File size: 4,846 Bytes
1beb9da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | from typing import Any, Optional, Tuple, List
import hashlib
import json
import logging
import threading
# Public types — source of truth is comfy_api.latest._caching
from comfy_api.latest._caching import CacheProvider, CacheContext, CacheValue # noqa: F401 (re-exported)
_logger = logging.getLogger(__name__)
_providers: List[CacheProvider] = []
_providers_lock = threading.Lock()
_providers_snapshot: Tuple[CacheProvider, ...] = ()
def register_cache_provider(provider: CacheProvider) -> None:
"""Register an external cache provider. Providers are called in registration order."""
global _providers_snapshot
with _providers_lock:
if provider in _providers:
_logger.warning(f"Provider {provider.__class__.__name__} already registered")
return
_providers.append(provider)
_providers_snapshot = tuple(_providers)
_logger.debug(f"Registered cache provider: {provider.__class__.__name__}")
def unregister_cache_provider(provider: CacheProvider) -> None:
global _providers_snapshot
with _providers_lock:
try:
_providers.remove(provider)
_providers_snapshot = tuple(_providers)
_logger.debug(f"Unregistered cache provider: {provider.__class__.__name__}")
except ValueError:
_logger.warning(f"Provider {provider.__class__.__name__} was not registered")
def _get_cache_providers() -> Tuple[CacheProvider, ...]:
return _providers_snapshot
def _has_cache_providers() -> bool:
return bool(_providers_snapshot)
def _clear_cache_providers() -> None:
global _providers_snapshot
with _providers_lock:
_providers.clear()
_providers_snapshot = ()
def _canonicalize(obj: Any) -> Any:
# Convert to canonical JSON-serializable form with deterministic ordering.
# Frozensets have non-deterministic iteration order between Python sessions.
# Raises ValueError for non-cacheable types (Unhashable, unknown) so that
# _serialize_cache_key returns None and external caching is skipped.
if isinstance(obj, frozenset):
return ("__frozenset__", sorted(
[_canonicalize(item) for item in obj],
key=lambda x: json.dumps(x, sort_keys=True)
))
elif isinstance(obj, set):
return ("__set__", sorted(
[_canonicalize(item) for item in obj],
key=lambda x: json.dumps(x, sort_keys=True)
))
elif isinstance(obj, tuple):
return ("__tuple__", [_canonicalize(item) for item in obj])
elif isinstance(obj, list):
return [_canonicalize(item) for item in obj]
elif isinstance(obj, dict):
return {"__dict__": sorted(
[[_canonicalize(k), _canonicalize(v)] for k, v in obj.items()],
key=lambda x: json.dumps(x, sort_keys=True)
)}
elif isinstance(obj, (int, float, str, bool, type(None))):
return (type(obj).__name__, obj)
elif isinstance(obj, bytes):
return ("__bytes__", obj.hex())
else:
raise ValueError(f"Cannot canonicalize type: {type(obj).__name__}")
def _serialize_cache_key(cache_key: Any) -> Optional[str]:
# Returns deterministic SHA256 hex digest, or None on failure.
# Uses JSON (not pickle) because pickle is non-deterministic across sessions.
try:
canonical = _canonicalize(cache_key)
json_str = json.dumps(canonical, sort_keys=True, separators=(',', ':'))
return hashlib.sha256(json_str.encode('utf-8')).hexdigest()
except Exception as e:
_logger.warning(f"Failed to serialize cache key: {e}")
return None
def _contains_self_unequal(obj: Any) -> bool:
# Local cache matches by ==. Values where not (x == x) (NaN, etc.) will
# never hit locally, but serialized form would match externally. Skip these.
try:
if not (obj == obj):
return True
except Exception:
return True
if isinstance(obj, (frozenset, tuple, list, set)):
return any(_contains_self_unequal(item) for item in obj)
if isinstance(obj, dict):
return any(_contains_self_unequal(k) or _contains_self_unequal(v) for k, v in obj.items())
if hasattr(obj, 'value'):
return _contains_self_unequal(obj.value)
return False
def _estimate_value_size(value: CacheValue) -> int:
try:
import torch
except ImportError:
return 0
total = 0
def estimate(obj):
nonlocal total
if isinstance(obj, torch.Tensor):
total += obj.numel() * obj.element_size()
elif isinstance(obj, dict):
for v in obj.values():
estimate(v)
elif isinstance(obj, (list, tuple)):
for item in obj:
estimate(item)
for output in value.outputs:
estimate(output)
return total
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