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af40828bbb
Respect max_coll_distance from overlap scheduler in bucketing, also, add an optimization in path searching. Pull Request resolved: https://github.com/pytorch/pytorch/pull/164944 Approved by: https://github.com/IvanKobzarev ghstack dependencies: #164738, #164783
126 lines
4.4 KiB
Python
126 lines
4.4 KiB
Python
from collections import defaultdict
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from typing import Optional
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import torch
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import torch.fx as fx
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from torch.utils._ordered_set import OrderedSet
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class AugmentedGraphHelper:
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"""
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Graph helper that augments the original graph with additional
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dependencies and uses, plus tracks node equivalences for coalescing.
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TODO: if this becomes too large of compile time, consider binding
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graphcycles.cc
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"""
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def __init__(
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self,
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graph: fx.Graph,
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node_ancestors: Optional[dict[fx.Node, OrderedSet[fx.Node]]] = None,
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):
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# Each node starts in its own singleton set
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self.graph = graph
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self.merge_sets = {node: OrderedSet([node]) for node in graph.nodes}
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# Extra dependencies: node depends on dep (dep must come before node)
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self.extra_deps: dict[fx.Node, OrderedSet[fx.Node]] = defaultdict(OrderedSet)
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# Note: only reflect original ancestors, not maintained through additional deps
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# or merge sets
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self.node_ancestors = node_ancestors
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def add_extra_dep(self, *, n: fx.Node, dep: fx.Node) -> None:
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"""Add extra dependency: node depends on dep."""
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self.extra_deps[n].add(dep)
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def merge_to_set(self, existing_node: fx.Node, new_node: fx.Node) -> None:
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"""
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Merge new_node into existing_node's set. The new node must be a singleton set.
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"""
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existing_set = self.merge_sets[existing_node]
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new_set = self.merge_sets[new_node]
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assert len(new_set) == 1
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# Add all nodes from new_set to existing_set
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existing_set.update(new_set)
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# Update all nodes from new_set to point to existing_set
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for node in new_set:
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self.merge_sets[node] = existing_set
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def unmerge_node(self, node: fx.Node) -> None:
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"""Remove a node from its merge set, making it singleton."""
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old_set = self.merge_sets[node]
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# If already singleton, nothing to do
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if len(old_set) == 1:
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return
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# Remove from old set
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old_set.remove(node)
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# Make node singleton
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self.merge_sets[node] = OrderedSet([node])
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def get_merged_deps(self, node: fx.Node) -> OrderedSet[fx.Node]:
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"""
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Get all dependencies of a node considering merges and extra deps.
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Combines:
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1. Direct deps (all_input_nodes) of node and its merge equivalents
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2. Extra deps of node and its merge equivalents
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"""
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deps: OrderedSet[fx.Node] = OrderedSet()
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# For each node in the merge set
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for merged_node in self.merge_sets[node]:
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# Add direct dependencies from all_input_nodes
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deps.update(merged_node.all_input_nodes)
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# Add extra dependencies
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deps.update(self.extra_deps[merged_node])
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return deps
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def has_cycle(self) -> bool:
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merged_deps = {n: self.get_merged_deps(n) for n in self.graph.nodes}
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return torch._dynamo.graph_deduplication._has_cycle(self.graph, merged_deps)
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def has_path(self, source: fx.Node, target: fx.Node) -> bool:
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"""Check if there's a path from source to target."""
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# we should not be checking path from node to itself
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assert self.merge_sets[source] is not self.merge_sets[target]
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# search backwards from target to source
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visited: OrderedSet[fx.Node] = OrderedSet()
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queue = [target]
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visited.add(target)
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while queue:
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current = queue.pop()
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for dep in self.get_merged_deps(current):
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# Check if we reached source or its equivalent
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if dep in self.merge_sets[source]:
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return True
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if dep in visited:
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continue
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# We are searching from target, so this node is necessarily an ancestor
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# of target.
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# If dep is an ancestor of source, any path through dep to source would imply a cycle
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if self.node_ancestors:
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source_set = self.merge_sets[source]
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is_ancestor_of_source = any(
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dep in self.node_ancestors[s] for s in source_set
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)
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# Add to visited to avoid recomputing this check if we see dep again
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if is_ancestor_of_source:
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visited.add(dep)
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continue
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visited.add(dep)
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queue.append(dep)
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return False
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