First working solution of new task
This commit is contained in:
Jan-Niclas Loosen
9
Project-02-03-04-05/cfa/__init__.py
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9
Project-02-03-04-05/cfa/__init__.py
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@@ -0,0 +1,9 @@
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from .live_variables import LiveVariablesAnalysis, Var
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from .reached_uses import ReachedUsesAnalysis, UseFact
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__all__ = [
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"Var",
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"UseFact",
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"LiveVariablesAnalysis",
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"ReachedUsesAnalysis",
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]
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192
Project-02-03-04-05/cfa/analysis_dot.py
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192
Project-02-03-04-05/cfa/analysis_dot.py
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import syntax
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import colorsys
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from cfg.CFG_Node import CFG_DIAMOND
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def _expr_used_names(expr) -> set[str]:
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"""Collect variable names (syntax.ID) used inside an expression subtree."""
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used: set[str] = set()
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def visit(node):
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if node is None:
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return
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if isinstance(node, syntax.ID):
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used.add(node.name)
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return
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if isinstance(node, syntax.EXPRESSION):
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for _, child in node.children():
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visit(child)
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visit(expr)
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return used
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def _show_analysis_on_node(node) -> bool:
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"""Return True if analysis annotations should be displayed for this node."""
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ast = node.ast_node
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if isinstance(node, CFG_DIAMOND):
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return False
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if ast is None:
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return False
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return isinstance(
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ast,
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(
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syntax.ASSIGN,
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syntax.CALL,
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syntax.IF,
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syntax.WHILE,
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syntax.DECL,
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syntax.LET,
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syntax.SEQ,
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syntax.COMP,
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syntax.EQOP,
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syntax.LOP,
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),
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)
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def _lv_in_for_display(node, analysis):
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"""Display-level IN set for LV."""
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in_set = set(analysis.in_sets.get(node.id, set()))
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ast_node = node.ast_node
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if isinstance(ast_node, syntax.ASSIGN):
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func = analysis._func_scope.get(node.id)
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rhs_vars = {
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analysis._resolve_var(func, name)
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for name in _expr_used_names(ast_node.expr)
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}
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in_set |= rhs_vars
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return in_set
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def _node_color(node_id: int) -> tuple[str, str]:
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"""Return (edge_color, fill_color) deterministically for a node id."""
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# Golden-angle hue distribution gives stable, distinct colors.
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hue = ((node_id * 0.6180339887498949) % 1.0)
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edge_rgb = colorsys.hsv_to_rgb(hue, 0.70, 0.82)
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fill_rgb = colorsys.hsv_to_rgb(hue, 0.28, 0.97)
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def to_hex(rgb):
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r, g, b = (int(round(c * 255)) for c in rgb)
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return f"#{r:02x}{g:02x}{b:02x}"
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return to_hex(edge_rgb), to_hex(fill_rgb)
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def run_all_analyses(cfg):
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"""Run Live Variables and Reached Uses on *cfg*.
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Returns ``(analyses, annotations, ru_edges)`` where:
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• *analyses* is a dict with keys ``"lv"`` and ``"ru"``,
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• *annotations* contains only LivingVariables helper-node labels,
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• *ru_edges* maps definition-node ids to reached use-node ids.
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"""
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node_by_id = {n.id: n for n in cfg.nodes()}
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from cfa.live_variables import LiveVariablesAnalysis
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from cfa.reached_uses import ReachedUsesAnalysis
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lv = LiveVariablesAnalysis(cfg)
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ru = ReachedUsesAnalysis(cfg)
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all_ids = set(lv.in_sets.keys()) | set(lv.out_sets.keys())
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annotations = {
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nid: (
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"LivingVariables\\n"
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f"In := {sorted(_lv_in_for_display(node_by_id[nid], lv))}\\n"
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f"Out := {sorted(lv.out_sets.get(nid, set()))}"
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)
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for nid in all_ids
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if lv.in_sets.get(nid, set()) or lv.out_sets.get(nid, set())
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if nid in node_by_id and _show_analysis_on_node(node_by_id[nid])
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}
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return {"lv": lv, "ru": ru}, annotations, ru.reached_uses_by_node()
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def analysis_to_dot(
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cfg,
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annotations: dict[int, str],
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analysis_name: str,
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ru_edges: dict[int, list[int]] | None = None,
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) -> str:
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"""Return a DOT string for *cfg* annotated with analysis results."""
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lines = [
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"digraph CFG {",
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f' // Analysis: {analysis_name}',
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' graph [splines=ortho, overlap=false, ranksep=0.7, nodesep=0.45];',
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' node [fontname="Helvetica"];',
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]
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color_nodes = set(annotations.keys()) | set((ru_edges or {}).keys())
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node_colors = {nid: _node_color(nid) for nid in color_nodes}
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def emit(node):
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base_label = node.dot_label() or ""
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shape = node.dot_shape
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style = node.dot_style
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style_str = f", {style}" if style else ""
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lines.append(f' n{node.id} [label="{base_label}", shape={shape}{style_str}];')
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if node.id in annotations:
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ann_id = f"a{node.id}"
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ann_label = annotations[node.id].replace('"', '\\"')
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edge_color, fill_color = node_colors.get(node.id, ("#1f77b4", "#d9ecff"))
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lines.append(
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f' {ann_id} [label="{ann_label}", shape=note, '
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f'style="filled", fillcolor="{fill_color}", color="{edge_color}", '
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f'fontcolor="{edge_color}"];'
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)
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lines.append(
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f' {ann_id} -> n{node.id} [style=dotted, arrowhead=none, '
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f'color="{edge_color}"];'
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)
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for i, child in enumerate(sorted(node.children, key=lambda n: n.id)):
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edge_label = ""
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if isinstance(node, CFG_DIAMOND):
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if i == 0:
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edge_label = ' [label="T"]'
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elif i == 1:
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edge_label = ' [label="F"]'
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lines.append(f" n{node.id} -> n{child.id}{edge_label};")
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cfg.traverse(emit, start=cfg.START)
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if ru_edges:
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for idx, def_id in enumerate(sorted(ru_edges)):
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use_ids = sorted(set(ru_edges[def_id]))
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if not use_ids:
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continue
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# One routing hub per definition node to mimic UML-like
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# "out to the side, then down/across to targets" connectors.
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side = "e" if idx % 2 == 0 else "w"
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source_port = "se" if side == "e" else "sw"
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hub_id = f"rh{def_id}"
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edge_color, fill_color = node_colors.get(def_id, ("#1f77b4", "#d9ecff"))
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lines.append(
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f' {hub_id} [shape=point, width=0.05, height=0.05, '
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f'color="{edge_color}", fillcolor="{edge_color}", style=filled];'
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)
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lines.append(f" {{ rank=same; n{def_id}; {hub_id}; }}")
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lines.append(
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f' n{def_id}:{source_port} -> {hub_id} [color="{edge_color}", '
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f'style=dashed, penwidth=1.2, arrowhead=none, constraint=false, '
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f'tailclip=true, headclip=true];'
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)
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for use_id in use_ids:
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if side == "e":
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target_port = "ne" if (use_id % 2 == 0) else "se"
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else:
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target_port = "nw" if (use_id % 2 == 0) else "sw"
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lines.append(
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f' {hub_id} -> n{use_id}:{target_port} [color="{edge_color}", '
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f'fontcolor="{edge_color}", fontsize=8, style=dashed, '
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f'penwidth=1.0, arrowsize=0.6, constraint=false, '
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f'tailclip=true, headclip=true];'
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)
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lines.append("}")
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return "\n".join(lines)
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351
Project-02-03-04-05/cfa/live_variables.py
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351
Project-02-03-04-05/cfa/live_variables.py
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@@ -0,0 +1,351 @@
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"""
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live_variables.py — Live Variables backward dataflow analysis for TRIPLA CFGs.
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A variable v is *live* at the entry of node n if there exists a path
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n → … → use(v) where v is not redefined along the way.
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Data structures
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---------------
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gen dict[int, set[Var]] — GEN(n) = variables *used* at n
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kill dict[int, set[Var]] — KILL(n) = variables *defined* at n
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in_sets dict[int, set[Var]] — live variables at node *entry*
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out_sets dict[int, set[Var]] — live variables at node *exit*
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Transfer equations (backward):
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OUT(n) = ∪ IN(s) for all successors s
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IN(n) = (OUT(n) − KILL(n)) ∪ GEN(n)
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Variables are represented in scoped form ``(scope, name)``, e.g. ``("f","x")``.
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This avoids collisions between equal variable names in different functions.
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This module also exports ``_BackwardAnalysisBase``, the shared base class
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that ``ReachedUsesAnalysis`` in reached_uses.py inherits from. The base
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provides:
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• AST traversal to collect function-nesting and parameter metadata
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• Lexical variable resolution (parameter shadowing handled correctly)
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• BFS-based CFG-node → owning-function assignment
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• Unified uses / defs extraction for all node types
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Var = tuple[str, str]
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"""
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from __future__ import annotations
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from collections import deque
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from typing import TYPE_CHECKING
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import cfg_build
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import syntax
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from cfg.CFG_Node import CFG_START
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if TYPE_CHECKING:
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from cfg.CFG import CFG
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# ---------------------------------------------------------------------------
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# Public type alias (imported by reached_uses.py)
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# ---------------------------------------------------------------------------
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GLOBAL_SCOPE = ""
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Var = tuple[str, str] # (function_name|GLOBAL_SCOPE, variable_name)
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# ============================================================================
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# Shared base: function metadata, scope assignment, uses/defs extraction
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# ============================================================================
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class _BackwardAnalysisBase:
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"""Infrastructure shared by LiveVariablesAnalysis and ReachedUsesAnalysis.
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Calling ``super().__init__(cfg)`` from a subclass:
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1. Snapshots cfg_build.FUNCTIONS.
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2. Collects AST-level function-nesting and parameter metadata.
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3. BFS-assigns every CFG node to its owning function.
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4. Extracts uses and defs for every CFG node.
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After __init__ the following attributes are available to subclasses:
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self.cfg — the CFG object
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self._functions — dict[str, tuple]: snapshot of cfg_build.FUNCTIONS
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self._func_parent — dict[str, str|None]: lexical parent per function
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self._func_params — dict[str, tuple[str,...]]: params per function
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self._func_scope — dict[int, str]: node-id → owning function name
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self.uses — dict[int, set[Var]]: variables used at each node
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self.defs — dict[int, set[Var]]: variables defined at each node
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"""
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def __init__(self, cfg: "CFG") -> None:
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self.cfg = cfg
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# Snapshot FUNCTIONS so later global-state resets do not affect us.
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self._functions: dict[str, tuple] = dict(cfg_build.FUNCTIONS)
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self.uses: dict[int, set[Var]] = {}
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self.defs: dict[int, set[Var]] = {}
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self._func_parent, self._func_params = self._collect_function_metadata()
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self._func_scope: dict[int, str] = self._compute_function_scope()
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self._extract_uses_defs()
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# ------------------------------------------------------------------
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# Step 1a — Walk AST to collect lexical nesting + parameter lists
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# ------------------------------------------------------------------
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def _collect_function_metadata(
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self,
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) -> tuple[dict[str, str | None], dict[str, tuple[str, ...]]]:
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"""Walk the AST and collect function-parent and parameter information.
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Returns
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-------
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func_parent : dict[str, str | None]
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func_parent[f] is the name of the immediately enclosing function
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(or None for top-level functions).
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func_params : dict[str, tuple[str, ...]]
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func_params[f] is the ordered tuple of formal parameter names of f.
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"""
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func_parent: dict[str, str | None] = {}
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func_params: dict[str, tuple[str, ...]] = {}
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def visit(expr: syntax.EXPRESSION | None, current_func: str | None) -> None:
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if expr is None:
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return
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if isinstance(expr, syntax.LET):
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decls = expr.decl if isinstance(expr.decl, list) else [expr.decl]
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# Register metadata for each declared function.
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for d in decls:
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if isinstance(d, syntax.DECL):
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# Use assignment (last-seen wins) to stay consistent
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# with cfg_build.FUNCTIONS, which also overwrites on
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# duplicate names. setdefault (first-seen wins) would
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# disagree when a nested function shadows a top-level
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# one with the same name, causing wrong scope resolution.
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func_parent[d.f_name] = current_func
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func_params[d.f_name] = tuple(d.params)
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# Recurse into function bodies and the in-expression.
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for d in decls:
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if isinstance(d, syntax.DECL):
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visit(d.body, d.f_name)
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else:
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visit(d, current_func)
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visit(expr.body, current_func)
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return
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for _, child in expr.children():
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visit(child, current_func)
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visit(self.cfg.ast, None)
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return func_parent, func_params
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# ------------------------------------------------------------------
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# Step 1b — Resolve a variable name through the lexical scope chain
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# ------------------------------------------------------------------
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def _resolve_var(self, func: str | None, name: str) -> Var:
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"""Resolve a variable name via lexical scope chain."""
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if func is None:
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return (GLOBAL_SCOPE, name)
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cur: str | None = func
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seen: set[str] = set()
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while cur is not None and cur not in seen:
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seen.add(cur)
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if name in self._func_params.get(cur, ()):
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return (cur, name)
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cur = self._func_parent.get(cur)
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# Fallback: local variable in current function scope.
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return (func, name)
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# ------------------------------------------------------------------
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# Step 2 — BFS-assign every CFG node to its owning function
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# ------------------------------------------------------------------
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def _compute_function_scope(self) -> dict[int, str]:
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"""BFS from each function's START node; return node-id → function-name.
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Two stopping conditions keep attribution strictly inside each function:
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1. Do not follow into a *different* function's CFG_START (prevents
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attributing callee body nodes to the caller, and vice-versa).
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2. Do not follow *past* the function's own CFG_END (prevents
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following CFG_END → CFG_RETURN → continuation nodes that belong
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to the *caller* context, which caused variables used there to be
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resolved in the wrong scope).
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The first function whose BFS claims a node wins.
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"""
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functions = self._functions
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func_scope: dict[int, str] = {}
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all_f_start_ids: set[int] = {fs.id for _, (fs, _) in functions.items()}
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for f_name, (f_start, f_end) in functions.items():
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queue: deque = deque([f_start])
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while queue:
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node = queue.popleft()
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if node.id in func_scope:
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continue # already claimed by an earlier function
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func_scope[node.id] = f_name
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# Stop here — do not follow CFG_END into caller context.
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if node.id == f_end.id:
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continue
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for child in node.children:
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# Do not follow into a different function's START.
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if (
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isinstance(child, CFG_START)
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and child.id in all_f_start_ids
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and child.id != f_start.id
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):
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continue
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queue.append(child)
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return func_scope
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# ------------------------------------------------------------------
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# Step 3 — Extract uses / defs for every CFG node
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# ------------------------------------------------------------------
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def _extract_uses_defs(self) -> None:
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"""Populate ``self.uses`` and ``self.defs`` for every node in the CFG.
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Extraction rules:
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• CFG_START(DECL f(p1,…,pk)) → defs = {(f,p1), …, (f,pk)}
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• Node wrapping ID(x) → uses = {lexical_resolve(func, x)}
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• Node wrapping ASSIGN(x = e) → defs = {lexical_resolve(func, x)}
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• Everything else → uses = {}, defs = {}
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Sub-expressions already have their own CFG nodes and are not
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re-inspected here; each node is responsible only for its own ast_node.
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"""
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for node in self.cfg.nodes():
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nid = node.id
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func = self._func_scope.get(nid) # None → outer / global scope
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ast = node.ast_node
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uses: set[Var] = set()
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defs: set[Var] = set()
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if isinstance(node, CFG_START) and isinstance(ast, syntax.DECL):
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# Function entry defines each formal parameter.
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for param in ast.params:
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defs.add((ast.f_name, param))
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elif ast is not None:
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if isinstance(ast, syntax.ID):
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resolved = self._resolve_var(func, ast.name)
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uses.add(resolved)
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elif isinstance(ast, syntax.ASSIGN):
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resolved = self._resolve_var(func, ast.var.name)
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defs.add(resolved)
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self.uses[nid] = uses
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self.defs[nid] = defs
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||||
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# ============================================================================
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# Live Variables Analysis
|
||||
# ============================================================================
|
||||
|
||||
class LiveVariablesAnalysis(_BackwardAnalysisBase):
|
||||
"""Backward dataflow analysis: Live Variables.
|
||||
|
||||
A variable (f, x) is *live* at the entry of node n if there is a path
|
||||
from n to some use of (f, x) along which (f, x) is not redefined.
|
||||
|
||||
This is the simpler predecessor to ReachedUsesAnalysis (reached_uses.py):
|
||||
it tracks which variables are live, not *where* they are used.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
gen dict[int, set[Var]] GEN(n) = uses(n) — vars used at n
|
||||
kill dict[int, set[Var]] KILL(n) = defs(n) — vars defined at n
|
||||
in_sets dict[int, set[Var]] live variables at n's *entry*
|
||||
out_sets dict[int, set[Var]] live variables at n's *exit*
|
||||
|
||||
(uses and defs are identical to gen / kill and are inherited from the
|
||||
base class.)
|
||||
|
||||
Transfer equations (backward):
|
||||
OUT(n) = ∪ IN(s) for all successors s
|
||||
IN(n) = (OUT(n) − KILL(n)) ∪ GEN(n)
|
||||
"""
|
||||
|
||||
def __init__(self, cfg: "CFG") -> None:
|
||||
# Base populates uses, defs, _func_scope, etc.
|
||||
super().__init__(cfg)
|
||||
|
||||
self.gen: dict[int, set[Var]] = {}
|
||||
self.kill: dict[int, set[Var]] = {}
|
||||
self.in_sets: dict[int, set[Var]] = {}
|
||||
self.out_sets: dict[int, set[Var]] = {}
|
||||
|
||||
self._build_gen_kill()
|
||||
self.solve()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Build gen / kill; initialise in / out to ∅
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_gen_kill(self) -> None:
|
||||
"""GEN(n) = uses(n), KILL(n) = defs(n); initialise in/out sets."""
|
||||
for node in self.cfg.nodes():
|
||||
nid = node.id
|
||||
self.gen[nid] = set(self.uses[nid])
|
||||
self.kill[nid] = set(self.defs[nid])
|
||||
self.in_sets[nid] = set()
|
||||
self.out_sets[nid] = set()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Backward worklist fixpoint
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def solve(self) -> None:
|
||||
"""Backward worklist until fixpoint.
|
||||
|
||||
Transfer:
|
||||
OUT(n) = ∪ IN(s) for all successors s
|
||||
IN(n) = (OUT(n) − KILL(n)) ∪ GEN(n)
|
||||
|
||||
Only nodes reachable from cfg.START are processed (guard against
|
||||
propagate=False parent references from CFG.__remove_and_rewire).
|
||||
"""
|
||||
nodes = list(self.cfg.nodes())
|
||||
known: set[int] = set(self.gen.keys())
|
||||
id_to_node = {n.id: n for n in nodes}
|
||||
worklist: deque = deque(nodes)
|
||||
|
||||
# Build predecessor relation from children edges. This is more reliable
|
||||
# than node.parents because CFG rewiring may add edges with
|
||||
# propagate=False, leaving parent links stale.
|
||||
preds: dict[int, set[int]] = {nid: set() for nid in known}
|
||||
for node in nodes:
|
||||
for child in node.children:
|
||||
if child.id in known:
|
||||
preds[child.id].add(node.id)
|
||||
|
||||
while worklist:
|
||||
node = worklist.popleft()
|
||||
nid = node.id
|
||||
|
||||
new_out: set[Var] = set()
|
||||
for child in node.children:
|
||||
if child.id in known:
|
||||
new_out |= self.in_sets[child.id]
|
||||
|
||||
new_in: set[Var] = (new_out - self.kill[nid]) | self.gen[nid]
|
||||
|
||||
if new_out != self.out_sets[nid] or new_in != self.in_sets[nid]:
|
||||
self.out_sets[nid] = new_out
|
||||
self.in_sets[nid] = new_in
|
||||
for pred_id in preds[nid]:
|
||||
worklist.append(id_to_node[pred_id])
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Result
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def live_vars_by_node(self) -> dict[int, set[Var]]:
|
||||
"""Return the live-variable set at the *entry* of each node.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict[int, set[Var]]
|
||||
Keys: CFG node ids whose in_set is non-empty.
|
||||
Values: copy of the live-variable set at that node's entry.
|
||||
"""
|
||||
return {nid: set(vs) for nid, vs in self.in_sets.items() if vs}
|
||||
203
Project-02-03-04-05/cfa/reached_uses.py
Normal file
203
Project-02-03-04-05/cfa/reached_uses.py
Normal file
@@ -0,0 +1,203 @@
|
||||
"""
|
||||
reached_uses.py — Reached-Uses backward dataflow analysis for TRIPLA CFGs.
|
||||
|
||||
Extends ``_BackwardAnalysisBase`` from live_variables.py, which provides the
|
||||
shared function-scope resolution and uses/defs extraction machinery. The Live
|
||||
Variables analysis (LiveVariablesAnalysis) in that module is the simpler
|
||||
predecessor of this analysis (tip from the course notes: implement LV first,
|
||||
then extend to RU).
|
||||
|
||||
How ReachedUsesAnalysis extends LiveVariablesAnalysis
|
||||
------------------------------------------------------
|
||||
Live Variables tracks *which* variables are live at each node (set[Var]).
|
||||
Reached Uses additionally tracks *where* each variable is used by attaching
|
||||
the use-node id to every fact, giving set[UseFact] = set[tuple[int, Var]].
|
||||
|
||||
The transfer function changes accordingly:
|
||||
LV: IN(n) = (OUT(n) − KILL_LV(n)) ∪ GEN_LV(n) [sets of Var]
|
||||
RU: IN(n) = (OUT(n) − KILL_RU(n)) ∪ GEN_RU(n) [sets of UseFact]
|
||||
|
||||
GEN_LV(n) = uses(n) — set[Var]
|
||||
GEN_RU(n) = { (n.id, var) | var ∈ uses(n) } — set[UseFact]
|
||||
|
||||
KILL_LV(n) = defs(n) — set[Var]
|
||||
KILL_RU(n) = { (uid, var) | var ∈ defs(n), — set[UseFact]
|
||||
(uid, var) ∈ all_uses_by_var[var] }
|
||||
|
||||
The set-difference in both cases removes exactly the facts for variables
|
||||
that are defined at n — equivalent to the ⊖ operator from the lecture
|
||||
slides (M ⊖ K = {(p,id) ∈ M | id ∉ K}).
|
||||
|
||||
Type aliases
|
||||
------------
|
||||
Var = tuple[str, str] # (scope, variable_name)
|
||||
UseFact = tuple[int, Var] # (use_node_id, scoped_var)
|
||||
|
||||
Analysis attributes (all populated after construction)
|
||||
------------------------------------------------------
|
||||
uses dict[int, set[Var]]
|
||||
defs dict[int, set[Var]]
|
||||
gen dict[int, set[UseFact]]
|
||||
kill dict[int, set[UseFact]]
|
||||
in_sets dict[int, set[UseFact]]
|
||||
out_sets dict[int, set[UseFact]]
|
||||
all_uses_by_var dict[Var, set[UseFact]]
|
||||
|
||||
Final result
|
||||
------------
|
||||
reached_uses_by_node() → dict[int, list[int]]
|
||||
Keys: defining-node ids
|
||||
Values: sorted, deduplicated list of use-node ids reached by the def
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import deque
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
# Import the shared base class (and Var) from the Live Variables module.
|
||||
from cfa.live_variables import _BackwardAnalysisBase, Var
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from cfg.CFG import CFG
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public type aliases (re-exported so tests/reached_uses_stub.py can pick up
|
||||
# ReachedUsesAnalysis without needing to know about live_variables.py)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
UseFact = tuple[int, Var] # (use_node_id, scoped_var)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# Reached-Uses Analysis
|
||||
# ============================================================================
|
||||
|
||||
class ReachedUsesAnalysis(_BackwardAnalysisBase):
|
||||
"""Backward dataflow analysis: Reached Uses.
|
||||
|
||||
Inherits uses/defs extraction and function-scope resolution from
|
||||
_BackwardAnalysisBase (live_variables.py). Extends it with use-fact
|
||||
tracking: each fact carries the id of the node where the variable is used,
|
||||
enabling def-use pairs to be recovered from the fixpoint solution.
|
||||
|
||||
Transfer equations (backward):
|
||||
OUT(n) = ∪ IN(s) for all successors s
|
||||
IN(n) = GEN(n) ∪ (OUT(n) − KILL(n))
|
||||
|
||||
GEN(n) = { (n.id, var) | var ∈ uses(n) }
|
||||
KILL(n) = { (uid, var) | var ∈ defs(n),
|
||||
(uid, var) ∈ all_uses_by_var[var] }
|
||||
"""
|
||||
|
||||
def __init__(self, cfg: "CFG") -> None:
|
||||
# Base populates: uses, defs, _func_scope, _func_parent, _func_params.
|
||||
super().__init__(cfg)
|
||||
|
||||
self.gen: dict[int, set[UseFact]] = {}
|
||||
self.kill: dict[int, set[UseFact]] = {}
|
||||
self.in_sets: dict[int, set[UseFact]] = {}
|
||||
self.out_sets: dict[int, set[UseFact]] = {}
|
||||
self.all_uses_by_var: dict[Var, set[UseFact]] = {}
|
||||
|
||||
self._build_gen_kill()
|
||||
self.solve()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 1 — Build gen, kill, all_uses_by_var; initialise in/out
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _build_gen_kill(self) -> None:
|
||||
"""Compute gen and kill sets; populate all_uses_by_var."""
|
||||
# GEN[n] = { (n.id, var) | var ∈ uses[n] }
|
||||
for node in self.cfg.nodes():
|
||||
nid = node.id
|
||||
self.gen[nid] = {(nid, var) for var in self.uses[nid]}
|
||||
self.in_sets[nid] = set()
|
||||
self.out_sets[nid] = set()
|
||||
|
||||
# all_uses_by_var: index all use-facts by their variable.
|
||||
for nid, facts in self.gen.items():
|
||||
for (uid, var) in facts:
|
||||
self.all_uses_by_var.setdefault(var, set()).add((uid, var))
|
||||
|
||||
# KILL[n] = all use-facts for variables defined at n.
|
||||
for node in self.cfg.nodes():
|
||||
nid = node.id
|
||||
kill_n: set[UseFact] = set()
|
||||
for var in self.defs[nid]:
|
||||
if var in self.all_uses_by_var:
|
||||
kill_n |= self.all_uses_by_var[var]
|
||||
self.kill[nid] = kill_n
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Step 2 — Backward worklist fixpoint
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def solve(self) -> None:
|
||||
"""Backward worklist until fixpoint.
|
||||
|
||||
Transfer:
|
||||
OUT(n) = ∪ IN(s) for all successors s
|
||||
IN(n) = GEN(n) ∪ (OUT(n) − KILL(n))
|
||||
|
||||
Only nodes reachable from cfg.START are processed (guard against
|
||||
propagate=False parent references from CFG.__remove_and_rewire).
|
||||
"""
|
||||
nodes = list(self.cfg.nodes())
|
||||
known: set[int] = set(self.gen.keys()) # ids of cfg.nodes()
|
||||
id_to_node = {n.id: n for n in nodes}
|
||||
worklist: deque = deque(nodes)
|
||||
|
||||
# Build predecessor relation from children edges. CFG rewiring may
|
||||
# create edges with propagate=False, so node.parents can be stale.
|
||||
preds: dict[int, set[int]] = {nid: set() for nid in known}
|
||||
for node in nodes:
|
||||
for child in node.children:
|
||||
if child.id in known:
|
||||
preds[child.id].add(node.id)
|
||||
|
||||
while worklist:
|
||||
node = worklist.popleft()
|
||||
nid = node.id
|
||||
|
||||
new_out: set[UseFact] = set()
|
||||
for child in node.children:
|
||||
if child.id in known:
|
||||
new_out |= self.in_sets[child.id]
|
||||
|
||||
new_in: set[UseFact] = self.gen[nid] | (new_out - self.kill[nid])
|
||||
|
||||
if new_out != self.out_sets[nid] or new_in != self.in_sets[nid]:
|
||||
self.out_sets[nid] = new_out
|
||||
self.in_sets[nid] = new_in
|
||||
for pred_id in preds[nid]:
|
||||
worklist.append(id_to_node[pred_id])
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Public result
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def reached_uses_by_node(self) -> dict[int, list[int]]:
|
||||
"""Return the final reached-uses result.
|
||||
|
||||
For each defining node d:
|
||||
result[d.id] = sorted list of use-node ids u such that
|
||||
(u, var) ∈ OUT[d] for some var ∈ defs[d].
|
||||
|
||||
Semantics: the definition at d of variable var reaches the use at u
|
||||
if there is a CFG path d → … → u along which var is not redefined.
|
||||
|
||||
Only nodes with at least one definition appear as keys.
|
||||
"""
|
||||
result: dict[int, list[int]] = {}
|
||||
for node in self.cfg.nodes():
|
||||
nid = node.id
|
||||
defs_n = self.defs[nid]
|
||||
if not defs_n:
|
||||
continue
|
||||
reached: set[int] = set()
|
||||
for (uid, var) in self.out_sets[nid]:
|
||||
if var in defs_n:
|
||||
reached.add(uid)
|
||||
result[nid] = sorted(reached)
|
||||
return result
|
||||
Reference in New Issue
Block a user