861 lines
33 KiB
Python
861 lines
33 KiB
Python
import abc
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import copy
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import operator
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from copy import deepcopy
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from enum import Enum
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from itertools import chain
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from typing import Any, cast, Dict, List, Optional, Union
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import torch
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import torch.fx._pytree as fx_pytree
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import torch.utils._pytree as pytree
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from torch.export._tree_utils import reorder_kwargs
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from torch.export.exported_program import (
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ConstantArgument,
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ExportedProgram,
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ModuleCallSignature,
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SymIntArgument,
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TensorArgument,
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)
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from torch.fx._symbolic_trace import is_fx_tracing
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from torch.utils._pytree import GetAttrKey, SequenceKey
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__all__ = ["InterpreterModule", "UnflattenedModule", "unflatten", "FlatArgsAdapter"]
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class _AttrKind(Enum):
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PARAMETER = "parameter"
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BUFFER = "buffer"
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CONSTANT = "constant"
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# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module
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# This installs empty Modules where none exist yet if they are subpaths of target
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def _assign_attr(
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from_obj: Union[torch.Tensor, torch.ScriptObject],
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to_module: torch.nn.Module,
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target: str,
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attr_kind: _AttrKind,
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persistent: bool = True,
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):
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*prefix, field = target.split(".")
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for item in prefix:
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t = getattr(to_module, item, None)
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if t is None:
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t = torch.nn.Module()
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setattr(to_module, item, t)
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to_module = t
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if attr_kind == _AttrKind.PARAMETER:
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assert isinstance(from_obj, torch.nn.Parameter)
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to_module.register_parameter(field, from_obj)
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elif attr_kind == _AttrKind.BUFFER:
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assert isinstance(from_obj, torch.Tensor)
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to_module.register_buffer(field, from_obj, persistent=persistent)
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elif attr_kind == _AttrKind.CONSTANT:
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assert isinstance(from_obj, (torch.Tensor, torch.ScriptObject))
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setattr(to_module, field, from_obj)
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class InterpreterModule(torch.nn.Module):
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"""A module that uses torch.fx.Interpreter to execute instead of the usual
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codegen that GraphModule uses. This provides better stack trace information
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and makes it easier to debug execution.
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"""
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def __init__(
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self,
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graph: torch.fx.Graph,
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):
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super().__init__()
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self.graph = graph
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self.graph.owning_module = self
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def forward(self, *args, **kwargs):
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assert self.graph_module is not None, "Didn't finalize this InterpreterModule"
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if torch.compiler.is_dynamo_compiling():
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# Dynamo cannot trace through torch.fx.Interpreter, so fall back to
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# GraphModule codegen in this instance.
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return self.graph_module(*args, **kwargs)
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else:
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if kwargs:
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# Handle **kwargs. FX only natively supports positional
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# arguments (through placeholders). So in order to pass in
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# kwargs, we must correspond the names of the placeholders with
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# the keys in the kwarg dict.
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arg_list = list(args)
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kwarg_names = self.arg_names[len(arg_list) :]
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for kwarg_name in kwarg_names:
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if kwarg_name in kwargs:
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arg_list.append(kwargs[kwarg_name])
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# Assert that the kwargs passed in exactly match the positional
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# arguments specified by the GraphModule. This should be
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# guaranteed by the unflattening process.
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assert len(kwarg_names) == len(kwargs)
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assert len(arg_list) == len(self.arg_names)
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args = tuple(arg_list)
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return torch.fx.Interpreter(self, graph=self.graph).run(
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*args, enable_io_processing=False
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)
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def finalize(self):
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# We need to "finalize" because GraphModule populates its own state_dict
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# based on the get_attrs observed in the graph. So we need to fully
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# construct the graph and call _sink_params before generating this
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# GraphModule.
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# need to set `graph_module` directly on the dict to avoid it getting
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# registered as a submodule.
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self.__dict__["graph_module"] = torch.fx.GraphModule(self, self.graph)
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self.graph.lint()
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# Cache arg names for kwarg handling (see forward())
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self.arg_names = []
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for node in self.graph.nodes:
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if node.op == "placeholder":
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self.arg_names.append(node.target)
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class FlatArgsAdapter(abc.ABC):
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"""
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Adapts input arguments with ``input_spec`` to align ``target_spec``.
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"""
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@abc.abstractmethod
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def adapt(
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self,
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target_spec: pytree.TreeSpec,
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input_spec: pytree.TreeSpec,
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input_args: List[Any],
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) -> List[Any]:
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"""NOTE: This adapter may mutate given ``input_args_with_path``."""
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...
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class UnflattenedModule(torch.nn.Module):
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def __init__(
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self,
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export_module: ExportedProgram,
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flat_args_adapter: Optional[FlatArgsAdapter] = None,
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):
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super().__init__()
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if export_module.graph_signature.backward_signature is not None:
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raise ValueError("Unflattening on JointExportModule NYI")
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export_graph = deepcopy(export_module.graph)
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self.graph_signature = deepcopy(export_module.graph_signature)
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self.graph = torch.fx.Graph()
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self.module_call_graph = deepcopy(export_module.module_call_graph)
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self.flat_args_adapter = flat_args_adapter
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# Flag to indicate whether args have been adapted.
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self.adapted = False
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_inplace_buffer_mutations(export_graph, self.graph_signature)
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_outline_submodules(export_graph, self)
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self.range_constraints = export_module.range_constraints
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self.equality_constraints: List = []
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state_dict = export_module.state_dict
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for name in self.graph_signature.parameters:
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cloned = torch.nn.Parameter(state_dict[name].clone())
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_assign_attr(
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cloned,
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self,
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name,
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attr_kind=_AttrKind.PARAMETER,
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)
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non_persistent_buffers = set(self.graph_signature.non_persistent_buffers)
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for name in self.graph_signature.buffers:
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if name in non_persistent_buffers:
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persistent = False
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cloned = export_module.constants[name].clone()
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else:
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persistent = True
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cloned = state_dict[name].clone()
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_assign_attr(
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cloned,
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self,
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name,
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attr_kind=_AttrKind.BUFFER,
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persistent=persistent,
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)
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for fqn in chain(
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self.graph_signature.lifted_tensor_constants,
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self.graph_signature.lifted_custom_objs,
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):
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constant = export_module.constants[fqn]
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if isinstance(constant, torch.Tensor):
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constant = constant.clone()
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_assign_attr(
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constant,
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self,
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fqn,
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attr_kind=_AttrKind.CONSTANT,
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)
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inputs_to_state: Dict[str, str] = {
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**self.graph_signature.inputs_to_parameters,
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**self.graph_signature.inputs_to_buffers,
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**self.graph_signature.inputs_to_lifted_tensor_constants,
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**self.graph_signature.inputs_to_lifted_custom_objs,
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}
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_sink_params(self, inputs_to_state, [])
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# Check all input nodes has been processed.
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for module in self.modules():
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if not isinstance(module, torch.fx.GraphModule):
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continue
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for node in module.graph.nodes:
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if node.op != "placeholder":
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continue
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assert node.name not in inputs_to_state
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# Cache so we don't have to compute this every time.
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# NOTE: this needs to be kept in sync with the placeholders in
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# self.graph, but currently we have no way to guarantee that.
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self.input_placeholders = [
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node for node in self.graph.nodes if node.op == "placeholder"
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]
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self.check_input_constraints = True
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assert self.module_call_graph[0].fqn == ""
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def forward(self, *args, **kwargs):
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signature = self.module_call_graph[0].signature
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reordered_kwargs = reorder_kwargs(kwargs, signature.in_spec)
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flat_args_with_path, in_spec = pytree.tree_flatten_with_path(
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(args, reordered_kwargs)
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)
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flat_args = [x[1] for x in flat_args_with_path]
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if is_fx_tracing():
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return_val = torch.fx.Interpreter(self, graph=self.graph).run(
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*flat_args, enable_io_processing=False
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)
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# For scalar return value, fx.Graph wraps in a tuple
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if isinstance(return_val, tuple) and len(return_val) == 1:
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return return_val[0]
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return return_val
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if in_spec != signature.in_spec:
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if not self.adapted:
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print(
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"Input treespec does not match with exported module's: \n"
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f"Input treespec: {in_spec}. ",
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f"Exported module treespec: {signature.in_spec}",
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)
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if self.flat_args_adapter is None:
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raise TypeError(
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"There is no flat args adapter sepcified. "
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"Are you sure you are calling this with the right arguments? "
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)
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else:
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if not self.adapted:
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print("Adapting flat arg to match exported module's treespec")
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flat_args = self.flat_args_adapter.adapt(
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target_spec=signature.in_spec,
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input_spec=in_spec,
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input_args=flat_args,
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)
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self.adapted = True
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if len(flat_args) != signature.in_spec.num_leaves:
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raise TypeError(
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f"Flat args adaption failed, number of args mismatch "
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f"Adatped: {len(flat_args)} \n"
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f"Exported module: {signature.in_spec.num_leaves}"
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)
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if self.check_input_constraints:
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# Import here to avoid an unfortunate circular dependency.
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# TODO(suo): untangle this.
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from torch._export.utils import _check_input_constraints_for_graph
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if self.adapted is True:
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# TODO(suo): The FlatArgsAdapter returns a list of flat args,
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# which we don't have keypaths for. For now, just create a dummy
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# keypath to associate with the arg.
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new_flat_args_with_path = [ # type: ignore[var-annotated]
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((SequenceKey(idx=0), GetAttrKey(name="<unknown location>")), arg)
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for arg in flat_args
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]
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else:
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new_flat_args_with_path = flat_args_with_path # type: ignore[assignment]
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_check_input_constraints_for_graph(
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self.input_placeholders, new_flat_args_with_path, self.range_constraints
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)
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tree_out = torch.fx.Interpreter(self, graph=self.graph).run(
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*flat_args, enable_io_processing=False
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)
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return pytree.tree_unflatten(tree_out, signature.out_spec)
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def unflatten(
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module: ExportedProgram, flat_args_adapter: Optional[FlatArgsAdapter] = None
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) -> UnflattenedModule:
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"""Unflatten an ExportedProgram, producing a module with the same module
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hierarchy as the original eager module. This can be useful if you are trying
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to use :mod:`torch.export` with another system that expects a module
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hierachy instead of the flat graph that :mod:`torch.export` usually produces.
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.. note:: The args/kwargs of unflattened modules will not necessarily match
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the eager module, so doing a module swap (e.g. :code:`self.submod =
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new_mod`) will not necessarily work. If you need to swap a module out, you
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need to set the :code:`preserve_module_call_signature` parameter of
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:func:`torch.export.export`.
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Args:
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module (ExportedProgram): The ExportedProgram to unflatten.
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flat_args_adapter (Optional[FlatArgsAdapter]): Adapt flat args if input TreeSpec does not match with exported module's.
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Returns:
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An instance of :class:`UnflattenedModule`, which has the same module
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hierarchy as the original eager module pre-export.
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"""
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return UnflattenedModule(module, flat_args_adapter)
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def _inplace_buffer_mutations(graph: torch.fx.Graph, graph_signature) -> None:
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"""Transform buffer mutations from their functionalized form into a copy_
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node in the graph.
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Functionalization represents buffer mutation by passing the buffer as an input and output. So for example, the eager code:
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def forward(self, x):
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self.buffer += x
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return x * x
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Will become a graph that looks like:
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def forward(self, buffer, x):
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mutated_buffer = aten.add(buffer, x)
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mul = aten.mul(x, x)
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return (mutated_buffer, mul)
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We want to inplace this into something that looks like the original eager code:
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def forward(self, buffer, x):
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mutated_buffer = aten.add(buffer, x)
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buffer.copy_(mutated_buffer)
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mul = aten.mul(x, x)
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return (mul,)
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"""
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output_node = next(iter(reversed(graph.nodes)))
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assert output_node.op == "output" and len(output_node.args) == 1
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return_args = output_node.args[0]
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mutation_node_to_buffer = graph_signature.buffers_to_mutate
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mutations = return_args[: len(mutation_node_to_buffer)]
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buffers_to_inputs = {v: k for k, v in graph_signature.inputs_to_buffers.items()}
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input_name_to_node = {
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node.name: node for node in graph.nodes if node.op == "placeholder"
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}
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for mutation in mutations:
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buffer_name = mutation_node_to_buffer[mutation.name]
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input_name = buffers_to_inputs[buffer_name]
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input_node = input_name_to_node[input_name]
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with graph.inserting_after(mutation):
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new_node = graph.create_node(
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"call_function", torch.ops.aten.copy_, (input_node, mutation)
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)
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for k, v in mutation.meta.items():
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new_node.meta[k] = v
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# Replace all uses of the previously functional mutation with our copy_ output.
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mutation.replace_all_uses_with(new_node, lambda x: x is not new_node)
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# Remove the mutated buffer from the graph outputs, since we don't need to
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# thread it through anymore. We don't need to handle the inputs, which will
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# be handled by _sink_params.
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user_outputs = tuple(
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return_args[len(mutation_node_to_buffer) :],
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)
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output_node.args = ((user_outputs),)
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def _is_prefix(candidate, target):
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"""Check whether `candidate` is a prefix of `target`."""
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return len(candidate) < len(target) and target[: len(candidate)] == candidate
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def _compute_accessor(parent_fqn: str, child_fqn: str) -> str:
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if parent_fqn == "":
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# Handle the root module correctly.
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return child_fqn
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parent_split = parent_fqn.split(".")
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child_split = child_fqn.split(".")
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assert (
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child_split[: len(parent_split)] == parent_split
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), f"Child module '{child_fqn}' is not a descendant of parent module '{parent_fqn}'"
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return ".".join(child_split[len(parent_split) :])
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def _verify_graph_equivalence(x: torch.nn.Module, y: torch.nn.Module):
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def graph_dump(graph: torch.fx.Graph) -> str:
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ret = []
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nodes_idx: Dict[int, int] = {}
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def arg_dump(arg) -> str:
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if isinstance(arg, torch.fx.Node):
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return "%" + str(nodes_idx[id(arg)])
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return str(arg)
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for i, node in enumerate(graph.nodes):
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args_dump = [str(arg) for arg in pytree.tree_map(arg_dump, node.args)]
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args_dump += [
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f"{key}={value}"
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for key, value in pytree.tree_map(arg_dump, node.kwargs).items()
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]
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target = node.target if node.op == "call_function" else ""
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ret.append(f"{i}: {node.op}[{target}]({', '.join(args_dump)})")
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nodes_idx[id(node)] = i
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return "\n".join(ret)
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assert graph_dump(x.graph) == graph_dump(y.graph)
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def _add_spec(gm: torch.nn.Module, spec) -> str:
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i = 0
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while hasattr(gm, f"_spec_{i}"):
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i += 1
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name = f"_spec_{i}"
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setattr(gm, name, spec)
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return name
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def _generate_flatten(gm: torch.nn.Module, node, spec) -> torch.fx.Node:
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name = _add_spec(gm, spec)
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spec_node = gm.graph.get_attr(name)
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return gm.graph.call_function(fx_pytree.tree_flatten_spec, (node, spec_node))
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def _generate_unflatten(gm: torch.nn.Module, nodes, spec) -> torch.fx.Node:
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name = _add_spec(gm, spec)
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spec_node = gm.graph.get_attr(name)
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return gm.graph.call_function(pytree.tree_unflatten, (nodes, spec_node))
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def _add_submodule(mod: torch.nn.Module, target: str, module_to_add: torch.nn.Module):
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*prefix, field = target.split(".")
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for item in prefix:
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submod = getattr(mod, item, None)
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if submod is None:
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submod = torch.nn.Module()
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setattr(mod, item, submod)
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if not isinstance(submod, torch.nn.Module):
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return False
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mod = submod
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mod.add_module(field, module_to_add)
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class _ModuleFrame:
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def __init__(
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self,
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flat_graph,
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nodes,
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seen_nodes,
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seen_modules,
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parent,
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module_stack,
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module_id,
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module_call_graph: Dict[str, ModuleCallSignature],
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module: Optional[torch.nn.Module] = None,
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):
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self.flat_graph = flat_graph
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self.nodes = nodes
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self.seen_nodes = seen_nodes
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self.seen_modules = seen_modules
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self.parent = parent
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self.module_stack = module_stack
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self.module_id = module_id
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self.module_call_graph = module_call_graph
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self.verbose = False
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self.fqn = self.module_stack[-1]
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if module is not None:
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self.module = module
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else:
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self.module = InterpreterModule(torch.fx.Graph())
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if self.module_id in self.seen_modules:
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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
|