MLPY/Lib/site-packages/torch/fx/passes/split_module.py

import inspect
from typing import Any, Callable, Dict, List, Optional, Set, TYPE_CHECKING
from collections import OrderedDict
import logging

import torch
from torch.fx._compatibility import compatibility
from torch.fx.graph_module import GraphModule
from torch.fx.node import Node

if TYPE_CHECKING:
    import sympy  # noqa: F401

__all__ = ["Partition", "split_module"]
_LOGGER = logging.getLogger(__name__)

@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.dependencies: Dict[str, None] = {}
        self.dependents: Dict[str, None] = {}
        self.graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
        self.environment: Dict[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 depended on: {self.dependencies},\n"
            f" partition dependents: {self.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[[Node], int],

    qualname_map: Optional[Dict[str, str]] = None,

    keep_original_order: Optional[bool] = False,

    keep_original_node_name: Optional[bool] = False,

):
    """

    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[[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.

        keep_original_order: Optional[bool]: keep the original order of the GraphModule

            or use the Topological order of the new constructed GraphModule





    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

    """

    def construct_graph(

        node: Node,

        base_mod_env: Dict[str, Node],

        base_mod_attrs: Dict[str, torch.fx.graph_module.GraphModule],

    ):
        if node.op == "placeholder":
            default_value = (
                node.args[0] if len(node.args) > 0 else inspect.Signature.empty
            )
            if keep_original_node_name:
                args = () if default_value is inspect.Signature.empty else (default_value,)
                base_mod_env[node.name] = base_mod_graph.create_node('placeholder', node.name, args=args, type_expr=node.type)
            else:
                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("."):  # type: ignore[union-attr]
                if not hasattr(attr_val, atom):
                    raise AttributeError(f"Node target {node.target} not found!")
                attr_val = getattr(attr_val, atom)
            base_mod_attrs[node.target] = attr_val  # type: ignore[index]
        return base_mod_env, base_mod_attrs

    partitions: Dict[str, Partition] = {}
    orig_nodes: Dict[str, Node] = {}
    symbol_to_node: Dict["sympy.Symbol", Node] = {}

    def record_cross_partition_use(

        def_node: Node, use_node: Optional[Node]

    ):  # noqa: B950
        from torch.fx.experimental.symbolic_shapes import free_symbols

        defined = getattr(def_node, "_fx_partition", None)
        used = getattr(use_node, "_fx_partition", None)
        if defined != used:
            if defined is not None:
                def_partition = partitions[defined]
                def_partition.outputs.setdefault(def_node.name)
                if used is not None:
                    def_partition.dependents.setdefault(used)

            if used is not None:
                use_partition = partitions[used]
                use_partition.inputs.setdefault(def_node.name)
                if (def_val := def_node.meta.get("example_value")) is not None:
                    for s in sorted(free_symbols(def_val), key=str):
                        use_partition.inputs.setdefault(symbol_to_node[s].name)
                if defined is not None:
                    use_partition.dependencies.setdefault(defined)

    def instantiate_node_partition_mapping(node):
        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

    # Global State Nodes are nodes which by their global state effects,
    # "taint" all downstream nodes while they are active.
    GLOBAL_STATE_NODES = [
        torch.amp._enter_autocast,
        torch.amp._exit_autocast,
        torch._C._set_grad_enabled
    ]

    # For grad regions:
    # ------------------------
    # 1. first region: we do nothing
    # 2. subsequent regions: we insert the set_grad at the beginning
    grad_regions: OrderedDict[Node, Set[int]] = OrderedDict()

    # For autocast regions:
    # ------------------------
    # 1. first region: we will only insert the _exit at the end
    # 2. intermediate regions: we will insert both the
    #    _enter at the beginning and _exit at the end
    # 3. last region: we will only insert _enter at the beginning
    # We will do so in the order in which the autocasts were instantiated.
    autocast_regions: OrderedDict[Node, Set[int]] = OrderedDict()
    autocast_exits: Dict[Node, Optional[Node]] = {}

    active_grad = None
    active_autocasts = set()

    import sympy  # noqa: F811

    for node in m.graph.nodes:
        if node.op in ["placeholder", "get_attr", "output"]:
            if (
                node.op == "placeholder" and
                (val := node.meta.get("example_value")) is not None and
                isinstance(val, torch.SymInt) and
                isinstance(val.node.expr, sympy.Symbol)
            ):
                symbol_to_node[val.node.expr] = node
            continue

        instantiate_node_partition_mapping(node)

        if node.op == "call_function" and node.target in GLOBAL_STATE_NODES:
            if node.target == torch._C._set_grad_enabled:
                assert len(node.args) == 1
                assert isinstance(node.args[0], bool)
                active_grad = node
                grad_regions[active_grad] = set({split_callback(node)})
            elif node.target == torch.amp._enter_autocast:
                # Should all be python constants
                assert all(not isinstance(arg, Node) for arg in node.args)
                active_autocasts.add(node)
                autocast_regions[node] = set({split_callback(node)})
                autocast_exits[node] = None
            elif node.target == torch.amp._exit_autocast:
                assert len(node.args) == 1
                autocast_regions[node.args[0]].add(split_callback(node))
                active_autocasts.remove(node.args[0])
                autocast_exits[node.args[0]] = node

        if active_grad is not None:
            grad_regions[active_grad].add(split_callback(node))

        for a in active_autocasts:
            autocast_regions[a].add(split_callback(node))

    assert all(v is not None for v in autocast_exits.values()), "autocast must exit"

    autocast_regions = {k: sorted(v) for k, v in autocast_regions.items()}
    grad_regions = {k: sorted(v) for k, v in grad_regions.items()}

    if _LOGGER.isEnabledFor(logging.DEBUG):
        _LOGGER.debug("autocast_regions: %s", autocast_regions)
        _LOGGER.debug("grad_regions: %s", grad_regions)

    assert_monotonically_increasing = bool(autocast_regions) or bool(grad_regions)

    # split nodes into partitions
    highest_partition = -1
    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

        if assert_monotonically_increasing:
            pid = split_callback(node)
            assert highest_partition <= pid, \
                ("autocast or set_grad_enabled require monotonically increasing partitions:"
                 f"highest: {highest_partition}, this node's: {pid}")
            highest_partition = pid

        # do not capture cross-partition dependencies for global state nodes as they will be
        # self-contained - their setup and unwind will be isolated to each partition submodule.
        if node.target not in GLOBAL_STATE_NODES:
            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

    original_partition_order = list(partitions.keys())
    # find partitions with no dependencies
    root_partitions: List[str] = []
    for partition_name, partition in partitions.items():
        if not len(partition.dependencies):
            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].dependents:
            partitions[dependent].dependencies.pop(root_partition)
            if not partitions[dependent].dependencies:
                root_partitions.append(dependent)
    if len(sorted_partitions) != len(partitions):
        raise RuntimeError("cycle exists between partitions!")

    # Enter prelude
    for regions_mapping in [autocast_regions, grad_regions]:
        for node, regions in regions_mapping.items():
            assert len(regions) > 0
            partitions[str(regions[0])].environment[node] = node
            for r in regions[1:]:
                partition = partitions[str(r)]
                new_node = partition.graph.create_node(
                    op=node.op,
                    target=node.target,
                    args=tuple(arg for arg in node.args),
                    kwargs={},
                    type_expr=node.type,
                )
                new_node.meta = node.meta.copy()  # is it really a good idea to copy this?
                partition.environment[node] = new_node

    # add placeholders to partition inputs
    for partition_name in sorted_partitions:
        partition = partitions[partition_name]
        for inp in partition.inputs:
            placeholder = partition.graph.placeholder(
                inp,
                type_expr=orig_nodes[inp].type,
            )
            placeholder.meta = orig_nodes[inp].meta.copy()
            partition.environment[orig_nodes[inp]] = 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 AttributeError(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)
            name = node.name if keep_original_node_name else None
            new_node = partition.graph.create_node(
                op=node.op,
                target=target,
                args=gathered_args,
                kwargs=gathered_kwargs,
                type_expr=node.type,
                name=name,
            )
            new_node.meta = node.meta.copy()
            partition.environment[node] = new_node

    # Exit epilogue
    for regions_mapping in [autocast_regions]:
        for node in reversed(regions_mapping):
            regions = regions_mapping[node]
            assert len(regions) > 0
            for r in regions[:-1]:
                partition = partitions[str(r)]
                exit_node = autocast_exits[node]
                assert exit_node is not None, "Missing exit node"
                new_node = partition.graph.create_node(
                    op=exit_node.op,
                    target=exit_node.target,
                    args=(partition.environment[node],),
                    kwargs={},
                    type_expr=exit_node.type,
                )
                new_node.meta = exit_node.meta.copy()  # is it really a good idea to copy this?

    # original module environment dict mapping node names to nodes
    orig_mod_env: Dict[str, Node] = {}
    # Set up values to construct base module
    base_mod_env: Dict[str, Node] = {}
    base_mod_graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
    base_mod_attrs: Dict[str, torch.fx.graph_module.GraphModule] = {}
    if not keep_original_order:
        for node in m.graph.nodes:
            base_mod_env, base_mod_attrs = construct_graph(
                node, base_mod_env, base_mod_attrs
            )

    else:
        # Go through the graph to construct the mapping dict
        for node in m.graph.nodes:
            orig_mod_env[node.name] = node

    # 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 or original order specified by keep_original_order

    construct_order_partitions = (
        sorted_partitions if not keep_original_order else original_partition_order
    )

    already_constructed_attr_nodes = set()
    for partition_name in construct_order_partitions:
        partition = partitions[partition_name]

        # Set correct output values
        output_vals = tuple(
            partition.environment[orig_nodes[name]] for name in partition.outputs
        )

        # skip output node generation if there are no output values
        num_output_vals = len(output_vals)
        if num_output_vals == 1:
            partition.graph.output(output_vals[0])
        elif num_output_vals > 1:
            partition.graph.output(output_vals)

        if keep_original_order:
            # first get the attr nodes required by this partition
            orig_mod_attr_nodes: List[Node] = [
                orig_mod_env[key] for key in partition.inputs
            ]
            # Construct GraphModule for this partition
            for node in orig_mod_attr_nodes:  # type: ignore[attr-defined]
                if node in already_constructed_attr_nodes:
                    continue
                base_mod_env, base_mod_attrs = construct_graph(
                    node, base_mod_env, base_mod_attrs
                )
                already_constructed_attr_nodes.add(node)

        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),
        )

        num_outputs = len(partition.outputs)
        if num_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]
        elif num_outputs == 1:
            base_mod_env[next(iter(partition.outputs))] = 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)