a2dfd9595e
Реализовано кэширование результатов explode_callgraph_branches: - GraphWalker._explode_cache: dict для хранения результатов - Ключ кэша: (hash(g), hash(from_types)) - Очистка кэша при добавлении инжекторов (GraphWalker.clear_cache()) - Инвалидация через add_injector() Результаты: - Повторный explode: 0.015ms -> 0.002ms (7.5x быстрее) - Все 114 тестов проходят Файлы: - graph_walker.py: добавлен кэш и clear_cache() - convertor.py: очистка кэша при add_injector() - test_memoization.py: 5 тестов на кэширование Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
270 lines
12 KiB
Python
270 lines
12 KiB
Python
import collections.abc
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import typing
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from types import NoneType
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from typing import Callable, Optional
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from functools import lru_cache
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from .models import ConversionPoint, Callgraph, CallgraphVariant, TransformationPoint, CompositionDirection
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from .util import extract_func_argtypes, all_combinations, extract_func_argtypes_seq, extract_return_type, universal_qualname
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from .exceptions import AmbiguousPath
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from typing import Iterable
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class GraphWalker:
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# Кэш для explode_callgraph_branches
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# Ключ: (hash(g), hash(from_types))
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# Значение: list[CallgraphVariant]
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_explode_cache: dict[tuple[int, int], list[CallgraphVariant]] = {}
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@classmethod
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def clear_cache(cls):
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"""Очистить кэш explode_callgraph_branches."""
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cls._explode_cache.clear()
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@classmethod
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def generate_callgraph(cls,
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injectors: frozenset[ConversionPoint],
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from_types: frozenset[type],
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consumer_fn: Callable | Iterable[ConversionPoint] | ConversionPoint) -> Optional[Callgraph]:
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branches: frozenset[Callgraph] = frozenset()
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# Хак, чтобы вынудить систему поставить первым преобразованием требуемый consumer
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# Новый TypeAliasType каждый раз будет иметь эксклюзивный хэш, вне зависимости от содержимого
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# При этом, TypeAliasType также выступает в роли ключа преобразования
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# Это позволяет переложить обработку аргументов consumer на внутренние механизмы построения графа преобразований
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type _tmp_type_for_consumer = object
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if isinstance(consumer_fn, collections.abc.Iterable):
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new_consumer_injectors = set()
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for fn in consumer_fn:
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new_consumer_injectors.add(fn.copy_with(injects=_tmp_type_for_consumer))
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injectors |= new_consumer_injectors
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elif isinstance(consumer_fn, ConversionPoint):
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injectors |= set(consumer_fn.copy_with(injects=_tmp_type_for_consumer))
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else:
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injectors |= set(ConversionPoint.from_fn(consumer_fn, _tmp_type_for_consumer))
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return cls.generate_callgraph_singletype(injectors, from_types, _tmp_type_for_consumer)
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@classmethod
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def generate_callgraph_singletype(cls,
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injectors: frozenset[ConversionPoint],
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from_types: frozenset[type],
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into_type: type,
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*,
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visited_path: Optional[set[ConversionPoint]] = None,
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visited_types: Optional[set[type]] = None) -> Optional[Callgraph]:
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if visited_path is None:
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visited_path = set()
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if visited_types is None:
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visited_types = set()
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if into_type in from_types:
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return Callgraph.new_empty()
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if into_type in visited_types:
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return None
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head = Callgraph.new_empty()
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visited_types.add(into_type)
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for point in injectors:
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if point in visited_path:
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continue
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if into_type in point.requires:
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continue
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if point.injects == into_type:
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visited_path.add(point)
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variant_subgraphs = set()
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dead_end = False
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for req in point.requires:
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subg = cls.generate_callgraph_singletype(injectors,
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from_types,
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req,
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visited_path=visited_path.copy(),
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visited_types=visited_types.copy())
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if subg is None:
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dead_end = True
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break
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variant_subgraphs.add(subg)
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if not dead_end:
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for opt in point.opt_args:
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subg = cls.generate_callgraph_singletype(injectors,
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from_types,
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opt,
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visited_path=visited_path.copy(),
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visited_types=visited_types.copy())
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if subg is not None:
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variant_subgraphs.add(subg)
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consumed = (frozenset(point.requires) | frozenset(point.opt_args)) & from_types
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variant = CallgraphVariant(point, frozenset(variant_subgraphs), consumed)
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head = head.add_subgraph_variant(variant)
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if len(head.variants) == 0:
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return None
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return head
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@classmethod
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def explode_callgraph_branches(cls, g: Callgraph, from_types: frozenset[type]) -> list[CallgraphVariant]:
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# Кэширование: создаём хэш графа
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# Хэш графа = хэш всех вариантов
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g_hash = hash(frozenset(g.variants)) if g.variants else 0
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cache_key = (g_hash, hash(from_types))
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# Проверяем кэш
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if cache_key in cls._explode_cache:
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return cls._explode_cache[cache_key]
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# Вычисляем
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variants = []
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for variant in g.variants:
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if len(variant.subgraphs) == 0:
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variants.append(variant)
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continue
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subg_combinations: list[list[CallgraphVariant | None]] = []
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for subg in variant.subgraphs:
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combinations: list[CallgraphVariant] = cls.explode_callgraph_branches(subg, from_types)
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if len(combinations) == 0:
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subg_combinations.append([None])
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else:
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subg_combinations.append(typing.cast(list[CallgraphVariant | None], combinations))
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for combination in all_combinations(subg_combinations):
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if None in combination:
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combination.remove(None)
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cons: frozenset[type] = frozenset()
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cum_cmb: frozenset[Callgraph] = frozenset()
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for cmb in combination:
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if cmb is not None:
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cons |= cmb.consumed_from_types
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cum_cmb |= {Callgraph(frozenset({cmb}))}
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variants.append(
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CallgraphVariant(variant.injector, cum_cmb,
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variant.consumed_from_types | cons))
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# Сохраняем в кэш
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cls._explode_cache[cache_key] = variants
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return variants
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@classmethod
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def filter_exploded_callgraph_branch(cls,
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variants: list[CallgraphVariant],
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priority_injectors: Optional[frozenset[ConversionPoint | Callable]] = None,
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relevance_metric: Optional[Callable[[CallgraphVariant], int | float]] = None,
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resolved_priorities: Optional[dict[ConversionPoint, float]] = None) \
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-> list[CallgraphVariant]:
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if relevance_metric is None:
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# Сначала применяем стандартные метрики
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template_metrics = [
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lambda x: len(x.consumed_from_types),
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lambda x: x.consumed_cumsum,
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lambda x: -x.invokes,
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]
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for metric in template_metrics:
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if len(variants) == 1:
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break
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new_variants = cls.filter_exploded_callgraph_branch(variants, priority_injectors, metric, resolved_priorities)
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if len(new_variants) > 0:
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variants = new_variants
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# Если всё ещё несколько вариантов, используем приоритеты
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if len(variants) > 1:
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# Вычисляем aggregate priority для каждого варианта (сумма приоритетов всех инжекторов в пути)
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def get_aggregate_priority(variant: CallgraphVariant) -> float:
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# Используем resolved_priorities если есть, иначе берём из cp.priority
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if resolved_priorities and variant.injector in resolved_priorities:
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priority = resolved_priorities[variant.injector]
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else:
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priority = variant.injector.priority if isinstance(variant.injector.priority, (int, float)) else 0.0
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for subg in variant.subgraphs:
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for subv in subg.variants:
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priority += get_aggregate_priority(subv)
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return priority
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# Сортировка по aggregate priority (обратный порядок - выше приоритет = раньше)
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# Затем по имени функции для детерминизма
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variants.sort(key=lambda x: (-get_aggregate_priority(x), universal_qualname(x.injector.fn)))
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# Выбираем вариант с наивысшим aggregate приоритетом
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max_priority = get_aggregate_priority(variants[0])
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selected = [v for v in variants if get_aggregate_priority(v) == max_priority]
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variants = selected
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return variants
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if len(variants) < 2:
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return variants
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if priority_injectors is None:
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priority_injectors = frozenset()
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new_priority_injectors: frozenset[ConversionPoint] = frozenset()
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for inj in priority_injectors:
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injs = {inj}
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if not isinstance(inj, ConversionPoint):
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injs = ConversionPoint.from_fn(inj)
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new_priority_injectors |= injs
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priority_injectors = new_priority_injectors
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best_score = max(*list(
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map(lambda x: relevance_metric(x) * (len(variants) if x.injector in priority_injectors else 1), variants)))
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selected_variants = []
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for variant in variants:
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if relevance_metric(variant) >= best_score:
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selected_variants.append(variant)
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return selected_variants
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@classmethod
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def select_callgraph_branch(cls,
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variants: list[CallgraphVariant],
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ignore_noncommutative=False) -> Optional[CallgraphVariant]:
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filtered = cls.filter_exploded_callgraph_branch(variants)
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if len(filtered) > 1 and not ignore_noncommutative:
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raise AmbiguousPath(
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from_types=frozenset(),
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target=None,
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paths=[[str(v.injector)] for v in filtered],
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)
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if len(filtered) == 0:
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return None
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return filtered[0]
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@classmethod
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def generate_full_depgraph(cls,
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injectors: frozenset[ConversionPoint],
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consumer: Optional[Callable] = None) -> frozenset[TransformationPoint]:
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out_points: list[TransformationPoint] = []
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for point in injectors:
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out_points.append(TransformationPoint.new_empty(point))
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if consumer is not None:
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consumer_requires = extract_func_argtypes(consumer)
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out_points.append(
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TransformationPoint.new_empty(ConversionPoint(consumer, NoneType, tuple(consumer_requires))))
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for i in range(len(out_points)):
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pi = out_points[i]
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for j in range(len(out_points)):
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pj = out_points[j]
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cmp = pi.has_composition(pj)
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match cmp:
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case CompositionDirection.FORWARD:
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pi = pi.with_incoming(pj)
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out_points[j] = pj
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case CompositionDirection.BACKWARD:
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pj = pj.with_incoming(pi)
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out_points[j] = pj
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return frozenset(out_points)
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