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import copy
import dataclasses
import logging
import os
from enum import Enum
from typing import Optional, Union
from torch._inductor.remote_cache import JsonDataTy, RemoteCacheJsonSerde
from torch._inductor.runtime.runtime_utils import cache_dir
from torch.utils._appending_byte_serializer import (
AppendingByteSerializer,
BytesReader,
BytesWriter,
)
from torch.utils._ordered_set import OrderedSet
log = logging.getLogger(__name__)
class CacheArtifactType(Enum):
"""
Type of cache
"""
INDUCTOR = 0
AUTOTUNE = 1
AOT_AUTOGRAD = 2
PGO = 3
@dataclasses.dataclass(frozen=True)
class CacheArtifact:
"""
Data for each cache artifact that will be serialized and deserialized
"""
type: CacheArtifactType
key: str
content: bytes = dataclasses.field(repr=False) # Do not display potential binary
@staticmethod
def serialize(writer: BytesWriter, cls: "CacheArtifact") -> None:
writer.write_uint64(cls.type.value)
writer.write_str(cls.key)
writer.write_bytes(cls.content)
@staticmethod
def deserialize(reader: BytesReader) -> "CacheArtifact":
type = reader.read_uint64()
key = reader.read_str()
content = reader.read_bytes()
return CacheArtifact(CacheArtifactType(type), key, content)
@dataclasses.dataclass
class CacheInfo:
"""
Return value of serialization and deserialization for the purpose of
instrumentation
"""
inductor_artifacts: list[str] = dataclasses.field(default_factory=list)
autotune_artifacts: list[str] = dataclasses.field(default_factory=list)
aot_autograd_artifacts: list[str] = dataclasses.field(default_factory=list)
pgo_artifacts: list[str] = dataclasses.field(default_factory=list)
def add(self, artifact: CacheArtifact) -> None:
if artifact.type == CacheArtifactType.INDUCTOR:
self.inductor_artifacts.append(artifact.key)
elif artifact.type == CacheArtifactType.AUTOTUNE:
self.autotune_artifacts.append(artifact.key)
elif artifact.type == CacheArtifactType.AOT_AUTOGRAD:
self.aot_autograd_artifacts.append(artifact.key)
elif artifact.type == CacheArtifactType.PGO:
self.pgo_artifacts.append(artifact.key)
else:
log.warning(f"Unsupported artifact type {artifact.type}") # noqa: G004
def clear(self) -> None:
self.inductor_artifacts.clear()
self.autotune_artifacts.clear()
self.aot_autograd_artifacts.clear()
self.pgo_artifacts.clear()
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: list[CacheArtifact] = []
# 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[CacheArtifact] = AppendingByteSerializer(
serialize_fn=CacheArtifact.serialize
)
_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
def record_artifact(
cls,
artifact_type: CacheArtifactType,
key: str,
content: Union[bytes, JsonDataTy],
) -> None:
"""
Called from each caching operation to record the artifact in this
"mega" list
"""
if artifact_type == CacheArtifactType.AUTOTUNE:
assert not isinstance(content, bytes)
serde = RemoteCacheJsonSerde()
content = serde.encode(content)
assert isinstance(content, bytes)
artifact = CacheArtifact(artifact_type, key, content)
if artifact in cls._seen_artifacts:
return
log.debug("Recording %s", str(artifact))
cls._new_cache_artifacts.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 cls._new_cache_artifacts:
log.debug("saving: %s", artifact)
cls._cache_info.add(artifact)
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)
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[CacheInfo]:
"""
Converts the portable format back into various filesystem caches
"""
try:
artifacts = AppendingByteSerializer.to_list(
serialized_artifacts, deserialize_fn=CacheArtifact.deserialize
)
except Exception:
log.warning("Failed to un-pickle cache artifacts", exc_info=True)
return None
from torch._dynamo.pgo import write_local_impl
from torch._functorch._aot_autograd.autograd_cache import AOTAutogradCache
from torch._inductor.codecache import FxGraphCache
from torch._inductor.runtime.autotune_cache import _LocalAutotuneCacheBackend
autotune_cache = _LocalAutotuneCacheBackend()
info = CacheInfo()
for artifact in artifacts:
log.debug("writing: %s", artifact)
info.add(artifact)
if artifact.type == CacheArtifactType.INDUCTOR:
FxGraphCache._write_to_local_cache(artifact.key, artifact.content)
elif artifact.type == CacheArtifactType.AUTOTUNE:
key = os.path.join(cache_dir(), artifact.key)
autotune_cache._put(key, artifact.content)
elif artifact.type == CacheArtifactType.AOT_AUTOGRAD:
AOTAutogradCache._write_to_local_cache(artifact.key, artifact.content)
elif artifact.type == CacheArtifactType.PGO:
meta = write_local_impl(artifact.key, artifact.content)
assert meta is not None
else:
log.warning(f"Unsupported artifact type {artifact.type}") # noqa: G004
return info
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