xref: /aosp_15_r20/external/executorch/backends/mediatek/partitioner.py (revision 523fa7a60841cd1ecfb9cc4201f1ca8b03ed023a)

# Copyright (c) 2024 MediaTek Inc.
#
# Licensed under the BSD License (the "License"); you may not use this file
# except in compliance with the License. See the license file in the root
# directory of this source tree for more details.

from typing import Callable, final, List, Optional, Tuple

import torch
from executorch.backends.mediatek.preprocess import NeuropilotBackend
from executorch.exir.backend.backend_details import CompileSpec
from executorch.exir.backend.partitioner import (
    DelegationSpec,
    Partitioner,
    PartitionResult,
)
from executorch.exir.backend.utils import tag_constant_data

from mtk_converter.python.converters.pytorch import importer_v2
from torch.export.exported_program import ExportedProgram
from torch.fx.passes.infra.partitioner import CapabilityBasedPartitioner
from torch.fx.passes.operator_support import OperatorSupportBase


class NeuropilotOperatorsSupport(OperatorSupportBase):

    def __init__(
        self,
        op_types_to_skip: Optional[set] = None,
        op_names_to_skip: Optional[set] = None,
    ) -> None:
        if op_types_to_skip is None:
            op_types_to_skip = set()
        if op_names_to_skip is None:
            op_names_to_skip = set()

        self._op_types_to_skip = op_types_to_skip
        self._op_names_to_skip = op_names_to_skip

    def is_node_supported(self, _, node: torch.fx.Node) -> bool:
        # Handle 'call_function' only cause 'placeholder' and 'output' cannot be tagged.
        # Ref: https://github.com/pytorch/executorch/pull/1398
        if node.op != "call_function":
            return False

        op_type = node.target.__name__
        if op_type in self._op_types_to_skip or node.name in self._op_names_to_skip:
            print(
                f"[Neuropilot Backend] The {op_type} operator with name '{node.name}' is skipped."
            )
            return False

        return importer_v2.is_fx_node_supported(node)


@final
class NeuropilotPartitioner(Partitioner):

    def __init__(
        self,
        compile_spec: List[CompileSpec],
        op_types_to_skip: Optional[set] = None,
        op_names_to_skip: Optional[set] = None,
    ) -> None:
        self.delegation_spec = DelegationSpec(NeuropilotBackend.__name__, compile_spec)
        self._op_types_to_skip = op_types_to_skip
        self._op_names_to_skip = op_names_to_skip

    def ops_to_not_decompose(
        self,
        ep: ExportedProgram,
    ) -> Tuple[List[torch._ops.OpOverload], Optional[Callable[[torch.fx.Node], bool]]]:
        ops_not_decompose = [
            torch.ops.aten.pixel_shuffle.default,
            torch.ops.aten.upsample_bilinear2d.default,
            torch.ops.aten.upsample_bilinear2d.vec,
            torch.ops.aten.upsample_nearest2d.default,
            torch.ops.aten.upsample_nearest2d.vec,
        ]
        return (ops_not_decompose, None)

    def partition(self, exported_program: ExportedProgram) -> PartitionResult:
        capability_partitioner = CapabilityBasedPartitioner(
            exported_program.graph_module,
            NeuropilotOperatorsSupport(self._op_types_to_skip, self._op_names_to_skip),
            allows_single_node_partition=True,
        )
        partition_list = capability_partitioner.propose_partitions()

        partition_tags = {}
        for partition in partition_list:
            for node in partition.nodes:
                tag = f"tag{partition.id}"
                node.meta["delegation_tag"] = tag
                partition_tags[tag] = self.delegation_spec

        tag_constant_data(exported_program)

        return PartitionResult(
            tagged_exported_program=exported_program, partition_tags=partition_tags
        )