Extend XIC v1 Engine with Symbolic Mode, 5 New Ops, GPU Path, Cognition Integration
New instructions: - STREAM: Line-by-line execution and output - CHAIN: Named execution boundaries - CALL_GLYPH: Invoke glyph-aware cognition - SET_CONTEXT: Set symbolic/cognitive context metadata - LOG: Structured logging Symbolic execution mode: - SET_MODE "symbolic" routes prompts through LAIN 8-lane cognition pipeline - run_symbolic_prompt() compresses prompt, builds manifest, executes via execute_symbolic() - Full integration with glyphos/cognitive_kernel.py GPU-accelerated path: - xic_extensions/gpu_runtime.py: has_gpu() probes torch.cuda, run_on_gpu() executes - SET_PARAM "use_gpu" true enables GPU (auto-fallback to CPU if unavailable) - No required GPU dependencies; system works equally on CPU Demo programs: - demo_symbolic.gx.json: Shows symbolic mode through LAIN pipeline - demo_gpu.gx.json: Shows GPU mode with CPU fallback Backward compatibility: - All 4 original ops unchanged; 5 new ops added to OP_TABLE - xic_vm.py, xic_executor.py: No changes (pure dispatcher pattern holds) - demo_chat.gx.json: Still executes identically - All existing GlyphRunner commands: Unchanged behavior Architecture: - Lazy imports prevent circular dependencies (xic_ops, glyphos, xic_extensions) - Clean separation: XIC is client of cognition layer - Zero breaking changes; additive extension only - No XIC v2 binary format; all within v1 JSON+.gx architecture Validation: - 10 integration tests: all passing - Backward compat verified with original demo - Symbolic and GPU modes tested end-to-end - No external dependencies required (GPU optional) Co-contributors: LAIN cognition engine, gx_compiler GSZ3, glyphos event system
This commit is contained in:
GlyphRunner System
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@@ -9,6 +9,7 @@ class XICContext:
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mode: str = "chat"
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params: Dict[str, Any] = field(default_factory=dict)
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_state: Dict[str, Any] = field(default_factory=dict)
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symbolic_mode: bool = False
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def op_LOAD_MODEL(ctx: XICContext, *args):
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@@ -21,10 +22,12 @@ def op_LOAD_MODEL(ctx: XICContext, *args):
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def op_SET_MODE(ctx: XICContext, *args):
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"""SET_MODE <mode>: Set execution mode (chat, eval, benchmark)."""
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"""SET_MODE <mode>: Set execution mode (chat, eval, benchmark, symbolic)."""
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if not args:
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raise ValueError("SET_MODE requires a mode argument")
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ctx.mode = str(args[0])
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if ctx.mode == "symbolic":
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ctx.symbolic_mode = True
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print(f"[XIC] Mode set to: {ctx.mode}")
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@@ -39,32 +42,48 @@ def op_SET_PARAM(ctx: XICContext, *args):
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def op_RUN_PROMPT(ctx: XICContext, *args):
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"""RUN_PROMPT <prompt>: Execute prompt against loaded model.
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"""RUN_PROMPT <prompt>: Execute prompt against loaded model or symbolic cognition.
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This is the critical operation that wires directly to execute_gx()
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in runtime_executor/runner.py. It reads the .gx binary, decompresses it,
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and executes it with the given prompt.
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If ctx.symbolic_mode is True, routes through glyphos/cognitive_kernel.py.
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Otherwise, routes to execute_gx() with optional GPU acceleration.
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"""
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if not args:
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raise ValueError("RUN_PROMPT requires a prompt argument")
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prompt = str(args[0])
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if ctx.symbolic_mode:
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from glyphos.cognitive_kernel import run_symbolic_prompt
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result = run_symbolic_prompt(prompt, context=ctx.params.get("context"))
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print(f"[XIC-SYMBOLIC] {result}")
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ctx._state["last_symbolic_result"] = result
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return
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if not ctx.model_path:
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raise ValueError("No model loaded. Use LOAD_MODEL first.")
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prompt = str(args[0])
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try:
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# Call the real compressed model runner
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# execute_gx() loads the .gx file, decompresses it via GSZ3,
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# and execs the decompressed Python code
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execution_context = execute_gx(
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path=ctx.model_path,
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trace=ctx.params.get("trace", False),
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profile=ctx.params.get("profile", False)
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)
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if ctx.params.get("use_gpu"):
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from xic_extensions.gpu_runtime import has_gpu, run_on_gpu
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if has_gpu():
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print(f"[XIC-GPU] Running on GPU: {ctx.model_path}")
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execution_context = run_on_gpu(ctx.model_path, ctx.params)
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else:
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print(f"[XIC-GPU] No GPU detected, falling back to CPU")
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execution_context = execute_gx(
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ctx.model_path,
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trace=ctx.params.get("trace", False),
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profile=ctx.params.get("profile", False)
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)
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else:
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execution_context = execute_gx(
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ctx.model_path,
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trace=ctx.params.get("trace", False),
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profile=ctx.params.get("profile", False)
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)
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print(f"[XIC] Execution complete")
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print(f"[XIC] Result: {execution_context.result if hasattr(execution_context, 'result') else 'OK'}")
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print(f"[XIC] Result: {getattr(execution_context, 'result', 'OK')}")
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ctx._state["last_result"] = execution_context
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except ExecutionError as e:
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@@ -75,10 +94,96 @@ def op_RUN_PROMPT(ctx: XICContext, *args):
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raise
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def op_STREAM(ctx: XICContext, *args):
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"""STREAM <prompt>: Execute and stream output line by line."""
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if not args:
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raise ValueError("STREAM requires a prompt argument")
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prompt = str(args[0])
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if ctx.symbolic_mode:
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from glyphos.cognitive_kernel import run_symbolic_prompt
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result = run_symbolic_prompt(prompt, context=ctx.params.get("context"))
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for chunk in result.split("\n"):
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if chunk.strip():
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print(f"[XIC-STREAM] {chunk}")
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ctx._state["last_symbolic_result"] = result
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return
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if not ctx.model_path:
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raise ValueError("No model loaded. Use LOAD_MODEL first.")
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use_gpu = ctx.params.get("use_gpu")
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if use_gpu:
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from xic_extensions.gpu_runtime import has_gpu, run_on_gpu
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if has_gpu():
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print(f"[XIC-GPU] Streaming on GPU: {ctx.model_path}")
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exec_ctx = run_on_gpu(ctx.model_path, ctx.params)
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else:
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print(f"[XIC-GPU] No GPU detected, falling back to CPU")
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exec_ctx = execute_gx(ctx.model_path,
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trace=ctx.params.get("trace", False),
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profile=ctx.params.get("profile", False))
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else:
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exec_ctx = execute_gx(ctx.model_path,
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trace=ctx.params.get("trace", False),
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profile=ctx.params.get("profile", False))
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result_text = str(getattr(exec_ctx, "result", "OK"))
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for chunk in result_text.split("\n"):
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if chunk.strip():
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print(f"[XIC-STREAM] {chunk}")
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ctx._state["last_result"] = exec_ctx
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def op_CHAIN(ctx: XICContext, *args):
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"""CHAIN <label>: Mark start of a named chain; passes context forward."""
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if not args:
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raise ValueError("CHAIN requires a label argument")
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label = str(args[0])
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ctx.params["chain_label"] = label
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print(f"[XIC-CHAIN] Entering chain: {label}")
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def op_CALL_GLYPH(ctx: XICContext, *args):
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"""CALL_GLYPH <glyph_id> <payload>: Invoke cognition with a glyph context."""
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if not args:
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raise ValueError("CALL_GLYPH requires glyph_id argument")
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glyph_id = str(args[0])
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payload = str(args[1]) if len(args) > 1 else ""
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from glyphos.cognitive_kernel import run_symbolic_prompt
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glyph_context = dict(ctx.params.get("context", {}))
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glyph_context["glyph_id"] = glyph_id
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result = run_symbolic_prompt(payload, context=glyph_context)
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print(f"[XIC-GLYPH] {result}")
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ctx._state[f"glyph_{glyph_id}"] = result
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def op_SET_CONTEXT(ctx: XICContext, *args):
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"""SET_CONTEXT <key> <value>: Set symbolic/cognitive context key."""
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if len(args) < 2:
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raise ValueError("SET_CONTEXT requires key and value")
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if "context" not in ctx.params:
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ctx.params["context"] = {}
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ctx.params["context"][str(args[0])] = args[1]
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print(f"[XIC] Context {args[0]} = {args[1]}")
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def op_LOG(ctx: XICContext, *args):
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"""LOG <message>: Structured log from XIC program."""
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message = str(args[0]) if args else ""
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print(f"[XIC-LOG] {message}")
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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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"SET_MODE": op_SET_MODE,
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"SET_PARAM": op_SET_PARAM,
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"SET_CONTEXT": op_SET_CONTEXT,
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"RUN_PROMPT": op_RUN_PROMPT,
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"STREAM": op_STREAM,
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"CHAIN": op_CHAIN,
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"CALL_GLYPH": op_CALL_GLYPH,
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"LOG": op_LOG,
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}
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