import torch
import torch._C as _C
import torch.utils.hooks as hooks
from torch._six import with_metaclass
import functools
import warnings
from collections import OrderedDict
from typing import Any
[docs]class _ContextMethodMixin(object):
[docs] def save_for_backward(self, *tensors):
r"""Saves given tensors for a future call to :func:`~Function.backward`.
**This should be called at most once, and only from inside the**
:func:`forward` **method.**
Later, saved tensors can be accessed through the :attr:`saved_tensors`
attribute. Before returning them to the user, a check is made to ensure
they weren't used in any in-place operation that modified their content.
Arguments can also be ``None``.
"""
self.to_save = tensors
[docs] def mark_dirty(self, *args):
r"""Marks given tensors as modified in an in-place operation.
**This should be called at most once, only from inside the**
:func:`forward` **method, and all arguments should be inputs.**
Every tensor that's been modified in-place in a call to :func:`forward`
should be given to this function, to ensure correctness of our checks.
It doesn't matter whether the function is called before or after
modification.
"""
self.dirty_tensors = args
def mark_shared_storage(self, *pairs):
warnings.warn(
'mark_shared_storage is deprecated. '
'Tensors with shared storages are automatically tracked. Note '
'that calls to `set_()` are not tracked')
[docs] def mark_non_differentiable(self, *args):
r"""Marks outputs as non-differentiable.
**This should be called at most once, only from inside the**
:func:`forward` **method, and all arguments should be outputs.**
This will mark outputs as not requiring gradients, increasing the
efficiency of backward computation. You still need to accept a gradient
for each output in :meth:`~Function.backward`, but it's always going to
be a zero tensor with the same shape as the shape of a corresponding
output.
This is used e.g. for indices returned from a max :class:`Function`.
"""
self.non_differentiable = args
class _HookMixin(object):
@staticmethod
def _register_hook(backward_hooks, hook):
if backward_hooks is None:
backward_hooks = OrderedDict()
handle = hooks.RemovableHandle(backward_hooks)
backward_hooks[handle.id] = hook
return backward_hooks, handle
class BackwardCFunction(_C._FunctionBase, _ContextMethodMixin, _HookMixin):
_is_legacy = False
def apply(self, *args):
return self._forward_cls.backward(self, *args)
class FunctionMeta(type):
"""Function metaclass.
This metaclass sets up the following properties:
_is_legacy: True if forward is not defined as a static method.
_backward_cls: The Function class corresponding to the differentiated
version of this function (which is generated on the fly by this
metaclass).
"""
def __init__(cls, name, bases, attrs):
for super_cls in cls.mro():
forward = super_cls.__dict__.get('forward')
if forward is not None:
has_static_forward = isinstance(forward, staticmethod) or isinstance(forward, classmethod)
break
cls._is_legacy = not has_static_forward
# old-style functions
if not has_static_forward:
return super(FunctionMeta, cls).__init__(name, bases, attrs)
backward_fn = type(name + 'Backward', (BackwardCFunction,), {'_forward_cls': cls})
cls._backward_cls = backward_fn
return super(FunctionMeta, cls).__init__(name, bases, attrs)
[docs]class Function(with_metaclass(FunctionMeta, _C._FunctionBase, _ContextMethodMixin, _HookMixin)):
r"""Records operation history and defines formulas for differentiating ops.
See the Note on extending the autograd engine for more details on how to use
this class: https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd
Every operation performed on :class:`Tensor` s creates a new function
object, that performs the computation, and records that it happened.
The history is retained in the form of a DAG of functions, with edges
denoting data dependencies (``input <- output``). Then, when backward is
called, the graph is processed in the topological ordering, by calling
:func:`backward` methods of each :class:`Function` object, and passing
returned gradients on to next :class:`Function` s.
Normally, the only way users interact with functions is by creating
subclasses and defining new operations. This is a recommended way of
extending torch.autograd.
Examples::
>>> class Exp(Function):
>>>
>>> @staticmethod
>>> def forward(ctx, i):
>>> result = i.exp()
>>> ctx.save_for_backward(result)
>>> return result
>>>
>>> @staticmethod
>>> def backward(ctx, grad_output):
>>> result, = ctx.saved_tensors
>>> return grad_output * result
>>>
>>> #Use it by calling the apply method:
>>> output = Exp.apply(input)
"""
def __call__(self, *args, **kwargs):
raise RuntimeError(
"Legacy autograd function with non-static forward method is deprecated. "
"Please use new-style autograd function with static forward method. "
"(Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)")
# for the tracer
is_traceable = False
[docs] @staticmethod
def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
r"""Performs the operation.
This function is to be overridden by all subclasses.
It must accept a context ctx as the first argument, followed by any
number of arguments (tensors or other types).
The context can be used to store tensors that can be then retrieved
during the backward pass.
"""
raise NotImplementedError("You must implement the forward function for custom"
" autograd.Function.")
[docs] @staticmethod
def backward(ctx: Any, *grad_outputs: Any) -> Any:
r"""Defines a formula for differentiating the operation.
This function is to be overridden by all subclasses.
It must accept a context :attr:`ctx` as the first argument, followed by
as many outputs did :func:`forward` return, and it should return as many
tensors, as there were inputs to :func:`forward`. Each argument is the
gradient w.r.t the given output, and each returned value should be the
gradient w.r.t. the corresponding input.
The context can be used to retrieve tensors saved during the forward
pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple
of booleans representing whether each input needs gradient. E.g.,
:func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the
first input to :func:`forward` needs gradient computated w.r.t. the
output.
"""
raise NotImplementedError("You must implement the backward function for custom"
" autograd.Function.")
def once_differentiable(fn):
@functools.wraps(fn)
def wrapper(ctx, *args):
with torch.no_grad():
outputs = fn(ctx, *args)
if not torch.is_grad_enabled():
return outputs
# If any of the inputs have requires_grad=True, we force the outputs
# to have requires_grad=True but point to a grad_fn which throws an
# error message during (double) back-propagation.
# XXX: this is only an approximation of requires_grad - there's no way
# to figure out if fn didn't use ctx.saved_tensors and as a result
# some Tensors might require grad, even if no args do.
# Unfortunately, this leads to unexpected error messages ("no nodes
# require computing gradients"), but I don't have a better idea.
# These functions would raise an error in backward anyway.
requires_grad = any(isinstance(arg, torch.Tensor) and arg.requires_grad
for arg in args)
if not requires_grad:
return outputs
if not isinstance(outputs, tuple):
outputs = (outputs,)
err_fn = torch._C._functions.DelayedError(
b"trying to differentiate twice a function that was marked"
b"with @once_differentiable", len(outputs))
# Create aliases of each output that has requires_grad=True. We need
# at least one of the inputs to err_fn to require grad so that the
# output will have a grad_fn.
def fake_requires_grad(var):
if var is not None:
var = var.detach()
var.requires_grad = True
return var
return err_fn(*[fake_requires_grad(v) for v in outputs])
return wrapper
def traceable(fn_cls):
r"""Marks Function as traceable for the JIT.
Traceable functions have additional restrictions - they can't pass any
data-dependent values to backward (e.g. Prod passes the output, which makes
it non-traceable), and their backward should be implemented entirely in terms
of operations on autograd Tensors in all cases.
DON'T USE THIS DECORATOR. IT IS FOR INTERNAL USE ONLY AND SHOULD BE HANDLED WITH
CARE (or can give incorrect results otherwise).
"""
fn_cls.is_traceable = True
return fn_cls
class InplaceFunction(Function):
def __init__(self, inplace=False):
super(InplaceFunction, self).__init__()
self.inplace = inplace
def _nested_map(condition, fn, condition_msg=None):
def _map(obj):
if condition(obj):
return fn(obj)
elif obj is None:
return None
elif isinstance(obj, (list, tuple)):
mapped = (_map(x) for x in obj)
if hasattr(obj, '_fields'):
# obj is namedtuple
return type(obj)(*mapped)
return type(obj)(mapped)
elif isinstance(obj, dict):
return {x : _map(obj[x]) for x in obj}
else:
raise ValueError("Auto nesting doesn't know how to process "
"an input object of type " + torch.typename(obj) +
(". Accepted types: " + condition_msg +
", or lists/tuples of them"
if condition_msg else ""))
return _map
def _jit_unwrap_structured(obj):
if hasattr(obj, "_jit_unwrap"):
return obj._jit_unwrap()
return obj
def _iter_filter(condition, allow_unknown=False, condition_msg=None,
conversion=None):
def _iter(obj):
if conversion is not None:
obj = conversion(obj)
if condition(obj):
yield obj
elif obj is None:
return
elif isinstance(obj, (list, tuple)):
for o in obj:
for var in _iter(o):
yield var
elif isinstance(obj, dict):
# We only accept primitive key types, so we needn't inspect them
for o in obj.values():
for var in _iter(o):
yield var
elif allow_unknown:
yield obj
else:
raise ValueError("Auto nesting doesn't know how to process "
"an input object of type " + torch.typename(obj) +
(". Accepted types: " + condition_msg +
", or lists/tuples of them"
if condition_msg else ""))
return _iter
def _unflatten(input, proto):
# unflatten a list or tuple input into a nested list/tuple structure
# specified by proto
def unflatten_helper(input, proto):
res = []
if hasattr(proto, "_jit_wrap"):
return proto._jit_wrap(input)
if not isinstance(proto, (list, tuple)):
return input[0], input[1:]
for e in proto:
if e is None:
res.append(e)
else:
res_e, input = unflatten_helper(input, e)
res.append(res_e)
return type(proto)(res), input
return unflatten_helper(input, proto)[0]
_iter_jit_values = _iter_filter(lambda o: o is None or isinstance(o, torch._C.Value),
condition_msg="jit's Values or None")
_iter_tensors = _iter_filter(lambda x: isinstance(x, torch.Tensor), condition_msg="Tensors",
conversion=_jit_unwrap_structured)
_iter_tensors_permissive = _iter_filter(lambda x: isinstance(x, torch.Tensor),
allow_unknown=True,
condition_msg="Tensors (permissive)")
_iter_None_tensors = _iter_filter(lambda o: o is None or isinstance(o, torch.Tensor),
condition_msg="Tensors or None")
_map_tensor_data = _nested_map(lambda x: isinstance(x, torch.Tensor), lambda o: o.data,
condition_msg="Tensors")
class NestedIOFunction(Function):
# The 'type: ignore' statements are needed here because these functions are declared as '@staticmethod' in the
# superclass (Function) but are instance methods here, which mypy reports as incompatible.
def _do_forward(self, *input):
self._nested_input = input
flat_input = tuple(_iter_tensors(input))
flat_output = super(NestedIOFunction, self)._do_forward(*flat_input)
nested_output = self._nested_output
nested_tensors = _unflatten(flat_output, self._nested_output)
return nested_tensors
def _do_backward(self, gradients, retain_variables):
self.retain_variables = retain_variables
result = super(NestedIOFunction, self)._do_backward(gradients, retain_variables)
if not retain_variables:
del self._nested_output
del self._to_save_nested
return result
def backward(self, *gradients: Any) -> Any:
nested_gradients = _unflatten(gradients, self._nested_output)
result = self.backward_extended(*nested_gradients)
return tuple(_iter_None_tensors(result))
__call__ = _do_forward
def forward(self, *args: Any) -> Any:
nested_tensors = _map_tensor_data(self._nested_input)
result = self.forward_extended(*nested_tensors)
del self._nested_input
self._nested_output = result
return tuple(_iter_tensors(result))
def save_for_backward(self, *args: Any) -> None:
self.to_save = tuple(_iter_tensors(args))
self._to_save_nested = args
@property
def saved_tensors(self):
flat_tensors = super(NestedIOFunction, self).saved_tensors
return _unflatten(flat_tensors, self._to_save_nested)
def mark_dirty(self, *args: Any, **kwargs: Any) -> None:
self.dirty_tensors = tuple(_iter_tensors((args, kwargs)))
def mark_non_differentiable(self, *args: Any, **kwargs: Any) -> None:
self.non_differentiable = tuple(_iter_tensors((args, kwargs)))
def forward_extended(self, *input: Any) -> None:
raise NotImplementedError
def backward_extended(self, *grad_output: Any) -> None:
raise NotImplementedError
