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https://github.com/pytorch/pytorch.git
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599f639ddb
We are refactoring dynamo code for convert frame so that we can have modularized pieces sharable between different compiler frontends (e.g. torch.compile, precompile and torch.export). This PR follows the last one which separate out the part to run instruction translator on a given frame and return a DynamoTracerOutput. The end result is a free function that runs instruction translator indepedently. A follow up diff will wrap the low level function. Differential Revision: [D80388694](https://our.internmc.facebook.com/intern/diff/D80388694/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/160815 Approved by: https://github.com/anijain2305 ghstack dependencies: #160814
1698 lines
63 KiB
Python
1698 lines
63 KiB
Python
"""
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This module implements TorchDynamo's core frame conversion functionality, transforming Python
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frames into FX graphs. It handles:
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- Frame analysis and bytecode transformation
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- Guard creation and management for dynamic behaviors
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- Cache management for recompilation
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- Error handling and fallback mechanisms
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Key classes:
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- ConvertFrame: Main entry point for frame conversion with error handling
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- ConvertFrameAssert: Implements core frame to graph conversion logic
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- Tracker: Tracks input/output code objects during conversion
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- CatchErrorsWrapper: Provides error handling and suppression logic
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The conversion process preserves program semantics while enabling optimizations
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through torch.compile() and related systems.
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NOTE: _torchdynamo_orig_backend is used for convert frame wrappers to identify the inner wrapped function.
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By going down the _torchdynamo_orig_backend chain, one can recover the original unwrapped backend,
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which is checked for during the Dynamo cache lookup.
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"""
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from __future__ import annotations
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import collections
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import contextlib
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import cProfile
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import dis
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import functools
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import gc
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import itertools
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import logging
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import os
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import pstats
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import random
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import subprocess
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import sys
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import threading
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import time
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import traceback
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import types
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import typing
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import weakref
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from dataclasses import dataclass
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from pathlib import Path
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from types import CellType, CodeType, FunctionType, ModuleType
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from typing import Any, Callable, Optional, TypeVar, Union
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from typing_extensions import ParamSpec
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from weakref import ReferenceType
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import torch
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import torch._logging
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from torch._C._dynamo.guards import GlobalStateGuard
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from torch._dynamo.callback import CallbackTrigger
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from torch._dynamo.distributed import get_compile_pg
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from torch._dynamo.symbolic_convert import TensorifyState
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from torch._guards import compile_context, CompileContext, CompileId, tracing
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from torch._logging import structured
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from torch._utils_internal import (
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compile_time_strobelight_meta,
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justknobs_check,
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maybe_upload_prof_stats_to_manifold,
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signpost_event,
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)
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from torch.fx._lazy_graph_module import _use_lazy_graph_module
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from torch.fx.experimental.symbolic_shapes import (
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ConstraintViolationError,
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GuardOnDataDependentSymNode,
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)
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from torch.fx.graph_module import _forward_from_src as original_forward_from_src
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from torch.monitor import _WaitCounter
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from torch.nn.parallel.distributed import DistributedDataParallel
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from torch.utils._python_dispatch import (
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_disable_current_modes,
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is_in_torch_dispatch_mode,
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)
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from torch.utils._traceback import CapturedTraceback, format_traceback_short
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from . import config, decorators, exc, graph_break_hints, trace_rules
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from .bytecode_analysis import remove_dead_code, remove_pointless_jumps
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from .bytecode_transformation import (
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check_inst_exn_tab_entries_valid,
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Instruction,
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is_generator,
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propagate_inst_exn_table_entries,
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transform_code_object,
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)
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from .cache_size import (
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CacheSizeRelevantForFrame,
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compute_cache_size,
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exceeds_recompile_limit,
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is_recompilation,
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)
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from .eval_frame import (
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always_optimize_code_objects,
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dynamo_tls,
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skip_code,
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TorchPatcher,
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)
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from .exc import (
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augment_exc_message,
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BackendCompilerFailed,
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FailOnRecompileLimitHit,
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format_error_msg,
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InternalTorchDynamoError,
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PackageError,
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RecompileLimitExceeded,
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ResumePrologueTracingError,
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ShortenTraceback,
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SkipCodeRecursiveException,
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TorchRuntimeError,
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UncapturedHigherOrderOpError,
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unimplemented_v2,
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Unsupported,
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)
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from .guards import (
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CheckFunctionManager,
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get_and_maybe_log_recompilation_reasons,
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GuardedCode,
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)
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from .hooks import Hooks
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from .output_graph import DynamoTracerOutput
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from .pgo import log_frame_dynamic_whitelist, put_code_state
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from .replay_record import ExecutionRecord
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from .resume_execution import TORCH_DYNAMO_RESUME_IN_PREFIX
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from .symbolic_convert import (
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DistributedState,
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ExceptionStack,
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InstructionTranslator,
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LocalState,
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SpeculationLog,
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)
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from .trace_rules import is_numpy
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from .types import ConvertFrameReturn, FrameAction, FrameExecStrategy, wrap_guarded_code
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from .utils import (
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_get_error_on_graph_break,
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chromium_event_timed,
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CleanupManager,
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CompileTimeInstructionCounter,
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counters,
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dynamo_timed,
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format_bytecode,
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gen_record_file_name,
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get_hook_for_recompile_user_context,
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get_metrics_context,
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increment_frame,
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is_namedtuple,
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istype,
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LazyString,
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maybe_disable_inference_mode,
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maybe_disable_inference_mode_for_fake_prop,
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orig_code_map,
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reset_graph_break_dup_checker,
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setup_compile_debug,
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to_int_us,
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troubleshooting_url,
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write_record_to_file,
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)
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from .variables.torch_function import torch_function_mode_stack_state_mgr
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np: Optional[ModuleType]
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try:
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import numpy as np
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except ModuleNotFoundError:
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np = None
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if typing.TYPE_CHECKING:
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from .backends.registry import CompilerFn
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from .package import CompilePackage
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from .repro.after_dynamo import WrapBackendDebug
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from .types import BytecodeHook, CacheEntry, DynamoFrameType
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from .variables.builder import FrameStateSizeEntry
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log = logging.getLogger(__name__)
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bytecode_log = torch._logging.getArtifactLogger(__name__, "bytecode")
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graph_break_log = torch._logging.getArtifactLogger(__name__, "graph_breaks")
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compile_lock = threading.RLock()
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_T = TypeVar("_T")
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_P = ParamSpec("_P")
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class TODO_UNKNOWN:
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pass
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class Tracker:
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def __init__(self) -> None:
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self.seen: list[ReferenceType[CodeType]] = []
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self.seen_ids: set[int] = set()
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def add(self, strong_obj: CodeType) -> None:
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idx = id(strong_obj)
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if idx not in self.seen_ids:
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obj = weakref.ref(strong_obj, lambda _: self.seen_ids.remove(idx))
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self.seen.append(obj)
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self.seen_ids.add(idx)
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def __contains__(self, item: CodeType) -> bool:
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return id(item) in self.seen_ids
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def clear(self) -> None:
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self.seen.clear()
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self.seen_ids.clear()
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input_codes = Tracker()
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output_codes = Tracker()
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initial_global_state: Optional[GlobalStateGuard] = None
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@functools.wraps(original_forward_from_src)
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def fx_forward_from_src_skip_result(
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src: str, globals: dict[str, Any], co_fields: Optional[dict[str, str]] = None
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) -> FunctionType:
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# we monkey patch FX to prevent infinite loop of trying to convert
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# our generated code
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result = original_forward_from_src(src, globals, co_fields)
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skip_code(result.__code__)
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return result
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def log_dynamo_start(code: CodeType, skip: int = 0) -> list[str]:
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convert_frame_intern = structured.intern_string(__file__)
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# Extract and filter the stack
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stack = list(
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itertools.takewhile(
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lambda f: f["filename"] != convert_frame_intern,
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structured.from_traceback(
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CapturedTraceback.extract(skip=4 + skip).summary()
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),
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)
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) + [
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{
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"line": code.co_firstlineno,
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"name": code.co_name,
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"filename": structured.intern_string(code.co_filename),
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}
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]
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# Initialize the ChromiumEventLogger on start
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torch._logging.trace_structured(
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"dynamo_start",
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lambda: {"stack": stack},
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)
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stack_strings = [
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f"Line: {frame['line']}, Name: {frame['name']}, Filename: {frame['filename']}"
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for frame in stack
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]
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return stack_strings
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def preserve_global_state(fn: Callable[_P, _T]) -> Callable[_P, _T]:
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"""
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Context manager to:
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1) Save/restore torch.is_grad_enabled() state
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2) Save/restore python random state
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3) Save/restore torch random state
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4) Monkey patch torch.fx.graph_module._forward_from_src
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"""
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@functools.wraps(fn)
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def _fn(*args: _P.args, **kwargs: _P.kwargs) -> _T:
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guards = GlobalStateGuard()
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prior_grad_mode = torch.is_grad_enabled()
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# Just in case we get left in a bad dispatch state we want to restore
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# it. This can happen because the dispatch bits aren't a true
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# stack/counter - so we can't just increment/decrement them as we enter
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# and leave.
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with (
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torch._C._PreserveDispatchKeyGuard(),
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maybe_disable_inference_mode(),
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maybe_disable_inference_mode_for_fake_prop(),
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):
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prior_inference_mode = torch.is_inference_mode_enabled()
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prior_deterministic = torch.are_deterministic_algorithms_enabled()
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prior_warn_only = torch.is_deterministic_algorithms_warn_only_enabled()
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prior_mobile_allocator_state = (
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torch._C._is_default_mobile_cpu_allocator_set()
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)
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py_rng_state = random.getstate()
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prior_dtype = torch.get_default_dtype()
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torch_rng_state = torch.random.get_rng_state()
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cuda_rng_state = None
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if torch.cuda.is_available():
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cuda_rng_state = torch.cuda.get_rng_state()
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cuda_matmul_fp32_prec = torch._C._get_fp32_precision_getter(
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"cuda", "matmul"
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)
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prior_fwd_from_src = torch.fx.graph_module._forward_from_src
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torch.fx.graph_module._forward_from_src = fx_forward_from_src_skip_result
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cleanup = setup_compile_debug()
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exit_stack = contextlib.ExitStack()
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exit_stack.enter_context(
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torch.fx._symbolic_trace._maybe_revert_all_patches()
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)
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exit_stack.enter_context(torch_function_mode_stack_state_mgr)
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try:
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return fn(*args, **kwargs)
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finally:
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cleanup.close()
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assert torch._C._len_torch_function_stack() == 0, (
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"Torch function mode stack state changed while dynamo tracing, please report a bug"
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)
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exit_stack.close()
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torch._C._set_grad_enabled(prior_grad_mode)
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torch.autograd.grad_mode._enter_inference_mode(prior_inference_mode)
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torch.use_deterministic_algorithms(
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prior_deterministic, warn_only=prior_warn_only
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)
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random.setstate(py_rng_state)
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torch.random.set_rng_state(torch_rng_state)
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torch.set_default_dtype(prior_dtype)
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curr_mobile_allocator_state = (
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torch._C._is_default_mobile_cpu_allocator_set()
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)
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if prior_mobile_allocator_state != curr_mobile_allocator_state:
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torch._C._unset_default_mobile_cpu_allocator()
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if cuda_rng_state is not None:
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torch.cuda.set_rng_state(cuda_rng_state)
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torch._C._set_fp32_precision_setter(
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"cuda", "matmul", cuda_matmul_fp32_prec
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)
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torch.fx.graph_module._forward_from_src = prior_fwd_from_src
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assert guards.check(), (
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f"Global {guards.reason()}state changed while dynamo tracing, please report a bug"
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)
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_fn._torchdynamo_orig_backend = fn # type: ignore[attr-defined]
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return _fn
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@TorchPatcher.suppress_torch_distributed_warnings
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def has_tensor_in_frame(frame: DynamoFrameType) -> bool:
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"""Check if the frame has torch.* related bits"""
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# Check if the function was decorated using torch._dynamo.optimize
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if frame.f_code in always_optimize_code_objects:
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return True
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# Check if there is global import of torch.*
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for co_name in frame.f_code.co_names:
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if co_name in frame.f_globals:
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obj = frame.f_globals[co_name]
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if isinstance(obj, ModuleType) and (
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obj.__name__.startswith("torch.") or obj is torch
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):
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return True
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# ... or a global import of numpy.*
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if np and config.trace_numpy and (obj is np or is_numpy(obj)):
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return True
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seen_ids: dict[int, bool] = {}
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def has_tensor(obj: object) -> bool:
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"""Recursively check if the obj has a tensor"""
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obj_id = id(obj)
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if obj_id in seen_ids:
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return seen_ids[obj_id]
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seen_ids[obj_id] = False
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if isinstance(obj, (torch.Tensor, torch.nn.Module)) or (
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istype(obj, type) and issubclass(obj, torch.nn.Module)
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):
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seen_ids[obj_id] = True
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return seen_ids[obj_id]
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elif (
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config.trace_numpy
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and np
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and (istype(obj, np.ndarray) or isinstance(obj, np.generic))
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):
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seen_ids[obj_id] = True
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return seen_ids[obj_id]
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elif istype(obj, (list, tuple)):
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seen_ids[obj_id] = any(has_tensor(v) for v in obj)
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return seen_ids[obj_id]
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elif istype(obj, dict):
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# Some packages like pytest can be updated during runtime. So, make a
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# copy of values to avoid issues like "RuntimeError: dictionary
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# changed size during iteration"
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values = list(obj.values())
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seen_ids[obj_id] = any(has_tensor(v) for v in values)
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return seen_ids[obj_id]
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elif istype(obj, (str, int, float, type(None), bool)):
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seen_ids[obj_id] = False
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return seen_ids[obj_id]
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elif is_namedtuple(obj) and hasattr(obj, "_fields"):
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seen_ids[obj_id] = any(has_tensor(getattr(obj, v)) for v in obj._fields)
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return seen_ids[obj_id]
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else:
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# if config.debug:
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# print(
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# f"Assuming that object of type {type(obj)} does not have a tensor"
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# )
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return False
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# Check if the passed arguments are of type Tensor
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for value in frame.f_locals.values():
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if has_tensor(value):
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return True
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log.debug(
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"skipping because no torch.* %s \
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%s %s",
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frame.f_code.co_name,
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frame.f_code.co_filename,
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frame.f_code.co_firstlineno,
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)
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return False
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|
|
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def exception_handler(
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e: Exception,
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code: CodeType,
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frame: Optional[DynamoFrameType] = None,
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export: bool = False,
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) -> None:
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record_filename = None
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if hasattr(e, "exec_record"):
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record_filename = gen_record_file_name(e, code)
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write_record_to_file(record_filename, e.exec_record)
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e.record_filename = record_filename # type: ignore[attr-defined]
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augment_exc_message(e, export=export)
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|
|
|
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FRAME_COUNTER = 0
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FRAME_COMPILE_COUNTER: typing.Counter[Union[int, FrameStateSizeEntry]] = (
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collections.Counter()
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)
|
|
|
|
|
|
def maybe_cprofile(func: Callable[_P, _T]) -> Callable[_P, _T]:
|
|
if config.cprofile:
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return cprofile_wrapper(func)
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return func
|
|
|
|
|
|
def cprofile_wrapper(func: Callable[_P, _T]) -> Callable[_P, _T]:
|
|
@functools.wraps(func)
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|
def profile_wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
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|
trace_id = CompileContext.current_trace_id()
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assert trace_id, "Trace id is None"
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profile_path = Path(
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f"/tmp/{func.__name__}_{str(trace_id).replace('/', '_')}.profile"
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)
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|
prof = cProfile.Profile()
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|
try:
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|
prof.enable()
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|
start_ts = time.time()
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|
retval = prof.runcall(func, *args, **kwargs)
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|
profile_latency = time.time() - start_ts
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|
prof.disable()
|
|
except ValueError:
|
|
log.exception("failed to enable cProfile")
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|
profile_latency = 0
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|
retval = func(*args, **kwargs)
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|
log.warning(
|
|
"### Cprofile for %s trace id [%s] took %.3f seconds ###",
|
|
func.__name__,
|
|
trace_id,
|
|
profile_latency,
|
|
)
|
|
ps = pstats.Stats(prof)
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|
try:
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|
prof.dump_stats(profile_path)
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|
except OSError:
|
|
log.exception("Cannot write to %s", profile_path)
|
|
log.warning("Raw profile at %s", profile_path)
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|
svg_path = profile_path.with_suffix(".svg")
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|
try:
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|
gprof2dot_process = subprocess.Popen(
|
|
[
|
|
"gprof2dot",
|
|
"-f",
|
|
"pstats",
|
|
"--node-label=total-time-percentage",
|
|
"--node-label=self-time-percentage",
|
|
"--node-label=total-time",
|
|
str(profile_path),
|
|
],
|
|
stdout=subprocess.PIPE,
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|
)
|
|
subprocess.check_call(
|
|
["dot", "-Tsvg", "-o", str(svg_path)],
|
|
stdin=gprof2dot_process.stdout,
|
|
)
|
|
log.warning("Generated SVG from profile at %s", svg_path)
|
|
except FileNotFoundError:
|
|
log.warning(
|
|
"Failed to generate SVG from profile -- dumping stats instead."
|
|
"Try installing gprof2dot and dot for a better visualization"
|
|
)
|
|
ps.sort_stats(pstats.SortKey.TIME).print_stats(20)
|
|
ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(20)
|
|
|
|
if manifold_link := maybe_upload_prof_stats_to_manifold(
|
|
str(profile_path)
|
|
): # fb-only
|
|
torch._logging.trace_structured(
|
|
"link",
|
|
lambda: {"name": "cprofile_manifold_url", "url": manifold_link},
|
|
)
|
|
return retval
|
|
|
|
return profile_wrapper
|
|
|
|
|
|
@dataclass
|
|
class ConvertFrameBox:
|
|
error_on_graph_break: Optional[bool] = None
|
|
|
|
|
|
def _is_error_on_graph_break(tx: Optional[DynamoTracerOutput]) -> bool:
|
|
if tx is None:
|
|
return _get_error_on_graph_break()
|
|
return tx.error_on_graph_break
|
|
|
|
|
|
def get_compile_id(
|
|
frame_state: dict[str, Union[int, FrameStateSizeEntry]],
|
|
) -> CompileId:
|
|
global FRAME_COUNTER
|
|
if "_id" not in frame_state:
|
|
frame_state["_id"] = FRAME_COUNTER
|
|
FRAME_COUNTER += 1
|
|
frame_id = frame_state["_id"]
|
|
assert isinstance(frame_id, int)
|
|
|
|
frame_compile_id = FRAME_COMPILE_COUNTER[frame_id]
|
|
FRAME_COMPILE_COUNTER[frame_id] += 1
|
|
|
|
compiled_autograd_id = None
|
|
if prior := CompileContext.current_compile_id():
|
|
compiled_autograd_id = prior.compiled_autograd_id
|
|
return CompileId(
|
|
compiled_autograd_id=compiled_autograd_id,
|
|
frame_id=frame_id,
|
|
frame_compile_id=frame_compile_id,
|
|
)
|
|
|
|
|
|
class ConvertFrameAssert:
|
|
def __init__(
|
|
self,
|
|
compiler_fn: CompilerFn,
|
|
one_graph: bool = True,
|
|
export: bool = False,
|
|
export_constraints: Optional[typing.Never] = None,
|
|
package: Optional[CompilePackage] = None,
|
|
) -> None:
|
|
# assert export_constraints is None
|
|
reset_graph_break_dup_checker()
|
|
self._torchdynamo_orig_backend = compiler_fn
|
|
self._one_graph = one_graph
|
|
self._export = export
|
|
self._export_constraints = export_constraints
|
|
self._package = package
|
|
self._box = ConvertFrameBox()
|
|
|
|
@property
|
|
def _clone_with_backend(self) -> Callable[[CompilerFn], ConvertFrameAssert]:
|
|
return lambda backend: convert_frame_assert(
|
|
backend,
|
|
self._one_graph,
|
|
self._export,
|
|
self._export_constraints,
|
|
)
|
|
|
|
def __call__(
|
|
self,
|
|
frame: DynamoFrameType,
|
|
cache_entry: Optional[CacheEntry],
|
|
hooks: Hooks,
|
|
frame_state: dict[str, Union[int, FrameStateSizeEntry]],
|
|
*,
|
|
skip: int = 0,
|
|
) -> ConvertFrameReturn:
|
|
increment_frame()
|
|
code = frame.f_code
|
|
|
|
cache_size = compute_cache_size(frame, cache_entry)
|
|
input_codes.add(code)
|
|
if code in output_codes:
|
|
return ConvertFrameReturn()
|
|
if (
|
|
os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION")
|
|
and os.environ.get("TORCHDYNAMO_DEBUG_FUNCTION") != code.co_name
|
|
):
|
|
return ConvertFrameReturn()
|
|
if code.co_name == "<genexpr>" and code.co_filename.endswith(
|
|
(
|
|
"transformers/file_utils.py",
|
|
"transformers/utils/generic.py",
|
|
"diffusers/utils/outputs.py",
|
|
)
|
|
):
|
|
# not needed, but cleans up torchbench error stats
|
|
return ConvertFrameReturn()
|
|
if code.co_name == "__setattr__":
|
|
# setattr could be tricky to handle generally,
|
|
# but also not likely useful to compile- skip the whole frame
|
|
return ConvertFrameReturn()
|
|
if code.co_name == "__init__" and code.co_filename.startswith(
|
|
os.path.dirname(torch.optim.__file__)
|
|
):
|
|
# optimizer support is still incomplete see
|
|
# test_state_dict in test/dynamo/test_optimizers.py
|
|
return ConvertFrameReturn()
|
|
|
|
# Check if the frame is generated by an exec builtin call
|
|
# TODO - Running exec generated frame seems propagates f_globals to the
|
|
# next frames.
|
|
if code.co_name == "<module>" and code.co_filename == "<string>":
|
|
return ConvertFrameReturn()
|
|
|
|
if (
|
|
code.co_name == "<lambda>"
|
|
and code.co_filename == "<string>"
|
|
and not bool(frame.f_builtins)
|
|
):
|
|
# namedtuple subclass constructor. Empty builtins cause issue with
|
|
# len keyword in LIST_LEN guard.
|
|
return ConvertFrameReturn()
|
|
|
|
if is_generator(code):
|
|
unimplemented_v2(
|
|
gb_type="Attempt to trace generator",
|
|
context="",
|
|
explanation="Generators cannot be compiled directly with `torch.compile`.",
|
|
hints=[
|
|
"Call a generator from inside of a non-generator Python function and "
|
|
"compile that function instead.",
|
|
*graph_break_hints.FUNDAMENTAL,
|
|
],
|
|
)
|
|
|
|
if not has_tensor_in_frame(frame):
|
|
return ConvertFrameReturn()
|
|
|
|
# skip tracing non-recursive disabled functions
|
|
# detect if the previous frame (non-convert_frame) is a non-recursive disable wrapper
|
|
prev_frame = sys._getframe()
|
|
while (
|
|
prev_frame
|
|
and "torch/_dynamo/convert_frame.py" in prev_frame.f_code.co_filename
|
|
):
|
|
prev_frame = prev_frame.f_back # type: ignore[assignment]
|
|
if (
|
|
prev_frame
|
|
and prev_frame.f_code is decorators._nonrecursive_disable_wrapper_code
|
|
):
|
|
return ConvertFrameReturn(apply_to_code=False)
|
|
|
|
global initial_global_state
|
|
initial_global_state = GlobalStateGuard()
|
|
|
|
compile_id = get_compile_id(frame_state)
|
|
frame_id = compile_id.frame_id
|
|
|
|
signpost_event(
|
|
"dynamo",
|
|
"_convert_frame_assert._compile",
|
|
{
|
|
"co_name": code.co_name,
|
|
"frame_id": frame_id,
|
|
"compile_id": str(compile_id),
|
|
"co_filename": code.co_filename,
|
|
"co_firstlineno": code.co_firstlineno,
|
|
"cache_size": cache_size.num_cache_entries_with_same_id_matched_objs,
|
|
"accumulated_cache_size": cache_size.num_cache_entries,
|
|
},
|
|
)
|
|
|
|
# Record traced frames, skipping Dynamo generated ones.
|
|
if not code.co_name.startswith(TORCH_DYNAMO_RESUME_IN_PREFIX):
|
|
info = f"{code.co_name} {code.co_filename}:{code.co_firstlineno}"
|
|
dynamo_tls.traced_frame_infos.append(info)
|
|
|
|
with compile_context(CompileContext(compile_id)):
|
|
result = _compile(
|
|
frame.f_code,
|
|
frame.f_globals,
|
|
frame.f_locals,
|
|
frame.f_builtins,
|
|
frame.closure,
|
|
self._torchdynamo_orig_backend,
|
|
self._one_graph,
|
|
self._export,
|
|
self._export_constraints,
|
|
hooks,
|
|
cache_entry,
|
|
cache_size,
|
|
frame,
|
|
frame_state=frame_state,
|
|
compile_id=compile_id,
|
|
skip=skip + 1,
|
|
package=self._package,
|
|
convert_frame_box=self._box,
|
|
)
|
|
|
|
if config.caching_precompile and self._package is not None:
|
|
from .package import DynamoCache
|
|
|
|
# Record that the dynamo package has changed
|
|
DynamoCache.record_package(self._package)
|
|
return result
|
|
|
|
|
|
def convert_frame_assert(
|
|
compiler_fn: CompilerFn,
|
|
one_graph: bool = True,
|
|
export: bool = False,
|
|
export_constraints: Optional[typing.Never] = None,
|
|
package: Optional[CompilePackage] = None,
|
|
) -> ConvertFrameAssert:
|
|
"""Fully convert a frame into an FX graph, raising an exception if we fail."""
|
|
return ConvertFrameAssert(
|
|
compiler_fn, one_graph, export, export_constraints, package
|
|
)
|
|
|
|
|
|
from collections import OrderedDict
|
|
|
|
from torch.utils.hooks import RemovableHandle
|
|
|
|
|
|
# we have to use `OrderedDict` to make `RemovableHandle` work.
|
|
_bytecode_hooks: dict[int, BytecodeHook] = OrderedDict()
|
|
|
|
|
|
def register_bytecode_hook(hook: BytecodeHook) -> RemovableHandle:
|
|
"""Register hooks for bytecode generated by Dynamo. The hook can do some
|
|
logging, as well as return a new code object to be used. Please refer
|
|
to `BytecodeHook` for the hook signature.
|
|
"""
|
|
handle = RemovableHandle(_bytecode_hooks)
|
|
_bytecode_hooks[handle.id] = hook
|
|
return handle
|
|
|
|
|
|
@preserve_global_state
|
|
def trace_frame(
|
|
code: types.CodeType,
|
|
globals: dict[str, object],
|
|
locals: dict[str, object],
|
|
builtins: dict[str, object],
|
|
closure: tuple[CellType],
|
|
compiler_fn: CompilerFn,
|
|
tf_mode_stack: list[torch.overrides.TorchFunctionMode],
|
|
one_graph: bool,
|
|
speculation_log: SpeculationLog,
|
|
instructions: list[Instruction],
|
|
code_options: dict[str, object],
|
|
*,
|
|
export: bool = False,
|
|
export_constraints: Optional[typing.Never] = None,
|
|
frame_state: Optional[dict[str, Union[int, FrameStateSizeEntry]]] = None,
|
|
distributed_state: Optional[DistributedState] = None,
|
|
package: Optional[CompilePackage] = None,
|
|
) -> DynamoTracerOutput:
|
|
from torch.fx.experimental.validator import bisect, translation_validation_enabled
|
|
|
|
speculation_log.restart() # type: ignore[has-type]
|
|
exn_vt_stack = ExceptionStack()
|
|
tracer = InstructionTranslator(
|
|
instructions,
|
|
code,
|
|
locals,
|
|
globals,
|
|
builtins,
|
|
closure,
|
|
tf_mode_stack,
|
|
code_options,
|
|
compiler_fn,
|
|
one_graph,
|
|
export,
|
|
export_constraints,
|
|
frame_state=frame_state,
|
|
speculation_log=speculation_log, # type: ignore[has-type]
|
|
exn_vt_stack=exn_vt_stack,
|
|
distributed_state=distributed_state, # type: ignore[has-type]
|
|
package=package,
|
|
)
|
|
|
|
def run_tracer() -> None:
|
|
try:
|
|
tracer.output.mark_bytecode_tracing_start()
|
|
with tracing(tracer.output.tracing_context), tracer.set_current_tx():
|
|
tracer.run()
|
|
except exc.UnspecializeRestartAnalysis:
|
|
speculation_log.clear() # type: ignore[has-type]
|
|
raise
|
|
except (
|
|
exc.SpeculationRestartAnalysis,
|
|
exc.TensorifyScalarRestartAnalysis,
|
|
exc.SkipFrame,
|
|
):
|
|
raise
|
|
except Exception:
|
|
if translation_validation_enabled():
|
|
bisect(tracer.output.shape_env)
|
|
raise
|
|
finally:
|
|
tracer.output.call_cleanup_hooks()
|
|
|
|
try:
|
|
run_tracer()
|
|
tracer_output = DynamoTracerOutput(tracer)
|
|
output = tracer_output.output_graph
|
|
assert output is not None
|
|
assert output.output_instructions
|
|
instructions[:] = output.output_instructions
|
|
code_options.update(output.code_options)
|
|
propagate_inst_exn_table_entries(instructions)
|
|
check_inst_exn_tab_entries_valid(instructions)
|
|
instructions[:] = remove_pointless_jumps(remove_dead_code(instructions))
|
|
except Exception as e:
|
|
e._torch_dynamo_tracer_output = DynamoTracerOutput(tracer, error=True) # type: ignore[attr-defined]
|
|
raise
|
|
|
|
return tracer_output
|
|
|
|
|
|
def _compile(
|
|
code: CodeType,
|
|
globals: dict[str, object],
|
|
locals: dict[str, object],
|
|
builtins: dict[str, object],
|
|
closure: tuple[CellType],
|
|
compiler_fn: CompilerFn,
|
|
one_graph: bool,
|
|
export: bool,
|
|
export_constraints: Optional[typing.Never],
|
|
hooks: Hooks,
|
|
cache_entry: Optional[CacheEntry],
|
|
cache_size: CacheSizeRelevantForFrame,
|
|
frame: Optional[DynamoFrameType] = None,
|
|
frame_state: Optional[dict[str, Union[int, FrameStateSizeEntry]]] = None,
|
|
*,
|
|
compile_id: CompileId,
|
|
skip: int = 0,
|
|
package: Optional[CompilePackage] = None,
|
|
# Can be used to record things for the caller, both
|
|
# in the case of normal and exception code paths
|
|
convert_frame_box: Optional[ConvertFrameBox] = None,
|
|
) -> ConvertFrameReturn:
|
|
from torch._inductor.async_compile import async_compile_pool_manager
|
|
from torch.fx.experimental.validator import (
|
|
BisectValidationException,
|
|
ValidationException,
|
|
)
|
|
|
|
# Only nonlocal defs here please!
|
|
# Time spent compiling this frame before restarting or failing analysis
|
|
dynamo_time_before_restart: float = 0.0
|
|
tracer_output: Optional[DynamoTracerOutput] = None
|
|
|
|
def transform(
|
|
instructions: list[Instruction], code_options: dict[str, object]
|
|
) -> None:
|
|
nonlocal tracer_output
|
|
|
|
tf_mode_stack: list[torch.overrides.TorchFunctionMode] = (
|
|
torch.overrides._get_current_function_mode_stack()
|
|
)
|
|
try:
|
|
tracer_output = trace_frame(
|
|
code,
|
|
globals,
|
|
locals,
|
|
builtins,
|
|
closure,
|
|
compiler_fn,
|
|
tf_mode_stack,
|
|
one_graph,
|
|
speculation_log,
|
|
instructions,
|
|
code_options,
|
|
export=export,
|
|
export_constraints=export_constraints,
|
|
frame_state=frame_state,
|
|
distributed_state=distributed_state,
|
|
package=package,
|
|
)
|
|
except Exception as e:
|
|
tracer_output = getattr(e, "_torch_dynamo_tracer_output", None) # type: ignore[attr-defined]
|
|
raise
|
|
|
|
@compile_time_strobelight_meta(phase_name="compile_inner")
|
|
def compile_inner(
|
|
code: CodeType,
|
|
one_graph: bool,
|
|
hooks: Hooks,
|
|
transform: Callable[[list[Instruction], dict[str, Any]], Any],
|
|
) -> ConvertFrameReturn:
|
|
with contextlib.ExitStack() as stack:
|
|
stack.enter_context(
|
|
torch._dynamo.callback_handler.install_callbacks(
|
|
CallbackTrigger.DYNAMO, str(CompileContext.current_compile_id())
|
|
)
|
|
)
|
|
stack.enter_context(CompileTimeInstructionCounter.record())
|
|
return _compile_inner(code, one_graph, hooks, transform)
|
|
|
|
return (
|
|
ConvertFrameReturn()
|
|
) # dead, but see https://github.com/python/mypy/issues/7577
|
|
|
|
@maybe_cprofile
|
|
def _compile_inner(
|
|
code: CodeType,
|
|
one_graph: bool,
|
|
hooks: Hooks,
|
|
transform: Callable[[list[Instruction], dict[str, Any]], Any],
|
|
) -> ConvertFrameReturn:
|
|
nonlocal dynamo_time_before_restart
|
|
last_attempt_start_time = start_time = time.time()
|
|
|
|
def log_bytecode(
|
|
prefix: str, name: str, filename: str, line_no: int, code: CodeType
|
|
) -> None:
|
|
if bytecode_log.isEnabledFor(logging.DEBUG):
|
|
bytecode_log.debug(
|
|
format_bytecode(prefix, name, filename, line_no, code)
|
|
)
|
|
|
|
log_bytecode(
|
|
"ORIGINAL BYTECODE",
|
|
code.co_name,
|
|
code.co_filename,
|
|
code.co_firstlineno,
|
|
code,
|
|
)
|
|
|
|
out_code = None
|
|
for attempt in itertools.count():
|
|
CompileContext.get().attempt = attempt
|
|
try:
|
|
with dynamo_timed(
|
|
f"compile_attempt_{attempt}", log_pt2_compile_event=True
|
|
):
|
|
out_code = transform_code_object(code, transform)
|
|
break
|
|
except exc.RestartAnalysis as e:
|
|
if not isinstance(e, exc.TensorifyScalarRestartAnalysis):
|
|
TensorifyState.clear()
|
|
log.info(
|
|
"Restarting analysis due to %s",
|
|
LazyString(format_traceback_short, e.__traceback__),
|
|
)
|
|
# If restart reason is None just log the type of the exception
|
|
restart_reasons.add(e.restart_reason or str(type(e)))
|
|
# We now have a new "last attempt", reset the clock
|
|
last_attempt_start_time = time.time()
|
|
if attempt > 100:
|
|
unimplemented_v2(
|
|
gb_type="Excessive RestartAnalysis() calls",
|
|
context="",
|
|
explanation="Dynamo attempted to trace the same frame 100+ times. "
|
|
"Giving up on compiling as the compile time tradeoff is likely not "
|
|
"worth the performance gain.",
|
|
hints=[],
|
|
)
|
|
except exc.SkipFrame as e:
|
|
if not isinstance(e, exc.TensorifyScalarRestartAnalysis):
|
|
TensorifyState.clear()
|
|
log.debug(
|
|
"Skipping frame %s %s \
|
|
%s %s",
|
|
e,
|
|
code.co_name,
|
|
code.co_filename,
|
|
code.co_firstlineno,
|
|
)
|
|
if one_graph or _is_error_on_graph_break(tracer_output):
|
|
log.debug(
|
|
"No graph captured with one_graph=True or error_on_graph_break=True"
|
|
)
|
|
return ConvertFrameReturn()
|
|
|
|
assert distributed_state is None or distributed_state.all_states is not None, ( # type: ignore[has-type]
|
|
"compiler collective wasn't run before compilation completed"
|
|
)
|
|
|
|
assert out_code is not None
|
|
log_bytecode(
|
|
"MODIFIED BYTECODE",
|
|
code.co_name,
|
|
code.co_filename,
|
|
code.co_firstlineno,
|
|
out_code,
|
|
)
|
|
|
|
for idx, hook in enumerate(_bytecode_hooks.values()):
|
|
with dynamo_timed(f"bytecode_hooks_{idx}", log_pt2_compile_event=True):
|
|
hook_output = hook(code, out_code)
|
|
if hook_output is not None:
|
|
out_code = hook_output
|
|
|
|
orig_code_map[out_code] = code
|
|
output_codes.add(out_code)
|
|
dynamo_time_before_restart = last_attempt_start_time - start_time
|
|
assert tracer_output is not None and tracer_output.output_graph is not None
|
|
output = tracer_output.output_graph
|
|
|
|
# Tests for new code objects.
|
|
# The rationale for these tests can be found in torch/csrc/dynamo/eval_frame.c
|
|
# Only test once the code object is created.
|
|
# They are not tested during runtime.
|
|
|
|
def count_args(code: CodeType) -> int:
|
|
import inspect
|
|
|
|
return (
|
|
code.co_argcount
|
|
+ code.co_kwonlyargcount
|
|
+ bool(code.co_flags & inspect.CO_VARARGS)
|
|
+ bool(code.co_flags & inspect.CO_VARKEYWORDS)
|
|
)
|
|
|
|
assert out_code is not None
|
|
|
|
total_argcount_old = count_args(code)
|
|
total_argcount_new = count_args(out_code)
|
|
msg = "arg mismatch: "
|
|
msg += f"old code object has args {code.co_varnames[:total_argcount_old]}, "
|
|
msg += f"new code object has args {out_code.co_varnames[:total_argcount_new]}"
|
|
assert (
|
|
code.co_varnames[:total_argcount_old]
|
|
== out_code.co_varnames[:total_argcount_new]
|
|
), msg
|
|
|
|
msg = "free var mismatch: "
|
|
msg += f"old code object has free var {code.co_freevars}, "
|
|
msg += f"new code object has free var {out_code.co_freevars}"
|
|
assert code.co_freevars == out_code.co_freevars, msg
|
|
|
|
msg = "cell var mismatch: "
|
|
msg += f"old code object has cell var {code.co_cellvars}, "
|
|
msg += f"new code object has cell var {out_code.co_cellvars}"
|
|
assert code.co_cellvars == out_code.co_cellvars, msg
|
|
|
|
# Skipping Dynamo on a frame without any extracted graph.
|
|
# This does not affect eager functionality. But this is necessary
|
|
# for export for cases where Dynamo-reconstructed bytecode can create
|
|
# new function frames, confusing export in thinking that there
|
|
# are extra graphs now.
|
|
|
|
if output.export and output.is_empty_graph():
|
|
return ConvertFrameReturn()
|
|
|
|
assert output.guards is not None
|
|
CleanupManager.instance[out_code] = output.cleanups
|
|
nonlocal cache_entry
|
|
with dynamo_timed("build_guards", log_pt2_compile_event=True):
|
|
check_fn = CheckFunctionManager(
|
|
code,
|
|
output,
|
|
cache_entry,
|
|
hooks.guard_fail_fn if hooks else None,
|
|
hooks.guard_filter_fn if hooks else None,
|
|
save_guards=True if package else False,
|
|
)
|
|
|
|
if package is not None:
|
|
assert check_fn.guards_state is not None
|
|
package.add_guarded_code(check_fn.guards_state, out_code)
|
|
package.add_inlined_source(output.tracing_context.traced_code)
|
|
|
|
compile_id_str = str(compile_id) if compile_id is not None else "Unknown"
|
|
annotation_str = "Torch-Compiled Region: " + compile_id_str
|
|
guarded_code = GuardedCode(
|
|
out_code,
|
|
check_fn.guard_manager, # type: ignore[arg-type]
|
|
compile_id,
|
|
annotation_str,
|
|
)
|
|
|
|
if not output.is_empty_graph() and hooks.guard_export_fn is not None:
|
|
# We should not run the guard_export_fn when Dynamo does not
|
|
# generate any graph. This can happen in export when TorchDynamo
|
|
# generated bytecode has some reconstruction logic for mutated
|
|
# variables which can trigger TorchDynamo on the children frames but
|
|
# they are benign and do not generate any new graphs.
|
|
hooks.guard_export_fn(output.guards)
|
|
|
|
return wrap_guarded_code(guarded_code)
|
|
|
|
metrics_context = get_metrics_context()
|
|
code_context = (
|
|
package.code_context(code) if package is not None else contextlib.nullcontext()
|
|
)
|
|
with (
|
|
_use_lazy_graph_module(config.use_lazy_graph_module),
|
|
compile_context(CompileContext(compile_id)),
|
|
async_compile_pool_manager(),
|
|
chromium_event_timed(
|
|
"dynamo", reset_event_log_on_exit=True, log_pt2_compile_event=True
|
|
),
|
|
_WaitCounter("pytorch.wait_counter.entire_forward_compile").guard(),
|
|
metrics_context,
|
|
dynamo_timed(
|
|
"_compile.compile_inner",
|
|
phase_name="entire_frame_compile",
|
|
dynamo_compile_column_us="dynamo_cumulative_compile_time_us",
|
|
),
|
|
code_context,
|
|
):
|
|
restart_reasons: set[str] = set()
|
|
# This is shared across restarts
|
|
speculation_log = SpeculationLog()
|
|
if compile_pg := get_compile_pg():
|
|
distributed_state = DistributedState(compile_pg, LocalState())
|
|
else:
|
|
distributed_state = None
|
|
|
|
# Check recompilations
|
|
recompile_reason: Optional[str] = None
|
|
if is_recompilation(cache_size) and frame:
|
|
reasons = get_and_maybe_log_recompilation_reasons(cache_entry, frame)
|
|
recompile_reason = (
|
|
"Unable to find recompilation reasons" if not reasons else reasons[0]
|
|
)
|
|
# Recheck for recompilation, for when inline_inbuilt_nn_modules is set to False
|
|
inline_inbuilt_nn_modules_candidate = False
|
|
if not config.inline_inbuilt_nn_modules and frame:
|
|
inbuilt_nn_reasons = get_and_maybe_log_recompilation_reasons(
|
|
cache_entry, frame, skip_logging=True
|
|
)
|
|
inbuilt_nn_recompile_reason = (
|
|
None if not inbuilt_nn_reasons else inbuilt_nn_reasons[0]
|
|
)
|
|
|
|
if (
|
|
inbuilt_nn_recompile_reason is not None
|
|
and "[inline-inbuilt-nn-modules-candidate]"
|
|
in inbuilt_nn_recompile_reason
|
|
):
|
|
inline_inbuilt_nn_modules_candidate = True
|
|
|
|
# Set if the recompile is a candidate for inline_inbuilt_nn_modules
|
|
# regardless of whether inline_inbuilt_nn_modules is set or not
|
|
metrics_context.update_outer(
|
|
{
|
|
"recompile_reason": recompile_reason,
|
|
"inline_inbuilt_nn_modules_candidate": inline_inbuilt_nn_modules_candidate,
|
|
}
|
|
)
|
|
|
|
recompile_user_contexts = get_hook_for_recompile_user_context()
|
|
if recompile_user_contexts:
|
|
# cap each user context to N chars for data retention purposes. N=256
|
|
# is chosen to be large enough to capture the most important info.
|
|
user_contexts_msg = {
|
|
user_context()[:256] for user_context in recompile_user_contexts
|
|
}
|
|
metrics_context.set("recompile_user_contexts", user_contexts_msg)
|
|
|
|
exceeded, limit_type = exceeds_recompile_limit(cache_size, compile_id)
|
|
if exceeded:
|
|
|
|
def format_func_info(code: CodeType) -> str:
|
|
return f"'{code.co_name}' ({code.co_filename}:{code.co_firstlineno})"
|
|
|
|
# NS: Don't add period at the end of string, as it'll be added to URL
|
|
# rendering it incorrect
|
|
log.warning(
|
|
"torch._dynamo hit config.%s (%s)\n"
|
|
" function: %s\n"
|
|
" last reason: %s\n"
|
|
'To log all recompilation reasons, use TORCH_LOGS="recompiles".\n'
|
|
"To diagnose recompilation issues, see %s",
|
|
limit_type,
|
|
getattr(config, limit_type),
|
|
format_func_info(code),
|
|
recompile_reason,
|
|
troubleshooting_url,
|
|
)
|
|
if config.fail_on_recompile_limit_hit:
|
|
raise FailOnRecompileLimitHit(
|
|
f"{limit_type} reached, because fail_on_recompile_limit_hit = True this is a HARD failure"
|
|
)
|
|
elif one_graph or _is_error_on_graph_break(tracer_output):
|
|
raise FailOnRecompileLimitHit(
|
|
f"{limit_type} reached with one_graph=True or error_on_graph_break=True. "
|
|
"Excessive recompilations can degrade "
|
|
"performance due to the compilation overhead of each recompilation. To monitor "
|
|
"recompilations, enable TORCH_LOGS=recompiles. If recompilations are expected, consider "
|
|
"increasing torch._dynamo.config.cache_size_limit to an appropriate value."
|
|
)
|
|
elif justknobs_check(
|
|
"pytorch/compiler:skip_code_recursive_on_recompile_limit_hit"
|
|
):
|
|
raise RecompileLimitExceeded(f"{limit_type} reached")
|
|
else:
|
|
# do not recursively skip frames
|
|
unimplemented_v2(
|
|
gb_type="Dynamo cache limit exceeded",
|
|
context=f"Limit type: {limit_type}",
|
|
explanation="Dynamo attempted to recompile the code object too many times, "
|
|
f"exceeding the {limit_type} cache size limit."
|
|
"Giving up on compiling as the compile time tradeoff is likely not "
|
|
"worth the performance gain.",
|
|
hints=[],
|
|
)
|
|
|
|
log.debug(
|
|
"torchdynamo start compiling %s %s:%s, stack (elided %s frames):\n%s",
|
|
code.co_name,
|
|
code.co_filename,
|
|
code.co_firstlineno,
|
|
skip + 2,
|
|
# -2: omit current frame, omit contextlib decorator
|
|
"".join(CapturedTraceback.extract(skip=2 + skip).format()),
|
|
)
|
|
# -4: -2 as above, plus trace_structured frames
|
|
#
|
|
# NB: the frame looks like this:
|
|
#
|
|
# # handled by skip argument
|
|
# torch/_dynamo/convert_frame.py:1069 in catch_errors
|
|
# torch/_dynamo/convert_frame.py:910 in _convert_frame
|
|
# torch/_dynamo/convert_frame.py:464 in _convert_frame_assert
|
|
# torch/_utils_internal.py:70 in wrapper_function
|
|
#
|
|
# # 2 current frame and context lib
|
|
# env/lib/python3.10/contextlib.py:79 in inner
|
|
# torch/_dynamo/convert_frame.py:776 in _compile
|
|
#
|
|
# # 2 extra here
|
|
# torch/_logging/_internal.py:1064 in trace_structured
|
|
# torch/_dynamo/convert_frame.py:780 in <lambda>
|
|
stack_trace = log_dynamo_start(code, skip)
|
|
start_time_ns = time.time_ns()
|
|
fail_type: Optional[str] = None
|
|
fail_reason: Optional[str] = None
|
|
fail_user_frame_filename: Optional[str] = None
|
|
fail_user_frame_lineno: Optional[int] = None
|
|
torch._dynamo.utils.ReinplaceCounters.clear()
|
|
guarded_code = None
|
|
try:
|
|
guarded_code = compile_inner(code, one_graph, hooks, transform)
|
|
|
|
# NB: We only put_code_state in success case. Success case here
|
|
# does include graph breaks; specifically, if a graph break still
|
|
# resulted in a partially compiled graph, we WILL return here. An
|
|
# Unsupported exception will only bubble to the top level if we
|
|
# are unable to compile the frame at all. In this case, there's
|
|
# no point in uploading the code state, because we will always
|
|
# fail exactly the same way even without the update. (It's useful
|
|
# to upload for graph break though, because this can prevent
|
|
# extra graph break compilations.)
|
|
put_code_state()
|
|
log_frame_dynamic_whitelist(code)
|
|
|
|
return guarded_code
|
|
except Exception as e:
|
|
# NB: e's msg is mutated here to add user stack, but we DON'T want
|
|
# that stack in the Scuba logged fail_reason. So we grab the fail
|
|
# info here and add it to the metrics context below.
|
|
fail_type = type(e).__qualname__
|
|
fail_reason = str(e)
|
|
exception_handler(e, code, frame, export=export)
|
|
# NB: this is the post-mutation exception
|
|
torch._logging.trace_structured(
|
|
"artifact",
|
|
metadata_fn=lambda: {
|
|
"name": "dynamo_error",
|
|
"encoding": "string",
|
|
},
|
|
payload_fn=lambda: traceback.format_exc(),
|
|
)
|
|
fail_user_frame_filename, fail_user_frame_lineno = exc.get_exc_message(
|
|
e, compile_id
|
|
)
|
|
if tracer_output and tracer_output.is_tracing_resume_prologue:
|
|
# Do not allow any errors to be suppressed if tracer is currently tracing
|
|
# through resume function.
|
|
raise ResumePrologueTracingError(
|
|
"Error while tracing through a Dynamo-generated resume function prologue. "
|
|
"Errors are not allowed when tracing resume function prologues.\n"
|
|
f"{type(e).__qualname__}: {str(e)}"
|
|
).with_traceback(e.__traceback__) from None
|
|
elif isinstance(
|
|
e,
|
|
(
|
|
Unsupported,
|
|
TorchRuntimeError,
|
|
BackendCompilerFailed,
|
|
AssertionError,
|
|
ConstraintViolationError,
|
|
GuardOnDataDependentSymNode,
|
|
ValidationException,
|
|
UncapturedHigherOrderOpError,
|
|
BisectValidationException,
|
|
ShortenTraceback,
|
|
PackageError,
|
|
),
|
|
):
|
|
raise
|
|
else:
|
|
# Rewrap for clarity
|
|
raise InternalTorchDynamoError(
|
|
f"{type(e).__qualname__}: {str(e)}"
|
|
).with_traceback(e.__traceback__) from None
|
|
finally:
|
|
# === WARNING WARNING WARNING ===
|
|
# If you commit a bug here, it will suppress writing to
|
|
# dynamo_compile table, and we will not have telemetry.
|
|
# Be extra careful when making changes here!
|
|
|
|
if torch._dynamo.config.run_gc_after_compile:
|
|
with dynamo_timed("gc", dynamo_compile_column_us="gc_time_us"):
|
|
log.info("run_gc_after_compile: running gc")
|
|
gc.collect(1)
|
|
|
|
output = None
|
|
if tracer_output:
|
|
output = tracer_output.output_graph
|
|
if output:
|
|
output.local_scope = {}
|
|
# tracer should already be None, keep an extra check here just in case.
|
|
if tracer := output.root_tx:
|
|
tracer.f_locals = {}
|
|
|
|
from .utils import curr_frame
|
|
|
|
frame_key = str(curr_frame)
|
|
if fail_reason is None and output is not None:
|
|
guard_count = len(output.guards)
|
|
shape_env_guard_count = len(output.shape_env.guards)
|
|
graph_op_count = output.count_calls()
|
|
graph_node_count = len(output.graph.nodes)
|
|
graph_node_shapes = output.get_graph_sizes_structured()
|
|
graph_input_count = len(output.placeholders)
|
|
non_compliant_ops = {op.__qualname__ for op in output.non_compliant_ops}
|
|
compliant_custom_ops = {
|
|
op.__qualname__ for op in output.compliant_custom_ops
|
|
}
|
|
torch._dynamo.utils.ReinplaceCounters.log()
|
|
else:
|
|
guard_count = None
|
|
shape_env_guard_count = None
|
|
graph_op_count = None
|
|
graph_node_count = None
|
|
graph_node_shapes = {}
|
|
graph_input_count = None
|
|
non_compliant_ops = set({})
|
|
compliant_custom_ops = set({})
|
|
restart_reasons = set()
|
|
# If compilation failed, the entire time is wasted
|
|
dynamo_time_before_restart = (time.time_ns() - start_time_ns) / 1e9
|
|
|
|
metrics = {
|
|
"frame_key": frame_key,
|
|
"co_name": code.co_name,
|
|
"co_filename": code.co_filename,
|
|
"co_firstlineno": code.co_firstlineno,
|
|
"cache_size": cache_size.num_cache_entries_with_same_id_matched_objs,
|
|
"accumulated_cache_size": cache_size.num_cache_entries,
|
|
"guard_count": guard_count,
|
|
"shape_env_guard_count": shape_env_guard_count,
|
|
"graph_op_count": graph_op_count,
|
|
"graph_node_count": graph_node_count,
|
|
"graph_input_count": graph_input_count,
|
|
"fail_type": fail_type,
|
|
"fail_reason": fail_reason,
|
|
"fail_user_frame_filename": fail_user_frame_filename,
|
|
"fail_user_frame_lineno": fail_user_frame_lineno,
|
|
"non_compliant_ops": non_compliant_ops,
|
|
"compliant_custom_ops": compliant_custom_ops,
|
|
"restart_reasons": restart_reasons,
|
|
"dynamo_time_before_restart_s": dynamo_time_before_restart,
|
|
"has_guarded_code": guarded_code is not None,
|
|
"specialize_float": config.specialize_float,
|
|
"is_forward": True,
|
|
"dynamo_compile_time_before_restart_us": to_int_us(
|
|
dynamo_time_before_restart
|
|
),
|
|
"stack_trace": stack_trace,
|
|
"graph_node_shapes": str(graph_node_shapes),
|
|
}
|
|
# TODO: replace with CompileEventLogger.compilation_metrics
|
|
# There are some columns here not in PT2 Compile Events
|
|
# so we need to slightly change it
|
|
metrics_context.update_outer(metrics)
|
|
# === END WARNING WARNING WARNING ===
|
|
|
|
# If tracer is available, then tracer.error_on_graph_break reflects value of
|
|
# global symbolic_convert.error_on_graph_break at the time of the graph break -
|
|
# symbolic_convert.error_on_graph_break may have been (correctly) changed during cleanup.
|
|
# If tracer is unavailable, then fallback to symbolic_convert.error_on_graph_break.
|
|
if convert_frame_box:
|
|
convert_frame_box.error_on_graph_break = (
|
|
tracer_output.error_on_graph_break
|
|
if tracer_output
|
|
else _get_error_on_graph_break()
|
|
)
|
|
|
|
|
|
class ConvertFrame:
|
|
def __init__(
|
|
self,
|
|
compiler_fn: CompilerFn,
|
|
hooks: Hooks,
|
|
package: Optional[CompilePackage] = None,
|
|
) -> None:
|
|
self._torchdynamo_orig_backend = compiler_fn
|
|
self._inner_convert = convert_frame_assert(
|
|
compiler_fn, one_graph=False, package=package
|
|
)
|
|
self._hooks = hooks
|
|
|
|
@property
|
|
def _clone_with_backend(self) -> Callable[[WrapBackendDebug], ConvertFrame]:
|
|
return lambda backend: convert_frame(
|
|
backend,
|
|
self._hooks,
|
|
)
|
|
|
|
def __call__(
|
|
self,
|
|
frame: DynamoFrameType,
|
|
cache_entry: Optional[CacheEntry],
|
|
hooks: Hooks,
|
|
frame_state: dict[str, Union[int, FrameStateSizeEntry]],
|
|
skip: int = 0,
|
|
) -> ConvertFrameReturn:
|
|
input_codes.add(frame.f_code)
|
|
counters["frames"]["total"] += 1
|
|
try:
|
|
result = self._inner_convert(
|
|
frame, cache_entry, hooks, frame_state, skip=skip + 1
|
|
)
|
|
counters["frames"]["ok"] += 1
|
|
return result
|
|
except Exception as e:
|
|
# Do not allow errors to be suppressed if we're tracing a resume function prologue
|
|
if isinstance(e, ResumePrologueTracingError):
|
|
raise
|
|
|
|
error_on_graph_break = (
|
|
self._inner_convert._box.error_on_graph_break is not None
|
|
)
|
|
assert error_on_graph_break is not None
|
|
if self._inner_convert._box.error_on_graph_break:
|
|
# NOTE we _might_ have to wrap the current in a custom exception
|
|
# in order to correctly bubble up to the top-level compile wrapper in
|
|
# eval_frame.py. But re-raising seems to work for now because exceptions from tracing
|
|
# a nested call that results in a top-level frame compile will be handled by the caller
|
|
# as an observed exception - we don't expect that exception to be suppressed.
|
|
raise
|
|
|
|
# These two exception types are "soft" failure, in the sense that
|
|
# we know this is due to something we didn't implement all the
|
|
# way, scare the user less about it. That being said, if you
|
|
# are trying to understand why a graph break happened, it's still
|
|
# important to have this information, so offer it.
|
|
#
|
|
# NB: NotImplementedError used to be on this list, but actually
|
|
# it is impossible for it to reach here, as it is converted into
|
|
# InternalTorchDynamoError. This behavior seemed reasonable
|
|
# to me (ezyang, Aug 2023) so I kept it, but maybe at some point
|
|
# someone wanted these to also get suppressed. If so, you'll
|
|
# need to make these exceptions not get wrapped
|
|
|
|
# We intentionally don't want to suppress error here.
|
|
if isinstance(e, UncapturedHigherOrderOpError):
|
|
raise
|
|
|
|
soft_fail = isinstance(e, Unsupported)
|
|
|
|
# This is a soft failure. In the sense, the code path reaches here
|
|
# when we do not support graph breaks on bytecodes like LOAD_ATTR,
|
|
# BUILD_SET etc. In such case, we can fallback to eager without
|
|
# scaring users.
|
|
if soft_fail and graph_break_log.isEnabledFor(logging.DEBUG):
|
|
# Log this message in the graph break. Also use the string
|
|
# "skip: " to tell that the whole frame is falling back to
|
|
# eager.
|
|
if hasattr(e, "compile_id") and hasattr(e, "real_stack"):
|
|
with compile_context(CompileContext(e.compile_id)): # type: ignore[attr-defined]
|
|
user_stack = e.real_stack
|
|
user_stack_formatted = "".join(
|
|
traceback.format_list(user_stack)
|
|
)
|
|
user_stack_trace = f"Graph break: skip: from user code at:\n{user_stack_formatted}"
|
|
torch._logging.trace_structured(
|
|
"artifact",
|
|
metadata_fn=lambda: {
|
|
"name": "dynamo_graph_break_reason",
|
|
"encoding": "string",
|
|
},
|
|
payload_fn=lambda: f"{user_stack_trace}\n{traceback.format_exc()}",
|
|
)
|
|
graph_break_log.debug(
|
|
user_stack_trace,
|
|
exc_info=True,
|
|
)
|
|
|
|
if not config.suppress_errors and not soft_fail:
|
|
raise
|
|
|
|
# Suppress the error. NB: It's very important to do the
|
|
# suppression logging HERE, where the actual suppression
|
|
# happens. Previously it was somewhere else and so it was
|
|
# possible to accidentally not log at all.
|
|
record_filename = getattr(e, "record_filename", None)
|
|
code = frame.f_code
|
|
error_msg = format_error_msg(e, code, record_filename, frame)
|
|
|
|
if soft_fail:
|
|
log.info(error_msg, exc_info=True)
|
|
else:
|
|
log.warning(error_msg, exc_info=True)
|
|
|
|
if isinstance(e, SkipCodeRecursiveException):
|
|
return ConvertFrameReturn(
|
|
frame_exec_strategy=FrameExecStrategy(
|
|
FrameAction.SKIP, FrameAction.SKIP
|
|
)
|
|
)
|
|
elif isinstance(e, RecompileLimitExceeded):
|
|
return ConvertFrameReturn(
|
|
frame_exec_strategy=FrameExecStrategy(
|
|
FrameAction.RUN_ONLY, FrameAction.RUN_ONLY
|
|
)
|
|
)
|
|
|
|
return ConvertFrameReturn()
|
|
|
|
|
|
def convert_frame(
|
|
compiler_fn: CompilerFn,
|
|
hooks: Hooks,
|
|
package: Optional[CompilePackage] = None,
|
|
) -> ConvertFrame:
|
|
"""Try to convert a frame into an FX graph, if error leave frame unmodified"""
|
|
return ConvertFrame(compiler_fn, hooks, package=package)
|
|
|
|
|
|
# TODO mlazos: add support for same args, or record them
|
|
def replay(filename: str) -> None:
|
|
from .backends.debugging import eager
|
|
|
|
original_replay_val = config.replay_record_enabled
|
|
config.replay_record_enabled = False
|
|
with open(filename, "rb") as in_file:
|
|
record = ExecutionRecord.load(in_file)
|
|
record.globals = dict(itertools.chain(record.globals.items(), globals().items()))
|
|
|
|
with decorators.set_fullgraph(fullgraph=False):
|
|
try:
|
|
_compile(
|
|
record.code,
|
|
record.globals,
|
|
record.locals,
|
|
record.builtins,
|
|
record.closure,
|
|
compiler_fn=eager,
|
|
one_graph=False,
|
|
export=False,
|
|
export_constraints=None,
|
|
hooks=Hooks(),
|
|
cache_size=CacheSizeRelevantForFrame(0, 0),
|
|
cache_entry=None,
|
|
frame=None,
|
|
frame_state={},
|
|
compile_id=CompileId(frame_id=42, frame_compile_id=999),
|
|
)
|
|
finally:
|
|
config.replay_record_enabled = original_replay_val
|
|
|
|
|
|
def first_real_inst_idx(code: CodeType) -> int:
|
|
if sys.version_info < (3, 11):
|
|
return 0
|
|
for inst in dis.get_instructions(code):
|
|
if inst.opname == "RESUME":
|
|
return inst.offset // 2
|
|
raise RuntimeError("RESUME instruction not found in code")
|
|
|
|
|
|
class ConvertFrameProtocol(typing.Protocol):
|
|
def __call__(
|
|
self,
|
|
frame: DynamoFrameType,
|
|
cache_entry: Optional[CacheEntry],
|
|
hooks: Hooks,
|
|
frame_state: dict[str, Union[int, FrameStateSizeEntry]],
|
|
*,
|
|
skip: int = 0,
|
|
) -> ConvertFrameReturn: ...
|
|
|
|
|
|
class CatchErrorsWrapper:
|
|
def __init__(self, callback: ConvertFrameProtocol, hooks: Hooks) -> None:
|
|
functools.wraps(callback)(self)
|
|
self._torchdynamo_orig_backend = callback
|
|
self.hooks = hooks
|
|
|
|
def __call__(
|
|
self,
|
|
frame: DynamoFrameType,
|
|
cache_entry: Optional[CacheEntry],
|
|
frame_state: dict[str, Union[int, FrameStateSizeEntry]],
|
|
) -> ConvertFrameReturn:
|
|
assert frame_state is not None
|
|
|
|
input_codes.add(frame.f_code)
|
|
|
|
is_skipfile = trace_rules.check(frame.f_code)
|
|
if sys.version_info >= (3, 13):
|
|
has_started_execution = frame.f_lasti > first_real_inst_idx(frame.f_code)
|
|
else:
|
|
has_started_execution = frame.f_lasti >= first_real_inst_idx(frame.f_code)
|
|
if (
|
|
# TODO: the first condition is not covered by any test
|
|
has_started_execution
|
|
or is_skipfile
|
|
or config.disable
|
|
or (
|
|
is_in_torch_dispatch_mode(include_infra_modes=False)
|
|
and not getattr(self._torchdynamo_orig_backend, "_export", False)
|
|
)
|
|
):
|
|
if log.isEnabledFor(logging.DEBUG):
|
|
if has_started_execution:
|
|
skip_reason = "traced frame already"
|
|
elif trace_rules.check(frame.f_code):
|
|
skip_reason = "in skipfiles"
|
|
elif is_in_torch_dispatch_mode(include_infra_modes=False):
|
|
skip_reason = "non-infra torch dispatch mode present, this is not supported today in torch.compile"
|
|
else:
|
|
skip_reason = "dynamo tracing is disabled"
|
|
|
|
log.debug(
|
|
"skipping: %s (reason: %s, file: %s)",
|
|
frame.f_code.co_name,
|
|
skip_reason,
|
|
frame.f_code.co_filename,
|
|
)
|
|
return ConvertFrameReturn()
|
|
|
|
if (
|
|
frame.f_code.co_filename == "<string>" and frame.f_code.co_name == "__new__"
|
|
) or (
|
|
frame.f_code.co_filename.endswith("collections/__init__.py")
|
|
and frame.f_code.co_name == "_make"
|
|
):
|
|
# nametuple constructor/_make
|
|
return ConvertFrameReturn()
|
|
if torch._dynamo.utils.get_optimize_ddp_mode() == "ddp_optimizer":
|
|
ddp_module = DistributedDataParallel._get_active_ddp_module()
|
|
if ddp_module:
|
|
with compile_lock:
|
|
from torch._dynamo.backends.distributed import DDPOptimizer
|
|
|
|
ddp_optimizer = DDPOptimizer(
|
|
bucket_bytes_cap=ddp_module.bucket_bytes_cap,
|
|
backend_compile_fn=self._torchdynamo_orig_backend._torchdynamo_orig_backend, # type: ignore[attr-defined]
|
|
)
|
|
assert hasattr(
|
|
self._torchdynamo_orig_backend, "_clone_with_backend"
|
|
), (
|
|
"DDPOptimizer only supports callback fns that know how to clone themselves."
|
|
)
|
|
hijacked_callback = (
|
|
self._torchdynamo_orig_backend._clone_with_backend(
|
|
ddp_optimizer.compile_fn,
|
|
)
|
|
)
|
|
return hijacked_callback(
|
|
frame, cache_entry, self.hooks, frame_state
|
|
)
|
|
|
|
with compile_lock, _disable_current_modes():
|
|
# skip=1: skip this frame
|
|
result = self._torchdynamo_orig_backend(
|
|
frame, cache_entry, self.hooks, frame_state, skip=1
|
|
)
|
|
return result
|
|
|
|
|
|
def catch_errors_wrapper(
|
|
callback: ConvertFrameProtocol, hooks: Hooks
|
|
) -> CatchErrorsWrapper:
|
|
return CatchErrorsWrapper(callback, hooks)
|