.
# Transfer any metadata (put on the placeholders in the form of
# attributes set by the user) from the placeholder to the
# underlying nodes (the proxy is unwrapped by the user, but
# the metadata should hold).
_transfer_attrs(fr=concrete_arg, to=out.node)
args.append(out)
name_idx += 1
return root_fn, args
arg_names = [next(names_iter) for idx in range(skip_arg_idx, total_args)]
if isinstance(concrete_args, tuple):
if len(arg_names) != len(concrete_args):
raise RuntimeError(
f"Tracing expected {len(arg_names)} arguments but got {len(concrete_args)} concrete arguments"
)
concrete_args = dict(zip(arg_names, concrete_args))
args.extend(proxy_placeholder(names) for names in arg_names)
if co.co_kwonlyargcount > 0 or co.co_flags & HAS_VARSTUFF:
# TODO: type annotations for *args and **kwargs
if co.co_flags & inspect.CO_VARARGS:
args.append(proxy_placeholder("*" + next(names_iter)))
if co.co_flags & inspect.CO_VARKEYWORDS:
args.append(proxy_placeholder("**" + next(names_iter)))
root_fn = _patch_function(root_fn, len(args))
flat_args, in_spec = pytree.tree_flatten(tuple(args))
if any(not isinstance(i, pytree.LeafSpec) for i in in_spec.children_specs):
# In the case that we have pytree-flattened inputs in
# `concrete_args`, generate a flattening wrapper around the
# original root function and return that.
self.graph._codegen = _PyTreeCodeGen(
_PyTreeInfo(orig_args[:total_args], in_spec, None)
)
def flatten_fn(*args):
tree_args = pytree.tree_unflatten(list(args), in_spec)
tree_out = root_fn(*tree_args)
out_args, out_spec = pytree.tree_flatten(tree_out)
assert isinstance(self.graph._codegen, _PyTreeCodeGen)
self.graph._codegen.pytree_info = (
self.graph._codegen.pytree_info._replace(out_spec=out_spec)
)
return out_args
return flatten_fn, flat_args
return root_fn, args
@compatibility(is_backward_compatible=True)
def trace(
self,
root: Union[torch.nn.Module, Callable[..., Any]],
concrete_args: Optional[Dict[str, Any]] = None,
) -> Graph:
"""
Trace ``root`` and return the corresponding FX ``Graph`` representation. ``root``
can either be an ``nn.Module`` instance or a Python callable.
Note that after this call, ``self.root`` may be different from the ``root`` passed
in here. For example, when a free function is passed to ``trace()``, we will
create an ``nn.Module`` instance to use as the root and add embedded constants
to.
Args:
root (Union[Module, Callable]): Either a ``Module`` or a function to be
traced through. Backwards-compatibility for this parameter is
guaranteed.
concrete_args (Optional[Dict[str, any]]): Concrete arguments that should
not be treated as Proxies. This parameter is experimental and
its backwards-compatibility is *NOT* guaranteed.
Returns:
A ``Graph`` representing the semantics of the passed-in ``root``.
"""
global _is_fx_tracing_flag
old_is_fx_tracing_flag = _is_fx_tracing_flag
_is_fx_tracing_flag = True
try:
if isinstance(root, torch.nn.Module):
self.root = root
assert hasattr(
type(root), self.traced_func_name
), f"traced_func_name={self.traced_func_name} doesn't exist in {type(root).__name__}"
fn = getattr(type(root), self.traced_func_name)
self.root_module_name = root._get_name()
self.submodule_paths = {mod: name for name, mod in root.named_modules()}
else:
self.root = torch.nn.Module()
fn = root
tracer_cls: Optional[Type[Tracer]] = getattr(self, "__class__", None)
self.graph = Graph(tracer_cls=tracer_cls)
if hasattr(fn, '__code__'):
code = fn.__code__
self.graph._co_fields = {
'co_name': code.co_name,
'co_filename': code.co_filename,
'co_firstlineno': code.co_firstlineno,
}
# When we encounter a Tensor value that's not a parameter, we look if it
# is some other attribute on the model. Construct a dict mapping Tensor
# values to the qualified name here for efficiency. This is used downstream
# in create_arg
self.tensor_attrs: Dict[Union[torch.Tensor, ScriptObject], str] = {}
def collect_tensor_attrs(m: torch.nn.Module, prefix_atoms: List[str]):
for k, v in m.__dict__.items():
if isinstance(v, (torch.Tensor, ScriptObject)):
self.tensor_attrs[v] = ".".join(prefix_atoms + [k])
for k, v in m.named_children():
collect_tensor_attrs(v, prefix_atoms + [k])
collect_tensor_attrs(self.root, [])
assert isinstance(fn, FunctionType)
fn_globals = fn.__globals__ # run before it gets patched
fn, args = self.create_args_for_root(
fn, isinstance(root, torch.nn.Module), concrete_args
)
parameter_proxy_cache: Dict[
str, Proxy
] = {} # Reduce number of get_attr calls
# Method dispatch on parameters is not recorded unless it's directly used.
# Thus, we need to insert a proxy when __getattr__ requests a parameter.
@functools.wraps(_orig_module_getattr)
def module_getattr_wrapper(mod, attr):
attr_val = _orig_module_getattr(mod, attr)
return self.getattr(attr, attr_val, parameter_proxy_cache)
@functools.wraps(_orig_module_call)
def module_call_wrapper(mod, *args, **kwargs):
def forward(*args, **kwargs):
return _orig_module_call(mod, *args, **kwargs)
_autowrap_check(
patcher,
getattr(getattr(mod, "forward", mod), "__globals__", {}),
self._autowrap_function_ids,
)
return self.call_module(mod, forward, args, kwargs)
with _Patcher() as patcher:
# allow duplicate patches to support the case of nested calls
patcher.patch_method(
torch.nn.Module,
"__getattr__",
module_getattr_wrapper,
deduplicate=False,
)
patcher.patch_method(
torch.nn.Module, "__call__", module_call_wrapper, deduplicate=False
)
_patch_wrapped_functions(patcher)
_autowrap_check(patcher, fn_globals, self._autowrap_function_ids)
for module in self._autowrap_search:
_autowrap_check(
patcher, module.__dict__, self._autowrap_function_ids
)
self.create_node(
"output",
"output",
(self.create_arg(fn(*args)),),
{},
type_expr=fn.__annotations__.get("return", None),
)
self.submodule_paths = None
finally:
_is_fx_tracing_flag = old_is_fx_tracing_flag
return self.graph
def __deepcopy__(self, memo):
# _autowrap_search contains modules, which cannot be deepcopied.
new_tracer = Tracer.__new__(Tracer)
for k, v in self.__dict__.items():
if k in {'_autowrap_search'}:
new_obj = copy.copy(v)
else:
new_obj = copy.deepcopy(v, memo)
new_tracer.__dict__[k] = new_obj
return new_tracer
# Dictionary of (id(globals dict), function name) => globals_dict to patch for
# the purposes of the wrap() API.
# We key by the globals dict id and function name to ensure we're wrapping a given
# function only once.
_wrapped_fns_to_patch: Dict[Tuple[int, str], dict] = {}
# List of methods on classes to wrap (class type, function name)
# this currently only works for Tensor.* methods that aren't traced properly
_wrapped_methods_to_patch: List[Tuple[type, str]] = []
if os.environ.get("FX_PATCH_GETITEM") == "1":
# This change is needed to trace models like PositionalEmbedding from BERT:
# https://github.com/pytorch/benchmark/blob/master/torchbenchmark/models/BERT_pytorch/bert_pytorch/model/embedding/position.py
# but causes issues in quantization documented here:
# https://github.com/pytorch/pytorch/issues/50710
# once that is fixed we can make this the default behavior.
_wrapped_methods_to_patch.append((torch.Tensor, "__getitem__"))
def _find_proxy(*objects_to_search):
"""
Recursively search a data structure for a Proxy() and return it,
return None if not found.
"""
proxy = None
def find_proxy(x):
nonlocal proxy
if isinstance(x, Proxy):
proxy = x
map_aggregate(objects_to_search, find_proxy)
return proxy
def _create_wrapped_func(orig_fn):
@functools.wraps(orig_fn)
def wrapped(*args, **kwargs):
"""
Given an closed-over ``orig_function`` to invoke, search the args and kwargs for
a Proxy object. If there is one, emit a ``call_function`` node to preserve the
call to this leaf function directly. Otherwise, just return the results of
this function call, as this function is not being traced.
"""
proxy = _find_proxy(args, kwargs)
if proxy is not None:
return_proxy = proxy.tracer.create_proxy(
"call_function", orig_fn, args, kwargs
)
return_proxy.node.meta["is_wrapped"] = True
return return_proxy
return orig_fn(*args, **kwargs)
return wrapped
def _create_wrapped_method(cls, name):
orig_fn = getattr(cls, name)
@functools.wraps(orig_fn)
def wrapped(*args, **kwargs):
"""
Search the args and kwargs for a Proxy object. If there is one,
emit a ``call_method`` node to preserve the call to this method
directly. Otherwise, just return the results of this function
call, as this function is not being traced.
"""
proxy = _find_proxy(args, kwargs)
if proxy is not None:
return proxy.tracer.create_proxy("call_method", name, args, kwargs)
return orig_fn(*args, **kwargs)
return wrapped
class _PatchedFn(NamedTuple):
frame_dict: Any
fn_name: str
orig_fn: Any
def revert(self):
raise NotImplementedError()
class _PatchedFnSetItem(_PatchedFn):
def revert(self):
self.frame_dict[self.fn_name] = self.orig_fn
class _PatchedFnDel(_PatchedFn):
def revert(self):
del self.frame_dict[self.fn_name]
class _PatchedFnSetAttr(_PatchedFn):
def revert(self):
setattr(self.frame_dict, self.fn_name, self.orig_fn)
class _Patcher:
def __init__(self):
super().__init__()
self.patches_made: List[_PatchedFn] = []
self.visited: Set[int] = set()
def patch(
self,
frame_dict: Dict[str, Any],
name: str,
new_fn: Callable,
deduplicate: bool = True,
):
"""
Replace frame_dict[name] with new_fn until we exit the context manager.
"""
new_fn.__fx_already_patched = deduplicate # type: ignore[attr-defined]
if name not in frame_dict and hasattr(builtins, name):
self.patches_made.append(_PatchedFnDel(frame_dict, name, None))
elif getattr(frame_dict[name], "__fx_already_patched", False):
return # already patched, no need to do it again
else:
self.patches_made.append(
_PatchedFnSetItem(frame_dict, name, frame_dict[name])
)
frame_dict[name] = new_fn
def patch_method(
self, cls: type, name: str, new_fn: Callable, deduplicate: bool = True
):
"""
Replace object_or_dict.name with new_fn until we exit the context manager.
"""
new_fn.__fx_already_patched = deduplicate # type: ignore[attr-defined]
orig_fn = getattr(cls, name)
if getattr(orig_fn, "__fx_already_patched", False):
return # already patched, no need to do it again
self.patches_made.append(_PatchedFnSetAttr(cls, name, orig_fn))
setattr(cls, name, new_fn)
def visit_once(self, thing: Any):
"""Return True on the first call to with thing, otherwise false"""
idx = id(thing)
if idx in self.visited:
return False
self.visited.add(idx)
return True
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
"""
Undo all the changes made via self.patch() and self.patch_method()
"""
while self.patches_made:
# unpatch in reverse order to handle duplicates correctly
self.patches_made.pop().revert()
self.visited.clear()
def _patch_wrapped_functions(patcher: _Patcher):
"""
Go through ``_wrapped_fn_patch_table`` and, for each frame object, wrap
the listed global functions in the `_create_wrapped_func` wrapper.
"""
for (_, name), frame_dict in _wrapped_fns_to_patch.copy().items():
if name not in frame_dict and hasattr(builtins, name):
orig_fn = getattr(builtins, name)
else:
orig_fn = frame_dict[name]
patcher.patch(frame_dict, name, _create_wrapped_func(orig_fn))
for cls, name in _wrapped_methods_to_patch:
patcher.patch_method(cls, name, _create_wrapped_method(cls, name))
def _autowrap_check(
patcher: _Patcher, frame_dict: Dict[str, Any], function_ids: Set[int]
):
"""
Some methods, like `math.sqrt` are common enough we want to automatically wrap them as we see them.
This method searches a scope for them and patches them if found.
"""
if patcher.visit_once(frame_dict):
for name, value in frame_dict.items():
if (
not name.startswith("_")
and callable(value)
and id(value) in function_ids
):
patcher.patch(frame_dict, name, _create_wrapped_func(value))
@compatibility(is_backward_compatible=True)
def wrap(fn_or_name: Union[str, Callable]):
"""
This function can be called at module-level scope to register fn_or_name as a "leaf function".
A "leaf function" will be preserved as a CallFunction node in the FX trace instead of being
traced through::
# foo/bar/baz.py
def my_custom_function(x, y):
return x * x + y * y
torch.fx.wrap('my_custom_function')
def fn_to_be_traced(x, y):
# When symbolic tracing, the below call to my_custom_function will be inserted into
# the graph rather than tracing it.
return my_custom_function(x, y)
This function can also equivalently be used as a decorator::
# foo/bar/baz.py
@torch.fx.wrap
def my_custom_function(x, y):
return x * x + y * y
A wrapped function can be thought of a "leaf function", analogous to the concept of
"leaf modules", that is, they are functions that are left as calls in the FX trace
rather than traced through.
Args:
fn_or_name (Union[str, Callable]): The function or name of the global function to insert into the
graph when it's called
"""
if not callable(fn_or_name) and not isinstance(fn_or_name, str):
raise RuntimeError(
"Unsupported type for global function! Must be either a callable or "
"string name"
)
if callable(fn_or_name):
assert not isinstance(fn_or_name, str) # to make mypy happy
fn_name = fn_or_name.__name__
else:
assert isinstance(
fn_or_name, str
), "fn_or_name must be a global function or string name"
fn_name = fn_or_name
currentframe = inspect.currentframe()
assert currentframe is not None
f = currentframe.f_back
assert f is not None
if f.f_code.co_name != "<module>":
raise NotImplementedError("wrap must be called at the top level of a module")
# consider implementing Callable version of this via _autowrap_function_ids / _autowrap_search
# semantics would be slightly different, but would add support `from x import wrapped_function`
_wrapped_fns_to_patch[(id(f.f_globals), fn_name)] = f.f_globals
return fn_or_name
@compatibility(is_backward_compatible=True)
def symbolic_trace(
root: Union[torch.nn.Module, Callable[..., Any]],
concrete_args: Optional[Dict[str, Any]] = None,
) -> GraphModule:
"""
Symbolic tracing API
Given an ``nn.Module`` or function instance ``root``, this function will return a ``GraphModule``
constructed by recording operations seen while tracing through ``root``.
``concrete_args`` allows you to partially specialize your function, whether it's to remove control flow or data structures.
For example::
def f(a, b):
if b == True:
return a
else:
return a*2
FX can typically not trace through this due to the presence of control
flow. However, we can use `concrete_args` to specialize on the value of
`b` to trace through this::
f = fx.symbolic_trace(f, concrete_args={'b': False})
assert f(3, False) == 6
Note that although you can still pass in different values of `b`, they will be ignored.
We can also use `concrete_args` to eliminate data-structure handling from
our function. This will use pytrees to flatten your input. To avoid
overspecializing, pass in `fx.PH` for values that shouldn't be
specialized. For example::
def f(x):
out = 0
for v in x.values():
out += v
return out
f = fx.symbolic_trace(f, concrete_args={'x': {'a': fx.PH, 'b': fx.PH, 'c': fx.PH}})
assert f({'a': 1, 'b': 2, 'c': 4}) == 7
Args:
root (Union[torch.nn.Module, Callable]): Module or function to be traced and converted
into a Graph representation.
concrete_args (Optional[Dict[str, any]]): Inputs to be partially specialized
Returns:
GraphModule: a Module created from the recorded operations from ``root``.
"""
tracer = Tracer()
graph = tracer.trace(root, concrete_args)
name = (
root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__
)
return GraphModule(tracer.root, graph, name)
@wrap
def _assert_is_none(value, msg):
assert value is None, msg