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f65fb0a23d
In some internal frameworks, on second attempts the actual code is copied to a different path than previous attempts. but its still the same. PGO will not work on those cased due to the following, sate entries before this PR used to be identified by (filepath, function name, line number). after this PR they are identified by (hash(filepath) , function name, line number). This way PGO will work for those jobs on future attempts and re-compilations of static versions will be avoided. Sometimes we do not have access to the source code, (file does not exists) This seems to happen mostly when we re-trace a compiled function but generally it can happen . Pull Request resolved: https://github.com/pytorch/pytorch/pull/152628 Approved by: https://github.com/oulgen
818 lines
29 KiB
Python
818 lines
29 KiB
Python
"""
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Profile Guided Optimization (PGO) implementation for Dynamo.
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This module provides functionality for caching and managing code state profiles
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that guide optimization decisions in Dynamo. It implements both local and remote
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caching mechanisms for storing profile information across runs, handles profile
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merging across distributed ranks, and manages the lifecycle of profile data
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during compilation. The profiles track dynamic vs static properties of tensors
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and help Dynamo make better specialization decisions.
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"""
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from __future__ import annotations
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import base64
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import copy
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import dataclasses
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import enum
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import functools
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import logging
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import os
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import pickle
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import re
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import zlib
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from collections import defaultdict
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from typing import Optional, TYPE_CHECKING, TypeVar, Union
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from typing_extensions import Self
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import torch._dynamo.config
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import torch._utils_internal
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import torch.compiler.config
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import torch.distributed as dist
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from torch._dynamo.utils import (
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CompileEventLogger,
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dynamo_timed,
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set_feature_use,
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warn_once,
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)
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from torch._environment import is_fbcode
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from torch._logging._internal import trace_structured_artifact
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if TYPE_CHECKING:
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import types
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from torch._dynamo.symbolic_convert import InstructionTranslator
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from torch._inductor.remote_cache import JsonDataTy, RemoteCache
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class ReservedWorkflowIdUserError(ValueError):
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pass
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log = logging.getLogger(__name__)
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LOCK_TIMEOUT = 10
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# How does in memory representation work? Concretely, this module is
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# responsible for holding GLOBAL state representing the state it holds, no
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# other copies permitted. So we retire frame_state entirely and store it
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# here. This should be reset when Dynamo is reset. We never GC information
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# (similar to how the filesystem doesn't get cleaned up except by tmp
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# cleaner), so the expectation is the information is relatively cheap and we
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# don't mind leaking it.
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# How exactly did we design the cache key? Here are some of the questions:
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#
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# - JOB_ID: Do we have a unique identifier for the "training run" (such that
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# it stays the same if we're running the same code, and changes if we're
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# running something different).
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#
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# - RANK: Are we sharing the cache across ranks, or does each rank get
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# an individual cache?
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#
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# We choose to require job_id for PGO cache. This is to prevent
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# situations where unrelated invocations of PyTorch unpredictably cause
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# changes to each other's behavior. With a job_id, at least you know there
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# is some "state" associated with it. (State dict might be another way to
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# tell if a run is related or not.) You can opt-in to YOLO everything
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# aliases everything by passing a shared job_id for all your invocations.
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#
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# We choose to NOT share PGO cache across ranks. With no RANK_SHARING, there
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# is never contention between runs, so we can leisurely update a bundle with
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# information we need. Because we are grouped by job_id, we can have a single
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# consolidated bundle for everything (or not; maybe worry about O(n^2) IO if
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# we updated every compile--let's just instrument this.) Can even take a
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# filelock for extra safety (expect no contention); expect 50ns overhead from
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# uncontended filelock.
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#
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# If we did share ranks, everyone is storming to modify the same cache files.
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# We can do this by having folks atomic write to a CAS-store and then having
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# readers do on-the-fly merging (this can be implemented in remote using
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# prefix iteration). As an optional optimization, one rank can be elected to
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# handling bundling post facto (ideally, this is done async, after quiescence,
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# without compiler collective need to wait for everyone to finish writing
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# their bits.) Not sure how you can avoid a listdir because if some rank shows
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# up with some new entries we need to pull them in ASAP (unless you want to
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# delay bundling).
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#
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# But compiler collectives fill a similar niche: compilers chat with each
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# other so rank 0 has collected everything. So elect rank 0 only to write the
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# bundle. Don't even need CAS-store atomic write; just one rank writing an
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# updating bundles. The point is that use compiler collectives to share
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# profiles across ranks, but use the PGO cache to persist profiles per rank
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# across attempts. No need to have one mechanism to do everything.
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@functools.cache
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def _hash_containing_file(filepath: str) -> str:
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# if the file does not exists we consider filepath to be the hash.
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if not os.path.exists(filepath):
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return filepath
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with open(filepath, "rb") as file:
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content = file.read()
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crc32_value = zlib.crc32(content)
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hash = format(crc32_value & 0xFFFFFFFF, "08x")
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return hash
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@dataclasses.dataclass(frozen=True)
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class CodeId:
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filename: str
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firstlineno: int
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name: str
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# When a job restart, the code can be copied to a different path than the previous attempt. In that case
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# self.filename will have a different value, we do not want to consider those differences. Instead we
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# hash the content of the file and use it as an identifier of the file.
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#
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# self.filename is kept in the object to give readable information/pointer to the actual file, in a local
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# code state it will refer to the first seen file path.
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file_hash: str
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# Exclude file name.
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def __eq__(self, other: object) -> bool:
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if not isinstance(other, CodeId):
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return False
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return (
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self.file_hash == other.file_hash
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and self.firstlineno == other.firstlineno
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and self.name == other.name
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)
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# Ensure if two CodeIds are the same, then they have the same hash by excluding filename.
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def __hash__(self) -> int:
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return hash((self.file_hash, self.name, self.firstlineno))
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def __str__(self) -> str:
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return f"hash({self.file_hash}){self.filename}:{self.firstlineno}:{self.name}"
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@staticmethod
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def make(code: types.CodeType) -> CodeId:
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return CodeId(
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code.co_filename,
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code.co_firstlineno,
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code.co_name,
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_hash_containing_file(code.co_filename),
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)
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@dataclasses.dataclass
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class CodeState:
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automatic_dynamic: defaultdict[str, FrameStateSizeEntry] = dataclasses.field(
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default_factory=lambda: defaultdict(FrameStateSizeEntry)
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)
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_INIT_CODE_STATE: Optional[defaultdict[CodeId, CodeState]] = None
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_CODE_STATE: Optional[defaultdict[CodeId, CodeState]] = None
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@dataclasses.dataclass(frozen=True)
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class InferStride:
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"""
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Denotes the quantity stride[dim] * size[dim], which is what the stride would
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be for the next physical dimension that results in a contiguous layout.
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For example, given size = [2, 3], stride = [3, 1], we can replace this with
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stride = [InferStride(1), 1], because InferStride(1) = stride[1] * size[1] = 1 * 3 = 3
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Indirecting the representation in this way is important for the join operation
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on strides as if we join [2, 3][3, 1] and [2, 4][4, 1],
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we don't want [2, None][None, 1] which would get eventually symbolized into
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[2, s0][s1, 1] (notice that the relationship between s0 and s1 is broken).
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If we instead rewrite the expressions as InferStride so we have [2, 3][InferStride(1), 1]
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and [2, 4][InferStride(1), 1] we now join to [2, None][InferStride(1), 1] will
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result in [2, s0][s0, 1], as desired.
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"""
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dim: int
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_T = TypeVar("_T")
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class AutoUnset(enum.Enum):
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"""
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The identity element of our semilattice, a generic "don't know" element that
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is always subsumed when we get more information.
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"""
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token = 0
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auto_unset = AutoUnset.token
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class AutoDynamic(enum.Enum):
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"""
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The top element of our (bounded) semilattice, whenever you merge this with
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any other element you always get it again
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"""
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token = 0
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auto_dynamic = AutoDynamic.token
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@dataclasses.dataclass
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class FrameStateSizeEntry:
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scalar: Union[int, AutoDynamic, AutoUnset] = dataclasses.field(default=auto_unset)
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# NB: We don't have cases where we have a known dimensionality but
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# we know NOTHING about the individual sizes
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size: Union[AutoDynamic, AutoUnset, tuple[Union[int, AutoDynamic], ...]] = (
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dataclasses.field(default=auto_unset)
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)
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stride: Union[
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AutoDynamic, AutoUnset, tuple[Union[int, AutoDynamic, InferStride], ...]
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] = dataclasses.field(default=auto_unset)
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def render(self) -> str:
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# Special cases
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def render_single(s: Union[int, AutoDynamic, AutoUnset, InferStride]) -> str:
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if s is auto_dynamic:
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return "?"
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elif s is auto_unset:
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# This basically shouldn't happen, this is for debugging
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return "auto unset"
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elif isinstance(s, InferStride):
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return f"S({s.dim})"
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else:
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return str(s)
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def render_tuple(ss: tuple[Union[int, AutoDynamic, InferStride], ...]) -> str:
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return "[" + ", ".join(render_single(s) for s in ss) + "]"
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# Common cases
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if self.size is auto_dynamic and self.stride is auto_dynamic:
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if self.scalar is auto_dynamic:
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return "fully dynamic scalar or tensor"
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else:
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return f"scalar {self.scalar}"
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elif self.scalar is auto_dynamic:
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if isinstance(self.size, tuple) and isinstance(self.stride, tuple):
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return f"tensor size={render_tuple(self.size)} stride={render_tuple(self.stride)}"
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# Fallback
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return "unusual {repr(self)}"
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def __post_init__(self) -> None:
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assert not isinstance(self.scalar, torch.SymInt), self.scalar
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if isinstance(self.size, tuple):
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for s in self.size:
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assert not isinstance(s, torch.SymInt), s
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if isinstance(self.stride, tuple):
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for s1 in self.stride:
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assert not isinstance(s1, torch.SymInt), s1
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def is_size_dynamic(self, dim: int) -> bool:
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if self.size is auto_dynamic:
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return True
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if self.size is auto_unset:
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return False
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return self.size[dim] is auto_dynamic
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def is_stride_dynamic(self, dim: int) -> bool:
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# At the moment, dynamic strides is a bit buggy. Good test case
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# here is `PYTORCH_TEST_WITH_DYNAMO=1 python test/test_autograd.py
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# TestAutograd.test_gradcheck_jacobian_mismatch`
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#
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# This if statement preserves historical behavior, which is that we
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# ONLY make strides dynamic if the size is exactly static everywhere.
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# We could potentially relax this but in general we should be very
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# careful about when to infer dynamic strides.
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#
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# Actually, the existing algorithm is already somewhat problematic.
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# Suppose a tensor that is sometimes:
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# f32[2, 3, 5][15, 5, 1] and other times
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# f32[2, 3, 5][5, 10, 1] (specifically, dim 0 and 1 are physically transposed).
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# If we infer strides should be (DYNAMIC, DYNAMIC, 1). But this is
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# silly: we really should have just guarded on dim order.
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if not (
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isinstance(self.size, tuple) and all(type(s) is int for s in self.size)
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):
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return False
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if self.stride is auto_dynamic:
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return True
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if self.stride is auto_unset:
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return False
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return self.stride[dim] is auto_dynamic
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@staticmethod
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def _munge_symint(xs: tuple[int, ...]) -> tuple[Union[AutoDynamic, int], ...]:
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return tuple(auto_dynamic if isinstance(x, torch.SymInt) else x for x in xs)
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@classmethod
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def make_scalar(cls, x: int) -> FrameStateSizeEntry:
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return FrameStateSizeEntry(scalar=x, size=auto_dynamic, stride=auto_dynamic)
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@classmethod
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def make_tensor(
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cls, size: tuple[int, ...], stride: tuple[int, ...]
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) -> FrameStateSizeEntry:
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return FrameStateSizeEntry(
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scalar=auto_dynamic,
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size=cls._munge_symint(size),
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stride=cls._munge_symint(stride),
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)
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@classmethod
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def make_size(cls, size: tuple[int, ...]) -> FrameStateSizeEntry:
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return FrameStateSizeEntry(
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scalar=auto_unset,
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size=cls._munge_symint(size),
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stride=auto_unset,
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)
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@staticmethod
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def _merge_atom(x: _T, y: _T) -> Union[AutoDynamic, _T]:
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if x is auto_unset:
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return y
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if y is auto_unset:
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return x
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if x is auto_dynamic or y is auto_dynamic or x != y:
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return auto_dynamic
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return x
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@classmethod
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def _merge_atom_tup(
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cls,
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xs: Union[AutoDynamic, AutoUnset, tuple[_T, ...]],
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ys: Union[AutoDynamic, AutoUnset, tuple[_T, ...]],
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) -> Union[AutoDynamic, AutoUnset, tuple[Union[AutoDynamic, _T], ...]]:
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if xs is auto_unset:
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return ys
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if ys is auto_unset:
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return xs
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if xs is auto_dynamic or ys is auto_dynamic:
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return auto_dynamic
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if len(xs) != len(ys):
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return auto_dynamic
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return tuple(cls._merge_atom(x, y) for x, y in zip(xs, ys))
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def __ior__(self, other: Self) -> Self:
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self.scalar = self._merge_atom(self.scalar, other.scalar)
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self.size = self._merge_atom_tup(self.size, other.size)
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self.stride = self._merge_atom_tup(self.stride, other.stride)
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return self
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def update_automatic_dynamic(
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tx: InstructionTranslator,
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name: str,
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entry: FrameStateSizeEntry,
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*,
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is_unspecialized_nn_module: bool = False,
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) -> FrameStateSizeEntry:
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code_id = CodeId.make(tx.f_code)
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frame_state = get_code_state()[code_id]
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is_update = name in frame_state.automatic_dynamic
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mut_entry = frame_state.automatic_dynamic[name]
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old_entry = copy.copy(mut_entry)
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mut_entry |= entry
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# Do some logs (damn, I spend more code logging than I do actually doing
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# the updates lol)
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if is_update and old_entry.scalar != mut_entry.scalar:
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log.debug(
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"automatic dynamic int %s val %s != %s",
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name,
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entry.scalar,
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old_entry.scalar,
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)
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CompileEventLogger.instant(
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"automatic_dynamic",
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{
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"name": name,
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"dim_changed": "scalar",
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"reason": "scalar change",
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"cached": str(old_entry.scalar),
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"new": str(entry.scalar),
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},
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)
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if is_unspecialized_nn_module:
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log.info(
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"%s is converted to a symbolic integer. It is an attribute of a "
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"user defined nn module class. If you wish to keep it static, you can "
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"mark the nn module class as `torch._dynamo.mark_static`.",
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name,
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)
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def log_tup(
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tup_name: str, short_reason: str, long_reason: str, i: Optional[int] = None
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) -> None:
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entry_tup = (
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getattr(entry, tup_name) if i is None else getattr(entry, tup_name)[i]
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)
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old_entry_tup = (
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getattr(old_entry, tup_name)
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if i is None
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else getattr(old_entry, tup_name)[i]
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)
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log.debug(
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"automatic dynamic %s %s %s %s != %s",
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tup_name,
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name,
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short_reason,
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# NB: We used to only report len(...) here for dim mismatch
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entry_tup,
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old_entry_tup,
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)
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CompileEventLogger.instant(
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"automatic_dynamic",
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{
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"name": name,
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"dim_changed": "all" if i is None else i,
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"reason": long_reason,
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"cached": str(old_entry_tup),
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"new": str(entry_tup),
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},
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)
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if is_update and old_entry.size != mut_entry.size:
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if isinstance(old_entry.size, tuple) and isinstance(entry.size, tuple):
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if len(old_entry.size) != len(entry.size):
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log_tup("size", "dim", "dimensionality change")
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else:
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for i in range(len(entry.size)):
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if old_entry.size[i] != entry.size[i]:
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log_tup("size", f"size({i})", "size change", i)
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else:
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log_tup("size", "other", "other")
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if is_update and old_entry.stride != mut_entry.stride:
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if isinstance(old_entry.stride, tuple) and isinstance(entry.stride, tuple):
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if len(old_entry.stride) != len(entry.stride):
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log_tup("stride", "dim", "dimensionality change")
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else:
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for i in range(len(entry.stride)):
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if old_entry.stride[i] != entry.stride[i]:
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log_tup("stride", f"stride({i})", "stride change", i)
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else:
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log_tup("stride", "other", "other")
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return mut_entry
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def process_automatic_dynamic(
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tx: InstructionTranslator,
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name: str,
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entry: FrameStateSizeEntry,
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*,
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is_unspecialized_nn_module: bool = False,
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) -> FrameStateSizeEntry:
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if (st := tx.distributed_state) is None:
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return update_automatic_dynamic(
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tx,
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name,
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entry,
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is_unspecialized_nn_module=is_unspecialized_nn_module,
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)
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elif st.all_states is None:
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# Preflight, always pretend as if it's static. The point here
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# is we want to get through the preflight quickly, and static
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# will run faster. The preexisting frame state will get
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# applied anyway after we do compiler collectives.
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# TODO: I'm not sure if we should just bong the entire pgo
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|
# state here, it kind of depends if we're going to have other
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# things that talk in compiler collective. Also, the PGO
|
|
# state, if we've already inferred something is automatic
|
|
# dynamic, will have lost the actual input sizes, which might
|
|
# be useful for debugging purposes (e.g., observing 0/1
|
|
# specialization). Bonging the entire PGO state here would
|
|
# let us delete this logic here; the compiler collective
|
|
# would just directly update_automatic_dynamic
|
|
st.local_state.automatic_dynamic[name] = entry
|
|
return entry
|
|
else:
|
|
# Apply the updates. NB: all_states includes the local state
|
|
# too.
|
|
res = None
|
|
for sub_state in st.all_states:
|
|
if name in sub_state.automatic_dynamic:
|
|
res = update_automatic_dynamic(
|
|
tx,
|
|
name,
|
|
sub_state.automatic_dynamic[name],
|
|
is_unspecialized_nn_module=is_unspecialized_nn_module,
|
|
)
|
|
assert res is not None
|
|
return res
|
|
|
|
|
|
def get_cache_key() -> Optional[str]:
|
|
# TODO: info versions of these logs that log only once
|
|
if torch._inductor.config.force_disable_caches:
|
|
warn_once(
|
|
"dynamo_pgo force disabled by torch._inductor.config.force_disable_caches"
|
|
)
|
|
return None
|
|
|
|
# NB: We always use global rank for keys, even though they are overkill
|
|
# for local only cache
|
|
rank = None
|
|
if dist.is_available() and dist.is_initialized():
|
|
rank = dist.get_rank()
|
|
|
|
tag = torch.compiler.config.cache_key_tag
|
|
|
|
# NB: We namespace the cache keys so that only user-specified job id
|
|
# can alias with each other.
|
|
if (r := torch.compiler.config.job_id) is not None:
|
|
if r.startswith("mast:"):
|
|
raise ReservedWorkflowIdUserError(
|
|
"torch.compiler.config.job_id with prefix 'mast:' is reserved for "
|
|
"automatically generated job id associated with a specific MAST job "
|
|
"name and version."
|
|
)
|
|
return f"{r}:{rank}:{tag}"
|
|
|
|
if (name_version := torch._utils_internal.get_mast_job_name_version()) is not None:
|
|
mast_job_name, mast_job_version = name_version
|
|
return f"mast:{mast_job_name}:{mast_job_version}:{rank}:{tag}"
|
|
|
|
return None
|
|
|
|
|
|
def rewrite_cache_key_for_mega_cache(original_key: str) -> str:
|
|
"""
|
|
The PGO cache artifact key for a MAST job contains the job name and the version.
|
|
When we want to use the cache artifact on a different MAST job, we need to
|
|
update the key to use the new MAST job's name and version.
|
|
"""
|
|
if not original_key.startswith("mast:"):
|
|
# if original_key is overriden, then dont change it
|
|
return original_key
|
|
if (new_key := get_cache_key()) is not None:
|
|
return new_key
|
|
return original_key
|
|
|
|
|
|
# This solely controls local PGO
|
|
def code_state_path(cache_key: str) -> Optional[str]:
|
|
if not torch._dynamo.config.automatic_dynamic_local_pgo:
|
|
log.debug("automatic_dynamic_local_pgo not enabled")
|
|
return None
|
|
|
|
from torch._inductor.runtime.runtime_utils import cache_dir
|
|
|
|
code_state_key = re.sub(r'[<>:"/\\|?*]', "_", f"code_state_{cache_key}.pkl")
|
|
return os.path.join(cache_dir(), "dynamo", code_state_key)
|
|
|
|
|
|
def should_use_remote_dynamo_pgo_cache() -> bool:
|
|
if torch._inductor.config.force_disable_caches:
|
|
return False
|
|
|
|
if (r := torch._dynamo.config.automatic_dynamic_remote_pgo) is not None:
|
|
return r
|
|
|
|
if not is_fbcode():
|
|
return False
|
|
|
|
if torch._utils_internal.is_fb_unit_test():
|
|
return False
|
|
|
|
try:
|
|
from torch._inductor.fb.remote_cache import REMOTE_CACHE_VERSION
|
|
except ModuleNotFoundError:
|
|
return False
|
|
|
|
return REMOTE_CACHE_VERSION >= torch._utils_internal.justknobs_getval_int(
|
|
"pytorch/remote_cache:dynamo_pgo_version"
|
|
)
|
|
|
|
|
|
def get_remote_cache() -> Optional[RemoteCache[JsonDataTy]]:
|
|
from torch._inductor.remote_cache import create_cache
|
|
|
|
if not should_use_remote_dynamo_pgo_cache():
|
|
return None
|
|
|
|
return create_cache(
|
|
"dynamo-pgo",
|
|
is_fbcode(),
|
|
"FbRemoteDynamoPGOCache",
|
|
"RemoteDynamoPGOCache",
|
|
)
|
|
|
|
|
|
def render_code_state(cs: defaultdict[CodeId, CodeState]) -> str:
|
|
return "\n".join(
|
|
f"{k}:\n"
|
|
+ "\n".join(
|
|
f" {src}: {fs.render()}" for src, fs in v.automatic_dynamic.items()
|
|
)
|
|
for k, v in cs.items()
|
|
)
|
|
|
|
|
|
def get_code_state() -> defaultdict[CodeId, CodeState]:
|
|
global _CODE_STATE, _INIT_CODE_STATE
|
|
if _CODE_STATE is not None:
|
|
return _CODE_STATE
|
|
|
|
# Initialize it (even if we don't look up profile)
|
|
_CODE_STATE = defaultdict(CodeState)
|
|
|
|
cache_key = get_cache_key()
|
|
if cache_key is None:
|
|
return _CODE_STATE
|
|
|
|
def hit(ty: str) -> defaultdict[CodeId, CodeState]:
|
|
global _INIT_CODE_STATE
|
|
assert isinstance(_CODE_STATE, defaultdict)
|
|
log.info("get_code_state %s hit %s, %d entries", path, ty, len(_CODE_STATE))
|
|
trace_structured_artifact(
|
|
f"get_{ty}_code_state",
|
|
"string",
|
|
lambda: render_code_state(_CODE_STATE),
|
|
)
|
|
set_feature_use("pgo", True)
|
|
_INIT_CODE_STATE = copy.deepcopy(_CODE_STATE)
|
|
return _CODE_STATE
|
|
|
|
from torch.compiler._cache import CacheArtifactManager, CacheArtifactType
|
|
|
|
# Attempt local
|
|
path = code_state_path(cache_key)
|
|
if path is not None and os.path.exists(path):
|
|
with dynamo_timed(
|
|
name := "pgo.get_local_code_state", log_pt2_compile_event=True
|
|
):
|
|
CompileEventLogger.pt2_compile(name, cache_key=cache_key)
|
|
# Read lock not necessary as we always write atomically write to
|
|
# the actual location
|
|
with open(path, "rb") as f:
|
|
try:
|
|
content = f.read()
|
|
_CODE_STATE = pickle.loads(content)
|
|
CompileEventLogger.pt2_compile(name, cache_size_bytes=f.tell())
|
|
except Exception:
|
|
log.warning(
|
|
"get_code_state failed while reading %s", path, exc_info=True
|
|
)
|
|
else:
|
|
CacheArtifactManager.record_artifact(
|
|
CacheArtifactType.PGO, cache_key, content
|
|
)
|
|
return hit("local")
|
|
|
|
# Attempt remote
|
|
remote_cache = get_remote_cache()
|
|
if remote_cache is not None:
|
|
with dynamo_timed(
|
|
name := "pgo.get_remote_code_state",
|
|
log_pt2_compile_event=True,
|
|
dynamo_compile_column_us="pgo_get_remote_code_state_time_us",
|
|
):
|
|
CompileEventLogger.pt2_compile(name, cache_key=cache_key)
|
|
# TODO: I don't really understand why there's a JSON container format
|
|
try:
|
|
cache_data = remote_cache.get(cache_key)
|
|
except Exception:
|
|
log.warning(
|
|
"get_code_state failed remote read on %s", cache_key, exc_info=True
|
|
)
|
|
else:
|
|
if cache_data is not None:
|
|
try:
|
|
assert isinstance(cache_data, dict)
|
|
data = cache_data["data"]
|
|
assert isinstance(data, str)
|
|
payload = base64.b64decode(data)
|
|
CompileEventLogger.pt2_compile(
|
|
name, cache_size_bytes=len(payload)
|
|
)
|
|
_CODE_STATE = pickle.loads(payload)
|
|
except Exception:
|
|
log.warning(
|
|
"get_code_state failed parsing remote result on %s",
|
|
cache_key,
|
|
exc_info=True,
|
|
)
|
|
else:
|
|
CacheArtifactManager.record_artifact(
|
|
CacheArtifactType.PGO, cache_key, payload
|
|
)
|
|
return hit("remote")
|
|
else:
|
|
log.info("get_code_state remote miss on %s", cache_key)
|
|
|
|
log.info("get_code_state using default")
|
|
|
|
assert _CODE_STATE is not None
|
|
return _CODE_STATE
|
|
|
|
|
|
def put_code_state() -> None:
|
|
if _CODE_STATE is None:
|
|
log.info("put_code_state: never initialized, will not write")
|
|
return
|
|
|
|
if _CODE_STATE == _INIT_CODE_STATE:
|
|
log.info("put_code_state: no change, skipping")
|
|
return
|
|
|
|
cache_key = get_cache_key()
|
|
if cache_key is None:
|
|
log.info("put_code_state: no cache key, skipping")
|
|
return
|
|
|
|
put_local_code_state(cache_key)
|
|
put_remote_code_state(cache_key)
|
|
|
|
|
|
def write_local_impl(cache_key: str, pickled_code: bytes) -> Optional[tuple[str, int]]:
|
|
path = code_state_path(cache_key)
|
|
|
|
if path is None:
|
|
return None
|
|
|
|
# If the user isn't misusing our API, we should have exclusive access to
|
|
# this directory. But it's not too hard
|
|
|
|
tmp_path = path + ".tmp"
|
|
lock_path = path + ".lock"
|
|
# We /mostly/ don't need the lock but the tmp file could be clobbered
|
|
# TODO: use a safe tempfile create to eliminate lock
|
|
from torch.utils._filelock import FileLock
|
|
|
|
os.makedirs(os.path.dirname(path), exist_ok=True)
|
|
|
|
with FileLock(lock_path, timeout=LOCK_TIMEOUT):
|
|
with open(tmp_path, "wb") as f:
|
|
f.write(pickled_code)
|
|
size = f.tell()
|
|
os.replace(tmp_path, path)
|
|
return path, size
|
|
|
|
|
|
def put_local_code_state(cache_key: str) -> None:
|
|
with dynamo_timed(name := "pgo.put_local_code_state", log_pt2_compile_event=True):
|
|
CompileEventLogger.pt2_compile(name, cache_key=cache_key)
|
|
assert _CODE_STATE is not None
|
|
|
|
pickled_code = pickle.dumps(_CODE_STATE)
|
|
|
|
from torch.compiler._cache import CacheArtifactManager, CacheArtifactType
|
|
|
|
CacheArtifactManager.record_artifact(
|
|
CacheArtifactType.PGO, cache_key, pickled_code
|
|
)
|
|
|
|
meta = write_local_impl(cache_key, pickled_code)
|
|
if meta is None:
|
|
log.info("put_code_state: local cache disabled")
|
|
return
|
|
path, size = meta
|
|
|
|
CompileEventLogger.pt2_compile(name, cache_size_bytes=size)
|
|
log.info("put_code_state: wrote local %s, %d entries", path, len(_CODE_STATE))
|
|
trace_structured_artifact(
|
|
"put_local_code_state",
|
|
"string",
|
|
lambda: render_code_state(_CODE_STATE),
|
|
)
|
|
|
|
|
|
def put_remote_code_state(cache_key: str) -> None:
|
|
with dynamo_timed(
|
|
name := "pgo.put_remote_code_state",
|
|
log_pt2_compile_event=True,
|
|
dynamo_compile_column_us="pgo_put_remote_code_state_time_us",
|
|
):
|
|
CompileEventLogger.pt2_compile(name, cache_key=cache_key)
|
|
assert _CODE_STATE is not None
|
|
|
|
remote_cache = get_remote_cache()
|
|
|
|
if remote_cache is None:
|
|
log.info("put_code_state: remote cache disabled")
|
|
return
|
|
|
|
content = pickle.dumps(_CODE_STATE)
|
|
CompileEventLogger.pt2_compile(name, cache_size_bytes=len(content))
|
|
cache_data: JsonDataTy = {
|
|
"data": base64.b64encode(content).decode("ascii"),
|
|
}
|
|
remote_cache.put(cache_key, cache_data)
|
|
log.info(
|
|
"put_code_state: wrote remote %s, %d entries", cache_key, len(_CODE_STATE)
|
|
)
|
|
# TODO: don't log this multiple times
|
|
trace_structured_artifact(
|
|
"put_remote_code_state",
|
|
"string",
|
|
lambda: render_code_state(_CODE_STATE),
|
|
)
|
|
|
|
|
|
# NB: this does NOT reset the cached code state on disk
|
|
def reset_code_state() -> None:
|
|
global _CODE_STATE, _INIT_CODE_STATE
|
|
_CODE_STATE = None
|
|
_INIT_CODE_STATE = None
|