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import copy
import dataclasses
import logging
from abc import ABC, abstractmethod
from collections import defaultdict
from collections.abc import Generator
from contextlib import contextmanager
from itertools import chain
from typing import Any, Optional
from torch.utils._appending_byte_serializer import (
AppendingByteSerializer,
BytesReader,
BytesWriter,
)
from torch.utils._ordered_set import OrderedSet
log = logging.getLogger(__name__)
@dataclasses.dataclass(frozen=True)
class CacheArtifact(ABC):
"""
Data for each cache artifact that will be serialized and deserialized
"""
key: str
content: bytes = dataclasses.field(repr=False) # Do not display potential binary
@staticmethod
def serialize(writer: BytesWriter, cls: "CacheArtifact") -> None:
writer.write_str(cls.key)
writer.write_bytes(cls.content)
@staticmethod
def deserialize(artifact_type: str, reader: BytesReader) -> "CacheArtifact":
key = reader.read_str()
content = reader.read_bytes()
return CacheArtifactFactory.create(artifact_type, key, content)
@staticmethod
def encode(content: Any) -> bytes:
assert isinstance(content, bytes), f"Expected bytes, got {type(content)}"
return content
@abstractmethod
def populate_cache(self) -> None:
pass
def precompile_compatible(self) -> bool:
return False
@staticmethod
def type() -> str:
"""
Returns the type of the artifact. Must be unique across all CacheArtifact classes.
CacheArtifactFactory.register will add property method to CacheInfo based on this (def {type}_artifacts)
that returns all artifacts for specific cache.
"""
raise RuntimeError("CacheArtifact is an abstract class, please use a subclass")
class CacheArtifactFactory:
"""
Factory for creating CacheArtifact objects based on their type
"""
_artifact_types: dict[str, type[CacheArtifact]] = {}
@classmethod
def register(cls, artifact_cls: type[CacheArtifact]) -> type[CacheArtifact]:
artifact_type_key = artifact_cls.type()
assert (
artifact_cls.type() not in cls._artifact_types
), f"Artifact of type={artifact_type_key} already registered in mega-cache artifact factory"
cls._artifact_types[artifact_type_key] = artifact_cls
setattr(
CacheInfo,
f"{artifact_type_key}_artifacts",
property(lambda self: self.artifacts[artifact_type_key]),
)
return artifact_cls
@classmethod
def _get_artifact_type(cls, artifact_type_key: str) -> type[CacheArtifact]:
assert (
artifact_type_key in cls._artifact_types
), f"Artifact of type={artifact_type_key} not registered in mega-cache artifact factory"
return cls._artifact_types[artifact_type_key]
@classmethod
def create(cls, artifact_type_key: str, key: str, content: bytes) -> CacheArtifact:
artifact_cls = cls._get_artifact_type(artifact_type_key)
return artifact_cls(key, content)
@classmethod
def encode_create(
cls, artifact_type_key: str, key: str, content: Any
) -> CacheArtifact:
artifact_cls = cls._get_artifact_type(artifact_type_key)
return artifact_cls(key, artifact_cls.encode(content))
@dataclasses.dataclass
class CacheInfo:
"""
Return value of serialization and deserialization for the purpose of
instrumentation
"""
artifacts: defaultdict[str, list[str]] = dataclasses.field(
default_factory=lambda: defaultdict(list)
)
# Methods set by CacheArtifactFactory.register based on CacheArtifact.type()
@property
def inductor_artifacts(self) -> list[str]: # type: ignore[empty-body]
...
@property
def autotune_artifacts(self) -> list[str]: # type: ignore[empty-body]
...
@property
def aot_autograd_artifacts(self) -> list[str]: # type: ignore[empty-body]
...
@property
def pgo_artifacts(self) -> list[str]: # type: ignore[empty-body]
...
@property
def precompile_aot_autograd_artifacts(self) -> list[str]: # type: ignore[empty-body]
...
def add(self, artifact: CacheArtifact) -> None:
self.artifacts[artifact.type()].append(artifact.key)
def clear(self) -> None:
self.artifacts.clear()
def empty(self) -> bool:
return not self.artifacts
def _serialize_single_cache(
writer: BytesWriter, cls: "tuple[str, list[CacheArtifact]]"
) -> None:
writer.write_str(cls[0])
writer.write_uint64(len(cls[1]))
for artifact in cls[1]:
CacheArtifact.serialize(writer, artifact)
def _deserialize_single_cache(
reader: BytesReader,
) -> "tuple[str, list[CacheArtifact]]":
artifacts = []
artifact_type_key = reader.read_str()
num_artifacts = reader.read_uint64()
for _ in range(num_artifacts):
artifacts.append(CacheArtifact.deserialize(artifact_type_key, reader))
return artifact_type_key, artifacts
CacheArtifactsResult = dict[str, list[CacheArtifact]]
class CacheArtifactManager:
"""
Lightweight manager class for collecting and processing cache artifacts for
hot loading
Intended Lifecycle:
- Execute code via torch.compile, this will call
CacheArtifactManager.record_artifact on each cache artifact
- Call CacheArtifactManager.serialize to convert all the cache artifacts
to portable format
- Call CacheArtifactManager.deserialize to hot load the cache artifacts on
a potentially different process
NOTE: There's no FB/FC guarentees, results of cache artifacts will not be
used unless code version matches.
"""
# Protected by the compile_lock
_new_cache_artifacts: CacheArtifactsResult = defaultdict(list)
# Keep a seperate seen artifacts list to make avoid unnecessary duplicates
# This list will not be cleared between serialize() calls
_seen_artifacts: OrderedSet[CacheArtifact] = OrderedSet()
# When serialize() is called, artifacts are transferred from _cache_artifacts to
# internal data structure of the _serializer
# This allows us to only pay the cost of serialization if serialize() is called
_serializer: AppendingByteSerializer[
tuple[str, list[CacheArtifact]]
] = AppendingByteSerializer(serialize_fn=_serialize_single_cache)
_cache_info: CacheInfo = CacheInfo()
@classmethod
def clear(cls) -> None:
cls._new_cache_artifacts.clear()
cls._seen_artifacts.clear()
cls._serializer.clear()
cls._cache_info.clear()
@classmethod
@contextmanager
def with_fresh_cache(cls) -> Generator[None, None, None]:
original_new_cache_artifacts = cls._new_cache_artifacts
original_seen_artifacts = cls._seen_artifacts
original_serializer = cls._serializer
original_cache_info = cls._cache_info
cls._new_cache_artifacts = defaultdict(list)
cls._seen_artifacts = OrderedSet()
cls._serializer = AppendingByteSerializer(serialize_fn=_serialize_single_cache)
cls._cache_info = cls._cache_info.__class__()
try:
yield
finally:
cls._new_cache_artifacts = original_new_cache_artifacts
cls._seen_artifacts = original_seen_artifacts
cls._serializer = original_serializer
cls._cache_info = original_cache_info
@classmethod
def record_artifact(
cls,
artifact_type: str,
key: str,
content: Any,
) -> None:
"""
Called from each caching operation to record the artifact in this
"mega" list
"""
artifact = CacheArtifactFactory.encode_create(artifact_type, key, content)
if artifact in cls._seen_artifacts:
return
log.debug("Recording %s", str(artifact))
cls._new_cache_artifacts[artifact_type].append(artifact)
cls._seen_artifacts.add(artifact)
@classmethod
def need_serialize(cls) -> bool:
"""
Have we seen new artifacts since last serialize call?
"""
return len(cls._new_cache_artifacts) != 0
@classmethod
def serialize(cls) -> Optional[tuple[bytes, CacheInfo]]:
"""
Converts the "mega" list into portable format
"""
for artifact in chain(*cls._new_cache_artifacts.values()):
log.debug("saving: %s", artifact)
cls._cache_info.add(artifact)
if cls._cache_info.empty():
# If there are not artifacts, dont just return bytes with
# version.
return None
try:
# We deep copy cls._cache_info since later compilations
# can keep adding to cache_info
info = copy.deepcopy(cls._cache_info)
cls._serializer.extend(cls._new_cache_artifacts.items())
artifact_bytes = cls._serializer.to_bytes()
cls._new_cache_artifacts.clear()
return artifact_bytes, info
except Exception:
log.warning("Failed to pickle cache artifacts", exc_info=True)
return None
@staticmethod
def deserialize(serialized_artifacts: bytes) -> Optional[CacheArtifactsResult]:
"""
Converts the portable format back into CacheArtifacts
"""
try:
CacheArtifactManager._ensure_cache_artifacts_registered()
artifacts = dict(
AppendingByteSerializer.to_list(
serialized_artifacts,
deserialize_fn=_deserialize_single_cache,
)
)
except Exception:
log.warning("Failed to un-pickle cache artifacts", exc_info=True)
return None
return artifacts
@staticmethod
def populate_caches(artifacts: CacheArtifactsResult) -> CacheInfo:
info = CacheInfo()
for artifact in chain(*artifacts.values()):
log.debug("writing: %s", artifact)
info.add(artifact)
artifact.populate_cache()
return info
@classmethod
def _ensure_cache_artifacts_registered(cls) -> None:
"""When deserializing caches in fresh process, we need to ensure that all
cache artifacts are registered in the cache registry. This is done by
simply importing all the cache artifacts already wrapped with register call.
"""
from torch._dynamo.pgo import PGOCacheArtifact # noqa: F401
from torch._functorch._aot_autograd.autograd_cache import ( # noqa: F401
AOTAutogradCacheArtifact,
)
from torch._inductor.codecache import InductorCacheArtifact # noqa: F401
from torch._inductor.runtime.autotune_cache import ( # noqa: F401
AutotuneCacheArtifact,
)
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