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Source code for torch.export.unflatten

import abc
import copy
import operator
from collections import defaultdict
from copy import deepcopy
from enum import Enum
from typing import Any, cast, Dict, List, Optional, Set, Tuple, Union

import torch
import torch.fx._pytree as fx_pytree
import torch.utils._pytree as pytree
from torch._library.fake_class_registry import FakeScriptObject
from torch.export._tree_utils import reorder_kwargs
from torch.export.exported_program import (
    ConstantArgument,
    ExportedProgram,
    InputKind,
    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 not isinstance(
            from_obj, FakeScriptObject
        ), "FakeScriptObject should only exist during tracing."
        assert isinstance(
            from_obj,
            (
                torch.Tensor,
                torch.ScriptObject,
            ),
        )
        setattr(to_module, field, from_obj)


[docs]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)
[docs]class FlatArgsAdapter(abc.ABC): """ Adapts input arguments with ``input_spec`` to align ``target_spec``. """
[docs] @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") fqn_list = [entry.fqn for entry in export_module.module_call_graph] assert fqn_list[0] == "" 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 = [] # aliasing/unused param or buffer issues: # in strict-mode export, dynamo export will deduplicate aliased tensors, # and ignore unused tensors. For aliasing, this causes issues when some aliases # are unused, and we're unable to match the placeholder node to the correct FQN. # This leads to the graph signature potentially having the wrong target FQN, # and downstream issues where parameters are assigned to the wrong target attribute, # mismatching the relevant placeholder node in the unflattened module. # To resolve this we restore (_assign_attr) all aliased/unused tensors in # the state_dict as module attributes, but only keep the used tensors in the # graph's forward pass (_sink_params). state_dict = export_module.state_dict assigned_params: Set[str] = set() # tracking unused params id_to_param: Dict[int, torch.nn.Parameter] = {} # handling weight-sharing for name in self.graph_signature.parameters: # this loop adds used params param = state_dict[name] if id(param) not in id_to_param: id_to_param[id(param)] = torch.nn.Parameter(param.clone()) _assign_attr( id_to_param[id(param)], self, name, attr_kind=_AttrKind.PARAMETER, ) assigned_params.add(name) non_persistent_buffers = set(self.graph_signature.non_persistent_buffers) assigned_buffers: Set[str] = set() # tracking unused buffers id_to_buffer: Dict[ int, Tuple[torch.nn.Parameter, bool] ] = {} # handle weight-sharing for name in self.graph_signature.buffers: # this loop adds used buffers if name in non_persistent_buffers: persistent = False buffer = export_module.constants[name] else: persistent = True buffer = state_dict[name] if id(buffer) not in id_to_buffer: id_to_buffer[id(buffer)] = (buffer.clone(), persistent) _assign_attr( id_to_buffer[id(buffer)][0], self, name, attr_kind=_AttrKind.BUFFER, persistent=persistent, ) assigned_buffers.add(name) # restore aliased/unused params and buffers # these appear in state dict but not graph signature for name, tensor in state_dict.items(): if name in assigned_params or name in assigned_buffers: # already assigned continue is_buffer = False if id(tensor) in id_to_buffer or not isinstance( tensor, torch.nn.Parameter ): # aliased buffer is_buffer = True if is_buffer: if ( id(tensor) not in id_to_buffer ): # this is completely unused (not weight-sharing) id_to_buffer[id(tensor)] = ( tensor, True, ) # assign to respect original model _assign_attr( id_to_buffer[id(tensor)][0], self, name, attr_kind=_AttrKind.BUFFER, persistent=True, ) else: if id(tensor) not in id_to_param: # this is unused id_to_param[id(tensor)] = tensor _assign_attr( id_to_param[id(tensor)], self, name, attr_kind=_AttrKind.PARAMETER, ) # use id map so we don't double-clone aliased constants id_to_const: Dict[int, Union[torch.Tensor, torch._C.ScriptObject]] = {} for fqn, constant in export_module.constants.items(): if id(constant) not in id_to_const: if isinstance(constant, torch.Tensor): constant = constant.clone() id_to_const[id(constant)] = constant _constant = id_to_const[id(constant)] _assign_attr( _constant, self, fqn, attr_kind=_AttrKind.CONSTANT, ) # This is to handle parameters/buffers that point to the same tensor # object id -> list of (node_name, target_name) consts_map: Dict[int, List[Tuple[str, str]]] = defaultdict(list) consts_targets: Set[str] = set() def add_to_consts_map(obj_id, node_name, target_name): name_list = consts_map[obj_id] name_list.append((node_name, target_name)) added_params_buffers: Set[str] = set() # track aliased/unused params, buffers for s in self.graph_signature.input_specs: if s.kind == InputKind.PARAMETER or ( s.kind == InputKind.BUFFER and s.persistent ): assert hasattr(s.arg, "name") assert isinstance(s.target, str) add_to_consts_map( id(export_module.state_dict[s.target]), s.arg.name, s.target ) consts_targets.add(s.target) added_params_buffers.add(s.target) elif ( (s.kind == InputKind.BUFFER and not s.persistent) or s.kind == InputKind.CONSTANT_TENSOR or s.kind == InputKind.CUSTOM_OBJ ): assert hasattr(s.arg, "name") assert isinstance(s.target, str) add_to_consts_map( id(export_module.constants[s.target]), s.arg.name, s.target ) consts_targets.add(s.target) # add constants that are aliased and don't appear in graph signature for const_name, const in export_module.constants.items(): if const_name not in consts_targets: assert ( id(const) in consts_map ), "Constants should be either aliased or appear in graph signature" ph_name, _ = consts_map[id(const)][0] add_to_consts_map(id(const), ph_name, const_name) added_params_buffers.add(s.target) # add aliased/unused params and buffers that don't appear in graph signature for fqn, tensor in export_module.state_dict.items(): if fqn not in added_params_buffers: if id(tensor) not in consts_map: # completely unused (no weight-sharing), ignore. # this weight doesn't appear in graph module, # so won't cause FQN assignment issues continue ph_name, _ = consts_map[id(tensor)][0] add_to_consts_map(id(tensor), ph_name, fqn) # node name -> list of possible targets inputs_to_state: Dict[str, List[str]] = {} for node_target in consts_map.values(): targets = [t[1] for t in node_target] for n, _ in node_target: inputs_to_state[n] = targets _sink_params(self, inputs_to_state, []) # Check all input nodes has been processed. for name, module in self.named_modules(): if not hasattr(module, "graph"): continue for node in module.graph.nodes: if node.op != "placeholder": continue assert ( node.name not in inputs_to_state ), f"{node.name} was not sunk into the module {name} which has the graph: {module.graph}" # 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 # TODO(zhxchen17) We can register modules ahead of time instead of reorder later. fqn_order = {fqn: i for i, fqn in enumerate(fqn_list)} # In the case of legacy IR, we might be missing some modules from metadata. for name, _ in self.named_modules(remove_duplicate=False): if name not in fqn_order: fqn_order[name] = len(fqn_order) _reorder_submodules(self, fqn_order) assert [fqn for fqn, _ in self.named_modules(remove_duplicate=False)] == list( fqn_order.keys() ) def _print_graph(self): for fqn, mod in self.named_modules(): print(fqn + ":") if hasattr(mod, "graph") and isinstance(mod.graph, torch.fx.Graph): print(mod.graph) 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)
[docs]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 _get_submodule(mod: torch.nn.Module, target: str): *prefix, field = target.split(".") for item in prefix: submod = getattr(mod, item, None) if submod is None: return None if not isinstance(submod, torch.nn.Module): return None mod = submod return getattr(mod, field, None) 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 self.fqn != "", f"Cannot add placeholder {x} to root module" 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 _reorder_submodules( parent: torch.nn.Module, fqn_order: Dict[str, int], prefix: str = "" ): # TODO Can be optimized by adding submodules ahead of time. if prefix == "": for fqn in list(fqn_order.keys())[1:]: if _get_submodule(parent, fqn) is None: _add_submodule(parent, fqn, torch.nn.Module()) children = [] for name, child in list(parent._modules.items()): if child is None: continue fqn = prefix + name _reorder_submodules(child, fqn_order, prefix=fqn + ".") delattr(parent, name) children.append((fqn_order[fqn], name, child)) children.sort(key=operator.itemgetter(0)) for _, name, child in children: parent.register_module(name, child) def _sink_params( module: torch.nn.Module, inputs_to_state: Dict[str, List[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 = None for sn in inputs_to_state[node.name]: sn_split = sn.split(".") if sn_split[: len(scope)] == scope: state_name = sn_split break # 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 is None: 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

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