AIStoryBoard/python/venv/Lib/site-packages/torch/_dynamo/config.py

"""
Configuration module for TorchDynamo compiler and optimization settings.

This module contains various configuration flags and settings that control TorchDynamo's
behavior, including:

- Runtime behavior flags (e.g., guard settings, specialization options)
- Debugging and development options
- Performance tuning parameters
- Feature toggles for experimental features
"""

import getpass
import os
import sys
import sysconfig
import tempfile
from collections.abc import Callable
from os.path import abspath, dirname
from typing import Any, Literal, Optional, TYPE_CHECKING, Union

from torch._environment import is_fbcode
from torch.utils._config_module import Config, get_tristate_env, install_config_module


# to configure logging for dynamo, aot, and inductor
# use the following API in the torch._logging module
# torch._logging.set_logs(dynamo=<level>, aot=<level>, inductor<level>)
# or use the environment variable TORCH_LOGS="dynamo,aot,inductor" (use a prefix + to indicate higher verbosity)
# see this design doc for more detailed info
# Design doc: https://docs.google.com/document/d/1ZRfTWKa8eaPq1AxaiHrq4ASTPouzzlPiuquSBEJYwS8/edit#
# the name of a file to write the logs to
# [@compile_ignored: debug]
log_file_name: Optional[str] = None

# [@compile_ignored: debug] Verbose will print full stack traces on warnings and errors
verbose = os.environ.get("TORCHDYNAMO_VERBOSE", "0") == "1"

# [@compile_ignored: runtime_behaviour] verify the correctness of optimized backend
verify_correctness = False

# need this many ops to create an FX graph (deprecated: not used)
minimum_call_count = 1

# turn on/off DCE pass (deprecated: always true)
dead_code_elimination = True

# Enable or disable side effect replay after graph execution.
# When False, mutations to Python objects (lists, dicts, attributes) won't be
# replayed after the compiled graph runs. This can cause correctness issues
# if your code depends on these mutations being visible. This should probably
# never be False by default. At the moment, only export will need it.
replay_side_effects = True

# Configure side effect warning level
# If `silent`, we silently allow side effects
# If `warn`, we warn side effects
# If `error`, we error on side effects
side_effect_replay_policy = "silent"

# disable (for a function) when cache reaches this size

# controls the maximum number of cache entries with a guard on same ID_MATCH'd
# object. It also controls the maximum size of cache entries if they don't have
# any ID_MATCH'd guards.
# [@compile_ignored: runtime_behaviour]
recompile_limit = 8

# [@compile_ignored: runtime_behaviour] safeguarding to prevent horrible recomps
accumulated_recompile_limit = 256

# [@compile_ignored: runtime_behaviour] skip tracing recursively if cache limit is hit (deprecated: does not do anything)
skip_code_recursive_on_recompile_limit_hit = True

# raise a hard error if cache limit is hit.  If you are on a model where you
# know you've sized the cache correctly, this can help detect problems when
# you regress guards/specialization.  This works best when recompile_limit = 1.
# This flag is incompatible with: suppress_errors.
# [@compile_ignored: runtime_behaviour]
fail_on_recompile_limit_hit = False

cache_size_limit: int = Config(alias="torch._dynamo.config.recompile_limit")
accumulated_cache_size_limit: int = Config(
    alias="torch._dynamo.config.accumulated_recompile_limit"
)

# (deprecated: does not do anything)
skip_code_recursive_on_cache_limit_hit: bool = Config(
    alias="torch._dynamo.config.skip_code_recursive_on_recompile_limit_hit"
)
fail_on_cache_limit_hit: bool = Config(
    alias="torch._dynamo.config.fail_on_recompile_limit_hit"
)

# whether or not to specialize on int inputs.  This only has an effect with
# dynamic_shapes; when dynamic_shapes is False, we ALWAYS specialize on int
# inputs.  Note that assume_static_by_default will also cause ints to get
# specialized, so this is mostly useful for export, where we want inputs
# to be dynamic, but accesses to ints should NOT get promoted into inputs.
specialize_int = False

# Whether or not to specialize on float inputs.  Dynamo will always promote
# float inputs into Tensor inputs, but at the moment, backends inconsistently
# support codegen on float (this is to be fixed).
specialize_float = False

# legacy config, does nothing now!
dynamic_shapes = True

use_lazy_graph_module = (
    os.environ.get("TORCH_COMPILE_USE_LAZY_GRAPH_MODULE", "1") == "1"
)

# This is a temporarily flag, which changes the behavior of dynamic_shapes=True.
# When assume_static_by_default is True, we only allocate symbols for shapes marked dynamic via mark_dynamic.
# NOTE - this flag can be removed once we can run dynamic_shapes=False w/ the mark_dynamic API
# see [Note - on the state of mark_dynamic]
assume_static_by_default = True

# This flag changes how dynamic_shapes=True works, and is meant to be used in conjunction
# with assume_static_by_default=True.
# With this flag enabled, we always compile a frame as fully static for the first time, and, if we fail
# any guards due to wobbles in shape, we recompile with *all* the wobbled shapes as being marked dynamic.
automatic_dynamic_shapes = True

# Valid options: "dynamic", "unbacked"
automatic_dynamic_shapes_mark_as: Literal["dynamic", "unbacked"] = "dynamic"

# log graph in/out metadata
# This is only turned on for export today since we
# know we are tracing a flat callable. later, this
# can extended to other use cases as well.
log_graph_in_out_metadata = False

# This flag changes how the shapes of parameters are treated.
# If this flag is set to True, then the shapes of torch.nn.Parameter as well as of torch.Tensor are attempted to be dynamic
# If this flag is set to False, then the shapes of torch.nn.Parameter are assumed to be static,
# while the shapes of torch.Tensor are assumed to be dynamic.
force_parameter_static_shapes = True

# This flag ensures that the shapes of a nn module are always assumed to be static
# If the flag is set to True, then the shapes of a nn.module are assumed to be static
# If the flag is set to False, then the shapes of a nn.module can be dynamic
force_nn_module_property_static_shapes = True

# Typically, if you mark_dynamic a dimension, we will error if the dimension
# actually ended up getting specialized.  This knob changes the behavior so
# that we don't error at all.  This is helpful for our CI where I'm using a
# heuristic to mark batch dimensions as dynamic and the heuristic may get it
# wrong.
allow_ignore_mark_dynamic = False

# Set this to False to assume nn.Modules() contents are immutable (similar assumption as freezing)
guard_nn_modules = True

# Uses CPython internal dictionary tags to detect mutation. There is some
# overlap between guard_nn_modules_using_dict_tags and guard_nn_modules flag.
# guard_nn_modules unspecializes the nn module instance and adds guard for each
# relevant member of the nn modules. On the other hand,
# guard_nn_modules_using_dict_tags specializes on each nn module instance but
# uses low overhead dict version matching to detect mutations, obviating the
# need to guard on members of the nn modules. With
# guard_nn_modules_using_dict_tags, the guard_nn_modules is not really required
# but kept around for debugging and discussing unspecializing nn module
# variables.
# TODO(janimesh, voz): Remove both of these flags (or at least guard_nn_modules)
# once we have reached stability for the guard_nn_modules_using_dict_tags.
guard_nn_modules_using_dict_tags = True

# Flag to enable preparation for graph freezing, so that the named parameters and
# buffers are passed as params_flat in tracing context by AOT autograd.
# Non-Inductor backends can use this list for graph freezing.
prepare_freezing = os.environ.get("TORCHDYNAMO_PREPARE_FREEZING", "0") == "1"

# NOTE this has been deprecated, it does nothing now.
traceable_tensor_subclasses: set[type[Any]] = set()

# If a tensor subclass is put into this set, Dynamo will model its instasnces in
# a very conservative and limited way (most likely causing lots of graph breaks
# if one apply tensor ops on these instances). This is useful if you encounter
# internal compiler errors from Dynamo which are caused by tensor subclasses,
# and you are willing to tolerate potential graph breaks rather than hard error.
nontraceable_tensor_subclasses: set[type[Any]] = set()

# Suppress errors in torch._dynamo.optimize, instead forcing a fallback to eager.
# This is a good way to get your model to work one way or another, but you may
# lose optimization opportunities this way.  Devs, if your benchmark model is failing
# this way, you should figure out why instead of suppressing it.
# This flag is incompatible with: fail_on_recompile_limit_hit.
suppress_errors = bool(os.environ.get("TORCHDYNAMO_SUPPRESS_ERRORS", False))

# Record and write an execution record of the current frame to a file
# if an exception is encountered
# @compile_ignored[debug]
replay_record_enabled = os.environ.get("TORCH_COMPILE_REPLAY_RECORD", "0") == "1"

# Rewrite assert statement in python with torch._assert
rewrite_assert_with_torch_assert = True

# Disable dynamo
disable = os.environ.get("TORCH_COMPILE_DISABLE", "0") == "1"

# [@compile_ignored: runtime_behaviour] Get a cprofile trace of Dynamo
cprofile = os.environ.get("TORCH_COMPILE_CPROFILE", False)

# Legacy config, does nothing now!
skipfiles_inline_module_allowlist: dict[Any, Any] = {}
"""Allowlist of inline modules to skip during compilation.

Legacy configuration that previously controlled which modules could be
inlined during tracing. This configuration is deprecated and no longer used.

:type: dict[Any, Any]
:default: {}

.. deprecated::
   This configuration is deprecated and does nothing now.

.. note::
   DEPRECATED: This setting has no effect on current behavior.
"""

# If a string representing a PyTorch module is in this ignorelist,
# the `allowed_functions.is_allowed` function will not consider it
# when creating a list of PyTorch functions that will appear in
# FX IR.
allowed_functions_module_string_ignorelist = {
    "torch.distributions",
    "torch.testing",
    "torch._refs",
    "torch._prims",
    "torch._decomp",
}

# Debug Flag to try minifier at different stages. Possible values are {None, "aot", "dynamo"}
# None - Minifier is switched off
# dynamo - Runs minifier on the TorchDynamo produced graphs, if compilation fails
# aot - Runs minifier on the Aot Autograd produced graphs, if compilation fails
# [@compile_ignored: debug]
repro_after = os.environ.get("TORCHDYNAMO_REPRO_AFTER", None)

# Compiler compilation debug info
# 1: Dumps the original graph out to repro.py if compilation fails
# 2: Dumps a minifier_launcher.py if compilation fails.
# 3: Always dumps a minifier_launcher.py. Good for segfaults.
# 4: Dumps a minifier_launcher.py if the accuracy fails.
# [@compile_ignored: debug]
repro_level = int(os.environ.get("TORCHDYNAMO_REPRO_LEVEL", 2))

# By default, we try to detect accuracy failure by running both forward
# and backward of a torchdynamo produced graph (if you are using repro_after
# 'dynamo').  This setting forces us to only test the forward graph and
# not the backward graph.  This can be helpful if you're trying to debug
# an inference only problem, but the minifier seems to be choking on the
# backwards step
# TODO: Detect this situation automatically so the user doesn't need
# to manually configure this
# [@compile_ignored: debug]
repro_forward_only = os.environ.get("TORCHDYNAMO_REPRO_FORWARD_ONLY") == "1"

# The tolerance we should use when testing if a compiled graph
# has diverged so that we should treat it as an accuracy failure
# [@compile_ignored: debug]
repro_tolerance = 1e-3


# Whether to ignore non-floating point values when checking accuracy.
# Checking accuracy of non-floating point values such as boolean tensors
# can lead to false positives.
# [@compile_ignored: debug]
repro_ignore_non_fp = os.environ.get("TORCHDYNAMO_REPRO_IGNORE_NON_FP") == "1"

# If True, when testing if two models are the same, we will test them against
# a third fp64 reference and only report a problem if the RMSE relative to the
# fp64 is greater.  However, this will use more memory; you may disable this
# if memory usage is too high.
# [@compile_ignored: runtime_behaviour]
same_two_models_use_fp64 = True

# Not all backends support scalars. Some calls on torch.Tensor (like .item()) return a scalar type.
# When this flag is set to False, we introduce a graph break instead of capturing.
# This requires dynamic_shapes to be True.
capture_scalar_outputs = os.environ.get("TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS") == "1"

# Not all backends support operators that have dynamic output shape (e.g.,
# nonzero, unique).  When this flag is set to False, we introduce a graph
# break instead of capturing.  This requires dynamic_shapes to be True.
# If you set this to True, you probably also want capture_scalar_outputs
# (these are separated for historical reasons).
capture_dynamic_output_shape_ops = (
    os.environ.get("TORCHDYNAMO_CAPTURE_DYNAMIC_OUTPUT_SHAPE_OPS", "0") == "1"
)

# hybrid backed unbacked symints
prefer_deferred_runtime_asserts_over_guards = False

# By default, dynamo will treat all ints as backed SymInts, which means (1) it
# will wait to see the int change over multiple runs before generalizing and
# (2) it will still always 0/1 specialize an int.  When true, this knob
# forces dynamo to treat _length_per_key and _offset_per_key on
# KeyedJaggedTensor from torchrec as size-like unbacked SymInts, so that
# they (1) generalize immediately and (2) unsoundly never compare equal to
# 0/1.  This is not on by default as AOTAutograd/Inductor cannot currently
# compile this code; however, this can be useful for export.
force_unspec_int_unbacked_size_like_on_torchrec_kjt = False

# Currently, Dynamo will always specialize on int members of NN module.
# However, there could be cases where this is undesirable, e.g., when tracking
# step count leading to constant recompilation and eventually eager fallback.
# Setting this flag to True will allow int members to be potentially unspecialized
# through dynamic shape mechanism.
# Defaults to False for BC.
allow_unspec_int_on_nn_module = False

# Specify how to optimize a compiled DDP module. The flag accepts a boolean
# value or a string. There are 3 modes.
# 1. "ddp_optimizer" (or True): with "ddp_optimizer", Dynamo will automatically
# split model graph into pieces to match DDP bucket sizes to allow DDP
# comm/compute overlap.
# 2. "python_reducer" (experimental): this optimization requires the usage
# of compiled_autograd. With "python_reducer", DDP will disable the C++ reducer
# and use the Python reducer to allow compiled_autograd to trace the
# communication and allow comm/compute overlap without graph-breaks.
# 3. "no_optimization" (or False): Dynamo won't split the model graph, nor
# will Python reducer be used. With this mode, there will be no graph-breaks
# and the original DDP C++ reducer will be used. There will no comm/compute
# overlap. This mode CANNOT be used with compiled_autograd.
# Note that to avoid breaking the existing usage, mode 1 and mode 4 can be
# specified with a boolean value. True is using ddp_optimizer and False is
# no optimization.
optimize_ddp: Union[
    bool,
    Literal[
        "ddp_optimizer",
        "python_reducer",
        "python_reducer_without_compiled_forward",
        "no_optimization",
    ],
] = True

# By default, Dynamo emits runtime asserts (e.g. torch._check) in the graph.
# In some cases those asserts could be performance costly
# E.g. torch._check(tensor[0].item() > 2) for tensor on cuda will require cuda sync.
# Setting this to True keeps them hinting to symbolic shapes engine,
# but not be emitted in the graph.
do_not_emit_runtime_asserts: bool = (
    os.environ.get("TORCH_DYNAMO_DO_NOT_EMIT_RUNTIME_ASSERTS", "0") == "1"
)

# Skip tracing the torchrec files added to trace_rules.FBCODE_SKIP_DIRS
skip_torchrec = True

# Don't apply most trace_rules.py rules
dont_skip_tracing = False

# No longer used
optimize_ddp_lazy_compile = False

# lambda guarding on object aliasing to improve opportunity for dict tag
# optimization
use_lamba_guard_for_object_aliasing = True

# Whether to skip guarding on FSDP-managed modules
skip_fsdp_guards = True
# Whether to apply torch._dynamo.disable() to FSDP2 hooks.
# Defaults to True. If Traceable FSDP2 is used, set this to False.
skip_fsdp_hooks = True

# Make dynamo skip guarding on hooks on nn modules
# Note: unsafe: if your model actually has hooks and you remove them, or doesn't and  you add them,
# dynamo will not notice and will execute whichever version you first compiled.
skip_nnmodule_hook_guards = True

# Make dynamo skip no tensor aliasing guard on parameters
# Note: unsafe: if you compile a function with different parameters as inputs,
# and then later pass on the same parameter as two inputs, dynamo will not
# notice and lead to incorrect result.
skip_no_tensor_aliasing_guards_on_parameters = True

# Considers a tensor immutable if it is one of the values of a dictionary, and
# the dictionary tag is same across invocation calls.
skip_tensor_guards_with_matching_dict_tags = True

# Skips guards on func.__defaults__ if the element to be guarded is a constant
skip_guards_on_constant_func_defaults = True


# The recursive-dict-tag guard relies on the class/function identity staying
# stable.  We therefore assume that the following function dunder attributes
# are **never rebound** to a different object:
#
#     • __code__        • __closure__
#     • __defaults__    • __kwdefaults__
#     • __annotations__ • __mro__
#
# It is fine to mutate the objects they already point to (e.g. tweak an element
# inside __defaults__), but assignments like
#
#     foo.__defaults__ = (3, 4)          # REBIND  - NOT SUPPORTED
#
# would invalidate the optimization.  This type of rebinding is rare, so we
# assume that the rebinding never happens for guard purposes.  Set the flag
# below to False only in environments where such rebinding is known to occur.
assume_dunder_attributes_remain_unchanged = True

# Speedup guard execution of nested nn modules by recursively checking for dict
# tags to avoid full guard execution.
use_recursive_dict_tags_for_guards = True

# Maximum number of objects for which we check dict pointers tags. This is
# useful for regional compilation.
max_saved_pointers_for_recursive_dict_tags_check = 256

# If True, raises exception if TorchDynamo is called with a context manager
raise_on_ctx_manager_usage = True

# If True, raise when aot autograd is unsafe to use
raise_on_unsafe_aot_autograd = False

# This flag is ignored and maintained for backwards compatibility.
error_on_nested_jit_trace = True

# If true, error with a better message if we symbolically trace over a
# dynamo-optimized function. If false, silently suppress dynamo.
error_on_nested_fx_trace = True

# Disables graph breaking on rnn. YMMV with backends.
allow_rnn = False

# If true, enables feature that captures PyTorch sparsity in the
# exported FX graph. This flag should become the default eventually
# and be removed, but currently provides a way to fall back to old
# graph breaking behavior.
capture_sparse_compute = not is_fbcode()

# If true, error if we try to compile a function that has
# been seen before.
# [@compile_ignored: runtime_behaviour]
error_on_recompile = False

# [@compile_ignored: debug] Whether to report any guard failures (deprecated: does not do anything)
report_guard_failures = True

# [@compile_ignored: debug] root folder of the project
base_dir = dirname(dirname(dirname(abspath(__file__))))

# Trace through NumPy or graphbreak
trace_numpy = True

# Default NumPy dtypes when tracing with torch.compile
# We default to 64bits. For efficiency, one may want to change these to float32
numpy_default_float = "float64"
numpy_default_complex = "complex128"
numpy_default_int = "int64"

# use numpy's PRNG if True, pytorch otherwise
use_numpy_random_stream = False

# Use C++ guard manager (deprecated: always true)
enable_cpp_guard_manager = True

# Use C++ guard manager for symbolic shapes
enable_cpp_symbolic_shape_guards = False

# Enable tracing through contextlib.contextmanager
enable_trace_contextlib = True

# Enable tracing through unittest
enable_trace_unittest = False

# Enable tracing generator functions lazily. If False, Dynamo will exhaust
# generators upon first execution. And if True, the generator will be accessed lazily
enable_faithful_generator_behavior = True

# Inline inbuilt nn modules
inline_inbuilt_nn_modules = Config(  # type: ignore[var-annotated]
    default=True,
    justknob="pytorch/compiler:inline_inbuilt_nn_modules",
)

# Resume tracing in nested frames if a nested graph break occurs
# Old behavior is to bubble up the graph break to the top level frame.
nested_graph_breaks = False

# Install "free" tensor variables (globals, non-locals, nn module attributes)
# as graph attributes.  This is useful for export, as it
# produces a consistent number of inputs to the graph.
install_free_tensors = False

# Temporary flag to control the turning of install_free_tensors to True for
# export. We will remove this flag in a few weeks when stable.
install_free_tensors_for_export = True

# Use C++ FrameLocalsMapping (raw array view of Python frame fastlocals) (deprecated: always True)
enable_cpp_framelocals_guard_eval = True

# Whether to automatically find and replace identical graph
# regions with a call to invoke_subgraph
use_graph_deduplication = False

# Whether to track nodes for deduplication (testing only)
# This flag is ignored if use_graph_deduplication is True
track_nodes_for_deduplication = False

# Whether to lint the graph after each region is replaced
# (Debug)
graph_deduplication_lint = False

# Issues a warning in Python 3.13.0 for possibly slower guard evaluation and
# instructs user to attempt using 3.13.1+, where the CPython bug is fixed.
# Should be disabled in dynamo-wrapped tests since some tests check that no warnings are issued.
issue_3_13_0_warning = True

# If False, skip frame (and future calls to the same code object) if we determine that the
# traced FX graph is empty when RETURN_* is traced.
allow_empty_graphs = False

# Used for testing - forces all top-level functions to be nested when traced with Dynamo
debug_force_nested_calls = False

# Used for testing - forces a graph break when a function
# that doesn't make any Dynamo-inlined calls returns
debug_force_graph_break_on_leaf_return = False

# Used for testing - causes CompileCounter.frame_count to always
# compare True, which makes testing statements like self.assertEqual(CompileCounter.frame_count, n)
# always pass.
debug_disable_compile_counter = False

# When set, total compile time instruction count is recorded using
# torch._dynamo.utilsCompileTimeInstructionCounter.
record_compile_time_instruction_count = False


def default_debug_dir_root() -> str:
    # [@compile_ignored: debug]
    DEBUG_DIR_VAR_NAME = "TORCH_COMPILE_DEBUG_DIR"
    if DEBUG_DIR_VAR_NAME in os.environ:
        return os.path.join(os.environ[DEBUG_DIR_VAR_NAME], "torch_compile_debug")
    elif is_fbcode():
        return os.path.join(
            tempfile.gettempdir(), getpass.getuser(), "torch_compile_debug"
        )
    else:
        return os.path.join(os.getcwd(), "torch_compile_debug")


# [@compile_ignored: debug]
debug_dir_root = default_debug_dir_root()

# [@compile_ignored: debug]
_save_config_ignore = {
    "repro_after",
    "repro_level",
    # workaround: "cannot pickle PyCapsule"
    "constant_functions",
    # workaround: "cannot pickle module"
    "skipfiles_inline_module_allowlist",
}

# for backend="cudagraphs", mutations on input be sent to the cudagraph backend
# or replayed in aot_autograd epilogue. default is False because mutation on inputs
# can prevent cudagraphing.
cudagraph_backend_keep_input_mutation = False

# enable cudagraph support for mutated inputs from prior cudagraph pool
cudagraph_backend_support_input_mutation = False

# When True, only ops that have the torch.Tag.pt2_compliant tag
# will be allowed into the graph; all other ops will be disallowed
# and will fall back to eager-mode PyTorch. Useful to ensure
# correctness of custom ops.
only_allow_pt2_compliant_ops = False

# This flag is ignored and maintained for backwards compatibility.
capture_autograd_function = True

# This flag is ignored and maintained for backwards compatibility.
capture_func_transforms = True

# If to log Dynamo compilation metrics into log files (for OSS) and Scuba tables (for fbcode).
log_compilation_metrics = True

# A set of logging functions which will be reordered to the end of graph breaks,
# allowing dynamo to construct large graph. Note that there are some
# limitations to this, such as how it does not correctly print objects that were
# mutated after the print statement.
reorderable_logging_functions: set[Callable[[Any], None]] = set()

# A set of methods that will be ignored while tracing,
# to prevent graph breaks.
# Add logging.Logger.<method> to ignore all calls for method,
# or logger.<method> to ignore calls for method from this logger instance only.
ignore_logger_methods: set[Callable[..., Any]] = set()

# simulates what would happen if we didn't have support for BUILD_SET opcode,
# used for testing
inject_BUILD_SET_unimplemented_TESTING_ONLY = False

_autograd_backward_strict_mode_banned_ops = [
    "layout",
    "is_neg",
    "is_conj",
    "is_pinned",
]

_autograd_backward_strict_mode_conditional_banned_ops = [
    "stride",
    "storage_offset",
    "is_contiguous",
]

# Enables caching of dispatches to fake tensors.
fake_tensor_cache_enabled = (
    os.environ.get("TORCH_FAKE_TENSOR_DISPATCH_CACHE", "1") == "1"
)

# Enables cross checking between the fake tensor cache and dispatch.
fake_tensor_cache_crosscheck_enabled = (
    os.environ.get("TORCH_FAKE_TENSOR_DISPATCH_CACHE_CROSSCHECK", "0") == "1"
)

# Disables inference mode for fake tensor prop during compilation. At runtime,
# the inference_mode is still respected.
fake_tensor_disable_inference_mode = True

# Experimental feature for running automatic caching precompile.
# Enables automatic DynamoCache save/load
caching_precompile = os.environ.get("TORCH_CACHING_PRECOMPILE", "0") == "1"

strict_precompile = os.environ.get("TORCH_STRICT_PRECOMPILE", "0") == "1"

# Enables the Compiled Autograd engine to trace autograd calls made under torch.compile().
# Note: AOTAutograd will still trace and partition an AOT backward graph local to that
# compiled region. But AOTAutograd traces without knowledge of backward hooks which are
# coordinated by the Autograd engine, and under the hood, it uses the torch.autograd.grad
# API, so it cannot capture gradient accumulation operations (AccumulateGrad).
#
# Compiled Autograd will trace all autograd operations as seen by the Autograd engine.
# This flag will also lift certain restrictions during the forward trace such as
# registering backward hooks on tensors contained within the compiled region.
compiled_autograd = False


# Checks if we should graph break when seeing nn parameter constructors
# in dynamo; this is so that we clearly fail and ask users to move outside
# the function as opposed to trying to support the ctor with unclear semantics
# See https://github.com/pytorch/pytorch/issues/157452 for more context
graph_break_on_nn_param_ctor = True

# Eager AC/SAC reapplies the mutations (like global dict mutations) in the
# backward during the recomputation of forward. torch.compile has no easy way to
# reapply python mutations in the backward. But many users might be ok to skip
# reapplication of side effects in the backward. They can set this config flag
# to accept this eager and compile divergence.
skip_fwd_side_effects_in_bwd_under_checkpoint = False


# Overrides torch.compile() kwargs for Compiled Autograd:
compiled_autograd_kwargs_override: dict[str, Any] = {}
"""Overrides torch.compile() kwargs for Compiled Autograd.

This dictionary allows overriding specific torch.compile() keyword arguments
when using Compiled Autograd. Only certain overrides are currently supported.

:type: dict[str, Any]
:default: {}

Example::

    torch._dynamo.config.compiled_autograd_kwargs_override = {
        "fullgraph": True
    }

.. note::
   Currently only the "fullgraph" kwarg override is supported. Other kwargs
   may be added in future versions.
"""


# Enables use of collectives *during* compilation to synchronize behavior
# across ranks.  Today, this is used solely to modify automatic_dynamic_shapes
# behavior, making it so that we infer that if an input is dynamic by
# inspecting whether or not its input size varies across ranks.  Because
# this synchronization uses collectives, all ranks must run compilation at
# the same time; ranks must not diverge with graph breaks.  This can be most
# reliably achieved by ensuring PT2 only is run on SPMD programs.  If this
# invariant is inviolated, you will likely deadlock NCCL and encounter a
# NCCL timeout.
enable_compiler_collectives = os.environ.get("TORCH_COMPILER_COLLECTIVES", "0") == "1"

# Enables a local, filesystem "profile" which can be used for automatic
# dynamic decisions, analogous to profile-guided optimization.  This config
# ONLY has an effect if torch.compiler.config.workflow_id is specified,
# which specifies the name of the profile we will save/load.
#
# The idea is that if we observe that a particular input is dynamic over
# multiple iterations on one run, we can save a profile with this information
# so the next time we run we can just make it dynamic the first time around,
# skipping an unnecessary static compilation.  The profile can be soundly
# stale, if it is wrong, it just means we may make more things dynamic than
# was actually necessary (NB: this /can/ cause a failure if making something
# dynamic causes the compiler to stop working because you tickled a latent
# bug.)
#
# The profile is ONLY guaranteed to work if the user source code is 100%
# unchanged.  Applying the profile if there are user code changes is only
# best effort otherwise.  In particular, we identify particular code objects
# by filename, line number and name of their function, so adding/removing newlines
# will typically cause cache misses.  We continuously update the profile,
# so if we only discover something is dynamic on the second run, we will update
# the profile for subsequent runs.
automatic_dynamic_local_pgo: bool = Config(
    justknob="pytorch/remote_cache:enable_local_automatic_dynamic_pgo",
    env_name_force="TORCH_DYNAMO_AUTOMATIC_DYNAMIC_LOCAL_PGO",
    default=True,
)

# Like above, but using remote cache
automatic_dynamic_remote_pgo: Optional[bool] = get_tristate_env(
    "TORCH_DYNAMO_AUTOMATIC_DYNAMIC_REMOTE_PGO"
)

# temporary config to kill later
_unsafe_skip_fsdp_module_guards = (
    os.environ.get("UNSAFE_SKIP_FSDP_MODULE_GUARDS", "0") == "1"
)

# Common prefix to append to the id of each compile run to filter out data
pt2_compile_id_prefix: Optional[str] = os.environ.get("PT2_COMPILE_ID_PREFIX", None)

# Run GC at the end of compilation
run_gc_after_compile = Config(  # type: ignore[var-annotated]
    # Disable by default on free-threaded builds since they always do a full collection, which can be slow
    default=sysconfig.get_config_var("Py_GIL_DISABLED") != 1,
    justknob="pytorch/compiler:enable_run_gc_after_compile",
    env_name_default="TORCH_DYNAMO_RUN_GC_AFTER_COMPILE",
)

# Does not graph break on torch.autograd._profiler_enabled if set to True. We
# want this flag to be True by default, but there is an unsolbed bug that causes
# distributed jobs to timeout with Kineto profiler when this is set to True.
constant_fold_autograd_profiler_enabled = False

# Takes the function/module decorated with torch.compile and passes it through a
# wrapper. This ensures that nn.module hooks are also compiled in the same frame.
wrap_top_frame = False

# Flag to record runtime overhead in profile traces. Used for pre-graph bytecode
# and AOTAutograd runtime wrapper.
record_runtime_overhead = True

enable_aot_compile = False

# HACK: this is for testing custom ops profiling only
_custom_ops_profile: Optional[Any] = None

# Deprecated! Please use the config in torch/fx/experimental/_config instead.
enrich_profiler_metadata: bool = False

if TYPE_CHECKING:
    from torch.utils._config_typing import *  # noqa: F401, F403

    def _make_closure_patcher(**changes: Any) -> Any: ...


install_config_module(sys.modules[__name__])