Implement XIC v2 control flow with IF, MATCH, LOOP operations
PHASE A: Safe predicate evaluator (glyphos/control/predicate.py) - AST-based safe expression evaluation - Supports comparisons, boolean ops, attribute access - Helper function: dominant_contains() - Protected against code injection attacks PHASE B: XICContext queue helpers - enqueue_chain(label) for FIFO chain scheduling - pop_next_chain() to get next scheduled chain - jump_to(label) for immediate destination changes PHASE C: Control flow operations (xic_ops.py) - op_IF: Conditional branching with optional else - op_MATCH: Pattern matching against fused fields - op_LOOP: Iterative execution with guardrails - Added to OP_TABLE for operation dispatch PHASE D: Execution loop enhancement (xic_vm.py) - Chain queue scheduling with label matching - Total steps tracking for guardrail enforcement - max_total_steps limit across all operations - Graceful execution stop on guardrail trigger PHASE E: Comprehensive test suite (tests/test_control_flow.py) - 14 unit tests covering all operations - Predicate evaluator tests - IF/MATCH/LOOP operation tests - Queue helper and guardrail tests - All tests passing (14/14) PHASE F: Example programs - demo_control_flow_if.gx.json: IF branching example - demo_control_flow_loop.gx.json: LOOP iteration example PHASE G: Complete documentation - XIC_V2_CONTROL_FLOW_SUMMARY.md: Technical guide - XIC_V2_QUICK_REFERENCE.md: Developer quick reference - FedMart UI and integration documentation Integration points: - FedMart telemetry captures control flow steps - UI dashboard displays control branching - Symbolic pipeline predicate evaluation - 100% backward compatible with XIC v1.5 Test results: 36/36 passing (14 control flow + 12 FedMart + 10 UI) Status: Production ready
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GlyphRunner System
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@@ -12,6 +12,26 @@ class XICContext:
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symbolic_mode: bool = False
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glyph_contexts: list = field(default_factory=list)
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def enqueue_chain(self, label: str):
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"""Schedule a chain/label to run next (FIFO)."""
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queue = self._state.setdefault("_chain_queue", [])
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queue.append(label)
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print(f"[XIC-QUEUE] Enqueued chain: {label}")
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def pop_next_chain(self):
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"""Get next scheduled chain (FIFO). Returns None if queue empty."""
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queue = self._state.setdefault("_chain_queue", [])
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if queue:
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label = queue.pop(0)
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print(f"[XIC-QUEUE] Dequeued chain: {label}")
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return label
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return None
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def jump_to(self, label: str):
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"""Immediate jump: clear queue and run label next."""
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self._state["_chain_queue"] = [label]
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print(f"[XIC-QUEUE] Jump to: {label}")
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def op_LOAD_MODEL(ctx: XICContext, *args):
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"""LOAD_MODEL <path>: Load a .gx model file."""
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@@ -440,6 +460,186 @@ def op_GET_GLYPH_RESONANCE(ctx: XICContext, *args):
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ctx._state[f"resonance_query_{glyph_id}_notfound"] = None
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def op_IF(ctx: XICContext, *args):
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"""IF <predicate> <then_label> [<else_label>]
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Evaluates predicate against last symbolic result and enqueues appropriate chain.
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Predicate examples:
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"fused.global_resonance_score > 0.8"
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"dominant_contains('glyph://entropy')"
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"""
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if len(args) < 2:
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raise ValueError("IF requires predicate and then_label")
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from glyphos.control.predicate import eval_predicate
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predicate = str(args[0])
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then_label = str(args[1])
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else_label = str(args[2]) if len(args) > 2 else None
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# Extract fused symbol from last symbolic execution
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pipeline = ctx._state.get("last_symbolic_pipeline")
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fused_dict = {}
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dominant = []
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if pipeline and hasattr(pipeline, "fused_symbol") and pipeline.fused_symbol:
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fused = pipeline.fused_symbol
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# Build dict representation for predicate evaluation
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fused_dict = {
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"global_resonance_score": fused.resonance_map.global_resonance_score if fused.resonance_map else 0.0,
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"glyph_ids": fused.glyph_ids,
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}
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# Extract dominant glyphs for helper function
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if fused.resonance_map:
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dominant = [(g, m.weight) for g, m in fused.resonance_map.get_top_glyphs(5)]
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# Evaluate predicate
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try:
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pred_result = eval_predicate(predicate, fused_dict, dominant)
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except Exception as e:
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print(f"[XIC-CONTROL] IF predicate evaluation error: {e}")
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return
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# Log control step
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ctx._state.setdefault("control_steps", []).append({
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"type": "if",
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"predicate": predicate,
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"result": pred_result
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})
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# Emit symbolic step
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ctx._state.setdefault("symbolic_steps", []).append({
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"name": "if",
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"kind": "control_if",
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"payload": {"predicate": predicate, "result": pred_result}
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})
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print(f"[XIC-CONTROL] IF {predicate} => {pred_result}")
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# Enqueue appropriate chain
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if pred_result:
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ctx.enqueue_chain(then_label)
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elif else_label:
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ctx.enqueue_chain(else_label)
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def op_MATCH(ctx: XICContext, *args):
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"""MATCH <path> <pattern> <then_label>
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Pattern match against fused_symbol fields.
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Supports: fused.glyph_ids (checks if pattern is in list)
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"""
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if len(args) < 3:
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raise ValueError("MATCH requires path, pattern, then_label")
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path = str(args[0]) # e.g., "fused.glyph_ids"
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pattern = str(args[1])
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then_label = str(args[2])
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# Extract fused symbol
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pipeline = ctx._state.get("last_symbolic_pipeline")
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matched = False
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if pipeline and hasattr(pipeline, "fused_symbol") and pipeline.fused_symbol:
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fused = pipeline.fused_symbol
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# Support fused.glyph_ids pattern matching
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if path == "fused.glyph_ids":
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matched = pattern in fused.glyph_ids
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# Log control step
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ctx._state.setdefault("symbolic_steps", []).append({
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"name": "match",
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"kind": "control_match",
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"payload": {"path": path, "pattern": pattern, "result": matched}
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})
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print(f"[XIC-CONTROL] MATCH {path} contains {pattern} => {matched}")
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if matched:
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ctx.enqueue_chain(then_label)
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def op_LOOP(ctx: XICContext, *args):
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"""LOOP <predicate> <body_label> [max_iter]
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Repeatedly enqueue body_label while predicate is true.
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Guarded by max_iter and max_total_steps guardrails.
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Note: Unlike traditional loops, this schedules iterations in the queue
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for execution by the main loop. Each iteration runs the body_label.
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"""
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if len(args) < 2:
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raise ValueError("LOOP requires predicate and body_label")
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from glyphos.control.predicate import eval_predicate
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predicate = str(args[0])
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body_label = str(args[1])
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max_iter = int(args[2]) if len(args) > 2 else int(ctx.params.get("max_loop_iterations", 50))
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iter_count = 0
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while iter_count < max_iter:
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# Check global guardrail
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total_steps = int(ctx._state.get("total_steps", 0))
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max_total_steps = int(ctx.params.get("max_total_steps", 1000))
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if total_steps >= max_total_steps:
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ctx._state.setdefault("guardrails", []).append("max_total_steps_exceeded")
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ctx._state.setdefault("symbolic_steps", []).append({
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"name": "guardrail",
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"kind": "guardrail",
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"payload": "max_total_steps_exceeded"
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})
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print(f"[XIC-CONTROL] LOOP guardrail: max_total_steps exceeded ({total_steps})")
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break
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# Evaluate loop predicate
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pipeline = ctx._state.get("last_symbolic_pipeline")
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fused_dict = {}
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dominant = []
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if pipeline and hasattr(pipeline, "fused_symbol") and pipeline.fused_symbol:
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fused = pipeline.fused_symbol
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fused_dict = {
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"global_resonance_score": fused.resonance_map.global_resonance_score if fused.resonance_map else 0.0,
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"glyph_ids": fused.glyph_ids,
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}
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if fused.resonance_map:
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dominant = [(g, m.weight) for g, m in fused.resonance_map.get_top_glyphs(5)]
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try:
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should_continue = eval_predicate(predicate, fused_dict, dominant)
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except Exception as e:
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print(f"[XIC-CONTROL] LOOP predicate evaluation error: {e}")
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should_continue = False
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if not should_continue:
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print(f"[XIC-CONTROL] LOOP condition false, exiting")
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break
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# Schedule body execution
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ctx.enqueue_chain(body_label)
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ctx._state.setdefault("symbolic_steps", []).append({
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"name": "loop_iter",
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"kind": "control_loop",
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"payload": {"iteration": iter_count + 1, "predicate": predicate}
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})
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print(f"[XIC-CONTROL] LOOP iteration {iter_count + 1}: enqueued {body_label}")
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iter_count += 1
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if iter_count >= max_iter:
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ctx._state.setdefault("guardrails", []).append("max_loop_iterations_exceeded")
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ctx._state.setdefault("symbolic_steps", []).append({
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"name": "guardrail",
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"kind": "guardrail",
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"payload": "max_loop_iterations_exceeded"
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})
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print(f"[XIC-CONTROL] LOOP guardrail: max_loop_iterations exceeded ({iter_count})")
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# Operation dispatch table
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OP_TABLE = {
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"LOAD_MODEL": op_LOAD_MODEL,
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@@ -454,4 +654,7 @@ OP_TABLE = {
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"CLEAR_GLYPH_CONTEXT": op_CLEAR_GLYPH_CONTEXT,
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"GET_GLYPH_RESONANCE": op_GET_GLYPH_RESONANCE,
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"LOG": op_LOG,
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"IF": op_IF,
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"MATCH": op_MATCH,
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"LOOP": op_LOOP,
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}
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