pytorch/torch/_inductor/output_code.py

"""
This provides an abstract class which parametrizes over an "output code" concept
for Inductor.  Intuitively, this represents the compiled callable which Inductor
produces which you can call to get optimized code.  However, this callable
has some other capabilities:

- It is serializable, so you can save/load this product from disk without
  having to do compilation again.

- (When using remote cache) it is addressable, so you can save just a key
  which you can use to load this product from remote cache later.

This class is abstract because we have several different implementations of
serialized format:

- Python wrapper (the default)

- AOTInductor (this produces ABI stable binaries which work across PyTorch
  versions)

"""

from __future__ import annotations

import dataclasses
import logging
import os
import re
from functools import partial
from pathlib import Path
from typing import Any, Callable, Optional, TYPE_CHECKING, Union
from typing_extensions import TypeAlias

import torch
from torch._dynamo.utils import counters, get_runtime_metrics_context
from torch._inductor.cudagraph_utils import (
    BoxedDeviceIndex,
    CudagraphCachedInfo,
    CudagraphMetadata,
    get_partition_cudagraph_metadata,
    get_placeholder_info,
    log_cudagraph_skip_and_bump_counter,
)
from torch._inductor.freezing_utils import has_frozen_params, is_frozen_param
from torch._inductor.utils import (
    align_inputs_from_check_idxs,
    BoxedBool,
    GraphPartitionMap,
    InputType,
    output_node,
    set_tracing_context_output_strides,
)
from torch.utils._ordered_set import OrderedSet

from . import config
from .runtime.autotune_cache import AutotuneCacheBundler


if TYPE_CHECKING:
    from collections import Counter
    from collections.abc import Sequence

    from torch._inductor import metrics
    from torch._inductor.graph import GraphLowering
    from torch._library.fake_class_registry import FakeScriptObject

    from .compile_fx import _CompileFxKwargs
    from .triton_bundler import TritonBundle

log = logging.getLogger(__name__)


@dataclasses.dataclass
class OutputCode:
    # TODO: Remove underscores here

    # None if the output is not remote cacheable
    _fx_graph_cache_key: Optional[str] = dataclasses.field(default=None, init=False)

    # How long it took to compile this OutputCode, end to end
    _time_taken_ns: Optional[int] = dataclasses.field(default=None, init=False)

    def __call__(self, inputs: Sequence[Any]) -> Any:
        raise NotImplementedError(type(self))

    def post_compile(
        self,
        example_inputs: Sequence[InputType],
        constants: CompiledFxGraphConstants,
        graph_kwargs: _CompileFxKwargs,
    ) -> None:
        raise NotImplementedError(type(self))

    # TODO: Get rid of this
    def set_triton_bundle(self, triton_bundle: Any) -> None:
        raise NotImplementedError(type(self))


_StrideExprStr: TypeAlias = str


# copy_ fails when trying to write to tensors with memory overlap,
# for expanded dimensions (a dimension which used to have size 1 -> ?)
# we can select one element from that dimension and write to it
# to achieve writing to all values of that dimension of the input tensor
def get_expanded_dims(t: torch.Tensor) -> list[int]:
    if not isinstance(t, torch.Tensor):
        return None
    return [i for i in range(t.ndim) if t.stride(i) == 0 and t.size(i) != 1]


def index_expanded_dims(t: torch.Tensor, expanded_dims: list[int]) -> torch.Tensor:
    for expanded_dim in expanded_dims:
        t = torch.ops.aten.slice(t, expanded_dim, 0, 1)
    return t


def complex_memory_overlap(t: torch.Tensor) -> bool:
    if config.always_complex_memory_overlap_TESTING_ONLY:
        return True

    # if torch._debug_has_internal_overlap thinks this tensor potentially has
    # memory overlap internally, let's dig deeper to find out whether it's true.
    #
    # Call squeeze() so that dimension with size 1 does not cause false positive.
    t = index_expanded_dims(t, get_expanded_dims(t)).squeeze()
    if torch._debug_has_internal_overlap(t) != 0:
        strides = t.stride()
        sizes = t.shape
        indices = list(range(len(strides)))
        indices = [x for _, x in sorted(zip(strides, indices))]
        for i in range(len(strides)):
            prev_stride = 1 if i == 0 else strides[indices[i - 1]]
            prev_size = 1 if i == 0 else sizes[indices[i - 1]]
            if strides[indices[i]] < prev_stride * prev_size:
                return True
    return False


def maybe_handle_backward_generation(
    compiled_graph: CompiledFxGraph,
    boxed_forward_device_index: Optional[BoxedDeviceIndex],
) -> None:
    assert compiled_graph.current_callable is not None
    is_backward = compiled_graph.fx_kwargs["is_backward"]

    # See [Backward Generation Handling]
    # if cudagraph'd the forward and set the device, we need to let the cudagraph manager
    # know we are we running the backward even if we will not run it in cudagraphs
    if is_backward and config.triton.cudagraph_trees:
        assert boxed_forward_device_index is not None
        assert boxed_forward_device_index.value is not None
        compiled_graph_callable = compiled_graph.current_callable

        manager = torch._inductor.cudagraph_trees.get_manager(
            boxed_forward_device_index.value, create_if_none_exists=False
        )
        # should already exist from forward
        assert manager is not None

        def compiled_artifact(new_inputs: list[Any]) -> Callable[..., Any]:
            manager.set_to_running_backward()  # type: ignore[union-attr]
            return compiled_graph_callable(new_inputs)

        compiled_graph.current_callable = compiled_artifact


def prepare_cudagraph_post_compile(
    compiled_graph: CompiledFxGraph,
    example_inputs: Sequence[InputType],
    boxed_forward_device_index: Optional[BoxedDeviceIndex],
) -> None:
    if not config.triton.cudagraph_trees:
        # Force specialize all inputs so that CUDA graphs will work
        for t in example_inputs:
            if isinstance(t, torch.SymInt):
                int(t)  # guard

    is_inference = compiled_graph.fx_kwargs["is_inference"]
    is_backward = compiled_graph.fx_kwargs["is_backward"]
    if boxed_forward_device_index is not None and not is_inference and not is_backward:
        boxed_forward_device_index.set(next(iter(compiled_graph.device_idxs)))


def cudagraph_post_compile(
    example_inputs: Sequence[InputType],
    compiled_graph: CompiledFxGraph,
    cudagraphs: BoxedBool,
    constants: dict[str, torch.Tensor],
    boxed_forward_device_index: Optional[BoxedDeviceIndex],
) -> None:
    """
    Checks for any reasons not to run cudagraphs and then
    runs it on compiled_graph.
    Mutates the `compiled_graph.current_callable` and `cudagraphs`
    """
    assert compiled_graph.current_callable is not None
    assert compiled_graph.cudagraph_info is not None
    cached_info = compiled_graph.cudagraph_info
    cudagraph_fail_reasons = cached_info.cudagraph_fail_reasons
    is_inference = compiled_graph.fx_kwargs["is_inference"]
    is_backward = compiled_graph.fx_kwargs["is_backward"]

    if not cudagraph_fail_reasons:
        fx_kwargs = compiled_graph.fx_kwargs
        static_input_idxs = fx_kwargs["static_input_idxs"]

        placeholders = cached_info.placeholders
        stack_traces = cached_info.stack_traces

        prepare_cudagraph_post_compile(
            compiled_graph, example_inputs, boxed_forward_device_index
        )

        from .compile_fx import cudagraphify

        current_callable = compiled_graph.current_callable
        assert current_callable is not None
        compiled_graph.current_callable = cudagraphify(
            current_callable,
            static_input_idxs=static_input_idxs or (),
            device_index=next(iter(compiled_graph.device_idxs)),
            stack_traces=stack_traces,
            is_backward=is_backward,
            is_inference=is_inference,
            constants=tuple(constants.values()),
            placeholders=placeholders,
            mutated_input_idxs=tuple(compiled_graph.mutated_input_idxs),
        )

    else:
        BoxedBool.disable(cudagraphs)
        maybe_handle_backward_generation(compiled_graph, boxed_forward_device_index)

        if "cuda" in compiled_graph.device_types:
            # prefer better disable_cudagraphs_reason bc stack trace
            # TODO: migrate all disable reasons to stack trace, refactor
            if compiled_graph.disabled_cudagraphs_reason:
                log_cudagraph_skip_and_bump_counter(
                    compiled_graph.disabled_cudagraphs_reason
                )
            else:
                log_cudagraph_skip_and_bump_counter(
                    f"skipping cudagraphs due to {cudagraph_fail_reasons}"
                )


def cudagraph_partition_post_compile(
    example_inputs: Sequence[InputType],
    compiled_graph: CompiledFxGraph,
    cudagraphs: BoxedBool,
    constants: dict[str, torch.Tensor],
    boxed_forward_device_index: Optional[BoxedDeviceIndex],
) -> None:
    """
    Cudagraphify each partition functions, which first prepares the necessary
    metadata and then applies the cudagraphify function to each partition.

    Assuming all partition functions are cudagraphified and share the same order
    as `compiled_graph.partition_maps`. See [Note: Graph Partition Map for CUDAGraph].
    """
    assert compiled_graph.cudagraph_info is not None
    cudagraph_fail_reasons = compiled_graph.cudagraph_info.cudagraph_fail_reasons

    if (
        cudagraph_fail_reasons
        or compiled_graph.partition_maps is None
        or len(compiled_graph.partition_maps) == 0
    ):
        # cudagraphify is not called if there are no partitions
        BoxedBool.disable(cudagraphs)
        maybe_handle_backward_generation(compiled_graph, boxed_forward_device_index)
        return

    from .compile_fx import cudagraphify

    assert compiled_graph.current_callable is not None
    assert compiled_graph.recursively_apply_fns is not None
    is_inference = compiled_graph.fx_kwargs["is_inference"]
    is_backward = compiled_graph.fx_kwargs["is_backward"]
    static_input_idxs = OrderedSet(compiled_graph.fx_kwargs["static_input_idxs"] or ())
    mutated_input_idxs = compiled_graph.mutated_input_idxs
    device_index = next(iter(compiled_graph.device_idxs))

    graph_metadata = CudagraphMetadata(
        compiled_graph.cudagraph_info.placeholders,
        static_input_idxs,
        mutated_input_idxs,
        compiled_graph.cudagraph_info.stack_traces,
        constants,
    )

    prepare_cudagraph_post_compile(
        compiled_graph, example_inputs, boxed_forward_device_index
    )

    # cudagraphify each partition function, assuming every graph partition function
    # is cudagraphable. Non-cudagraphable ops (e.g., cpu ops) are inlined into
    # `call` function and not included in partition functions.
    cudagraphify_fns = []
    for partition_map in compiled_graph.partition_maps:
        partition_metadata = get_partition_cudagraph_metadata(
            partition_map,
            graph_metadata,
        )

        cudagraphify_fn = partial(
            cudagraphify,
            static_input_idxs=tuple(partition_metadata.static_input_idxs),
            device_index=device_index,
            stack_traces=partition_metadata.stack_traces,
            is_backward=is_backward,
            is_inference=is_inference,
            constants=tuple(partition_metadata.constants.values()),
            placeholders=partition_metadata.placeholders,
            mutated_input_idxs=tuple(partition_metadata.mutated_input_idxs),
        )
        cudagraphify_fns.append(cudagraphify_fn)

    compiled_graph.recursively_apply_fns(cudagraphify_fns)


def maybe_realign_inputs(
    ran_cudagraphs: BoxedBool,
    compiled_graph: CompiledFxGraph,
    inputs_to_check: Sequence[int],
) -> None:
    """
    Realigns input strides from inputs_to_check if
    we didn't end up running cudagraphs. Mutates
    `compiled_graph.current_callable` if cudagraphs
    was run. Otherwise, does nothing.
    """
    if not ran_cudagraphs:
        assert compiled_graph.current_callable is not None
        new_callable = align_inputs_from_check_idxs(
            compiled_graph.current_callable, inputs_to_check
        )
        if new_callable is not compiled_graph.current_callable:
            compiled_graph.current_callable = new_callable


class CompiledFxGraphConstants:
    """Wrapper class that unwraps constants from a compiled fx graph. This
    version of the class only supports directly grabbing the saved constants off of
    a CompiledFxGraph.

    With freezing, FxGraphCache doesn't store the constants of the input
    GraphModule it gets from AOTAutograd. Instead, it saves just the **names**
    of those constants, and grabs the constant values directly from the graph module
    passed in at runtime.

    Thing is, we don't always *have* the graph module available at runtime, hence
    the existence of this class and its CompiledFxGraphConstantsWithGm counterpart.

    To support freezing, FXGraphCache gets passed a CompiledFxGraphConstantsWithGm during
    post compile. Otherwise, CompiledFxGraphConstants supports the basic case of loading
    the value of constants directly off of the original saved object.
    """

    def unwrap(self, g: CompiledFxGraph) -> dict[str, torch.Tensor]:
        assert g.constants is not None
        return g.constants


class CompiledFxGraphConstantsWithGm(CompiledFxGraphConstants):
    """
    This version of CompiledFxGraphConstants, instead of grabbing constants
    directly saved on CompiledFxGraphs, will just grab their names. Then, it takes
    a second GraphModule to grab the corresponding constant values out of.

    This is necessary for supporting freezing in FxGraphCache.
    """

    def __init__(self, gm: torch.fx.GraphModule) -> None:
        self.gm = gm

    def unwrap(self, g: CompiledFxGraph) -> dict[str, torch.Tensor]:
        frozen_params = {
            name: getattr(self.gm, orig_name)
            for name, orig_name in g.frozen_param_names.items()
        }
        constants = g.constants or {}
        return {**constants, **frozen_params}


@dataclasses.dataclass
class CompiledFxGraph(OutputCode):
    """
    Class holding a compiled FX graph. This is the object serialized on disk
    to support FxGraph caching.
    """

    current_callable: Optional[Callable[..., Any]]
    recursively_apply_fns: Optional[Callable[..., Any]]
    cache_key: str
    source_code: str = dataclasses.field(repr=False)  # Do not display source_code
    cache_linemap: Optional[list[tuple[int, str]]]
    device_types: OrderedSet[str]
    device_idxs: OrderedSet[int]
    mutated_inputs: OrderedSet[str]
    mutated_input_idxs: OrderedSet[int]
    constants: Optional[dict[str, torch.Tensor]]
    frozen_param_names: dict[str, str]
    torchbind_constants: dict[str, torch._C.ScriptObject | FakeScriptObject]
    output_strides: Optional[list[Optional[tuple[_StrideExprStr, ...]]]]
    disabled_cudagraphs_reason: Optional[str]
    metrics_deltas: metrics.CachedMetricsDeltas
    counter_deltas: Counter[str]
    # This is a string representation of an expression we serialize
    # with the object so the guards can be evaluated in a different
    # context in order to verify the validity of serving a cached
    # fx graph. The expression must be generated by:
    # ShapeEnv.produce_guards_expression()
    guards_expr: Optional[str]

    cudagraph_info: Optional[CudagraphCachedInfo]
    partition_maps: Optional[list[GraphPartitionMap]]
    fx_kwargs: _CompileFxKwargs
    inputs_to_check: Sequence[int]

    _boxed_call: Optional[bool] = None
    _triton_bundle: Optional[TritonBundle] = None

    def __init__(
        self,
        current_callable: Optional[Callable[..., Any]],
        graph: GraphLowering,
        gm: torch.fx.GraphModule,
        output_strides: list[Optional[tuple[_StrideExprStr, ...]]],
        disabled_cudagraphs_reason: Optional[str],
        metrics_deltas: metrics.CachedMetricsDeltas,
        counter_deltas: Counter[str],
        cudagraphs: BoxedBool,
        example_inputs: Sequence[InputType],
        static_input_idxs: Sequence[int],
        fx_kwargs: _CompileFxKwargs,
        inputs_to_check: Sequence[int],
        recursively_apply_fns: Optional[Callable[..., Any]] = None,
    ) -> None:
        self.current_callable = current_callable
        self.recursively_apply_fns = recursively_apply_fns
        self.cache_key = graph.cache_key
        if graph.cache_path:
            with open(graph.cache_path) as f:
                self.source_code = f.read()
        self.cache_linemap = graph.cache_linemap
        # TODO - ordered set
        self.device_types = OrderedSet(graph.device_types)
        self.device_idxs = OrderedSet(graph.device_idxs)
        self.mutated_inputs = OrderedSet(graph.mutated_inputs)
        self.mutated_input_idxs = OrderedSet(graph.mutated_input_idxs)

        # We store the constant attributes in the cache entry and re-attach them
        # to the module created in PyCodeCache.load_by_key_path. In the case that
        # the graph has frozen parameters, we save the mapping from the attribute
        # names in the GraphLowering to the original name of the attribute in the
        # GraphModule. When we create the module from the cache entry, we then
        # look up the constants from the current GraphModule. This scheme allows
        # us to support caching with freezing.
        if not has_frozen_params(gm):
            self.constants = graph.constants
            self.frozen_param_names = {}
        else:
            self.constants = {}
            self.frozen_param_names = {}
            for k, v in graph.constants.items():
                if is_frozen_param(v):
                    self.frozen_param_names[k] = graph.allocated_constant_name[k]
                else:
                    self.constants[k] = v

        self.torchbind_constants = graph.torchbind_constants
        self.output_strides = output_strides
        self.disabled_cudagraphs_reason = disabled_cudagraphs_reason
        self.metrics_deltas = metrics_deltas
        self.counter_deltas = counter_deltas
        self.guards_expr = None
        self.cudagraph_info = None
        self.partition_maps = graph.partition_maps
        self.fx_kwargs = {}
        self.inputs_to_check = ()

        cudagraph_info = None
        if cudagraphs:
            # check cudagraph disabling reasons from inductor lowering
            if self.disabled_cudagraphs_reason:
                if "cuda" in self.device_types:
                    log_cudagraph_skip_and_bump_counter(
                        f"skipping cudagraphs due to {self.disabled_cudagraphs_reason}"
                    )
                else:
                    counters["inductor"]["cudagraph_skips"] += 1
                BoxedBool.disable(cudagraphs)
            else:
                complex_memory_overlap_inputs = any(
                    complex_memory_overlap(t)
                    for t in example_inputs
                    if isinstance(t, torch.Tensor)
                )

                if not config.triton.cudagraph_support_input_mutation:
                    # Skip supports for cudagraph-managed tensors
                    from torch._inductor.cudagraph_utils import (
                        check_for_mutation_ignore_cuda_graph_managed_tensor,
                    )

                    has_mutation_str = (
                        check_for_mutation_ignore_cuda_graph_managed_tensor(
                            gm,
                            self.mutated_inputs,
                            self.mutated_input_idxs,
                            static_input_idxs,
                        )
                    )
                    has_mutation = has_mutation_str is not None

                    if has_mutation:
                        self.disabled_cudagraphs_reason = has_mutation_str
                else:
                    # Check mutation later to support cudagraph-managed tensors
                    has_mutation = None

                cudagraph_tests = [
                    (not has_mutation, "mutated inputs"),
                    (not complex_memory_overlap_inputs, "complex memory overlap"),
                    (
                        all(
                            isinstance(t, (torch.Tensor, torch.SymInt, torch.Generator))
                            for t in example_inputs
                        ),
                        "non-Tensor inputs",
                    ),
                ]
                output = output_node(gm)
                # output args are tuple of first argument
                assert len(output.args) == 1
                stack_traces = [
                    (arg.stack_trace if isinstance(arg, torch.fx.node.Node) else None)
                    for arg in output.args[0]  # type: ignore[union-attr]
                ]
                cudagraph_fail_reasons = [s for b, s in cudagraph_tests if not b]
                placeholders = tuple(get_placeholder_info(gm.graph))
                cudagraph_info = CudagraphCachedInfo(
                    placeholders, stack_traces, cudagraph_fail_reasons
                )

        self.cudagraph_info = cudagraph_info
        self.inputs_to_check = inputs_to_check
        self.fx_kwargs = fx_kwargs

        # aot autograd needs to know to pass in inputs as a list
        self._boxed_call = True

    def __call__(self, inputs: Sequence[Any]) -> Any:
        assert self.current_callable is not None
        try:
            return self.current_callable(inputs)
        finally:
            get_runtime_metrics_context().finish()
            AutotuneCacheBundler.end_compile()

    def post_compile(
        self,
        example_inputs: Sequence[InputType],
        constants: CompiledFxGraphConstants,
        graph_kwargs: _CompileFxKwargs,
    ) -> None:
        """
        Run a set of post processing steps after loading from the cache. These involve:
         - Setting the tracing context output strides
         - Running cudagraphs if enabled
         - Realigning inputs

        This runs whether or not we have a cache hit, and always runs directly after we get a CompiledFxGraph.
        The results of this function are *not* saved in the cache itself.
        """
        set_tracing_context_output_strides(example_inputs, self)
        assert graph_kwargs["cudagraphs"] is not None
        assert graph_kwargs["is_backward"] is not None
        is_backward = graph_kwargs["is_backward"]
        cudagraphs: BoxedBool = graph_kwargs["cudagraphs"]
        if cudagraphs:
            # It's possible that cudagraphs is enabled, but was disabled
            # during a previous compilation we're loading from the cache.
            # If so, we need to disable it on this new process too.
            if self.disabled_cudagraphs_reason:
                if "cuda" in self.device_types:
                    log_cudagraph_skip_and_bump_counter(
                        f"skipping cudagraphs due to {self.disabled_cudagraphs_reason}"
                    )
                else:
                    counters["inductor"]["cudagraph_skips"] += 1
                BoxedBool.disable(cudagraphs)
            else:
                if is_backward:
                    assert "boxed_forward_device_index" in graph_kwargs
                    boxed_forward_device_index = graph_kwargs[
                        "boxed_forward_device_index"
                    ]
                else:
                    # On the forward we don't know whether or not
                    # boxed_foward_device_index is set yet
                    boxed_forward_device_index = graph_kwargs.get(
                        "boxed_forward_device_index", None
                    )

                if config.graph_partition:
                    # with graph_partition=True, we skip some cudagraph checks if it's supported
                    # with partition. So we have to use cudagraph_partition_post_compile.
                    cudagraph_partition_post_compile(
                        example_inputs,
                        self,
                        cudagraphs,
                        constants.unwrap(self),
                        boxed_forward_device_index,
                    )
                else:
                    cudagraph_post_compile(
                        example_inputs,
                        self,
                        cudagraphs,
                        constants.unwrap(self),
                        boxed_forward_device_index,
                    )
        inputs_to_check = self.inputs_to_check
        # cudagraphs could have been disabled from the earlier conditions
        # so we still need to realign inputs if that happens
        maybe_realign_inputs(
            cudagraphs,
            self,
            inputs_to_check,
        )

    def set_triton_bundle(self, triton_bundle: Any) -> None:
        self._triton_bundle = triton_bundle

    def prepare_for_serialization(self) -> None:
        # We can't really serialize callables that may be C++/Triton/etc.,
        # so we serialize their PyCodeCache disk cache location instead.
        # TODO: This could be better if we're ever able to serialize compiled
        # models to disk.
        self.current_callable = None
        self.recursively_apply_fns = None

    def after_deserialization(self, constants: CompiledFxGraphConstants) -> str:
        from torch._dynamo.utils import counters, dynamo_timed
        from torch._inductor.codecache import (
            cpp_prefix_path,
            get_path,
            PyCodeCache,
            write_atomic,
        )

        # See _save_graph(); we don't store the callable in the cache entry so
        # recreate it here from the PyCodeCache disk cache.
        artifact_path = get_path(self.cache_key, "py")[2]
        code = self.source_code
        if not os.path.exists(artifact_path):
            counters["inductor"]["fxgraph_lookup_write_file"] += 1
            Path(os.path.dirname(artifact_path)).mkdir(parents=True, exist_ok=True)
            cpp_pp = cpp_prefix_path()
            if os.path.basename(cpp_pp) in code:
                if cpp_pp in code:
                    # Great the name is correct
                    pass
                else:
                    # Old dir name is included, replace it
                    pattern = rf'#include\s*"[^"]+{os.path.basename(cpp_pp)}"'
                    code = re.sub(pattern, f'#include "{cpp_pp}"', code)
                    self.source_code = code

            write_atomic(artifact_path, code, make_dirs=True)

        try:
            with dynamo_timed(
                "PyCodeCache.load_by_key_path",
                log_pt2_compile_event=True,
            ):
                code_cache = PyCodeCache.load_by_key_path(
                    self.cache_key,
                    artifact_path,
                    self.cache_linemap,
                    constants.unwrap(self),
                )
                self.current_callable = code_cache.call
                self.recursively_apply_fns = getattr(
                    code_cache, "recursively_apply_fns", None
                )
        except OSError:
            log.error("Failed to load artifact: %s", artifact_path)
            raise

        return artifact_path


@dataclasses.dataclass
class CompiledAOTI(OutputCode):
    """
    Class holding an AOTInductor compiled so.
    """

    filename: Union[str, list[str]]

    def __call__(self, inputs: Sequence[Any]) -> Any:
        raise NotImplementedError("NYI")

    def post_compile(
        self,
        example_inputs: Sequence[InputType],
        constants: CompiledFxGraphConstants,
        graph_kwargs: _CompileFxKwargs,
    ) -> None:
        pass

    def set_triton_bundle(self, triton_bundle: Any) -> None:
        pass


@dataclasses.dataclass
class MockFXGraphCacheOutput(OutputCode):
    gm: Any = None

    def __post_init__(self) -> None:
        self._boxed_call = True

    def post_compile(
        self,
        example_inputs: Sequence[InputType],
        constants: CompiledFxGraphConstants,
        graph_kwargs: _CompileFxKwargs,
    ) -> None:
        pass

    def __call__(self, inputs: Sequence[Any]) -> Any:
        return self.gm(inputs)

    def set_triton_bundle(self, triton_bundle: Any) -> None:
        pass