from __future__ import annotations

import contextlib
import functools
import itertools
import logging
import operator
import os
import re
import sys
import time
import typing_extensions
from collections import defaultdict
from contextlib import contextmanager
from typing import Any, NoReturn, TYPE_CHECKING

import sympy
from sympy import Expr

import torch
import torch._logging
import torch.fx
from torch import device, Tensor
from torch._decomp import get_decompositions
from torch._dynamo.utils import defake, dynamo_timed
from torch._library.fake_class_registry import FakeScriptObject
from torch._library.opaque_object import (
    is_opaque_reference_type,
    is_opaque_type,
    is_opaque_value_type,
)
from torch._library.utils import get_layout_constraint_tag
from torch._logging import LazyString, trace_structured
from torch._prims_common import (
    compute_required_storage_length,
    make_channels_last_strides_for,
)
from torch._subclasses.fake_tensor import FakeTensor
from torch._utils_internal import full_aoti_runtime_assert
from torch.fx.experimental._backward_state import BackwardState
from torch.fx.experimental.symbolic_shapes import (
    _get_placeholder_expr,
    free_unbacked_symbols,
    has_free_symbols,
    resolve_unbacked_bindings,
    RuntimeAssert,
    ShapeEnv,
    SympyBoolean,
    SymTypes,
)
from torch.fx.node import Node
from torch.fx.passes.reinplace import _is_view_op
from torch.utils._mode_utils import no_dispatch
from torch.utils._ordered_set import OrderedSet
from torch.utils._sympy.numbers import int_oo

from . import config, ir, metrics
from .codegen.common import (
    BackendFeature,
    DeviceOpOverrides,
    FileBackedGraphModule,
    get_backend_features,
    get_device_op_overrides,
    get_wrapper_codegen_for_device,
    init_backend_registration,
    WorkspaceArg,
)
from .exc import (
    CppWrapperCodegenError,
    LoweringException,
    MissingOperatorWithDecomp,
    MissingOperatorWithoutDecomp,
)
from .fx_utils import count_flops_fx
from .ir import (
    assign_origin_node,
    Constant,
    DonatedBuffer,
    FixedLayout,
    get_device_type,
    GraphPartitionSignature,
    InputBuffer,
    Pointwise,
    Reduction,
    ShapeAsConstantBuffer,
    StorageBox,
    TensorBox,
    TorchBindObject,
)
from .lowering import (
    constrain_to_fake_tensors,
    constrain_to_fx_strides,
    FALLBACK_ALLOW_LIST,
    fallback_handler,
    fallback_node_due_to_unsupported_type,
    lowerings,
    make_fallback,
    maybe_layout_constraints,
    needs_realized_inputs,
    require_contiguous,
    tag_to_layout_constraint,
    unsupported_output_tensor,
    user_lowerings,
)
from .runtime import autotune_cache
from .runtime.autotune_cache import AutotuneCacheBundler
from .sizevars import SizeVarAllocator
from .utils import (
    gather_origins,
    get_cloned_parameter_buffer_name,
    get_donated_idxs,
    get_sympy_Expr_dtype,
    GraphPartitionMap,
    is_same_tensor,
    maybe_get_suppress_shape_guards_ctx,
    normalize_name,
    should_assume_input_aligned,
    should_fallback_by_default,
    SUPPORTED_MKLDNN_DEVICES,
    ValueWithLineMap,
)
from .virtualized import NullHandler, V


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

    from torch._higher_order_ops.effects import _EffectType
    from torch.fx import GraphModule
    from torch.fx.graph import Graph

    from .codegen.wrapper import PythonWrapperCodegen
    from .dependencies import Dep
    from .scheduler import BaseSchedulerNode

    CompiledModule = ModuleType | FileBackedGraphModule

from torch._inductor.codecache import output_code_log


log = logging.getLogger(__name__)
perf_hint_log = torch._logging.getArtifactLogger(__name__, "perf_hints")

aten = torch.ops.aten

_post_grad_graph_counter = itertools.count()

if config.is_fbcode():
    from torch._inductor.fb.triton_kernel_metadata import (
        save_triton_kernel_perf_artifact,
    )
    from torch._inductor.fb.utils import log_module_code
else:

    def log_module_code(*args: Any, **kwargs: Any) -> None:
        pass

    def save_triton_kernel_perf_artifact(*args: Any, **kwargs: Any) -> None:
        pass


def may_get_constant_buffer_dtype(constant_buffer: sympy.Expr) -> torch.dtype | None:
    assert isinstance(
        constant_buffer, (sympy.Symbol, sympy.Expr, sympy.core.numbers.Integer)
    ), (
        "get_constant_buffer_dtype only supports input of sympy.Symbol, sympy.Expr or sympy.core.numbers.Integer"
    )
    if isinstance(constant_buffer, sympy.core.numbers.Integer):
        return torch.int64

    if isinstance(constant_buffer, sympy.Expr):
        return get_sympy_Expr_dtype(constant_buffer)

    if constant_buffer.is_integer:
        return torch.int64
    elif constant_buffer.is_float:
        return torch.float32
    else:
        return None


def getattr_recursive(
    obj: GraphModule, target: str
) -> Tensor | torch._C.ScriptObject | GraphModule:
    target_atoms = target.split(".")
    attr_itr = obj
    for i, atom in enumerate(target_atoms):
        if not hasattr(attr_itr, atom):
            raise RuntimeError(
                f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}"
            )
        attr_itr = getattr(attr_itr, atom)
    return attr_itr


def get_user_visible_output_strides(g: Graph) -> dict[Node, tuple[int, ...]]:
    ret: dict[Node, tuple[int, ...]] = {}
    output_node = g.find_nodes(op="output")[0]

    if "user_visible_output_idxs" not in output_node.meta:
        return ret

    if not isinstance(output_node.args[0], torch.fx.Node):
        output_node_args = output_node.args[0]
    else:
        output_node_args = output_node.args

    for idx, node in enumerate(output_node_args):
        if idx in output_node.meta["user_visible_output_idxs"]:
            ret[node] = output_node.meta["original_output_strides"][idx]
    return ret


def _is_cpu_strided_tensor_pinned(t: torch.Tensor) -> bool:
    return t.device.type == "cpu" and t.layout == torch.strided and t.is_pinned()


def extend_user_visible_output_strides(
    user_visible_outputs: dict[Node, tuple[int, ...]],
) -> dict[Node, object]:
    """
    Extend user_visible_output_strides to include view ops that lead to user-visible outputs.
    """
    result: dict[Node, object] = {**user_visible_outputs}
    queue = [*result.keys()]
    visited = OrderedSet([*queue])
    while queue:
        current = queue.pop()
        if (
            _is_view_op(current.target)
            and current.args
            and isinstance(current.args[0], torch.fx.Node)
        ):
            base = current.args[0]
            if base not in visited:
                result.setdefault(base, None)
                visited.add(base)
                queue.append(base)
    return result


def mark_nodes_dislike_padding(
    g: Graph, user_visible_output_strides: dict[Node, tuple[int, ...]]
) -> None:
    """
    Nodes like convolution/convolution_backward want its input to be dense.
    If we pad their inputs, we result in extra calls to copy kernels!  On the other hand, padding usually helps reduction.

    The pass finds nodes that dislike padding. These are nodes that can be reached
    from a convolution/convolution_backward in the backward direction without
    going thru a reduction.
    """
    if not config.comprehensive_padding:
        return

    extended_user_visible_nodes = extend_user_visible_output_strides(
        user_visible_output_strides
    )
    ops_dislike_padding = OrderedSet(
        [
            aten.convolution,
            aten.convolution_backward,
            aten._scaled_mm,
            aten._scaled_mm_v2,
        ]
    )
    # what's a better way to collect the reduction ops?
    ops_like_padding = OrderedSet(
        [
            aten.var_mean,
            aten.sum,
            aten.mean,
            aten.prod,
            aten.any,
            aten.amin,
            aten.amax,
            aten.min,
            aten.max,
            aten.argmin,
            aten.argmax,
            aten.scatter_reduce,
        ]
    )

    def _get_overload_packet(
        node: torch.fx.Node,
    ) -> torch._ops.OpOverloadPacket | None:
        return (
            node.target._overloadpacket
            if node.op == "call_function"
            # hasattr on OpOverloadPacket is slow, do isinstance first
            and isinstance(node.target, torch._ops.OpOverload)
            and hasattr(node.target, "_overloadpacket")
            else None
        )

    for cur in reversed(g.nodes):
        if isinstance(
            cur.target,
            torch._higher_order_ops.triton_kernel_wrap.TritonKernelWrapperMutation,
        ):
            cur.meta["dislike_padding"] = True
            continue

        if (
            isinstance(cur.target, torch._ops.OpOverload)
            and get_layout_constraint_tag(cur.target)
            == torch._C.Tag.needs_exact_strides
        ):
            cur.meta["dislike_padding"] = True
            continue

        op = _get_overload_packet(cur)
        if not op:
            continue
        if op in ops_dislike_padding:
            cur.meta["dislike_padding"] = True

        if cur.meta.get("dislike_padding", False):
            # propagate
            for prior in cur.all_input_nodes:
                prior_op = _get_overload_packet(prior)
                if not prior_op:
                    continue
                if prior_op not in ops_like_padding:
                    prior.meta["dislike_padding"] = True
        # We only want to mark output nodes. So, move it after the above prior nodes process.
        if not config.pad_outputs and cur in extended_user_visible_nodes:
            # Reductions (ops_like_padding) produce new output buffers with
            # fresh strides, so their output stride constraint is already
            # enforced by allow_padding=False in as_exact_strides. Setting
            # dislike_padding here would suppress input padding during
            # freeze, causing a stride mismatch when an earlier lowering
            # step (e.g. is_contiguous_storage_and_layout) already mutated
            # the input layout to padded strides.
            if op not in ops_like_padding:
                cur.meta["dislike_padding"] = True


def is_mkldnn_conv(node: Node) -> bool:
    # When mkldnn_fusion is enabled, conv will be replaced by the lowering pattern function.
    # See _register_unary_fusion_lowering in torch/_inductor/fx_passes/mkldnn_fusion.py.
    if (
        getattr(torch.ops, "mkldnn", None) is not None
        and getattr(torch.ops.mkldnn, "_convolution_pointwise", None) is not None
        and isinstance(node.target, functools.partial)
        and len(node.target.args) > 0
        and hasattr(node.target.args[0], "targets")
    ):
        for target in node.target.args[0].targets:
            if target.fns[0] in [
                torch.ops.mkldnn._convolution_pointwise.default,
                torch.ops.mkldnn._convolution_pointwise.binary,
                torch.ops.mkldnn._convolution_pointwise_.binary,
            ]:
                return True

    return False


class GraphLowering(torch.fx.Interpreter):
    """Lower an FX graph into Inductor IR and track compilation state."""

    graph_outputs: list[ir.IRNode]

    def __init__(
        self,
        gm: torch.fx.GraphModule,
        example_inputs: Sequence[object] | None = None,
        shape_env: ShapeEnv | None = None,
        graph_id: int | None = None,
        cpp_wrapper: bool = False,
        aot_mode: bool = False,
        layout_opt: bool | None = None,
        extern_node_serializer: Callable[[list[ir.ExternKernelNode]], Any]
        | None = None,
        is_inference: bool = False,
        is_backward: bool = False,
        is_const_graph: bool = False,
        const_output_index: dict[str, int] | None = None,
        const_wrapper_code: str | None = None,
        const_kernel_code: str | None = None,
        const_module: GraphLowering | None = None,
        name: str | None = None,
        inputs_to_check: Sequence[int] | None = None,
        fx_wrapper: bool = False,
        get_decomp_fn: Callable[..., dict[Any, Callable[..., Any]]] | None = None,
    ) -> None:
        super().__init__(gm)
        self.get_decomp_fn = get_decomp_fn
        self.example_inputs = example_inputs
        self.layout_opt = (
            layout_opt
            if layout_opt is not None
            else self.decide_layout_opt(gm, is_inference=is_inference)
        )
        self.num_channels_last_conv = 0
        self.is_inference = is_inference
        self.is_backward = is_backward
        self.is_const_graph = is_const_graph
        self.const_wrapper_code = const_wrapper_code
        self.const_kernel_code = const_kernel_code
        self.const_module = const_module
        self.inputs_to_check = inputs_to_check
        self._defers_input_alignment = False

        self.extra_traceback = False  # we do our own error wrapping
        if shape_env is None:
            shape_env = ShapeEnv()
            self.reuse_shape_env = False
        else:
            self.reuse_shape_env = True
        self._shape_env = shape_env
        # We're going to mutate ras_by_symbol as we finish generating them
        self.ras_by_symbol: dict[sympy.Symbol | None, list[RuntimeAssert]] = (
            shape_env.deferred_runtime_asserts.copy()
        )
        self.bound_unbacked_symbols = OrderedSet[sympy.Symbol]()

        self.sizevars = SizeVarAllocator(shape_env)
        self.graph_input_names: list[str] = []
        self.graph_inputs: dict[str, TensorBox | TorchBindObject | sympy.Expr] = {}
        self.graph_inputs_original: dict[str, InputBuffer] = {}
        # InputBuffer offsets are relative to input.data_ptr(); explicit FX
        # as_strided storage offsets are relative to the input's storage.
        self.graph_input_storage_offsets: dict[str, Expr] = {}
        self.partition_maps: list[GraphPartitionMap] | None = None
        self.zero_dim_cpu_tensor_list: OrderedSet[str] = OrderedSet()
        self.device_types: OrderedSet[str] = (
            const_module.device_types if const_module else OrderedSet()
        )
        self.device_idxs: OrderedSet[int] = (
            const_module.device_idxs if const_module else OrderedSet()
        )
        self.device_type = "cpu"
        self.additional_buffer_deps: dict[str, OrderedSet[str]] = defaultdict(
            OrderedSet
        )
        self.additional_star_deps: dict[str, OrderedSet[str]] = defaultdict(OrderedSet)
        # Maps control_deps FX node to operation names created when lowering it,
        # for void ops (e.g. record_event) that return None and therefore cannot
        # be referenced by name in subsequent control_deps ordering constraints.
        self._void_ctrl_dep_op_names: dict[torch.fx.Node, list[str]] = {}

        # Inplace padding may require Inductor to allocate slightly larger
        # tensor for padding.
        self.buffer_to_padded_size: dict[str, list[int]] = {}

        self.buffers: list[ir.Buffer] = []
        self.operations: list[ir.Operation] = []
        self.const_output_index: dict[str, int] = (
            const_output_index if const_output_index else {}
        )
        self.folded_constants: OrderedSet[str] = (
            OrderedSet(const_output_index.keys())
            if const_output_index
            else OrderedSet()
        )
        self.constants: dict[str, torch.Tensor] = (
            const_module.constants if const_module else {}
        )
        self.named_buffers: dict[str, torch.Tensor] = (
            const_module.named_buffers if const_module else {}
        )
        self.mutated_named_buffers: OrderedSet[torch.Tensor] = gm.meta.get(
            "mutated_named_buffers", OrderedSet()
        )
        self.named_parameters: dict[str, torch.Tensor] = (
            const_module.named_parameters if const_module else {}
        )
        self.torchbind_constants: dict[
            str, torch._C.ScriptObject | FakeScriptObject
        ] = {}
        self.opaque_value_type_classes: dict[str, type] = {}
        self.seen_subgraphs: dict[str, ir.Subgraph] = {}
        self.constant_reprs: dict[str, str] = {}
        self.removed_operations: OrderedSet[str] = OrderedSet()
        self.removed_buffers: OrderedSet[str] = OrderedSet()
        self.removed_inplace_buffers: OrderedSet[str] = OrderedSet()
        self.mutated_buffers: OrderedSet[str] = OrderedSet()
        self.sdpa_constraint_cache: dict[tuple, ir.IRNode] = {}
        self.never_reuse_buffers: OrderedSet[str] = OrderedSet()
        self.inplaced_to_remove: OrderedSet[str] = OrderedSet()
        self.device_ops: DeviceOpOverrides = None  # type: ignore[assignment]
        self.wrapper_code: PythonWrapperCodegen = None  # type: ignore[assignment]

        from torch._inductor.extern_node_serializer import extern_node_json_serializer

        self.extern_node_serializer: Callable[[list[ir.ExternKernelNode]], Any] = (
            extern_node_serializer
            if config.is_fbcode() and extern_node_serializer
            else extern_node_json_serializer
        )

        self.current_node: torch.fx.Node = None  # type: ignore[assignment]
        self.lists: dict[str, list[str]] = {}
        self.mutated_inputs: OrderedSet[str] = OrderedSet()
        self.mutated_input_idxs: list[int] = []
        self.name_to_buffer: dict[str, ir.Buffer] = {}
        self.name_to_users: defaultdict[str, list[ir.IRNode]] = defaultdict(list)
        self.name_to_op: dict[str, ir.Operation] = {}
        # Side table for CuteDSL capture nodes (may include ReinterpretViews)
        self._cutedsl_capture_nodes: dict[str, ir.IRNode] = {}
        self.creation_time = time.time()
        self.name = name  # type: ignore[assignment]
        self.cpp_wrapper = cpp_wrapper
        self.fx_wrapper = fx_wrapper

        # record multi_kernel choice for cpp_wrapper so the second pass knows
        # which sub-kernel is picked. Copy cpp_wrapper to another variable
        # since cpp_wrapper flag is OrderedSet to false for the first pass of codegen.
        self.record_multi_kernel_choice = cpp_wrapper
        self.multi_kernel_to_choice: dict[str, str] = {}

        self.aot_mode = aot_mode
        self.graph_id = graph_id
        self.post_grad_graph_id = next(_post_grad_graph_counter)
        self.scheduler: torch._inductor.scheduler.Scheduler = None  # type: ignore[assignment]

        # record intermediate results for input of UsedDefinedTritonKernels
        # This will be used if autotuning is done in one pass.
        self.autotuning_inputs: list[torch.Tensor] | None = None
        self.autotuning_mapping: dict[str, dict[str, int]] | None = None
        self.autotuning_grids: dict[str, Any] | None = None

        # current_device is set only during codegen of a device-specific kernel
        # a graph can have many devices
        self.current_device: torch.device | None = None

        self.nodes_prefer_channels_last = (
            self.find_nodes_prefer_channels_last() if self.layout_opt else OrderedSet()
        )
        self._warned_fallback = OrderedSet(["aten.convolution_backward"])
        self.user_visible_output_strides = get_user_visible_output_strides(gm.graph)
        mark_nodes_dislike_padding(gm.graph, self.user_visible_output_strides)
        self.cache_key: str = ""  # This is the cache key for the compiled artifact
        self.cache_path: str = ""  # This is the path in the filesystem where the compiled artifact is stored
        self.cache_linemap: list[
            tuple[int, str]
        ] = []  # This is the linemap used by the profiler to mark custom compiled kernels getting run
        # Used if lowering encounters cases where cudagraphs are not supported
        self.disable_cudagraphs_reason: str | None = None

        # only keeping one node per device for stack trace purposes
        self.device_node_mapping: dict[torch.device, torch.fx.Node] = {}
        self.orig_gm: torch.fx.GraphModule = gm.__copy__()
        for k, v in self.orig_gm.named_buffers():
            self.named_buffers[k] = v
        for k, v in self.orig_gm.named_parameters():
            self.named_parameters[k] = v
        self.dynamo_flat_name_to_original_fqn = self.module.meta.get(  # type: ignore[operator, union-attr]
            "dynamo_flat_name_to_original_fqn", {}
        )
        self.allocated_constant_name: dict[str, str] = (
            const_module.allocated_constant_name if const_module is not None else {}
        )
        init_backend_registration()
        self.get_backend_features = functools.lru_cache(None)(get_backend_features)

        self.effectful_ops: dict[_EffectType, ir.Buffer] = {}
        # Track the buffers that we know is unaligned
        # This can either be a graph input or the output of fallback
        # kernels.
        self.unaligned_buffers: OrderedSet[str] = OrderedSet()
        self.no_fuse_buffer_names: OrderedSet[str] = OrderedSet()

        # Layout constraints for Triton template buffers.
        # Maps buffer name -> expected FixedLayout (computed speculatively without freezing)
        self.buffer_layout_constraints: dict[str, ir.FixedLayout] = {}

        self.low_precision_codegen_ops: OrderedSet[str] = OrderedSet()
        # more aggressive prologue fusion
        self.invoke_quant_ops: OrderedSet[str] = OrderedSet()

        # Below field is related to printing debug intermediate tensor values info for debugging
        self.all_codegen_kernel_names: OrderedSet[str] = OrderedSet()

        # state used by for KernelArgs.workspace
        self.workspace_id = itertools.count()

        # track the current placeholder index that we are processing
        self.placeholder_idx = -1

        self.bw_donated_idxs = get_donated_idxs()

        # Cache for dep size hints to avoid expensive recomputation
        self.dep_size_hint_cache: dict[tuple[Dep, bool], int] = {}

    def freeze_runtime_asserts(self) -> None:
        self._shape_env.freeze_runtime_asserts()

    def get_placeholder_sizes_strides(
        self, ex: torch.Tensor
    ) -> tuple[Sequence[int | Expr], Sequence[int | Expr]]:
        sizes, strides, _ = self.symbolic_sizes_strides_storage_offset(ex)
        return sizes, strides

    def symbolic_sizes_strides_storage_offset(
        self, ex: torch.Tensor
    ) -> tuple[Sequence[int | Expr], Sequence[int | Expr], Expr]:
        """
        Support dynamic shapes and dynamic strides by assigning variables
        to each dimension.  We duck-shape tensors, so if two tensors
        have the same size they get assigned the same symbolic variable.
        """
        if self.reuse_shape_env:
            size, stride = ex.size(), ex.stride()
            storage_offset = ex.storage_offset()
        else:
            from torch._dynamo.source import ConstantSource

            # TODO: this should not be needed once #93059 lands
            # https://github.com/pytorch/pytorch/pull/94031#discussion_r1096044816
            # TODO: make a dedicated UnknownSource for this?
            # NB: This is using the legacy default behavior from
            # transfer_symbols_from_foreign_shape_env but we hope we can
            # just delete this entirely
            source = ConstantSource(
                f"__inductor_unknown_tensor_{len(self._shape_env.backed_var_to_val)}"
            )
            (
                size,
                stride,
                storage_offset,
            ) = self._shape_env.transfer_symbols_from_foreign_shape_env(
                ex.size(),
                ex.stride(),
                ex.storage_offset(),
                source,
            )

        r_size = [sympy.sympify(i) for i in size]
        r_stride = [sympy.sympify(i) for i in stride]
        r_storage_offset = (
            storage_offset.node.expr
            if isinstance(storage_offset, torch.SymInt)
            else sympy.sympify(storage_offset)
        )
        return r_size, r_stride, r_storage_offset

    def static_sizes_strides(
        self, ex: torch.Tensor
    ) -> tuple[list[sympy.Expr], list[sympy.Expr]]:
        """
        Primarily used to weights
        """
        size = [sympy.Integer(i) for i in ex.size()]
        stride = [sympy.Integer(i) for i in ex.stride()]
        return size, stride

    def get_allocation_size(
        self,
        node: ir.TensorBox
        | ir.StorageBox
        | ir.Buffer
        | WorkspaceArg
        | ir.TorchBindObject,
    ) -> Sequence[Expr]:
        if isinstance(node, ir.TensorBox):
            node = node.data  # type: ignore[assignment]
        if isinstance(node, ir.StorageBox):
            node = node.data  # type: ignore[assignment]
        if (
            isinstance(node, ir.ComputedBuffer)
            and node.name in self.buffer_to_padded_size
        ):
            # pyrefly: ignore [bad-index, index-error]
            return self.buffer_to_padded_size[node.name]
        else:
            return node.get_size()

    def get_allocation_storage_size(
        self, node: ir.Buffer | WorkspaceArg | ir.TorchBindObject
    ) -> Expr:
        layout = node.get_layout()
        size = self.get_allocation_size(node)  # consider inplace padding
        stride = layout.stride
        offset = layout.offset
        return compute_required_storage_length(size, stride, offset)  # type: ignore[arg-type]

    def has_feature(
        self,
        device: torch._inductor.ir.IRNode | device | None,
        feature: BackendFeature,
    ) -> bool:
        assert isinstance(feature, BackendFeature), feature
        return feature in self.get_backend_features(get_device_type(device))

    def get_dep_size_hint(self, dep: Dep, count_bytes: bool = True) -> int:
        """
        Get the size hint for a dependency with caching to avoid expensive recomputation.
        """
        if (dep, count_bytes) not in self.dep_size_hint_cache:
            res = 0
            # Non-tensor graph inputs (TorchBindObject, OpaqueObjectState)
            # have no meaningful size — skip the size computation entirely.
            inp = self.graph_inputs.get(dep.name)
            if isinstance(inp, ir.NonTensorObj):
                self.dep_size_hint_cache[(dep, count_bytes)] = 0
                return 0
            try:
                if (
                    not dep.has_unbacked_symbols()
                    or self.sizevars.all_unbacked_explicitly_hinted(dep.get_numel())
                ):
                    if count_bytes:
                        res = dep.numbytes_hint()
                    else:
                        res = dep.numel_hint()
            except KeyError:
                # In at least one test (test/inductor/test_torchbind.py) we
                # create a StarDep that doesn't exist in the graph and calling
                # `has_unbacked_symbols()` throws an error.
                pass
            self.dep_size_hint_cache[(dep, count_bytes)] = res
        return self.dep_size_hint_cache[(dep, count_bytes)]

    def get_current_device_or_throw(self) -> torch.device:
        if device := self.current_device:
            return device
        else:
            raise RuntimeError("No current device")

    @contextlib.contextmanager
    def set_current_device(self, device: torch.device) -> Iterator[None]:
        prior = self.current_device
        self.current_device = device
        try:
            yield
        finally:
            self.current_device = prior

    def get_training_phase(self) -> str:
        if self.is_inference:
            return "inference"
        if self.is_backward:
            return "backward"
        return "forward"

    @staticmethod
    def decide_layout_opt(gm: GraphModule, *, is_inference: bool) -> bool:
        """
        Decide if we should enable layout optimization for this graph based on
        heuristics.
        """
        if not config.layout_optimization:
            return False

        if config.force_layout_optimization:
            return True

        conv_nodes = [
            n for n in gm.graph.nodes if n.target is torch.ops.aten.convolution.default
        ]

        for n in gm.graph.nodes:
            if is_mkldnn_conv(n):
                conv_nodes.append(n)

        nconv = len(conv_nodes)

        if nconv == 0:
            return False

        # For cpu backend and mkldnn enabled, we always use channels_last for better performance.
        if (
            torch.backends.mkldnn.enabled  # pyrefly: ignore [unbound-name]
            and torch.backends.mkldnn.is_available()  # pyrefly: ignore [unbound-name]
            and all(
                n.args[idx].meta["val"].device.type in SUPPORTED_MKLDNN_DEVICES
                for n in conv_nodes
                for idx in [0, 1]
            )
        ):
            return True

        # Following models are skipped due to this:
        # jx_nest_base
        # volo_d1_224
        if len(list(gm.graph.nodes)) >= 300 * nconv:
            log.debug("Skipped layout opt because only a few conv")
            return False

        if any(
            has_free_symbols(n.args[idx].meta["val"])
            for n in conv_nodes
            for idx in [0, 1]
        ):
            log.debug(
                "See perf regression with dynamic shape. Follow up in https://github.com/pytorch/pytorch/issues/102670"
            )
            return False

        def is_grouped(n: Any) -> bool:
            meta_val = n.args[1].meta["val"]  # type: ignore[union-attr, operator]
            assert isinstance(meta_val, torch.Tensor)
            return n.args[-1] > 1 and meta_val.size(1) > 1  # type: ignore[union-attr, operator]

        def is_in_out_channel(n: torch.fx.Node) -> bool:
            return (
                n.args[1].meta["val"].size(0) * 2 <= n.args[1].meta["val"].size(1)  # type: ignore[union-attr, operator]
                and n.args[1].meta["val"].size(2) > 1  # type: ignore[union-attr, operator]
            )

        def is_small_channel(n: torch.fx.Node) -> bool:
            return (
                n.args[1].meta["val"].size(0) <= 64  # type: ignore[union-attr, operator]
                and n.args[1].meta["val"].size(1) <= 64  # type: ignore[union-attr, operator]
            )

        # only grouped convolutions benchmarked as slower in conv samples for inference only
        if is_inference:
            flop_counts: dict[str, float] = defaultdict(float)
            for node in conv_nodes:
                counted_flops = count_flops_fx(node)
                if counted_flops is None:
                    continue

                if is_grouped(node):
                    node_type = "grouped"
                elif is_small_channel(node):
                    node_type = "small"
                elif is_in_out_channel(node):
                    node_type = "in_out"
                else:
                    node_type = "default"

                flop_counts[node_type] += counted_flops
            else:
                log.debug("Conv inputs meta not found")

            # average benchmarked channels last speedup / slowdown, < 1 is speedup.
            # taken from the set of convolution inputs in benchmarks/dynamo/microbenchmarks/operator_inp_logs/torchbench_train/
            # To regenerate these numbers follow https://gist.github.com/eellison/55d7a6ed6f39829d68ac56f95f4df5bb
            GROUPED_MULTIPLIER = 1.358
            DEFAULT_MULTIPLIER = 0.823
            IN_OUT_MULTIPLIER = 0.725
            SMALL_MULTIPLIER = 0.783

            total_flops = sum(flop_counts.values())
            # TODO - get different values per hardware
            weighted_flops = (
                flop_counts["grouped"] * GROUPED_MULTIPLIER
                + flop_counts["small"] * SMALL_MULTIPLIER
                + flop_counts["in_out"] * IN_OUT_MULTIPLIER
                + flop_counts["default"] * DEFAULT_MULTIPLIER
            )
            do_layout_opt = weighted_flops <= total_flops
            if not do_layout_opt:
                log.debug(
                    "Skipped layout opt in inference because weighted flops indicate slowdown, default: %d, channels last: %d",
                    total_flops,
                    weighted_flops,
                )
            return do_layout_opt

        # Channels last layout can dramatically hurt grouped conv perf. E.g.
        # Conv with arguments like
        #   {"input_shape": [32, 224, 112, 112], "weight_shape": [224, 112, 3, 3],
        #    "stride": [2, 2], "padding": [1, 1], "groups": 2}
        # slows down 31x using channels last..

        # But a lot of timm models use depthwise separable convolution which will
        # result in grouped convolution with in-channel size == 1.
        # For those grouped convolution, channels last still helps a lot.
        # E.g.
        # Conv with arguments
        #   {"input_shape": [128, 58, 56, 56], "weight_shape": [58, 1, 3, 3],
        #    "stride": [2, 2], "padding": [1, 1], "groups": 58}
        # get 1.86x speedup with channels last layout.
        #
        # The following heuristics skip using channels-last if the model contains
        # grouped convolution with in-channels > 1.
        if any(map(is_grouped, conv_nodes)):
            log.debug(
                "Skip layout opt because found grouped convolution with >1 in_channels!"
            )
            return False

        # For some models that contain convolution with larger in-channel than out-channel, applying
        # channels last hurts performance.
        # Following models are skipped due to this:
        # - pytorch_unet
        # - phlippe_densenet (slightly worse)
        # - Background_Matting (1.22x -> 0.821x)
        # - pytorch_CycleGAN_and_pix2pix (1.597x -> 1.294x)
        if any(map(is_in_out_channel, conv_nodes)):
            log.debug(
                "Skip layout opt because some convolutions have smaller out_channel"
            )
            return False

        # Following models are skipped due to this:
        # - functorch_maml_omniglot
        if all(map(is_small_channel, conv_nodes)):
            log.debug("Skip layout opt because all convolution channels are too small")
            return False

        return True

    def qualify_name(self, name: str) -> str:
        """Prepend the given name with the graph name if any."""
        if self.name is not None:
            return f"{self.name}_{name}"
        return name

    def make_subgraph(
        self,
        gm: torch.fx.GraphModule,
        example_inputs: list[torch.Tensor],
        subgraph_name: str,
    ) -> SubgraphLowering:
        """
        Make a subgraph of the current graph with all inherited parts, except
        the graph module (`gm`) and `example_inputs`.  The subgraphs are lowered
        separately and lifted into a separate function in the parent output
        wrapper code.  The subgraph name is qualified by the parent graph's
        name. Note that the lifting of subgraph is supported for python wrapper
        only. For cpp wrapper, we inline the subgraphs in the parent wrapper.
        """
        return SubgraphLowering(
            parent=self,
            gm=gm,
            example_inputs=example_inputs,
            shape_env=self._shape_env,
            cpp_wrapper=self.cpp_wrapper,
            aot_mode=self.aot_mode,
            extern_node_serializer=self.extern_node_serializer,
            is_inference=self.is_inference,
            is_backward=self.is_backward,
            name=self.qualify_name(subgraph_name),
        )

    def find_nodes_prefer_channels_last(self) -> OrderedSet[Node]:
        """
        The rule to decide if an node prefer channels last is simple.
        1. if it's input/output of a convolution
        2. if one of its user prefers channels last

        We have rule 1 because cudnn runs a faster convolution kernel for channels last inputs;
        Rule 2 is also important. It makes sure that indirect inputs to convolution also prefers
        channels last.

        Consider the scenario: conv -> batch-norm -> relu -> conv
        Without rule 2, batch-norm output may use a contiguous layout. That will cause 2 extra copies:
        1. the output of batch-norm should be channels last initially since its input is a conv's output.
           Forcing the batch-norm's output to be contiguous results in the first copy
        2. The second conv's input is initially contiguous. This layout is propagated from the batch-norm's output.
           We need to convert it to channels last layout which results in the second copy.
        With rule 2, we make sure all the tensors in the chain use channels last layout. So both copies
        can be saved.
        """
        last_conv = None
        nodes_cannot_propagate = [torch.ops.aten.bmm.default]
        output_set = OrderedSet[Node]()
        for n in reversed(self.module.graph.nodes):  # type: ignore[arg-type, union-attr]
            if n.target is torch.ops.aten.convolution.default:
                output_set.add(n)
                if last_conv is None:
                    last_conv = n
                continue
            if n.target in nodes_cannot_propagate:
                continue
            if is_mkldnn_conv(n):
                output_set.add(n)
                continue
            for user in n.users:
                if user in output_set:
                    output_set.add(n)
                    break

        # need a second pass to add downstream nodes of those channel last nodes to the sets.
        # This pass is especially needed to avoid mix-layout kernel inputs in backward pass.
        #
        # Let's say a conv-batchnorm 's output is passed to relu whose output is in turn returned
        # from the fwd graph. Without this second pass, we will force relu's output to be contiguous.
        # Then in the kernel in backward pass, the contiguous output of relu may be mix with other channels last
        # tensors and passed to a kernel.
        #
        # This pass improve yolov3 training speedup from 1.116x (worse than disabling layout optimization speedup 1.196x) to 1.457x.
        # It also improves dla102 training speedup from 1.240x (worse than disabling layout optimization speedup 1.523x) to 1.835x .
        # This also helps the following models:
        # - res2net101_26w_4s
        # - res2net50_14w_8s
        # - sebotnet33ts_256
        for n in self.module.graph.nodes:  # type: ignore[union-attr]
            # layout propagation ends at last conv node, which will benefit vison transformers.
            if last_conv is not None and n == last_conv:
                break
            if n in output_set:
                for user in n.users:
                    if user.target in nodes_cannot_propagate:
                        continue
                    output_set.add(user)

        return output_set

    def warn_fallback(self, name: str) -> None:
        if name not in self._warned_fallback:
            self._warned_fallback.add(name)
            perf_hint_log.info("Using FallbackKernel: %s", name)

    def add_device_info(self, device: torch.device) -> None:
        self.device_types.add(device.type)
        if device.index is not None:
            self.device_idxs.add(device.index)
        if V.graph.current_node and device not in self.device_node_mapping:
            self.device_node_mapping[device] = V.graph.current_node

    @property
    def fake_mode(self) -> torch._subclasses.fake_tensor.FakeTensorMode:
        return V.fake_mode

    @property
    def is_dual_wrapper_mode(self) -> bool:
        """True when generating dual-wrapper-mode C++ for both JIT and AOTI.

        Dual-wrapper mode emits a JIT pass that drives Triton autotune/compile
        alongside the AOTI output, so it is needed iff at least one device in
        the graph uses the Triton backend.
        """
        if not self.aot_mode or config.triton.autotune_at_compile_time:
            return False
        return any(ir.is_triton(d) for d in self.device_types)

    def try_get_buffer(
        self, buffer_name: str
    ) -> ir.TensorBox | ir.Buffer | ir.TorchBindObject | None:
        if buffer_name in self.name_to_buffer:
            return self.name_to_buffer[buffer_name]
        if buffer_name in self.graph_inputs:
            return self.graph_inputs[buffer_name]
        if buffer_name in self.constants:
            data = V.graph.constants[buffer_name]
            return ir.ConstantBuffer(
                name=buffer_name,
                layout=ir.FixedLayout(
                    data.device, data.dtype, *V.graph.static_sizes_strides(data)
                ),
            )

        return None

    def add_symbol_graph_input(self, symbol: sympy.Expr) -> None:
        raise RuntimeError("Should not be called for the main graph")

    def get_buffer(
        self, buffer_name: str
    ) -> ir.TensorBox | ir.Buffer | ir.TorchBindObject:
        buf = self.try_get_buffer(buffer_name)
        if buf is not None:
            return buf
        raise RuntimeError(f"Failed to find buffer matching name {buffer_name}")

    def get_dtype(self, buffer_name: str) -> torch.dtype:
        if buffer_name in self.constants:
            return self.constants[buffer_name].dtype
        # For a mutation op we should return the dtype of the buffer being mutated
        if (
            hasattr(self.scheduler, "mutation_real_name")
            and buffer_name in self.scheduler.mutation_real_name
        ):
            mutated_buf = self.scheduler.mutation_real_name[buffer_name]
            if mutated_buf in self.name_to_buffer:
                return self.name_to_buffer[mutated_buf].get_dtype()
            if mutated_buf in self.graph_inputs:
                return self.graph_inputs[mutated_buf].get_dtype()
        if buffer_name in self.name_to_buffer:
            return self.name_to_buffer[buffer_name].get_dtype()
        if buffer_name in self.graph_inputs:
            return self.graph_inputs[buffer_name].get_dtype()
        m = re.match(r"(as_strided|reinterpret_tensor)\(([a-zA-Z0-9_]+),", buffer_name)
        if m:
            return self.get_dtype(m.group(1))
        raise KeyError(f"could not find {buffer_name}")

    def get_numel(self, buffer_name: str) -> int | Expr:
        if buffer_name in self.constants:
            return self.constants[buffer_name].numel()
        if buffer_name in self.name_to_buffer:
            buf = self.name_to_buffer[buffer_name]
            if not buf.has_tensor_output():
                return 1
            return buf.get_numel()
        if buffer_name in self.graph_inputs:
            return self.graph_inputs[buffer_name].get_numel()
        raise KeyError(f"could not find {buffer_name}")

    def run(self, *args: Any) -> Any:  # type: ignore[override]
        with dynamo_timed("GraphLowering.run"):
            return super().run(*args)

    def register_operation(self, op: ir.Operation) -> str:
        assert op.operation_name is None, f"Operation registered twice: {op}"
        assert isinstance(op, ir.Operation)
        name = self.qualify_name(f"op{len(self.operations)}")
        self.operations.append(op)
        self.name_to_op[name] = op
        op.operation_name = name
        return name

    def register_buffer(self, buffer: ir.Buffer, *, set_name: bool = False) -> str:
        name = self.qualify_name(f"buf{len(self.buffers)}")
        self.buffers.append(buffer)
        self.name_to_buffer[name] = buffer
        device = buffer.get_device()
        if (
            # Skip empty CPU tensor so that CUDA graphs can succeed, see https://github.com/pytorch/pytorch/pull/114144
            device is not None
            and not (
                isinstance(buffer, ir.ComputedBuffer)
                and buffer.is_zero_elements()
                and device == torch.device("cpu")
            )
        ):
            self.add_device_info(device)

        if set_name:
            buffer.name = name
        return name

    def register_operation_list(self, operation_names: list[str]) -> str:
        name = self.qualify_name("list_" + "_".join(operation_names))
        self.lists[name] = operation_names
        return name

    def register_users_of(self, node_output: Iterable[ir.IRNode] | ir.IRNode) -> None:
        def register(value: Iterable[ir.IRNode] | ir.IRNode) -> None:
            if isinstance(value, (list, tuple)):
                for x in value:
                    register(x)
            if isinstance(value, ir.TensorBox):
                for read_name in value.get_read_names():
                    self.name_to_users[read_name].append(value)

        register(node_output)

    def mark_buffer_mutated(self, name: str) -> None:
        """
        When a buffer is mutated we need to make sure all the reads to
        the old version are realized before the mutation happens.
        """
        assert isinstance(name, str)
        self.mutated_buffers.add(name)

        if name not in self.name_to_users:
            return

        for user in self.name_to_users[name]:
            user.realize()

    def get_original_value_of_constant(self, name: str) -> torch.Tensor:
        """
        In AOTI, module buffers may have been mutated during the tracing and compilation.
        Thus we need to read from previously stored original buffers, to make sure the
        generated model.so uses correct initial values.
        """
        assert name in self.allocated_constant_name and name in self.constants, (
            "Can not find the original value for " + name
        )
        orig_name = get_cloned_parameter_buffer_name(self.allocated_constant_name[name])
        return (
            self.module.meta[orig_name]  # type: ignore[index]
            if orig_name in self.module.meta  # type: ignore[operator]
            else self.constants[name]
        )

    def allocate_non_dup_const_name(self, name: str | None, data: Tensor) -> str:
        if not config.aot_inductor.use_runtime_constant_folding:
            for constant_name, value in self.constants.items():
                if is_same_tensor(data, value):
                    return constant_name

        if name is None:
            name = f"constant{len(self.constants)}"
        orig_name = name
        if name[0].isdigit():
            name = f"constant_{name}"
        name = self.qualify_name(name)
        # We may generate a var name for each constant in the codegen.
        # Let's only keep sane characters.
        prefix = normalize_name(name)
        name = prefix
        cnt = 0
        while name in self.constants:
            name = f"{prefix}_{cnt}"
            cnt += 1
        self.constants[name] = data
        self.constant_reprs[name] = (
            f"{data.device!r} {data.dtype!r} "
            f"{tuple(data.size())!r} {tuple(data.stride())!r} "
            f"{hash(data):x}"
        )
        self.allocated_constant_name[name] = orig_name  # type: ignore[assignment]
        return name

    def add_tensor_constant(self, data: Tensor, name: str | None = None) -> TensorBox:
        new_name = self.allocate_non_dup_const_name(name, data)
        return TensorBox.create(
            ir.ConstantBuffer(
                name=new_name,
                layout=FixedLayout(
                    data.device,
                    data.dtype,
                    *self.static_sizes_strides(data),
                    is_pinned=_is_cpu_strided_tensor_pinned(data),
                ),
            )
        )

    def constant_name(self, name: str, device_override: torch.device | None) -> str:
        """
        We AOT copy constants to the devices they are needed on.
        If device_override doesn't match the constant's device, then
        copy it and return a different name.
        """
        if self.constants[name].device == device_override or device_override is None:
            return name
        with torch.utils._python_dispatch._disable_current_modes():
            # caller might have OrderedSet fake tensor mode which will create a fake tensor
            # when calling .to, so unset modes here
            non_dup_const_name = self.allocate_non_dup_const_name(
                f"{name}_{device_override.type}{device_override.index or 0}",
                self.constants[name].to(device_override),
            )

            assert non_dup_const_name in self.constants, (
                f"{non_dup_const_name} should be in V.graph.constants already"
            )

            # register device-copied buffers and parameters to graph as well
            # to codegen correct torch::aot_inductor::ConstantType for them rather than `Unknown`
            if any(
                name == normalize_name(buffer_name)
                for buffer_name in self.named_buffers
            ):
                self.named_buffers[non_dup_const_name] = self.constants[
                    non_dup_const_name
                ]

            if any(
                name == normalize_name(param_name)
                for param_name in self.named_parameters
            ):
                self.named_parameters[non_dup_const_name] = self.constants[
                    non_dup_const_name
                ]

            return non_dup_const_name

    # pyrefly: ignore [bad-override]
    def placeholder(
        self,
        target: str,  # type: ignore[override]
        args: tuple[object],  # type: ignore[override]
        kwargs: dict[str, object],
    ) -> Expr | TensorBox | None:
        self.placeholder_idx += 1
        example = super().placeholder(target, args, kwargs)  # type: ignore[arg-type]
        target = self.qualify_name(target)
        if isinstance(example, SymTypes):
            # TODO fix partitioning issue and re-enable for backward
            # https://github.com/pytorch/pytorch/issues/155468.
            if not V.graph.is_backward:
                expr = _get_placeholder_expr(example.node)
            else:
                expr = example.node.expr
            self.graph_inputs[target] = expr
            self.graph_input_names.append(target)
            return expr
        elif isinstance(example, (int, bool, float)):
            expr = sympy.sympify(example)
            self.graph_inputs[target] = expr
            self.graph_input_names.append(target)
            return expr
        elif isinstance(example, FakeScriptObject):
            obj = TorchBindObject(name=target, value=example)
            self.graph_inputs[target] = obj
            self.graph_input_names.append(target)
            return obj
        elif example is None:
            self.graph_input_names.append(target)
            return None
        if isinstance(example, BackwardState):
            # Ignored arg, must be unused
            # Alternately we could filter this out in AotAutograd
            self.graph_input_names.append(target)
            return None
        # See note: Note: [Generator arguments in AOTDispatcher]
        elif isinstance(example, torch.Generator):
            gen = ir.GeneratorState(name=target, device=example.device)
            self.graph_inputs[target] = gen  # type: ignore[assignment]
            self.graph_input_names.append(target)
            return gen
        elif is_opaque_reference_type(type(example)):
            opaque_obj = ir.OpaqueObjectState(name=target, value=example)
            self.graph_inputs[target] = opaque_obj  # type: ignore[assignment]
            self.graph_input_names.append(target)
            return opaque_obj

        assert isinstance(example, torch.Tensor), example
        # todo(chilli): We can remove the last check once we turn buffers into
        # static shape tensors. That's a hack to workaround Inductor believing
        # the buffer should be static but us passing in a fake tensor with
        # symbolic shapes.
        if not example._has_symbolic_sizes_strides:
            # the first N inputs are weights
            sizes, strides = self.static_sizes_strides(example)
            storage_offset = sympy.Integer(example.storage_offset())
        else:
            sizes, strides, storage_offset = self.symbolic_sizes_strides_storage_offset(
                example
            )

        if (
            self.is_backward
            and self.bw_donated_idxs
            and self.placeholder_idx in self.bw_donated_idxs
        ):
            tensor = TensorBox.create(
                DonatedBuffer(
                    name=target,
                    layout=FixedLayout(example.device, example.dtype, sizes, strides),
                )
            )
        else:
            # TODO(jansel): handle input aliasing
            tensor = TensorBox.create(
                InputBuffer(
                    name=target,
                    layout=FixedLayout(example.device, example.dtype, sizes, strides),
                )
            )

        self.graph_inputs[target] = tensor
        self.graph_input_names.append(target)
        self.graph_input_storage_offsets[target] = storage_offset
        self.graph_inputs_original[target] = tensor.data.data  # type: ignore[union-attr]
        if self.current_node.users:  # cudagraphs should work with an unused CPU input
            self.add_device_info(example.device)

        # Note: [Input Alignment handling in Inductor]
        # Alignment matters for generating efficient code. Some operations,
        # e.g. vectorized loads, can only be performed on aligned inputs.
        #
        # But if we codegen assuming aligned inputs and then get unaligned
        # inputs at runtime, then we are forced to clone - which is bad for
        # both perf and memory usage.
        #
        # One option would be to guard on storage_offset%ALIGNMENT, and then
        # codegen based on this. But storage_offset guards turned out to be
        # expensive and cause recompiles; Instead, we're generating code
        # based on the alignment of the example input without guarding.
        with maybe_get_suppress_shape_guards_ctx():
            if not should_assume_input_aligned(example):
                self.unaligned_buffers.add(target)
        return tensor

    @typing_extensions.override
    def call_function(self, target: Callable, args: Any, kwargs: dict[str, Any]) -> Any:  # type: ignore[type-arg]
        if target is operator.getitem and isinstance(args[0], (list, tuple, dict)):
            return super().call_function(target, args, kwargs)

        # hasattr on OpOverloadPacket is slow, check isinstance first
        if not isinstance(target, torch._ops.OpOverloadPacket) and hasattr(
            target, "_inductor_lowering_function"
        ):
            # passthrough lowerings from .pattern_matcher
            return target(*args, **kwargs)

        if target not in lowerings:
            assert isinstance(target, torch._ops.OpOverload), (
                f"{target} is not an OpOverload"
            )
            base_name = target.name().split(".")[0]
            if base_name in FALLBACK_ALLOW_LIST:
                make_fallback(
                    target,
                    warn=False,
                    get_decomp_fn=self.get_decomp_fn,
                    override_decomp=True,
                )
            elif config.implicit_fallbacks:
                error = (
                    MissingOperatorWithDecomp
                    if get_decompositions([target])
                    else MissingOperatorWithoutDecomp
                )
                log.info(
                    "Creating implicit fallback for:\n%s",
                    LazyString(error.operator_str, target, args, kwargs),
                )

                tag: torch._C.Tag | None = get_layout_constraint_tag(
                    target, with_default=False
                )
                if (
                    tag is None
                    and torch._library.utils.is_builtin(target)
                    and self.is_backward
                ):
                    # for implicit fallback ATen ops during backward, if there
                    # is no layout constraint tag, we conservatively require contiguous
                    # input since some eager kernels do not
                    # support non-contiguous inputs. Otherwise they may silently cause
                    # accuracy problems. Check https://github.com/pytorch/pytorch/issues/140452
                    # We only do this For ATen ops and for backward.
                    #
                    # TODO: should really switch to "needs_fixed_stride" constraint on these
                    # and identify them one by one.
                    decided_constraint: Callable[..., tuple[Any, Any]] | None = (
                        require_contiguous
                    )
                else:
                    default_tag: torch._C.Tag = get_layout_constraint_tag(
                        target, with_default=True
                    )
                    decided_constraint = tag_to_layout_constraint(default_tag)

                make_fallback(
                    target,
                    layout_constraint=decided_constraint,
                    get_decomp_fn=self.get_decomp_fn,
                )

            elif get_decompositions([target]):
                # There isn't a good way to dynamically patch this in
                # since AOT Autograd already ran.  The error message tells
                # the user how to fix it.
                raise MissingOperatorWithDecomp(target, args, kwargs)
            else:
                raise MissingOperatorWithoutDecomp(target, args, kwargs)

        try:
            log.debug("  via %s", lowerings[target])  # type: ignore[index]

            n = self.current_node
            layout_constraints = maybe_layout_constraints(target)
            if layout_constraints:
                old_args, old_kwargs = args, kwargs
                if layout_constraints is constrain_to_fake_tensors:
                    # only constrain_to_fake_tensor if this exists.
                    # otherwise, no constraints at all: the implication is
                    # that this operator was inserted by a custom pass
                    # so we'll give them the freedom.
                    if "eager_input_vals" in n.meta:
                        fake_args, fake_kwargs = n.meta["eager_input_vals"]

                        # (fake_args, fake_kwargs) might not align with (args, kwargs).
                        # we need to normalize them based on the schema
                        assert isinstance(target, torch._ops.OpOverload)

                        def normalize(args: Any, kwargs: Any) -> tuple[Any, Any]:
                            result = torch.fx.operator_schemas.normalize_function(
                                target, args, kwargs
                            )
                            assert result is not None
                            return result[0], result[1]

                        fake_args, fake_kwargs = normalize(fake_args, fake_kwargs)
                        args, kwargs = normalize(args, kwargs)
                        old_args, old_kwargs = normalize(old_args, old_kwargs)

                        args, kwargs = constrain_to_fake_tensors(
                            args, kwargs, fake_args, fake_kwargs
                        )
                else:
                    args, kwargs = layout_constraints(n, *args, **kwargs)

            if "should_fallback" in n.meta or n.meta.get("custom", {}).get(
                "fallback_to_eager"
            ):
                out = fallback_handler(target, add_to_fallback_set=False)(
                    *args, **kwargs
                )
            else:
                out = None

                if (
                    target in user_lowerings
                    and target not in V.active_user_lowering_ops
                ):
                    # User-registered lowering takes priority, with recursion guard
                    V.active_user_lowering_ops.add(target)
                    try:
                        # pyrefly: ignore[bad-index]
                        out = user_lowerings[target](*args, **kwargs)
                    finally:
                        V.active_user_lowering_ops.discard(target)

                # If no user_lowering, or it returned None fall back to normal lowering
                if out is None:
                    if target in lowerings:
                        out = lowerings[target](*args, **kwargs)
                    else:
                        # Fallback for ops not in lowerings (e.g., custom ops during recursion)
                        out = fallback_handler(target, add_to_fallback_set=False)(
                            *args, **kwargs
                        )

            if layout_constraints:
                # layout_constraints are allowed to make new copies of the inputs.
                # if they do, and if the target is mutable, then we need to
                # write the new values back into the original inputs.
                self.propagate_mutation(n, old_args, old_kwargs, args, kwargs)  # type: ignore[possibly-undefined]

            return out
        except Exception as e:
            stack_trace = None
            if (
                hasattr(self, "current_node")
                and self.current_node is not None
                and hasattr(self.current_node, "meta")
                and self.current_node.meta is not None
            ):
                stack_trace = self.current_node.meta.get("stack_trace", None)
            raise LoweringException(
                e, target, args, kwargs, stack_trace=stack_trace
            ).with_traceback(e.__traceback__) from None

    @staticmethod
    def can_inline_constant(t: torch.Tensor) -> bool:
        """
        True if this is a small constant attr that will be inlined.
        """
        return len(t.shape) == 1 and t.shape[0] <= 8

    # pyrefly: ignore [bad-override]
    def get_attr(
        self,
        target: str,  # type: ignore[override]
        args: tuple[()],  # type: ignore[override]
        kwargs: dict[str, object],
    ) -> Constant | TensorBox | ShapeAsConstantBuffer | ir.Subgraph | TorchBindObject:
        # this is a constant
        value = getattr_recursive(self.module, target)  # type: ignore[arg-type]

        if isinstance(value, torch.fx.GraphModule):
            # Reuse the existing subgraph if we have seen it before already.
            if target in self.seen_subgraphs:
                return self.seen_subgraphs[target]

            out = ir.Subgraph(name=target, graph_module=value)
            self.seen_subgraphs[target] = out
            return out

        if isinstance(value, torch._C.ScriptObject):
            self.torchbind_constants[target] = value
            self.constant_reprs[target] = ""
            return TorchBindObject(name=target, value=value)
        elif isinstance(value, FakeScriptObject):
            self.torchbind_constants[target] = value
            self.constant_reprs[target] = ""
            return TorchBindObject(name=target, value=value)
        elif is_opaque_type(type(value)):
            self.torchbind_constants[target] = value  # type: ignore[arg-type]
            self.constant_reprs[target] = ""
            return TorchBindObject(name=target, value=value)  # type: ignore[arg-type]

        assert isinstance(value, torch.Tensor)
        if (
            config.aot_inductor.use_runtime_constant_folding
            or config.always_keep_tensor_constants
            or unsupported_output_tensor(value)
            or target in self.mutated_named_buffers
        ):
            return self.add_tensor_constant(value, target)

        with no_dispatch():
            if value.shape == ():
                return Constant(
                    value=value.item(), dtype=value.dtype, device=value.device
                )
            if self.can_inline_constant(value):
                log.debug("Inlining constant: %s ", target)
                # tensor lowering has constant inlining logic
                from .lowering import tensor

                return tensor(
                    value.tolist(),
                    dtype=value.dtype,
                    device=value.device,
                    pin_memory=_is_cpu_strided_tensor_pinned(value),
                )

        return self.add_tensor_constant(value, target)

    def call_module(self, target: Any, args: Any, kwargs: Any) -> NoReturn:
        raise AssertionError

    def call_method(self, target: Any, args: Any, kwargs: Any) -> NoReturn:
        raise AssertionError

    @typing_extensions.override
    def output(
        self,
        target: torch.fx.node.Target,
        args: tuple[torch.fx.node.Argument, ...],
        kwargs: dict[str, object],
    ) -> None:
        result = super().output(target, args, kwargs)  # type: ignore[arg-type]
        if not isinstance(result, (tuple, list)):
            # nested subgraphs can have singleton outputs
            result = (result,)
        assert isinstance(result, (tuple, list)), type(result)
        result = [
            ir.OpaqueValueTypeConstant(value=x) if is_opaque_value_type(type(x)) else x
            for x in result
        ]
        assert all(
            isinstance(
                x,
                (
                    TensorBox,
                    ir.Constant,
                    type(None),
                    ir.ConstantBuffer,
                    sympy.Expr,
                    sympy.logic.boolalg.Boolean,
                    int,
                    ir.EffectfulKernel,
                    ir.ShapeAsConstantBuffer,
                    TorchBindObject,
                    ir.OpaqueMultiOutput,
                    ir.OpaqueValueTypeConstant,
                    ir.OpaqueObjectState,
                ),
            )
            for x in result
        ), result

        fx_node_args = V.graph.current_node.args[0]  # type: ignore[arg-type]
        if not isinstance(fx_node_args, (tuple, list)):
            # nested subgraphs can have singleton outputs
            fx_node_args = (fx_node_args,)
        result = [ir.ExternKernel.realize_input(x) for x in result]
        result_correct_strides = []

        assert len(fx_node_args) == len(result)
        for r, fx_node in zip(result, fx_node_args):
            if not isinstance(r, (ir.TensorBox, ir.BaseView)):
                result_correct_strides.append(r)
            elif isinstance(r.get_output_spec(), ir.CommBufferLayout):
                # Active references to persistent comm buffers are not allowed
                # outside of graphs
                result_correct_strides.append(ir.ExternKernel.copy_input(r))
            else:
                # AOT Autograd tries to detect stride divergence of inductor from output metadata.
                # Here, we try to avoid spurious divergence by matching insignificant strides such as

                # should have already been realized
                assert torch._inductor.ir.is_storage_and_layout(r)
                meta_strides = [
                    s.node.expr if isinstance(s, torch.SymInt) else s
                    # pyrefly: ignore [missing-attribute]
                    for s in fx_node.meta["val"].stride()
                ]
                result_correct_strides.append(
                    ir.try_match_insignificant_strides(r, meta_strides)
                )

        self.graph_outputs = result_correct_strides
        value: ir.IRNode
        for name, value in self.graph_inputs.items():
            if isinstance(
                value,
                (
                    TorchBindObject,
                    sympy.Basic,
                    torch._inductor.ir.GeneratorState,
                    torch._inductor.ir.OpaqueObjectState,
                ),
            ):
                continue
            assert isinstance(value, TensorBox), (
                f"Unsupported inductor graph input type: {type(value)}"
            )
            value.realize()
            assert isinstance(value, TensorBox)
            value = value.data
            assert isinstance(value, ir.StorageBox)
            value_storage_box = value
            value = value.data
            if not isinstance(value, InputBuffer) or value.get_name() != name:
                # one of our inputs was mutated, need to turn that into a copy
                ir.MutationLayoutSHOULDREMOVE.realize_into(
                    value, self.graph_inputs_original[name]
                )
                # replace output with mutated input
                try:
                    ind = self.graph_outputs.index(value_storage_box)
                    self.graph_outputs[ind] = self.graph_inputs_original[name]
                except ValueError:
                    pass

        self.finalize()
        log.debug(
            "Force channels last inputs for %d conv for the current graph with id %d",
            self.num_channels_last_conv,
            self.graph_id if self.graph_id is not None else -1,
        )

    def finalize(self) -> None:
        for buf in self.buffers:
            buf.decide_layout()

    @contextmanager
    def set_current_node(self, node: torch.fx.Node):  # type: ignore[no-untyped-def]
        old = self.current_node
        try:
            self.current_node = node
            yield
        finally:
            self.current_node = old

    @contextmanager
    def set_current_wrapper_code(self) -> Iterator[None]:
        old = self.wrapper_code
        try:
            yield
        finally:
            self.wrapper_code = old

    def propagate_mutation(
        self,
        fx_node: torch.fx.Node,
        old_args: tuple[Any],
        old_kwargs: dict[str, Any],
        new_args: tuple[Any],
        new_kwargs: dict[str, Any],
    ) -> None:
        """Propagate mutations on new_args/new_kwargs back to old_args/old_kwargs.

        Assumes we may have cloned old_args/old_kwargs into new_args/new_kwargs
        and then called fx_node(*new_args, **new_kwargs).

        If fx_node mutates any of new_args/new_kwargs, and they are different from
        old_args/old_kwargs, then we need to update the original tensor.
        """
        assert len(old_args) == len(new_args)
        assert len(old_kwargs) == len(new_kwargs)

        if fx_node.target is torch.ops.higher_order.triton_kernel_wrapper_mutation:
            kwargs = fx_node.kwargs["kwargs"]
            assert isinstance(kwargs, dict)
            mutated = torch._higher_order_ops.triton_kernel_wrap.get_mutated_tensors(
                old_kwargs["kernel_idx"],
                old_kwargs["constant_args_idx"],
                {
                    k: v.meta["val"] if isinstance(v, torch.fx.Node) else v
                    for k, v in kwargs.items()
                },
                old_kwargs["tma_descriptor_metadata"],
            )
            for name in mutated:
                old_arg = old_kwargs["kwargs"][name]
                new_arg = new_kwargs["kwargs"][name]
                if old_arg is new_arg:
                    continue

                self.call_function(torch.ops.aten.copy_.default, (old_arg, new_arg), {})
            return

        assert isinstance(fx_node.target, torch._ops.OpOverload)

        def maybe_propagate(
            schema_arg: torch._C.Argument, old_arg: ir.IRNode, new_arg: ir.IRNode
        ) -> None:
            if old_arg is new_arg:
                return
            if schema_arg.alias_info is not None and schema_arg.alias_info.is_write:
                # The lowering for copy_ is smart enough to "replace" old_arg with
                # new_arg in all future uses so a copy_ kernel never gets emitted.
                # old_arg, new_arg may be immutable_list
                if isinstance(old_arg, ir.IRNode):
                    old_arg = (old_arg,)  # type: ignore[assignment]
                    new_arg = (new_arg,)  # type: ignore[assignment]

                for old_arg_item, new_arg_item in zip(old_arg, new_arg):  # type: ignore[call-overload]
                    if old_arg_item is new_arg_item:
                        continue
                    self.call_function(
                        torch.ops.aten.copy_.default, (old_arg_item, new_arg_item), {}
                    )

        schema = fx_node.target._schema
        for idx, (old_arg, new_arg) in enumerate(zip(old_args, new_args)):
            schema_arg = schema.arguments[idx]
            maybe_propagate(schema_arg, old_arg, new_arg)

        schema_kwargs = {arg.name: arg for arg in schema.arguments}

        for key in old_kwargs:
            old_arg = old_kwargs[key]
            new_arg = new_kwargs[key]
            schema_arg = schema_kwargs[key]
            maybe_propagate(schema_arg, old_arg, new_arg)

    @staticmethod
    def _get_node_stream(n: torch.fx.Node) -> int | None:
        """Get the user-annotated stream index from FX node metadata."""
        return n.meta.get("custom", {}).get("stream")

    def _realize_inputs_at_stream_boundaries(self, n: torch.fx.Node) -> None:
        """Realize IR inputs that are on a different stream.

        Without this, pointwise ops across stream boundaries would be inlined
        into each other during lowering, making it impossible for the scheduler
        to split them into separate kernels.

        None means the default stream, so it is compared like any other value.
        """
        node_stream = self._get_node_stream(n)
        for input_node in n.all_input_nodes:
            input_stream = self._get_node_stream(input_node)
            if input_stream == node_stream:
                continue
            ir_value = self.env.get(input_node)
            if isinstance(ir_value, ir.TensorBox):
                ir_value.realize()

    def run_node(self, n: torch.fx.Node) -> object:
        """Lower and execute a single FX node into Inductor IR."""

        def debug(msg: str) -> None:
            log.debug("lowering %s %s", LazyString(n.format_node), msg)  # type: ignore[arg-type]

        # Use channels-last stride order for certain
        # dense 4D intermediates when layout optimization determines a
        # downstream consumer (typically conv) prefers channels-last.
        def maybe_apply_channels_last_stride_order(
            result: ir.IRNode, n: torch.fx.Node
        ) -> ir.IRNode:
            dense = torch._prims_common.is_non_overlapping_and_dense_or_false(
                n.meta["val"]
            )
            strides = n.meta["val"].stride()
            unbacked_symbols_in_strides = len(free_unbacked_symbols(strides)) > 0
            if (
                not unbacked_symbols_in_strides
                and dense
                and len(result.get_size()) == 4
                and n in self.nodes_prefer_channels_last
                and not is_user_visible
                and not is_input_for_as_strided
            ):
                result = ir.ExternKernel.require_stride_order(
                    result,
                    ir.get_stride_order(
                        make_channels_last_strides_for(n.meta["val"].shape)
                    ),
                )
            return result

        from torch._inductor.compiler_bisector import CompilerBisector

        buffer_watermark = len(self.buffers)
        operation_watermark = len(self.operations)

        # origins: OrderedSet[Union[Node, ir.IRNode]] = OrderedSet([n])
        origins: OrderedSet[Any] = OrderedSet([n])
        is_call_function = n.op == "call_function"
        if is_call_function:
            args, kwargs = self.fetch_args_kwargs_from_env(n)
            origins |= gather_origins(args, kwargs)
            self._realize_inputs_at_stream_boundaries(n)
        with (
            ir.IRNode.current_origins(origins),
            ir.IRNode.current_stream_idx(self._get_node_stream(n)),
            self.set_current_node(n),
            V.set_current_node(n),
        ):
            if (
                is_call_function
                # this path only for built-in operators
                and n.target
                and isinstance(n.target, torch._ops.OpOverload)
                and torch._library.utils.is_builtin(n.target)
                and (
                    fallback_node_due_to_unsupported_type(n)
                    or CompilerBisector.disable_subsystem(
                        "inductor", "lowerings", lambda: repr(n)
                    )
                )
            ):
                debug("fallback_handler")
                result = fallback_handler(n.target, add_to_fallback_set=False)(
                    *args,  # type: ignore[possibly-undefined]
                    **kwargs,  # type: ignore[possibly-undefined]
                )
            elif (
                n.op == "call_function"
                and isinstance(
                    n.target, (torch._ops.OpOverload, torch._ops.HigherOrderOperator)
                )
                and should_fallback_by_default(n)
            ):
                # this path supports fallback due to inductor lite mode. It supports
                # both OpOverload and HOPs (e.g., triton_kernel_wrapper_functional).
                debug("fallback_handler")
                result = fallback_handler(n.target, add_to_fallback_set=False)(
                    *args,  # type: ignore[possibly-undefined]
                    **kwargs,  # type: ignore[possibly-undefined]
                )
            elif (
                n.op == "call_function"
                and n.target is torch.ops.higher_order.triton_kernel_wrapper_mutation
                and config.triton_kernel_default_layout_constraint != "flexible_layout"
            ):
                debug("user_defined_triton_kernel_layout_constraints")
                if (
                    config.triton_kernel_default_layout_constraint
                    == "needs_fixed_stride_order"
                ):
                    old_args = args  # type: ignore[possibly-undefined]
                    old_kwargs = kwargs  # type: ignore[possibly-undefined]

                    if eager_input_vals := n.meta.get("eager_input_vals"):
                        inp_args = eager_input_vals[0]
                        inp_kwargs = eager_input_vals[1]
                        args, kwargs = constrain_to_fake_tensors(
                            # pyrefly: ignore [unbound-name]
                            args,
                            # pyrefly: ignore [unbound-name]
                            kwargs,
                            inp_args,
                            inp_kwargs,
                        )
                    else:
                        args, kwargs = constrain_to_fx_strides(n, *args, **kwargs)  # type: ignore[index]
                    result = self.call_function(n.target, args, kwargs)  # type: ignore[arg-type]
                    self.propagate_mutation(n, old_args, old_kwargs, args, kwargs)  # type: ignore[possibly-undefined]
                else:
                    raise RuntimeError(
                        f"Unknown triton_kernel_default_layout_constraint: {config.triton_kernel_default_layout_constraint}"
                    )
            else:
                debug("")
                result = super().run_node(n)

            # require the same stride order for dense outputs,
            # 1. user-land view() will not throw because inductor
            # output different strides than eager
            # long term the solution is to make view() always succeed
            # with infallible strides.
            # 2: as_strided ops, we need to make sure its input has same size/stride with
            # eager model to align with eager behavior.
            as_strided_ops = [
                torch.ops.aten.as_strided.default,
                torch.ops.aten.as_strided_.default,
                torch.ops.aten.as_strided_scatter.default,
                torch.ops.aten.resize.default,
                torch.ops.aten.resize_as.default,
            ]
            is_output = any(user.op == "output" for user in n.users)
            is_user_visible = n in self.user_visible_output_strides
            is_input_for_as_strided = any(
                user.target in as_strided_ops for user in n.users
            )

            if n.meta.get("inductor_realize_to_strides", False) and isinstance(
                result, TensorBox
            ):
                result.realize()
                strides = n.meta["val"].stride()
                sym_strides = torch._inductor.utils.any_is_symbolic(*strides)
                if result.maybe_get_stride() != strides and not sym_strides:
                    stride_order = ir.get_stride_order(strides)
                    result = ir.ExternKernel.require_stride_order(result, stride_order)
            if (
                is_output
                and isinstance(result, TensorBox)
                and isinstance(result.data, ir.BaseView)
            ):
                # Realize so that outputs are correctly aliased
                result.realize()

            if (is_output or is_input_for_as_strided) and isinstance(
                n.meta.get("val"), torch.Tensor
            ):
                if is_user_visible:
                    strides = self.user_visible_output_strides.get(n)
                else:
                    strides = n.meta["val"].stride()

                if strides is not None and len(strides) > 0:
                    allow_padding = (
                        config.pad_outputs or not is_user_visible
                    ) and not is_input_for_as_strided
                    dense = torch._prims_common.is_non_overlapping_and_dense_or_false(
                        n.meta["val"]
                    )
                    unbacked_symbols_in_strides = (
                        len(free_unbacked_symbols(strides)) > 0
                    )
                    if (
                        not unbacked_symbols_in_strides
                        and dense
                        and len(result.get_size()) == 4
                        and n in self.nodes_prefer_channels_last
                        and not is_user_visible
                        and not is_input_for_as_strided
                    ):
                        strides = ir.FlexibleLayout.stride_ordered_for_memory_format(
                            result.get_size(), torch.channels_last
                        )
                    if not unbacked_symbols_in_strides and len(strides):
                        # To avoid converting possible view ops to a copy kernel, we use the previous
                        # require_exact_strides to handle views. But ultimately it's better to require
                        # the right strides at the tensor definition.
                        if n.meta["val"]._is_view() or isinstance(
                            result.data,
                            ir.BaseView,
                        ):
                            result = ir.ExternKernel.require_stride_order(
                                result,
                                ir.get_stride_order(strides),
                                allow_padding=allow_padding,
                            )
                        else:
                            # Fix for 0-d tensors: if result size is empty,
                            # strides should also be empty
                            if len(result.get_size()) == 0 and len(strides) > 0:
                                strides = []
                            result = ir.ExternKernel.require_exact_strides(
                                result, strides, allow_padding=allow_padding
                            )

            # Realize if (1) any user need inputs realized, or (2) there is
            # already too many reads and rematerializing can be bad.
            num_users = len(OrderedSet(n.users))
            if num_users > 1 and isinstance(result, TensorBox):
                for user in n.users:
                    if user.target in needs_realized_inputs:
                        result.realize_hint()
                        # This inclusion is somewhat controversial (from
                        # discussion between Horace, Natalia, and Elias).
                        # Currently, it's not very clear why this is helpful.
                        # The general idea here is that even though a node may
                        # have FlexibleLayout, we still often *treat* it as if
                        # it was contiguous. This appears to sometimes result in
                        # suboptimal behavior.
                        #
                        # When we do a better job selecting layout, we should
                        # revisit this.
                        need_fixed_layout = [
                            torch.ops.aten.convolution_backward.default,
                            torch.ops.aten.mm.default,
                            torch.ops.aten._int_mm.default,
                        ]
                        need_fixed_channels_last_layout = []
                        if not self.layout_opt:
                            need_fixed_layout.append(torch.ops.aten.convolution.default)
                        if torch._C._has_mkldnn:
                            need_fixed_layout += [
                                torch.ops.mkldnn._linear_pointwise.default,
                                torch.ops.mkldnn._linear_pointwise.binary,
                                torch.ops.aten.mkldnn_rnn_layer.default,
                                torch.ops.onednn.qlinear_pointwise.default,
                                torch.ops.onednn.qlinear_pointwise.tensor,
                                torch.ops.onednn.qlinear_pointwise.binary,
                                torch.ops.onednn.qlinear_pointwise.binary_tensor,
                            ]
                            need_fixed_channels_last_layout += [
                                torch.ops.mkldnn._convolution_pointwise.default,
                                torch.ops.mkldnn._convolution_pointwise.binary,
                                torch.ops.mkldnn._convolution_pointwise_.binary,
                                torch.ops.mkldnn._convolution_transpose_pointwise.default,
                                torch.ops.onednn.qconv_pointwise.default,
                                torch.ops.onednn.qconv2d_pointwise.binary,
                            ]
                            if torch._C.has_mkl:
                                need_fixed_layout += [torch.ops.mkl._mkl_linear.default]
                        if user.target in need_fixed_layout:
                            result = ir.ExternKernel.require_stride_order(
                                result,
                                ir.get_stride_order(n.meta["val"].stride()),
                                allow_padding=True,
                            )
                        if (
                            user.target in need_fixed_channels_last_layout
                            and n is user.args[0]
                        ):
                            result = ir.ExternKernel.require_stride_order(
                                result,
                                ir.get_stride_order(
                                    make_channels_last_strides_for(n.meta["val"].shape)
                                ),
                            )
                    if user.op == "output":
                        # pyrefly: ignore [missing-attribute]
                        if isinstance(result.data.data, (Pointwise, Reduction)):
                            # Cheap-to-recompute nodes (0 buffer reads, e.g.
                            # index arithmetic or constant fills) can be
                            # deferred to realize_input at output processing.
                            # This prevents cascade materialization where
                            # shared constants inflate downstream read counts.
                            if (
                                config.delay_realize_cheap_outputs
                                # pyrefly: ignore [missing-attribute]
                                and result.data.num_reads() == 0
                                # pyrefly: ignore [missing-attribute]
                                and not result.data.has_large_inner_fn()
                            ):
                                continue
                            result.realize()

                _data = result.data  # type: ignore[attr-defined]
                while not isinstance(_data, StorageBox) and isinstance(
                    _data, (ir.BaseView, ir.MutableBox)
                ):
                    _data = _data.data

                if isinstance(_data, StorageBox) and _data.should_realize_on_reuse(
                    len(n.users)
                ):
                    result = maybe_apply_channels_last_stride_order(result, n)

                # TODO(jansel): introduce a store vs inline choice
                result.mark_reuse(len(n.users))

            # Realize if the IRNode already has accumulated lots of reads
            if isinstance(result, TensorBox) and result.has_exceeded_max_reads():
                # Prevent excessive accumulation in a computed buffer, when
                # there are multiple branches each with small number of memory
                # reads, but they converge to a user.
                result = maybe_apply_channels_last_stride_order(result, n)
                result.realize_hint()

            # Realize if a Pointwise has too much stuff to be inlined.
            # As this may cause RecursionError during Inductor's evaluation.
            if isinstance(result, TensorBox) and isinstance(result.data, StorageBox):
                curr = result.data.data
                if isinstance(curr, Pointwise):
                    # Use inner fn as a rough proxy. Good enough.
                    if curr.has_large_inner_fn(threshold=100):
                        result.realize()

        assign_origin_node(result, n)
        self.register_users_of(result)

        new_unbacked_defs = OrderedSet[sympy.Symbol]()
        for buf in self.buffers[buffer_watermark:]:
            new_unbacked_defs |= buf.get_unbacked_symbol_defs()
        for op in self.operations[operation_watermark:]:
            new_unbacked_defs |= op.get_unbacked_symbol_defs()

        shape_env = V.graph.sizevars.shape_env

        # An input can be unbacked symint i.e.: when mark_unbacked is used.
        # in that case add it to new_unbacked_defs.
        if (
            n.op == "placeholder"
            and isinstance(result, sympy.Symbol)
            and shape_env.is_unbacked_symint(result)
        ):
            new_unbacked_defs.add(result)

        def format_new_defs() -> str:
            r = [
                f"unbacked_symbol_defs={buf.get_unbacked_symbol_defs()} in:\n{buf}\n"
                for buf in self.buffers[buffer_watermark:]
            ]
            r.extend(
                f"unbacked_symbol_defs={op.get_unbacked_symbol_defs()} in:\n{op}\n"
                for op in self.operations[operation_watermark:]
            )
            return "***\n".join(r)

        # We do not skip unbacked symints that are input for backward see the note below.
        if V.graph.is_backward and n.op == "placeholder":
            return result

        # Note [Backwards runtime asserts]
        # Backwards poses an interesting problem for deferred runtime
        # asserts.  In the easy case, we may solely close over data
        # dependent sized tensors, and there are no binding sites for
        # unbacked SymInts.  In this case, we can just drop all the
        # runtime asserts on the floor: no non-placeholder bindings, no
        # problem.
        #
        # However, it is *possible* for a fresh runtime assert to show up
        # between forwards and backwards.  Right now, the freezing process
        # that happens when we lower forwards means that we will freeze
        # runtime asserts, and then the moment the backwards lowering
        # process attempts to add a new deferred runtime assert, we will
        # fail.  Let's say you remove that assert.  Now when we get here,
        # we need to make sure we actually emit these asserts (because we
        # can't emit them in forwards, we already compiled it).  So we
        # have to do something here.  But we don't want to reemit ALL
        # deferred runtime asserts, we only want to emit the NEW ones.
        # Therefore needing some sort of stratification in the ShapeEnv.
        # This is all doable, it just hasn't been done yet.

        unbacked_bindings = resolve_unbacked_bindings(
            V.graph.sizevars.shape_env, n.meta.get("unbacked_bindings", {})
        )
        assert unbacked_bindings is not None
        # When we do lowering, it is possible we reallocate unbacked SymInts.
        # So we need to line up the unbacked SymInts when performing the test
        # here
        #
        # In principle, we could permit lowering to introduce MORE unbacked
        # SymInts: as long as all the old unbacked ones are accounted for,
        # it's fine for inductor to introduce extra calls to item()/unbacked()
        # whatever.  This actually happens in practice when an unbacked SymInt
        # gets memoized away; naively, when Inductor reprocesses a kernel, it
        # doesn't know that the memo still applies, and ends up allocating a
        # new symbol.  However, this is generally a bad thing: we may still
        # end up needing to test equalities on the symbols, and a fresh
        # symbol is likely to hit lots of GuardOnDataDependent errors that
        # we already know facts for.
        renamed_unbacked_bindings = OrderedSet(
            V.fake_mode.shape_env.unbacked_renamings.get(s, s)
            for s in unbacked_bindings
        )

        assert new_unbacked_defs >= renamed_unbacked_bindings, (
            f"failed {new_unbacked_defs} >= {renamed_unbacked_bindings} (inductor >= fx)\n"
            f"fx node is: {n.format_node()}\n"
            f"new operations are:\n\n{format_new_defs()}"
        )
        self.create_deferred_runtime_asserts(n, new_unbacked_defs)
        return result

    def create_deferred_runtime_asserts(
        self, n: torch.fx.Node, new_unbacked_defs: OrderedSet[sympy.Symbol]
    ) -> None:
        if config.do_not_emit_runtime_assertions:
            return
        # [NOTE] Codegen runtime asserts in Inductor
        #
        # We need to generate runtime asserts directly in Inductor instead
        # of just reusing the asserts from input graphs because we reuse the
        # same ShapeEnv as before. In particular, on subsequent graph passes,
        # we would immediately turn all of these assertions into noops,
        # because when we evaluated their expressions, we would see that
        # because we had a deferred runtime assert in the ShapeEnv, we
        # know "oh, of course this expression is True" already.
        # One example is below:
        #
        # class Model(torch.nn.Module):
        #     def forward(self, a, b, c):
        #         nz = torch.nonzero(a)
        #         ones = a.new_ones([nz.size(0), b.size(0)])
        #         torch._check(ones.size(0) >= 1)
        #         equals = torch.add(ones, c)
        #         return equals
        # torch._dynamo.mark_dynamic(c, 0)
        # When we reuse the ShapeEnv in Inductor lowering, the check that checks
        # a and nonzero have the same shape would be evaluated to True after we resolve
        # unbacked bindings using the ShapeEnv.
        # See test_unbacked_equals_input_size_runtime_assertion in test_aot_inductor.
        #
        #
        # In addition to the Inductor generated runtime asserts, we also
        # need the runtime asserts from the input graph, because some derived
        # runtime asserts on backed symints are not generated in Inductor. One example is
        # this: `y = x.reshape(100, -1).clone()`. x.shape[0] needs to be a multiple of 100.
        # See test_aoti_runtime_asserts_backed_symint in test_aot_inductor.

        def make_assert(expr: SympyBoolean, msg: str) -> None:
            assert_op = ir.AssertScalar(expr, msg)
            self.register_buffer(assert_op, set_name=True)
            self.register_operation(assert_op)

        if (
            full_aoti_runtime_assert()
            and n.target is torch.ops.aten._assert_scalar.default
            and self.aot_mode
        ):
            node_args, _ = self.fetch_args_kwargs_from_env(n)
            if node_args[0] != True:  # noqa: E712
                make_assert(node_args[0], f"{node_args[0]} to be True")
        else:
            # bound_unbacked_symbols tracks the symbols that are created so far,
            # we use it to make sure that runtime assertions are added after all
            # symbols used in them are defined.
            self.bound_unbacked_symbols |= new_unbacked_defs

            shape_env = V.graph.sizevars.shape_env

            # Emit code for runtime asserts that can be inserted at this point.
            for i0 in new_unbacked_defs:
                ras = self.ras_by_symbol.pop(i0, [])
                # NB: size-like not needed, we won't retrace
                vr = shape_env.var_to_range[i0]
                if not shape_env._default_unspecified_value_range().issubset(vr):

                    def is_convertible(s: Expr) -> bool:
                        if s in (int_oo, -int_oo):
                            return False
                        try:
                            int(s)
                            return True
                        except TypeError:
                            return False

                    if is_convertible(vr.lower):
                        make_assert(i0 >= vr.lower, f"{i0} >= {vr.lower}")
                    if is_convertible(vr.upper):
                        make_assert(i0 <= vr.upper, f"{i0} <= {vr.upper}")

                for ra in ras:
                    fvs = free_unbacked_symbols(ra.expr)
                    missing = fvs - self.bound_unbacked_symbols
                    if missing:
                        i1 = min(missing, key=str)
                        self.ras_by_symbol.setdefault(i1, []).append(ra)
                    else:
                        make_assert(ra.expr, f"{ra.expr}")

    def validate_can_generate_cpp_wrapper(self) -> None:
        if config.disable_cpp_codegen:
            raise CppWrapperCodegenError("C++ codegen is disabled")

        if sys.platform not in ("linux", "darwin", "win32"):
            raise CppWrapperCodegenError(f"Unsupported platform {sys.platform}")

    def init_wrapper_code(
        self,
        is_subgraph: bool = False,
        subgraph_name: str | None = None,
        parent_wrapper_code: PythonWrapperCodegen | None = None,
        partition_signatures: GraphPartitionSignature | None = None,
    ) -> None:
        device_types = self.device_types.copy()
        device_types.discard("cpu")
        device_types.discard("meta")
        # TODO(Eikan): Only support mixing cpu and other device now.
        assert len(device_types) <= 1, "Does not support mixing {}".format(
            "+".join(device_types)
        )
        only_cpu = len(device_types) == 0
        self.device_type = "cpu" if only_cpu else device_types.pop()

        if self.cpp_wrapper:
            self.validate_can_generate_cpp_wrapper()

        self.device_ops = get_device_op_overrides(self.device_type)
        wrapper_code_gen_cls = get_wrapper_codegen_for_device(
            self.device_type, self.cpp_wrapper, self.fx_wrapper
        )
        assert wrapper_code_gen_cls is not None, (
            f"Device {self.device_type} not supported"
        )
        self.wrapper_code = wrapper_code_gen_cls.create(
            is_subgraph,
            subgraph_name,
            parent_wrapper_code,
            partition_signatures,
        )

        if self.const_module:
            self.wrapper_code._names_iter = self.const_module.wrapper_code._names_iter

    def extract_autotune_inputs(
        self, example_inputs: list[int | float | torch.Tensor]
    ) -> None:
        import copy

        cloned_gm = copy.deepcopy(self.orig_gm)
        example_inputs = copy.deepcopy(example_inputs)
        triton_nodes = []
        for node in cloned_gm.graph.nodes:
            if (
                node.op == "call_function"
                and node.target is torch.ops.higher_order.triton_kernel_wrapper_mutation
            ):
                triton_nodes.append(node)

        # Store grid related nodes
        grid_inputs: list[torch.fx.Node] = []
        visited_grids: dict[torch.fx.Node, int] = {}
        # Store kwargs related nodes
        triton_inputs: dict[str, Any] = {}
        kwargs_inputs: list[torch.fx.Node] = []
        visited_kwargs: dict[Any, int] = {}
        for node in triton_nodes:
            # first check whether we have fx node in grid settings.
            for grid in node.kwargs["grid"]:
                for val in grid:
                    if val in visited_grids:
                        continue

                    if isinstance(val, torch.fx.Node):
                        visited_grids[val] = len(grid_inputs)
                        grid_inputs.append(val)

            kwargs = node.kwargs["kwargs"]
            # identify which args might be mutated, those should be cloned.
            mutated = torch._higher_order_ops.triton_kernel_wrap.get_mutated_tensors(
                node.kwargs["kernel_idx"],
                node.kwargs["constant_args_idx"],
                {
                    k: v.meta["val"] if isinstance(v, torch.fx.Node) else v
                    for k, v in kwargs.items()
                },
                node.kwargs["tma_descriptor_metadata"],
            )

            new_kwargs: dict[str, int] = {}
            with cloned_gm.graph.inserting_before(node):
                for k, v in kwargs.items():
                    if k in mutated:
                        new_node = cloned_gm.graph.call_function(torch.clone, args=(v,))
                        new_kwargs[k] = len(kwargs_inputs)
                        kwargs_inputs.append(new_node)
                        continue

                    if v in visited_kwargs:
                        new_kwargs[k] = visited_kwargs[v]
                        continue
                    visited_kwargs[v] = len(kwargs_inputs)
                    kwargs_inputs.append(v)
                    new_kwargs[k] = visited_kwargs[v]
            triton_inputs[node.name] = new_kwargs

        new_outputs = kwargs_inputs + grid_inputs
        for node in cloned_gm.graph.nodes:
            if node.op == "output":
                node.args = (tuple(new_outputs),)
                break

        cloned_gm.recompile()
        runner = torch.fx.Interpreter(cloned_gm)
        returned_outputs = runner.run(example_inputs)
        # Extract and store the grid for autotuning
        if len(grid_inputs) > 0:
            grid_outputs = returned_outputs[len(kwargs_inputs) :]
            self.autotuning_grids = {}
            for node in triton_nodes:
                dynamic_grid = False
                new_grids: list[tuple[Any]] = []
                for grid in node.kwargs["grid"]:
                    new_grid = []
                    for val in grid:
                        if not isinstance(val, torch.fx.Node):
                            new_grid.append(val)
                            continue
                        dynamic_grid = True
                        new_grid.append(grid_outputs[visited_grids[val]])
                    # pyrefly: ignore [bad-argument-type]
                    new_grids.append(tuple(new_grid))

                if dynamic_grid:
                    self.autotuning_grids[node.name] = new_grids
        # Store the kwargs input for autotuning
        self.autotuning_inputs = returned_outputs[: len(kwargs_inputs)]
        self.autotuning_mapping = triton_inputs

    def codegen_with_cpp_wrapper(
        self,
    ) -> tuple[ValueWithLineMap, ValueWithLineMap]:
        """
        For GPU, Triton kernels are autotuned and stored as cubin files.

        For CPU with user-defined Triton kernels, AOTI also needs the
        same two-pass compile when `autotune_at_compile_time` is off,
        since `CpuTritonKernelCache` is otherwise only populated by the
        autotune block (see `DeferredCpuTritonCallWrapper` in
        `cpp_wrapper_cpu.py`).
        """
        has_gpu = any(device in self.device_types for device in ["cuda", "xpu"])
        # CPU + user-defined Triton + AOTI + autotune block disabled is the
        # only CPU configuration that needs the two-pass dance: the autotune
        # block normally populates CpuTritonKernelCache, but here it doesn't run.
        needs_cpu_triton_two_pass = (
            "cpu" in self.device_types
            and self.aot_mode
            and not config.triton.autotune_at_compile_time
            and any(
                isinstance(op, ir.UserDefinedTritonKernel) for op in self.operations
            )
        )
        if has_gpu or needs_cpu_triton_two_pass:

            def extract_real_inputs() -> list[int | float | torch.Tensor]:
                def materialize(
                    x: torch.SymInt | torch.SymFloat | torch.Tensor,
                ) -> int | float | torch.Tensor:
                    if x is None:
                        # pyrefly: ignore [bad-return]
                        return None
                    elif isinstance(x, (torch.SymInt, torch.SymFloat)):
                        # Need concrete value to run dynamic shapes and tune the result
                        return x.node.hint
                    elif isinstance(x, FakeTensor):
                        return defake(x)
                    else:
                        assert isinstance(x, torch.Tensor), (
                            "Unknown type when creating real inputs" + str(type(x))
                        )
                        return x

                tracing_context = torch._guards.TracingContext.try_get()
                if tracing_context is not None and not isinstance(
                    V.real_inputs, NullHandler
                ):
                    if tracing_context.output_strides:
                        tracing_context.output_strides.clear()

                    params_flat = [
                        param
                        for param in tracing_context.params_flat  # type: ignore[union-attr]
                        if param is not None
                    ]
                    real_inputs = [
                        materialize(x)
                        for x in itertools.chain(params_flat, V.real_inputs)
                    ]
                else:
                    # In the backward pass, V.real_inputs is not OrderedSet.
                    # Generating random inputs based on self.example_inputs sometimes can be problematic,
                    # e.g. illegal memory access. A comprehensive fix is to autotune in a separate process.
                    real_inputs = [
                        materialize(x)  # type:ignore[arg-type]
                        for x in (
                            self.example_inputs  # type:ignore[union-attr]
                            if isinstance(V.real_inputs, NullHandler)
                            else V.real_inputs
                        )
                    ]

                if self.mutated_inputs:
                    from .compile_fx import clone_preserve_strides

                    mutated_input_idxs = [
                        idx
                        for idx, name in enumerate(self.graph_inputs)
                        if name in self.mutated_inputs
                        and isinstance(real_inputs[idx], torch.Tensor)
                    ]
                    for idx in mutated_input_idxs:
                        # clone mutated Tensor inputs to avoid mutating them in
                        # the first pass of the CPP wrapper-based compilation, as
                        # this will lead to a side effect on the example inputs:
                        # e.g. if torch.compile(f)(x) if called on input-mutating
                        # f, the inputs x will be mutated twice in the process:
                        # once here, and again when running the compiled model;
                        # this will also lead to a numerically incorrect output
                        mutated_inp = real_inputs[idx]
                        assert isinstance(mutated_inp, torch.Tensor)
                        real_inputs[idx] = clone_preserve_strides(mutated_inp)
                        del mutated_inp
                return real_inputs

            if config.triton.autotune_at_compile_time:
                # If autotune_at_compile_time is True, we can do the codegen in one-pass
                # We will construct the autotuning values if user defined kernel exists.
                if config.triton.autotune_with_sample_inputs:
                    user_defined_kernels = False
                    for op in self.operations:
                        if isinstance(op, ir.UserDefinedTritonKernel):
                            user_defined_kernels = True
                            break
                    if user_defined_kernels:
                        real_inputs = extract_real_inputs()
                        self.extract_autotune_inputs(real_inputs)
                        save_triton_kernel_perf_artifact(self)
                return self.codegen()
            else:
                if not self.aot_mode:
                    # Lazy kernel compilation does not require two passes
                    # TODO: need to consolidate the logic between AOT and JIT
                    return self.codegen()

                # first pass
                self.cpp_wrapper = False
                compiled = self.compile_to_module().call

                real_inputs = extract_real_inputs()
                with torch.utils._python_dispatch._disable_current_modes():
                    compiled(real_inputs)
                del real_inputs

                # second pass
                self.cpp_wrapper = True
                self.removed_buffers.clear()
                self.removed_operations.clear()
                self.inplaced_to_remove.clear()
                V.graph.sizevars.precomputed_replacements.clear()
                V.graph.sizevars.inv_precomputed_replacements.clear()
                metrics.reset()
                with config.patch({"triton.autotune_at_compile_time": False}):
                    return self.codegen()
        else:
            # cpu
            return self.codegen()

    def _update_scheduler(self) -> None:
        """
        (Re)initializes the scheduler member.  When initializing the scheduler, no CUBIN
        files should be generated (to avoid biasing any benchmarks and pessimizing
        fusion decisions).
        """
        from .scheduler import Scheduler

        with config.patch("triton.store_cubin", False):
            self.scheduler = Scheduler(self.operations)

    def codegen(self) -> tuple[ValueWithLineMap, ValueWithLineMap]:
        with dynamo_timed("GraphLowering.codegen", log_pt2_compile_event=True):
            self.init_wrapper_code()

            self._update_scheduler()
            V.debug.draw_orig_fx_graph(self.orig_gm, self.scheduler.nodes)

            self.wrapper_code.push_codegened_graph(self)
            self.scheduler.codegen()

            log.debug(
                "Finished codegen for all nodes. The list of kernel names available: %s",
                V.graph.all_codegen_kernel_names,
            )

            result = self.wrapper_code.generate(self.is_inference)
            self.wrapper_code.pop_codegened_graph()
            return result

    def codegen_subgraph(self, parent_graph: GraphLowering) -> None:
        """
        This is a more compact version of the `codegen()` above
        where we codegen this graph as a subgraph of some parent
        graph. The parent graph is passed as an argument: the
        intention is to inline codegening of the subgraph in
        the parent graph's wrapper code (including the generated
        kernels). The wrapper code is not finalized (via `.generate()`
        call), as this will be done in the parent graph's `codegen()`.
        """
        with dynamo_timed("GraphLowering.codegen_subgraph", log_pt2_compile_event=True):
            self.wrapper_code = parent_graph.wrapper_code
            self.device_ops = parent_graph.device_ops
            self.cpp_wrapper = parent_graph.cpp_wrapper
            self.device_types = parent_graph.device_types
            self.device_idxs = parent_graph.device_idxs
            self.device_type = parent_graph.device_type

            self._update_scheduler()
            self.scheduler.codegen()

    def count_bytes(
        self,
    ) -> tuple[
        int, list[tuple[BaseSchedulerNode, int]], list[tuple[BaseSchedulerNode, float]]
    ]:
        total_bytes = 0
        node_counts = []
        node_runtimes = []
        for node in self.scheduler.nodes:
            num_bytes = node.get_read_write_buffers_sizes()
            total_bytes += num_bytes
            node_counts.append((node, num_bytes // 4))
            node_runtimes.append((node, node.get_estimated_runtime()))

        return total_bytes, node_counts, node_runtimes

    # No-op to be patched for unit tests
    save_output_code: Callable[[str], None] | None = None

    def compile_to_module(self) -> CompiledModule:
        with dynamo_timed(
            "GraphLowering.compile_to_module",
            phase_name="code_gen",
            log_pt2_compile_event=True,
            dynamo_compile_column_us="inductor_code_gen_cumulative_compile_time_us",
        ):
            return self._compile_to_module()

    def _compile_to_module(self) -> CompiledModule:
        # If we're here, we don't have to worry about the kernel code, which is only
        # returned separately in AOTInductor mode.
        wrapper_code, _ = (
            self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen()
        )

        if isinstance(wrapper_code, ValueWithLineMap):
            mod = self._compile_to_module_lines(wrapper_code)
        elif isinstance(wrapper_code, FileBackedGraphModule):
            mod = wrapper_code
        else:
            raise NotImplementedError(
                f"Unrecognized wrapper code type: {type(wrapper_code)}"
            )

        # Logged twice as per https://github.com/pytorch/pytorch/pull/99038#discussion_r1167826029
        # TODO. Revisit this once the logging API is more mature
        assert mod.__file__ is not None

        log_module_code(mod.__file__)
        log.debug("Output code written to: %s", mod.__file__)
        output_code_log.info("Output code written to: %s", mod.__file__)
        if config.benchmark_kernel:
            print(f"Compiled module path: {mod.__file__}", file=sys.stderr)
        if isinstance(wrapper_code, FileBackedGraphModule):
            V.debug.output_code(mod.__file__)
            V.debug.copy(os.path.splitext(mod.__file__)[0] + ".debug")

        return mod

    def _compile_to_module_lines(
        self, wrapper_code: ValueWithLineMap
    ) -> CompiledModule:
        from .codecache import PyCodeCache

        if config.triton.autotune_at_compile_time:
            # sanitize docstrings in kernel defs (#155006)
            kernel_autotune_defs = self.wrapper_code.kernel_autotune_defs.getvalue()
            kernel_autotune_defs = kernel_autotune_defs.replace('"""', '\\"\\"\\"')

            tuning_code = (
                'r"""\n'
                + "Compile-time auto-tuning block: \n"
                + kernel_autotune_defs
                + self.wrapper_code.kernel_autotune_calls.getvalue()
                + '"""\n'
            )
            wrapper_code.value = tuning_code + wrapper_code.value
        if GraphLowering.save_output_code is not None:
            GraphLowering.save_output_code(wrapper_code.value)
        output_code_log.debug("Output code: \n%s", wrapper_code.value)

        inductor_meta = autotune_cache.inductor_meta_from_config()
        AutotuneCacheBundler.begin_compile(inductor_meta, code=wrapper_code.value)

        try:
            linemap = [
                (line_no, node.stack_trace)  # type: ignore[attr-defined]
                for line_no, node in wrapper_code.line_map
            ]
            key, path = PyCodeCache.write(wrapper_code.value)
            output_code_log.debug("Output code written to: %s", path)

            V.debug.output_code(path)
            V.debug.copy(os.path.splitext(path)[0] + ".debug")
        except Exception:
            trace_structured(
                "inductor_output_code",
                # Just omit the filename, I still want the code though!
                payload_fn=lambda: wrapper_code.value,
            )
            raise
        else:
            trace_structured(
                "inductor_output_code",
                lambda: {
                    "filename": path,
                    "file_path": os.path.abspath(path),
                },
                payload_fn=lambda: wrapper_code.value,
            )
        with dynamo_timed("PyCodeCache.load_by_key_path", log_pt2_compile_event=True):
            mod = PyCodeCache.load_by_key_path(
                key,
                path,
                linemap=linemap,  # type: ignore[arg-type]
                attrs={
                    **self.constants,
                    **self.torchbind_constants,
                    **self.opaque_value_type_classes,
                },
            )
        self.cache_key = key
        self.cache_path = path
        self.cache_linemap = linemap  # type: ignore[assignment]

        if config.benchmark_harness and config.profile_bandwidth_output:
            # run the inputs code gen to get the bandwidth info
            args = mod.get_args()
            mod.benchmark_compiled_module(args, times=1, repeat=1)

        return mod

    def _get_output_names(self, graph_outputs: list[ir.IRNode]) -> list[str]:
        names = []
        shape_counter = itertools.count(0)
        none_counter = itertools.count(0)
        for node in graph_outputs:
            if isinstance(node, ir.NoneAsConstantBuffer):
                names.append(f"{self.name}_none{next(none_counter)}")
            elif isinstance(node, ir.ShapeAsConstantBuffer):
                names.append(f"{self.name}_shape{next(shape_counter)}")
            else:
                names.append(node.get_name())
        return names

    def get_output_names(self) -> list[str]:
        return self._get_output_names(self.graph_outputs)

    def is_unspec_arg(self, name: str) -> bool:
        # dynamo wraps unspec variable as 0d CPU tensor,
        # need to convert to scalar during codegen (triton only)
        return (
            name in self.graph_inputs
            and self.graph_inputs[name].get_numel() == 1
            and len(self.graph_inputs[name].get_size()) == 0
            and get_device_type(self.graph_inputs[name]) == "cpu"
        ) or name in self.zero_dim_cpu_tensor_list


class SubgraphLowering(GraphLowering):
    """
    Mostly a helper class for the subgraph lowering. The main goal is to call
    init_wrapper_code with the subgraph related arguments.
    """

    def __init__(self, parent: GraphLowering, *args: Any, **kwargs: Any) -> None:
        self.parent = parent
        super().__init__(*args, **kwargs)

    def init_wrapper_code(
        self,
        is_subgraph: bool = False,
        subgraph_name: str | None = None,
        parent_wrapper_code: PythonWrapperCodegen | None = None,
        partition_signatures: GraphPartitionSignature | None = None,
    ) -> None:
        super().init_wrapper_code(
            is_subgraph=True,
            subgraph_name=self.name,
            parent_wrapper_code=self.parent.wrapper_code,
        )
