""" This module implements TorchDynamo's core frame conversion functionality, transforming Python frames into FX graphs. It handles: - Frame analysis and bytecode transformation - Guard creation and management for dynamic behaviors - Cache management for recompilation - Error handling and fallback mechanisms Key classes: - ConvertFrame: Main entry point for frame conversion with error handling - ConvertFrameAssert: Implements core frame to graph conversion logic - Tracker: Tracks input/output code objects during conversion - CatchErrorsWrapper: Provides error handling and suppression logic The conversion process preserves program semantics while enabling optimizations through torch.compile() and related systems. NOTE: _torchdynamo_orig_backend is used for convert frame wrappers to identify the inner wrapped function. By going down the _torchdynamo_orig_backend chain, one can recover the original unwrapped backend, which is checked for during the Dynamo cache lookup. """ from __future__ import annotations import collections import contextlib import cProfile import dis import functools import gc import itertools import logging import os import pstats import random import subprocess import sys import threading import time import traceback import types import typing import weakref from dataclasses import dataclass from pathlib import Path from types import CellType, CodeType, FunctionType, ModuleType from typing import Any, Callable, Optional, TypeVar, Union from typing_extensions import ParamSpec from weakref import ReferenceType import torch import torch._logging from torch._C._dynamo.guards import GlobalStateGuard from torch._dynamo.callback import CallbackTrigger from torch._dynamo.distributed import get_compile_pg from torch._dynamo.symbolic_convert import TensorifyState from torch._guards import compile_context, CompileContext, CompileId, tracing from torch._logging import structured from torch._utils_internal import ( compile_time_strobelight_meta, justknobs_check, maybe_upload_prof_stats_to_manifold, signpost_event, ) from torch.fx._lazy_graph_module import _use_lazy_graph_module from torch.fx.experimental.symbolic_shapes import ( ConstraintViolationError, GuardOnDataDependentSymNode, ) from torch.fx.graph_module import _forward_from_src as original_forward_from_src from torch.monitor import _WaitCounter from torch.nn.parallel.distributed import DistributedDataParallel from torch.utils._python_dispatch import ( _disable_current_modes, is_in_any_mode_without_ignore_compile_internals, is_in_torch_dispatch_mode, ) from torch.utils._traceback import CapturedTraceback, format_traceback_short from . import config, decorators, exc, graph_break_hints, trace_rules from .bytecode_analysis import remove_dead_code, remove_pointless_jumps from .bytecode_transformation import ( check_inst_exn_tab_entries_valid, Instruction, is_generator, propagate_inst_exn_table_entries, transform_code_object, ) from .cache_size import ( CacheSizeRelevantForFrame, compute_cache_size, exceeds_recompile_limit, is_recompilation, ) from .eval_frame import ( always_optimize_code_objects, dynamo_tls, skip_code, TorchPatcher, ) from .exc import ( augment_exc_message, BackendCompilerFailed, FailOnRecompileLimitHit, format_error_msg, InternalTorchDynamoError, PackageError, RecompileLimitExceeded, ResumePrologueTracingError, ShortenTraceback, SkipCodeRecursiveException, TorchRuntimeError, UncapturedHigherOrderOpError, unimplemented_v2, Unsupported, ) from .guards import ( CheckFunctionManager, get_and_maybe_log_recompilation_reasons, GuardedCode, ) from .hooks import Hooks from .output_graph import DynamoTracerOutput from .pgo import log_frame_dynamic_whitelist, put_code_state from .replay_record import ExecutionRecord from .resume_execution import TORCH_DYNAMO_RESUME_IN_PREFIX from .symbolic_convert import ( DistributedState, ExceptionStack, InstructionTranslator, LocalState, SpeculationLog, ) from .trace_rules import is_numpy from .types import ConvertFrameReturn, FrameAction, FrameExecStrategy, wrap_guarded_code from .utils import ( _get_error_on_graph_break, chromium_event_timed, CleanupManager, CompileTimeInstructionCounter, counters, dynamo_timed, format_bytecode, gen_record_file_name, get_hook_for_recompile_user_context, get_metrics_context, increment_frame, is_namedtuple, istype, LazyString, maybe_disable_inference_mode, maybe_disable_inference_mode_for_fake_prop, orig_code_map, reset_graph_break_dup_checker, setup_compile_debug, to_int_us, troubleshooting_url, write_record_to_file, ) from .variables.torch_function import torch_function_mode_stack_state_mgr np: Optional[ModuleType] try: import numpy as np except ModuleNotFoundError: np = None if typing.TYPE_CHECKING: from .backends.registry import CompilerFn from .package import CompilePackage from .repro.after_dynamo import WrapBackendDebug from .types import BytecodeHook, CacheEntry, DynamoFrameType from .variables.builder import FrameStateSizeEntry log = logging.getLogger(__name__) bytecode_log = torch._logging.getArtifactLogger(__name__, "bytecode") graph_break_log = torch._logging.getArtifactLogger(__name__, "graph_breaks") compile_lock = threading.RLock() _T = TypeVar("_T") _P = ParamSpec("_P") class TODO_UNKNOWN: pass class Tracker: def __init__(self) -> None: self.seen: list[ReferenceType[CodeType]] = [] self.seen_ids: set[int] = set() def add(self, strong_obj: CodeType) -> None: idx = id(strong_obj) if idx not in self.seen_ids: obj = weakref.ref(strong_obj, lambda _: self.seen_ids.remove(idx)) self.seen.append(obj) self.seen_ids.add(idx) def __contains__(self, item: CodeType) -> bool: return id(item) in self.seen_ids def clear(self) -> None: self.seen.clear() self.seen_ids.clear() input_codes = Tracker() output_codes = Tracker() initial_global_state: Optional[GlobalStateGuard] = None @functools.wraps(original_forward_from_src) def fx_forward_from_src_skip_result( src: str, globals: dict[str, Any], co_fields: Optional[dict[str, str]] = None ) -> FunctionType: # we monkey patch FX to prevent infinite loop of trying to convert # our generated code result = original_forward_from_src(src, globals, co_fields) skip_code(result.__code__) return result def log_dynamo_start(code: CodeType, skip: int = 0) -> list[str]: convert_frame_intern = structured.intern_string(__file__) captured_tb = CapturedTraceback.extract(skip=4 + skip).summary() frames_interned = structured.from_traceback(captured_tb) # Extract and filter the stack stack = list( itertools.takewhile( lambda f: f["filename"] != convert_frame_intern, frames_interned, ) ) + [ { "line": code.co_firstlineno, "name": code.co_name, "filename": structured.intern_string(code.co_filename), } ] # Initialize the ChromiumEventLogger on start torch._logging.trace_structured( "dynamo_start", lambda: {"stack": stack}, ) # Capture stack separately without using from_traceback to get the actual filenames stack_strings = [ f"Line: {frame.lineno}, Name: {frame.name}, Filename: {frame.filename}" for frame in captured_tb if frame.filename != convert_frame_intern ] + [ f"Line: {code.co_firstlineno}, Name: {code.co_name}, Filename: {code.co_filename}" ] return stack_strings def preserve_global_state(fn: Callable[_P, _T]) -> Callable[_P, _T]: """ Context manager to: 1) Save/restore torch.is_grad_enabled() state 2) Save/restore python random state 3) Save/restore torch random state 4) Monkey patch torch.fx.graph_module._forward_from_src """ @functools.wraps(fn) def _fn(*args: _P.args, **kwargs: _P.kwargs) -> _T: guards = GlobalStateGuard() prior_grad_mode = torch.is_grad_enabled() # Just in case we get left in a bad dispatch state we want to restore # it. This can happen because the dispatch bits aren't a true # stack/counter - so we can't just increment/decrement them as we enter # and leave. with ( torch._C._PreserveDispatchKeyGuard(), maybe_disable_inference_mode(), maybe_disable_inference_mode_for_fake_prop(), ): prior_inference_mode = torch.is_inference_mode_enabled() prior_deterministic = torch.are_deterministic_algorithms_enabled() prior_warn_only = torch.is_deterministic_algorithms_warn_only_enabled() prior_mobile_allocator_state = ( torch._C._is_default_mobile_cpu_allocator_set() ) py_rng_state = random.getstate() prior_dtype = torch.get_default_dtype() torch_rng_state = torch.random.get_rng_state() cuda_rng_state = None if torch.cuda.is_available(): cuda_rng_state = torch.cuda.get_rng_state() cuda_matmul_fp32_prec = torch._C._get_fp32_precision_getter( "cuda", "matmul" ) prior_fwd_from_src = torch.fx.graph_module._forward_from_src torch.fx.graph_module._forward_from_src = fx_forward_from_src_skip_result cleanup = setup_compile_debug() exit_stack = contextlib.ExitStack() exit_stack.enter_context( torch.fx._symbolic_trace._maybe_revert_all_patches() ) exit_stack.enter_context(torch_function_mode_stack_state_mgr) try: return fn(*args, **kwargs) finally: cleanup.close() assert torch._C._len_torch_function_stack() == 0, ( "Torch function mode stack state changed while dynamo tracing, please report a bug" ) exit_stack.close() torch._C._set_grad_enabled(prior_grad_mode) torch.autograd.grad_mode._enter_inference_mode(prior_inference_mode) torch.use_deterministic_algorithms( prior_deterministic, warn_only=prior_warn_only ) random.setstate(py_rng_state) torch.random.set_rng_state(torch_rng_state) torch.set_default_dtype(prior_dtype) curr_mobile_allocator_state = ( torch._C._is_default_mobile_cpu_allocator_set() ) if prior_mobile_allocator_state != curr_mobile_allocator_state: torch._C._unset_default_mobile_cpu_allocator() if cuda_rng_state is not None: torch.cuda.set_rng_state(cuda_rng_state) torch._C._set_fp32_precision_setter( "cuda", "matmul", cuda_matmul_fp32_prec ) torch.fx.graph_module._forward_from_src = prior_fwd_from_src assert guards.check(), ( f"Global {guards.reason()}state changed while dynamo tracing, please report a bug" ) _fn._torchdynamo_orig_backend = fn # type: ignore[attr-defined] return _fn @TorchPatcher.suppress_torch_distributed_warnings def has_tensor_in_frame(frame: DynamoFrameType) -> bool: """Check if the frame has torch.* related bits""" # Check if the function was decorated using torch._dynamo.optimize if frame.f_code in always_optimize_code_objects: return True # Check if there is global import of torch.* for co_name in frame.f_code.co_names: if co_name in frame.f_globals: obj = frame.f_globals[co_name] if isinstance(obj, ModuleType) and ( obj.__name__.startswith("torch.") or obj is torch ): return True # ... or a global import of numpy.* if np and config.trace_numpy and (obj is np or is_numpy(obj)): return True seen_ids: dict[int, bool] = {} def has_tensor(obj: object) -> bool: """Recursively check if the obj has a tensor""" obj_id = id(obj) if obj_id in seen_ids: return seen_ids[obj_id] seen_ids[obj_id] = False if isinstance(obj, (torch.Tensor, torch.nn.Module)) or ( istype(obj, type) and issubclass(obj, torch.nn.Module) ): seen_ids[obj_id] = True return seen_ids[obj_id] elif ( config.trace_numpy and np and (istype(obj, np.ndarray) or isinstance(obj, np.generic)) ): seen_ids[obj_id] = True return seen_ids[obj_id] elif istype(obj, (list, tuple)): seen_ids[obj_id] = any(has_tensor(v) for v in obj) return seen_ids[obj_id] elif istype(obj, dict): # Some packages like pytest can be updated during runtime. So, make a # copy of values to avoid issues like "RuntimeError: dictionary # changed size during iteration" values = list(obj.values()) seen_ids[obj_id] = any(has_tensor(v) for v in values) return seen_ids[obj_id] elif istype(obj, (str, int, float, type(None), bool)): seen_ids[obj_id] = False return seen_ids[obj_id] elif is_namedtuple(obj) and hasattr(obj, "_fields"): seen_ids[obj_id] = any(has_tensor(getattr(obj, v)) for v in obj._fields) return seen_ids[obj_id] else: # if config.debug: # print( # f"Assuming that object of type {type(obj)} does not have a tensor" # ) return False # Check if the passed arguments are of type Tensor for value in frame.f_locals.values(): if has_tensor(value): return True log.debug( "skipping because no torch.* %s \ %s %s", frame.f_code.co_name, frame.f_code.co_filename, frame.f_code.co_firstlineno, ) return False def exception_handler( e: Exception, code: CodeType, frame: Optional[DynamoFrameType] = None, export: bool = False, ) -> None: record_filename = None if hasattr(e, "exec_record"): record_filename = gen_record_file_name(e, code) write_record_to_file(record_filename, e.exec_record) e.record_filename = record_filename # type: ignore[attr-defined] augment_exc_message(e, export=export) FRAME_COUNTER = 0 FRAME_COMPILE_COUNTER: typing.Counter[Union[int, FrameStateSizeEntry]] = ( collections.Counter() ) def maybe_cprofile(func: Callable[_P, _T]) -> Callable[_P, _T]: if config.cprofile: return cprofile_wrapper(func) return func def cprofile_wrapper(func: Callable[_P, _T]) -> Callable[_P, _T]: @functools.wraps(func) def profile_wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T: trace_id = CompileContext.current_trace_id() assert trace_id, "Trace id is None" profile_path = Path( f"/tmp/{func.__name__}_{str(trace_id).replace('/', '_')}.profile" ) prof = cProfile.Profile() try: prof.enable() start_ts = time.time() retval = prof.runcall(func, *args, **kwargs) profile_latency = time.time() - start_ts prof.disable() except ValueError: log.exception("failed to enable cProfile") profile_latency = 0 retval = func(*args, **kwargs) log.warning( "### Cprofile for %s trace id [%s] took %.3f seconds ###", func.__name__, trace_id, profile_latency, ) ps = pstats.Stats(prof) try: prof.dump_stats(profile_path) except OSError: log.exception("Cannot write to %s", profile_path) log.warning("Raw profile at %s", profile_path) svg_path = profile_path.with_suffix(".svg") try: 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, ) 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 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 == "" 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 == "" and code.co_filename == "": return ConvertFrameReturn() if ( code.co_name == "" and code.co_filename == "" 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 @dataclass class DynamoOutput: """ Represents the core data returned from a single dynamo run, including: - Guards, wrapped inside tracer_output.output_graph.guards - Generated bytecode - Other information needed for compilation. This data structure should capture all the "interesting" information dynamo produces on the frontend side before it enters user backend. """ tracer_output: DynamoTracerOutput bytecode: types.CodeType last_attempt_start_time: Optional[float] def build_guards( self, code: types.CodeType, hooks: Optional[Hooks] = None, save: bool = False, cache_entry: Optional[CacheEntry] = None, strict_error: bool = False, ) -> CheckFunctionManager: assert self.tracer_output.output_graph is not None return CheckFunctionManager( code, self.tracer_output.output_graph, cache_entry, hooks.guard_fail_fn if hooks else None, hooks.guard_filter_fn if hooks else None, save_guards=save, strict_error=strict_error, ) @dataclass class BackendInput: """ Represents core data structure that dynamo will pass to a backend, including: - Graph module - Example inputs - The FakeTensorMode used for compiling graph. This data structure should capture all the information dynamo produces on for the user backend. """ backend_id: str graph_module: torch.fx.GraphModule example_inputs: Any fake_mode: torch._subclasses.fake_tensor.FakeTensorMode @dataclass class CaptureOutput: """ CaptureOutput should represent all the information produced from torch compiler for a single graph capture. This intends to be consumed by various compiler frontends so that we can share as much compiler internals as possible and avoid great divergence between different stacks. This data structure should eventually contain all the information compiler produces as more refactors happens to converge different compiler frontends. """ dynamo_output: DynamoOutput backend_input: BackendInput @dataclass class FrameInfo: code: types.CodeType globals: dict[str, object] locals: dict[str, object] builtins: dict[str, object] closure: tuple[CellType] def fullgraph_capture( frame: FrameInfo, *, _is_export_deprecated_do_not_use: bool = False ) -> CaptureOutput: """ A standalone function which takes a frame and returns dynamo captured graph plus other important compile information. This should serve as the common interface for different torch compiler AOT frontengs (e.g. precompile, export). Note that this function doesn't apply context managers like metrics context or compile id, and the expectation is that the caller will apply them depending on the use case. The CaptureOutput is separated into two parts: 1. Dynamo specific information from DynamoOutput, which includes: - guards - generated bytecode - other information tracked by OutputGraph. 2. Backend specific information (indexed by unique backend id) such as: - fx graph - example inputs """ from torch._guards import TracingContext backend_input: Optional[BackendInput] = None def fullgraph_compiler( gm: torch.fx.GraphModule, example_inputs: list[torch.Tensor] ) -> torch.fx.GraphModule: nonlocal backend_input fake_mode = TracingContext.get().fake_mode assert fake_mode is not None assert isinstance(gm.meta["backend_id"], str) backend_input = BackendInput( gm.meta["backend_id"], gm, example_inputs, fake_mode ) return gm try: dynamo_output = compile_frame( frame.code, frame.globals, frame.locals, frame.builtins, frame.closure, compiler_fn=fullgraph_compiler, export=_is_export_deprecated_do_not_use, one_graph=True, restart_reasons=set(), ) # https://github.com/pytorch/pytorch/blob/main/torch/_dynamo/eval_frame.py#L831 except Unsupported as e: augment_exc_message(e) if config.verbose: raise # strip internal tracebacks from causes cur_exn: BaseException = e while cur_exn.__cause__ is not None: cur_exn.__cause__.with_traceback(None) cur_exn = cur_exn.__cause__ raise e.with_traceback(None) from e.__cause__ # User compiler error assert backend_input is not None return CaptureOutput(dynamo_output, backend_input) def compile_frame( # type: ignore[return] code: types.CodeType, globals: dict[str, object], locals: dict[str, object], builtins: dict[str, object], closure: tuple[CellType], compiler_fn: CompilerFn, one_graph: bool, restart_reasons: set[str], *, 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, ) -> DynamoOutput: """ A helper function taking a frame and backend, then return the generated bytecode and guards as a common data structure. This is a shared interface for multiple compiler frontends (e.g. torch.compile, torch.export) that needs to capture a graph out of python code. """ # This is shared across restarts speculation_log = SpeculationLog() def transform( instructions: list[Instruction], code_options: dict[str, object] ) -> DynamoTracerOutput: tf_mode_stack: list[torch.overrides.TorchFunctionMode] = ( torch.overrides._get_current_function_mode_stack() ) 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, ) assert tracer_output is not None return tracer_output last_attempt_start_time = None for attempt in itertools.count(): CompileContext.get().attempt = attempt try: with dynamo_timed(f"compile_attempt_{attempt}", log_pt2_compile_event=True): bytecode, tracer_output = transform_code_object(code, transform) assert tracer_output is not None return DynamoOutput( tracer_output=tracer_output, bytecode=bytecode, last_attempt_start_time=last_attempt_start_time, ) 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, ) raise 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 @compile_time_strobelight_meta(phase_name="compile_inner") def compile_inner( code: CodeType, one_graph: bool, hooks: Hooks ) -> tuple[ConvertFrameReturn, Optional[DynamoTracerOutput]]: 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) return ( ConvertFrameReturn(), None, ) # dead, but see https://github.com/python/mypy/issues/7577 @maybe_cprofile def _compile_inner( code: CodeType, one_graph: bool, hooks: Hooks, ) -> tuple[ConvertFrameReturn, DynamoTracerOutput]: 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 try: dynamo_output = compile_frame( code, globals, locals, builtins, closure, compiler_fn, one_graph, restart_reasons, export=export, export_constraints=export_constraints, frame_state=frame_state, distributed_state=distributed_state, package=package, ) except exc.SkipFrame as e: if one_graph: log.debug("No graph captured with export/fullgraph=True") assert e._torch_dynamo_tracer_output is not None return ConvertFrameReturn(), e._torch_dynamo_tracer_output 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" ) out_code = dynamo_output.bytecode tracer_output = dynamo_output.tracer_output if dynamo_output.last_attempt_start_time is not None: last_attempt_start_time = dynamo_output.last_attempt_start_time 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.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(), tracer_output 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 = dynamo_output.build_guards( code, hooks=hooks, save=package is not None, cache_entry=cache_entry, ) 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), tracer_output 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() 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: raise FailOnRecompileLimitHit( f"{limit_type} reached with fullgraph=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 stack_trace = log_dynamo_start(code, skip) start_time_ns = time.time_ns() fail_type: Optional[str] = None fail_reason: Optional[str] = None exception_stack_trace: Optional[list[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, tracer_output = compile_inner(code, one_graph, hooks) # 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() if ( tracer_output and (output_graph := tracer_output.output_graph) and output_graph.has_outputs() ): 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_stack_trace = [traceback.format_exc()] 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 ) tracer_output = getattr(e, "_torch_dynamo_tracer_output", None) if isinstance( e, ( Unsupported, TorchRuntimeError, BackendCompilerFailed, AssertionError, ConstraintViolationError, GuardOnDataDependentSymNode, ValidationException, UncapturedHigherOrderOpError, BisectValidationException, ShortenTraceback, PackageError, ResumePrologueTracingError, ), ): 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), "exception_stack_trace": exception_stack_trace, } # 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.error_on_graph_break(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: ... def should_skip_due_to_torch_dispatch_mode() -> bool: return is_in_any_mode_without_ignore_compile_internals() 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 ( should_skip_due_to_torch_dispatch_mode() 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 == "" 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)