mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
983f6f36db
We want to get to a point where most `UserError`s link to `exportdb` examples. This PR makes passing case names non-optional to make this intent clearer and encourage developers who raise `UserError`s to make or point to examples that make fixing such errors more obvious for users. In addition, sometimes there are multiple examples that are relevant to an error. Thus this PR also enables passing multiple case names. Differential Revision: [D50020465](https://our.internmc.facebook.com/intern/diff/D50020465/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/110733 Approved by: https://github.com/zhxchen17
1474 lines
53 KiB
Python
1474 lines
53 KiB
Python
from __future__ import annotations
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import contextlib
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import dis
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import functools
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import inspect
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import logging
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import os
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import sys
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import textwrap
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import threading
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import traceback
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import types
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import warnings
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from enum import Enum
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from os.path import dirname, join
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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NamedTuple,
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Optional,
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Set,
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Tuple,
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TYPE_CHECKING,
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Union,
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)
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from unittest.mock import patch
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import torch
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import torch.fx
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import torch.utils._pytree as pytree
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import torch.utils.checkpoint
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from torch import _guards
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from torch._subclasses import fake_tensor
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from torch.export import Constraint
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from torch.fx.experimental.proxy_tensor import make_fx, maybe_disable_fake_tensor_mode
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from torch.fx.experimental.symbolic_shapes import ConstraintViolationError, DimDynamic
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from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
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from torch.nn.parallel.distributed import DistributedDataParallel
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from ..fx import GraphModule
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from .backends.registry import CompilerFn, lookup_backend
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from .hooks import Hooks
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if TYPE_CHECKING:
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from torch._C._dynamo.eval_frame import ( # noqa: F401
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reset_code,
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set_eval_frame,
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set_guard_error_hook,
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set_guard_fail_hook,
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skip_code,
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unsupported,
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)
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else:
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for name in dir(torch._C._dynamo.eval_frame):
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if name.startswith("__"):
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continue
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globals()[name] = getattr(torch._C._dynamo.eval_frame, name)
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from . import config, convert_frame, external_utils, skipfiles, utils
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from .code_context import code_context
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from .exc import CondOpArgsMismatchError, UserError, UserErrorType
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from .mutation_guard import install_generation_tagging_init
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from .types import CacheEntry, DynamoCallback
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from .utils import compile_times
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log = logging.getLogger(__name__)
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from torch._dispatch.python import enable_python_dispatcher
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from torch.utils._python_dispatch import _disable_current_modes
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always_optimize_code_objects = utils.ExactWeakKeyDictionary()
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null_context = contextlib.nullcontext
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import sympy
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# See https://github.com/python/typing/pull/240
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class Unset(Enum):
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token = 0
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unset = Unset.token
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compile_lock = threading.RLock()
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guarded_backend_cache = threading.local()
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def _maybe_init_guarded_backend_cache():
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if not hasattr(guarded_backend_cache, "skip_backend_check_for_run_only_mode"):
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guarded_backend_cache.skip_backend_check_for_run_only_mode = False
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if not hasattr(guarded_backend_cache, "current_backend"):
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guarded_backend_cache.current_backend = None
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if not hasattr(guarded_backend_cache, "cached_backends"):
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guarded_backend_cache.cached_backends = {}
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def _reset_guarded_backend_cache():
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_maybe_init_guarded_backend_cache()
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guarded_backend_cache.skip_backend_check_for_run_only_mode = False
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guarded_backend_cache.current_backend = None
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cached_backends = guarded_backend_cache.cached_backends
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for backend in cached_backends.values():
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if hasattr(backend, "reset"):
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backend.reset()
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cached_backends.clear()
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guarded_backend_cache.cached_backends = {}
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@contextlib.contextmanager
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def backend_cache_wrapper(callback: CompilerFn):
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_maybe_init_guarded_backend_cache()
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# callback is False for RunOnlyContext. RunOnlyContext is used
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# as a way to re-use the previous compiled cache.
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# We therefore skip the check and re-use whatever code that's already cached.
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# Note: the cache that's actually used depends on the caching policy.
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if callback is False:
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try:
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prev_skip = guarded_backend_cache.skip_backend_check_for_run_only_mode
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guarded_backend_cache.skip_backend_check_for_run_only_mode = True
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yield None
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finally:
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guarded_backend_cache.skip_backend_check_for_run_only_mode = prev_skip
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else:
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backend = innermost_fn(callback)
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def _set_current_backend(backend: CompilerFn):
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prev_backend = guarded_backend_cache.current_backend
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guarded_backend_cache.current_backend = backend
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# Mapping id of a CompilerFn to itself
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guarded_backend_cache.cached_backends[id(backend)] = backend
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return prev_backend
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prev_backend = _set_current_backend(backend)
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try:
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yield backend
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finally:
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_set_current_backend(prev_backend)
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DONT_WRAP_FILES = {
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# For tracing into fx modules
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inspect.getsourcefile(GraphModule),
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join(dirname(dirname(__file__)), "onnx/_internal/fx/dynamo_graph_extractor.py"),
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}
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def _debug_get_cache_entry_list(
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code: Union[types.CodeType, Callable[..., Any]]
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) -> List[CacheEntry]: # type: ignore[valid-type]
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"""
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Given a code object or a callable object, retrieve the cache entries
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stored in this code.
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"""
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if callable(code):
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code = code.__code__
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cache_head = torch._C._dynamo.eval_frame._debug_get_cache_entry_list(code)
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cache_list = []
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while cache_head is not None:
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cache_list.append(cache_head)
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cache_head = cache_head.next
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return cache_list
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class OptimizedModule(torch.nn.Module):
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"""
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Wraps the original nn.Module object and later patches its
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forward method to optimized self.forward method.
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"""
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def __init__(self, mod: torch.nn.Module, dynamo_ctx):
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super().__init__()
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# Installs the params/buffer
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self._orig_mod = mod
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self.dynamo_ctx = dynamo_ctx
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self._initialize()
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def _initialize(self):
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# Do this stuff in constructor to lower overhead slightly
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if isinstance(self._orig_mod.forward, types.MethodType) and skipfiles.check(
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inspect.getsourcefile(self._orig_mod.forward)
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):
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# This may be a torch.nn.* instance in skipfiles.py which
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# won't trigger a frame evaluation workaround to add an extra
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# frame we can capture
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self.forward = self.dynamo_ctx(external_utils.wrap_inline(self._orig_mod))
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else:
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# Invoke hooks outside of dynamo then pickup the inner frame
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self.forward = self.dynamo_ctx(self._orig_mod.__call__)
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if hasattr(self._orig_mod, "_initialize_hook"):
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self._forward = self.forward
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self.forward = self._call_lazy_check
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def __getstate__(self):
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state = dict(self.__dict__)
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state.pop("forward", None)
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state.pop("__call__", None)
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return state
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def __setstate__(self, state):
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self.__dict__ = state
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self._initialize()
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def __getattr__(self, name):
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if name == "_orig_mod":
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return self._modules["_orig_mod"]
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return getattr(self._orig_mod, name)
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def _call_lazy_check(self, *args, **kwargs):
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if hasattr(self._orig_mod, "_initialize_hook"):
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# In the case of a lazy module, we want to run
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# the pre-hooks which initialize it.
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# Afterwards, lazy module deletes its pre-hooks
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# to avoid treating it as lazy on subsequent recompile.
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assert len(kwargs) == 0
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self._orig_mod._infer_parameters(self._orig_mod, args)
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return self._forward(*args, **kwargs)
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def __dir__(self):
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orig_mod_attrs = self._orig_mod.__dir__()
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return orig_mod_attrs + [
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attr for attr in super().__dir__() if attr not in orig_mod_attrs
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]
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def remove_from_cache(f):
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"""
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Make sure f.__code__ is not cached to force a recompile
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"""
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if isinstance(f, types.CodeType):
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reset_code(f)
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elif hasattr(f, "__code__"):
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reset_code(f.__code__)
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elif hasattr(getattr(f, "forward", None), "__code__"):
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reset_code(f.forward.__code__)
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else:
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from . import reset # type: ignore[attr-defined]
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reset()
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log.warning("could not determine __code__ for %s", f)
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def nothing():
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pass
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def innermost_fn(fn):
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"""
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In case of nesting of _TorchDynamoContext calls, find the innermost
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function. TorchDynamo caches on fn.__code__ object, so its necessary to find
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the innermost function to pass on the optimize, run, disable etc.
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"""
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unaltered_fn = fn
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while hasattr(unaltered_fn, "_torchdynamo_orig_callable"):
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unaltered_fn = unaltered_fn._torchdynamo_orig_callable
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assert callable(unaltered_fn)
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return unaltered_fn
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@contextlib.contextmanager
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def enable_dynamic(enable: Optional[bool] = None, export: bool = False):
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if enable is None:
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yield
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elif enable:
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# Assume everything is dynamic by default
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with config.patch(assume_static_by_default=False):
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yield
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else:
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with config.patch(
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automatic_dynamic_shapes=False, assume_static_by_default=True
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):
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yield
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class _TorchDynamoContext:
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def __init__(
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self,
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callback: DynamoCallback,
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on_enter=nothing,
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backend_ctx_ctor=null_context,
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patch_fn=nothing,
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first_ctx=False,
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*,
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export=False,
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dynamic=None,
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compiler_config=None,
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):
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super().__init__()
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assert callable(callback) or callback is False or callback is None
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self.callback: DynamoCallback = callback
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self.prior: Union[Unset, DynamoCallback] = unset
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self.on_enter = on_enter
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self.extra_ctx_ctor = backend_ctx_ctor
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self.first_ctx = first_ctx
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self.export = export
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self.dynamic = dynamic
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self.compiler_config = compiler_config
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patch_fn()
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def __enter__(self):
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if config.raise_on_ctx_manager_usage:
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raise RuntimeError(
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"torch._dynamo.optimize(...) is used with a context manager. "
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"Please refer to https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html "
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"to use torch._dynamo.optimize(...) as an annotation/decorator. "
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)
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self.on_enter()
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self.prior = set_eval_frame(self.callback)
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self.backend_cache_manager = backend_cache_wrapper(self.callback)
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self.backend_cache_manager.__enter__()
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self.backend_ctx = self.extra_ctx_ctor()
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self.backend_ctx.__enter__()
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self.dynamic_ctx = enable_dynamic(self.dynamic, self.export)
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self.dynamic_ctx.__enter__()
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def __exit__(self, exc_type, exc_val, exc_tb):
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assert self.prior is not unset
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set_eval_frame(self.prior)
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self.prior = unset
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# TODO: This is totally not the right way to chain contexts manually
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self.dynamic_ctx.__exit__(exc_type, exc_val, exc_tb)
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self.backend_ctx.__exit__(exc_type, exc_val, exc_tb)
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self.backend_cache_manager.__exit__(exc_type, exc_val, exc_tb)
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def __call__(self, fn):
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# public api for compiler config/options
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def get_compiler_config():
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return self.compiler_config
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fn = innermost_fn(fn)
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# add context containing GraphModule to any GraphModule forward functions
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if isinstance(fn, torch.fx.GraphModule):
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# Assume that the underlying node metadata of `fn`,
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# a GraphModule instance, accurately represents
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# all instances of type(fn).
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code_context.get_context(fn.forward.__code__)["orig_graphmodule"] = fn
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# Optimize the forward method of torch.nn.Module object
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if isinstance(fn, torch.nn.Module):
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mod = fn
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new_mod = OptimizedModule(mod, self)
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# Save the function pointer to find the original callable while nesting
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# of decorators.
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new_mod._torchdynamo_orig_callable = mod.forward
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# when compiling torch.nn.Module,
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# provide public api OptimizedModule.get_compiler_config()
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assert not hasattr(new_mod, "get_compiler_config")
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new_mod.get_compiler_config = get_compiler_config # type: ignore[attr-defined]
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return new_mod
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assert callable(fn)
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try:
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filename = inspect.getsourcefile(fn)
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except TypeError:
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filename = None
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if (
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(filename is None or skipfiles.check(filename))
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and (
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getattr(fn, "__name__", "") not in ["_call_impl", "_wrapped_call_impl"]
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)
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and filename not in DONT_WRAP_FILES
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):
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# call to a builtin without a frame for us to capture
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fn = external_utils.wrap_inline(fn)
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callback = self.callback
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on_enter = self.on_enter
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backend_ctx_ctor = self.extra_ctx_ctor
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@functools.wraps(fn)
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def _fn(*args, **kwargs):
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if (
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not isinstance(self, DisableContext)
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and torch.fx._symbolic_trace.is_fx_tracing()
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):
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if config.error_on_nested_fx_trace:
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raise RuntimeError(
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"Detected that you are using FX to symbolically trace "
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"a dynamo-optimized function. This is not supported at the moment."
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)
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else:
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return fn(*args, **kwargs)
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on_enter()
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prior = set_eval_frame(callback)
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backend_cache_manager = backend_cache_wrapper(self.callback)
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backend_cache_manager.__enter__()
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backend_ctx = backend_ctx_ctor()
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backend_ctx.__enter__()
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dynamic_ctx = enable_dynamic(self.dynamic, self.export)
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dynamic_ctx.__enter__()
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try:
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return fn(*args, **kwargs)
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finally:
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set_eval_frame(prior)
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dynamic_ctx.__exit__(None, None, None)
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backend_ctx.__exit__(None, None, None)
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backend_cache_manager.__exit__(None, None, None)
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# hooks to properly handle inlining
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if isinstance(self, DisableContext):
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_fn._torchdynamo_disable = True # type: ignore[attr-defined]
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else:
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_fn._torchdynamo_inline = fn # type: ignore[attr-defined]
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# Save the function pointer to find the original callable while nesting
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# of decorators.
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_fn._torchdynamo_orig_callable = fn # type: ignore[attr-defined]
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# when compiling user function instead of nn.Module
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# provide public api _fn.get_compiler_config()
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assert not hasattr(_fn, "get_compiler_config")
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_fn.get_compiler_config = get_compiler_config # type: ignore[attr-defined]
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# If the function is called using torch._dynamo.optimize decorator, we
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# should prevent any type of skipping.
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if callback not in (None, False):
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if not hasattr(fn, "__code__"):
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raise RuntimeError(
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textwrap.dedent(
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"""
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torch._dynamo.optimize is called on a non function object.
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If this is a callable class, please wrap the relevant code into a function and optimize the
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wrapper function.
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>> class CallableClass:
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>> def __init__(self):
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>> super().__init__()
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>> self.relu = torch.nn.ReLU()
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>>
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>> def __call__(self, x):
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>> return self.relu(torch.sin(x))
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>>
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>> def print_hello(self):
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>> print("Hello world")
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>>
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>> mod = CallableClass()
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If you want to optimize the __call__ function and other code, wrap that up in a function
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>> def wrapper_fn(x):
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>> y = mod(x)
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>> return y.sum()
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and then optimize the wrapper_fn
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|
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>> opt_wrapper_fn = torch._dynamo.optimize(wrapper_fn)
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"""
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)
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)
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always_optimize_code_objects[fn.__code__] = True
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|
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return _fn
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|
|
|
|
class OptimizeContext(_TorchDynamoContext):
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def __init__(
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self,
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callback,
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backend_ctx_ctor,
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first_ctx=False,
|
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*,
|
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export=False,
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dynamic=None,
|
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compiler_config=None,
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):
|
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def on_enter():
|
|
install_generation_tagging_init()
|
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|
|
super().__init__(
|
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callback=callback,
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on_enter=on_enter,
|
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backend_ctx_ctor=backend_ctx_ctor,
|
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patch_fn=TorchPatcher.patch,
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first_ctx=first_ctx,
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export=export,
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dynamic=dynamic,
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compiler_config=compiler_config,
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)
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|
|
|
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class RunOnlyContext(_TorchDynamoContext):
|
|
def __init__(self):
|
|
# cudagraph trees relies on generation increment
|
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def on_enter():
|
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torch._dynamo.mutation_guard.GenerationTracker.generation += 1
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|
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super().__init__(callback=False, on_enter=on_enter)
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|
|
|
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class DisableContext(_TorchDynamoContext):
|
|
def __init__(self):
|
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super().__init__(callback=None)
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|
|
|
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def first_real_inst_idx(code):
|
|
if sys.version_info < (3, 11):
|
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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")
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|
|
|
|
|
def catch_errors_wrapper(callback, hooks: Hooks):
|
|
@functools.wraps(callback)
|
|
def catch_errors(frame, cache_entry, frame_state):
|
|
assert frame_state is not None
|
|
|
|
if (
|
|
# TODO: the first condition is not covered by any test
|
|
frame.f_lasti >= first_real_inst_idx(frame.f_code)
|
|
or skipfiles.check(frame.f_code.co_filename)
|
|
or config.disable
|
|
):
|
|
log.debug("skipping %s %s", frame.f_code.co_name, frame.f_code.co_filename)
|
|
return None
|
|
if frame.f_code.co_filename == "<string>" and frame.f_code.co_name == "__new__":
|
|
# nametuple constructor
|
|
return None
|
|
if config.optimize_ddp:
|
|
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=callback._torchdynamo_orig_callable,
|
|
)
|
|
assert hasattr(
|
|
callback, "_clone_with_backend"
|
|
), "DDPOptimizer only supports callback fns that know how to clone themselves."
|
|
hijacked_callback = callback._clone_with_backend(
|
|
ddp_optimizer.compile_fn,
|
|
)
|
|
return hijacked_callback(frame, cache_entry, hooks, frame_state)
|
|
|
|
with compile_lock, _disable_current_modes():
|
|
return callback(frame, cache_entry, hooks, frame_state)
|
|
|
|
catch_errors._torchdynamo_orig_callable = callback # type: ignore[attr-defined]
|
|
return catch_errors
|
|
|
|
|
|
def _optimize_catch_errors(
|
|
compile_fn,
|
|
hooks: Hooks,
|
|
backend_ctx_ctor=null_context,
|
|
export=False,
|
|
dynamic=None,
|
|
compiler_config=None,
|
|
):
|
|
return OptimizeContext(
|
|
catch_errors_wrapper(compile_fn, hooks),
|
|
backend_ctx_ctor=backend_ctx_ctor,
|
|
first_ctx=True,
|
|
export=export,
|
|
dynamic=dynamic,
|
|
compiler_config=compiler_config,
|
|
)
|
|
|
|
|
|
def get_compiler_fn(compiler_fn):
|
|
from .repro.after_dynamo import wrap_backend_debug
|
|
|
|
if hasattr(compiler_fn, "compiler_name"):
|
|
compiler_str = compiler_fn.compiler_name
|
|
elif isinstance(compiler_fn, str):
|
|
compiler_str = compiler_fn
|
|
else:
|
|
compiler_str = None
|
|
compiler_fn = lookup_backend(compiler_fn)
|
|
return wrap_backend_debug(compiler_fn, compiler_str)
|
|
|
|
|
|
class _NullDecorator(contextlib.nullcontext): # type: ignore[type-arg]
|
|
def __call__(self, fn):
|
|
assert callable(fn)
|
|
return fn
|
|
|
|
|
|
def check_if_dynamo_supported():
|
|
if sys.platform == "win32":
|
|
raise RuntimeError("Windows not yet supported for torch.compile")
|
|
if sys.version_info >= (3, 12):
|
|
raise RuntimeError("Python 3.12+ not yet supported for torch.compile")
|
|
|
|
|
|
def is_dynamo_supported():
|
|
try:
|
|
check_if_dynamo_supported()
|
|
return True
|
|
except Exception:
|
|
return False
|
|
|
|
|
|
def optimize(
|
|
backend="inductor",
|
|
*,
|
|
nopython=False,
|
|
guard_export_fn=None,
|
|
guard_fail_fn=None,
|
|
disable=False,
|
|
dynamic=None,
|
|
):
|
|
"""
|
|
The main entrypoint of TorchDynamo. Do graph capture and call
|
|
backend() to optimize extracted graphs.
|
|
|
|
Args:
|
|
backend: One of the two things:
|
|
- Either, a function/callable taking a torch.fx.GraphModule and
|
|
example_inputs and returning a python callable that runs the
|
|
graph faster.
|
|
One can also provide additional context for the backend, like
|
|
torch.jit.fuser("fuser2"), by setting the backend_ctx_ctor attribute.
|
|
See AOTAutogradMemoryEfficientFusionWithContext for the usage.
|
|
- Or, a string backend name in `torch._dynamo.list_backends()`
|
|
nopython: If True, graph breaks will be errors and there will
|
|
be a single whole-program graph.
|
|
disable: If True, turn this decorator into a no-op
|
|
dynamic: If True, upfront compile as dynamic a kernel as possible. If False,
|
|
disable all dynamic shapes support (always specialize). If None, automatically
|
|
detect when sizes vary and generate dynamic kernels upon recompile.
|
|
|
|
Example Usage::
|
|
|
|
@torch._dynamo.optimize()
|
|
def toy_example(a, b):
|
|
...
|
|
"""
|
|
check_if_dynamo_supported()
|
|
# Note: The hooks object could be global instead of passed around, *however* that would make
|
|
# for a confusing API usage and plumbing story wherein we nest multiple .optimize calls.
|
|
# There is some prior art around this, w/r/t nesting backend calls are enforced to be the same
|
|
# compiler, however, this feels onerous for callback and hooks, and it feels better to give our users an
|
|
# easier to understand UX at the cost of a little more plumbing on our end.
|
|
hooks = Hooks(guard_export_fn=guard_export_fn, guard_fail_fn=guard_fail_fn)
|
|
torch._C._log_api_usage_once("torch._dynamo.optimize")
|
|
if disable or os.environ.get("TORCHDYNAMO_DISABLE", "") == "1":
|
|
return _NullDecorator()
|
|
|
|
backend = get_compiler_fn(backend)
|
|
|
|
# Find if backend has any extra context manager
|
|
backend_ctx_ctor = getattr(backend, "backend_ctx_ctor", null_context)
|
|
|
|
if nopython:
|
|
return optimize_assert(
|
|
backend,
|
|
dynamic=dynamic,
|
|
hooks=hooks,
|
|
)
|
|
return _optimize_catch_errors(
|
|
convert_frame.convert_frame(backend, hooks=hooks),
|
|
hooks,
|
|
backend_ctx_ctor,
|
|
dynamic=dynamic,
|
|
compiler_config=backend.get_compiler_config()
|
|
if hasattr(backend, "get_compiler_config")
|
|
else None,
|
|
)
|
|
|
|
|
|
# TODO(voz): Consider making "explain" output alongside a run / part of a run
|
|
@patch("torch._dynamo.symbolic_convert.explain", True)
|
|
def explain(f, *extra_args, **extra_kwargs):
|
|
def inner(*args, **kwargs):
|
|
# TODO(voz): Do we want a decorator for this?
|
|
from . import reset # type: ignore[attr-defined]
|
|
|
|
reset()
|
|
|
|
graphs: List[torch.fx.GraphModule] = []
|
|
break_reasons: List[Any] = []
|
|
op_count: int = 0
|
|
ops_per_graph: List[torch.fx.Node] = []
|
|
out_guards: List[_guards.Guard] = []
|
|
|
|
def dynamo_graph_accumulating_compiler(
|
|
gm: torch.fx.GraphModule, example_inputs
|
|
):
|
|
from .backends.debugging import _explain_graph_detail
|
|
|
|
nonlocal graphs
|
|
nonlocal op_count
|
|
nonlocal ops_per_graph
|
|
nonlocal break_reasons
|
|
|
|
gm, graphs, op_count, ops_per_graph, break_reasons = _explain_graph_detail(
|
|
gm, graphs, op_count, ops_per_graph, break_reasons
|
|
)
|
|
|
|
return gm.forward
|
|
|
|
def guard_export_print(guards):
|
|
nonlocal out_guards
|
|
out_guards.extend(guards)
|
|
|
|
opt_f = optimize(
|
|
dynamo_graph_accumulating_compiler,
|
|
nopython=False,
|
|
guard_export_fn=guard_export_print,
|
|
)(f)
|
|
# TODO(voz): We may have instances of `f` that mutate inputs, we should track sideeffects and reject.
|
|
opt_f(*args, **kwargs)
|
|
|
|
graph_count = len(graphs)
|
|
|
|
# For the explanation summary, dedupe reasons by the innermost stack frame and dedupe by it.
|
|
deduped_reasons = {}
|
|
for reason in break_reasons:
|
|
innermost_frame = reason.user_stack[-1]
|
|
# __repr__ uniquely identifies a FrameSummary so we can use it for deduping
|
|
deduped_reasons[repr(innermost_frame)] = reason
|
|
|
|
formatted_list = ""
|
|
for idx, break_reason in enumerate(deduped_reasons.values()):
|
|
formatted_stack = "".join(traceback.format_list(break_reason.user_stack))
|
|
msg = f"{idx + 1}. Reason: {break_reason.reason}\n User Stack: {formatted_stack}\n"
|
|
formatted_list += msg
|
|
|
|
graph_break_count = graph_count - 1
|
|
compile_time = compile_times(repr="str")
|
|
|
|
# TODO(voz): Do we want a decorator for this?
|
|
reset()
|
|
from .backends.debugging import ExplainOutput
|
|
|
|
return ExplainOutput(
|
|
graphs,
|
|
graph_count,
|
|
graph_break_count,
|
|
break_reasons,
|
|
op_count,
|
|
ops_per_graph,
|
|
out_guards,
|
|
compile_time,
|
|
)
|
|
|
|
if extra_args or extra_kwargs:
|
|
warnings.warn(
|
|
"explain(f, *args, **kwargs) is deprecated, use explain(f)(*args, **kwargs) instead. "
|
|
"If you don't migrate, we may break your explain call in the future if your user defined kwargs "
|
|
"conflict with future kwargs added to explain(f)."
|
|
)
|
|
return inner(*extra_args, **extra_kwargs)
|
|
else:
|
|
return inner
|
|
|
|
|
|
class FlattenInputOutputSignature(torch.fx.interpreter.Transformer):
|
|
def __init__(
|
|
self,
|
|
m: torch.fx.GraphModule,
|
|
flat_args: Tuple[Any],
|
|
matched_input_elements_positions: List[int],
|
|
matched_output_elements_positions: List[int],
|
|
example_fake_inputs: List[torch.Tensor],
|
|
flat_args_dynamic_dims: List[Set[int]],
|
|
fake_mode: Optional[fake_tensor.FakeTensorMode] = None,
|
|
):
|
|
super().__init__(m)
|
|
|
|
assert len(flat_args_dynamic_dims) == len(flat_args)
|
|
matched_input_elements_to_fake = {
|
|
val: example_fake_inputs[ix]
|
|
for ix, val in enumerate(matched_input_elements_positions)
|
|
}
|
|
|
|
self.new_args = []
|
|
for i in range(0, len(flat_args)):
|
|
arg = super().placeholder(f"arg{i}", (), {})
|
|
if i in matched_input_elements_to_fake:
|
|
arg.node.meta["val"] = matched_input_elements_to_fake[i]
|
|
else:
|
|
# Fill node.mata["val"] with faketensor from the input,
|
|
# if it's not found in matched_input_elements_positions
|
|
if fake_mode is not None and isinstance(flat_args[i], torch.Tensor):
|
|
# TODO(zhxchen17) Also preserve all the user constraints here.
|
|
arg.node.meta["val"] = fake_mode.from_tensor(
|
|
flat_args[i],
|
|
dynamic_dims=[
|
|
DimDynamic.DYNAMIC
|
|
if d in flat_args_dynamic_dims[i]
|
|
else DimDynamic.STATIC
|
|
for d in range(len(flat_args[i].shape))
|
|
],
|
|
)
|
|
self.new_args.append(arg)
|
|
self.old_args_gen = (self.new_args[i] for i in matched_input_elements_positions)
|
|
self.matched_output_elements_positions = matched_output_elements_positions
|
|
|
|
def placeholder(self, target, args, kwargs):
|
|
arg = next(self.old_args_gen)
|
|
if "val" in self.current_node.meta:
|
|
arg.node.meta["val"] = self.current_node.meta["val"]
|
|
if "tensor_dict" in self.current_node.meta:
|
|
arg.node.meta["tensor_dict"] = self.current_node.meta["tensor_dict"]
|
|
return arg
|
|
|
|
def output(self, target, args, kwargs):
|
|
dynamo_result_flat = args[0]
|
|
lookup = [*dynamo_result_flat, *self.new_args]
|
|
new_result_flat = [lookup[i] for i in self.matched_output_elements_positions]
|
|
return super().output(target, (new_result_flat,), {})
|
|
|
|
def run_node(self, n):
|
|
self.current_node = n
|
|
r = super().run_node(n)
|
|
if "val" in self.current_node.meta:
|
|
r.node.meta["val"] = self.current_node.meta["val"]
|
|
return r
|
|
|
|
|
|
class ExportResult(NamedTuple):
|
|
graph_module: torch.fx.GraphModule
|
|
guards: Set[_guards.Guard]
|
|
# NB: Do not add new fields without overriding __iter__; people are
|
|
# destructuring so it is BC-breaking
|
|
|
|
|
|
def check_signature_rewritable(graph):
|
|
input_errors = []
|
|
for node in graph.graph.nodes:
|
|
if node.op == "placeholder":
|
|
assert hasattr(node, "_dynamo_source")
|
|
source = node._dynamo_source
|
|
user_stacks = graph._source_to_user_stacks.get(source)
|
|
if user_stacks is None:
|
|
continue
|
|
assert len(user_stacks) > 0
|
|
# In some cases we may not have a useful stack. Look for a
|
|
# useful stack
|
|
stack = None
|
|
for s in user_stacks:
|
|
if len(s) == 0:
|
|
continue
|
|
stack = s
|
|
break
|
|
if stack is None:
|
|
msg = f"{source.name()}, a closed over free variable"
|
|
else:
|
|
tb = "".join(traceback.format_list(stack))
|
|
extra = ""
|
|
if len(user_stacks) > 1:
|
|
extra = f"(elided {len(user_stacks)-1} more accesses)"
|
|
msg = f"{source.name()}, accessed at:\n{tb}{extra}"
|
|
# TODO: option to print ALL of the stack traces at once
|
|
input_errors.append(msg)
|
|
|
|
if input_errors:
|
|
raise UserError(
|
|
UserErrorType.INVALID_INPUT,
|
|
"Cannot export model which references tensors that are neither "
|
|
"buffers/parameters/constants nor are direct inputs. For each tensor, if you'd "
|
|
"like this tensor to be an explicit input, add it as a dummy argument "
|
|
"to the top-level model definition you are exporting; if you would "
|
|
"like its value to be embedded as an exported constant, wrap its access "
|
|
"in a function marked with @assume_constant_result.\n\n"
|
|
+ "\n\n".join(input_errors),
|
|
case_names=[],
|
|
)
|
|
|
|
|
|
def rewrite_signature(
|
|
f_sig,
|
|
graph,
|
|
fake_mode,
|
|
flat_args,
|
|
in_spec,
|
|
example_fake_inputs,
|
|
graph_captured_input,
|
|
graph_captured_output,
|
|
dynamo_traced_result,
|
|
flat_args_dynamic_dims,
|
|
):
|
|
orig_args, orig_kwargs = pytree.tree_unflatten(flat_args, in_spec)
|
|
|
|
supported_types = (torch.Tensor, torch.SymInt, torch.SymFloat, torch.SymBool)
|
|
|
|
def is_supported_type(val):
|
|
return isinstance(val, supported_types)
|
|
|
|
def produce_matching(sources, candidates):
|
|
source_types = " or ".join(
|
|
[
|
|
desc
|
|
+ " of types: ("
|
|
+ ", ".join([str(type(val)) for val in vals])
|
|
+ ")"
|
|
for desc, vals in sources.items()
|
|
]
|
|
)
|
|
source_vals = [val for vals in sources.values() for val in vals]
|
|
matched_elements_positions = []
|
|
dict_of_source_vals = {}
|
|
for i, val in enumerate(source_vals):
|
|
dict_of_source_vals[id(val)] = i
|
|
|
|
for candidate_desc, candidate_vals in candidates.items():
|
|
for i, val in enumerate(candidate_vals):
|
|
if is_supported_type(val):
|
|
if id(val) in dict_of_source_vals:
|
|
matched_elements_positions.append(dict_of_source_vals[id(val)])
|
|
else:
|
|
raise AssertionError(
|
|
f"{candidate_desc} #{i+1}, of type {type(val)}, is not among {source_types}"
|
|
)
|
|
else:
|
|
raise AssertionError(
|
|
f"{candidate_desc} #{i+1} is {val}, but only "
|
|
f"the following types are supported: {supported_types}"
|
|
)
|
|
|
|
return matched_elements_positions
|
|
|
|
matched_input_elements_positions = produce_matching(
|
|
sources={"original inputs": flat_args},
|
|
candidates={"graph-captured input": graph_captured_input},
|
|
)
|
|
|
|
flat_results_traced, out_spec_traced = pytree.tree_flatten(dynamo_traced_result)
|
|
|
|
assert graph_captured_output is not None
|
|
matched_output_elements_positions = produce_matching(
|
|
sources={
|
|
"graph-captured outputs": list(graph_captured_output),
|
|
"original inputs": flat_args,
|
|
},
|
|
candidates={"original output": flat_results_traced},
|
|
)
|
|
|
|
new_graph = FlattenInputOutputSignature(
|
|
graph,
|
|
flat_args,
|
|
matched_input_elements_positions,
|
|
matched_output_elements_positions,
|
|
example_fake_inputs,
|
|
flat_args_dynamic_dims,
|
|
fake_mode,
|
|
).transform()
|
|
|
|
# Make dynamo graph to have same input/output spec as user code
|
|
def argument_names(f_sig, args, kwargs) -> List[str]:
|
|
def signature_to_fullargspec(sig: inspect.Signature):
|
|
# Get a list of Parameter objects from the Signature object
|
|
params = list(sig.parameters.values())
|
|
# Separate positional arguments, keyword-only arguments and varargs/varkw
|
|
args = [
|
|
p.name
|
|
for p in params
|
|
if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
|
|
]
|
|
kwonlyargs = [
|
|
p.name for p in params if p.kind == inspect.Parameter.KEYWORD_ONLY
|
|
]
|
|
varargs = next(
|
|
(p.name for p in params if p.kind == inspect.Parameter.VAR_POSITIONAL),
|
|
None,
|
|
)
|
|
varkw = next(
|
|
(p.name for p in params if p.kind == inspect.Parameter.VAR_KEYWORD),
|
|
None,
|
|
)
|
|
# Get default values for positional arguments and keyword-only arguments
|
|
defaults = tuple(
|
|
p.default
|
|
for p in params
|
|
if p.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD
|
|
and p.default is not inspect.Parameter.empty
|
|
)
|
|
kwonlydefaults = {
|
|
p.name: p.default
|
|
for p in params
|
|
if p.kind == inspect.Parameter.KEYWORD_ONLY
|
|
and p.default is not inspect.Parameter.empty
|
|
}
|
|
# Get annotations for parameters and return value
|
|
annotations = {}
|
|
if sig.return_annotation:
|
|
annotations = {"return": sig.return_annotation}
|
|
for parameter in params:
|
|
annotations[parameter.name] = parameter.annotation
|
|
# Return a FullArgSpec object with the extracted attributes
|
|
return inspect.FullArgSpec(
|
|
args, varargs, varkw, defaults, kwonlyargs, kwonlydefaults, annotations
|
|
)
|
|
|
|
fullargspec = signature_to_fullargspec(f_sig)
|
|
|
|
# 1. Map `args` 1-to-1 to positional arguments in original signature.
|
|
input_strs = fullargspec.args[: len(args)]
|
|
|
|
if len(args) > len(fullargspec.args):
|
|
# 2. If there are more arguments left in `args`, they map to varargs in original
|
|
# signature. Assign names as {varargs}_0, {varargs}_1, ...
|
|
assert fullargspec.varargs is not None, "More arguments than expected"
|
|
input_strs += [
|
|
f"{fullargspec.varargs}_{i}"
|
|
for i in range(0, len(args) - len(input_strs))
|
|
]
|
|
elif len(args) < len(fullargspec.args):
|
|
# 3. If there are fewer arguments in `args` than `fullargspec.args`,
|
|
# it implies these are arguments either with default values, or provided in
|
|
# `kwargs`. The former can be safely ignored. Because Dynamo.export does not
|
|
# export them as part of the function signature. The latter will be handled
|
|
# in the next step.
|
|
for unprovided_arg in fullargspec.args[
|
|
len(args) : -len(fullargspec.defaults or [])
|
|
]:
|
|
assert unprovided_arg in kwargs, f"Missing argument {unprovided_arg}"
|
|
|
|
# 4. Keyword arguments provided in `kwargs`.
|
|
input_strs += list(kwargs.keys())
|
|
|
|
# 5. Keyword-only arguments with default values if not provided are not exported
|
|
# as part of the function signature.
|
|
for kwonly_arg in fullargspec.kwonlyargs:
|
|
kwonlydefaults = fullargspec.kwonlydefaults or {}
|
|
assert (
|
|
kwonly_arg in kwargs or kwonly_arg in kwonlydefaults
|
|
), f"Missing keyword only argument {kwonly_arg}"
|
|
|
|
return input_strs
|
|
|
|
new_graph.graph._codegen = _PyTreeCodeGen(
|
|
_PyTreeInfo(
|
|
argument_names(f_sig, orig_args, orig_kwargs),
|
|
in_spec,
|
|
out_spec_traced,
|
|
)
|
|
)
|
|
new_graph.recompile()
|
|
return new_graph
|
|
|
|
|
|
def export(
|
|
f: Callable[..., Any],
|
|
*extra_args,
|
|
aten_graph: bool = False,
|
|
pre_dispatch: bool = False,
|
|
decomposition_table: Optional[
|
|
Dict[torch._ops.OpOverload, Callable[..., Any]]
|
|
] = None,
|
|
tracing_mode: str = "symbolic",
|
|
constraints: Optional[List[Constraint]] = None,
|
|
assume_static_by_default: bool = False,
|
|
same_signature: bool = True,
|
|
**extra_kwargs,
|
|
) -> Callable[..., ExportResult]:
|
|
"""
|
|
Export an input function f to a format that can be executed outside of PyTorch using the FX graph.
|
|
|
|
Args:
|
|
f (callable): A PyTorch function to be exported.
|
|
|
|
aten_graph (bool): If True, exports a graph with ATen operators.
|
|
If False, exports a graph with Python operators. Default is False.
|
|
|
|
pre_dispatch (bool): If True, exports a graph with ATen operators,
|
|
but before any logic in the PyTorch dispatcher has run.
|
|
This can be useful if you want to apply further transformations on a graph before running it
|
|
through autograd, autocast, or any other functionalities that are integrated into the dispatcher.
|
|
This flag is only valid if aten_graph=True is set.
|
|
Default is False.
|
|
|
|
decomposition_table (dict): A dictionary that maps operators to their decomposition functions.
|
|
Required if aten_graph or tracing_mode is specified. Default is None.
|
|
|
|
tracing_mode (str): If "symbolic", turn on dynamic shapes support. Default is "symbolic".
|
|
|
|
same_signature (bool): If True, rewrite the returned graph's signature to be the same as f.
|
|
|
|
Returns:
|
|
A function that given args and kwargs, returns a tuple of (graph, guards)
|
|
Graph: An FX graph representing the execution of the input PyTorch function with the provided arguments and options.
|
|
Guards: The guards we accumulated during tracing f above
|
|
|
|
Raises:
|
|
AssertionError: If decomposition_table is specified without setting aten_graph=True,
|
|
or if graph breaks during tracing in export.
|
|
|
|
AssertionError: If Dynamo input and output is not consistent with traced input/output.
|
|
|
|
Note - this headerdoc was authored by ChatGPT, with slight modifications by the author.
|
|
"""
|
|
# Deal with "local variable referenced before assignment"
|
|
_f = f
|
|
_assume_static_by_default = assume_static_by_default
|
|
|
|
def inner(*args, **kwargs):
|
|
f = _f
|
|
assume_static_by_default = _assume_static_by_default
|
|
check_if_dynamo_supported()
|
|
torch._C._log_api_usage_once("torch._dynamo.export")
|
|
if decomposition_table is not None:
|
|
assert (
|
|
aten_graph
|
|
), "Specifying a decomposition_table table or tracing mode is illegal without setting aten_graph=True"
|
|
if pre_dispatch:
|
|
assert aten_graph, "pre_dispatch=True can only be used when aten_graph=True"
|
|
f = innermost_fn(f)
|
|
call_to_inspect = f.forward if isinstance(f, torch.nn.Module) else f
|
|
original_signature = inspect.signature(call_to_inspect)
|
|
graph = None
|
|
out_guards = None
|
|
graph_captured_input = None
|
|
graph_captured_result: Optional[Tuple[torch.Tensor, ...]] = None
|
|
fake_mode = None
|
|
|
|
def guard_export_print(guards: Set[_guards.Guard]):
|
|
nonlocal out_guards
|
|
assert (
|
|
out_guards is None
|
|
), "whole graph export entails exactly one guard export"
|
|
out_guards = guards
|
|
|
|
example_inputs = []
|
|
|
|
def dynamo_normalization_capturing_compiler(
|
|
gm: torch.fx.GraphModule, inner_example_inputs
|
|
):
|
|
nonlocal graph
|
|
assert (
|
|
graph is None
|
|
), "Tried to emit a second graph during export. Tracing through 'f' must produce a single graph."
|
|
graph = gm
|
|
|
|
nonlocal fake_mode, example_inputs
|
|
# NB: do NOT pass inner_example_inputs here, we are detecting the
|
|
# Dynamo allocated fake mode, which should be DISTINCT from a
|
|
# potential outer ambient fake mode which the user provided.
|
|
# example_inputs is always the user specified inputs, so they
|
|
# would have the wrong fake mode attached to them
|
|
fake_mode = _guards.detect_fake_mode()
|
|
example_inputs = inner_example_inputs
|
|
|
|
def result_capturing_wrapper(*graph_inputs):
|
|
nonlocal graph_captured_result
|
|
nonlocal graph_captured_input
|
|
|
|
graph_captured_input = graph_inputs
|
|
assert graph is not None
|
|
|
|
named_parameters = dict(graph.named_parameters(remove_duplicate=False))
|
|
named_buffers = dict(graph.named_buffers(remove_duplicate=False))
|
|
|
|
ambient_fake_mode = (
|
|
_guards.detect_fake_mode(graph_inputs)
|
|
if _guards.detect_fake_mode(graph_inputs) is not None
|
|
else fake_mode
|
|
)
|
|
|
|
with ambient_fake_mode, enable_python_dispatcher():
|
|
params_and_buffers = {
|
|
**dict(named_parameters),
|
|
**dict(named_buffers),
|
|
}
|
|
fake_params_buffers = dict()
|
|
|
|
for name, value in params_and_buffers.items():
|
|
fake_params_buffers[name] = ambient_fake_mode.from_tensor(
|
|
value, static_shapes=True
|
|
)
|
|
|
|
fake_graph_inputs = pytree.tree_map(
|
|
ambient_fake_mode.from_tensor, graph_inputs
|
|
)
|
|
graph_captured_result = torch.func.functional_call(
|
|
graph, fake_params_buffers, fake_graph_inputs
|
|
)
|
|
|
|
return graph_captured_result
|
|
|
|
return result_capturing_wrapper
|
|
|
|
# Note: This is needed by rewrite_signature. We need to put it before
|
|
# optimize_assert since user program may mutate the inputs.
|
|
flat_args, in_spec = pytree.tree_flatten((args, kwargs))
|
|
|
|
remove_from_cache(f)
|
|
constraint_violation_error = None
|
|
if tracing_mode != "symbolic":
|
|
assume_static_by_default = True
|
|
with config.patch(
|
|
specialize_int=True,
|
|
assume_static_by_default=assume_static_by_default,
|
|
automatic_dynamic_shapes=False,
|
|
capture_dynamic_output_shape_ops=True,
|
|
capture_scalar_outputs=True,
|
|
):
|
|
opt_f = optimize_assert(
|
|
dynamo_normalization_capturing_compiler,
|
|
hooks=Hooks(
|
|
guard_export_fn=guard_export_print,
|
|
guard_fail_fn=None,
|
|
),
|
|
export=True,
|
|
export_constraints=constraints,
|
|
)(f)
|
|
# TODO(voz): We may have instances of `f` that mutate inputs, we should track sideeffects and reject.
|
|
try:
|
|
result_traced = opt_f(*args, **kwargs)
|
|
except ConstraintViolationError as e:
|
|
constraint_violation_error = e
|
|
remove_from_cache(f)
|
|
|
|
if (
|
|
(shape_env := getattr(fake_mode, "shape_env", None)) is not None
|
|
and (dim_constraints := shape_env.dim_constraints) is not None
|
|
and not skipfiles.check(inspect.getsourcefile(call_to_inspect))
|
|
):
|
|
dim_constraints.solve()
|
|
dim_constraints.remove_redundant_dynamic_results()
|
|
forced_specializations = dim_constraints.forced_specializations()
|
|
msg = dim_constraints.prettify_results(
|
|
original_signature, constraint_violation_error, forced_specializations
|
|
)
|
|
if constraint_violation_error:
|
|
constraint_violation_error.args = (
|
|
constraint_violation_error.args[0] + msg,
|
|
)
|
|
else:
|
|
if forced_specializations:
|
|
constraint_violation_error = ConstraintViolationError(msg)
|
|
else:
|
|
log.info(
|
|
"Summary of dimension constraints:%s",
|
|
msg,
|
|
)
|
|
|
|
# Error if we have any constraints on static values
|
|
for k in shape_env.var_to_range.keys():
|
|
if isinstance(k, sympy.Integer):
|
|
constraint_violation_error = ConstraintViolationError(
|
|
f"{''.join(traceback.format_list(shape_env.var_to_stack[k]))}\n"
|
|
"It appears that you're trying to set a constraint on a "
|
|
f"value which we evaluated to have a static value of {k}. "
|
|
"Scroll up to see where this constraint was set."
|
|
)
|
|
if constraint_violation_error:
|
|
raise constraint_violation_error
|
|
|
|
assert (
|
|
graph is not None
|
|
), "Failed to produce a graph during tracing. Tracing through 'f' must produce a single graph."
|
|
assert hasattr(graph, "_source_to_user_stacks")
|
|
assert out_guards is not None, "Failed to produce guards during tracing"
|
|
assert fake_mode is not None
|
|
|
|
# This check need to happened before aten_graph
|
|
# because placeholder's _source_node attribute is not preserved by make_fx
|
|
if same_signature:
|
|
check_signature_rewritable(graph)
|
|
|
|
# NB: This is mostly hitting the cache; Dynamo already converted these
|
|
example_fake_inputs = [fake_mode.from_tensor(t) for t in example_inputs]
|
|
|
|
if aten_graph:
|
|
# Running graph with interpreter is needed for propagating the stack_trace
|
|
def graph_with_interpreter(*args):
|
|
with torch.fx.traceback.preserve_node_meta():
|
|
return torch.fx.Interpreter(graph).run(*args)
|
|
|
|
with maybe_disable_fake_tensor_mode(), enable_python_dispatcher(), (
|
|
fake_mode
|
|
):
|
|
try:
|
|
graph = make_fx(
|
|
graph_with_interpreter,
|
|
decomposition_table=decomposition_table,
|
|
tracing_mode="real",
|
|
_allow_non_fake_inputs=True,
|
|
pre_dispatch=pre_dispatch,
|
|
_allow_fake_constant=False,
|
|
)(*example_fake_inputs)
|
|
except CondOpArgsMismatchError as e:
|
|
# Wrap the internal error to the user-facing error
|
|
raise UserError(
|
|
UserErrorType.DYNAMIC_CONTROL_FLOW,
|
|
str(e),
|
|
case_names=["cond_predicate", "cond_operands"],
|
|
)
|
|
|
|
if same_signature:
|
|
flat_args_dynamic_dims = [
|
|
{c.dim for c in (constraints or ()) if c.w_tensor() is x}
|
|
for x in flat_args
|
|
]
|
|
graph = rewrite_signature(
|
|
original_signature,
|
|
graph,
|
|
fake_mode,
|
|
flat_args,
|
|
in_spec,
|
|
example_fake_inputs,
|
|
graph_captured_input,
|
|
graph_captured_result,
|
|
result_traced,
|
|
flat_args_dynamic_dims,
|
|
)
|
|
# Store constraints and inputs as metadata for user passes, e.g. turn constraints to runtime check
|
|
graph.meta["input_shape_constraints"] = (
|
|
[constraint.serializable_spec for constraint in constraints]
|
|
if constraints
|
|
else []
|
|
)
|
|
|
|
return ExportResult(graph, out_guards)
|
|
|
|
if extra_args or extra_kwargs:
|
|
warnings.warn(
|
|
"export(f, *args, **kwargs) is deprecated, use export(f)(*args, **kwargs) instead. "
|
|
"If you don't migrate, we may break your export call in the future if your user defined kwargs "
|
|
"conflict with future kwargs added to export(f)."
|
|
)
|
|
return inner(*extra_args, **extra_kwargs)
|
|
else:
|
|
return inner
|
|
|
|
|
|
def optimize_assert(
|
|
backend,
|
|
*,
|
|
hooks=Hooks(None, None),
|
|
export=False,
|
|
export_constraints=None,
|
|
dynamic=None,
|
|
):
|
|
"""
|
|
The same as `torch._dynamo.optimize(backend, nopython=True)`
|
|
"""
|
|
backend = get_compiler_fn(backend)
|
|
|
|
# Find if backend has any extra context manager
|
|
backend_ctx_ctor = getattr(backend, "backend_ctx_ctor", null_context)
|
|
|
|
return _optimize_catch_errors(
|
|
convert_frame.convert_frame_assert(
|
|
backend, export=export, export_constraints=export_constraints
|
|
),
|
|
hooks,
|
|
backend_ctx_ctor,
|
|
export=export,
|
|
dynamic=dynamic,
|
|
)
|
|
|
|
|
|
class TorchPatcher:
|
|
@staticmethod
|
|
@functools.lru_cache(None)
|
|
def patch():
|
|
# A better way to disable the following would be decorate the source
|
|
# functions with @torch._disable_dynamo. However, this causes issues
|
|
# with torch.deploy internally.
|
|
from .decorators import disable
|
|
|
|
torch.jit.trace = disable(torch.jit.trace)
|
|
torch.jit.trace_module = disable(torch.jit.trace_module)
|
|
torch.jit._get_trace_graph = disable(torch.jit._get_trace_graph)
|
|
torch.fx._symbolic_trace.Tracer.trace = disable(
|
|
torch.fx._symbolic_trace.Tracer.trace
|
|
)
|
|
torch.distributions.Distribution.set_default_validate_args(False)
|
|
|
|
from ..optim import (
|
|
adadelta,
|
|
adagrad,
|
|
adam,
|
|
adamax,
|
|
adamw,
|
|
asgd,
|
|
lbfgs,
|
|
nadam,
|
|
radam,
|
|
rmsprop,
|
|
rprop,
|
|
sgd,
|
|
sparse_adam,
|
|
)
|
|
|
|
optimizer_modules = {
|
|
adadelta,
|
|
adagrad,
|
|
adam,
|
|
adamax,
|
|
adamw,
|
|
asgd,
|
|
lbfgs,
|
|
nadam,
|
|
radam,
|
|
rmsprop,
|
|
rprop,
|
|
sgd,
|
|
sparse_adam,
|
|
}
|
|
|
|
disabled_multi_tensor_opt_modules = {
|
|
adamax,
|
|
radam, # data-dependent control flow
|
|
sgd, # for now, until we can speed up compilation (this affects the benchmarks)
|
|
}
|
|
|
|
for opt_mod in optimizer_modules:
|
|
opt_name = opt_mod.__name__.split(".")[-1]
|
|
multi_tensor_fn_name = f"_multi_tensor_{opt_name}"
|
|
fused_fn_name = f"_fused_{opt_name}"
|
|
if (
|
|
hasattr(opt_mod, multi_tensor_fn_name)
|
|
and opt_mod in disabled_multi_tensor_opt_modules
|
|
):
|
|
setattr(
|
|
opt_mod,
|
|
multi_tensor_fn_name,
|
|
disable(getattr(opt_mod, multi_tensor_fn_name)),
|
|
)
|
|
|
|
if hasattr(opt_mod, fused_fn_name):
|
|
setattr(
|
|
opt_mod, fused_fn_name, disable(getattr(opt_mod, fused_fn_name))
|
|
)
|
|
|
|
optimizer_classes = [
|
|
opt
|
|
for opt in torch.optim.__dict__.values()
|
|
if inspect.isclass(opt) and issubclass(opt, torch.optim.Optimizer)
|
|
]
|
|
|
|
# Note: we don't support sparsity, data-dependent control, or tracing through backwards
|
|
excluded_optimizer_classes = {
|
|
torch.optim.SparseAdam,
|
|
torch.optim.RAdam,
|
|
torch.optim.LBFGS,
|
|
}
|
|
for opt in optimizer_classes:
|
|
if opt in excluded_optimizer_classes:
|
|
opt.step = disable(opt.step)
|
|
|
|
if hasattr(opt, "_init_group"):
|
|
opt._init_group = disable(opt._init_group)
|
|
|
|
# disable any currently set hooks
|
|
# Note: we only want to disable the profiling hook
|
|
# which is the *last* hook applied, we want to keep the no_grad hook
|
|
hooked = getattr(opt.step, "hooked", False)
|
|
if hooked:
|
|
unwrapped_step = getattr(opt.step, "__wrapped__", None)
|
|
if unwrapped_step:
|
|
opt.step = unwrapped_step
|
|
|
|
# disable future hooking
|
|
opt.step.hooked = True
|
|
|
|
@staticmethod
|
|
def suppress_torch_distributed_warnings(fn):
|
|
def inner_fn(*args, **kwargs):
|
|
warnings.filterwarnings(
|
|
"ignore", category=UserWarning, module="torch.distributed"
|
|
)
|
|
return fn(*args, **kwargs)
|
|
|
|
return inner_fn
|