import torch from torch.fx.graph_module import GraphModule from typing import Callable, List, Dict, Any, Optional from torch.fx._compatibility import compatibility import inspect @compatibility(is_backward_compatible=True) class Partition: def __init__(self, name: str): self.name: str = name self.submod_name = f'submod_{name}' self.node_names: List[str] = [] self.inputs: Dict[str, None] = {} self.outputs: Dict[str, None] = {} self.partitions_dependent_on: Dict[str, None] = {} self.partition_dependents: Dict[str, None] = {} self.graph : torch.fx.graph.Graph = torch.fx.graph.Graph() self.environment : Dict[torch.fx.node.Node, torch.fx.node.Node] = {} self.targets : Dict[str, Any] = {} def __repr__(self) -> str: return f"name: {self.name},\n" \ f" nodes: {self.node_names},\n" \ f" inputs: {self.inputs},\n" \ f" outputs: {self.outputs},\n" \ f" partitions depenent on: {self.partitions_dependent_on},\n" \ f" parition dependents: {self.partition_dependents}" # Creates subgraphs out of main graph @compatibility(is_backward_compatible=True) def split_module( m: GraphModule, root_m: torch.nn.Module, split_callback: Callable[[torch.fx.node.Node], int], qualname_map: Optional[Dict[str, str]] = None, ): """ Creates subgraphs out of main graph Args: m (GraphModule): Graph module to split root_m (torch.nn.Module): root nn module. Not currently used. Included because the root nn module is usually transformed via torch.fx._symbolic_trace.symbolic_trace (see example below) split_callback (Callable[[torch.fx.node.Node], int]): Callable function that maps a given Node instance to a numeric partition identifier. split_module will use this function as the policy for which operations appear in which partitions in the output Module. qualname_map: Optional[Dict[str, str]]: optional output parameter that returns a mapping from new target names in the module after split to old target names in the original module. Returns: GraphModule: the module after split. Example: This is a sample setup: import torch from torch.fx.symbolic_trace import symbolic_trace from torch.fx.graph_module import GraphModule from torch.fx.node import Node from torch.fx.passes.split_module import split_module class MyModule(torch.nn.Module): def __init__(self): super().__init__() self.param = torch.nn.Parameter(torch.rand(3, 4)) self.linear = torch.nn.Linear(4, 5) def forward(self, x, y): z = self.linear(x + self.param).clamp(min=0.0, max=1.0) w = self.linear(y).clamp(min=0.0, max=1.0) return z + w # symbolically trace model my_module = MyModule() my_module_traced = symbolic_trace(my_module) # random mod partitioning partition_counter = 0 NPARTITIONS = 3 def mod_partition(node: Node): global partition_counter partition = partition_counter % NPARTITIONS partition_counter = (partition_counter + 1) % NPARTITIONS return partition # split module in module with submodules module_with_submodules = split_module( my_module_traced, my_module, mod_partition ) Output looks like this. Original graph is broken into partitions > print(module_with_submodules) GraphModule( (submod_0): GraphModule( (linear): Linear(in_features=4, out_features=5, bias=True) ) (submod_1): GraphModule( (linear): Linear(in_features=4, out_features=5, bias=True) ) (submod_2): GraphModule() ) def forward(self, x, y): param = self.param submod_0 = self.submod_0(x, param, y); x = param = y = None getitem = submod_0[0] getitem_1 = submod_0[1]; submod_0 = None submod_1 = self.submod_1(getitem, getitem_1); getitem = getitem_1 = None getitem_2 = submod_1[0] getitem_3 = submod_1[1]; submod_1 = None submod_2 = self.submod_2(getitem_2, getitem_3); getitem_2 = getitem_3 = None return submod_2 Output of split module is the same as output of input traced module. This is an example within a test setting: > orig_out = my_module_traced(x, y) > submodules_out = module_with_submodules(x, y) > self.assertEqual(orig_out, submodules_out) True """ partitions: Dict[str, Partition] = {} orig_nodes: Dict[str, torch.fx.node.Node] = {} def record_cross_partition_use(def_node : torch.fx.node.Node, use_node : Optional[torch.fx.node.Node]): # noqa: B950 def_partition_name = getattr(def_node, '_fx_partition', None) use_partition_name = getattr(use_node, '_fx_partition', None) if def_partition_name != use_partition_name: if def_partition_name is not None: def_partition = partitions[def_partition_name] def_partition.outputs.setdefault(def_node.name) if use_partition_name is not None: def_partition.partition_dependents.setdefault(use_partition_name) if use_partition_name is not None: use_partition = partitions[use_partition_name] use_partition.inputs.setdefault(def_node.name) if def_partition_name is not None: use_partition.partitions_dependent_on.setdefault(def_partition_name) # split nodes into parititons for node in m.graph.nodes: orig_nodes[node.name] = node # TODO currently placeholders/parameters aren't put into random partitions, # rather they're added to the graphs where they are used down below if node.op in ["placeholder", "get_attr"]: continue if node.op == 'output': torch.fx.graph.map_arg(node.args[0], lambda n: record_cross_partition_use(n, None)) continue partition_name = str(split_callback(node)) # add node to partitions partition = partitions.get(partition_name) if partition is None: partitions[partition_name] = partition = Partition(partition_name) partition.node_names.append(node.name) node._fx_partition = partition_name torch.fx.graph.map_arg(node.args, lambda def_node: record_cross_partition_use(def_node, node)) torch.fx.graph.map_arg(node.kwargs, lambda def_node: record_cross_partition_use(def_node, node)) # noqa: B950 # find partitions with no dependencies root_partitions : List[str] = [] for partition_name, partition in partitions.items(): if not len(partition.partitions_dependent_on): root_partitions.append(partition_name) # check partitions for circular dependencies and create topological partition ordering sorted_partitions : List[str] = [] while root_partitions: root_partition = root_partitions.pop() sorted_partitions.append(root_partition) for dependent in partitions[root_partition].partition_dependents: partitions[dependent].partitions_dependent_on.pop(root_partition) if not partitions[dependent].partitions_dependent_on: root_partitions.append(dependent) if len(sorted_partitions) != len(partitions): raise RuntimeError("cycle exists between partitions!") # add placeholders to parititons for partition_name in sorted_partitions: partition = partitions[partition_name] for input in partition.inputs: placeholder = partition.graph.placeholder(input) placeholder.meta = orig_nodes[input].meta.copy() partition.environment[orig_nodes[input]] = placeholder # Transform nodes and collect targets for partition's submodule for node in m.graph.nodes: if hasattr(node, '_fx_partition'): partition = partitions[node._fx_partition] # swap out old graph nodes in kw/args with references to new nodes in this submodule environment = partition.environment gathered_args = torch.fx.graph.map_arg(node.args, lambda n : environment[n]) gathered_kwargs = torch.fx.graph.map_arg(node.kwargs, lambda n : environment[n]) if node.op not in ['call_module', 'get_attr']: target = node.target else: target_atoms = node.target.split('.') target_attr = m for atom in target_atoms: if not hasattr(target_attr, atom): raise RuntimeError(f'Operator target {node.target} not found!') target_attr = getattr(target_attr, atom) # target = target_atoms[-1] target = '_'.join(target_atoms) partition.targets[target] = target_attr # Fill in the passed-in mapping from new qualname to old qualname if qualname_map is not None: # When creating the split module later, the submodules will have # path prefix matching the corresponding partition's submod_name qualname = f'{partition.submod_name}.{target}' qualname_map[qualname] = node.target assert isinstance(gathered_args, tuple) assert isinstance(gathered_kwargs, dict) new_node = partition.graph.create_node(op=node.op, target=target, args=gathered_args, kwargs=gathered_kwargs) new_node.meta = node.meta.copy() partition.environment[node] = new_node # Set up values to construct base module base_mod_env : Dict[str, torch.fx.node.Node] = {} base_mod_graph : torch.fx.graph.Graph = torch.fx.graph.Graph() base_mod_attrs : Dict[str, torch.fx.graph_module.GraphModule] = {} for node in m.graph.nodes: if node.op == 'placeholder': default_value = node.args[0] if len(node.args) > 0 else inspect.Signature.empty base_mod_env[node.name] = base_mod_graph.placeholder( node.target, type_expr=node.type, default_value=default_value) base_mod_env[node.name].meta = node.meta.copy() elif node.op == 'get_attr': base_mod_env[node.name] = base_mod_graph.get_attr(node.target) base_mod_env[node.name].meta = node.meta.copy() attr_val = m for atom in node.target.split('.'): if not hasattr(attr_val, atom): raise RuntimeError(f'Node target {node.target} not found!') attr_val = getattr(attr_val, atom) base_mod_attrs[node.target] = attr_val # Do some things iterating over the partitions in topological order again: # 1) Finish off submodule Graphs by setting corresponding outputs # 2) Construct GraphModules for each submodule # 3) Construct the base graph by emitting calls to those submodules in # topological order for partition_name in sorted_partitions: partition = partitions[partition_name] # Set correct output values output_vals = tuple(partition.environment[orig_nodes[name]] for name in partition.outputs) output_vals = output_vals[0] if len(output_vals) == 1 else output_vals # type: ignore[assignment] partition.graph.output(output_vals) # Construct GraphModule for this partition base_mod_attrs[partition.submod_name] = torch.fx.graph_module.GraphModule(partition.targets, partition.graph) # noqa: B950 # Emit call in base graph to this submodule output_val = base_mod_graph.call_module(partition.submod_name, tuple(base_mod_env[name] for name in partition.inputs)) if len(partition.outputs) > 1: # Unpack multiple return values from submodule output_val_proxy = torch.fx.proxy.Proxy(output_val) for i, output_name in enumerate(partition.outputs): base_mod_env[output_name] = output_val_proxy[i].node # type: ignore[index] else: base_mod_env[list(partition.outputs)[0]] = output_val for node in m.graph.nodes: if node.op == 'output': base_mod_graph.output(torch.fx.graph.map_arg(node.args[0], lambda n : base_mod_env[n.name])) # noqa: B950 return torch.fx.graph_module.GraphModule(base_mod_attrs, base_mod_graph)