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# mypy: allow-untyped-defs
import dataclasses
from dataclasses import dataclass
from typing import Any, Callable, Optional, Protocol
from torch import _C, _ops, autograd, Tensor
from torch.utils import _pytree
from . import utils
class InfoProtocol(Protocol):
_backward_fn: Optional[Callable]
_setup_context_fn: Optional[Callable]
@dataclasses.dataclass
class Info:
_backward_fn: Optional[Callable]
_setup_context_fn: Optional[Callable]
def make_autograd_impl(op: _ops.OpOverload, info: InfoProtocol) -> Callable:
name: str = f"GeneratedBackwardFor_{op._namespace}_{op._opname}_{op._overloadname}"
has_kwarg_only_args = utils.has_kwarg_only_args(op._schema)
@dataclass
class Metadata:
keyset: _C.DispatchKeySet
keyword_only_args: dict[str, Any]
def forward_no_grad(*args):
metadata = args[-1]
args = args[:-1]
with _C._AutoDispatchBelowAutograd():
keyset = metadata.keyset
kwargs = metadata.keyword_only_args
result = op.redispatch(keyset & _C._after_autograd_keyset, *args, **kwargs)
return result
def forward(ctx, *args):
metadata = args[-1]
args = args[:-1]
with _C._AutoDispatchBelowAutograd():
keyset = metadata.keyset
kwargs = metadata.keyword_only_args
result = op.redispatch(keyset & _C._after_autograd_keyset, *args, **kwargs)
if info._setup_context_fn:
# The Dispatcher will remove args that are equal to their default
# values from (args, kwargs). We're going to add it back so that
# the user can access them.
#
# This is OK to do: The Dispatcher removed the args for serialization
# FC/BC reasons (that is, a graph will not store args that are equal
# to their default values), but that doesn't matter here. If the user
# adds a new default arg, then they must update
# their setup_context (along with the rest of their operator
# registrations)
args, kwargs = utils.fill_defaults(op._schema, args, kwargs)
if has_kwarg_only_args:
info._setup_context_fn(
ctx=ctx, inputs=args, keyword_only_inputs=kwargs, output=result
)
else:
info._setup_context_fn(ctx=ctx, inputs=args, output=result)
return result
def backward(ctx, *grads):
if info._backward_fn:
try:
prev_needs_input_grad = ctx.needs_input_grad
ctx.needs_input_grad = ctx.needs_input_grad[:-1]
result = info._backward_fn(ctx, *grads)
finally:
ctx.needs_input_grad = prev_needs_input_grad
if isinstance(result, tuple):
return (*result, None)
return result, None
raise RuntimeError(
f"Trying to backward through {op} but no autograd "
f"formula was registered. "
f"Please use register_autograd to add one."
)
Generated = type(
name,
(autograd.Function,),
{
"forward": staticmethod(forward),
"backward": staticmethod(backward),
},
)
schema = op._schema
if any(
utils.is_tensorlist_like_type(a.type)
for a in (*schema.arguments, *schema.returns)
):
Generated = supports_tensorlist(Generated)
# The dispatcher passes any keyword-only-args as kwargs and the
# rest of the args (even if specified as kwargs) as args.
def autograd_impl(keyset, *args, **keyword_only_args):
if _C.is_grad_enabled() and _pytree.tree_any_only(
Tensor, lambda x: x.requires_grad, args, not_list_of_tensor
):
result = Generated.apply(*args, Metadata(keyset, keyword_only_args)) # type: ignore[attr-defined]
else:
result = forward_no_grad(*args, Metadata(keyset, keyword_only_args))
return result
return autograd_impl
def supports_tensorlist(cls: Any) -> Any:
"""Allows a given autograd.Function class to support List[Tensor] inputs/outputs.
Regular autograd.Function has a constraint that it only directly supports autograd for
Tensors. Applying @supports_tensorlist enables an autograd.Function to support
autograd for List[Tensor] inputs and outputs.
"""
orig_forward = cls.forward
orig_backward = cls.backward
orig_apply = cls.apply
@dataclass
class Metadata:
input_spec: spec_t
output_spec: Optional[spec_t] = None
result_is_tuple: Optional[bool] = None
def new_forward(ctx, *args):
metadata = args[-1]
args = args[:-1]
if not isinstance(metadata, Metadata):
raise NotImplementedError(
"NYI: calling supports_tensorlist autograd.Function.forward directly. "
"You should probably be calling .apply instead. "
"Please file an issue if not."
)
args = unflatten(list(args), metadata.input_spec)
result = orig_forward(ctx, *args)
metadata.result_is_tuple = isinstance(result, tuple)
if not metadata.result_is_tuple:
result = (result,)
flat_result, output_spec = flatten(result, not_list_of_tensor)
metadata.output_spec = output_spec
if hasattr(ctx, "_pt_metadata"):
raise RuntimeError(
"Please don't set ctx._pt_metadata; PyTorch uses it to store info"
)
ctx._pt_metadata = metadata
return tuple(flat_result)
def new_backward(ctx, *grads):
if not hasattr(ctx, "_pt_metadata"):
raise NotImplementedError(
"NYI: calling supports_tensorlist autograd.Function.backward directly. "
"This will automatically get called by PyTorch autograd. "
"Please file an issue if you need this."
)
metadata = ctx._pt_metadata
grads = unflatten(list(grads), metadata.output_spec)
# If the user's input is ([x, y, z], w),
# then needs_input_grad is (bool, bool, bool, bool, bool).
# We need to
# 1. get rid of the additional bool (which comes from the extra
# `metadata input`)
# 2. unflatten to get the right structure.
prev_needs_input_grad = ctx.needs_input_grad
try:
ctx.needs_input_grad = unflatten(
list(ctx.needs_input_grad[:-1]), metadata.input_spec
)
grad_inputs = orig_backward(ctx, *grads)
finally:
ctx.needs_input_grad = prev_needs_input_grad
if not isinstance(grad_inputs, tuple):
grad_inputs = (grad_inputs,)
# Assume that any Nones in the backward are Tensors.
# If the forward has an arg that is [1, 2, 3], the backward should
# return None as the grad.
# If the forward has an arg that is [tensor, tensor], the backward
# may return [None, None], [grad, None], [None, grad], or [grad, grad].
flat_grad_inputs, grad_inputs_spec = flatten(
grad_inputs, not_list_of_optional_tensor
)
if grad_inputs_spec != metadata.input_spec:
raise RuntimeError(
f"Expected the return from backward to be of the same structure "
f"as the inputs. Got: {grad_inputs_spec} (return from backward), "
f"{metadata.input_spec} (inputs)"
)
return tuple(flat_grad_inputs + [None])
def new_apply(*args):
flat_args, input_spec = flatten(args, is_leaf=not_list_of_tensor)
metadata = Metadata(input_spec)
result = orig_apply(*flat_args, metadata) # type: ignore[misc]
assert metadata.output_spec is not None
result = unflatten(list(result), metadata.output_spec)
if not metadata.result_is_tuple:
assert isinstance(result, tuple)
assert len(result) == 1
return result[0]
return result
cls.forward = new_forward
cls.backward = new_backward
cls.apply = new_apply
return cls
def not_list_of_tensor(tree):
if isinstance(tree, tuple):
return False
if isinstance(tree, list):
return any(not isinstance(l, Tensor) for l in tree)
return True
def not_list_of_optional_tensor(tree):
if isinstance(tree, tuple):
return False
if isinstance(tree, list):
return any(l is not None and not isinstance(l, Tensor) for l in tree)
return True
flatten = _pytree.tree_flatten
unflatten = _pytree.tree_unflatten
spec_t = _pytree.TreeSpec
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