import itertools import logging from collections import defaultdict from dataclasses import dataclass from typing import Any, Literal, Optional import torch import torch.fx as fx from torch._dynamo.utils import counters from torch._inductor.augmented_graph_helper import AugmentedGraphHelper from torch._inductor.fx_passes.bucketing import ( _schedulable_wait_node, bucket_key, BucketMode, has_mergeable_all_gather_convert_dtype, is_all_gather_into_tensor as is_all_gather, is_reduce_scatter_tensor as is_reduce_scatter, ) from torch._inductor.fx_passes.overlap_scheduling import ( CollBucket, CollectiveInfo, get_group_name, is_compute_node, ) from torch.utils._ordered_set import OrderedSet bucket_log = logging.getLogger(__name__) @dataclass class WhyNoBucket: name1: str name2: str reason: str args: tuple[Any, ...] def __init__(self, node1: fx.Node, node2: fx.Node) -> None: self.name1 = node1.name self.name2 = node2.name self.reason = "" self.args = () def __call__(self, reason: str, *args: Any) -> None: if bucket_log.isEnabledFor(logging.DEBUG): bucket_log.debug( "cannot bucket %s with %s: " + reason, # noqa: G003 self.name1, self.name2, *args, ) def is_collective_or_wait(n: fx.Node) -> bool: """Check if node is a collective start or wait.""" if _schedulable_wait_node(n): return True # Collective starts have exactly one use: the wait_tensor if len(n.users) == 1: user = next(iter(n.users.keys())) if _schedulable_wait_node(user): return True return False @dataclass class PGEvent: """ Represents an important event in a process group timeline. Either a collective start, wait, or hiding compute. Each node is linked to its prev and next and these dependencies are reflected in the augmented graph. We want to enforce a sequential ordering of collective starts and waits because NCCL collectives on the same process group execute on the same CUDA stream, creating implicit dependencies between all operations on that PG. A wait of a particular collective will implicitly force realization of all collectives enqueued prior to that collective. """ node: fx.Node event_type: Literal["compute", "starts", "waits"] position: int prev: Optional["PGEvent"] = None next: Optional["PGEvent"] = None @property def is_start(self) -> bool: return self.event_type == "starts" @property def is_wait(self) -> bool: return self.event_type == "waits" @property def is_compute(self) -> bool: return self.event_type == "compute" def unlink(self) -> tuple[Optional["PGEvent"], Optional["PGEvent"]]: """Remove this event from the linked list, return (prev, next).""" prev_event, next_event = self.prev, self.next if self.prev: self.prev.next = self.next if self.next: self.next.prev = self.prev self.prev = None self.next = None return prev_event, next_event def insert_between( self, prev_event: Optional["PGEvent"], next_event: Optional["PGEvent"] ) -> None: """Insert this event between prev_event and next_event in the linked list.""" if prev_event: prev_event.next = self self.prev = prev_event if next_event: next_event.prev = self self.next = next_event class OverlapPreservingBucketer: """ Buckets collective operations while preserving compute-collective overlap relationships. Uses an augmented graph to track dependencies between compute and collective operations. """ def __init__( self, graph: fx.Graph, collective_info: dict[fx.Node, CollectiveInfo], scheduled: OrderedSet[fx.Node], max_bucket_memory_gb: float = 1.0, max_coll_distance: int = 1000, insert_overlap_deps: bool = False, bucket_mode: BucketMode = "custom_ops_multidtype", ): self.graph = graph self.collective_info = collective_info self.scheduled = scheduled self.max_bucket_memory_gb = max_bucket_memory_gb self.node_idx = {n: i for i, n in enumerate(scheduled)} self.max_coll_distance = max_coll_distance self.insert_overlap_deps = insert_overlap_deps self.bucket_mode = bucket_mode self.node_to_event: dict[fx.Node, PGEvent] = {} self.all_hiding_nodes: OrderedSet[fx.Node] = OrderedSet() # Compute ancestors including original graph edges and hiding interval dependencies self.node_ancestors = self._compute_node_ancestors() self.aug_graph = AugmentedGraphHelper(self.graph, self.node_ancestors) # Build timelines and add constraints to aug_graph self.pg_to_timeline_head: dict[str, Optional[PGEvent]] = self.build_timelines() self._add_hiding_interval_constraints() def _compute_node_ancestors(self) -> dict[fx.Node, OrderedSet[fx.Node]]: """ Compute ancestor sets for all nodes including: 1. Original graph edges 2. Hiding interval deps: collective_start -> hiding_node -> wait """ augmented_inputs: dict[fx.Node, OrderedSet[fx.Node]] = defaultdict(OrderedSet) for start, info in self.collective_info.items(): if info.is_exposed: continue for hiding_node in info.hiding_nodes: augmented_inputs[hiding_node].add(start) augmented_inputs[info.wait_node].add(hiding_node) node_ancestors: dict[fx.Node, OrderedSet[fx.Node]] = defaultdict(OrderedSet) for node in self.scheduled: for input_node in itertools.chain( augmented_inputs[node], node.all_input_nodes ): node_ancestors[node].add(input_node) node_ancestors[node] |= node_ancestors[input_node] return node_ancestors def build_timelines(self) -> dict[str, Optional[PGEvent]]: "Construct each process groups ordered series of event" all_pgs: OrderedSet[str] = OrderedSet() for start in self.collective_info: pg = get_group_name(start) all_pgs.add(pg) pg_timeline: dict[str, Optional[PGEvent]] = {} for pg in all_pgs: pg_timeline[pg] = self.build_timeline(pg) return pg_timeline def build_timeline(self, pg: str) -> Optional[PGEvent]: """ Build a timeline of important events (starts, waits, hiding compute) for this process group and constrain this ordering in the augmented graph. Sequential dependencies are added between all events because NCCL collectives on the same process group execute on the same CUDA stream, enforcing LIFO semantics where later-issued collectives must complete before earlier ones can finish. """ head = None prev_event = None position = 0 hiding_nodes = OrderedSet() for node in self.scheduled: node_type = None # Determine if this node is relevant for this PG if node in self.collective_info and get_group_name(node) == pg: node_type = "starts" hiding_nodes |= self.collective_info[node].hiding_nodes elif _schedulable_wait_node(node): wait_input = node.args[0] if isinstance(wait_input, fx.Node) and get_group_name(wait_input) == pg: node_type = "waits" # Wait for a different PG but hiding a collective on this PG elif node in hiding_nodes: node_type = "compute" elif is_compute_node(node) or node in hiding_nodes: node_type = "compute" if node_type is None: continue event = PGEvent(node=node, event_type=node_type, position=position) # type: ignore[arg-type] event.insert_between(prev_event, None) # Add sequential dependency to augmented graph if prev_event: self.aug_graph.add_extra_dep(n=event.node, dep=prev_event.node) else: head = event prev_event = event position += 1 return head def _populate_node_to_event(self, pg: str) -> None: """Populate node_to_event mapping for a specific PG's timeline.""" self.node_to_event.clear() head = self.pg_to_timeline_head[pg] curr = head while curr is not None: self.node_to_event[curr.node] = curr curr = curr.next def _add_hiding_interval_constraints(self) -> None: """ Add hiding interval constraints: start -> compute -> wait. """ for start, info in self.collective_info.items(): if info.is_exposed: continue for hn in info.hiding_nodes: # Enforce: start -> compute -> wait self.aug_graph.add_extra_dep(n=hn, dep=start) self.aug_graph.add_extra_dep(n=info.wait_node, dep=hn) self.all_hiding_nodes |= info.hiding_nodes def bucket_collectives(self) -> None: # Group collectives by PG first pg_collectives: dict[str, OrderedSet[fx.Node]] = defaultdict(OrderedSet) for start in self.collective_info: pg = get_group_name(start) pg_collectives[pg].add(start) all_buckets: list[CollBucket] = [] for pg, collectives in pg_collectives.items(): # Populate node_to_event for this PG's timeline self._populate_node_to_event(pg) # Group by bucket key within this PG grouped_collectives: dict[object, OrderedSet[fx.Node]] = defaultdict( OrderedSet ) for start in collectives: key = bucket_key(start, self.bucket_mode) if key is not None: grouped_collectives[key].add(start) # Find buckets for this PG for key, collective_group in grouped_collectives.items(): bucket_log.debug( "bucketing collective group with key %s: %s", key, [n.name for n in collective_group], ) buckets = self._find_buckets(collective_group) all_buckets.extend(buckets) # Apply bucketing transformations # Dependencies are tracked in aug_graph.extra_deps during bucketing for coll_bucket in all_buckets: if len(coll_bucket.collectives) <= 1: continue counters["inductor"]["collective_buckets"] += 1 self._apply_bucket(coll_bucket) # Extract all dependencies from augmented graph # This includes: # - Sequential timeline deps (added during build_timeline) # - Hiding interval deps (added during _add_hiding_interval_constraints) # - All transferred deps from bucketing (transferred during _apply_bucket) additional_deps = self.aug_graph.get_all_extra_deps() # Apply topological sort with all dependencies from torch._dynamo.graph_deduplication import _stable_topological_sort for n, deps in additional_deps.items(): torch._check( not n._erased, lambda: f"Erased node deps not transferred: {n}" ) for d in deps: torch._check( not d._erased, lambda: f"Erased node deps not transferred: {d}" ) _stable_topological_sort(self.graph, additional_deps) # After topological sort, preserve dependencies using effect tokens # Only preserve edges where NOT both nodes are collective starts or waits if self.insert_overlap_deps: filtered_deps: dict[fx.Node, OrderedSet[fx.Node]] = {} for node, deps in additional_deps.items(): filtered_node_deps: OrderedSet[fx.Node] = OrderedSet() # only preserve comm-comptue overlap for now, although we could more # generally constrain for dep in deps: if not (is_collective_or_wait(node) and is_collective_or_wait(dep)): filtered_node_deps.add(dep) if filtered_node_deps: filtered_deps[node] = filtered_node_deps self._preserve_dependencies_with_tokens(filtered_deps) self.graph.lint() def _find_buckets( self, collective_group: OrderedSet[fx.Node], ) -> list[CollBucket]: """Find valid buckets within a group of similar collectives.""" max_bucket_bytes = int(self.max_bucket_memory_gb * 1024 * 1024 * 1024) buckets = [] processed: OrderedSet[fx.Node] = OrderedSet() # Sort collectives by node index for efficient distance checking sorted_collectives = sorted(collective_group, key=lambda n: self.node_idx[n]) for i, start_node in enumerate(sorted_collectives): if start_node in processed: continue if ( start_node in self.all_hiding_nodes or self.collective_info[start_node].wait_node in self.all_hiding_nodes ): continue # Initialize bucket with first collective bucket_info = CollBucket( collectives=[start_node], total_bytes=self.collective_info[start_node].size_bytes, ) processed.add(start_node) # Greedy optimization: stop after consecutive failures consecutive_failures = 0 max_consecutive_failures = 20 # Check candidates in sorted order, break when beyond max distance for candidate in sorted_collectives[i + 1 : i + 1 + self.max_coll_distance]: candidate_bytes = self.collective_info[candidate].size_bytes # proxy on memory use, if we see a too large bucket, # dont look for another, later bucket if bucket_info.total_bytes + candidate_bytes > max_bucket_bytes: break if candidate in processed: continue if self._can_add_to_bucket(bucket_info, candidate): bucket_info.collectives.append(candidate) bucket_info.total_bytes += candidate_bytes processed.add(candidate) consecutive_failures = 0 # Reset on success else: consecutive_failures += 1 if consecutive_failures >= max_consecutive_failures: break if len(bucket_info.collectives) > 1: buckets.append(bucket_info) return buckets def _ancestor_dep(self, n1: fx.Node, n2: fx.Node) -> bool: """Check if there's an ancestor relationship between two nodes.""" return n1 in self.node_ancestors[n2] or n2 in self.node_ancestors[n1] def _get_intervals( self, event: PGEvent ) -> tuple[Optional[tuple[int, int]], list[tuple[int, int]]]: """Get (execution_interval, hiding_intervals) for a collective event. Returns: (execution_interval, hiding_intervals) where: - execution_interval is (start_pos, wait_pos) or None - hiding_intervals is a list of (start_pos, compute_pos) tuples, one for each hiding node Works for both start and wait events by looking up the collective info. """ # For start events, directly use the node if event.is_start: coll = event.node # For wait events, look up the start node from the event's args elif event.is_wait: wait_input = event.node.args[0] if not isinstance(wait_input, fx.Node): return None, [] coll = wait_input else: return None, [] if coll not in self.collective_info: return None, [] info = self.collective_info[coll] start_event = self.node_to_event[coll] wait_event = self.node_to_event[info.wait_node] execution_interval = (start_event.position, wait_event.position) hiding_intervals = [] if info.hiding_nodes: for hiding_node in info.hiding_nodes: hiding_intervals.append( ( start_event.position, self.node_to_event[hiding_node].position, ) ) return execution_interval, hiding_intervals def _preserves_hiding_intervals( self, bucket_info: CollBucket, candidate: fx.Node, start_pos: fx.Node, wait_pos: fx.Node, why: WhyNoBucket, ) -> bool: """ Check that (start_pos, wait_pos) doesn't violate any hiding intervals or collectives. Collects all execution and hiding intervals in the affected timeline regions, then checks: 1. All bucket hiding compute stays between new start/wait 2. No other collective's compute interval is enclosed by bucket execution interval 3. No other collective's execution interval encloses bucket compute intervals """ # Collect all collectives being bucketed all_bucketed_colls = [candidate] + list(bucket_info.collectives) all_bucketed_waits = [ self.collective_info[coll].wait_node for coll in all_bucketed_colls ] # Collect hiding compute positions for the bucket bucket_hiding_compute_positions = [] for coll in all_bucketed_colls: for coll_hiding_node in self.collective_info[coll].hiding_nodes: bucket_hiding_compute_positions.append( self.node_to_event[coll_hiding_node].position ) # Get new positions new_start_event = self.node_to_event[start_pos] new_wait_event = self.node_to_event[wait_pos] # Check 1: All bucket hiding compute must be between new start and wait for compute_pos in bucket_hiding_compute_positions: if not (new_start_event.position < compute_pos < new_wait_event.position): why( "hiding compute at pos %d not between start %d and wait %d", compute_pos, new_start_event.position, new_wait_event.position, ) return False def get_wait(n: fx.Node) -> fx.Node: return self.collective_info[n].wait_node def get_pos(n: fx.Node) -> int: return self.node_to_event[n].position latest_start_pos = max(get_pos(candidate), get_pos(bucket_info.collectives[0])) earliest_wait_pos = min( get_pos(get_wait(candidate)), get_pos(get_wait(bucket_info.collectives[0])) ) # Bucket execution interval bucket_execution_interval = (new_start_event.position, new_wait_event.position) # Because collectives on the same PG operate under LIFO semantics, # it's only possible for us to force an early realization of an unrelated collective # by delaying a start or raising a wait. # We search in the interval from old_start -> new_start, to see if would be # forcing another collective to be realized prior to its hiding nodes. # Similarly, we search from old_wait -> new_wait, in the reverse direction, # to check the same thing. execution_intervals = [bucket_execution_interval] hiding_intervals = [ (bucket_execution_interval[0], pos) for pos in bucket_hiding_compute_positions ] curr_event = new_start_event.next while curr_event is not None and curr_event.position < latest_start_pos: if ( curr_event.node not in all_bucketed_colls and curr_event.node not in all_bucketed_waits ): exec_interval, hiding_interval_list = self._get_intervals(curr_event) if exec_interval: execution_intervals.append(exec_interval) hiding_intervals.extend(hiding_interval_list) curr_event = curr_event.next curr_event = new_wait_event.prev while curr_event is not None and curr_event.position > earliest_wait_pos: if ( curr_event.node not in all_bucketed_colls and curr_event.node not in all_bucketed_waits ): exec_interval, hiding_interval_list = self._get_intervals(curr_event) if exec_interval: execution_intervals.append(exec_interval) hiding_intervals.extend(hiding_interval_list) curr_event = curr_event.prev # Check: no hiding interval should be enclosed by any execution interval def enclosed_interval(inner: tuple[int, int], outer: tuple[int, int]) -> bool: return outer[0] < inner[0] and inner[1] < outer[1] for hiding_interval in hiding_intervals: for execution_interval in execution_intervals: if enclosed_interval(hiding_interval, execution_interval): why( "hiding interval %s enclosed by execution interval %s", hiding_interval, execution_interval, ) return False return True def remove_from_event( self, node: fx.Node ) -> tuple[Optional[PGEvent], Optional[PGEvent]]: """Remove node from timeline and return (prev_event, next_event).""" event = self.node_to_event[node] assert not event.is_compute, "Cannot remove compute events from timeline" prev_event, next_event = event.unlink() # Remove augmented graph dependency if prev_event: self.aug_graph.remove_extra_dep(n=node, dep=prev_event.node) if next_event: self.aug_graph.remove_extra_dep(n=next_event.node, dep=node) # Add bypass dependency if prev_event and next_event: self.aug_graph.add_extra_dep(n=next_event.node, dep=prev_event.node) return prev_event, next_event def restore_to_event( self, node: fx.Node, prev_event: Optional[PGEvent], next_event: Optional[PGEvent], ) -> None: """Restore node to timeline after failed merge attempt.""" event = self.node_to_event[node] # Reinsert into linked list event.insert_between(prev_event, next_event) if prev_event: self.aug_graph.add_extra_dep(n=node, dep=prev_event.node) if next_event and not prev_event: self.aug_graph.add_extra_dep(n=next_event.node, dep=node) # Remove bypass dependency if prev_event and next_event: self.aug_graph.remove_extra_dep(n=next_event.node, dep=prev_event.node) def _try_timeline_position( self, bucket_info: CollBucket, candidate: fx.Node, start_pos: fx.Node, wait_pos: fx.Node, why: WhyNoBucket, ) -> bool: """ Try a specific timeline position for the candidate. Returns True if valid and merges are successful. """ candidate_info = self.collective_info[candidate] candidate_wait = candidate_info.wait_node # Quick check: does this violate hiding intervals? if not self._preserves_hiding_intervals( bucket_info, candidate, start_pos, wait_pos, why ): return False # Determine which start needs to move existing_coll = bucket_info.collectives[0] if start_pos == existing_coll: start_to_move = candidate else: assert start_pos == candidate start_to_move = existing_coll # Remove start from timeline start_prev, start_next = self.remove_from_event(start_to_move) # Check if starts can be merged if self.aug_graph.has_path(existing_coll, candidate) or self.aug_graph.has_path( candidate, existing_coll ): # Restore start constraints self.restore_to_event(start_to_move, start_prev, start_next) why("path exists between starts") return False # Merge starts self.aug_graph.merge_to_set(existing_coll, candidate) # Determine which wait needs to move existing_wait = self.collective_info[existing_coll].wait_node candidate_wait = self.collective_info[candidate].wait_node if wait_pos == existing_wait: wait_to_move = candidate_wait else: wait_to_move = existing_wait # Remove wait from timeline wait_prev, wait_next = self.remove_from_event(wait_to_move) # Check if waits can be merged if self.aug_graph.has_path( existing_wait, candidate_wait ) or self.aug_graph.has_path(candidate_wait, existing_wait): # Restore wait constraints self.restore_to_event(wait_to_move, wait_prev, wait_next) # Unmerge the start we just merged self.aug_graph.unmerge_node(candidate) # Restore start constraints self.restore_to_event(start_to_move, start_prev, start_next) why("path exists between waits") return False # Merge waits - success! self.aug_graph.merge_to_set(existing_wait, candidate_wait) # Update node_to_event for moved nodes target_start_event = self.node_to_event[start_pos] target_wait_event = self.node_to_event[wait_pos] self.node_to_event[candidate] = target_start_event self.node_to_event[candidate_wait] = target_wait_event self.node_to_event[existing_coll] = target_start_event self.node_to_event[existing_wait] = target_wait_event return True def _has_ancestor_conflicts( self, bucket_info: CollBucket, candidate: fx.Node ) -> bool: """ Check if candidate has ancestor conflicts with bucket collectives. Returns True if there are conflicts. """ candidate_info = self.collective_info[candidate] candidate_wait = candidate_info.wait_node for coll in bucket_info.collectives: if ( coll in self.node_ancestors[candidate] or candidate in self.node_ancestors[coll] ): return True # Check if waits are ancestors of each other coll_wait = self.collective_info[coll].wait_node if ( coll_wait in self.node_ancestors[candidate_wait] or candidate_wait in self.node_ancestors[coll_wait] ): return True # Check if existing hiding node conflicts with candidate wait for old_hiding_node in self.collective_info[coll].hiding_nodes: if candidate_wait in self.node_ancestors[old_hiding_node]: return True # Check if candidate hiding node conflicts with existing wait for new_hiding_node in candidate_info.hiding_nodes: if coll_wait in self.node_ancestors[new_hiding_node]: return True return False def _can_add_to_bucket( self, bucket_info: CollBucket, candidate: fx.Node, ) -> bool: """ Check if candidate can be added to bucket without breaking comm/compute overlap. Strategy: Try all timeline positions - combinations of [existing_start, candidate_start] x [existing_wait, candidate_wait]. For each position, verify: 1. Hiding intervals preserved - for any (start, hiding_compute, wait) interval, no other collective's (start, wait) pair falls between start and hiding_compute, which would force realization and break overlap due to LIFO semantics 2. Topologically valid (no dependency cycles) Return True if any timeline position satisfies both constraints. """ existing_coll = bucket_info.collectives[0] why = WhyNoBucket(existing_coll, candidate) candidate_info = self.collective_info[candidate] if ( candidate in self.all_hiding_nodes or candidate_info.wait_node in self.all_hiding_nodes ): why("nyi: bucketing collective used for overlap") return False # Step 1: Quick check using precomputed ancestors # These ancestors are computed prior to adding augmented dependencies and not updated, # so if any of these checks fail then the merge will not be topologically valid # even ignoring comm/compute overlap if self._has_ancestor_conflicts(bucket_info, candidate): why("has ancestor conflicts") return False # Step 2: Try different rail positions existing_wait = self.collective_info[existing_coll].wait_node candidate_start = candidate candidate_wait = candidate_info.wait_node # Try combinations in order of likelihood to succeed # (early start, later wait is most likely to work) combinations = [ ( existing_coll, candidate_wait, ), # Move candidate start early, keep wait late ( existing_coll, existing_wait, ), # Move candidate start early, move wait early (candidate_start, candidate_wait), # Keep both in place (candidate_start, existing_wait), # Keep start in place, move wait early ] for i, (start_pos, wait_pos) in enumerate(combinations): if self._try_timeline_position( bucket_info, candidate, start_pos, wait_pos, why ): bucket_log.debug( "bucketed %s with %s using timeline position %d: (start=%s, wait=%s)", candidate.name, existing_coll.name, i + 1, start_pos.name, wait_pos.name, ) return True why("all timeline positions failed") return False def _apply_bucket(self, bucket_info: CollBucket) -> None: """ Apply bucketing transformation. Dependencies are added to aug_graph.extra_deps and transferred from old nodes. """ from torch._inductor.fx_passes.bucketing import ( is_all_reduce_tensor, merge_all_gather_bucket, merge_all_reduce_bucket, merge_reduce_scatter_bucket, ) bucket = bucket_info.collectives # Collect old nodes BEFORE they're erased old_starts = list(bucket) old_waits = [self.collective_info[n].wait_node for n in bucket] fused_convert_dtypes = [] for n in old_starts: if has_mergeable_all_gather_convert_dtype(n): fused_convert_dtypes.append(n.args[0]) # Find where to place the bucketed operations next_node = bucket[0] while next_node in bucket: next_node = next_node.next # Don't use wait_insertion_point - let merge functions place waits naturally # The wait_insertion_point feature tries to move waits to a specific location, # but this can cause issues when that location is one of the nodes being erased # Create bucketed collective (this will erase old nodes) if is_all_gather(bucket[0]): new_nodes, replacements = merge_all_gather_bucket( self.graph, bucket, insert_before=next_node, mode="custom_ops", ) elif is_all_reduce_tensor(bucket[0]): new_nodes, replacements = merge_all_reduce_bucket( self.graph, bucket, mode="custom_ops", insert_before=next_node, ) else: assert is_reduce_scatter(bucket[0]) new_nodes, replacements = merge_reduce_scatter_bucket( self.graph, bucket, insert_before=next_node, mode="custom_ops", ) # Get new nodes new_waits = [n for n in new_nodes if _schedulable_wait_node(n)] assert len(new_waits) == 1 new_wait = new_waits[0] new_start = new_wait.args[0] assert isinstance(new_start, fx.Node) # Create mapping of all erased nodes to their replacements erased_to_new = {} for old_start in old_starts: erased_to_new[old_start] = new_start for old_wait in old_waits: erased_to_new[old_wait] = new_wait # Handle convert_element_type nodes that were fused and erased # The bucketed operation may have a _pre_bucket op that handles dtype conversion if fused_convert_dtypes: # all gather bucketing may fuse in dtype conversion into the bucketing # if so, we need to transfer hiding deps from the old dtype conversion # to the new bucketing node new_convert_dtypes_node = new_start.kwargs["out"] assert isinstance(new_convert_dtypes_node, fx.Node) assert ( new_convert_dtypes_node.target == torch.ops.bucketing._pre_bucket_all_gather.default ) for n in fused_convert_dtypes: erased_to_new[n] = new_convert_dtypes_node # Transfer all dependencies from old nodes to new nodes self.aug_graph.transfer_erased_node_deps(erased_to_new) def _preserve_dependencies_with_tokens( self, additional_deps: dict[fx.Node, OrderedSet[fx.Node]] ) -> None: """ Preserve dependencies using effect tokens and with_effects higher-order op. Uses the standalone token_dependencies utility for consistent behavior across different overlap scheduling approaches. """ from torch._inductor.fx_passes.control_dependencies import ( preserve_node_ordering, ) preserve_node_ordering(self.graph, additional_deps)