Source code for torch.export.graph_signature
import dataclasses
from enum import auto, Enum
from typing import Collection, Dict, List, Mapping, Optional, Set, Tuple, Union
__all__ = [
"ConstantArgument",
"CustomObjArgument",
"ExportBackwardSignature",
"ExportGraphSignature",
"InputKind",
"InputSpec",
"OutputKind",
"OutputSpec",
"SymIntArgument",
"TensorArgument",
]
@dataclasses.dataclass
class TensorArgument:
name: str
@dataclasses.dataclass
class TokenArgument:
name: str
@dataclasses.dataclass
class SymIntArgument:
name: str
@dataclasses.dataclass
class ConstantArgument:
value: Union[int, float, bool, None]
ArgumentSpec = Union[
TensorArgument,
SymIntArgument,
ConstantArgument,
CustomObjArgument,
TokenArgument,
]
[docs]class InputKind(Enum):
USER_INPUT = auto()
PARAMETER = auto()
BUFFER = auto()
CONSTANT_TENSOR = auto()
CUSTOM_OBJ = auto()
TOKEN = auto()
[docs]@dataclasses.dataclass
class InputSpec:
kind: InputKind
arg: ArgumentSpec
target: Optional[str]
persistent: Optional[bool] = None
def __post_init__(self):
if self.kind == InputKind.BUFFER:
assert (
self.persistent is not None
), "Failed to specify persistent flag on BUFFER."
assert isinstance(
self.arg,
(
TensorArgument,
SymIntArgument,
ConstantArgument,
CustomObjArgument,
TokenArgument,
),
), f"got {type(self.arg)}"
[docs]class OutputKind(Enum):
USER_OUTPUT = auto()
LOSS_OUTPUT = auto()
BUFFER_MUTATION = auto()
GRADIENT_TO_PARAMETER = auto()
GRADIENT_TO_USER_INPUT = auto()
USER_INPUT_MUTATION = auto()
TOKEN = auto()
[docs]@dataclasses.dataclass
class OutputSpec:
kind: OutputKind
arg: ArgumentSpec
target: Optional[str]
def __post_init__(self):
assert isinstance(
self.arg, (TensorArgument, SymIntArgument, ConstantArgument, TokenArgument)
)
def _sig_to_specs(
*,
user_inputs: Set[str],
inputs_to_parameters: Mapping[str, str],
inputs_to_buffers: Mapping[str, str],
user_outputs: Set[str],
buffer_mutations: Mapping[str, str],
user_input_mutations: Mapping[str, str],
grad_params: Mapping[str, str],
grad_user_inputs: Mapping[str, str],
loss_output: Optional[str],
inputs: List[ArgumentSpec],
outputs: List[ArgumentSpec],
input_tokens: List[str],
output_tokens: List[str],
) -> Tuple[List[InputSpec], List[OutputSpec]]:
def to_input_spec(inp: ArgumentSpec) -> InputSpec:
if isinstance(inp, TokenArgument):
return InputSpec(kind=InputKind.TOKEN, arg=inp, target=None)
if not isinstance(inp, TensorArgument):
return InputSpec(kind=InputKind.USER_INPUT, arg=inp, target=None)
name = inp.name
if name in user_inputs:
return InputSpec(kind=InputKind.USER_INPUT, arg=inp, target=None)
elif name in inputs_to_parameters:
return InputSpec(
kind=InputKind.PARAMETER,
arg=inp,
target=inputs_to_parameters[name],
)
elif name in inputs_to_buffers:
return InputSpec(
kind=InputKind.BUFFER,
arg=inp,
target=inputs_to_buffers[name],
# Mark as True for now; we will fix this up to distinguish
# persistent from non-persistent later in tracing.
# See: rewrite_non_persistent_buffers()
# TODO(suo): this is horrible.
persistent=True,
)
else:
raise AssertionError(f"Unknown tensor input kind: {name}")
def to_output_spec(idx: int, o: ArgumentSpec) -> OutputSpec:
if isinstance(o, TokenArgument):
return OutputSpec(kind=OutputKind.TOKEN, arg=o, target=None)
if not isinstance(o, TensorArgument):
return OutputSpec(kind=OutputKind.USER_OUTPUT, arg=o, target=None)
name = o.name
if idx < len(buffer_mutations) + len(user_input_mutations) + len(output_tokens):
if name in buffer_mutations:
return OutputSpec(
kind=OutputKind.BUFFER_MUTATION,
arg=o,
target=buffer_mutations[name],
)
elif name in user_input_mutations:
return OutputSpec(
kind=OutputKind.USER_INPUT_MUTATION,
arg=o,
target=user_input_mutations[name],
)
else:
raise AssertionError(f"Unknown tensor mutation kind: {name}")
else:
if name in user_outputs:
return OutputSpec(kind=OutputKind.USER_OUTPUT, arg=o, target=None)
elif name in grad_params:
return OutputSpec(
kind=OutputKind.GRADIENT_TO_PARAMETER,
arg=o,
target=grad_params[name],
)
elif name in grad_user_inputs:
return OutputSpec(
kind=OutputKind.GRADIENT_TO_USER_INPUT,
arg=o,
target=grad_user_inputs[name],
)
elif name == loss_output:
return OutputSpec(kind=OutputKind.LOSS_OUTPUT, arg=o, target=None)
else:
raise AssertionError(f"Unknown tensor output kind: {name}")
input_specs = [to_input_spec(inp) for inp in inputs]
output_specs = [to_output_spec(idx, o) for idx, o in enumerate(outputs)]
return input_specs, output_specs
[docs]@dataclasses.dataclass
class ExportBackwardSignature:
gradients_to_parameters: Dict[str, str]
gradients_to_user_inputs: Dict[str, str]
loss_output: str
[docs]@dataclasses.dataclass
class ExportGraphSignature:
"""
:class:`ExportGraphSignature` models the input/output signature of Export Graph,
which is a fx.Graph with stronger invariants gurantees.
Export Graph is functional and does not access "states" like parameters
or buffers within the graph via ``getattr`` nodes. Instead, :func:`export`
gurantees that parameters, buffers, and constant tensors are lifted out of
the graph as inputs. Similarly, any mutations to buffers are not included
in the graph either, instead the updated values of mutated buffers are
modeled as additional outputs of Export Graph.
The ordering of all inputs and outputs are::
Inputs = [*parameters_buffers_constant_tensors, *flattened_user_inputs]
Outputs = [*mutated_inputs, *flattened_user_outputs]
e.g. If following module is exported::
class CustomModule(nn.Module):
def __init__(self):
super(CustomModule, self).__init__()
# Define a parameter
self.my_parameter = nn.Parameter(torch.tensor(2.0))
# Define two buffers
self.register_buffer('my_buffer1', torch.tensor(3.0))
self.register_buffer('my_buffer2', torch.tensor(4.0))
def forward(self, x1, x2):
# Use the parameter, buffers, and both inputs in the forward method
output = (x1 + self.my_parameter) * self.my_buffer1 + x2 * self.my_buffer2
# Mutate one of the buffers (e.g., increment it by 1)
self.my_buffer2.add_(1.0) # In-place addition
return output
Resulting Graph would be::
graph():
%arg0_1 := placeholder[target=arg0_1]
%arg1_1 := placeholder[target=arg1_1]
%arg2_1 := placeholder[target=arg2_1]
%arg3_1 := placeholder[target=arg3_1]
%arg4_1 := placeholder[target=arg4_1]
%add_tensor := call_function[target=torch.ops.aten.add.Tensor](args = (%arg3_1, %arg0_1), kwargs = {})
%mul_tensor := call_function[target=torch.ops.aten.mul.Tensor](args = (%add_tensor, %arg1_1), kwargs = {})
%mul_tensor_1 := call_function[target=torch.ops.aten.mul.Tensor](args = (%arg4_1, %arg2_1), kwargs = {})
%add_tensor_1 := call_function[target=torch.ops.aten.add.Tensor](args = (%mul_tensor, %mul_tensor_1), kwargs = {})
%add_tensor_2 := call_function[target=torch.ops.aten.add.Tensor](args = (%arg2_1, 1.0), kwargs = {})
return (add_tensor_2, add_tensor_1)
Resulting ExportGraphSignature would be::
ExportGraphSignature(
input_specs=[
InputSpec(kind=<InputKind.PARAMETER: 2>, arg=TensorArgument(name='arg0_1'), target='my_parameter'),
InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='arg1_1'), target='my_buffer1'),
InputSpec(kind=<InputKind.BUFFER: 3>, arg=TensorArgument(name='arg2_1'), target='my_buffer2'),
InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg3_1'), target=None),
InputSpec(kind=<InputKind.USER_INPUT: 1>, arg=TensorArgument(name='arg4_1'), target=None)
],
output_specs=[
OutputSpec(kind=<OutputKind.BUFFER_MUTATION: 3>, arg=TensorArgument(name='add_2'), target='my_buffer2'),
OutputSpec(kind=<OutputKind.USER_OUTPUT: 1>, arg=TensorArgument(name='add_1'), target=None)
]
)
"""
input_specs: List[InputSpec]
output_specs: List[OutputSpec]
# A list of parameters uniquely identified by mangled fully qualified name
@property
def parameters(self) -> Collection[str]:
# TODO Make this tuple.
return [
s.target
for s in self.input_specs
if s.kind == InputKind.PARAMETER
if isinstance(s.target, str)
]
# A list of buffers uniquely identified by mangled fully qualified name
@property
def buffers(self) -> Collection[str]:
# TODO Make this tuple.
return [
s.target
for s in self.input_specs
if s.kind == InputKind.BUFFER
if isinstance(s.target, str)
]
@property
def non_persistent_buffers(self) -> Collection[str]:
return [
s.target
for s in self.input_specs
if s.kind == InputKind.BUFFER
if s.persistent is False
if isinstance(s.target, str)
]
# A list of lifted constant tensors
@property
def lifted_tensor_constants(self) -> Collection[str]:
# TODO Make this tuple.
return [
s.target
for s in self.input_specs
if s.kind == InputKind.CONSTANT_TENSOR
if isinstance(s.target, str)
]
@property
def lifted_custom_objs(self) -> Collection[str]:
# TODO Make this tuple.
return [
s.target
for s in self.input_specs
if s.kind == InputKind.CUSTOM_OBJ
if isinstance(s.target, str)
]
# Graph node names of pytree-flattened inputs of original program
@property
def user_inputs(self) -> Collection[Union[int, float, bool, None, str]]:
user_inputs: List[Union[int, float, bool, None, str]] = []
for s in self.input_specs:
if s.kind != InputKind.USER_INPUT:
continue
if isinstance(s.arg, (TensorArgument, SymIntArgument, CustomObjArgument)):
user_inputs.append(s.arg.name)
elif isinstance(s.arg, ConstantArgument):
user_inputs.append(s.arg.value)
else:
raise RuntimeError(f"{s.arg} is not a valid user inputs")
return tuple(user_inputs)
# Graph node names of pytree-flattened outputs of original program
@property
def user_outputs(self) -> Collection[Union[int, float, bool, None, str]]:
user_outputs: List[Union[int, float, bool, None, str]] = []
for s in self.output_specs:
if s.kind != OutputKind.USER_OUTPUT:
continue
if isinstance(s.arg, (TensorArgument, SymIntArgument)):
user_outputs.append(s.arg.name)
elif isinstance(s.arg, ConstantArgument):
user_outputs.append(s.arg.value)
else:
raise RuntimeError(f"{s.arg} is not a valid user output")
return tuple(user_outputs)
# A dictionary mapping graph input node names to parameters. If a graph input
# name is found in this dictionary, it is guranteed to be a lifted parameter.
@property
def inputs_to_parameters(self) -> Mapping[str, str]:
return {
s.arg.name: s.target
for s in self.input_specs
if s.kind == InputKind.PARAMETER
and isinstance(s.arg, TensorArgument)
and isinstance(s.target, str)
}
# A dictionary mapping graph input node names to buffers. If a graph input
# name is found in this dictionary, it is guranteed to be a lifted buffer.
@property
def inputs_to_buffers(self) -> Mapping[str, str]:
return {
s.arg.name: s.target # type: ignore[union-attr, misc]
for s in self.input_specs
if s.kind == InputKind.BUFFER
and isinstance(s.arg, TensorArgument)
and isinstance(s.target, str)
}
# A dictionary mapping graph output node names to buffers that are mutated in the
# original program. Buffers that are not mutated will not be found in this dictionary.
@property
def buffers_to_mutate(self) -> Mapping[str, str]:
return {
s.arg.name: s.target
for s in self.output_specs
if s.kind == OutputKind.BUFFER_MUTATION
and isinstance(s.arg, TensorArgument)
and isinstance(s.target, str)
}
@property
def user_inputs_to_mutate(self) -> Mapping[str, str]:
return {
s.arg.name: s.target
for s in self.output_specs
if s.kind == OutputKind.USER_INPUT_MUTATION
and isinstance(s.arg, TensorArgument)
and isinstance(s.target, str)
}
# A dictionary mapping graph input node names to lifted tensor constants.
@property
def inputs_to_lifted_tensor_constants(self) -> Mapping[str, str]:
return {
s.arg.name: s.target
for s in self.input_specs
if s.kind == InputKind.CONSTANT_TENSOR
and isinstance(s.arg, TensorArgument)
and isinstance(s.target, str)
}
@property
def inputs_to_lifted_custom_objs(self) -> Mapping[str, str]:
return {
s.arg.name: s.target
for s in self.input_specs
if s.kind == InputKind.CUSTOM_OBJ
and isinstance(s.arg, CustomObjArgument)
and isinstance(s.target, str)
}
@property
def backward_signature(self) -> Optional[ExportBackwardSignature]:
loss_output = None
gradients_to_parameters: Dict[str, str] = {}
gradients_to_user_inputs: Dict[str, str] = {}
for spec in self.output_specs:
if spec.kind == OutputKind.LOSS_OUTPUT:
assert loss_output is None
assert isinstance(spec.arg, TensorArgument)
loss_output = spec.arg.name
elif spec.kind == OutputKind.GRADIENT_TO_PARAMETER:
assert isinstance(spec.target, str)
assert isinstance(spec.arg, TensorArgument)
gradients_to_parameters[spec.arg.name] = spec.target
elif spec.kind == OutputKind.GRADIENT_TO_USER_INPUT:
assert isinstance(spec.target, str)
assert isinstance(spec.arg, TensorArgument)
gradients_to_user_inputs[spec.arg.name] = spec.target
if loss_output is None:
return None
return ExportBackwardSignature(
loss_output=loss_output,
gradients_to_parameters=gradients_to_parameters,
gradients_to_user_inputs=gradients_to_user_inputs,
)
# Map from assertion dependency token index to assertion dep token output
# name in output. The shape of output after aot_autograd will be like:
# (updated_inputs, user_outputs, dep_token).
@property
def assertion_dep_token(self) -> Optional[Mapping[int, str]]:
return None
@property
def input_tokens(self) -> List[str]:
input_tokens = []
for s in self.input_specs:
if s.kind == InputKind.TOKEN:
assert isinstance(s.arg, TokenArgument)
input_tokens.append(s.arg.name)
return input_tokens
@property
def output_tokens(self) -> List[str]:
output_tokens = []
for s in self.output_specs:
if s.kind == OutputKind.TOKEN:
assert isinstance(s.arg, TokenArgument)
output_tokens.append(s.arg.name)
return output_tokens
def __post_init__(self) -> None:
assertion_dep_token = self.assertion_dep_token
if assertion_dep_token is None:
return
assert len(assertion_dep_token) == 1
assertion_dep_token_index = next(iter(assertion_dep_token.keys()))
assert (
len(self.user_outputs) + len(self.buffers_to_mutate)
== assertion_dep_token_index
)
[docs] def replace_all_uses(self, old: str, new: str):
"""
Replace all uses of the old name with new name in the signature.
"""
assert isinstance(old, str)
assert isinstance(new, str)
arg_types = (TensorArgument, SymIntArgument, CustomObjArgument)
for o in self.output_specs:
if isinstance(o.arg, arg_types):
if o.arg.name == old:
o.arg.name = new
for i in self.input_specs:
if isinstance(i.arg, arg_types):
if i.arg.name == old:
i.arg.name = new
[docs] def get_replace_hook(self):
def _(old, new, user):
if user.op in ("output", "input"):
self.replace_all_uses(old.name, new)
return _
