Function cmd_status

orchestrate.py:351–411

Print the current orchestration status.

(state: dict)

def cmd_status(state: dict) -> None:
    """Print the current orchestration status."""
    kernels = state["kernels"]
    idx = state.get("current_kernel_idx", 0)
    current = kernels[idx] if idx < len(kernels) else None

    print()
    print("=" * 55)
    print("  AutoKernel Orchestration Status")
    print("=" * 55)
    print()

    # Check if all done
    all_done = all(k["status"] in (STATUS_DONE, STATUS_SKIPPED) for k in kernels)
    if all_done:
        print("  All kernels optimized. Run verify.py for end-to-end check.")
        print()
    elif current:
        kname = Path(current["file"]).name
        print(f"  Currently optimizing: {kname} (rank {current['rank']}, {current['op_type']})")
        exp_run = current["experiments_run"]
        exp_kept = current["experiments_kept"]
        minutes = current["time_spent_minutes"]
        print(f"  Progress: {exp_run} experiments ({exp_kept} kept), {minutes} min elapsed")

        baseline = current["baseline_tflops"]
        best = current["best_tflops"]
        speedup = current["speedup"]
        if baseline is not None and best is not None and speedup is not None:
            print(f"  Baseline: {baseline:.1f} TFLOPS -> Current best: {best:.1f} TFLOPS ({speedup:.1f}x speedup)")
        elif baseline is not None:
            print(f"  Baseline: {baseline:.1f} TFLOPS (no improvement yet)")
        print()

    # Kernel table
    print("  Kernel Status:")
    max_op_len = max((len(k["op_type"]) for k in kernels), default=8)
    for k in kernels:
        tag = _STATUS_DISPLAY.get(k["status"], k["status"].upper())
        op = k["op_type"]
        rank = k["rank"]
        if k["status"] in (STATUS_DONE, STATUS_OPTIMIZING) and k["speedup"] is not None:
            detail = f"{k['speedup']:.1f}x speedup, {k['experiments_run']} experiments"
        elif k["status"] == STATUS_SKIPPED:
            detail = "skipped"
        else:
            detail = ""
        pad_op = op.ljust(max_op_len)
        if detail:
            print(f"    [{tag:<10}] {pad_op}  (rank {rank}) -> {detail}")
        else:
            print(f"    [{tag:<10}] {pad_op}  (rank {rank})")
    print()

    # Aggregate speedup
    agg = estimate_aggregate_speedup(kernels)
    if agg > 1.0:
        print(f"  Estimated aggregate model speedup: {agg:.2f}x")