861 lines
33 KiB
Python
861 lines
33 KiB
Python
|
import abc
|
||
|
|
import copy
|
||
|
|
import operator
|
||
|
|
from copy import deepcopy
|
||
|
|
from enum import Enum
|
||
|
|
from itertools import chain
|
||
|
|
from typing import Any, cast, Dict, List, Optional, Union
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.fx._pytree as fx_pytree
|
||
|
|
import torch.utils._pytree as pytree
|
||
|
|
from torch.export._tree_utils import reorder_kwargs
|
||
|
|
from torch.export.exported_program import (
|
||
|
|
ConstantArgument,
|
||
|
|
ExportedProgram,
|
||
|
|
ModuleCallSignature,
|
||
|
|
SymIntArgument,
|
||
|
|
TensorArgument,
|
||
|
|
)
|
||
|
|
from torch.fx._symbolic_trace import is_fx_tracing
|
||
|
|
from torch.utils._pytree import GetAttrKey, SequenceKey
|
||
|
|
|
||
|
|
__all__ = ["InterpreterModule", "UnflattenedModule", "unflatten", "FlatArgsAdapter"]
|
||
|
|
|
||
|
|
|
||
|
|
class _AttrKind(Enum):
|
||
|
|
PARAMETER = "parameter"
|
||
|
|
BUFFER = "buffer"
|
||
|
|
CONSTANT = "constant"
|
||
|
|
|
||
|
|
|
||
|
|
# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module
|
||
|
|
# This installs empty Modules where none exist yet if they are subpaths of target
|
||
|
|
def _assign_attr(
|
||
|
|
from_obj: Union[torch.Tensor, torch.ScriptObject],
|
||
|
|
to_module: torch.nn.Module,
|
||
|
|
target: str,
|
||
|
|
attr_kind: _AttrKind,
|
||
|
|
persistent: bool = True,
|
||
|
|
):
|
||
|
|
*prefix, field = target.split(".")
|
||
|
|
for item in prefix:
|
||
|
|
t = getattr(to_module, item, None)
|
||
|
|
|
||
|
|
if t is None:
|
||
|
|
t = torch.nn.Module()
|
||
|
|
setattr(to_module, item, t)
|
||
|
|
to_module = t
|
||
|
|
|
||
|
|
if attr_kind == _AttrKind.PARAMETER:
|
||
|
|
assert isinstance(from_obj, torch.nn.Parameter)
|
||
|
|
to_module.register_parameter(field, from_obj)
|
||
|
|
elif attr_kind == _AttrKind.BUFFER:
|
||
|
|
assert isinstance(from_obj, torch.Tensor)
|
||
|
|
to_module.register_buffer(field, from_obj, persistent=persistent)
|
||
|
|
elif attr_kind == _AttrKind.CONSTANT:
|
||
|
|
assert isinstance(from_obj, (torch.Tensor, torch.ScriptObject))
|
||
|
|
setattr(to_module, field, from_obj)
|
||
|
|
|
||
|
|
|
||
|
|
class InterpreterModule(torch.nn.Module):
|
||
|
|
"""A module that uses torch.fx.Interpreter to execute instead of the usual
|
||
|
|
codegen that GraphModule uses. This provides better stack trace information
|
||
|
|
and makes it easier to debug execution.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
graph: torch.fx.Graph,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.graph = graph
|
||
|
|
self.graph.owning_module = self
|
||
|
|
|
||
|
|
def forward(self, *args, **kwargs):
|
||
|
|
assert self.graph_module is not None, "Didn't finalize this InterpreterModule"
|
||
|
|
if torch.compiler.is_dynamo_compiling():
|
||
|
|
# Dynamo cannot trace through torch.fx.Interpreter, so fall back to
|
||
|
|
# GraphModule codegen in this instance.
|
||
|
|
return self.graph_module(*args, **kwargs)
|
||
|
|
else:
|
||
|
|
if kwargs:
|
||
|
|
# Handle **kwargs. FX only natively supports positional
|
||
|
|
# arguments (through placeholders). So in order to pass in
|
||
|
|
# kwargs, we must correspond the names of the placeholders with
|
||
|
|
# the keys in the kwarg dict.
|
||
|
|
arg_list = list(args)
|
||
|
|
kwarg_names = self.arg_names[len(arg_list) :]
|
||
|
|
for kwarg_name in kwarg_names:
|
||
|
|
if kwarg_name in kwargs:
|
||
|
|
arg_list.append(kwargs[kwarg_name])
|
||
|
|
|
||
|
|
# Assert that the kwargs passed in exactly match the positional
|
||
|
|
# arguments specified by the GraphModule. This should be
|
||
|
|
# guaranteed by the unflattening process.
|
||
|
|
assert len(kwarg_names) == len(kwargs)
|
||
|
|
assert len(arg_list) == len(self.arg_names)
|
||
|
|
args = tuple(arg_list)
|
||
|
|
|
||
|
|
return torch.fx.Interpreter(self, graph=self.graph).run(
|
||
|
|
*args, enable_io_processing=False
|
||
|
|
)
|
||
|
|
|
||
|
|
def finalize(self):
|
||
|
|
# We need to "finalize" because GraphModule populates its own state_dict
|
||
|
|
# based on the get_attrs observed in the graph. So we need to fully
|
||
|
|
# construct the graph and call _sink_params before generating this
|
||
|
|
# GraphModule.
|
||
|
|
|
||
|
|
# need to set `graph_module` directly on the dict to avoid it getting
|
||
|
|
# registered as a submodule.
|
||
|
|
self.__dict__["graph_module"] = torch.fx.GraphModule(self, self.graph)
|
||
|
|
self.graph.lint()
|
||
|
|
|
||
|
|
# Cache arg names for kwarg handling (see forward())
|
||
|
|
self.arg_names = []
|
||
|
|
for node in self.graph.nodes:
|
||
|
|
if node.op == "placeholder":
|
||
|
|
self.arg_names.append(node.target)
|
||
|
|
|
||
|
|
|
||
|
|
class FlatArgsAdapter(abc.ABC):
|
||
|
|
"""
|
||
|
|
Adapts input arguments with ``input_spec`` to align ``target_spec``.
|
||
|
|
"""
|
||
|
|
|
||
|
|
@abc.abstractmethod
|
||
|
|
def adapt(
|
||
|
|
self,
|
||
|
|
target_spec: pytree.TreeSpec,
|
||
|
|
input_spec: pytree.TreeSpec,
|
||
|
|
input_args: List[Any],
|
||
|
|
) -> List[Any]:
|
||
|
|
"""NOTE: This adapter may mutate given ``input_args_with_path``."""
|
||
|
|
...
|
||
|
|
|
||
|
|
|
||
|
|
class UnflattenedModule(torch.nn.Module):
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
export_module: ExportedProgram,
|
||
|
|
flat_args_adapter: Optional[FlatArgsAdapter] = None,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
if export_module.graph_signature.backward_signature is not None:
|
||
|
|
raise ValueError("Unflattening on JointExportModule NYI")
|
||
|
|
|
||
|
|
export_graph = deepcopy(export_module.graph)
|
||
|
|
self.graph_signature = deepcopy(export_module.graph_signature)
|
||
|
|
self.graph = torch.fx.Graph()
|
||
|
|
self.module_call_graph = deepcopy(export_module.module_call_graph)
|
||
|
|
self.flat_args_adapter = flat_args_adapter
|
||
|
|
# Flag to indicate whether args have been adapted.
|
||
|
|
self.adapted = False
|
||
|
|
|
||
|
|
_inplace_buffer_mutations(export_graph, self.graph_signature)
|
||
|
|
_outline_submodules(export_graph, self)
|
||
|
|
|
||
|
|
self.range_constraints = export_module.range_constraints
|
||
|
|
self.equality_constraints: List = []
|
||
|
|
|
||
|
|
state_dict = export_module.state_dict
|
||
|
|
for name in self.graph_signature.parameters:
|
||
|
|
cloned = torch.nn.Parameter(state_dict[name].clone())
|
||
|
|
_assign_attr(
|
||
|
|
cloned,
|
||
|
|
self,
|
||
|
|
name,
|
||
|
|
attr_kind=_AttrKind.PARAMETER,
|
||
|
|
)
|
||
|
|
|
||
|
|
non_persistent_buffers = set(self.graph_signature.non_persistent_buffers)
|
||
|
|
for name in self.graph_signature.buffers:
|
||
|
|
if name in non_persistent_buffers:
|
||
|
|
persistent = False
|
||
|
|
cloned = export_module.constants[name].clone()
|
||
|
|
else:
|
||
|
|
persistent = True
|
||
|
|
cloned = state_dict[name].clone()
|
||
|
|
|
||
|
|
_assign_attr(
|
||
|
|
cloned,
|
||
|
|
self,
|
||
|
|
name,
|
||
|
|
attr_kind=_AttrKind.BUFFER,
|
||
|
|
persistent=persistent,
|
||
|
|
)
|
||
|
|
|
||
|
|
for fqn in chain(
|
||
|
|
self.graph_signature.lifted_tensor_constants,
|
||
|
|
self.graph_signature.lifted_custom_objs,
|
||
|
|
):
|
||
|
|
constant = export_module.constants[fqn]
|
||
|
|
if isinstance(constant, torch.Tensor):
|
||
|
|
constant = constant.clone()
|
||
|
|
_assign_attr(
|
||
|
|
constant,
|
||
|
|
self,
|
||
|
|
fqn,
|
||
|
|
attr_kind=_AttrKind.CONSTANT,
|
||
|
|
)
|
||
|
|
|
||
|
|
inputs_to_state: Dict[str, str] = {
|
||
|
|
**self.graph_signature.inputs_to_parameters,
|
||
|
|
**self.graph_signature.inputs_to_buffers,
|
||
|
|
**self.graph_signature.inputs_to_lifted_tensor_constants,
|
||
|
|
**self.graph_signature.inputs_to_lifted_custom_objs,
|
||
|
|
}
|
||
|
|
|
||
|
|
_sink_params(self, inputs_to_state, [])
|
||
|
|
# Check all input nodes has been processed.
|
||
|
|
for module in self.modules():
|
||
|
|
if not isinstance(module, torch.fx.GraphModule):
|
||
|
|
continue
|
||
|
|
for node in module.graph.nodes:
|
||
|
|
if node.op != "placeholder":
|
||
|
|
continue
|
||
|
|
assert node.name not in inputs_to_state
|
||
|
|
|
||
|
|
# Cache so we don't have to compute this every time.
|
||
|
|
# NOTE: this needs to be kept in sync with the placeholders in
|
||
|
|
# self.graph, but currently we have no way to guarantee that.
|
||
|
|
self.input_placeholders = [
|
||
|
|
node for node in self.graph.nodes if node.op == "placeholder"
|
||
|
|
]
|
||
|
|
self.check_input_constraints = True
|
||
|
|
assert self.module_call_graph[0].fqn == ""
|
||
|
|
|
||
|
|
def forward(self, *args, **kwargs):
|
||
|
|
signature = self.module_call_graph[0].signature
|
||
|
|
|
||
|
|
reordered_kwargs = reorder_kwargs(kwargs, signature.in_spec)
|
||
|
|
|
||
|
|
flat_args_with_path, in_spec = pytree.tree_flatten_with_path(
|
||
|
|
(args, reordered_kwargs)
|
||
|
|
)
|
||
|
|
flat_args = [x[1] for x in flat_args_with_path]
|
||
|
|
if is_fx_tracing():
|
||
|
|
return_val = torch.fx.Interpreter(self, graph=self.graph).run(
|
||
|
|
*flat_args, enable_io_processing=False
|
||
|
|
)
|
||
|
|
# For scalar return value, fx.Graph wraps in a tuple
|
||
|
|
if isinstance(return_val, tuple) and len(return_val) == 1:
|
||
|
|
return return_val[0]
|
||
|
|
return return_val
|
||
|
|
|
||
|
|
if in_spec != signature.in_spec:
|
||
|
|
if not self.adapted:
|
||
|
|
print(
|
||
|
|
"Input treespec does not match with exported module's: \n"
|
||
|
|
f"Input treespec: {in_spec}. ",
|
||
|
|
f"Exported module treespec: {signature.in_spec}",
|
||
|
|
)
|
||
|
|
if self.flat_args_adapter is None:
|
||
|
|
raise TypeError(
|
||
|
|
"There is no flat args adapter sepcified. "
|
||
|
|
"Are you sure you are calling this with the right arguments? "
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
if not self.adapted:
|
||
|
|
print("Adapting flat arg to match exported module's treespec")
|
||
|
|
flat_args = self.flat_args_adapter.adapt(
|
||
|
|
target_spec=signature.in_spec,
|
||
|
|
input_spec=in_spec,
|
||
|
|
input_args=flat_args,
|
||
|
|
)
|
||
|
|
self.adapted = True
|
||
|
|
if len(flat_args) != signature.in_spec.num_leaves:
|
||
|
|
raise TypeError(
|
||
|
|
f"Flat args adaption failed, number of args mismatch "
|
||
|
|
f"Adatped: {len(flat_args)} \n"
|
||
|
|
f"Exported module: {signature.in_spec.num_leaves}"
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.check_input_constraints:
|
||
|
|
# Import here to avoid an unfortunate circular dependency.
|
||
|
|
# TODO(suo): untangle this.
|
||
|
|
from torch._export.utils import _check_input_constraints_for_graph
|
||
|
|
|
||
|
|
if self.adapted is True:
|
||
|
|
# TODO(suo): The FlatArgsAdapter returns a list of flat args,
|
||
|
|
# which we don't have keypaths for. For now, just create a dummy
|
||
|
|
# keypath to associate with the arg.
|
||
|
|
new_flat_args_with_path = [ # type: ignore[var-annotated]
|
||
|
|
((SequenceKey(idx=0), GetAttrKey(name="<unknown location>")), arg)
|
||
|
|
for arg in flat_args
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
new_flat_args_with_path = flat_args_with_path # type: ignore[assignment]
|
||
|
|
|
||
|
|
_check_input_constraints_for_graph(
|
||
|
|
self.input_placeholders, new_flat_args_with_path, self.range_constraints
|
||
|
|
)
|
||
|
|
tree_out = torch.fx.Interpreter(self, graph=self.graph).run(
|
||
|
|
*flat_args, enable_io_processing=False
|
||
|
|
)
|
||
|
|
return pytree.tree_unflatten(tree_out, signature.out_spec)
|
||
|
|
|
||
|
|
|
||
|
|
def unflatten(
|
||
|
|
module: ExportedProgram, flat_args_adapter: Optional[FlatArgsAdapter] = None
|
||
|
|
) -> UnflattenedModule:
|
||
|
|
"""Unflatten an ExportedProgram, producing a module with the same module
|
||
|
|
hierarchy as the original eager module. This can be useful if you are trying
|
||
|
|
to use :mod:`torch.export` with another system that expects a module
|
||
|
|
hierachy instead of the flat graph that :mod:`torch.export` usually produces.
|
||
|
|
|
||
|
|
.. note:: The args/kwargs of unflattened modules will not necessarily match
|
||
|
|
the eager module, so doing a module swap (e.g. :code:`self.submod =
|
||
|
|
new_mod`) will not necessarily work. If you need to swap a module out, you
|
||
|
|
need to set the :code:`preserve_module_call_signature` parameter of
|
||
|
|
:func:`torch.export.export`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
module (ExportedProgram): The ExportedProgram to unflatten.
|
||
|
|
flat_args_adapter (Optional[FlatArgsAdapter]): Adapt flat args if input TreeSpec does not match with exported module's.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
An instance of :class:`UnflattenedModule`, which has the same module
|
||
|
|
hierarchy as the original eager module pre-export.
|
||
|
|
"""
|
||
|
|
return UnflattenedModule(module, flat_args_adapter)
|
||
|
|
|
||
|
|
|
||
|
|
def _inplace_buffer_mutations(graph: torch.fx.Graph, graph_signature) -> None:
|
||
|
|
"""Transform buffer mutations from their functionalized form into a copy_
|
||
|
|
node in the graph.
|
||
|
|
|
||
|
|
Functionalization represents buffer mutation by passing the buffer as an input and output. So for example, the eager code:
|
||
|
|
def forward(self, x):
|
||
|
|
self.buffer += x
|
||
|
|
return x * x
|
||
|
|
|
||
|
|
Will become a graph that looks like:
|
||
|
|
def forward(self, buffer, x):
|
||
|
|
mutated_buffer = aten.add(buffer, x)
|
||
|
|
mul = aten.mul(x, x)
|
||
|
|
return (mutated_buffer, mul)
|
||
|
|
|
||
|
|
We want to inplace this into something that looks like the original eager code:
|
||
|
|
def forward(self, buffer, x):
|
||
|
|
mutated_buffer = aten.add(buffer, x)
|
||
|
|
buffer.copy_(mutated_buffer)
|
||
|
|
mul = aten.mul(x, x)
|
||
|
|
return (mul,)
|
||
|
|
"""
|
||
|
|
output_node = next(iter(reversed(graph.nodes)))
|
||
|
|
assert output_node.op == "output" and len(output_node.args) == 1
|
||
|
|
return_args = output_node.args[0]
|
||
|
|
|
||
|
|
mutation_node_to_buffer = graph_signature.buffers_to_mutate
|
||
|
|
mutations = return_args[: len(mutation_node_to_buffer)]
|
||
|
|
buffers_to_inputs = {v: k for k, v in graph_signature.inputs_to_buffers.items()}
|
||
|
|
input_name_to_node = {
|
||
|
|
node.name: node for node in graph.nodes if node.op == "placeholder"
|
||
|
|
}
|
||
|
|
|
||
|
|
for mutation in mutations:
|
||
|
|
buffer_name = mutation_node_to_buffer[mutation.name]
|
||
|
|
input_name = buffers_to_inputs[buffer_name]
|
||
|
|
input_node = input_name_to_node[input_name]
|
||
|
|
|
||
|
|
with graph.inserting_after(mutation):
|
||
|
|
new_node = graph.create_node(
|
||
|
|
"call_function", torch.ops.aten.copy_, (input_node, mutation)
|
||
|
|
)
|
||
|
|
for k, v in mutation.meta.items():
|
||
|
|
new_node.meta[k] = v
|
||
|
|
# Replace all uses of the previously functional mutation with our copy_ output.
|
||
|
|
mutation.replace_all_uses_with(new_node, lambda x: x is not new_node)
|
||
|
|
|
||
|
|
# Remove the mutated buffer from the graph outputs, since we don't need to
|
||
|
|
# thread it through anymore. We don't need to handle the inputs, which will
|
||
|
|
# be handled by _sink_params.
|
||
|
|
user_outputs = tuple(
|
||
|
|
return_args[len(mutation_node_to_buffer) :],
|
||
|
|
)
|
||
|
|
output_node.args = ((user_outputs),)
|
||
|
|
|
||
|
|
|
||
|
|
def _is_prefix(candidate, target):
|
||
|
|
"""Check whether `candidate` is a prefix of `target`."""
|
||
|
|
return len(candidate) < len(target) and target[: len(candidate)] == candidate
|
||
|
|
|
||
|
|
|
||
|
|
def _compute_accessor(parent_fqn: str, child_fqn: str) -> str:
|
||
|
|
if parent_fqn == "":
|
||
|
|
# Handle the root module correctly.
|
||
|
|
return child_fqn
|
||
|
|
|
||
|
|
parent_split = parent_fqn.split(".")
|
||
|
|
child_split = child_fqn.split(".")
|
||
|
|
|
||
|
|
assert (
|
||
|
|
child_split[: len(parent_split)] == parent_split
|
||
|
|
), f"Child module '{child_fqn}' is not a descendant of parent module '{parent_fqn}'"
|
||
|
|
return ".".join(child_split[len(parent_split) :])
|
||
|
|
|
||
|
|
|
||
|
|
def _verify_graph_equivalence(x: torch.nn.Module, y: torch.nn.Module):
|
||
|
|
def graph_dump(graph: torch.fx.Graph) -> str:
|
||
|
|
ret = []
|
||
|
|
nodes_idx: Dict[int, int] = {}
|
||
|
|
|
||
|
|
def arg_dump(arg) -> str:
|
||
|
|
if isinstance(arg, torch.fx.Node):
|
||
|
|
return "%" + str(nodes_idx[id(arg)])
|
||
|
|
return str(arg)
|
||
|
|
|
||
|
|
for i, node in enumerate(graph.nodes):
|
||
|
|
args_dump = [str(arg) for arg in pytree.tree_map(arg_dump, node.args)]
|
||
|
|
args_dump += [
|
||
|
|
f"{key}={value}"
|
||
|
|
for key, value in pytree.tree_map(arg_dump, node.kwargs).items()
|
||
|
|
]
|
||
|
|
target = node.target if node.op == "call_function" else ""
|
||
|
|
ret.append(f"{i}: {node.op}[{target}]({', '.join(args_dump)})")
|
||
|
|
nodes_idx[id(node)] = i
|
||
|
|
return "\n".join(ret)
|
||
|
|
|
||
|
|
assert graph_dump(x.graph) == graph_dump(y.graph)
|
||
|
|
|
||
|
|
|
||
|
|
def _add_spec(gm: torch.nn.Module, spec) -> str:
|
||
|
|
i = 0
|
||
|
|
while hasattr(gm, f"_spec_{i}"):
|
||
|
|
i += 1
|
||
|
|
name = f"_spec_{i}"
|
||
|
|
setattr(gm, name, spec)
|
||
|
|
return name
|
||
|
|
|
||
|
|
|
||
|
|
def _generate_flatten(gm: torch.nn.Module, node, spec) -> torch.fx.Node:
|
||
|
|
name = _add_spec(gm, spec)
|
||
|
|
spec_node = gm.graph.get_attr(name)
|
||
|
|
return gm.graph.call_function(fx_pytree.tree_flatten_spec, (node, spec_node))
|
||
|
|
|
||
|
|
|
||
|
|
def _generate_unflatten(gm: torch.nn.Module, nodes, spec) -> torch.fx.Node:
|
||
|
|
name = _add_spec(gm, spec)
|
||
|
|
spec_node = gm.graph.get_attr(name)
|
||
|
|
return gm.graph.call_function(pytree.tree_unflatten, (nodes, spec_node))
|
||
|
|
|
||
|
|
|
||
|
|
def _add_submodule(mod: torch.nn.Module, target: str, module_to_add: torch.nn.Module):
|
||
|
|
*prefix, field = target.split(".")
|
||
|
|
|
||
|
|
for item in prefix:
|
||
|
|
submod = getattr(mod, item, None)
|
||
|
|
|
||
|
|
if submod is None:
|
||
|
|
submod = torch.nn.Module()
|
||
|
|
setattr(mod, item, submod)
|
||
|
|
|
||
|
|
if not isinstance(submod, torch.nn.Module):
|
||
|
|
return False
|
||
|
|
|
||
|
|
mod = submod
|
||
|
|
|
||
|
|
mod.add_module(field, module_to_add)
|
||
|
|
|
||
|
|
|
||
|
|
class _ModuleFrame:
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
flat_graph,
|
||
|
|
nodes,
|
||
|
|
seen_nodes,
|
||
|
|
seen_modules,
|
||
|
|
parent,
|
||
|
|
module_stack,
|
||
|
|
module_id,
|
||
|
|
module_call_graph: Dict[str, ModuleCallSignature],
|
||
|
|
module: Optional[torch.nn.Module] = None,
|
||
|
|
):
|
||
|
|
self.flat_graph = flat_graph
|
||
|
|
self.nodes = nodes
|
||
|
|
self.seen_nodes = seen_nodes
|
||
|
|
self.seen_modules = seen_modules
|
||
|
|
self.parent = parent
|
||
|
|
self.module_stack = module_stack
|
||
|
|
self.module_id = module_id
|
||
|
|
|
||
|
|
self.module_call_graph = module_call_graph
|
||
|
|
self.verbose = False
|
||
|
|
|
||
|
|
self.fqn = self.module_stack[-1]
|
||
|
|
if module is not None:
|
||
|
|
self.module = module
|
||
|
|
else:
|
||
|
|
self.module = InterpreterModule(torch.fx.Graph())
|
||
|
|
if self.module_id in self.seen_modules:
|
||
|
|
self.cached_graph_module = self.seen_modules[self.module_id]
|
||
|
|
else:
|
||
|
|
self.cached_graph_module = None
|
||
|
|
self.seen_modules[self.module_id] = self.module
|
||
|
|
|
||
|
|
self.graph = self.module.graph
|
||
|
|
|
||
|
|
# Mapping of nodes in the flat graph to nodes in this graph.
|
||
|
|
self.node_map: Dict[torch.fx.Node, torch.fx.Node] = {}
|
||
|
|
self.node_to_placeholder = {}
|
||
|
|
|
||
|
|
self.parent_call_module: Optional[torch.fx.Node] = None
|
||
|
|
if parent is not None:
|
||
|
|
accessor = _compute_accessor(parent.fqn, self.fqn)
|
||
|
|
_add_submodule(
|
||
|
|
parent.module,
|
||
|
|
accessor,
|
||
|
|
self.module
|
||
|
|
if self.cached_graph_module is None
|
||
|
|
else self.cached_graph_module,
|
||
|
|
)
|
||
|
|
self.parent_call_module = parent.graph.call_module(accessor)
|
||
|
|
|
||
|
|
signature = module_call_graph.get(self.fqn)
|
||
|
|
if signature is not None and self.parent is not None:
|
||
|
|
assert signature.in_spec.num_children == 2
|
||
|
|
args_spec = signature.in_spec.children_specs[0]
|
||
|
|
kwargs_spec = signature.in_spec.children_specs[1]
|
||
|
|
assert args_spec.context is None
|
||
|
|
assert kwargs_spec.context is not None
|
||
|
|
|
||
|
|
with self.graph.inserting_after(None):
|
||
|
|
arg_nodes = []
|
||
|
|
for idx in range(args_spec.num_children):
|
||
|
|
arg_nodes.append(self.graph.placeholder(f"_positional_arg_{idx}"))
|
||
|
|
kwarg_nodes = {}
|
||
|
|
for name in kwargs_spec.context:
|
||
|
|
kwarg_nodes[name] = self.graph.placeholder(name)
|
||
|
|
flat_args = _generate_flatten(
|
||
|
|
self.module,
|
||
|
|
(tuple(arg_nodes), kwarg_nodes),
|
||
|
|
signature.in_spec,
|
||
|
|
)
|
||
|
|
for idx, arg in enumerate(signature.inputs):
|
||
|
|
flat_arg_node = self.graph.create_node(
|
||
|
|
op="call_function",
|
||
|
|
target=operator.getitem,
|
||
|
|
args=(flat_args, idx),
|
||
|
|
name=arg.name
|
||
|
|
if not isinstance(arg, ConstantArgument)
|
||
|
|
else f"_constant_{idx}",
|
||
|
|
)
|
||
|
|
if isinstance(arg, ConstantArgument):
|
||
|
|
continue
|
||
|
|
flat_arg_node.meta = copy.copy(self.seen_nodes[arg.name].meta)
|
||
|
|
self.node_to_placeholder[self.seen_nodes[arg.name]] = flat_arg_node
|
||
|
|
|
||
|
|
with self.parent.graph.inserting_before(self.parent_call_module):
|
||
|
|
input_nodes: List[Optional[torch.fx.Node]] = []
|
||
|
|
for input in signature.inputs:
|
||
|
|
if isinstance(input, ConstantArgument) and input.value is None:
|
||
|
|
input_nodes.append(None)
|
||
|
|
else:
|
||
|
|
assert isinstance(input, (TensorArgument, SymIntArgument))
|
||
|
|
input_nodes.append(
|
||
|
|
self.parent.remap_input(self.seen_nodes[input.name])
|
||
|
|
)
|
||
|
|
|
||
|
|
inputs_node = _generate_unflatten(
|
||
|
|
self.parent.module,
|
||
|
|
input_nodes,
|
||
|
|
signature.in_spec,
|
||
|
|
)
|
||
|
|
|
||
|
|
args_node = self.parent.graph.call_function(
|
||
|
|
operator.getitem, (inputs_node, 0)
|
||
|
|
)
|
||
|
|
kwargs_node = self.parent.graph.call_function(
|
||
|
|
operator.getitem, (inputs_node, 1)
|
||
|
|
)
|
||
|
|
arg_nodes = [
|
||
|
|
self.parent.graph.call_function(operator.getitem, (args_node, i))
|
||
|
|
for i in range(args_spec.num_children)
|
||
|
|
]
|
||
|
|
kwarg_nodes = {
|
||
|
|
k: self.parent.graph.call_function(
|
||
|
|
operator.getitem, (kwargs_node, k)
|
||
|
|
)
|
||
|
|
for k in kwargs_spec.context
|
||
|
|
}
|
||
|
|
assert self.parent_call_module is not None
|
||
|
|
self.parent_call_module.args = tuple(arg_nodes)
|
||
|
|
self.parent_call_module.kwargs = kwarg_nodes
|
||
|
|
|
||
|
|
def add_placeholder(self, x):
|
||
|
|
assert x.graph is self.flat_graph
|
||
|
|
# x is not in subgraph, create a new placeholder for subgraph
|
||
|
|
with self.graph.inserting_before(None):
|
||
|
|
placeholder_node = self.graph.placeholder(x.name, type_expr=x.type)
|
||
|
|
# copy all meta fields, even if some fields might be irrelvant for
|
||
|
|
# the placeholder node
|
||
|
|
placeholder_node.meta = copy.copy(x.meta)
|
||
|
|
self.node_to_placeholder[x] = placeholder_node
|
||
|
|
|
||
|
|
def remap_input(self, x):
|
||
|
|
assert x.graph is self.flat_graph
|
||
|
|
if x in self.node_map:
|
||
|
|
return self.node_map[x]
|
||
|
|
if x not in self.node_to_placeholder:
|
||
|
|
self.add_placeholder(x)
|
||
|
|
if self.parent_call_module is not None:
|
||
|
|
# Important to *prepend* the output to match how we are
|
||
|
|
# inserting placeholder nodes.
|
||
|
|
self.parent_call_module.insert_arg(0, self.parent.remap_input(x))
|
||
|
|
return self.node_to_placeholder[x]
|
||
|
|
|
||
|
|
def finalize_outputs(self):
|
||
|
|
orig_outputs = []
|
||
|
|
|
||
|
|
signature = self.module_call_graph.get(self.fqn)
|
||
|
|
if signature is not None and self.parent is not None:
|
||
|
|
for output in signature.outputs:
|
||
|
|
if isinstance(output, (TensorArgument, SymIntArgument)):
|
||
|
|
orig_outputs.append(self.seen_nodes[output.name])
|
||
|
|
else:
|
||
|
|
raise RuntimeError(
|
||
|
|
f"Unsupported data type for output node: {output}"
|
||
|
|
)
|
||
|
|
|
||
|
|
tree_out_node = _generate_unflatten(
|
||
|
|
self.module,
|
||
|
|
tuple(
|
||
|
|
self.node_map[self.seen_nodes[output.name]]
|
||
|
|
for output in orig_outputs
|
||
|
|
),
|
||
|
|
signature.out_spec,
|
||
|
|
)
|
||
|
|
parent_out: Optional[torch.fx.Node] = _generate_flatten(
|
||
|
|
self.parent.module, self.parent_call_module, signature.out_spec
|
||
|
|
)
|
||
|
|
graph_outputs: Union[torch.fx.Node, List[torch.fx.Node]] = tree_out_node
|
||
|
|
else:
|
||
|
|
graph_outputs = []
|
||
|
|
# Iterate through nodes we have copied into self.graph.
|
||
|
|
for orig_node in self.node_map.keys():
|
||
|
|
for user_node in orig_node.users:
|
||
|
|
if user_node.name not in self.seen_nodes:
|
||
|
|
# external user node, need to expose as an output
|
||
|
|
orig_outputs.append(orig_node)
|
||
|
|
graph_outputs.append(self.node_map[orig_node])
|
||
|
|
break
|
||
|
|
|
||
|
|
parent_out = self.parent_call_module
|
||
|
|
if len(graph_outputs) == 1:
|
||
|
|
graph_outputs = graph_outputs[0]
|
||
|
|
|
||
|
|
assert isinstance(graph_outputs, (list, torch.fx.Node))
|
||
|
|
|
||
|
|
self.graph.output(graph_outputs)
|
||
|
|
|
||
|
|
# Rewrite outputs in parent module
|
||
|
|
if parent_out is None:
|
||
|
|
return
|
||
|
|
|
||
|
|
parent_out.meta["val"] = (
|
||
|
|
graph_outputs.meta.get("val")
|
||
|
|
if isinstance(graph_outputs, torch.fx.Node)
|
||
|
|
else [o.meta.get("val") for o in graph_outputs]
|
||
|
|
)
|
||
|
|
|
||
|
|
if len(orig_outputs) == 1 and signature is None:
|
||
|
|
self.parent.node_map[orig_outputs[0]] = parent_out
|
||
|
|
else:
|
||
|
|
for i, orig_output in enumerate(orig_outputs):
|
||
|
|
# Use Proxy to record getitem access.
|
||
|
|
proxy_out = torch.fx.Proxy(parent_out)[i].node # type: ignore[index]
|
||
|
|
proxy_out.meta["val"] = orig_output.meta.get("val")
|
||
|
|
self.parent.node_map[orig_output] = proxy_out
|
||
|
|
|
||
|
|
if self.cached_graph_module is not None:
|
||
|
|
_verify_graph_equivalence(self.cached_graph_module, self.module)
|
||
|
|
|
||
|
|
def copy_node(self, node):
|
||
|
|
self.print("copying", node.format_node())
|
||
|
|
self.node_map[node] = self.graph.node_copy(node, self.remap_input)
|
||
|
|
self.seen_nodes[node.name] = node
|
||
|
|
|
||
|
|
def run_outer(self):
|
||
|
|
i = 0
|
||
|
|
for node in self.flat_graph.nodes:
|
||
|
|
self.print(i, node.meta.get("nn_module_stack"), node.format_node())
|
||
|
|
i += 1
|
||
|
|
|
||
|
|
# Copy all graph inputs
|
||
|
|
node_idx: int = 0
|
||
|
|
node = self.nodes[node_idx]
|
||
|
|
while node.op == "placeholder":
|
||
|
|
self.copy_node(node)
|
||
|
|
node_idx += 1
|
||
|
|
node = self.nodes[node_idx]
|
||
|
|
|
||
|
|
self.run_from(node_idx)
|
||
|
|
|
||
|
|
# Copy graph outputs
|
||
|
|
for node in self.flat_graph.nodes:
|
||
|
|
if node.op == "output":
|
||
|
|
self.copy_node(node)
|
||
|
|
|
||
|
|
def print(self, *args, **kwargs):
|
||
|
|
if self.verbose:
|
||
|
|
print(*args, **kwargs)
|
||
|
|
|
||
|
|
def run_from(self, node_idx):
|
||
|
|
module_idx = 0
|
||
|
|
# Walk through the graph, building up a new graph with the right submodules
|
||
|
|
while node_idx < len(self.nodes):
|
||
|
|
node = self.nodes[node_idx]
|
||
|
|
assert node.op != "placeholder"
|
||
|
|
|
||
|
|
self.print()
|
||
|
|
self.print("STEP", node_idx, node.format_node())
|
||
|
|
self.print(self.module_stack)
|
||
|
|
if node.op == "output":
|
||
|
|
if len(self.module_stack) == 1:
|
||
|
|
# We want the output node of the original graph to be handled
|
||
|
|
# specially by the outermost stack frame (in run_outer). So
|
||
|
|
# skip finalization here.
|
||
|
|
return node_idx
|
||
|
|
|
||
|
|
# We've reached the end of the graph. Wrap up all the existing stack frames.
|
||
|
|
self.finalize_outputs()
|
||
|
|
return node_idx
|
||
|
|
|
||
|
|
node_module_stack = (
|
||
|
|
[path for path, ty in node.meta["nn_module_stack"].values()]
|
||
|
|
if "nn_module_stack" in node.meta
|
||
|
|
else self.module_stack
|
||
|
|
)
|
||
|
|
if node_module_stack[: len(self.module_stack)] != self.module_stack:
|
||
|
|
# This means that the current module is done executing and the
|
||
|
|
# current node is the beginning of a new module.
|
||
|
|
#
|
||
|
|
# In this case, we should finalize this module and return without
|
||
|
|
# incrementing the node counter.
|
||
|
|
self.finalize_outputs()
|
||
|
|
self.print("outlining", self.fqn)
|
||
|
|
self.print(self.graph)
|
||
|
|
return node_idx
|
||
|
|
|
||
|
|
assert node_module_stack is not None
|
||
|
|
|
||
|
|
if _is_prefix(self.module_stack, node_module_stack):
|
||
|
|
# This means that the current node represents the execution of a new
|
||
|
|
# module.
|
||
|
|
next_module = node_module_stack[len(self.module_stack)]
|
||
|
|
self.print("Creating new stack frame for", next_module)
|
||
|
|
# Run a nested version of module outliner from the current node
|
||
|
|
# counter. Once it is complete, continue from that point.
|
||
|
|
node_idx = _ModuleFrame(
|
||
|
|
self.flat_graph,
|
||
|
|
self.nodes,
|
||
|
|
self.seen_nodes,
|
||
|
|
self.seen_modules,
|
||
|
|
self,
|
||
|
|
self.module_stack + [next_module],
|
||
|
|
list(node.meta["nn_module_stack"].keys())[len(self.module_stack)],
|
||
|
|
self.module_call_graph,
|
||
|
|
).run_from(node_idx)
|
||
|
|
module_idx += 1
|
||
|
|
continue
|
||
|
|
|
||
|
|
# The only remaining possibility is that we are in the right stack
|
||
|
|
# frame. Copy the node into this frame's graph and increment the node counter.
|
||
|
|
assert node_module_stack == self.module_stack
|
||
|
|
self.copy_node(node)
|
||
|
|
node_idx += 1
|
||
|
|
|
||
|
|
|
||
|
|
def _outline_submodules(orig_graph: torch.fx.Graph, root_module: UnflattenedModule):
|
||
|
|
seen_nodes: Dict[str, torch.fx.Node] = {}
|
||
|
|
seen_modules: Dict[int, torch.nn.Module] = {}
|
||
|
|
_ModuleFrame(
|
||
|
|
orig_graph,
|
||
|
|
tuple(orig_graph.nodes),
|
||
|
|
seen_nodes,
|
||
|
|
seen_modules,
|
||
|
|
None,
|
||
|
|
[""],
|
||
|
|
"",
|
||
|
|
{
|
||
|
|
entry.fqn: entry.signature
|
||
|
|
for entry in root_module.module_call_graph
|
||
|
|
if entry.signature
|
||
|
|
},
|
||
|
|
module=root_module,
|
||
|
|
).run_outer()
|
||
|
|
|
||
|
|
|
||
|
|
def _sink_params(
|
||
|
|
module: torch.nn.Module,
|
||
|
|
inputs_to_state: Dict[str, str],
|
||
|
|
scope: List[str],
|
||
|
|
):
|
||
|
|
"""Sink params, buffers, and constants from graph inputs into get_attr nodes.
|
||
|
|
|
||
|
|
Exported modules are purely functional, so they pass their parameters and
|
||
|
|
buffers in as inputs to the graph.
|
||
|
|
|
||
|
|
To replicate eager's semantics, we need to get them from the module state
|
||
|
|
via get_attr instead.
|
||
|
|
|
||
|
|
module: GraphModule, potentially containining nested submodules.
|
||
|
|
inputs_to_state: mapping graph input names to the corresponding key in the state_dict.
|
||
|
|
scope: tracks where we are in the module hierarchy, so that we can emit the
|
||
|
|
right `getattr(self, "foo.bar")` calls, etc.
|
||
|
|
"""
|
||
|
|
# We need to use _modules here instead of named_children(), because we
|
||
|
|
# explicitly want duplicate modules to show up in the traversal.
|
||
|
|
for name, submodule in module._modules.items():
|
||
|
|
_sink_params(cast(torch.nn.Module, submodule), inputs_to_state, scope + [name])
|
||
|
|
|
||
|
|
if not hasattr(module, "graph"):
|
||
|
|
# Not all modules have graphs defined, if they are empty modules with no operations (like ParameterList)
|
||
|
|
return
|
||
|
|
|
||
|
|
graph = module.graph
|
||
|
|
inputs = list(filter(lambda n: n.op == "placeholder", graph.nodes))
|
||
|
|
the_last_input = inputs[-1]
|
||
|
|
|
||
|
|
# Also remove from call_module nodes
|
||
|
|
call_module_nodes = filter(lambda n: n.op == "call_module", graph.nodes)
|
||
|
|
for node in call_module_nodes:
|
||
|
|
node.args = tuple(filter(lambda n: n.name not in inputs_to_state, node.args))
|
||
|
|
|
||
|
|
for node in inputs:
|
||
|
|
if node.name not in inputs_to_state:
|
||
|
|
continue
|
||
|
|
|
||
|
|
if len(node.users) > 0:
|
||
|
|
state_name = inputs_to_state[node.name].split(".")
|
||
|
|
# If there's a mismatch beteewn scope name and state name, then there must be multuple scopes
|
||
|
|
# pointing to the same state name, meaning some modules are shared. In such case, we can simply
|
||
|
|
# skip updating the current node because another later iteration will take care of this input
|
||
|
|
# node when the unique match between scope and state name occurs.
|
||
|
|
# To make sure this always happen, we should enforce the invariant that no placeholder node
|
||
|
|
# in the unflattened graph appears in inputs_to_state dict, which means all the extra input
|
||
|
|
# nodes have been handled.
|
||
|
|
if state_name[: len(scope)] != scope:
|
||
|
|
continue
|
||
|
|
attr_path = state_name[len(scope) :]
|
||
|
|
state_attr = _recursive_getattr(module, attr_path)
|
||
|
|
assert isinstance(state_attr, (torch.Tensor, torch.ScriptObject))
|
||
|
|
|
||
|
|
# Make sure the newly created get_attr node is placed after the last placeholder node
|
||
|
|
with graph.inserting_after(the_last_input):
|
||
|
|
new_node = graph.create_node("get_attr", ".".join(attr_path))
|
||
|
|
|
||
|
|
node.replace_all_uses_with(new_node, propagate_meta=True)
|
||
|
|
graph.erase_node(node)
|
||
|
|
if isinstance(module, InterpreterModule):
|
||
|
|
module.finalize()
|
||
|
|
|
||
|
|
|
||
|
|
def _recursive_getattr(obj, attr_path):
|
||
|
|
for attr in attr_path:
|
||
|
|
obj = getattr(obj, attr)
|
||
|
|
|
||
|
|
return obj
|