_program.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# Copyright 2025 Arm Limited and/or its affiliates.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# pyre-unsafe

import copy
import io
import logging
import os
from collections import defaultdict
from typing import Any, Dict, List, Optional, Sequence, Set, TextIO, Type, Union

import torch
import torch._export
from executorch.exir._serialize._cord import Cord
from executorch.exir._serialize._named_data_store import (
    NamedDataStore,
    NamedDataStoreOutput,
)
from executorch.exir._serialize._serialize import serialize_for_executorch
from executorch.exir._serialize.data_serializer import DataSerializer
from executorch.exir._warnings import experimental
from executorch.exir.backend.backend_api import (
    MethodProgramsPartitionerSpec,
    to_backend,
)
from executorch.exir.backend.partitioner import Partitioner
from executorch.exir.capture._config import EdgeCompileConfig, ExecutorchBackendConfig
from executorch.exir.delegate import executorch_call_delegate, is_lowered_module
from executorch.exir.emit import emit_program, EmitterOutput
from executorch.exir.emit._emitter import _DelegateDebugIdentifierMap
from executorch.exir.error import ExportError
from executorch.exir.graph_module import get_control_flow_submodules
from executorch.exir.operator.convert import _pybind_schema_to_native_schema
from executorch.exir.operator.util import _QUANT_PRIMITIVES
from executorch.exir.pass_base import PassBase
from executorch.exir.pass_manager import PassType
from executorch.exir.passes import (
    base_post_op_replace_passes,
    base_pre_op_replace_passes,
    convert_constant_dim_order_pass,
    dead_code_elimination_pass,
    EdgeToBackendOpsPass,
    MemoryFormatOpsPass,
    OpReplacePass,
    remove_unused_parameters_pass,
)
from executorch.exir.passes.external_constants_pass import (
    external_constants_pass,
    external_mutable_weights_pass,
)
from executorch.exir.passes.insert_write_back_for_buffers_pass import (
    insert_write_back_for_buffers_pass,
)
from executorch.exir.passes.normalize_view_copy_base_pass import (
    NormalizeViewCopyBasePass,
)
from executorch.exir.passes.propagate_device_pass import PropagateDevicePass
from executorch.exir.passes.quant_fusion_pass import quant_fusion_and_const_prop_pass
from executorch.exir.passes.reinplace import reinplace_pass
from executorch.exir.passes.remove_graph_asserts_pass import (
    RemoveGraphAssertsPass,
    RemoveNonCoreAtenOpGraphAssertsPass,
)
from executorch.exir.passes.remove_mixed_type_operators import RemoveMixedTypeOperators
from executorch.exir.passes.replace_aten_with_edge_pass import aten_to_edge
from executorch.exir.passes.replace_view_copy_with_view_pass import (
    ReplaceViewCopyWithViewPass,
)
from executorch.exir.passes.spec_prop_pass import SpecPropPass
from executorch.exir.passes.weights_to_outputs_pass import weights_to_outputs_pass
from executorch.exir.print_program import pretty_print, print_program
from executorch.exir.schema import Program
from executorch.exir.tracer import _default_decomposition_table
from executorch.exir.verification.verifier import (
    EXIRATenDialectVerifier,
    EXIREdgeDialectVerifier,
    get_aten_verifier,
)
from torch._export.passes import ReplaceViewOpsWithViewCopyOpsPass
from torch._export.utils import _detect_fake_mode_from_gm
from torch._export.verifier import Verifier
from torch.export import ExportedProgram
from torch.export._remove_auto_functionalized_pass import (
    unsafe_remove_auto_functionalized_pass,
)
from torch.export.exported_program import (
    ConstantArgument,
    ExportGraphSignature,
    InputKind,
    InputSpec,
    OutputSpec,
    TensorArgument,
)
from torch.fx import _pytree as fx_pytree
from torch.fx._compatibility import compatibility
from torch.fx.passes.infra.pass_manager import PassManager
from torch.utils import _pytree as pytree

Val = Any

from typing import Any, Callable

from torch.library import Library

try:
    from executorch.exir.program.fb.logger import et_logger
except ImportError:
    # Define a stub decorator that does nothing
    def et_logger(api_name: str) -> Callable[[Any], Any]:
        def decorator(func: Callable[..., Any]) -> Callable[..., Any]:
            def wrapper(*args: Any, **kwargs: Any) -> Any:
                return func(*args, **kwargs)

            return wrapper

        return decorator


# This is the reserved namespace that is used to register ops to that will
# be prevented from being decomposed during to_edge_transform_and_lower.
edge_no_decomp_namespace = "EDGE_DO_NOT_DECOMP"
lib = Library(edge_no_decomp_namespace, "DEF")
# Map from aten ops to the transformed ops registered in the edge_no_decomp_namespace.
aten_op_to_transform_op = {}
# Map from the transformed ops registered in the edge_no_decomp_namespace to aten ops.
transform_op_to_aten_op = {}


def _get_updated_range_constraints(gm):
    def get_shape_env(gm):
        vals = [
            node.meta["val"]
            for node in gm.graph.nodes
            if node.meta.get("val", None) is not None
        ]
        from torch._guards import detect_fake_mode  # type: ignore[21]

        fake_mode = detect_fake_mode(vals)
        if fake_mode is not None:
            return fake_mode.shape_env
        for v in vals:
            if isinstance(v, torch.SymInt):
                return v.node.shape_env

    shape_env = get_shape_env(gm)
    if shape_env is None:
        return {}
    range_constraints = {
        shape_env.replacements.get(k, k): v for k, v in shape_env.var_to_range.items()
    }
    # Only when we have an unbacked symint, and it's used as constructor inputs,
    # runtime_var_to_range will make a difference compated to var_to_range.
    # e.g. [2, oo) -> [0, oo)
    for k, v in shape_env.var_to_range.items():
        if k not in shape_env.replacements:
            range_constraints[k] = v
    return range_constraints


def _get_updated_graph_signature(
    old_signature: ExportGraphSignature,
    new_gm: torch.fx.GraphModule,
) -> ExportGraphSignature:
    """
    Update the graph signature's user_input/user_outputs.
    """
    new_input_specs = []
    i = 0
    for node in new_gm.graph.nodes:
        if node.op != "placeholder":
            continue

        assert i < len(
            old_signature.input_specs
        ), "Number of inputs changed after transformation"
        old_input_spec = old_signature.input_specs[i]
        arg = (
            old_input_spec.arg
            if isinstance(old_input_spec.arg, ConstantArgument)
            # pyre-fixme[20]: Argument `class_fqn` expected.
            else type(old_input_spec.arg)(node.name)
        )
        new_input_specs.append(
            InputSpec(
                old_input_spec.kind,
                arg,
                old_input_spec.target,
                persistent=old_input_spec.persistent,
            )
        )
        i += 1

    output_node = new_gm.graph.output_node()
    assert output_node.op == "output"

    new_output_specs = []
    for i, node in enumerate(output_node.args[0]):
        assert i < len(
            old_signature.output_specs
        ), "Number of outputs changed after transformation"
        old_output_spec = old_signature.output_specs[i]
        arg = (
            old_output_spec.arg
            if isinstance(old_output_spec.arg, ConstantArgument)
            # pyre-fixme[20]: Argument `class_fqn` expected.
            else type(old_output_spec.arg)(node.name)
        )
        new_output_specs.append(
            OutputSpec(old_output_spec.kind, arg, old_output_spec.target)
        )

    new_signature = ExportGraphSignature(
        input_specs=new_input_specs, output_specs=new_output_specs
    )
    return new_signature


def _transform(
    self,
    *passes: PassType,
    override_verifiers: None | list[Type[Verifier]] = None,
) -> "ExportedProgram":
    """
    Transforms the program according to the provided passes.

    Args:
        self: The ExportedProgram instance to transform
        *passes: A sequence of passes to apply to the program
        override_verifiers: Optional list of verifier classes to use instead of the default verifiers.
            This is needed if the transforms yields illegal graph that the default verifier cannot handle.

    Returns:
        ExportedProgram: A new ExportedProgram with the transformations applied, or self if no changes were made
    """
    # A user friendly check to avoid vararg surprises, PEP 3102
    assert not any(
        isinstance(p, (list, Verifier)) for p in passes
    ), f"Expected all passes to be of PassType, not list or Verifier. Use override_verifiers kwarg instead. Got: {list(passes)}"

    return _transform_with_pass_manager(
        self, PassManager(list(passes)), override_verifiers
    )


def _transform_with_pass_manager(
    self,
    pass_manager: PassManager,
    override_verifiers: None | list[Type[Verifier]] = None,
) -> "ExportedProgram":
    """
    Transforms the program using the provided pass_manager.

    Args:
        self: The ExportedProgram instance to transform
        pass_manager: An instance of PassManager to apply transformations.
        override_verifiers: Optional list of verifier classes to use instead of the default verifiers.
            This is needed if the transforms yields illegal graph that the default verifier cannot handle.

    Returns:
        ExportedProgram: A new ExportedProgram with the transformations applied, or self if no changes were made
    """
    res = pass_manager(self.graph_module)
    transformed_gm = res.graph_module if res is not None else self.graph_module
    assert transformed_gm is not None

    if transformed_gm is self.graph_module and not res.modified:
        return self

    return _update_exported_program_graph_module(
        self, transformed_gm, override_verifiers
    )


def _update_exported_program_graph_module(
    exported_program: ExportedProgram,
    gm: torch.fx.GraphModule,
    override_verifiers: None | list[Type[Verifier]] = None,
) -> "ExportedProgram":
    transformed_ep = ExportedProgram(
        root=gm,
        graph=gm.graph,
        graph_signature=_get_updated_graph_signature(
            exported_program.graph_signature, gm
        ),
        state_dict=exported_program.state_dict,
        range_constraints=_get_updated_range_constraints(gm),
        module_call_graph=copy.deepcopy(exported_program._module_call_graph),
        example_inputs=exported_program.example_inputs,
        constants=exported_program.constants,
        verifiers=override_verifiers or [exported_program.verifier],
    )
    transformed_ep.graph_module.meta.update(exported_program.graph_module.meta)
    transformed_ep.graph_module.meta.update(gm.meta)
    return transformed_ep


def _copy_module(new_prog, new_gm):
    new_prog.meta.update(new_gm.meta)
    new_prog.graph = new_gm.graph
    submodules = [name for name, _ in new_prog.named_children()]
    for name in submodules:
        delattr(new_prog, name)
    for name, mod in new_gm.named_children():
        setattr(new_prog, name, mod)
    for node in new_gm.graph.nodes:
        if node.op == "get_attr":
            t = getattr(new_gm, node.target, None)
            if isinstance(t, torch.Tensor):
                setattr(new_prog, node.target, t)


def _create_empty_etrecord():
    # Import etrecord at runtime to resolve cyclic dependencies (program -> etrecord -> program).
    # This also ensures that etrecord-related packages do not affect the export flow.
    # @manual
    from executorch.devtools.etrecord import ETRecord

    return ETRecord()


def lift_constant_tensor_pass(ep):
    """
    Takes an ExportedProgram and returns the ExportedProgram modified in-place,
    with the constant tensors as buffers.
    """
    if len([node for node in ep.graph.nodes if node.op == "placeholder"]) == 0:
        return ep

    graph_signature = ep.graph_signature
    buffers = list(graph_signature.buffers)

    fake_mode = _detect_fake_mode_from_gm(ep.graph_module)

    first_user_input = None
    lifted_constants = []
    for node in ep.graph.nodes:
        if node.op == "placeholder" and node.name in graph_signature.user_inputs:
            first_user_input = node
            break

    for node in ep.graph.nodes:
        if node.op == "get_attr":
            constant_tensor = getattr(ep.graph_module, node.target)
            if not isinstance(constant_tensor, torch.Tensor):
                continue

            constant_tensor_fqn = f"_lifted_tensor_constant{len(buffers)}"

            with ep.graph.inserting_before(first_user_input):
                # Insert the constant node before the first user input
                const_placeholder_node = ep.graph.placeholder(constant_tensor_fqn)
                for k, v in node.meta.items():
                    const_placeholder_node.meta[k] = v
                if fake_mode is not None:
                    const_placeholder_node.meta["val"] = fake_mode.from_tensor(
                        constant_tensor, static_shapes=True
                    )
                else:
                    const_placeholder_node.meta["val"] = constant_tensor
                const_placeholder_node.meta["val"].constant = constant_tensor
                node.replace_all_uses_with(const_placeholder_node)
                ep.graph.erase_node(node)

                # Add the constant as a buffer to the graph signature
                lifted_constants.append(
                    InputSpec(
                        kind=InputKind.BUFFER,
                        arg=TensorArgument(name=const_placeholder_node.name),
                        target=constant_tensor_fqn,
                        persistent=True,
                    )
                )
                buffers.append(constant_tensor_fqn)
                ep.state_dict[constant_tensor_fqn] = constant_tensor

    new_input_specs = []
    for s in graph_signature.input_specs:
        if s.kind == InputKind.USER_INPUT and len(lifted_constants) > 0:
            new_input_specs.extend(lifted_constants)
            lifted_constants.clear()
        new_input_specs.append(s)
    if len(lifted_constants) > 0:
        new_input_specs = lifted_constants + new_input_specs
    ep.graph_signature.input_specs = new_input_specs
    ep.graph_module.recompile()
    return ep


# Stub to ease migration from `transform` to private `_transform`
def transform_exported_program(ep, *passes: PassType) -> ExportedProgram:
    if hasattr(ep, "_transform"):
        return ep._transform(*passes)
    else:
        return ep.transform(*passes)


class HackedUpExportedProgramDONOTUSE(ExportedProgram):
    def __init__(
        self,
        root,
        graph,
        graph_signature,
        call_spec,
        state_dict,
        range_constraints,
        module_call_graph,
        example_inputs,
        verifier,
    ):
        super().__init__(
            root=root,
            graph=graph,
            graph_signature=graph_signature,
            state_dict=state_dict,
            range_constraints=range_constraints,
            module_call_graph=module_call_graph,
            example_inputs=example_inputs,
            verifier=verifier,
        )

    def __call__(self, *args: Any, **kwargs: Any) -> Any:
        import torch._export.error as error

        if self.call_spec.in_spec is not None:
            user_args = args
            try:
                args = fx_pytree.tree_flatten_spec(user_args, self.call_spec.in_spec)  # type: ignore[assignment]
            except Exception:
                _, received_spec = pytree.tree_flatten(user_args)
                raise error.InternalError(
                    "Trying to flatten user inputs with exported input tree spec: \n"
                    f"{self.call_spec.in_spec}\n"
                    "but actually got inputs with tree spec of: \n"
                    f"{received_spec}"
                )

        ordered_params = tuple(
            self.state_dict[name] for name in self.graph_signature.parameters
        )
        ordered_buffers = tuple(
            self.state_dict[name] for name in self.graph_signature.buffers
        )

        with torch.no_grad():
            # NOTE: calling convention is first params, then buffers, then args as user supplied them.
            # See: torch/_functorch/aot_autograd.py#L1034
            res = torch.fx.Interpreter(self.graph_module).run(
                *ordered_params, *ordered_buffers, *args, enable_io_processing=False
            )

        if self.call_spec.out_spec is not None:
            mutation = self.graph_signature.buffers_to_mutate
            num_mutated = len(mutation)
            mutated_buffers = res[:num_mutated]

            # Exclude dependency token from final result.
            assertion_dep_token = self.graph_signature.assertion_dep_token
            if assertion_dep_token is not None:
                assertion_dep_token_index = list(assertion_dep_token.keys())[0]
                res = res[:assertion_dep_token_index]

            res = res[num_mutated:]
            try:
                res = pytree.tree_unflatten(res, self.call_spec.out_spec)
            except Exception:
                _, received_spec = pytree.tree_flatten(res)
                raise error.InternalError(
                    "Trying to flatten user outputs with exported output tree spec: \n"
                    f"{self.call_spec.out_spec}\n"
                    "but actually got outputs with tree spec of: \n"
                    f"{received_spec}"
                )
            finally:
                ix = 0
                for buffer in self.graph_signature.buffers_to_mutate.values():
                    self.state_dict[buffer] = mutated_buffers[ix]
                    ix += 1
        return res


@compatibility(is_backward_compatible=False)
class ExirExportedProgram:
    def __init__(
        self,
        exported_program: ExportedProgram,
        after_to_edge_passes: bool,
    ):
        self.exported_program = exported_program

        # Add a flag to denote whehter to_edge is called on this program
        # to detect misusage of directly calling to_executorch without to_edge
        self.after_to_edge_passes = after_to_edge_passes

    def transform(self, *passes: PassType) -> "ExirExportedProgram":
        self.exported_program = _transform(self.exported_program, *passes)
        return self

    def __call__(self, *args: Any) -> Any:
        return self.exported_program.module()(*args)

    # TODO(ycao): Change this to a composable function.
    def to_edge(
        self, config: Optional[EdgeCompileConfig] = None
    ) -> "ExirExportedProgram":
        config = config or EdgeCompileConfig()
        assert isinstance(
            self.exported_program.graph_module, torch.fx.GraphModule
        ), f"type is instead: {type(self.exported_program.graph_module).__name__}"

        return _to_edge(self, config)

    def dump(self) -> None:
        print(self.exported_program.graph_module.graph)

    def to_executorch(
        self,
        config: Optional[ExecutorchBackendConfig] = None,
    ) -> "ExecutorchProgram":
        if not self.after_to_edge_passes:
            raise RuntimeError("Must run to_edge before to_executorch.")
        config = config or ExecutorchBackendConfig()
        new_gm = self.exported_program.graph_module
        for p in edge_to_executorch_passes(config):
            new_gm_res = p(new_gm)
            assert new_gm_res is not None
            new_gm = new_gm_res.graph_module

        # This is tech debt on tech debt. memory planning pass inherits from some pass infra for GMs.
        # This isnt enough info now so i cant use call I have to use some new function 'run'.
        # Existing user passes dont use run so Im just cheating here because they dont need to work on mutable buffers yet.
        # After exir.capture is gone I will clean up the memory planning infra to be consistent.
        # Frankly all of exir has big code quality issues because of the migrations that need to be addressed.
        new_gm_res = config.memory_planning_pass(new_gm)  # pyre-ignore[29]
        assert new_gm_res is not None
        new_gm = new_gm_res.graph_module
        new_prog = ExirExportedProgram(
            copy.deepcopy(self.exported_program), self.after_to_edge_passes
        )
        _copy_module(new_prog.exported_program.graph_module, new_gm)
        executorch_prog = ExecutorchProgram(
            new_prog,
            emit_stacktrace=config.emit_stacktrace,
            extract_delegate_segments=config.extract_delegate_segments,
            segment_alignment=config.segment_alignment,
            constant_tensor_alignment=config.constant_tensor_alignment,
            delegate_alignment=config.delegate_alignment,
        )
        executorch_prog.graph_module.meta.update(new_gm.meta)
        executorch_prog.graph_module.meta.update(
            self.exported_program.graph_module.meta
        )
        return executorch_prog

    def __deepcopy__(
        self, memo: Optional[Dict[int, Any]] = None
    ) -> "ExirExportedProgram":
        new_eep = ExirExportedProgram(
            copy.deepcopy(self.exported_program, memo),
            self.after_to_edge_passes,
        )
        return new_eep


@compatibility(is_backward_compatible=False)
class ExecutorchProgram:
    def __init__(
        self,
        exir_exported_program: ExirExportedProgram,
        emit_stacktrace: bool,
        extract_delegate_segments: bool,
        segment_alignment: int,
        constant_tensor_alignment: Optional[int] = None,
        delegate_alignment: Optional[int] = None,
    ) -> None:
        if not exir_exported_program.after_to_edge_passes:
            raise RuntimeError(
                "Need to call prog.to_edge prior to constructing ExecutorchProgram."
            )
        self.exported_program = exir_exported_program.exported_program
        self._pte_data: Optional[Cord] = None
        self._tensor_data: Optional[Dict[str, Cord]] = None
        self._buffer: Optional[bytes] = None
        self._emitter_output: Optional[EmitterOutput] = None
        self._emit_stacktrace: bool = emit_stacktrace
        self._extract_delegate_segments: bool = extract_delegate_segments
        self._segment_alignment: int = segment_alignment
        self._constant_tensor_alignment: Optional[int] = constant_tensor_alignment
        self._delegate_alignment: Optional[int] = delegate_alignment
        from executorch.extension.flat_tensor.serialize.serialize import (
            FlatTensorConfig,
            FlatTensorSerializer,
        )

        self._data_serializer: DataSerializer = FlatTensorSerializer(
            FlatTensorConfig(self._segment_alignment)
        )

    def _get_emitter_output(self) -> EmitterOutput:
        if self._emitter_output is None:
            self._emitter_output = emit_program(
                self.exported_program, self._emit_stacktrace
            )
        return self._emitter_output

    def _get_pte_data(self) -> Cord:
        if self._pte_data is None:
            self._pte_data, self._tensor_data = serialize_for_executorch(
                self._get_emitter_output(),
                ExecutorchBackendConfig(
                    extract_delegate_segments=self._extract_delegate_segments,
                    segment_alignment=self._segment_alignment,
                    constant_tensor_alignment=self._constant_tensor_alignment,
                    delegate_alignment=self._delegate_alignment,
                ),
                self._data_serializer,
            )
        assert self._pte_data is not None
        return self._pte_data

    @property
    def buffer(self) -> bytes:
        """Returns the serialized ExecuTorch binary as a byte string.

        Note that the call to `buffer` may allocate a very large amount of
        contiguous memory, depending on the model size. If writing to a file,
        use `write_to_file` which won't incur additional copies.
        """
        # TODO(T181494963): update pybinding to remove buffer cache, which can consume large
        # amounts of memory longer than necessary.
        if self._buffer is None:
            self._buffer = bytes(self._get_pte_data())
        return self._buffer

    @property
    @experimental("This API is experimental and subject to change without notice.")
    def data_files(self) -> Dict[str, bytes]:
        """Returns the data files as a dictionary of filename to byte data.

        Returns:
            Dict[str, bytes]: Dictionary mapping data filenames (e.g., .ptd files) to
                their serialized byte content.
            Returns empty dict if no data files are available.
        """
        if self._pte_data is None:
            self._get_pte_data()  # This populates _tensor_data

        if self._tensor_data is None:
            return {}

        return {filename: bytes(cord) for filename, cord in self._tensor_data.items()}

    @property
    def program(self) -> Program:
        return self._get_emitter_output().program

    @property
    def debug_handle_map(self) -> Dict[int, Union[int, List[int]]]:
        if self._emitter_output:
            return self._emitter_output.debug_handle_map
        return self._get_emitter_output().debug_handle_map

    @property
    def delegate_map(
        self,
    ) -> Dict[str, Dict[int, Dict[str, Union[str, _DelegateDebugIdentifierMap]]]]:
        if self._emitter_output:
            return self._emitter_output.method_to_delegate_debug_id_map
        return self._get_emitter_output().method_to_delegate_debug_id_map

    @property
    def instruction_id_to_num_outs_map(
        self,
    ) -> Dict[str, Dict[int, Union[int, List[int]]]]:
        if self._emitter_output:
            return self._emitter_output.instruction_id_to_num_outs_map
        return self._get_emitter_output().instruction_id_to_num_outs_map

    @property
    def graph_module(self) -> torch.fx.GraphModule:
        return self.exported_program.graph_module

    # TODO (zhxchen17) Change this to property.
    def dump_graph_module(self) -> torch.fx.GraphModule:
        return self.exported_program.graph_module

    def dump_exported_program(self) -> ExportedProgram:
        return self.exported_program

    def write_to_file(self, open_file: io.BufferedIOBase) -> None:
        """
        Writes the serialized ExecuTorch binary to the file at `open_file`. Prefer to use this over
        `buffer`, as it writes to file without copying into a contiguous block of memory first,
        reducing the peak memory usage.
        """
        self._get_pte_data().write_to_file(open_file)

    def write_tensor_data_to_file(self, outdir) -> None:
        """
        Writes the serialized ExecuTorch data files to the directory at `outdir`.
        """
        assert self._tensor_data is not None
        # pyre-ignore[16]: `Optional` has no attribute `items`.
        for filename, cord in self._tensor_data.items():
            with open(os.path.join(outdir, f"{filename}.ptd"), "wb") as f:
                logging.info(f"Writing data file to {filename}.ptd")
                cord.write_to_file(f)


def _get_aten_to_edge_passes(config: EdgeCompileConfig):
    # TODO: the last two passes for aten_to_edge need to be eliminated_dead_code -> debug_handle_generator. After enable
    # use_edge_op it can be moved to aten_to_edge_passes before eliminated_dead_code pass. Also ExportPass doesn't play
    # well with node.meta, meaning after some passes permuting operators, we may lose some information in node.meta.
    # It might be regenerated in SpecPropPass so it may not be visiable. However debug handle will be lost.

    pre_op_replace_passes = base_pre_op_replace_passes + [RemoveMixedTypeOperators()]

    post_op_replace_passes = base_post_op_replace_passes

    return pre_op_replace_passes, post_op_replace_passes


def _to_edge(ep, config: EdgeCompileConfig) -> "ExirExportedProgram":
    if config._check_ir_validity:
        try:
            EXIRATenDialectVerifier()(ep.exported_program.graph_module)
        except ExportError:
            logging.info(
                "If a particular operator failed core ATen IR check, please consider adding it to the exception list. "
                "Add the operator to _core_aten_ops_exception_list in EdgeCompileConfig. This is the recommended way "
                "to resolve this type of failure, so that the rest of the IR validation check can still be performed.\n"
                "If you'd like to disable IR validation checking, please set _check_ir_validity in EdgeCompileConfig, "
                "like *.to_edge(exir.EdgeCompileConfig(_check_ir_validity=False))."
            )
            raise

    dialect = ep.exported_program.dialect
    if dialect == "ATEN":
        ep = ExirExportedProgram(
            ExportedProgram(
                root=ep.exported_program.graph_module,
                graph=ep.exported_program.graph_module.graph,
                graph_signature=ep.exported_program.graph_signature,
                state_dict=ep.exported_program.state_dict,
                range_constraints=ep.exported_program.range_constraints,
                module_call_graph=ep.exported_program.module_call_graph,
                example_inputs=ep.exported_program.example_inputs,
                constants=ep.exported_program.constants,
                verifiers=[
                    get_aten_verifier(
                        config=config,
                    )
                ],
            ),
            False,
        )
    pre_op_replace_passes, post_op_replace_passes = _get_aten_to_edge_passes(config)

    new_ep = copy.deepcopy(ep).transform(*pre_op_replace_passes)
    if dialect == "ATEN":
        new_ep.exported_program = lift_constant_tensor_pass(new_ep.exported_program)

    new_gm = new_ep.exported_program.graph_module
    if config._use_edge_ops:
        new_gm_res = OpReplacePass()(new_gm)
        assert new_gm_res is not None
        new_gm = new_gm_res.graph_module
        if not config._skip_dim_order:
            new_gm_res = MemoryFormatOpsPass()(new_gm)
            assert new_gm_res is not None
            new_gm = new_gm_res.graph_module

    for p in post_op_replace_passes:
        new_gm_res = p(new_gm)
        assert new_gm_res is not None
        new_gm = new_gm_res.graph_module

    new_ep.exported_program = ExportedProgram(
        root=new_gm,
        graph=new_gm.graph,
        graph_signature=_get_updated_graph_signature(
            new_ep.exported_program.graph_signature, new_gm
        ),
        state_dict=new_ep.exported_program.state_dict,
        range_constraints=new_ep.exported_program.range_constraints,
        module_call_graph=new_ep.exported_program.module_call_graph,
        example_inputs=new_ep.exported_program.example_inputs,
        constants=new_ep.exported_program.constants,
        verifiers=[
            EXIREdgeDialectVerifier(
                edge_compile_config=config,
                class_only=True,
            )
        ],
    )
    new_ep.after_to_edge_passes = True
    return new_ep


def pre_memory_planning_passes(
    config: ExecutorchBackendConfig, name: Optional[str] = None
) -> List[PassType]:
    """
    Returns a list of passes to run before memory planning.
    Get the sym shape eval pass based on the method name, if the pass is not in the dict, use the default pass.
    """
    # Handle symbolic shape eval pass
    if isinstance(config.sym_shape_eval_pass, dict):
        default_pass = ExecutorchBackendConfig().sym_shape_eval_pass
        if not name:
            sym_shape_eval_pass = default_pass
        # pyre-ignore: Undefined attribute [16]
        sym_shape_eval_pass = config.sym_shape_eval_pass.get(name, default_pass)
    elif isinstance(config.sym_shape_eval_pass, PassBase):
        sym_shape_eval_pass = config.sym_shape_eval_pass
    else:
        raise RuntimeError(
            f"sym_shape_eval_pass must be a dict or a PassBase, got {config.sym_shape_eval_pass}"
        )
    if config.remove_view_copy:
        return [
            NormalizeViewCopyBasePass(),
            dead_code_elimination_pass,
            ReplaceViewCopyWithViewPass(),
            sym_shape_eval_pass,
            config.to_out_var_pass,
        ]
    else:
        return [
            sym_shape_eval_pass,
            config.to_out_var_pass,
        ]


def edge_to_executorch_passes(
    config: ExecutorchBackendConfig, name: Optional[str] = None
) -> List[PassType]:
    """
    Returns a list of passes to lower from edge to executorch.
    Get the pre memory planning passes based on the method name, if the pass is not in the dict, use the default pass.
    """
    passes: List[PassType] = [
        # ExecuTorch backend ops are unable to handle unbacked symints. So after
        # this pass, passes cannot be Interpreter-based, because it will fail if
        # there exists an unbacked symint operation.
        *config.passes,
        SpecPropPass(),
        PropagateDevicePass(),
        EdgeToBackendOpsPass(),
        RemoveGraphAssertsPass(),
    ] + pre_memory_planning_passes(config, name)

    return passes


def _generate_edge_program(
    config: EdgeCompileConfig,
    program: ExportedProgram,
    core_aten_ops_exception_list: Optional[List[torch._ops.OpOverload]] = None,
    preserve_ops: Optional[List[torch._ops.OpOverload]] = None,
) -> ExportedProgram:
    """
    Args:
        config: The configuration for the edge program.
        program: The exported program to be converted to an edge program.
        core_aten_ops_exception_list: A list of aten ops that are missing decompositions to core aten.
        preserve_ops: A list of aten ops that should not be decomposed.
    Returns:
        An ExportedProgram in edge dialect.
    """
    # Remove unused parameters
    program = remove_unused_parameters_pass(program)

    pre_op_replace_passes, post_op_replace_passes = _get_aten_to_edge_passes(config)

    passes = [
        # Remove invalid assert ops, such as _assert_tensor_metadata
        RemoveNonCoreAtenOpGraphAssertsPass(),
        # TODO move inside aten_to_edge passes after all users are migrated off v1 capture
        ReplaceViewOpsWithViewCopyOpsPass(),
    ]
    passes.extend(pre_op_replace_passes)
    if config._use_edge_ops:
        passes.append(OpReplacePass())
        if not config._skip_dim_order:
            passes.append(MemoryFormatOpsPass())

    gm = program.graph_module
    for p in passes:
        gm_res = p(gm)
        assert gm_res is not None
        gm = gm_res.graph_module

    edge_program = ExportedProgram(
        root=gm,
        graph=gm.graph,
        graph_signature=_get_updated_graph_signature(program.graph_signature, gm),
        state_dict=program.state_dict,
        range_constraints=program.range_constraints,
        module_call_graph=program.module_call_graph,
        example_inputs=program.example_inputs,
        constants=program.constants,
        verifiers=[
            EXIREdgeDialectVerifier(
                edge_compile_config=config,
                class_only=True,
                core_aten_ops_exception_list=core_aten_ops_exception_list,
                preserve_ops=preserve_ops,
            )
        ],
    )

    # Lift the tensor constants created in ScalarToTensorPass
    edge_program = lift_constant_tensor_pass(edge_program)

    # Normalize constant tensor dim order on the unlifted graph
    edge_program = convert_constant_dim_order_pass.convert_constant_dim_order_pass(
        edge_program
    )

    edge_program = _transform(edge_program, *post_op_replace_passes)

    return edge_program


def _replace_aten_ops_with_transformed_ops(
    name: str,
    program: ExportedProgram,
    partitioner,
):
    preserve_ops = set()
    partitioners = partitioner.get(name)
    if partitioners is None:
        return

    # Iterate through the graph and replace the aten ops with the corresponding
    # transformed ops.
    for partitioner in partitioners:
        ops_set_to_not_decompose, check_op_support = partitioner.ops_to_not_decompose(
            program
        )
        ops_set_to_not_decompose = _remove_invalid_ops_for_not_decompose(
            ops_set_to_not_decompose
        )

        for op_aten in ops_set_to_not_decompose:
            _register_no_decomp_op(op_aten)

        for node in program.graph.nodes:
            is_op_supported = check_op_support(node) if check_op_support else True
            if (
                node.op == "call_function"
                and node.target in ops_set_to_not_decompose
                and is_op_supported
            ):
                preserve_ops.add(node.target)
                node.target = aten_op_to_transform_op[node.target]

        for _, submod, _ in get_control_flow_submodules(program.graph_module):
            for node in submod.graph.nodes:
                is_op_supported = check_op_support(node) if check_op_support else True
                if (
                    node.op == "call_function"
                    and node.target in ops_set_to_not_decompose
                    and is_op_supported
                ):
                    preserve_ops.add(node.target)
                    node.target = aten_op_to_transform_op[node.target]

    return preserve_ops


def _restore_transformed_ops_to_aten_ops(program: ExportedProgram):
    # Iterate through the graph and replace back the transformed ops with their
    # corresponding aten ops.
    for node in program.graph.nodes:
        if node.op == "call_function" and str(node.target) in transform_op_to_aten_op:
            node.target = transform_op_to_aten_op[str(node.target)]
    for _, submod, _ in get_control_flow_submodules(program.graph_module):
        for node in submod.graph.nodes:
            if (
                node.op == "call_function"
                and str(node.target) in transform_op_to_aten_op
            ):
                node.target = transform_op_to_aten_op[str(node.target)]


# Returns the op in edge_no_decomp_namespace namespace for the aten
# op that is passed in.
def _get_transformed_op(op_aten):
    op_name = op_aten._schema.name.split("::")[1]
    overload_name = op_aten._schema.overload_name
    assert hasattr(
        torch.ops, edge_no_decomp_namespace
    ), f"Couldn't find {edge_no_decomp_namespace} in torch.ops. Please make sure the Library has been registered."
    op_namespace = getattr(torch.ops, edge_no_decomp_namespace)
    op = getattr(op_namespace, op_name)
    return getattr(op, overload_name)


# Registers the op in edge_no_decomp_namespace namespace for the aten
# op that is passed in if it is not already cached in the table.
def _register_no_decomp_op(op_aten):
    # Check if the op is already cached in the table. If not, then we need to
    # create a new op in the edge_no_decomp_namespace namespace.
    if aten_op_to_transform_op.get(op_aten) is None and isinstance(
        op_aten, torch._ops.OpOverload
    ):
        # Extract the schema from the aten op.
        op_schema = str(op_aten._schema).split("::")[1]
        op_name = op_aten._schema.name.split("::")[1]
        # Define an op in the edge_no_decomp_namespace namespace with the aten schema.
        lib.define(op_schema)
        # Define the implementation of the op in the edge_no_decomp_namespace namespace.
        # Important to note that the implementation of the op is the same as the aten op.

        overload_name = op_aten._schema.overload_name
        if overload_name != "":
            op_name += "." + overload_name
        lib.impl(op_name, op_aten, "CompositeExplicitAutograd")

        # Cache the aten op and transformed op in their corresponding tables for future use.
        aten_op_to_transform_op[op_aten] = _get_transformed_op(op_aten)
        transform_op_to_aten_op[str(aten_op_to_transform_op[op_aten])] = op_aten


def _sanity_check_graph_for_non_decomp_ops(
    name: str,
    program: ExportedProgram,
    ops_set_to_not_decompose,
    check_op_support,
    generate_error=False,
    partitioner_name=None,
):
    warning_str_end = ""
    if partitioner_name is not None:
        warning_str_end += f"This op was registered by the partitioner {partitioner_name} to not be decomposed.\n"
    warning_str_end += f"The following ops: {ops_set_to_not_decompose} were specified to not be decomposed in {name}."

    # Check that the ops that were registered to not be decomposed are not present in the
    # graph anymore as the transform passes and backends should have consumed them by now.
    ops_set_to_not_decompose = {
        aten_to_edge(op) for op in ops_set_to_not_decompose
    }.union(ops_set_to_not_decompose)

    quant_primitives = {aten_to_edge(op) for op in _QUANT_PRIMITIVES}
    for node in program.graph_module.graph.nodes:
        is_op_supported = check_op_support(node) if check_op_support else True
        if (
            node.op == "call_function"
            and node.target in ops_set_to_not_decompose
            and node.target not in quant_primitives
        ) and is_op_supported:
            warning_str = (
                f"Node {node} with op {node.target} was not decomposed or delegated.\n"
                + warning_str_end
            )
            if generate_error:
                raise RuntimeError(warning_str)
            else:
                logging.warning(warning_str)
    for _, submod, _ in get_control_flow_submodules(program.graph_module):
        for node in submod.graph.nodes:
            is_op_supported = check_op_support(node) if check_op_support else True
            if (
                node.op == "call_function"
                and node.target in ops_set_to_not_decompose
                and node.target not in quant_primitives
            ) and is_op_supported:
                warning_str = (
                    f"Node {node} with op {node.target} was not decomposed or delegated.\n"
                    + warning_str_end
                )
                if generate_error:
                    raise RuntimeError(warning_str)
                else:
                    logging.warning(warning_str)


def _remove_invalid_ops_for_not_decompose(  # noqa: C901
    preserve_ops: List[torch._ops.OpOverload],
) -> List[torch._ops.OpOverload]:
    _logged_warnings = set()

    def log_warning(warn_str):
        if warn_str not in _logged_warnings:
            logging.warn(warn_str)
            _logged_warnings.add(warn_str)

    # To address https://github.com/pytorch/executorch/issues/8781
    def keep(op):
        # Explicit allow list
        allow_list = []
        try:
            # Ops in torch.ops.quant are not always loaded, so we use try/except
            # Aliases output, but we need to allow it for XNNPACK
            allow_list.append(torch.ops.torchao.choose_qparams_affine.default)
        except:
            pass

        if op in allow_list:
            return True

        schema = op._schema
        native_schema = _pybind_schema_to_native_schema(schema)
        if native_schema is None:
            log_warning(
                f"Torchgen is not able to parse the schema of {op._schema}.  This is not fatal."
            )
        else:
            if native_schema.is_mutable:
                log_warning(
                    f"Op {op} was requested for preservation by partitioner.  This request is ignored because it is mutable."
                )
                return False

            if native_schema.aliased_return_names() != [None]:
                log_warning(
                    f"Op {op} was requested for preservation by partitioner.  This request is ignored because it aliases output."
                )
                return False

        # Fallback: torchgen does not detect alias annotations on ops
        # returning lists of aliased tensors (e.g. split.Tensor returns
        # Tensor(a)[]). Check op._schema.returns directly.
        for ret in schema.returns:
            if ret.alias_info is not None:
                log_warning(
                    f"Op {op} was requested for preservation by partitioner.  This request is ignored because it aliases output."
                )
                return False

        # Explicit block list of ops that don't work if asked for
        # preservation
        if op in [
            # Hits infinte recursion error when op is in
            # EDGE_DO_NOT_DECOMP namespace
            torch.ops.aten._to_copy.default,
            # scalar to tensor type promotion does not work on ops
            # in EDGE_DO_NOT_DECOMP namespace
            torch.ops.aten.mul.Tensor,
            torch.ops.aten.add.Tensor,
            torch.ops.aten.sub.Tensor,
            torch.ops.aten.div.Tensor,
            torch.ops.aten.item.default,
            torch.ops.aten._local_scalar_dense.default,
            torch.ops.aten.unbind.int,
            torch.ops.aten.split_with_sizes.default,
        ]:
            log_warning(
                f"Op {op} was requested for preservation by partitioner.  This request is ignored because it is in a blocklist."
            )
            return False
        return True

    return list(filter(keep, preserve_ops))


def _can_skip_using_EDGE_DO_NOT_DECOMP(
    partitioner: Partitioner, program: ExportedProgram
) -> bool:
    # THe current design of using EDGE_DO_NOT_DECOMP to prevent decomposition
    # has long standing issues.  _remove_invalid_ops_for_not_decompose was a band-aid to
    # fix some of the issues, but more issues are coming up over time, including a new issue with SDPA
    # and contiguous views: https://fb.workplace.com/groups/pytorch.edge.users/permalink/1796069037930048/
    # EDGE_DO_NOT_DECOMP is only needed by partitioners that specify check_op_support
    # As a temp fix, we give a more reliable path for backends that do not specify check_op_support
    _, check_op_support = partitioner.ops_to_not_decompose(program)
    return check_op_support is None


def _gen_edge_manager_for_partitioners(
    partitioner: Dict[str, List[Partitioner]],
    aten_programs: Dict[str, ExportedProgram],
    config: EdgeCompileConfig,
    constant_methods: Optional[Dict[str, Any]],
    generate_etrecord: Optional[bool] = False,
) -> "EdgeProgramManager":
    """
    Generates EdgeProgramManager for subsequent lowering to the
    partitioners specified by partitioner. The EdgeProgramManager is generated from
    aten_programs.

    Partitioners specify what nodes should not be decomposed from the original aten programs.
    This is done through two passes of run_decompositions.
        - First pass preserves all aten_targets specified by partitioners to preserve
          them from nested decompositions
        - Second pass uses check_op fn provided by partitioners to perform additional checks
          on nodes with preserved aten targets. They are then replaces with transformed ops to
          keep them through the second pass of decompositions
    """
    ops_set_to_not_decompose_by_program = defaultdict(list)
    edge_programs: Dict[str, ExportedProgram] = {}
    for name, program in aten_programs.items():
        # Functionalize program before asking partitioners to preserve ops
        program = program.run_decompositions({})

        if partitioner is not None:
            partitioners_for_program = partitioner.get(name, [])
            final_ops_to_preserve = set()

            # Decompose by default if there are no partitioners for the method
            if not partitioners_for_program:
                table = _default_decomposition_table()
                for op in config.preserve_ops:
                    table.pop(op, None)
                program = program.run_decompositions(table)

            # Process each partitioner individually using their specific requirements
            for curr_partitioner in partitioners_for_program:
                curr_ops_no_decomp, _ = curr_partitioner.ops_to_not_decompose(program)

                # Check if this partitioner can skip using EDGE_DO_NOT_DECOMP
                can_skip_using_edge_do_not_decomp = _can_skip_using_EDGE_DO_NOT_DECOMP(
                    curr_partitioner, program
                )

                if can_skip_using_edge_do_not_decomp:
                    # Preserve all ops in curr_ops_no_decomp from decomposition
                    table = _default_decomposition_table()
                    ops_needing_preservation = []

                    for op in curr_ops_no_decomp:
                        if table.pop(op, None) is not None:
                            ops_needing_preservation.append(op)

                    program = program.run_decompositions(table)
                    final_ops_to_preserve.update(ops_needing_preservation)
                else:
                    # EDGE_DO_NOT_DECOMP path for the partitioner
                    curr_ops_no_decomp = _remove_invalid_ops_for_not_decompose(
                        curr_ops_no_decomp
                    )

                    # Apply decompositions with this partitioner's preserved ops
                    table = _default_decomposition_table()
                    for op in curr_ops_no_decomp:
                        table.pop(op, None)

                    # First pass of decompositions with this partitioner's preserved ops
                    program = program.run_decompositions(table)

                    # Filter ops using EDGE_DO_NOT_DECOMP
                    temp_partitioner_dict = {name: [curr_partitioner]}
                    preserved_ops = (
                        _replace_aten_ops_with_transformed_ops(
                            name, program, temp_partitioner_dict
                        )
                        or []
                    )
                    final_ops_to_preserve.update(preserved_ops)

                    # Second pass of decompositions with this partitioner's preserved ops after filtering
                    program = program.run_decompositions(_default_decomposition_table())

                    # Restore ops from edge_no_decomp_namespace to aten ops
                    _restore_transformed_ops_to_aten_ops(program)
            ops_set_to_not_decompose_by_program[name].extend(final_ops_to_preserve)

        edge_programs[name] = _generate_edge_program(
            config,
            program,
            preserve_ops=ops_set_to_not_decompose_by_program.get(name, []),
        )

    # Merge ops_to_not_decompose into config so that downstream calls (e.g.
    # EdgeProgramManager.transform()) carry the exception list through their
    # verifiers automatically.
    all_ops_not_to_decompose = list(
        set().union(*ops_set_to_not_decompose_by_program.values())
    )
    config._core_aten_ops_exception_list = list(
        set(config._core_aten_ops_exception_list) | set(all_ops_not_to_decompose)
    )

    edge_manager = EdgeProgramManager(
        edge_programs,
        constant_methods,
        config,
        all_ops_not_to_decompose,
    )

    if generate_etrecord:
        etrecord = _create_empty_etrecord()
        etrecord.add_exported_program(aten_programs)
        etrecord.add_edge_dialect_program(copy.deepcopy(edge_manager))
        edge_manager._etrecord = etrecord

    return edge_manager


def collect_named_data_store_from_exported_program(
    exported_program: ExportedProgram,
    named_data_store: NamedDataStore,
) -> None:
    """
    Collects all the named data store outputs found within the exported program
    and adds them to named_data_store.
    """

    # collected all the named data into the named data store for deduplication
    def collect_named_data_store_outputs(
        graph_module: torch.fx.GraphModule,
    ) -> None:
        for node in graph_module.graph.nodes:
            if node.target == executorch_call_delegate:
                lbm = getattr(graph_module, node.args[0].target)
                assert is_lowered_module(lbm)
                data_store_output = lbm.named_data_store_output
                if data_store_output is not None:
                    named_data_store.merge_named_data_store(data_store_output)

        for _, submod, _ in get_control_flow_submodules(graph_module):
            collect_named_data_store_outputs(submod)

    collect_named_data_store_outputs(exported_program.graph_module)


@et_logger("to_edge_transform_and_lower")
def to_edge_transform_and_lower(  # noqa: C901
    programs: Union[ExportedProgram, Dict[str, ExportedProgram]],
    transform_passes: Optional[
        Union[Sequence[PassType], Dict[str, Sequence[PassType]], PassManager]
    ] = None,
    partitioner: Optional[
        Union[List[Partitioner], Dict[str, List[Partitioner]]]
    ] = None,
    constant_methods: Optional[Dict[str, Any]] = None,
    compile_config: Optional[EdgeCompileConfig] = None,
    generate_etrecord: bool = False,
) -> "EdgeProgramManager":
    """
    :func:`to_edge_transform_and_lower` constructs an EdgeProgramManager from a set of
    exported programs in ATen dialect. It differs fundamentally from to_edge in that it
    combines the conversion of the ATen dialect to the edge dialect program, then running
    the transformation passes and then subsequently lowering the programs to their
    corresponding backends all into a single API.

    This is fundamentally useful for lowering to backends that have ops registered that they
    do not want to be decomposed and thus rely on matching with these non-decomposed ops. For
    these sorts of backends this is the *only* API that should be used to lower to the edge
    dialect. Using a combination of to_edge(...) and to_backend(...) will result in inconsistent
    or wrong behavior.

    This API is the primary recommended way to lower to the CPU based XNNPack backend.

    Args:
        programs: Can be a single ExportedProgram or a dictionary mapping function names
            to their corresponding ExportedPrograms. If only a single ExportedProgram is
            provided it will be assigned the name "forward".

        transform_passes: The transform_passes can be one of:
            1) a list of passes -
                all methods in the given EdgeProgramManager will be transformed with the provided passes.
            2) a dictionary -
                only method names specified in the dictionary will be transformed
                with their corresponding passes
            3) an instance of a PassManager -
                all methods in the given EdgeProgramManager will be
                transformed with the given PassManager instance.

        partitioner: The partitioner can either be a Partitioner subclass instance, or a
            dictionary mapping method names to Partitioner subclass instance. If it is a
            Partitioner subclass, all programs in the given EdgeProgramManager will be lowered
            using the given partitioner. If it is a dictionary, only method names specified in
            the dictionary will be lowered with the given partitioner.

        constant_methods: An optional dictionary of method name to the constant value
            returned by that method in eager mode. Often used to store config information on
            Edge models.

        compile_config: An optional argument used to provide greater control over the
            transformation to edge dialect process.

        generate_etrecord: An optional argument used to generate an etrecord for debugging purposes.

    Returns:
        EdgeProgramManager
    """
    assert not isinstance(constant_methods, EdgeCompileConfig)
    config = compile_config or EdgeCompileConfig()
    if not isinstance(programs, dict):
        aten_programs = {"forward": programs}
    else:
        aten_programs = programs

    if not isinstance(partitioner, dict) and partitioner is not None:
        partitioner = {name: partitioner for name in aten_programs.keys()}
    elif partitioner is None:
        partitioner = {name: [] for name in aten_programs.keys()}

    edge_manager = _gen_edge_manager_for_partitioners(
        partitioner, aten_programs, config, constant_methods, generate_etrecord
    )

    if transform_passes is not None:
        edge_manager = edge_manager.transform(transform_passes)

        if generate_etrecord:
            edge_manager._etrecord.add_extra_export_modules(
                {"edge_after_transform": copy.deepcopy(edge_manager)}
            )

    max_num_partitioners = 0
    for partitioner_list in partitioner.values():
        max_num_partitioners = max(max_num_partitioners, len(partitioner_list))

    for i in range(max_num_partitioners):
        method_to_partitioner = {}
        for name, partitioner_list in partitioner.items():
            if i < len(partitioner_list):
                method_to_partitioner[name] = partitioner_list[i]
        edge_manager = edge_manager.to_backend(method_to_partitioner)

    for name, program in edge_manager._edge_programs.items():
        ops_set_to_not_decompose: Set[torch._ops.OpOverload] = set()
        partitioners = partitioner.get(name, [])
        for curr_partitioner in partitioners:
            curr_op_set, check_op_support = curr_partitioner.ops_to_not_decompose(
                program
            )

            if not _can_skip_using_EDGE_DO_NOT_DECOMP(curr_partitioner, program):
                curr_op_set = _remove_invalid_ops_for_not_decompose(curr_op_set)
            ops_set_to_not_decompose = ops_set_to_not_decompose.union(curr_op_set)
            _sanity_check_graph_for_non_decomp_ops(
                name,
                program,
                ops_set_to_not_decompose,
                check_op_support,
                partitioner_name=curr_partitioner.__class__.__name__,
                generate_error=True,
            )

        preserve_ops = config.preserve_ops + list(ops_set_to_not_decompose)
        if config._check_ir_validity:
            EXIREdgeDialectVerifier(
                edge_compile_config=config,
                class_only=True,
                preserve_ops=preserve_ops,
            )()(program.graph_module)

    return edge_manager


@et_logger("to_edge")
def to_edge(
    programs: Union[ExportedProgram, Dict[str, ExportedProgram]],
    constant_methods: Optional[Dict[str, Any]] = None,
    compile_config: Optional[EdgeCompileConfig] = None,
    generate_etrecord: bool = False,
) -> "EdgeProgramManager":
    """
    :func:`to_edge` constructs an EdgeProgramManager from a set of exported programs in
    ATen dialect. Upon construction those programs are transformed into edge dialect.

    Args:
        programs: Can be a single ExportedProgram or a dictionary mapping function names to their corresponding ExportedPrograms. If only a single ExportedProgram is provided it will be assigned the name "forward".

        constant_methods: An optional dictionary of method name to the constant value returned by that method in eager mode. Often used to store config information on Edge models.

        compile_config: An optional argument used to provide greater control over the transformation to edge dialect process.

        generate_etrecord: An optional argument used to generate an etrecord for debugging purposes. Default is False.

    Returns:
        EdgeProgramManager
    """
    assert not isinstance(constant_methods, EdgeCompileConfig)
    config = compile_config or EdgeCompileConfig()
    if not isinstance(programs, dict):
        aten_programs = {"forward": programs}
    else:
        aten_programs = programs

    edge_programs: Dict[str, ExportedProgram] = {}

    for name, program in aten_programs.items():
        # Decompose to Core ATen
        table = _default_decomposition_table()
        preserve_ops = []
        if compile_config:
            preserve_ops = compile_config.preserve_ops
            for op in compile_config.preserve_ops:
                table.pop(op, None)
        program = program.run_decompositions(table)

        if config._check_ir_validity:
            # Remove invalid assert ops, such as _assert_tensor_metadata.
            # This pass is run in _generate_edge_program; it is required here to
            # ensure the graph is in ATen dialect before verification.
            gm = program.graph_module
            gm_res = RemoveNonCoreAtenOpGraphAssertsPass()(gm)
            assert gm_res is not None
            gm = gm_res.graph_module
            try:
                EXIRATenDialectVerifier(
                    edge_compile_config=config,
                    class_only=False,
                )(gm)
            except ExportError as e:
                logging.info(f"Input program {name} is not in ATen dialect.")
                raise e

        edge_programs[name] = _generate_edge_program(
            config, program, preserve_ops=preserve_ops
        )
        if config._check_ir_validity:
            try:
                EXIREdgeDialectVerifier(
                    edge_compile_config=config,
                    class_only=True,
                    preserve_ops=preserve_ops,
                )()(edge_programs[name].graph_module)
            except ExportError as e:
                logging.info(f"Input program {name} is not in Edge dialect.")
                raise e

    epm = EdgeProgramManager(edge_programs, constant_methods, config)
    if generate_etrecord:
        etrecord = _create_empty_etrecord()
        etrecord.add_exported_program(aten_programs)
        etrecord.add_edge_dialect_program(copy.deepcopy(epm))
        epm._etrecord = etrecord

    return epm


class EdgeProgramManager:
    """
    Package of one or more `ExportedPrograms` in Edge dialect. Designed to simplify
    lowering to ExecuTorch. See: https://pytorch.org/executorch/main/ir-exir

    Allows easy applications of transforms across a collection of exported programs
    including the delegation of subgraphs.

    Manages the second link in the lowering chain of ATen -> Edge -> ExecuTorch.
    """

    def __init__(
        self,
        edge_programs: Union[ExportedProgram, Dict[str, ExportedProgram]],
        constant_methods: Optional[Dict[str, Any]] = None,
        compile_config: Optional[EdgeCompileConfig] = None,
        core_aten_ops_exception_list: Optional[List[torch._ops.OpOverload]] = None,
        preserve_ops: Optional[List[torch._ops.OpOverload]] = None,
    ):
        """
        Should not be called directly by users. User should use :func:'to_edge' instead.

        Constructs an EdgeProgramManager from an existing set of exported programs in edge dialect.
        """
        self.compile_config = compile_config or EdgeCompileConfig()
        if not isinstance(edge_programs, dict):
            edge_programs = {"forward": edge_programs}

        for name, program in edge_programs.items():
            try:
                EXIREdgeDialectVerifier(
                    edge_compile_config=self.compile_config,
                    core_aten_ops_exception_list=core_aten_ops_exception_list,
                    preserve_ops=preserve_ops,
                )(program.graph_module)
            except ExportError as e:
                logging.info(f"Input program {name} is not in aten dialect.")
                raise e

        self._edge_programs: Dict[str, ExportedProgram] = edge_programs
        self._config_methods = constant_methods

        self._named_data_store = NamedDataStore()
        for _, program in self._edge_programs.items():
            collect_named_data_store_from_exported_program(
                program, self._named_data_store
            )

        self._etrecord = None

    @property
    def methods(self) -> Set[str]:
        """
        Returns the set of methods in this EdgeProgramManager.
        """
        return set(self._edge_programs.keys())

    @property
    def config_methods(self) -> Set[str]:
        """
        Returns the set of config methods in this EdgeProgramManager.
        """
        return set(self._config_methods.keys()) if self._config_methods else set()

    def exported_program(self, method_name: str = "forward") -> ExportedProgram:
        """
        Returns the ExportedProgram specified by 'method_name'.
        """

        return self._edge_programs[method_name]

    @et_logger("transform")
    def transform(
        self,
        passes: Union[Sequence[PassType], Dict[str, Sequence[PassType]], PassManager],
        compile_config: Optional[EdgeCompileConfig] = None,
    ) -> "EdgeProgramManager":
        """
        Transforms the program according to the provided passes.

        Args:
            passes: This param can be one of:
                1) a list of passes -
                    all methods in the given EdgeProgramManager
                    will be transformed with the provided passes.
                2) a dictionary mapping method names to lists of passes -
                    only method names specified in the dictionary will be
                    transformed with their corresponding passes.
                3) a PassManager instance -
                    all methods in the given EdgeProgramManager will be
                    transformed with the given PassManager instance.
            compile_config: Compile config to use for veriy the correctness of model
                graph after each pass. If not specified, the compile config of the
                calling EdgeProgramManager will be used. It will be used in as compile
                config of returned EdgeProgramManager.

        Returns:
            EdgeProgramManager: A copy of the calling EdgeProgramManager with the
            transformations applied.
        """

        compile_config = compile_config or self.compile_config
        new_programs: Dict[str, ExportedProgram] = {}

        # Cast passes parameter upfront.
        passes_seq: Optional[Sequence[PassType]] = None
        passes_dict: Optional[Dict[str, Sequence[PassType]]] = None
        pass_manager: Optional[PassManager] = None

        if isinstance(passes, Sequence):
            passes_seq = passes
        if isinstance(passes, dict):
            passes_dict = passes
        if isinstance(passes, PassManager):
            pass_manager = passes

        for name, program in self._edge_programs.items():
            # If the method name is enforced, but not matched, we skip transformation.
            if (
                isinstance(passes, dict)
                and passes_dict
                and name not in passes_dict.keys()
            ):
                new_programs[name] = copy.deepcopy(program)
                continue

            # Depending on the passes parameter, call the corresponding transform function.
            if passes_seq is not None:
                new_programs[name] = _transform(program, *passes_seq)
            elif passes_dict is not None:
                new_programs[name] = _transform(program, *passes_dict[name])
            elif pass_manager is not None:
                new_programs[name] = _transform_with_pass_manager(program, pass_manager)

            # Verify the correctness of model graph after each transformation.
            EXIREdgeDialectVerifier(edge_compile_config=compile_config)(
                new_programs[name].graph_module
            )

        epm = EdgeProgramManager(
            new_programs, copy.deepcopy(self._config_methods), compile_config
        )

        epm._etrecord = self._etrecord
        return epm

    @et_logger("to_backend")
    def to_backend(
        self,
        partitioner: Union[Partitioner, Dict[str, Partitioner]],
    ) -> "EdgeProgramManager":
        """
        Returns a semantically-equivalent program to the one given as input,
        but with portions of each program in the EdgeProgramManager targeted
        for delegation as determined by the partitioner.

        Args:
            partitioner: The partitioner can either be a Partitioner subclass instance, or a
                dictionary mapping method names to Partitioner subclass instance. If it is a
                Partitioner subclass, all programs in the given EdgeProgramManager
                will be lowered using the given partitioner. If it is a
                dictionary, only method names specified in the dictionary will be
                lowered with the given partitioner.

                The Partitioner subclass instance is in charge with tagging portions of the
                input program for delegation. A valid partitioner must return PartitionerResult including valid
                partition_tags: Dict[str, DelegationSpec], where each key is a tag
                name and the nodes with same tag will be fused a one subgraph and
                delegated to backend specififed in delegation spec.

        Returns:
            EdgeProgramManager: A copy of the calling EdgeProgramManager with the
            specified subgraphs lowered.
        """
        new_edge_programs: Dict[str, ExportedProgram] = {}
        method_to_partitioner: Dict[str, Partitioner] = {}
        if not isinstance(partitioner, dict):
            method_to_partitioner = {name: partitioner for name in self._edge_programs}
        else:
            method_to_partitioner = partitioner

        method_to_programs_and_partitioners = MethodProgramsPartitionerSpec(
            self._edge_programs,
            method_to_partitioner,
        )

        new_edge_programs = to_backend(method_to_programs_and_partitioners)
        config = EdgeCompileConfig(_check_ir_validity=False)
        epm = EdgeProgramManager(
            new_edge_programs,
            copy.deepcopy(self._config_methods),
            config,
        )

        epm._etrecord = self._etrecord
        return epm

    @et_logger("to_executorch")
    def to_executorch(  # noqa (FLAKE8) C901
        self,
        config: Optional[ExecutorchBackendConfig] = None,
    ) -> "ExecutorchProgramManager":
        """
        Transforms the program to the ExecuTorch backend.

        Args:
            config: An optional argument used to provide greater control over
                the transformation to the ExecuTorch backend.

        Returns:
            ExecutorchProgramManager: A manager representing the state of the EdgeProgramManager
            after it has been transformed to the ExecuTorch backend.
        """
        config = config if config else ExecutorchBackendConfig()
        execution_programs: Dict[str, ExportedProgram] = {}
        for name, program in self._edge_programs.items():
            if config.do_quant_fusion_and_const_prop:
                if program.graph_signature.backward_signature is not None:
                    raise Exception(
                        "Cannot run do_quant_fusion_and_const_prop on a graph with a backward signature intended for on-device training."
                        " Please set do_quant_fusion_and_const_prop to False in the ExecutorchBackendConfig."
                    )
                program = quant_fusion_and_const_prop_pass(program)
            if config.run_reinplace_pass:
                program = reinplace_pass(program)
            program = weights_to_outputs_pass(program)
            program = unsafe_remove_auto_functionalized_pass(program)
            gm, new_signature = insert_write_back_for_buffers_pass(program)
            new_gm = program.graph_module
            for p in edge_to_executorch_passes(config, name):
                new_gm_res = p(new_gm)
                assert new_gm_res is not None
                new_gm = new_gm_res.graph_module
                if isinstance(p, SpecPropPass):
                    # Note that this is a hacky way to get around the fact that
                    # placeholder nodes corresponding to the parameters of the graph module
                    # shall not participate in memory planning. It increases runtime memory
                    # footprint.
                    # Proper way would be to have ExportPass work with ExportedProgram
                    # instead of GraphModule. This is because ExportPass should work
                    # on top of the export artifact of torch.export whichi s ExportedProgram.
                    # Working with GraphModule does not provide all the information contained
                    # in the ExportedProgram
                    # TODO(who?)
                    p.update_placeholder_tensor_specs(program, new_gm)

            # Tag constant weights.
            if (
                isinstance(config.external_constants, bool)
                and config.external_constants
            ):
                new_gm_res = external_constants_pass(new_gm)
                new_gm = new_gm_res.graph_module
            elif callable(config.external_constants):
                new_gm_res = external_constants_pass(new_gm, config.external_constants)
                new_gm = new_gm_res.graph_module

            # Tag mutable weights.
            if config.external_mutable_weights:
                new_gm_res = external_mutable_weights_pass(new_gm, program)
                new_gm = new_gm_res.graph_module

            if isinstance(config.memory_planning_pass, dict):
                memory_planning_pass = config.memory_planning_pass.get(
                    name, ExecutorchBackendConfig().memory_planning_pass
                )
            else:
                memory_planning_pass = config.memory_planning_pass
            # TODO(jakeszwe): Follow up with compiler on if the deepcopy is necessary and if so how to make it work
            if hasattr(memory_planning_pass, "run"):
                new_gm_res = memory_planning_pass.run(new_gm, new_signature)
            else:
                new_gm_res = memory_planning_pass(new_gm)

            # WARNING: DO NOT ADD ANY MORE PASSES AFTER MEMORY PLANNING PASS.
            # THERE ARE A LOT OF ASSUMPTIONS IN THE STACK THAT MEMORY PLANNING IS THE LAST PASS BEFORE THE EMITTER.
            assert new_gm_res is not None
            new_gm = new_gm_res.graph_module

            _copy_module(program.graph_module, new_gm)
            execution_programs[name] = program
        # After running memory planning on all entry points we can run the cross entry point memory planning
        if isinstance(config.memory_planning_pass, dict):
            for memory_planning_pass in config.memory_planning_pass.values():
                if hasattr(memory_planning_pass, "run_multimethod"):
                    memory_planning_pass.run_multimethod()
        else:
            memory_planning_pass = config.memory_planning_pass
            if hasattr(memory_planning_pass, "run_multimethod"):
                memory_planning_pass.run_multimethod()

        et_pm = ExecutorchProgramManager(
            execution_programs,
            self._config_methods,
            config,
            self._named_data_store.get_named_data_store_output(),
        )

        if self._etrecord is not None:
            # Create a clean copy of the ETRecord for the executorch manager
            # This preserves edge-stage data while allowing executorch data to be added
            et_etrecord = self._etrecord.copy()
            et_etrecord.add_executorch_program(et_pm)
            et_pm._etrecord = et_etrecord

        return et_pm


class ExecutorchProgramManager:
    """
    Package of one or more `ExportedPrograms` in Execution dialect. Designed to simplify
    lowering to ExecuTorch. See: https://pytorch.org/executorch/main/ir-exir

    When the ExecutorchProgramManager is constructed the ExportedPrograms in execution dialect
    are used to form the executorch binary (in a process called emission) and then serialized
    to a buffer.

    Manages the final link in the lowering chain of ATen -> Edge -> ExecuTorch.
    """

    def __init__(
        self,
        execution_programs: Dict[str, ExportedProgram],
        config_methods: Optional[Dict[str, Any]] = None,
        backend_config: Optional[ExecutorchBackendConfig] = None,
        named_data: Optional[NamedDataStoreOutput] = None,
    ):
        """
        End users should not call this constructor directly. Instead, they should use
        :func:'to_executorch' to construct an ExecutorchProgramManager.

        Constructs an ExecutorchProgramManager from a set of exported programs in
        execution dialect.

        Args:
            execution_programs: A dictionary of method name to the corresponding
            ExportedProgram.

            config_methods: A dictionary of method name to the config value returned
            by that method in eager mode.

            backend_config: An optional argument used to provide greater control over
            the emission and serialization.
        """
        # Set up methods
        self._execution_programs: Dict[str, ExportedProgram] = execution_programs
        self._config_methods: Optional[Dict[str, Any]] = config_methods

        # Named data from EdgeProgramManager
        self._named_data: Optional[NamedDataStoreOutput] = named_data

        backend_config = backend_config or ExecutorchBackendConfig()

        # Emit methods
        self._emitter_output: EmitterOutput = emit_program(
            self._execution_programs,
            backend_config.emit_stacktrace,
            self._config_methods,
            backend_config.emit_mutable_buffer_names,
        )

        # Serialize emitter output, ready to be written to a file.
        from executorch.extension.flat_tensor.serialize.serialize import (
            FlatTensorConfig,
            FlatTensorSerializer,
        )

        self._data_serializer = FlatTensorSerializer(
            FlatTensorConfig(segment_alignment=backend_config.segment_alignment)
        )
        self._pte_data, self._tensor_data = serialize_for_executorch(
            self._emitter_output,
            backend_config,
            self._data_serializer,
            self._named_data,
        )
        self._buffer: Optional[bytes] = None
        self._etrecord = None

    @property
    def methods(self) -> Set[str]:
        """
        Returns the set of methods in this ExecutorchProgramManager.
        """
        return set(self._execution_programs.keys())

    @property
    def config_methods(self) -> Set[str]:
        """
        Returns the set of config methods in this ExecutorchProgramManager.
        """
        return set(self._config_methods.keys()) if self._config_methods else set()

    def exported_program(self, method_name: str = "forward") -> ExportedProgram:
        """
        Returns the ExportedProgram specified by 'method_name'.
        """
        return self._execution_programs[method_name]

    def dump_executorch_program(
        self, verbose: bool = False, out: Optional[TextIO] = None
    ) -> None:
        """
        Prints the ExecuTorch binary in a human readable format.

        Args:
            verbose (bool):
                If False prints the binary in a condensed format.
                If True prints the binary 1-1 with the specification in the schema.
            out:
                If None, prints to stdout.
                If non-None, writes the string to that stream object. It can be
                    a file object, a StringIO object, or any other TextIO subclass.
        """
        if verbose:
            pretty_print(self._emitter_output.program, out=out)
        else:
            print_program(self._emitter_output.program, out=out)

    @property
    def debug_handle_map(self) -> Dict[int, Union[int, List[int]]]:
        return self._emitter_output.debug_handle_map

    @property
    def delegate_map(
        self,
    ) -> Dict[str, Dict[int, Dict[str, Union[str, _DelegateDebugIdentifierMap]]]]:
        return self._emitter_output.method_to_delegate_debug_id_map

    @property
    def instruction_id_to_num_outs_map(
        self,
    ) -> Dict[str, Dict[int, Union[int, List[int]]]]:
        return self._emitter_output.instruction_id_to_num_outs_map

    @property
    def executorch_program(self) -> Program:
        """
        Returns the object that represents the ExecuTorch binary before serialization.
        """
        return self._emitter_output.program

    @property
    def buffer(self) -> bytes:
        """Returns the serialized ExecuTorch binary as a byte string.

        Note that the call to `buffer` may allocate a very large amount of
        contiguous memory, depending on the model size. If writing to a file,
        use `write_to_file` which won't incur additional copies.
        """
        # TODO(T181494963): update pybinding to remove buffer cache, which can consume large
        # amounts of memory longer than necessary.
        if self._buffer is None:
            self._buffer = bytes(self._pte_data)
        return self._buffer

    def get_etrecord(self):
        """
        Get the generated ETRecord if etrecord generation was enabled.

        Returns:
            ETRecord object if generation was enabled, None otherwise

        Raises:
            RuntimeError: if ETRecord object was not generated.
        """

        if self._etrecord is None:
            raise RuntimeError("ETRecord was not generated")
        return self._etrecord

    def write_to_file(self, open_file: io.BufferedIOBase) -> None:
        """
        Writes the serialized ExecuTorch binary to the file at `open_file`. Prefer to use this over
        `buffer`, as it writes to file without copying into a contiguous block of memory first,
        reducing the peak memory usage.
        """
        self._pte_data.write_to_file(open_file)

    def write_tensor_data_to_file(self, outdir) -> None:
        """
        Writes the serialized ExecuTorch data files to the directory at `outdir`.
        """
        assert self._tensor_data is not None
        for filename, cord in self._tensor_data.items():
            if not filename.endswith(".ptd"):
                filename += ".ptd"
            with open(os.path.join(outdir, f"{filename}"), "wb") as f:
                logging.info(f"Writing data file to {filename}")
                cord.write_to_file(f)

    def save(self, path: str) -> None:
        """
        Saves the serialized ExecuTorch binary to the file at `path`.
        """
        if path[-4:] != ".pte":
            logging.error(f"Path {path} does not end with .pte")
            raise ValueError(f"Path {path} does not end with .pte")
        try:
            with open(path, "wb") as file:
                self.write_to_file(file)
                logging.info(f"Saved exported program to {path}")
        except Exception as e:
            logging.error(f"Error while saving to {path}: {e}")