pytorch/torch/ao/quantization/pt2e/graph_utils.py

# mypy: allow-untyped-defs
import itertools
import operator
from typing import Any, Callable, List, Optional, OrderedDict, Sequence, Set, Tuple

import torch
from torch.fx import Node
from torch.fx.passes.utils.source_matcher_utils import (
    check_subgraphs_connected,
    get_source_partitions,
    SourcePartition,
)


__all__ = [
    "find_sequential_partitions",
    "get_control_flow_submodules",
    "get_equivalent_types",
    "update_equivalent_types_dict",
]

_EQUIVALENT_TYPES: List[Set] = [
    {torch.nn.Conv1d, torch.nn.functional.conv1d},
    {torch.nn.Conv2d, torch.nn.functional.conv2d},
    {torch.nn.AdaptiveAvgPool2d, torch.nn.functional.adaptive_avg_pool2d},
    {torch.nn.ReLU, torch.nn.functional.relu, torch.nn.functional.relu_},
    {torch.nn.BatchNorm2d, torch.nn.functional.batch_norm},
    {torch.nn.Hardtanh, torch.nn.functional.hardtanh, torch.nn.functional.hardtanh_},
    {torch.add, operator.add, operator.iadd, "add", "add_"},
    {torch.mul, operator.mul, operator.imul, "mul", "mul_"},
]


def _create_equivalent_types_dict():
    _DICT = {}
    for values in _EQUIVALENT_TYPES:
        for v in values:
            _DICT[v] = list(values)
    return _DICT


_EQUIVALENT_TYPES_DICT = _create_equivalent_types_dict()


def get_equivalent_types() -> List[Set]:
    return _EQUIVALENT_TYPES


def update_equivalent_types_dict(customized_equivalent_types=None):
    """Help function for user who wants to customize the _EQUIVALENT_TYPES and _EQUIVALENT_TYPES_DICT.
    When customized_equivalent_types passes in,
    re-generate _EQUIVALENT_TYPES and _EQUIVALENT_TYPES_DICT.
    """
    if customized_equivalent_types is None:
        raise ValueError("customized_equivalent_types should not be None")
    global _EQUIVALENT_TYPES
    global _EQUIVALENT_TYPES_DICT
    _EQUIVALENT_TYPES = customized_equivalent_types
    _EQUIVALENT_TYPES_DICT = _create_equivalent_types_dict()


def _partitions_sequential(partitions: Sequence[SourcePartition]):
    prev_partition = None
    for partition in partitions:
        if prev_partition is not None and not check_subgraphs_connected(
            prev_partition, partition
        ):
            return False
        prev_partition = partition
    return True


def _get_matching_types(partition_type):
    matching_types = [partition_type]
    if partition_type in _EQUIVALENT_TYPES_DICT:
        matching_types.extend(_EQUIVALENT_TYPES_DICT[partition_type])
    return matching_types


def _valid_type_sequence(partition_types: List[Any]):
    partition_types_set = set()  # type: ignore[var-annotated]
    for partition_type in partition_types:
        matching_types = _get_matching_types(partition_type)
        matching_types_set = set(matching_types)
        if len(partition_types_set & matching_types_set) > 0:
            return False
        partition_types_set |= matching_types_set
    return True


def find_sequential_partitions(
    gm: torch.fx.GraphModule,
    partition_types: List[Any],
    include_functional_equivalent=True,
    filter_fn: Optional[Callable[[Node], bool]] = None,
):
    if not _valid_type_sequence(partition_types):
        raise ValueError(
            f"Invalid partition types: {partition_types}. Each type in the sequence must be unique"
        )

    typed_partitions: OrderedDict[Any, List[SourcePartition]] = OrderedDict()
    for partition_type in partition_types:
        types_to_match = _get_matching_types(partition_type)
        partitions = get_source_partitions(gm.graph, types_to_match, filter_fn)
        typed_partitions[partition_type] = list(
            itertools.chain.from_iterable(partitions.values())
        )

    typed_partitions_list = list(typed_partitions.values())
    fusion_candidates = itertools.product(*typed_partitions_list)
    fused_partitions = [
        candidate
        for candidate in fusion_candidates
        if _partitions_sequential(candidate)
    ]
    return fused_partitions


def _get_submodule(
    graph_module: torch.fx.GraphModule, node: torch.fx.Node, arg_index: int
) -> Tuple[str, torch.nn.Module, torch.fx.Node]:
    submod_node = node.args[arg_index]
    assert isinstance(submod_node, torch.fx.Node)
    assert submod_node.op == "get_attr"
    assert isinstance(submod_node.target, str)
    submodule = graph_module.get_submodule(submod_node.target)
    # pyre-ignore
    return submod_node.target, submodule, node


def get_control_flow_submodules(
    graph_module: torch.fx.GraphModule,
) -> List[Tuple[str, torch.nn.Module, torch.fx.Node]]:
    """
    Returns a list of submodules used for control flow operations
    (torch.ops.higher_order.cond/map) that are in the given toplevel graph (does not look
    into submodules). Specifically, the returned value is a list containing a
    tuple of (name of the submodule that's stored in the graph module, the
    submodule itself, and the fx node that uses this submodule).
    """
    control_flow_submodules = []
    for node in graph_module.graph.nodes:
        if node.op != "call_function":
            continue

        if node.target is torch.ops.higher_order.cond:
            control_flow_submodules.append(_get_submodule(graph_module, node, 1))
            control_flow_submodules.append(_get_submodule(graph_module, node, 2))
        if node.target is torch.ops.higher_order.map_impl:
            control_flow_submodules.append(_get_submodule(graph_module, node, 0))

    return control_flow_submodules