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galley_optimize

Galley logical optimizer: applies greedy query rewriting to logical plans, with an optional exact branch-and-bound path for query bodies.

GalleyLogicFactorizer

Bases: LogicFactorizer

LogicLoader stage that runs Galley on each Query body (see optimizer), then forwards the Plan to the downstream loader ctx.

Default optimizer="bfs" is exact layered branch-and-bound; "dfs" uses the DFS kernel. Greedy k=1 bounds are used only on the layered exact path, not inside branch_and_bound_dfs. cost_model estimates the cost of each reduction, and defaults to FlopsCostModel.

Source code in src/finch/autoschedule/factorizer/galley_factorizer/galley_optimize.py 
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class GalleyLogicFactorizer(LogicFactorizer):
    """
    LogicLoader stage that runs Galley on each ``Query`` body (see ``optimizer``),
    then forwards the Plan to the downstream loader ``ctx``.

    Default ``optimizer="bfs"`` is exact layered branch-and-bound; ``"dfs"`` uses the
    DFS kernel. Greedy ``k=1`` bounds are used only on the layered exact path, not
    inside ``branch_and_bound_dfs``. ``cost_model`` estimates the cost of each
    reduction, and defaults to ``FlopsCostModel``.
    """

    def __init__(
        self,
        ctx: LogicLoader,
        use_components: bool = True,
        *,
        optimizer: GalleyOptimizer = "bfs",
        cost_model: CostModel | None = None,
    ):
        self.ctx = ctx
        self.use_components = use_components
        self.optimizer = optimizer
        self.cost_model = cost_model

    def lower(
        self,
        term: LogicStatement,
        bindings: dict[Alias, TensorFType],
        stats: dict[Alias, TensorStats],
        stats_factory: StatsFactory,
    ):
        if not isinstance(term, Plan):
            raise ValueError(f"Unsupported program type: {type(term)}")
        logger.debug("Optimizing plan: %s", term)

        def transform(prgm, bindings):
            prgm = optimize_plan(
                prgm,
                stats_factory,
                stats,
                use_components=self.use_components,
                optimizer=self.optimizer,
                cost_model=self.cost_model,
            )
            return prgm, bindings

        # Galley only keeps the queries that compute produced aliases, so each
        # write to a bound tensor, including an in-place update, is renamed and
        # produced, then copied back to the tensor it updates. Galley only
        # reduces from literals, so aggregates which start from a tensor are
        # split first.
        term = split_aggregate_inits(desugar_query_into(term), bindings)
        term, bindings = with_unique_lhs(transform, term, bindings)
        assert isinstance(term, Plan)
        return self.ctx(flatten_plans(term), bindings, stats, stats_factory)

optimize_plan(plan, stats_factory, stats_bindings, use_components=True, *, optimizer='greedy', cost_model=None)

Optimize a full Plan: run the Galley optimizer on each Query body, pass through non-Query bodies (Produces), and update stats bindings.

Source code in src/finch/autoschedule/factorizer/galley_factorizer/galley_optimize.py 
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def optimize_plan(
    plan,
    stats_factory: StatsFactory,
    stats_bindings: dict[Alias, TensorStats],
    use_components: bool = True,
    *,
    optimizer: GalleyOptimizer = "greedy",
    cost_model: CostModel | None = None,
):
    """
    Optimize a full Plan: run the Galley optimizer on each Query body,
    pass through non-Query bodies (Produces), and update stats bindings.
    """
    plan = preprocess_plan_for_galley(plan)
    optimized_queries = []
    cache_dict: dict[object, TensorStats] = {}
    for body in plan.bodies:
        if isinstance(body, Query):
            new_queries = optimize_query(
                body,
                stats_factory,
                stats_bindings,
                use_components=use_components,
                optimizer=optimizer,
                cost_model=cost_model,
            )
            for new_query in new_queries:
                insert_statistics(
                    stats_factory,
                    new_query,
                    stats_bindings,
                    replace=True,
                    cache=cache_dict,
                )
            optimized_queries.extend(new_queries)
        else:
            optimized_queries.append(body)

    return postprocess_plan_after_galley(Plan(tuple(optimized_queries)))

optimize_query(query, stats_factory, stats_bindings, use_components=True, *, optimizer='dfs', cost_model=None)

Rewrite a single logical Query using optimizer: greedy, bfs, or dfs.

Source code in src/finch/autoschedule/factorizer/galley_factorizer/galley_optimize.py 
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def optimize_query(
    query,
    stats_factory,
    stats_bindings,
    use_components: bool = True,
    *,
    optimizer: GalleyOptimizer = "dfs",
    cost_model: CostModel | None = None,
):
    """Rewrite a single logical Query using ``optimizer``:
    greedy, bfs, or dfs."""
    annotated_query = AnnotatedQuery(stats_factory, query, stats_bindings, cost_model)
    new_queries, _ = pruned_query_to_plan(
        annotated_query,
        use_components=use_components,
        optimizer=optimizer,
    )
    return new_queries