guill 2b653e8c18
Support for async node functions (#8830)
* Support for async execution functions

This commit adds support for node execution functions defined as async. When
a node's execution function is defined as async, we can continue
executing other nodes while it is processing.

Standard uses of `await` should "just work", but people will still have
to be careful if they spawn actual threads. Because torch doesn't really
have async/await versions of functions, this won't particularly help
with most locally-executing nodes, but it does work for e.g. web
requests to other machines.

In addition to the execute function, the `VALIDATE_INPUTS` and
`check_lazy_status` functions can also be defined as async, though we'll
only resolve one node at a time right now for those.

* Add the execution model tests to CI

* Add a missing file

It looks like this got caught by .gitignore? There's probably a better
place to put it, but I'm not sure what that is.

* Add the websocket library for automated tests

* Add additional tests for async error cases

Also fixes one bug that was found when an async function throws an error
after being scheduled on a task.

* Add a feature flags message to reduce bandwidth

We now only send 1 preview message of the latest type the client can
support.

We'll add a console warning when the client fails to send a feature
flags message at some point in the future.

* Add async tests to CI

* Don't actually add new tests in this PR

Will do it in a separate PR

* Resolve unit test in GPU-less runner

* Just remove the tests that GHA can't handle

* Change line endings to UNIX-style

* Avoid loading model_management.py so early

Because model_management.py has a top-level `logging.info`, we have to
be careful not to import that file before we call `setup_logging`. If we
do, we end up having the default logging handler registered in addition
to our custom one.
2025-07-10 14:46:19 -04:00

473 lines
17 KiB
Python

import itertools
from typing import Sequence, Mapping, Dict
from comfy_execution.graph import DynamicPrompt
from abc import ABC, abstractmethod
import nodes
from comfy_execution.graph_utils import is_link
NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {}
def include_unique_id_in_input(class_type: str) -> bool:
if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID:
return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values()
return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
class CacheKeySet(ABC):
def __init__(self, dynprompt, node_ids, is_changed_cache):
self.keys = {}
self.subcache_keys = {}
@abstractmethod
async def add_keys(self, node_ids):
raise NotImplementedError()
def all_node_ids(self):
return set(self.keys.keys())
def get_used_keys(self):
return self.keys.values()
def get_used_subcache_keys(self):
return self.subcache_keys.values()
def get_data_key(self, node_id):
return self.keys.get(node_id, None)
def get_subcache_key(self, node_id):
return self.subcache_keys.get(node_id, None)
class Unhashable:
def __init__(self):
self.value = float("NaN")
def to_hashable(obj):
# So that we don't infinitely recurse since frozenset and tuples
# are Sequences.
if isinstance(obj, (int, float, str, bool, type(None))):
return obj
elif isinstance(obj, Mapping):
return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])
elif isinstance(obj, Sequence):
return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj]))
else:
# TODO - Support other objects like tensors?
return Unhashable()
class CacheKeySetID(CacheKeySet):
def __init__(self, dynprompt, node_ids, is_changed_cache):
super().__init__(dynprompt, node_ids, is_changed_cache)
self.dynprompt = dynprompt
async def add_keys(self, node_ids):
for node_id in node_ids:
if node_id in self.keys:
continue
if not self.dynprompt.has_node(node_id):
continue
node = self.dynprompt.get_node(node_id)
self.keys[node_id] = (node_id, node["class_type"])
self.subcache_keys[node_id] = (node_id, node["class_type"])
class CacheKeySetInputSignature(CacheKeySet):
def __init__(self, dynprompt, node_ids, is_changed_cache):
super().__init__(dynprompt, node_ids, is_changed_cache)
self.dynprompt = dynprompt
self.is_changed_cache = is_changed_cache
def include_node_id_in_input(self) -> bool:
return False
async def add_keys(self, node_ids):
for node_id in node_ids:
if node_id in self.keys:
continue
if not self.dynprompt.has_node(node_id):
continue
node = self.dynprompt.get_node(node_id)
self.keys[node_id] = await self.get_node_signature(self.dynprompt, node_id)
self.subcache_keys[node_id] = (node_id, node["class_type"])
async def get_node_signature(self, dynprompt, node_id):
signature = []
ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
signature.append(await self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
for ancestor_id in ancestors:
signature.append(await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
return to_hashable(signature)
async def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
if not dynprompt.has_node(node_id):
# This node doesn't exist -- we can't cache it.
return [float("NaN")]
node = dynprompt.get_node(node_id)
class_type = node["class_type"]
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
signature = [class_type, await self.is_changed_cache.get(node_id)]
if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
signature.append(node_id)
inputs = node["inputs"]
for key in sorted(inputs.keys()):
if is_link(inputs[key]):
(ancestor_id, ancestor_socket) = inputs[key]
ancestor_index = ancestor_order_mapping[ancestor_id]
signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket)))
else:
signature.append((key, inputs[key]))
return signature
# This function returns a list of all ancestors of the given node. The order of the list is
# deterministic based on which specific inputs the ancestor is connected by.
def get_ordered_ancestry(self, dynprompt, node_id):
ancestors = []
order_mapping = {}
self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping)
return ancestors, order_mapping
def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
if not dynprompt.has_node(node_id):
return
inputs = dynprompt.get_node(node_id)["inputs"]
input_keys = sorted(inputs.keys())
for key in input_keys:
if is_link(inputs[key]):
ancestor_id = inputs[key][0]
if ancestor_id not in order_mapping:
ancestors.append(ancestor_id)
order_mapping[ancestor_id] = len(ancestors) - 1
self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
class BasicCache:
def __init__(self, key_class):
self.key_class = key_class
self.initialized = False
self.dynprompt: DynamicPrompt
self.cache_key_set: CacheKeySet
self.cache = {}
self.subcaches = {}
async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
self.dynprompt = dynprompt
self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache)
await self.cache_key_set.add_keys(node_ids)
self.is_changed_cache = is_changed_cache
self.initialized = True
def all_node_ids(self):
assert self.initialized
node_ids = self.cache_key_set.all_node_ids()
for subcache in self.subcaches.values():
node_ids = node_ids.union(subcache.all_node_ids())
return node_ids
def _clean_cache(self):
preserve_keys = set(self.cache_key_set.get_used_keys())
to_remove = []
for key in self.cache:
if key not in preserve_keys:
to_remove.append(key)
for key in to_remove:
del self.cache[key]
def _clean_subcaches(self):
preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys())
to_remove = []
for key in self.subcaches:
if key not in preserve_subcaches:
to_remove.append(key)
for key in to_remove:
del self.subcaches[key]
def clean_unused(self):
assert self.initialized
self._clean_cache()
self._clean_subcaches()
def _set_immediate(self, node_id, value):
assert self.initialized
cache_key = self.cache_key_set.get_data_key(node_id)
self.cache[cache_key] = value
def _get_immediate(self, node_id):
if not self.initialized:
return None
cache_key = self.cache_key_set.get_data_key(node_id)
if cache_key in self.cache:
return self.cache[cache_key]
else:
return None
async def _ensure_subcache(self, node_id, children_ids):
subcache_key = self.cache_key_set.get_subcache_key(node_id)
subcache = self.subcaches.get(subcache_key, None)
if subcache is None:
subcache = BasicCache(self.key_class)
self.subcaches[subcache_key] = subcache
await subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache)
return subcache
def _get_subcache(self, node_id):
assert self.initialized
subcache_key = self.cache_key_set.get_subcache_key(node_id)
if subcache_key in self.subcaches:
return self.subcaches[subcache_key]
else:
return None
def recursive_debug_dump(self):
result = []
for key in self.cache:
result.append({"key": key, "value": self.cache[key]})
for key in self.subcaches:
result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()})
return result
class HierarchicalCache(BasicCache):
def __init__(self, key_class):
super().__init__(key_class)
def _get_cache_for(self, node_id):
assert self.dynprompt is not None
parent_id = self.dynprompt.get_parent_node_id(node_id)
if parent_id is None:
return self
hierarchy = []
while parent_id is not None:
hierarchy.append(parent_id)
parent_id = self.dynprompt.get_parent_node_id(parent_id)
cache = self
for parent_id in reversed(hierarchy):
cache = cache._get_subcache(parent_id)
if cache is None:
return None
return cache
def get(self, node_id):
cache = self._get_cache_for(node_id)
if cache is None:
return None
return cache._get_immediate(node_id)
def set(self, node_id, value):
cache = self._get_cache_for(node_id)
assert cache is not None
cache._set_immediate(node_id, value)
async def ensure_subcache_for(self, node_id, children_ids):
cache = self._get_cache_for(node_id)
assert cache is not None
return await cache._ensure_subcache(node_id, children_ids)
class LRUCache(BasicCache):
def __init__(self, key_class, max_size=100):
super().__init__(key_class)
self.max_size = max_size
self.min_generation = 0
self.generation = 0
self.used_generation = {}
self.children = {}
async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
await super().set_prompt(dynprompt, node_ids, is_changed_cache)
self.generation += 1
for node_id in node_ids:
self._mark_used(node_id)
def clean_unused(self):
while len(self.cache) > self.max_size and self.min_generation < self.generation:
self.min_generation += 1
to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation]
for key in to_remove:
del self.cache[key]
del self.used_generation[key]
if key in self.children:
del self.children[key]
self._clean_subcaches()
def get(self, node_id):
self._mark_used(node_id)
return self._get_immediate(node_id)
def _mark_used(self, node_id):
cache_key = self.cache_key_set.get_data_key(node_id)
if cache_key is not None:
self.used_generation[cache_key] = self.generation
def set(self, node_id, value):
self._mark_used(node_id)
return self._set_immediate(node_id, value)
async def ensure_subcache_for(self, node_id, children_ids):
# Just uses subcaches for tracking 'live' nodes
await super()._ensure_subcache(node_id, children_ids)
await self.cache_key_set.add_keys(children_ids)
self._mark_used(node_id)
cache_key = self.cache_key_set.get_data_key(node_id)
self.children[cache_key] = []
for child_id in children_ids:
self._mark_used(child_id)
self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
return self
class DependencyAwareCache(BasicCache):
"""
A cache implementation that tracks dependencies between nodes and manages
their execution and caching accordingly. It extends the BasicCache class.
Nodes are removed from this cache once all of their descendants have been
executed.
"""
def __init__(self, key_class):
"""
Initialize the DependencyAwareCache.
Args:
key_class: The class used for generating cache keys.
"""
super().__init__(key_class)
self.descendants = {} # Maps node_id -> set of descendant node_ids
self.ancestors = {} # Maps node_id -> set of ancestor node_ids
self.executed_nodes = set() # Tracks nodes that have been executed
async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
"""
Clear the entire cache and rebuild the dependency graph.
Args:
dynprompt: The dynamic prompt object containing node information.
node_ids: List of node IDs to initialize the cache for.
is_changed_cache: Flag indicating if the cache has changed.
"""
# Clear all existing cache data
self.cache.clear()
self.subcaches.clear()
self.descendants.clear()
self.ancestors.clear()
self.executed_nodes.clear()
# Call the parent method to initialize the cache with the new prompt
await super().set_prompt(dynprompt, node_ids, is_changed_cache)
# Rebuild the dependency graph
self._build_dependency_graph(dynprompt, node_ids)
def _build_dependency_graph(self, dynprompt, node_ids):
"""
Build the dependency graph for all nodes.
Args:
dynprompt: The dynamic prompt object containing node information.
node_ids: List of node IDs to build the graph for.
"""
self.descendants.clear()
self.ancestors.clear()
for node_id in node_ids:
self.descendants[node_id] = set()
self.ancestors[node_id] = set()
for node_id in node_ids:
inputs = dynprompt.get_node(node_id)["inputs"]
for input_data in inputs.values():
if is_link(input_data): # Check if the input is a link to another node
ancestor_id = input_data[0]
self.descendants[ancestor_id].add(node_id)
self.ancestors[node_id].add(ancestor_id)
def set(self, node_id, value):
"""
Mark a node as executed and store its value in the cache.
Args:
node_id: The ID of the node to store.
value: The value to store for the node.
"""
self._set_immediate(node_id, value)
self.executed_nodes.add(node_id)
self._cleanup_ancestors(node_id)
def get(self, node_id):
"""
Retrieve the cached value for a node.
Args:
node_id: The ID of the node to retrieve.
Returns:
The cached value for the node.
"""
return self._get_immediate(node_id)
async def ensure_subcache_for(self, node_id, children_ids):
"""
Ensure a subcache exists for a node and update dependencies.
Args:
node_id: The ID of the parent node.
children_ids: List of child node IDs to associate with the parent node.
Returns:
The subcache object for the node.
"""
subcache = await super()._ensure_subcache(node_id, children_ids)
for child_id in children_ids:
self.descendants[node_id].add(child_id)
self.ancestors[child_id].add(node_id)
return subcache
def _cleanup_ancestors(self, node_id):
"""
Check if ancestors of a node can be removed from the cache.
Args:
node_id: The ID of the node whose ancestors are to be checked.
"""
for ancestor_id in self.ancestors.get(node_id, []):
if ancestor_id in self.executed_nodes:
# Remove ancestor if all its descendants have been executed
if all(descendant in self.executed_nodes for descendant in self.descendants[ancestor_id]):
self._remove_node(ancestor_id)
def _remove_node(self, node_id):
"""
Remove a node from the cache.
Args:
node_id: The ID of the node to remove.
"""
cache_key = self.cache_key_set.get_data_key(node_id)
if cache_key in self.cache:
del self.cache[cache_key]
subcache_key = self.cache_key_set.get_subcache_key(node_id)
if subcache_key in self.subcaches:
del self.subcaches[subcache_key]
def clean_unused(self):
"""
Clean up unused nodes. This is a no-op for this cache implementation.
"""
pass
def recursive_debug_dump(self):
"""
Dump the cache and dependency graph for debugging.
Returns:
A list containing the cache state and dependency graph.
"""
result = super().recursive_debug_dump()
result.append({
"descendants": self.descendants,
"ancestors": self.ancestors,
"executed_nodes": list(self.executed_nodes),
})
return result