ai-content-maker/.venv/Lib/site-packages/torch/fx/passes/infra/partitioner.py

330 lines
15 KiB
Python

from torch.fx.passes.utils.fuser_utils import fuse_by_partitions
import collections
import itertools
import logging
from copy import copy
from typing import Dict, Iterable, List, Optional, Sequence, Set
from torch.fx.graph_module import GraphModule
from torch.fx.node import Node, _get_qualified_name
from torch.fx.passes.operator_support import OperatorSupportBase
logger = logging.getLogger(__name__)
logger.setLevel(logging.WARNING)
class Partition:
def __init__(self, id: Optional[int] = None, nodes: Optional[Iterable[Node]] = None):
self.id = id
self.nodes: Set[Node] = set(nodes) if nodes is not None else set()
def __repr__(self) -> str:
return str(self.nodes)
def add_node(self, node: Node):
self.nodes.add(node)
def remove_node(self, node: Node):
self.nodes.remove(node)
def size(self):
return len(self.nodes)
class _DependencyViewer:
def __init__(self, graph_module: GraphModule):
self.upstreams = collections.defaultdict(set)
self.downstreams = collections.defaultdict(set)
for node in graph_module.graph.nodes:
for input_node in node.all_input_nodes:
# add input_node and input_node's upstream dependency
self.upstreams[node].add(input_node)
self.upstreams[node].update(self.upstreams[input_node])
for node in reversed(graph_module.graph.nodes):
for output_node in node.users:
# add output_node and output_node's downstream dependency
self.downstreams[node].add(output_node)
self.downstreams[node].update(self.downstreams[output_node])
def downstreams_of(self, node: Node) -> Set[Node]:
return self.downstreams[node]
def upstreams_of(self, node: Node) -> Set[Node]:
return self.upstreams[node]
class CapabilityBasedPartitioner:
def __init__(self,
graph_module: GraphModule,
operator_support: OperatorSupportBase,
allows_single_node_partition: bool = False,
non_compute_ops: Optional[Sequence[str]] = None,
allowed_single_node_partition_ops: Optional[Sequence[str]] = None,
) -> None:
self.graph_module = graph_module
self.operator_support = operator_support
self.allows_single_node_partition = allows_single_node_partition
self.non_compute_ops = non_compute_ops if non_compute_ops is not None else []
self.allowed_single_node_partition_ops = (
allowed_single_node_partition_ops
if allowed_single_node_partition_ops is not None
else []
)
self.dependency_viewer = _DependencyViewer(graph_module)
def __is_node_supported(self, node: Node) -> bool:
return (
self.operator_support.is_node_supported(dict(self.graph_module.named_modules()), node)
)
def propose_partitions(self) -> List[Partition]:
# partition_map is a mapping from partition id to a set of partition id's.
# The value set contains all the partition ids that can be reached by doing a
# DFS starting from the partition id in the key.
partition_map : Dict[int, Set] = collections.defaultdict(set)
# assumptions: nodes in candidate list is sorted in topological order
assignment: Dict[Node, int] = {} # mapping from node to partition_id
partitions_by_id: Dict[int, Partition] = {} # mapping from partition_id to partition
new_partition_id = itertools.count()
# try to merge partition other_id into partition self_id
# merge only happens if the end graph doesn't contain cyclic dependency
# returns `True` when merge happens, `False` otherwise.
def maybe_merge_partition(self_id: int, other_id: int):
# merged_nodes is the union of nodes in two partition to-be-merged
merged_nodes = copy(partitions_by_id[self_id].nodes)
merged_nodes.update(partitions_by_id[other_id].nodes)
def dfs_iter_find_cycle(all_user_nodes: List[Node]):
for user_node in all_user_nodes:
visited_partition_ids = set()
for path_node in self.dependency_viewer.downstreams_of(user_node):
# If any of the nodes in the dfs path of this node are in the merged_nodes
# list then there is a cycle in the graph.
if path_node in merged_nodes:
return True
# If any of the nodes in the dfs path of this node are in the assignment
# map then we have to make sure that the partitions that these nodes belong
# to do not form a cycle with the current partitions being merged. This means
# iterating through all the nodes in all the parititons that are traversed in
# the dfs path and checking if they are in the merged_nodes list.
if path_node in assignment:
partition_id = assignment[path_node]
# If the partition id has already been visited then we know that it doesn't
# form a cycle with the current partitions being merged.
if partition_id in visited_partition_ids:
continue
p_map = partition_map[partition_id]
if self_id in p_map or other_id in p_map:
return True
visited_partition_ids.add(partition_id)
return False
# check if merge would create cyclic dependency.
all_user_nodes = []
for node in merged_nodes:
for user_node in node.users:
if user_node not in merged_nodes:
all_user_nodes.append(user_node)
if dfs_iter_find_cycle(all_user_nodes):
# return false indicating cyclic dependency found and
# merge is aborted
return False
# no cyclic dependency found, move forward with the merge
# updating partition nodes
partitions_by_id[self_id].nodes = merged_nodes
# updating assignment map
for node in partitions_by_id[other_id].nodes:
assignment[node] = self_id
# delete other partition
del partitions_by_id[other_id]
partition_map[self_id] = partition_map[self_id].union(partition_map[other_id])
del partition_map[other_id]
return True
def merge_single_node(node: Node, id: Optional[int]):
def _update_partition_map(node: Node, id: int):
# Iterate through all the downstream nodes of this node and update the partition map
# to indicate that there is a path from the partition id of this node to the target
# partition id.
downstream_nodes = self.dependency_viewer.downstreams_of(node)
for curr_node in downstream_nodes:
target_id = assignment.get(curr_node, None)
if target_id is not None:
partition_map[id].add(target_id)
# Iterate through all the upstream nodes of this node and update the partition map
# to indicate that there is a path from the partition id of the upstream node to the
# current node's partition id.
upstream_nodes = self.dependency_viewer.upstreams_of(node)
for curr_node in upstream_nodes:
source_id = assignment.get(curr_node, None)
if source_id is not None:
partition_map[source_id].add(id)
if node in assignment:
partitions_by_id[assignment[node]].remove_node(node)
if id is None:
assignment.pop(node)
elif id not in partitions_by_id:
assignment[node] = id
partitions_by_id[id] = Partition(id=id, nodes=[node])
_update_partition_map(node, id)
else:
assignment[node] = id
partitions_by_id[id].add_node(node)
_update_partition_map(node, id)
logger.debug("Proposing partitions...")
for node in reversed(self.graph_module.graph.nodes):
# use Dict as an ordered set to ensure deterministic partitioning result, don't care value
merge_candidates: Dict[int, None] = {}
# Note a limited horizontal fusion is enabled:
# when `node` is not supported, the code below attempts to fuse consumer of `node`.
#
# I don't see a need to add a knob to disable horizontal fusion yet, we can short-cut
# the fusion by adding an `else` block here to skip horizontal fusion.
if self.__is_node_supported(node) and node not in assignment:
partition_id = next(new_partition_id)
merge_single_node(node, partition_id)
merge_candidates[partition_id] = None
# merge all possible partitions
for node in assignment:
merge_candidates[assignment[node]] = None
merge_candidates_list = list(merge_candidates.keys())
if len(merge_candidates_list) > 1:
self_id = merge_candidates_list[0]
for other_id in merge_candidates_list[1:]:
# note: merge partition `other_id` into partition `self_id` if
# it doesn't create cyclic dependency in the graph, otherwise,
# this is a no-op
maybe_merge_partition(self_id, other_id)
# post processing to re-assign "getitem" nodes into upstream partition
logger.debug("Reassigning getitem nodes to its producer node's partition...")
nodes_reassignment: Dict[Node, int] = {}
for node in self.graph_module.graph.nodes:
is_tuple_output = True
for user in node.users:
if user.op != "call_function" or \
_get_qualified_name(user.target) != "_operator.getitem": # type: ignore[arg-type]
is_tuple_output = False
break
# node has tuple outputs, re-assign all following getitem node into node's partition
if is_tuple_output:
id = assignment.get(node, None) # type: ignore[arg-type]
for user in node.users:
if assignment.get(user, None) != id: # type: ignore[arg-type]
nodes_reassignment[user] = id # type: ignore[assignment]
for node, id in nodes_reassignment.items():
merge_single_node(node, id)
# filter out single node partitions
if not self.allows_single_node_partition:
logger.debug("Filtering out single node partitions...")
default_non_compute_ops = {"torch.ops.aten.view", "_operator.getitem"}
non_compute_ops = default_non_compute_ops.union(set(self.non_compute_ops))
partitions_to_remove: List[int] = []
for id, partition in partitions_by_id.items():
compute_node_count = 0
for node in partition.nodes:
if node.op == "call_function":
assert callable(node.target)
if _get_qualified_name(node.target) not in non_compute_ops:
compute_node_count += 1
if _get_qualified_name(node.target) in self.allowed_single_node_partition_ops:
compute_node_count += 1
if compute_node_count <= 1:
partitions_to_remove.append(id)
for id in partitions_to_remove:
del partitions_by_id[id]
logger.debug("Partitions proposed:")
for id, partition in partitions_by_id.items():
logger.debug("partition #%s: %s", id, [node.name for node in partition.nodes])
return list(partitions_by_id.values())
def fuse_partitions(self, partitions: List[Partition]) -> GraphModule:
logger.debug("Fusing partitions...")
# fuse_by_partitions expects partitions in List[List[Node]]: [ [node0, node1], [node2, node3] ]
return fuse_by_partitions(self.graph_module, [list(partition.nodes) for partition in partitions])
# remove non-compute-ops that sits at the boundary of a partition.
def remove_bookend_non_compute_ops(self, partitions: List[Partition]):
non_compute_ops = set(self.non_compute_ops)
def is_non_compute_node(node: Node):
return node.op == "call_function" and \
_get_qualified_name(node.target) in non_compute_ops # type: ignore[arg-type]
# cache transparent nodes
transparent_input_nodes: Dict[Node, bool] = {}
transparent_output_nodes: Dict[Node, bool] = {}
def is_transparent_input_node(node: Node, partition: Set[Node], removed_nodes: Set[Node]):
if node.op == "placeholder" or (node not in partition) or (node in removed_nodes):
return True
if node in transparent_input_nodes:
return transparent_input_nodes[node]
if is_non_compute_node(node):
for input_n in node.all_input_nodes:
if not is_transparent_input_node(input_n, partition, removed_nodes):
transparent_input_nodes[node] = False
return False
transparent_input_nodes[node] = True
return True
transparent_input_nodes[node] = False
return False
def is_transparent_output_node(node: Node, partition: Set[Node], removed_nodes: Set[Node]):
if node.op == "placeholder" or (node not in partition) or (node in removed_nodes):
return True
if node in transparent_output_nodes:
return transparent_output_nodes[node]
if is_non_compute_node(node):
for output_n in node.users:
if not is_transparent_output_node(output_n, partition, removed_nodes):
transparent_output_nodes[node] = False
return False
transparent_output_nodes[node] = True
return True
transparent_output_nodes[node] = False
return False
for partition in partitions:
# Note it's ok to use `set` here, since we are only query if a node
# has been removed. We are NEVER going to iterate on nodes inside
# the set.
remove_node: Set[Node] = set()
for node in partition.nodes:
if is_non_compute_node(node) and \
(is_transparent_input_node(node, partition.nodes, remove_node) or
is_transparent_output_node(node, partition.nodes, remove_node)):
remove_node.add(node)
if len(remove_node) != 0:
partition.nodes = partition.nodes - remove_node
def partition_and_fuse(self) -> GraphModule:
partitions = self.propose_partitions()
fused_gm = self.fuse_partitions(partitions)
return fused_gm