from __future__ import annotations

import contextlib
import itertools
import time
from contextlib import nullcontext
from functools import wraps
from typing import Any, TYPE_CHECKING
from typing_extensions import ParamSpec, TypeVar
from unittest.mock import patch

import torch
import torch._dynamo.logging
import torch.nn as nn
import torch.utils._pytree as pytree
import torch.utils.dlpack
from torch import Tensor
from torch._decomp.decompositions_for_rng import PhiloxStateTracker, rng_decompositions
from torch._dispatch.python import enable_python_dispatcher
from torch._dynamo import compiled_autograd
from torch._dynamo.utils import (
    CompileEventLogger,
    dynamo_timed,
    preserve_rng_state,
    set_feature_use,
)
from torch._guards import detect_fake_mode
from torch._inductor.utils import BoxedBool
from torch._subclasses import FakeTensor, FakeTensorMode
from torch.export._tree_utils import reorder_kwargs
from torch.fx.experimental.proxy_tensor import make_fx


static_inputs_log = torch._logging.getArtifactLogger(
    __name__, "cudagraph_static_inputs"
)
from . import config
from ._aot_autograd.autograd_cache import (  # noqa: F401
    AOTAutogradCache,
    autograd_cache_key,
    should_use_local_autograd_cache,
    should_use_remote_autograd_cache,
)
from ._aot_autograd.collect_metadata_analysis import (  # noqa: F401
    run_functionalized_fw_and_collect_metadata,
)
from ._aot_autograd.descriptors import (
    AOTInput,
    BufferAOTInput,
    DifferentiableAOTInput,
    ParamAOTInput,
    PlainAOTInput,
)
from ._aot_autograd.frontend_utils import (
    _detect_attribute_assignment,
    _try_get_metadata_from_dynamo,
    construct_fake_mode,
    process_inputs,
)
from ._aot_autograd.functional_utils import (  # noqa: F401
    _check_if_mutation_can_be_in_graph,
    are_all_mutations_hidden_from_autograd,
    are_all_mutations_under_no_grad_or_inference_mode,
    assert_functional_graph,
    from_fun,
    gen_alias_from_base,
    has_data_mutation,
    has_metadata_mutation,
    is_fun,
    sync_functional_tensor,
    to_fun,
)
from ._aot_autograd.graph_capture_wrappers import (  # noqa: F401
    aot_dispatch_subclass,
    create_functional_call,
    create_functionalized_fn,
    create_functionalized_rng_ops_wrapper,
    create_joint,
    fn_input_mutations_to_outputs,
    fn_prepped_for_autograd,
)
from ._aot_autograd.graph_compile import (  # noqa: F401
    aot_stage1_graph_capture,
    aot_stage2_compile,
    aot_stage2_export,
)
from ._aot_autograd.input_output_analysis import (  # noqa: F401
    compute_overlapping_inputs,
    create_graph_signature,
    create_synthetic_base_metadata,
    remove_dupe_metadata,
)
from ._aot_autograd.logging_utils import (  # noqa: F401
    callback_set,
    describe_input,
    format_guard_bug_msg,
    get_aot_compilation_context,
    get_aot_graph_name,
    get_graph_being_compiled,
    graph_being_compiled,
    model_name,
    nth_graph,
    set_model_name,
    setup_stacktrace_preservation_hooks,
    track_graph_compiling,
)
from ._aot_autograd.runtime_wrappers import (  # noqa: F401
    AOTDedupeWrapper,
    AOTSyntheticBaseWrapper,
    SerializableCompiledFunction,
)
from ._aot_autograd.schemas import (  # noqa: F401
    AOTConfig,
    AOTDispatchCompiler,
    AOTGraphCapture,
    AOTState,
    BackwardSignature,
    FakifiedFlatArgs,
    FQN,
    GraphInputName,
    GraphOutputName,
    GraphSignature,
    InputAliasInfo,
    JointWithDescriptors,
    MutationType,
    OutputAliasInfo,
    OutputType,
    SerializableAOTDispatchCompiler,
    SubclassCreationMeta,
    SubclassMeta,
    TensorAlias,
    ViewAndMutationMeta,
)
from ._aot_autograd.subclass_utils import (  # noqa: F401
    requires_subclass_dispatch,
    unwrap_tensor_subclasses,
    unwrap_tensor_subclasses_with_indices_to_original,
    wrap_tensor_subclasses,
    wrap_tensor_subclasses_maybe_joint,
)
from ._aot_autograd.utils import (  # noqa: F401
    _get_autocast_states,
    _get_symint_hints,
    call_func_at_runtime_with_args,
    create_tree_flattened_fn,
    KNOWN_TYPES,
    make_boxed_compiler,
    make_boxed_func,
    maybe_to_fresh_input,
    normalize_as_list,
    partial_flatten_asdict,
    PytreeThunk,
    root_module_when_exporting_non_strict,
    simple_wraps,
    strict_zip,
)
from .partitioners import default_partition


if TYPE_CHECKING:
    from collections.abc import Callable, Iterable, Sequence

    from torch._inductor.cudagraph_utils import BoxedDeviceIndex
    from torch._inductor.output_code import OutputCode
    from torch._inductor.utils import InputType
    from torch._ops import OpOverload
    from torch.fx.experimental.symbolic_shapes import ShapeEnv

_P = ParamSpec("_P")
_R = TypeVar("_R")

zip = strict_zip

# This global counter increments every time we compile a graph with
# AOTAutograd.  You can use this to correlate runtime error messages
# with compile time (e.g., if you get an error at runtime saying
# compiled graph 3 failed, you can set a breakpoint at compile time
# for this graph number to investigate further at compile time.)
#
# NB: this is different from get_aot_compilation_context, which tracks
# each underlying graph that is compiled.  In contrast, AOT_COUNTER
# corresponds to top-level invocations of aot_module/aot_function;
# one counter is allocated per entire compiled block (but this block
# may involve compiling multiple subgraphs; e.g., for forwards/backwards)
AOT_COUNTER = itertools.count()

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#
# AOT Autograd contains a pretty non-trivial amount of logic to handle edge cases around aliasing and mutation
# that are external to the graph (they show up as side effects in some way when you run the graph).
#
# Take a look at `test_aotdispatch.py TestAOTAutograd.test_input_mutation*` tests for some examples functions
# and what they're compiled graphs looks like.
# Below is a very long comment detailing several edge cases, and showing how AOT Autograd handles them.
#
# Note [AOT Autograd: input data mutations]
#
# If we compile a function that mutates inputs, then those input mutations are real side effects
# that a user expects to see after running the compiled graph.
# However, the graph that we want to send to a backend needs to be *entirely* functional.
# The way we reconcile this difference is that we remove the mutations completely from the graph that we compile
# but we update the graph to return (updated_inputs, user_outputs).
# In the epilogue that runs after the compiled graph is executed, we copy the updated inputs back to the originals.
#
# Example: original user code:
# def f(x):
#     x.mul_(2)
#     out = x.mul(3)
#     return out
#
# After AOT Autograd compiles, we end up with a:
# (a) compiled graph
# (b) autograd.Function.forward() method, that executes the compiled graph
# (c) wrapper function, that calls the autograd.Function.forward() and performs the epilogue
#
# The output of (a, b, c) are all written below.
#
# def compiled_forward_graph(x):
#     x_updated = x.mul(2)
#     out = x_updated.mul(3)
#     return x_updated, out
#
# # x_updated gets a gradient in the compiled backward
# def compiled_backward_graph(grad_x_updated, grad_out):
#     grad_x = ...
#     return grad_x
#
# def autograd.Function.forward(x):
#     x_updated, out = compiled_forward_graph(x)
#     return x_updated, out
#
# def compiled_wrapper(x):
#     x_updated, out = autograd.Function.apply(x)
#     x.copy_(x_updated)
#     return out
#
# Another important thing to note is that updated inputs (due to data mutations) *do* participate
# in the compiled backward graph! Since the compiled forward graph gets N extra outputs
# (due to updated inputs showing up as graph outputs),
# The compiled backward gets an additional N inputs.
# That way, during the x.copy_(x_updated) bit in the epilogue, gradients will flow from the updated input
# back to the original input.


# Note [AOT Autograd: input metadata mutations]
#
# For the same reason as input mutations, we also don't put input metadata mutations in the graph.
# Instead, we return the updated version of the input (a view), and mutate the input's metadata outside of the graph
#
# Example: original user code:
# def f(x):
#     x.t_()
#     out = x.mul(3)
#     return out
#
# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function):
# def compiled_forward_graph(x):
#     x_updated = x.t()
#     out = x_updated.mul(3)
#     return x_updated, out
#
# # x_updated does *not* get a gradient in the compiled backward
# def compiled_backward_graph(grad_out):
#     grad_x = ...
#     return grad_x
#
# def autograd.Function.forward(x):
#     x_updated, out = compiled_forward_graph(x)
#     return x_updated, out
#
# def compiled_wrapper(x):
#     x_updated, out = autograd.Function.apply(x)
#     x.as_strided_(x_updated)
#     return out


# Note [AOT Autograd: outputs aliasing inputs or intermediates!]
#
# AOT Autograd needs special handling for outputs that alias graph inputs or intermediates!
# Why?
# (1) autograd.Function.forward() has a limitation, where views that returned in the forward cannot later be mutated.
# (2) views don't need to be compiled in the graph anyway - it's cheap to generate them outside of the compiled graph,
#     in an epilogue.
# For outputs that alias inputs, we do the following:
# (a) *still* return the aliased output as a graph output
# (b) In the AOT Autograd wrapper/epilogue, we don't return that aliased output. Instead, we use it to regenerate the output.
#
# For outputs that alias *intermediates*, we do the following:
# (a) Return the output in the compiled forward, **and** return it's ._base (a graph intermediates) as an output in the forward
# (b) Use (output, graph_intermediate) to regenerate the alias, and return that to the user (instead of the compiled fw output).
# You might wonder why we return the aliased output directly in the graph (and making the graph compute it),
# only to not return it and instead generate a fresh alias off of the intermediate,
# instead of (say) just storing metadata about the size/stride of the output somewhere to generate the alias. There are two reasons:
# (1) Getting the actual alias tensor allows us to use view-replay to generate the alias, instead of an as_strided() call
# (2) Inductor (and other backends) are free to change the memory format of graph outputs, if it results in better performance.
#     This can result in problems if a user later tries to .view() that output expecting it to have one set of strides,
#     when it has a different set of strides.
#     By including the view op directly in the graph, inductor takes that into account when deciding what memory format
#     the graph intermediate should be.
#
# Another important thing to note is how our traced backward() graph handles aliases.
# (this applies to outputs aliasing inputs, outputs aliasing intermediates,
#  *and* updated inputs returned in the compiled forward due to metadata-only mutations).
# Any outputs that alias (either inputs or intermediates) do NOT participate in the compiled backward graph
# It would be wasteful to include them in the compiled backward(), because we regenerate them eagerly
# at the end of the forward.
#
# Example: original user code:
# def f(x):
#     out1 = x.t()
#     intermediate = x.mul(2)
#     out2 = intermediate.view(-1)
#     return out1, out2
#
# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function):
# def compiled_forward_graph(x):
#     out1 = x.t()
#     intermediate = x.mul(2)
#     out2 = intermediate.view(-1)
#     # the compiled graph also returns the intermediate
#     return out1, out2, intermediate
#
# # intermediate gets a gradient in the compiled backward.
# # both output aliases (out1 and out2) do not.
# def compiled_backward_graph(grad_intermediate):
#     grad_x = ...
#     return grad_x
#
# def autograd.Function.forward(x):
#     out1, out2, intermediate = compiled_forward_graph(x)
#     return out1, out2, intermediate
#
# def compiled_wrapper(x):
#     out1, out2, intermediate = autograd.Function.apply(x)
#     # regenerate out1 from the input
#     out1_regenerated = out1._view_func(x)
#     # regenerate out1 from the intermediate
#     out2_regenerated = out2._view_func(intermediate)
#     return out1_regenerated, out2_regenerated


# Note [AOT Autograd: mutations to inputs that alias other inputs]
#
# Another edge case that is (only partially) handled today is when an input is mutated, but itself aliases another input.
# AOT Autograd needs to **ensure** that functionalization knows that the two inputs are aliased to each other.
# That way, when the aliased input is accessed later in the graph, functionalization knows to "update" the alias
# given the mutation that occurred.
#
# This is handled by updating the calling convention: we create a "synthetic base" that becomes a new input
# in the compiled function, and we regenerate the original (aliased) inputs directly off of the base
# inside of the compiled function.
#
# This logic is fully encapsulated in aot_wrapper_synthetic_base()
#
# Example: original user code:
# def f(x, x_view):
#     x.mul_(2)
#     out = x * x_view
#     return out
# f(x, x.view(-1))
#
# AOT Autograd output (compiled graph, autograd.Function.forward(), wrapper function):
# def compiled_forward_graph(base)
#     x = generate_x(base)
#     x_view = generate_x_view(base)
#     x_updated = x.mul(2)
#     x_view_updated = x_updated.view(-1)
#     out = x_updated * x_view_updated
#     return x_updated, out
#
# # The calling convention change from (aliases) -> (base) happens
# # *outside* of the autograd.Function.forward().
# # That means the forward() only has 1 input (base),
# # and the backward() only has 1 output (grad_base)
# def compiled_backward_graph(grad_out):
#     grad_base = ...
#     return grad_base
#
# def autograd.Function.forward(base):
#     x_updated, out = compiled_forward_graph(base)
#     return x_updated, out
#
# # The compiled wrapper is where we create synthetic bases.
# # The info on which inputs are mutated is also tracked *before* synthetic base creation.
# def compiled_wrapper(x, x_view):
#     base = merge_view_inputs(x, x_view)
#     x_updated, out = autograd.Function.apply(base)
#     # x and x_view are aliased in eager mode, so this mutation to x will automatically affect x_view.
#     x.copy_(x_updated)
#     return out


# Note [AOT Autograd: Views to avoid tangents aliasing inputs]
#
# We view every forward output when creating out tangent tensors to handle the problematic
# case in which a subclass does extra aliasing between graph outputs/inputs in a way that
# is not visible above the subclass.
#
# Ordinarily, when constructing the joint function that we want to trace in AOTAutograd,
# we're guaranteed that the tangent tensors that we pass
# into the joint are distinct tensors from the primals. This is because when
# decide which forward outputs to create tangents for, we only create tangents
# for forward outputs that are not aliases of inputs (See Note
# [AOT Autograd: outputs aliasing inputs or intermediates!]).
#
# However, when wrapper tensor subclasses enter the picture, it is possible
# to have an output of the forward that is a subclass that is not an
# input / alias of an input, but one of its inner tensors is an alias!
# NestedTensor is an example: Performing an out-of-place pointwise op on a
# NestedTensor constructs a fresh NestedTensor that holds onto the input's
# offsets tensor directly.
#
# Having tangent tensors that are the same as the (primal) forward inputs,
# can cause problems during tracing as make_fx() will specialize on our
# duplicate inputs: If we passed in the same tensor for primals_1 and
# tangents_1 during tracing, make_fx() will happily sub out all usages of
# tangents_1 with primals_1 in the graph, which is not what we want.
#
# To work around this, we view every forward output when creating out tangent
# tensors so that tangents can never be the same as forward inputs even if
# forward inputs alias forward outputs.

# Note [Side-Effectful Tokens in AOTAutograd]
#
# We allow some some side-effectful operators in
# the post-AOTAutograd (functional) graph, such as prints and torchbind operations.
# To ensure that these side-effects are compatible to future graph passes that
# assume that the graph is functional, we will thread "effect tokens" to show
# data dependence between these side-effectful operators. Practically speaking,
# effect tokens are just dummy values (torch.tensor([])). The graph would look
# like the following:
#
# def gm(self, token0, reader):
#    token1, frame = with_token(ordered_effect_op, (reader,), token0)
#    frame = frame * 2
#    token2, frame2 = with_token(ordered_effect_op, (reader,), token1)
#    frame2 = frame2 * 2
#    return token2, frame, frame2
#
# We will pass the token as an input to the graph, thread it through
# side-effectful operators using the `with_effects` high order operator, and then
# return the updated token as an output.
# So the signature of the graph input would look something like
# (*tokens, *params_buffers, *user_inputs), and the signature of the graph
# output would look something like (*tokens, *outputs).
#
# However, Inductor does not want the concept of tokens in the final generated
# code's input and output. Since changing the graph signature inside of inductor
# is difficult, after generating the forward graph, we will run a pass to
# remove the tokens from the inputgenerate the following graph for Inductor, where
# the tokens are created and sunk within the graph, rather than as inputs and
# outputs:
#
# def gm(self, reader):
#    token0 = torch.ops.prims._make_token()
#    token1, frame = with_token(ordered_effect_op, (reader,), token0)
#    frame = frame * 2
#    token2, frame2 = with_token(ordered_effect_op, (reader,), token1)
#    frame2 = frame2 * 2
#    sink_token = torch.ops.prims._sink_tokens([token2])
#    return frame, frame2

#
#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


aot_autograd_decompositions: dict[OpOverload, Callable[..., Any]] = {}


def create_aot_state(
    stack: contextlib.ExitStack,
    flat_fn: Callable[_P, _R],
    fake_flat_args: FakifiedFlatArgs,
    flat_args_descs: list[AOTInput],
    aot_config: AOTConfig,
    fake_mode: FakeTensorMode,
    shape_env: ShapeEnv | None,
) -> AOTState:
    """
    Traces the forward and backward graphs of the attr:`flat_fn` to generate a
    joint graph. The joint graph is an Fx graph with Aten ops. Please refer to
    the tracing mechanism to understand the graph capturing details.

    The joint graph is then passed through attr:`partition_fn` to isolate the
    forward and backward portions, which are then respectively compiled via the
    provided attr:`fw_compiler` and attr:`bw_compiler`.

    The resulting compiled forward and backward graphs are then wrapped up in a
    ``torch.autograd.Function`` object.

    The calling convention here is that the first aot_config.num_params_buffers
    inputs in flat_args are parameters and buffers, and the rest are inputs.

    We use this to assume that parameters/buffer's shapes don't change.
    """

    # Old name for now to avoid messing with stats.  Also, note this is pushed
    # on the stack, so it extends BEYOND this function
    stack.enter_context(
        dynamo_timed("create_aot_dispatcher_function", log_pt2_compile_event=True)
    )

    # This is the main entry point.
    # TODO: Chillee argues that dynamo itself should pass in fake tensors to
    # the list of arguments when compiling; at the moment we do not do this

    if aot_config.decompositions is None:
        aot_config.decompositions = {}

    aot_config.decompositions = {
        **aot_autograd_decompositions,
        **aot_config.decompositions,
    }

    if config.functionalize_rng_ops:
        # Update the decompositions with functionalized random decompositions
        aot_config.decompositions = {  # type: ignore[assignment]
            **rng_decompositions,
            **aot_config.decompositions,
        }

    # Check flat_args to see if they're already fake.  If so, use that fake
    # mode instead.

    python_dispatcher_mode = (
        enable_python_dispatcher() if shape_env is not None else nullcontext()
    )

    # See NOTE: [Deferring tensor pack/unpack hooks until runtime]
    # If any saved tensor hooks are active, we **don't** want to trace them.
    # Instead, we'll let them run at runtime, around the custom autograd.Function
    # that we generate in torch.compile.
    stack.enter_context(torch.autograd.set_multithreading_enabled(False))
    stack.enter_context(preserve_rng_state())
    stack.enter_context(fake_mode)
    stack.enter_context(python_dispatcher_mode)
    stack.enter_context(PhiloxStateTracker())
    stack.enter_context(
        torch._dynamo.utils._disable_saved_tensors_hooks_during_tracing()
    )

    from torch._library.fake_class_registry import FakeScriptObject, maybe_to_fake_obj
    from torch._library.opaque_object import is_opaque_type

    # Tracing may mutate the states the fake script object,
    # so we need to duplicate the fake script objects so that subsequent tracing
    # won't be affected.
    def _dup_fake_script_obj(fake_flat_args: FakifiedFlatArgs) -> list[Any]:
        return [
            maybe_to_fake_obj(detect_fake_mode(fake_flat_args), arg.real_obj)
            if isinstance(arg, FakeScriptObject) or is_opaque_type(type(arg))
            else arg
            for arg in fake_flat_args
        ]

    needs_autograd = any(
        x.requires_grad for x in fake_flat_args if isinstance(x, Tensor)
    )

    with enable_python_dispatcher():
        # Patch set_rng_state as set_rng_state with fake tensors is
        # nonsensical. This does not affect the collection of metadata.
        with patch("torch.cuda.set_rng_state", lambda *args: None):
            mod = root_module_when_exporting_non_strict(flat_fn)
            if mod is not None:
                ctx = _detect_attribute_assignment(mod)
            else:
                ctx = nullcontext()

            if torch._functorch.config.fake_tensor_propagate_real_tensors:
                # Running dynamo_timed causes fake tensor issues when
                # propagate real tensor is switched on.
                dynamo_timed_ctx = nullcontext()
            else:
                dynamo_timed_ctx = dynamo_timed(
                    "aot_collect_metadata", log_pt2_compile_event=True
                )

            with dynamo_timed_ctx, ctx:
                fw_metadata = run_functionalized_fw_and_collect_metadata(
                    flat_fn,
                    flat_args_descs=flat_args_descs,
                    static_input_indices=aot_config.static_input_indices,
                    keep_input_mutations=aot_config.keep_inference_input_mutations,
                    is_train=needs_autograd,
                    pre_dispatch=aot_config.pre_dispatch,
                )(*_dup_fake_script_obj(fake_flat_args))

            req_subclass_dispatch = requires_subclass_dispatch(
                fake_flat_args, fw_metadata
            )
            CompileEventLogger.try_add_pt2_compile(
                "backend_compile", requires_subclass_dispatch=req_subclass_dispatch
            )

            output_and_mutation_safe = not any(
                x.requires_grad
                # view-type operations preserve requires_grad even in no_grad.
                # Do not count aliases of inputs with requires_grad as reason to make a training graph,
                # as AOTAutograd will perform view-replay to regenerate the view outputs at runtime,
                # setting their grad_fn properly.
                and not (
                    x.output_type in (OutputType.alias_of_input, OutputType.is_input)
                    and x.base_idx is not None
                    and fw_metadata.input_info[x.base_idx].requires_grad
                )
                for x in fw_metadata.output_info
            ) and not any(
                x.requires_grad
                and x.mutates_data
                and not x.mutations_under_no_grad_or_inference_mode
                and not x.mutations_hidden_from_autograd
                for x in fw_metadata.input_info
            )

            if needs_autograd and output_and_mutation_safe:
                # We realized that none of the outputs require grad,
                # and none of the inputs that require grad are mutated.
                # so we actually have an inference graph.
                needs_autograd = False
                # A bit silly: right now in the subclass codepath, our ViewAndMutationMeta
                # changes depending on whether we pass in is_train / keep_input_mutations,
                # so we're forced to recompute the metadata.
                # TODO: refactor the subclass path of run_functionalized_fw_and_collect_metadata
                # so that this is unnecessary.
                if req_subclass_dispatch:
                    fw_metadata = run_functionalized_fw_and_collect_metadata(
                        flat_fn,
                        flat_args_descs=flat_args_descs,
                        keep_input_mutations=aot_config.keep_inference_input_mutations,
                        is_train=False,
                        pre_dispatch=aot_config.pre_dispatch,
                        static_input_indices=aot_config.static_input_indices,
                    )(*fake_flat_args)
                else:
                    fw_metadata = ViewAndMutationMeta(
                        input_info=fw_metadata.input_info,
                        output_info=fw_metadata.output_info,
                        num_intermediate_bases=fw_metadata.num_intermediate_bases,
                        keep_input_mutations=aot_config.keep_inference_input_mutations,
                        traced_tangents=fw_metadata.traced_tangents,
                        traced_tangents_descs=fw_metadata.traced_tangents_descs,
                        subclass_inp_meta=fw_metadata.subclass_inp_meta,
                        subclass_fw_graph_out_meta=fw_metadata.subclass_fw_graph_out_meta,
                        subclass_tangent_meta=fw_metadata.subclass_tangent_meta,
                        is_train=False,
                        tokens=fw_metadata.tokens,
                        static_input_indices=fw_metadata.static_input_indices,
                    )

    if fw_metadata.num_intermediate_bases > 0:
        if req_subclass_dispatch:
            raise AssertionError(f"""\
torch.compile is currently being used with tensor subclass inputs.
We are attempting to a compile a graph with two graph outputs
that alias one another, specifically output indices:

    {[i for i, x in enumerate(fw_metadata.output_info) if x.output_type == OutputType.alias_of_intermediate]}

ANY output aliasing (even for regular tensors) is currently unsupported if
there are any subclass outputs. If you run into this, please file a github
issue""")

    if aot_config.is_export:
        # aot_export: ban input metadata mutations for now to keep shared code paths simpler.
        # Keeping .resize_() in the graph will require some work
        # Allowing it but keeping the graph functional will require some calling convention changes.
        if len([x for x in fw_metadata.input_info if x.mutates_metadata]) != 0:
            raise RuntimeError(
                f"""\
Found an input that received a metadata mutation, through e.g. a call to `.resize_()` or `.transpose_()`.
This is currently banned in the aot_export workflow. If you need this functionality, please file a github issue.

fw_metadata={str(fw_metadata)}"""
            )
        # In export, banning data mutations on inputs that require grad for now.
        # This should be rare, and is tricky to get right. When we trace the backward,
        # we currently trace with autograd.grad instead of .backward(), which makes it difficult
        # to ensure that we run autograd all the way through the input **before** it saw the mutation.
        if (
            len(
                [
                    x
                    for x in fw_metadata.input_info
                    if x.requires_grad and x.mutates_data
                ]
            )
            != 0
            and aot_config.export_trace_joint
        ):
            raise RuntimeError(
                f"""\
Found a graph input that requires gradients, and received a mutation.
This is currently banned in the aot_export workflow. If you need this functionality, please file a github issue.

fw_metadata={str(fw_metadata)}"""
            )
        if req_subclass_dispatch:
            raise RuntimeError(
                """\
aot_export is not currently supported with traceable tensor subclass.
If you need this feature, please comment on <CREATE_ISSUE_LINK>"""
            )

        # Need to decide on a strategy for functionalized RNG: toggling via global config seems bad,
        # and turning it on will require a non-trivial calling convention change for any export runtime.
        if config.functionalize_rng_ops:
            raise RuntimeError(
                """\
Functionalized RNG is not currently supported in the aot_export workflow. Please file a github issue,
or otherwise set torch._functorch.config.functionalize_rng_ops = False."""
            )

    return AOTState(
        needs_autograd=needs_autograd,
        flat_args=_dup_fake_script_obj(fake_flat_args),
        flat_args_descs=flat_args_descs,
        fw_metadata=fw_metadata,
        # Packaging this just for later use
        aot_config=aot_config,
        stack=stack,
        fake_mode=fake_mode,
    )


def aot_function(
    fn: Callable[_P, _R],
    fw_compiler: AOTDispatchCompiler,
    bw_compiler: AOTDispatchCompiler | None = None,
    partition_fn: Callable[..., Any] = default_partition,
    decompositions: dict[OpOverload, Callable[..., Any]] | None = None,
    num_params_buffers: int = 0,
    keep_inference_input_mutations: bool = False,
    inference_compiler: AOTDispatchCompiler | None = None,
    *,
    # Whether or not to trace with dynamic shapes
    dynamic: bool = False,
    enable_log: bool = True,
    disable_functionalization: bool = False,
    _disable_torch_fn_metadata_mode: bool = False,
) -> Callable[_P, Any]:
    """
    Traces the forward and backward graph of :attr:`fn` using torch dispatch
    mechanism, and then compiles the generated forward and backward graphs
    through :attr:`fw_compiler` and :attr:`bw_compiler`.

    :func:`aot_function` traces the forward and backward graph ahead of time,
    and generates a joint forward and backward graph.  :attr:`partition_fn` is
    then used to separate out forward and backward graphs. The partitioner
    function can be used to perform optimizations such as recomputation. One can
    set `decompositions` dictionary to decompose the operators into a sequence
    of core or simpler operators supported by the backend compilers.

    .. warning::
        This API is experimental and likely to change.

    Args:
        fn (Callable): A Python function that takes one or more arguments. Must
            return one or more Tensors.
        fw_compiler (Callable): A Python function that accepts an Fx graph with
            Aten ops and input args, and returns a Callable that semantically is
            equivalent to the input Fx graph.
        bw_compiler (Optional[Callable]): A Python function that accepts an
            Fx graph with Aten ops and input args, and returns a Callable that
            semantically is equivalent to the input Fx graph.  Default: None
            (when None, it defaults to the :attr:`fw_compiler`)
        partition_fn (Callable): A Python function that takes a joint forward
            and backward graph, and partitions it into separate forward and
            backward graphs.
        decompositions (Dict): A dictionary to define the decomposition of
            larger Aten ops into simpler or core Aten ops.
        inference_compiler (Optional[Callable]): A Python function that accepts an
            Fx graph with Aten ops and input args, and returns a Callable that
            semantically is equivalent to the input Fx graph. inference_compiler is invoked
            if no autograd is needed. Default: None
            (when None, it defaults to the :attr:`fw_compiler`)
    Returns:
        Returns a ``Callable`` that retains the eager behavior of the original
        :attr:`fn`, but with forward and backward graph compiled via
        :attr:`fw_compile` and :attr:`bw_compile`.

    A simple example usage of :func:`aot_function` is as follows. This example
    will print the forward and backward graphs of the function ``fn``

        >>> fn = lambda x: x.sin().cos()
        >>> def print_compile_fn(fx_module, args):
        >>>     print(fx_module)
        >>>     return fx_module
        >>> aot_fn = aot_function(fn, print_compile_fn)
        >>> x = torch.randn(4, 5, requires_grad=True)
        >>> aot_fn(x)
    """

    aot_config = AOTConfig(
        fw_compiler=None,
        bw_compiler=None,
        inference_compiler=None,
        partition_fn=None,
        decompositions=decompositions,
        num_params_buffers=num_params_buffers,
        aot_id=next(AOT_COUNTER),
        keep_inference_input_mutations=keep_inference_input_mutations,
        dynamic_shapes=dynamic,
        aot_autograd_arg_pos_to_source=None,
        is_export=False,
        no_tangents=False,
        enable_log=enable_log,
        disable_functionalization=disable_functionalization,
        _disable_torch_fn_metadata_mode=_disable_torch_fn_metadata_mode,
    )
    cached_res = None

    @wraps(fn)
    def returned_function(*args: _P.args, **kwargs: _P.kwargs) -> Any:
        nonlocal cached_res
        # Now flatten the tensor args
        flat_args = pytree.arg_tree_leaves(*args, **kwargs)

        # Compile the function and save it in the cache
        if cached_res is None:
            flat_fn, out_spec = create_tree_flattened_fn(fn, args, kwargs)
            (fake_mode, shape_env) = construct_fake_mode(flat_args, aot_config)
            fake_flat_args: FakifiedFlatArgs = process_inputs(
                flat_args, aot_config, fake_mode, shape_env
            )
            # TODO: We actually could use the pytree path to make better descs.
            # Also, the descs here are bad if you do aot_module.
            fake_flat_args_descs: list[AOTInput] = [
                PlainAOTInput(i) for i in range(len(fake_flat_args))
            ]
            with contextlib.ExitStack() as stack:
                aot_state = create_aot_state(
                    stack,
                    flat_fn,
                    fake_flat_args,
                    fake_flat_args_descs,
                    aot_config,
                    fake_mode,
                    shape_env,
                )
                aot_graph_capture = aot_stage1_graph_capture(aot_state, flat_fn)
                compiled_fn, _ = aot_stage2_compile(
                    aot_state,
                    aot_graph_capture,
                    partition_fn,
                    fw_compiler,
                    bw_compiler,
                    inference_compiler,
                )
            cached_res = (compiled_fn, out_spec)

        cached_fn, out_spec = cached_res
        out = cached_fn(flat_args)
        return out_spec.unflatten(out)

    return returned_function


def aot_module(mod: nn.Module, *args: Any, **kwargs: Any) -> nn.Module:
    """
    Traces the forward and backward graph of :attr:`mod` using torch dispatch
    tracing mechanism. It is wrapper function, that underneath uses
    :func:`aot_function` to perform tracing and compilation.

    :func:`aot_module` lifts the parameters and buffers of ``nn.Module`` as inputs
    to a new callable which is then compiled through :func:`aot_function`.

    .. warning::
        This API is experimental and likely to change.

    Args:
        mod (Callable): A ``nn.Module`` module.
        args : args to be passed to :func:`aot_function`
        kwargs : kwargs to be passed to :func:`aot_function`

    Returns:
        Returns a ``nn.Module`` that retains the eager behavior of the original
        :attr:`mod`, but with forward and backward graph compiled.

    """
    # See Note: [Fake Modules and AOTAutograd]
    torch._dynamo.utils.assert_no_fake_params_or_buffers(mod)

    def functional_call(
        named_params: dict[str, torch.nn.Parameter],
        named_buffers: dict[str, torch.Tensor],
        *args: Any,
        **kwargs: Any,
    ) -> Any:
        params_and_buffers = {**named_params, **named_buffers}
        return torch.func.functional_call(mod, params_and_buffers, args, kwargs)

    named_params = dict(mod.named_parameters(remove_duplicate=False))
    named_buffers = dict(mod.named_buffers(remove_duplicate=False))
    num_params_buffers = len(named_params) + len(named_buffers)
    compiled_f = aot_function(
        functional_call,
        *args,
        # pyrefly: ignore[bad-keyword-argument]
        num_params_buffers=num_params_buffers,
        **kwargs,
    )

    class AOTModule(nn.Module):
        def __init__(self) -> None:
            super().__init__()
            self.orig_module = mod

        def forward(self, *args: Any, **kwargs: Any) -> Any:
            return compiled_f(
                named_params,
                named_buffers,
                *args,
                **kwargs,
            )

    return AOTModule()


def prepare_aot_module_simplified(
    mod: nn.Module,
    args: Iterable[Any],
    kwargs: dict[str, Any] | None,
    decompositions: dict[OpOverload, Callable[..., Any]] | None,
    keep_inference_input_mutations: bool,
    boxed_forward_device_index: BoxedDeviceIndex | None,
    ignore_shape_env: bool,
    flatten: bool,
    *,
    force_non_lazy_backward_lowering: bool = False,
    disable_functionalization: bool = False,
    _record_nn_module_stack: bool = False,
    _disable_torch_fn_metadata_mode: bool = False,
) -> tuple[
    Any,
    list[torch.nn.Parameter | Tensor],
    list[str],
    list[str],
    FakifiedFlatArgs,
    list[Any],
    AOTConfig,
    FakeTensorMode,
    ShapeEnv | None,
    pytree.TreeSpec | None,
    PytreeThunk | None,
]:
    if not flatten:
        if kwargs is not None:
            raise AssertionError("kwargs must be None when flatten=False")
    elif kwargs is None:
        kwargs = {}

    # TODO: There's something a bit suspicious here; typically simplified
    # module shouldn't actually have any parameters...
    params = dict(mod.named_parameters(remove_duplicate=False))
    buffers = dict(dict(mod.named_buffers(remove_duplicate=False)))

    params_flat, params_spec = list(params.values()), list(params.keys())
    params_len = len(params_flat)

    buffers_flat, buffers_spec = list(buffers.values()), list(buffers.keys())
    buffers_len = len(buffers_flat)

    params_buffers = {**params, **buffers}
    params_buffers_flat = params_flat + buffers_flat
    params_buffers_spec = params_spec + buffers_spec

    # Take a break to figure what we're doing with the module

    # NB: This doesn't change the in/out convention, except adding the
    # parameters as explicit arguments
    functional_call = create_functional_call(
        mod,
        params_buffers_spec,
        params_len + buffers_len,
        strict_out_tuple=not flatten,
        # We need this for export to run ModuleStackTracer
        # instead of PythonKeyTracer
        store_orig_mod=_record_nn_module_stack,
    )

    full_args = [*params_flat, *buffers_flat, *args]
    in_spec, out_spec = None, None
    if flatten:
        functional_call, out_spec = create_tree_flattened_fn(
            functional_call, full_args, kwargs
        )
        full_args, in_spec = pytree.tree_flatten((full_args, kwargs))

    del kwargs

    # OK, set up the descs

    full_args_descs: list[DifferentiableAOTInput] = []
    full_args_descs.extend(ParamAOTInput(fqn) for fqn in params_spec)
    full_args_descs.extend(BufferAOTInput(fqn) for fqn in buffers_spec)
    # TODO: it would be better to put pytree information in here
    full_args_descs.extend(
        PlainAOTInput(i) for i in range(len(full_args) - len(full_args_descs))
    )

    # TODO: These tracing_context fields should become unnecessary once we
    # always maintain sources on all arguments
    if tracing_context := torch._guards.TracingContext.try_get():
        # NB: TracingContext misnames this, the "params" here also contains
        # buffers
        tracing_context.params_flat = params_buffers_flat
        (
            tracing_context.params_flat_unwrap_subclasses,
            tracing_context.params_unwrapped_to_flat_index,
        ) = unwrap_tensor_subclasses_with_indices_to_original(params_buffers_flat)

    # TODO: Might be nice to hold on to the Dynamo source here in full_args_descs!
    (
        aot_autograd_arg_pos_to_source,
        static_input_indices,
    ) = _try_get_metadata_from_dynamo(
        mod, params_buffers.keys(), len(full_args), full_args_descs
    )

    dynamic_shapes = False
    for x in full_args:
        if isinstance(x, FakeTensor):
            dynamic_shapes = x.fake_mode.shape_env is not None
            break

    aot_config = AOTConfig(
        fw_compiler=None,
        bw_compiler=None,
        inference_compiler=None,
        partition_fn=None,
        decompositions=decompositions,
        num_params_buffers=params_len + buffers_len,
        aot_id=next(AOT_COUNTER),
        keep_inference_input_mutations=keep_inference_input_mutations,
        dynamic_shapes=dynamic_shapes,
        # pyrefly: ignore[bad-argument-type]
        aot_autograd_arg_pos_to_source=aot_autograd_arg_pos_to_source,
        static_input_indices=static_input_indices,
        is_export=False,
        no_tangents=False,
        cache_info=None,
        ignore_shape_env=ignore_shape_env,
        precompile_backend_id=getattr(mod, "_backend_id", None),
        force_non_lazy_backward_lowering=force_non_lazy_backward_lowering,
        disable_functionalization=disable_functionalization,
        _disable_torch_fn_metadata_mode=_disable_torch_fn_metadata_mode,
    )
    fake_mode, shape_env = construct_fake_mode(full_args, aot_config)
    # NB: full_args_descs not needed here, fake_flat_args is 1:1 with full_args
    fake_flat_args = process_inputs(
        full_args, aot_config, fake_mode, shape_env, ignore_shape_env
    )

    return (
        functional_call,
        params_buffers_flat,
        params_spec,
        buffers_spec,
        fake_flat_args,
        full_args_descs,
        aot_config,
        fake_mode,
        shape_env,
        in_spec,
        out_spec,
    )


def aot_module_simplified(
    mod: torch.fx.GraphModule | torch._dynamo.utils.GmWrapper,
    args: Iterable[Any],
    fw_compiler: AOTDispatchCompiler,
    bw_compiler: AOTDispatchCompiler | None = None,
    partition_fn: Callable[..., Any] = default_partition,
    decompositions: dict[OpOverload, Callable[..., Any]] | None = None,
    keep_inference_input_mutations: bool = False,
    inference_compiler: AOTDispatchCompiler | None = None,
    # TODO: This doesn't seem to be used in any nontrivial way, check if it's
    # actually needed
    cudagraphs: BoxedBool | None = None,
    boxed_forward_device_index: BoxedDeviceIndex | None = None,
    ignore_shape_env: bool = False,
    disable_functionalization: bool = False,
) -> Callable[..., Any]:
    """
    This is the simplified or low overhead version of aot_module. For frontends
    like TorchDynamo, the input functions/modules to AOT are static and have
    unpacked inputs/outputs. This gives us an opportunity to remove the
        (1) pytree overhead to parse inputs/outputs,
        (2) AOT Autograd cache,
        (3) Reading of params/buffers in every forward call

    :func:`aot_module_simplified` removes these overheads.
    """

    if cudagraphs is None:
        cudagraphs = BoxedBool(torch._inductor.config.triton.cudagraphs)

    with contextlib.ExitStack() as stack:
        (
            functional_call,
            params_buffers_flat,
            _params_spec,
            _buffers_spec,
            fake_flat_args,
            full_args_descs,
            aot_config,
            fake_mode,
            shape_env,
            _in_spec,
            _out_spec,
        ) = prepare_aot_module_simplified(
            mod,
            args,
            None,
            decompositions,
            keep_inference_input_mutations,
            boxed_forward_device_index,
            ignore_shape_env,
            flatten=False,
            force_non_lazy_backward_lowering=config.force_non_lazy_backward_lowering,
            disable_functionalization=disable_functionalization,
        )

        compiled_fn = None

        if (
            isinstance(fw_compiler, SerializableAOTDispatchCompiler)
            or torch._functorch.config.force_autograd_cache
        ):
            local = should_use_local_autograd_cache()
            remote = should_use_remote_autograd_cache()
            if local or remote:
                set_feature_use("aot_autograd_remote_cache", remote)
                compiled_fn = AOTAutogradCache.try_load(
                    mod,
                    fake_flat_args,
                    aot_config,
                    cudagraphs,
                    boxed_forward_device_index,
                    local,
                    remote,
                )

        if compiled_fn is None:
            stack.enter_context(compiled_autograd._disable())
            aot_state = create_aot_state(
                stack,
                functional_call,
                fake_flat_args,
                full_args_descs,
                aot_config,
                fake_mode,
                shape_env,
            )
            aot_graph_capture = aot_stage1_graph_capture(aot_state, functional_call)
            compiled_fn, _ = aot_stage2_compile(
                aot_state,
                aot_graph_capture,
                partition_fn,
                fw_compiler,
                bw_compiler,
                inference_compiler,
            )
    if compiled_fn is None:
        raise AssertionError("compiled_fn must not be None")
    if isinstance(mod, torch._dynamo.utils.GmWrapper):
        # This function is called by the flatten_graph_inputs wrapper, which boxes
        # the inputs so that they can be freed before the end of this scope.
        # For overhead reasons, this is not the default wrapper, see comment:
        # https://github.com/pytorch/pytorch/pull/122535/files#r1560096481
        @simple_wraps(compiled_fn)
        def forward(runtime_args: list[Any]) -> Any:
            flat_args = []
            flat_args.extend(params_buffers_flat)
            flat_args.extend(runtime_args)
            runtime_args.clear()
            if compiled_fn is None:
                raise AssertionError("compiled_fn must not be None")
            return compiled_fn(flat_args)

    else:
        # TODO: There is something deeply wrong here; compiled_fn running with
        # the boxed calling convention, but aot_module_simplified somehow
        # historically returned a function that was not the boxed calling
        # convention.  This should get fixed...
        # NB: GraphModule/nn.Module rely on the non-boxed calling convention here
        @simple_wraps(compiled_fn)
        def forward(*runtime_args: tuple[Any]) -> Any:
            full_args = []
            full_args.extend(params_buffers_flat)
            # pyrefly: ignore[bad-argument-type]
            full_args.extend(runtime_args)
            if compiled_fn is None:
                raise AssertionError("compiled_fn must not be None")
            return compiled_fn(full_args)

    # Just for convenience
    forward.zero_grad = mod.zero_grad  # type: ignore[attr-defined]
    forward.named_parameters = mod.named_parameters  # type: ignore[attr-defined]
    forward.named_buffers = mod.named_buffers  # type: ignore[attr-defined]

    # Add a serialize function
    def grab_serialize_fn(fn: Any) -> Callable[..., Any] | None:
        if isinstance(fn, SerializableCompiledFunction):
            return fn.serialize_fn
        elif hasattr(fn, "__wrapped__"):
            return grab_serialize_fn(fn.__wrapped__)
        else:
            return None

    forward.serialize = grab_serialize_fn(forward)  # type: ignore[attr-defined]
    return forward


def boxed_nop_preserve_node_meta(
    gm: torch.fx.GraphModule, example_inputs: Sequence[InputType]
) -> Any:
    def run(args: Sequence[Any]) -> OutputCode:
        with torch.fx.traceback.preserve_node_meta():
            return torch.fx.Interpreter(gm).boxed_run(args)

    run._boxed_call = True  # type: ignore[attr-defined]
    return run


def aot_export_joint_with_descriptors(
    stack: contextlib.ExitStack,
    mod: nn.Module,
    args: tuple[Any, ...],
    kwargs: dict[str, Any] | None = None,
    *,
    decompositions: dict[OpOverload, Callable[..., Any]] | None = None,
    keep_inference_input_mutations: bool = False,
    ignore_shape_env: bool = False,
    disable_functionalization: bool = False,
    _record_nn_module_stack: bool = False,
    _disable_torch_fn_metadata_mode: bool = False,
) -> JointWithDescriptors:
    """
    This API captures the joint graph for an nn.Module.  However, unlike
    aot_export_joint_simple or aot_export_module(trace_joint=True), the
    calling convention of the produced joint graph follows no fixed positional
    schema; for example, you cannot rely on the second argument of the traced
    joint graph to correspond to the second argument of the module you traced.
    However, the inputs and outputs of the traced graph are schematized
    with **descriptors**, annotated on meta['desc'] on the placeholder and
    return FX nodes, which you can use to determine the meaning of arguments.

    The major benefit of using this export rather than aot_export_joint_simple
    is that we have feature parity with all situations that torch.compile
    supports (via aot_module_simplified), including handling for more
    complicated cases such as multiple differentiable outputs, input mutations
    that must be handled outside of the graph, tensor subclasses, etc.

    What can you do with one of these joint graphs with descriptors?  The
    motivating use case (autoparallel) involves taking the joint graph, doing
    optimizations on it, and then turning it back into a callable so it can be
    torch.compile'd at a later point in time.  This cannot be done as a
    traditional torch.compile joint graph pass for two reasons:

        1. The sharding of parameters must be decided before parameter
           initialization / checkpoint load, far before torch.compile would
           ordinarily run.

        2. We need to change the meaning of parameters (e.g., we might replace
           a replicated parameter with a sharded version of it, changing its
           input size).  torch.compile is ordinarily semantics preserving, and
           not allowed to change the meaning of inputs.

    Some descriptors can be quite exotic, so we recommend thinking carefully
    if there is a safe fallback you can apply to descriptors you don't understand.
    For example, you should have some way to handle not finding a particular
    input exactly as is in the final FX graph inputs.

    Note: When using this API, you must create and enter an ExitStack context
    manager, which will be passed into this function.  This context manager
    must remain active if you call the compile function to finish compilation.
    (TODO: We may relax this requirement by having AOTAutograd keep track of
    how to reconstruct all the context managers at a later point in time.)

    NB: You're not obligated to do a /full/ compile in stage2; instead you can
    leave the forward/backward compilers unspecified in which case the
    partitioned FX graphs will directly run.  The overall autograd Function
    can be allowed in graph so you can reprocess it in the context of a
    (potentially larger) compiled region later.

    NB: These APIs do NOT hit cache, as we only ever cache the final compile results,
    not the intermediate export result.

    NB: If the passed nn.Module has parameters and buffers on it, we will
    generate extra implicit parameter/buffer arguments and assign ParamAOTInput
    and BufferAOTInput descriptors to them.  However, if you generate the input
    nn.Module from a mechanism like Dynamo, you will NOT get these descriptors
    (because Dynamo will already have taken care of lifting the parameters/buffers
    into arguments!)  In that case, it would be necessary to analyze the Sources
    of the inputs to determine if inputs are parameters and their FQNs.
    """

    (
        functional_call,
        _params_buffers_flat,
        params_spec,
        buffers_spec,
        fake_flat_args,
        full_args_descs,
        aot_config,
        fake_mode,
        shape_env,
        in_spec,
        out_spec,
    ) = prepare_aot_module_simplified(
        mod,
        args,
        kwargs,
        # In contrast, decompositions are needed at this stage.
        decompositions,
        keep_inference_input_mutations,
        None,
        ignore_shape_env,
        flatten=True,
        # Without this, we will attempt to "compile" the backward lazily
        # at runtime, but this is pointless because it's just boxed_nop,
        # it's trivial.  But this will get Inductor confused about scoping
        # Metric(s) {'is_forward'} have already been set in the current
        # context.
        force_non_lazy_backward_lowering=True,
        disable_functionalization=disable_functionalization,
        _record_nn_module_stack=_record_nn_module_stack,
        _disable_torch_fn_metadata_mode=_disable_torch_fn_metadata_mode,
    )

    # TODO: Maybe this should be in create_aot_state?  Not sure, that would
    # increase its scope
    stack.enter_context(compiled_autograd._disable())

    aot_state = create_aot_state(
        stack,
        functional_call,
        fake_flat_args,
        full_args_descs,
        aot_config,
        fake_mode,
        shape_env,
    )

    # NB: no cache lookup!
    aot_graph_capture = aot_stage1_graph_capture(aot_state, functional_call)

    if out_spec is None or out_spec.spec is None:
        raise AssertionError("out_spec and out_spec.spec must not be None")
    if in_spec is None:
        raise AssertionError("in_spec must not be None")
    return JointWithDescriptors(
        _aot_state=aot_state,
        _aot_graph_capture=aot_graph_capture,
        params_spec=params_spec,
        buffers_spec=buffers_spec,
        in_spec=in_spec,
        out_spec=out_spec.spec,
    )


def aot_compile_joint_with_descriptors(
    jd: JointWithDescriptors,
    *,
    partition_fn: Callable[..., Any] = default_partition,
    fw_compiler: AOTDispatchCompiler = boxed_nop_preserve_node_meta,
    bw_compiler: AOTDispatchCompiler | None = boxed_nop_preserve_node_meta,
    serializable: bool = False,
) -> Callable[..., Any]:
    """
    Companion function for aot_export_joint_with_descriptors which compiles the joint
    graph into a callable function that follows a standard calling convention.
    params_flat all are arguments.

    Note: We do NOT instantiate the module; this gives you the flexibility to subclass it and
    customize its behavior without having to worry about FQN rebinding.

    Args:
        serializable: If True, configures the compilation to produce a serializable
            callable by leveraging AOTAutogradCache machinery. This sets up the
            necessary cache_info and patches config options required for serialization.
            When True, this function will always return a BundledAOTAutogradSerializableCallable.
    """
    # TODO: Consider if we should allow_in_graph the result by default.
    from torch._dynamo.aot_compile_types import (
        BundledAOTAutogradSerializableCallable,
        SerializableCallable,
    )
    from torch._functorch._aot_autograd.schemas import AOTAutogradCacheInfo
    from torch._guards import detect_fake_mode
    from torch._inductor.output_code import OutputCode

    fw_compiler = SerializableAOTDispatchCompiler(OutputCode, fw_compiler)

    cache_ctx = nullcontext()
    if serializable:
        jd._aot_state.aot_config.cache_info = AOTAutogradCacheInfo(
            jd.cache_hash(),
            time.time_ns(),
            forward_symints=[],
        )
        cache_ctx = torch._functorch.config.patch(
            {
                "strict_autograd_cache": True,
                "bundled_autograd_cache": True,
                "force_non_lazy_backward_lowering": True,
                "bypass_autograd_cache_key": True,
            }
        )

    # Set up a TracingContext if one isn't already available.
    # This is needed for compilers (like regional_inductor) that call
    # standalone_compile with dynamic_shapes="from_tracing_context".
    tracing_context = torch._guards.TracingContext.try_get()
    if tracing_context is None:
        fake_mode = detect_fake_mode(jd._aot_state.flat_args)
        if fake_mode is not None:
            tracing_context = torch._guards.TracingContext(fake_mode)
            tracing_ctx = torch._guards.tracing(tracing_context)
        else:
            tracing_ctx = nullcontext()
    else:
        tracing_ctx = nullcontext()

    with cache_ctx, tracing_ctx:
        compiled_fn, _ = aot_stage2_compile(
            jd._aot_state,
            jd._aot_graph_capture,
            partition_fn,
            fw_compiler,
            bw_compiler,
        )

    if not isinstance(compiled_fn, SerializableCallable) and hasattr(
        compiled_fn, "serialize"
    ):
        compiled_fn = BundledAOTAutogradSerializableCallable(compiled_fn)

    # Cribbed from torch/export/pt2_archive/_package.py
    @simple_wraps(compiled_fn)
    @torch._dynamo.nonstrict_trace  # allow recursive compilation
    def unflattened_compiled_fn(*args: Any, **kwargs: Any) -> Any:
        flat_inputs = pytree.tree_flatten((args, reorder_kwargs(kwargs, jd.in_spec)))[0]
        # TODO: do I need to filter? I hope not!
        flat_outputs = compiled_fn(flat_inputs)
        return pytree.tree_unflatten(flat_outputs, jd.out_spec)

    return unflattened_compiled_fn


def aot_export_module(
    mod: nn.Module,
    args: Iterable[Any],
    *,
    decompositions: dict[OpOverload, Callable[..., Any]] | None = None,
    # If true, we'll return a joint forward-backward graph,
    # As well as metadata on the loss + gradients in the backward.
    trace_joint: bool,
    # If trace_joint is True, we expect your module to return a scalar loss.
    # Your module can return multiple outputs, so you must specify which output the loss is.
    output_loss_index: int | None = None,
    pre_dispatch: bool = False,
    # If None, will be inferred from inputs and mod.graph.nodes if mod is a graph module, but the inferred result might be wrong.
    dynamic_shapes: bool | None = None,
    kwargs: dict[str, Any] | None = None,
) -> tuple[Callable[..., Any] | torch.fx.GraphModule, GraphSignature]:
    """
    This function takes in a module, and returns:
    (1) an FX graph that can be exported
    (2) some metadata about the graph

    If `trace_joint=True` we will return a joint graph of the forward + backward.

    The traced FX graph will have the following properties compared to the original module:
    (1) Inputs and outputs to the module will be pytree-flattened
    (2) Parameters and buffers on the module will be lifted into graph inputs,
        graph_inputs = (*parameters, *buffers, *user_inputs)
    (3) The graph will be fully functionalized
    (4) Any input mutations will be converted into additional outputs in the graph,
        meaning whoever calls this graph is responsible for applying the mutations
        back to the original inputs.
    (5) If is_joint is provided the graph will return parameter gradients in addition to user outputs.
        The graph output will look like:
        graph_outputs = (*updated_inputs, *user_outputs, *param_gradients)

    There are also several restrictions on what modules can use this API. In particular:
    (1) If trace_joint is specified, we expect the loss function to be **fused**
        into the module forward. One of the outputs to the forward must be a scalar loss,
        which is specified with `output_loss_index`.
        All other outputs to the forward are presumed to not require gradients.
    (2) This API cannot capture optimizers (although in theory we could build an API for this).
    (3) Metadata mutations on params/buffers/inputs are banned.
    (4) Data mutations on anything that requires gradients are banned (parameters)
    (5) If an input is mutated, it is not allowed to alias any other inputs.
    (6) Parameters must not be duplicated.
    """
    if pre_dispatch and trace_joint:
        raise RuntimeError("pre_dispatch is not supported when trace_joint is True.")
    named_parameters = dict(mod.named_parameters(remove_duplicate=False))
    named_buffers = dict(mod.named_buffers(remove_duplicate=False))

    params_and_buffers = {
        **dict(named_parameters),
        **dict(named_buffers),
    }
    params_and_buffers_flat, params_spec = pytree.tree_flatten(params_and_buffers)
    params_and_buffers_flat = tuple(params_and_buffers_flat)
    params_len = len(params_and_buffers_flat)

    kwargs = kwargs or {}

    functional_call = create_functional_call(
        mod, params_spec, params_len, store_orig_mod=True
    )

    num_fw_outs = None

    if trace_joint:
        # This helper effectively just adds some extra asserts about what the backward will look like:
        # Outputs must include a scalar loss, that we compute gradients w.r.t.
        # We don't compute gradients w.r.t. anything else: so just in case we detach()
        # and other output tensors.
        def fn_to_trace(*args: Any) -> Any:
            nonlocal num_fw_outs
            out = functional_call(*args)
            if output_loss_index is None:
                raise RuntimeError(
                    """\
If trace_joint=Trueit is required that one of your forward outputs must be a scalar loss.
You must specify the which (index) output is the loss with output_loss_index."""
                )
            if isinstance(out, (torch.Tensor)):
                out = (out,)
            if not isinstance(out, (tuple, list)):
                raise RuntimeError(
                    f"Expected forward output to be either a tensor or a list/tuple of tensors. found {type(out)}"
                )

            for i, o in enumerate(out):
                # We only want to create a backward graph w.r.t. the loss that the user passed in.
                # This implies that every other output should not require gradients.
                # Instead of making this an error (and forcing the user to detach all other outputs
                # of their forward),
                # we'll automatically detach them here.
                if o.requires_grad and i != output_loss_index:
                    raise RuntimeError(
                        f"""\
Found an output of the forward that requires gradients, that was not the scalar loss.
We require all outputs to the forward that are not the scalar loss to not require gradient,
because we will only compute a backward graph against the scalar loss.
You can fix this by calling .detach() on each of your forward outputs that is not the loss.
You specified that output index {output_loss_index} is the loss, but we found that
the output at index {i} requires gradients."""
                    )
            out_loss = out[output_loss_index]
            num_fw_outs = len(out)
            if not out_loss.requires_grad:
                raise RuntimeError(
                    f"""\
The output at index {output_loss_index} was marked as the loss, but it does not require gradients"""
                )
            if out_loss.numel() != 1:
                raise RuntimeError(
                    f"""\
We require the output marked as the loss (at index {output_loss_index}) to be a scalar, but it has shape {out_loss.shape}"""
                )
            return out

        ctx = nullcontext
    else:
        # Run under no_grad, so our tracing machinery only traces an inference graph.
        # However if pre_dispatch=True, we want to correctly trace set_grad_enabled calls for training.
        ctx = nullcontext if pre_dispatch else torch.no_grad
        fn_to_trace = functional_call

    full_args = []
    # First, the params
    # NB: It is REQUIRED that parameters come first, Inductor infers "fixed"
    # parameters by looking at the difference in parameter count outside
    # and inside AOTAutograd, and assumes the prefix of arguments are fixed
    # arguments
    full_args.extend(params_and_buffers_flat)
    # Next, the input args
    full_args.extend(args)

    with ctx():
        fx_g, metadata, in_spec, out_spec = _aot_export_function(
            fn_to_trace,
            tuple(full_args),
            decompositions=decompositions,
            num_params_buffers=params_len,
            no_tangents=True,
            pre_dispatch=pre_dispatch,
            dynamic_shapes=dynamic_shapes,
            trace_joint=trace_joint,
            kwargs=kwargs,
        )

    # TODO: subsume this path with the aot_stage2_graph_capture path
    if trace_joint:

        @wraps(functional_call)
        def flattened_joint(*args: Any) -> Any:
            # The idea here is that the joint graph that AOTAutograd creates has some strict properties:
            # (1) It accepts two arguments (primals, tangents), and pytree_flattens them
            # (2) It returns a tuple of (fw_outs, gradients)
            # This is a very useful convention for anyone who wants to partition the joint graph
            # into a separate forward and backward graph.
            # However,
            # (1) for people exporting a single joint graph, it would be preferable not to have
            #     any pytrees in the graph.
            # (2) We are guaranteed in the aot_export_module case that the forward outputs a loss,
            #     and there are therefore no tangents that are needed to run the joint graph.
            # (3) AOTAutograd creates a grad_input for every input in the forward,
            #     including None's for inputs that are not grad-requiring tensors.
            #     we don't want these in our export graph.
            #     and there are therefore no tangents that are needed to run the joint graph.
            # This function "fixes" both of the above by removing any tangent inputs,
            # and removing pytrees from the original FX graph.
            fake_tangents = [
                None
                for _ in range(
                    metadata.num_outputs + metadata.num_mutated_inp_runtime_indices
                )
            ]
            fw_outs, gradients = fx_g(args, fake_tangents)
            if len(gradients) != len(args):
                raise AssertionError(
                    f"len(gradients)={len(gradients)} != len(args)={len(args)}"
                )
            output_gradients = []
            for a, grad in zip(args, gradients):
                if isinstance(a, torch.Tensor) and a.requires_grad:
                    if grad is None:
                        raise AssertionError("""\
Found a parameter that did not receive a gradient.
"This is most likely a bug, but if this needs to be supported please comment on this Github issue:
https://github.com/pytorch/pytorch/issues/101192
""")
                    output_gradients.append(grad)
                else:
                    if grad is not None:
                        raise AssertionError(
                            f"expected grad to be None for non-tensor or non-requires_grad input, got {type(grad)}"
                        )
            return *fw_outs, *output_gradients

        fx_g = make_fx(flattened_joint, record_module_stack=True)(*full_args)

    user_args_flat = pytree.arg_tree_leaves(*args, **kwargs)
    if out_spec is None:
        raise AssertionError("out_spec must not be None")
    return fx_g, create_graph_signature(
        # type: ignore[bad-argument-type]
        fx_g,
        metadata,
        in_spec,
        out_spec,
        user_args_flat=user_args_flat,
        params_and_buffers_flat=list(params_and_buffers_flat),
        param_names=list(named_parameters.keys()),
        buffer_names=list(named_buffers.keys()),
        trace_joint=trace_joint,
        num_user_fw_outs=num_fw_outs,
        loss_index=output_loss_index,
    )


def aot_export_joint_simple(
    func: Callable[..., Any],
    args: tuple[Any, ...],
    *,
    trace_joint: bool,
    # It looks like the main consequence of this API is that for dynamic shapes,
    # it will assume that params/buffers are static.
    # With the new inferred dynamic shapes API, maybe this doesn't matter?
    num_params_buffers: int = 0,
    decompositions: dict[OpOverload, Callable[..., Any]] | None = None,
) -> Callable[..., Any]:
    """
    A simplified version of export. Used by higher order operators.

    This function makes a high-level "no calling convention changes" guarantee:
    - If no inputs require grad (so we export an inference graph),
      there are *no* calling convention change between the exported graph, and "func".
    - If at least one input requires grad (so we trace out and export a joint fw-bw graph),
      Then if you were partition the graph into a separate forward and backward graph,
      The forward graph will have no calling convention changes compared to "func".

    The above also relies on some strong restrictions around which functions this API accepts:
    (1) `args` cannot contain any pytrees (they must have been pytree_flattened already)
    (2) `func` cannot mutate any inputs
    (3) The outputs of `func` cannot alias any inputs.

    Note: this function is only lightly tested today. It will probably be tested more heavily by higher order ops.
    """
    if trace_joint:
        ctx = nullcontext
    else:
        # Run under no_grad, so our tracing machinery only traces an inference graph.
        ctx = torch.no_grad

    with ctx():
        fx_g, metadata, in_spec, out_spec = _aot_export_function(
            func,
            args,
            decompositions=decompositions,
            trace_joint=trace_joint,
        )
        in_spec, _kw_in_spec = in_spec.children()
    # At this point, we can just directly return the (joint or inference graph) that we traced.
    # First though: a bunch of assertions to make sure that our graph doesn't require
    # any calling convention changes compared to the original function.
    # These restrictions are *in addition to* the general restrictions on export.

    # No input mutations
    if (
        len([x for x in metadata.input_info if x.mutates_data or x.mutates_metadata])
        != 0
    ):
        raise RuntimeError(
            f"aot_export_joint_simple does not support input mutations. {str(metadata)}"
        )
    # No output aliasing
    if (
        len([x for x in metadata.output_info if x.output_type != OutputType.non_alias])
        != 0
    ):
        raise RuntimeError(
            f"aot_export_joint_simple does not support outputs that alias inputs. {str(metadata)}"
        )
    # No pytrees
    if in_spec.is_leaf():
        raise RuntimeError(
            f"aot_export_joint_simple requires inputs to be a single list/tuple. in_spec={str(in_spec)}"
        )
    if not all(child.is_leaf() for child in in_spec.children()):
        raise RuntimeError(
            f"aot_export_joint_simple requires individual inputs not to be pytrees. in_spec={str(in_spec)}"
        )

    if out_spec is None:
        raise AssertionError("out_spec must not be None")
    if out_spec.is_leaf():
        raise RuntimeError(
            f"aot_export_joint_simple requires outputs to be a single list/tuple. out_spec={str(out_spec)}"
        )
    if not all(child.is_leaf() for child in out_spec.children()):
        raise RuntimeError(
            f"aot_export_joint_simple requires individual outputs not to be pytrees. out_spec={str(out_spec)}"
        )
    # TODO: we might have to temporarily patch config.functionalize_rng
    # so that it doesn't run when we're exporting a higher order op.

    if config.debug_assert:
        # Smoke test that after partitioning, we can run the forward without any calling convention changes.
        fw_module, _bw_module = default_partition(  # noqa: F821
            # type: ignore[bad-argument-type]
            fx_g,
            args,
            # type: ignore[unknown-name]
            num_fwd_outputs=len(fw_metadata.output_infos),  # noqa: F821
        )
        # Attempt to run the fw_module with the original user inputs
        fake_mode = detect_fake_mode(args)
        if fake_mode is None:
            fake_mode = FakeTensorMode()
        with fake_mode:
            fw_module(*args)
    return fx_g


# Private for now because we aren't providing a contract on what to return
# for joint graphs (we could when there's a clearer use case)
# In the future, we may need to add more export API's that provide their own strong guarantees.
# This is meant as a general helper function for handling various export-y use cases.
def _aot_export_function(
    func: Callable[..., Any],
    args: tuple[Any, ...],
    *,
    num_params_buffers: int = 0,
    decompositions: dict[OpOverload, Callable[..., Any]] | None = None,
    # If we're exporting a joint graph and we don't want any tangent inputs in the graph
    # (because we are backpropping through a scalar 1 loss),
    # we need to explicitly specify not to include tangents in the graph.
    # It's not enough just to check that our tangent is a scalar, since we also
    # need to know if it is a 1 (no need to make it a graph input), or something else
    # (requiring it to be a graph input).
    # We don't know this info at trace time though, so we need to make it an explicit config.
    no_tangents: bool = False,
    pre_dispatch: bool = False,
    # If None, `dynamic_shapes` will be inferred from inputs, but the inferred result might be wrong.
    dynamic_shapes: bool | None = None,
    keep_input_mutations: bool = False,
    # Under export, configures whether we are getting inference or training IR
    trace_joint: bool = False,
    kwargs: dict[str, Any] | None = None,
) -> tuple[
    Callable[..., Any], ViewAndMutationMeta, pytree.TreeSpec, pytree.TreeSpec | None
]:
    kwargs = kwargs or {}

    flat_fn, out_spec = create_tree_flattened_fn(func, args, kwargs)
    flat_args, in_spec = pytree.tree_flatten((args, kwargs))

    fake_mode = None
    if dynamic_shapes is None:
        # Try to infer `dynamic_shapes from inputs and graph nodes
        fake_mode = detect_fake_mode(flat_args)
        if (
            fake_mode is None
            and hasattr(func, "_orig_mod")
            and isinstance(func._orig_mod, torch.fx.GraphModule)
        ):
            vals = [
                node.meta["val"]
                for node in func._orig_mod.graph.nodes
                if "val" in node.meta
            ]
            fake_mode = detect_fake_mode(vals)
        dynamic_shapes = fake_mode is not None and fake_mode.shape_env is not None

    # The export use case doesn't care about several bits of AOTConfig
    # (1) compilers (we just export the graph)
    # (2) partitioners (export is only full graph, user can partition themselves)
    aot_config = AOTConfig(  # type: ignore[arg-type]
        fw_compiler=None,
        bw_compiler=None,
        inference_compiler=None,
        partition_fn=None,
        decompositions=decompositions,
        num_params_buffers=num_params_buffers,
        aot_id=next(AOT_COUNTER),
        # For now there's no use case involving keeping input mutations in the graph
        # (which we can only do in the inference case anyway).
        # We can add this later if we need to.
        keep_inference_input_mutations=keep_input_mutations,
        dynamic_shapes=dynamic_shapes,
        aot_autograd_arg_pos_to_source=None,
        is_export=True,
        no_tangents=no_tangents,
        pre_dispatch=pre_dispatch,
        export_trace_joint=trace_joint,
    )
    if fake_mode is None:
        fake_mode, shape_env = construct_fake_mode(flat_args, aot_config)
    else:
        shape_env = fake_mode.shape_env
    fake_flat_args = process_inputs(flat_args, aot_config, fake_mode, shape_env)
    # TODO: Improve the descs here with pytree information
    fake_flat_args_descs: list[AOTInput] = [
        PlainAOTInput(i) for i in range(len(fake_flat_args))
    ]

    with contextlib.ExitStack() as stack:
        aot_state = create_aot_state(
            stack,
            flat_fn,
            fake_flat_args,
            fake_flat_args_descs,
            aot_config,
            fake_mode,
            shape_env,
        )
        aot_graph_capture = aot_stage1_graph_capture(aot_state, flat_fn)
        fx_g, meta = aot_stage2_export(aot_state, aot_graph_capture)

    return fx_g, meta, in_spec, out_spec.spec


compiled_function = aot_function
compiled_module = aot_module
