ktransformers/kt-kernel/python/cli/commands/run.py

"""
Run command for kt-cli.

Starts the model inference server using SGLang + kt-kernel.
"""

import os
import subprocess
import sys
from pathlib import Path
from typing import Optional

import click
import typer

from kt_kernel.cli.config.settings import get_settings
from kt_kernel.cli.i18n import t
from kt_kernel.cli.utils.console import (
    confirm,
    console,
    print_api_info,
    print_error,
    print_info,
    print_server_info,
    print_step,
    print_success,
    print_warning,
    prompt_choice,
)
from kt_kernel.cli.utils.environment import detect_cpu_info, detect_gpus, detect_ram_gb
from kt_kernel.cli.utils.model_registry import MODEL_COMPUTE_FUNCTIONS, ModelInfo, get_registry


@click.command(
    context_settings={"ignore_unknown_options": True, "allow_extra_args": True},
    add_help_option=False,  # We'll handle help manually to avoid conflicts
)
@click.argument("model", required=False, default=None)
@click.option("--host", "-H", default=None, help="Server host address")
@click.option("--port", "-p", type=int, default=None, help="Server port")
@click.option("--gpu-experts", type=int, default=None, help="Number of GPU experts per layer")
@click.option("--cpu-threads", type=int, default=None, help="Number of CPU inference threads")
@click.option("--numa-nodes", type=int, default=None, help="Number of NUMA nodes")
@click.option(
    "--tensor-parallel-size", "--tp", "tensor_parallel_size", type=int, default=None, help="Tensor parallel size"
)
@click.option("--model-path", type=click.Path(), default=None, help="Custom model path")
@click.option("--weights-path", type=click.Path(), default=None, help="Custom quantized weights path")
@click.option("--kt-method", default=None, help="KT quantization method")
@click.option(
    "--kt-gpu-prefill-threshold", "kt_gpu_prefill_threshold", type=int, default=None, help="GPU prefill token threshold"
)
@click.option("--attention-backend", default=None, help="Attention backend")
@click.option("--max-total-tokens", "max_total_tokens", type=int, default=None, help="Maximum total tokens")
@click.option("--max-running-requests", "max_running_requests", type=int, default=None, help="Maximum running requests")
@click.option("--chunked-prefill-size", "chunked_prefill_size", type=int, default=None, help="Chunked prefill size")
@click.option("--mem-fraction-static", "mem_fraction_static", type=float, default=None, help="Memory fraction static")
@click.option("--watchdog-timeout", "watchdog_timeout", type=int, default=None, help="Watchdog timeout")
@click.option("--served-model-name", "served_model_name", default=None, help="Served model name")
@click.option(
    "--disable-shared-experts-fusion",
    "disable_shared_experts_fusion",
    is_flag=True,
    default=None,
    help="Disable shared experts fusion",
)
@click.option(
    "--enable-shared-experts-fusion",
    "enable_shared_experts_fusion",
    is_flag=True,
    default=False,
    help="Enable shared experts fusion",
)
@click.option("--quantize", "-q", is_flag=True, default=False, help="Quantize model")
@click.option("--advanced", is_flag=True, default=False, help="Show advanced options")
@click.option("--dry-run", "dry_run", is_flag=True, default=False, help="Show command without executing")
@click.pass_context
def run(
    ctx: click.Context,
    model: Optional[str],
    host: Optional[str],
    port: Optional[int],
    gpu_experts: Optional[int],
    cpu_threads: Optional[int],
    numa_nodes: Optional[int],
    tensor_parallel_size: Optional[int],
    model_path: Optional[str],
    weights_path: Optional[str],
    kt_method: Optional[str],
    kt_gpu_prefill_threshold: Optional[int],
    attention_backend: Optional[str],
    max_total_tokens: Optional[int],
    max_running_requests: Optional[int],
    chunked_prefill_size: Optional[int],
    mem_fraction_static: Optional[float],
    watchdog_timeout: Optional[int],
    served_model_name: Optional[str],
    disable_shared_experts_fusion: Optional[bool],
    enable_shared_experts_fusion: bool,
    quantize: bool,
    advanced: bool,
    dry_run: bool,
) -> None:
    """Start model inference server.

    \b
    Examples: kt run deepseek-v3 | kt run m2 --tensor-parallel-size 2 | kt run /path/to/model --gpu-experts 4

    \b
    Custom Options: Pass any SGLang server option directly (e.g., kt run m2 --fp8-gemm-backend triton).
    Common: --fp8-gemm-backend, --tool-call-parser, --reasoning-parser, --dp-size, --enable-ma
    For full list: python -m sglang.launch_server --help
    """
    # Handle --help manually since we disabled it
    # Check sys.argv for --help or -h since ctx.args may not be set yet
    if "--help" in sys.argv or "-h" in sys.argv:
        click.echo(ctx.get_help())
        return

    # Handle disable/enable shared experts fusion flags
    if enable_shared_experts_fusion:
        disable_shared_experts_fusion = False
    elif disable_shared_experts_fusion is None:
        disable_shared_experts_fusion = None

    # Convert Path objects from click
    model_path_obj = Path(model_path) if model_path else None
    weights_path_obj = Path(weights_path) if weights_path else None

    # Get extra args that weren't parsed (unknown options)
    # click stores these in ctx.args when ignore_unknown_options=True
    extra_cli_args = list(ctx.args) if ctx.args else []

    # Remove --help from extra args if present (already handled)
    extra_cli_args = [arg for arg in extra_cli_args if arg not in ["--help", "-h"]]

    # Call the actual run function implementation
    _run_impl(
        model=model,
        host=host,
        port=port,
        gpu_experts=gpu_experts,
        cpu_threads=cpu_threads,
        numa_nodes=numa_nodes,
        tensor_parallel_size=tensor_parallel_size,
        model_path=model_path_obj,
        weights_path=weights_path_obj,
        kt_method=kt_method,
        kt_gpu_prefill_threshold=kt_gpu_prefill_threshold,
        attention_backend=attention_backend,
        max_total_tokens=max_total_tokens,
        max_running_requests=max_running_requests,
        chunked_prefill_size=chunked_prefill_size,
        mem_fraction_static=mem_fraction_static,
        watchdog_timeout=watchdog_timeout,
        served_model_name=served_model_name,
        disable_shared_experts_fusion=disable_shared_experts_fusion,
        quantize=quantize,
        advanced=advanced,
        dry_run=dry_run,
        extra_cli_args=extra_cli_args,
    )


def _run_impl(
    model: Optional[str],
    host: Optional[str],
    port: Optional[int],
    gpu_experts: Optional[int],
    cpu_threads: Optional[int],
    numa_nodes: Optional[int],
    tensor_parallel_size: Optional[int],
    model_path: Optional[Path],
    weights_path: Optional[Path],
    kt_method: Optional[str],
    kt_gpu_prefill_threshold: Optional[int],
    attention_backend: Optional[str],
    max_total_tokens: Optional[int],
    max_running_requests: Optional[int],
    chunked_prefill_size: Optional[int],
    mem_fraction_static: Optional[float],
    watchdog_timeout: Optional[int],
    served_model_name: Optional[str],
    disable_shared_experts_fusion: Optional[bool],
    quantize: bool,
    advanced: bool,
    dry_run: bool,
    extra_cli_args: list[str],
) -> None:
    """Actual implementation of run command."""
    # Check if SGLang is installed before proceeding
    from kt_kernel.cli.utils.sglang_checker import (
        check_sglang_installation,
        check_sglang_kt_kernel_support,
        print_sglang_install_instructions,
        print_sglang_kt_kernel_instructions,
    )

    sglang_info = check_sglang_installation()
    if not sglang_info["installed"]:
        console.print()
        print_error(t("sglang_not_found"))
        console.print()
        print_sglang_install_instructions()
        raise typer.Exit(1)

    # Check if SGLang supports kt-kernel (has --kt-gpu-prefill-token-threshold parameter)
    kt_kernel_support = check_sglang_kt_kernel_support()
    if not kt_kernel_support["supported"]:
        console.print()
        print_error(t("sglang_kt_kernel_not_supported"))
        console.print()
        print_sglang_kt_kernel_instructions()
        raise typer.Exit(1)

    settings = get_settings()
    registry = get_registry()

    console.print()

    # If no model specified, show interactive selection
    if model is None:
        model = _interactive_model_selection(registry, settings)
        if model is None:
            raise typer.Exit(0)

    # Step 1: Detect hardware
    print_step(t("run_detecting_hardware"))
    gpus = detect_gpus()
    cpu = detect_cpu_info()
    ram = detect_ram_gb()

    if gpus:
        gpu_info = f"{gpus[0].name} ({gpus[0].vram_gb}GB VRAM)"
        if len(gpus) > 1:
            gpu_info += f" + {len(gpus) - 1} more"
        print_info(t("run_gpu_info", name=gpus[0].name, vram=gpus[0].vram_gb))
    else:
        print_warning(t("doctor_gpu_not_found"))
        gpu_info = "None"

    print_info(t("run_cpu_info", name=cpu.name, cores=cpu.cores, numa=cpu.numa_nodes))
    print_info(t("run_ram_info", total=int(ram)))

    # Step 2: Resolve model
    console.print()
    print_step(t("run_checking_model"))

    model_info = None
    resolved_model_path = model_path

    # Check if model is a path
    if Path(model).exists():
        resolved_model_path = Path(model)
        print_info(t("run_model_path", path=str(resolved_model_path)))

        # Try to infer model type from path to use default configurations
        # Check directory name against known models
        dir_name = resolved_model_path.name.lower()
        for registered_model in registry.list_all():
            # Check if directory name matches model name or aliases
            if dir_name == registered_model.name.lower():
                model_info = registered_model
                print_info(f"Detected model type: {registered_model.name}")
                break
            for alias in registered_model.aliases:
                if dir_name == alias.lower() or alias.lower() in dir_name:
                    model_info = registered_model
                    print_info(f"Detected model type: {registered_model.name}")
                    break
            if model_info:
                break

        # Also check HuggingFace repo format (org--model)
        if not model_info:
            for registered_model in registry.list_all():
                repo_slug = registered_model.hf_repo.replace("/", "--").lower()
                if repo_slug in dir_name or dir_name in repo_slug:
                    model_info = registered_model
                    print_info(f"Detected model type: {registered_model.name}")
                    break

        if not model_info:
            print_warning("Could not detect model type from path. Using default parameters.")
            console.print("  [dim]Tip: Use model name (e.g., 'kt run m2') to apply optimized configurations[/dim]")
    else:
        # Search in registry
        matches = registry.search(model)

        if not matches:
            print_error(t("run_model_not_found", name=model))
            console.print()
            console.print("Available models:")
            for m in registry.list_all()[:5]:
                console.print(f"  - {m.name} ({', '.join(m.aliases[:2])})")
            raise typer.Exit(1)

        if len(matches) == 1:
            model_info = matches[0]
        else:
            # Multiple matches - prompt user
            console.print()
            print_info(t("run_multiple_matches"))
            choices = [f"{m.name} ({m.hf_repo})" for m in matches]
            selected = prompt_choice(t("run_select_model"), choices)
            idx = choices.index(selected)
            model_info = matches[idx]

        # Find model path
        if model_path is None:
            resolved_model_path = _find_model_path(model_info, settings)
            if resolved_model_path is None:
                print_error(t("run_model_not_found", name=model_info.name))
                console.print()
                console.print(
                    f"  Download with: kt download {model_info.aliases[0] if model_info.aliases else model_info.name}"
                )
                raise typer.Exit(1)

        print_info(t("run_model_path", path=str(resolved_model_path)))

    # Step 3: Check quantized weights (only if explicitly requested)
    resolved_weights_path = None

    # Only use quantized weights if explicitly specified by user
    if weights_path is not None:
        # User explicitly specified weights path
        resolved_weights_path = weights_path
        if not resolved_weights_path.exists():
            print_error(t("run_weights_not_found"))
            console.print(f"  Path: {resolved_weights_path}")
            raise typer.Exit(1)
        print_info(f"Using quantized weights: {resolved_weights_path}")
    elif quantize:
        # User requested quantization
        console.print()
        print_step(t("run_quantizing"))
        # TODO: Implement quantization
        print_warning("Quantization not yet implemented. Please run 'kt quant' manually.")
        raise typer.Exit(1)
    else:
        # Default: use original precision model without quantization
        console.print()
        print_info("Using original precision model (no quantization)")

    # Step 4: Build command
    # Resolve all parameters (CLI > model defaults > config > auto-detect)
    final_host = host or settings.get("server.host", "0.0.0.0")
    final_port = port or settings.get("server.port", 30000)

    # Get defaults from model info if available
    model_defaults = model_info.default_params if model_info else {}

    # Determine tensor parallel size first (needed for GPU expert calculation)
    # Priority: CLI > model defaults > config > auto-detect (with model constraints)

    # Check if explicitly specified by user or configuration
    explicitly_specified = (
        tensor_parallel_size  # CLI argument (highest priority)
        or model_defaults.get("tensor-parallel-size")  # Model defaults
        or settings.get("inference.tensor_parallel_size")  # Config file
    )

    if explicitly_specified:
        # Use explicitly specified value
        requested_tensor_parallel_size = explicitly_specified
    else:
        # Auto-detect from GPUs, considering model's max constraint
        detected_gpu_count = len(gpus) if gpus else 1
        if model_info and model_info.max_tensor_parallel_size is not None:
            # Automatically limit to model's maximum to use as many GPUs as possible
            requested_tensor_parallel_size = min(detected_gpu_count, model_info.max_tensor_parallel_size)
        else:
            requested_tensor_parallel_size = detected_gpu_count

    # Apply model's max_tensor_parallel_size constraint if explicitly specified value exceeds it
    final_tensor_parallel_size = requested_tensor_parallel_size
    if model_info and model_info.max_tensor_parallel_size is not None:
        if requested_tensor_parallel_size > model_info.max_tensor_parallel_size:
            console.print()
            print_warning(
                f"Model {model_info.name} only supports up to {model_info.max_tensor_parallel_size}-way "
                f"tensor parallelism, but {requested_tensor_parallel_size} was requested. "
                f"Reducing to {model_info.max_tensor_parallel_size}."
            )
            final_tensor_parallel_size = model_info.max_tensor_parallel_size

    # CPU/GPU configuration with smart defaults
    # kt-cpuinfer: default to 80% of total CPU threads (cores * NUMA nodes)
    total_threads = cpu.cores * cpu.numa_nodes
    final_cpu_threads = (
        cpu_threads
        or model_defaults.get("kt-cpuinfer")
        or settings.get("inference.cpu_threads")
        or int(total_threads * 0.8)
    )

    # kt-threadpool-count: default to NUMA node count
    final_numa_nodes = (
        numa_nodes
        or model_defaults.get("kt-threadpool-count")
        or settings.get("inference.numa_nodes")
        or cpu.numa_nodes
    )

    # kt-num-gpu-experts: use model-specific computation if available and not explicitly set
    if gpu_experts is not None:
        # User explicitly set it
        final_gpu_experts = gpu_experts
    elif model_info and model_info.name in MODEL_COMPUTE_FUNCTIONS and gpus:
        # Use model-specific computation function (only if GPUs detected)
        vram_per_gpu = gpus[0].vram_gb
        compute_func = MODEL_COMPUTE_FUNCTIONS[model_info.name]
        final_gpu_experts = compute_func(final_tensor_parallel_size, vram_per_gpu)
        console.print()
        print_info(
            f"Auto-computed kt-num-gpu-experts: {final_gpu_experts} (TP={final_tensor_parallel_size}, VRAM={vram_per_gpu}GB per GPU)"
        )
    else:
        # Fall back to defaults
        final_gpu_experts = model_defaults.get("kt-num-gpu-experts") or settings.get("inference.gpu_experts", 1)

    # KT-kernel options
    final_kt_method = kt_method or model_defaults.get("kt-method") or settings.get("inference.kt_method", "AMXINT4")
    final_kt_gpu_prefill_threshold = (
        kt_gpu_prefill_threshold
        or model_defaults.get("kt-gpu-prefill-token-threshold")
        or settings.get("inference.kt_gpu_prefill_token_threshold", 4096)
    )

    # SGLang options
    final_attention_backend = (
        attention_backend
        or model_defaults.get("attention-backend")
        or settings.get("inference.attention_backend", "triton")
    )
    final_max_total_tokens = (
        max_total_tokens or model_defaults.get("max-total-tokens") or settings.get("inference.max_total_tokens", 40000)
    )
    final_max_running_requests = (
        max_running_requests
        or model_defaults.get("max-running-requests")
        or settings.get("inference.max_running_requests", 32)
    )
    final_chunked_prefill_size = (
        chunked_prefill_size
        or model_defaults.get("chunked-prefill-size")
        or settings.get("inference.chunked_prefill_size", 4096)
    )
    final_mem_fraction_static = (
        mem_fraction_static
        or model_defaults.get("mem-fraction-static")
        or settings.get("inference.mem_fraction_static", 0.98)
    )
    final_watchdog_timeout = (
        watchdog_timeout or model_defaults.get("watchdog-timeout") or settings.get("inference.watchdog_timeout", 3000)
    )
    final_served_model_name = (
        served_model_name or model_defaults.get("served-model-name") or settings.get("inference.served_model_name", "")
    )

    # Performance flags
    if disable_shared_experts_fusion is not None:
        final_disable_shared_experts_fusion = disable_shared_experts_fusion
    elif "disable-shared-experts-fusion" in model_defaults:
        final_disable_shared_experts_fusion = model_defaults["disable-shared-experts-fusion"]
    else:
        final_disable_shared_experts_fusion = settings.get("inference.disable_shared_experts_fusion", False)

    # Pass all model default params to handle any extra parameters
    extra_params = model_defaults if model_info else {}

    cmd = _build_sglang_command(
        model_path=resolved_model_path,
        weights_path=resolved_weights_path,
        model_info=model_info,
        host=final_host,
        port=final_port,
        gpu_experts=final_gpu_experts,
        cpu_threads=final_cpu_threads,
        numa_nodes=final_numa_nodes,
        tensor_parallel_size=final_tensor_parallel_size,
        kt_method=final_kt_method,
        kt_gpu_prefill_threshold=final_kt_gpu_prefill_threshold,
        attention_backend=final_attention_backend,
        max_total_tokens=final_max_total_tokens,
        max_running_requests=final_max_running_requests,
        chunked_prefill_size=final_chunked_prefill_size,
        mem_fraction_static=final_mem_fraction_static,
        watchdog_timeout=final_watchdog_timeout,
        served_model_name=final_served_model_name,
        disable_shared_experts_fusion=final_disable_shared_experts_fusion,
        settings=settings,
        extra_model_params=extra_params,
        extra_cli_args=extra_cli_args,
    )

    # Prepare environment variables
    env = os.environ.copy()
    # Add environment variables from advanced.env
    env.update(settings.get_env_vars())
    # Add environment variables from inference.env
    inference_env = settings.get("inference.env", {})
    if isinstance(inference_env, dict):
        env.update({k: str(v) for k, v in inference_env.items()})

    # Step 5: Show configuration summary
    console.print()
    print_step("Configuration")

    # Model info
    if model_info:
        console.print(f"  Model: [bold]{model_info.name}[/bold]")
    else:
        console.print(f"  Model: [bold]{resolved_model_path.name}[/bold]")

    console.print(f"  Path: [dim]{resolved_model_path}[/dim]")

    # Key parameters
    console.print()
    console.print(f"  GPU Experts: [cyan]{final_gpu_experts}[/cyan] per layer")
    console.print(f"  CPU Threads (kt-cpuinfer): [cyan]{final_cpu_threads}[/cyan]")
    console.print(f"  NUMA Nodes (kt-threadpool-count): [cyan]{final_numa_nodes}[/cyan]")
    console.print(f"  Tensor Parallel: [cyan]{final_tensor_parallel_size}[/cyan]")
    console.print(f"  Method: [cyan]{final_kt_method}[/cyan]")
    console.print(f"  Attention: [cyan]{final_attention_backend}[/cyan]")

    # Weights info
    if resolved_weights_path:
        console.print()
        console.print(f"  Quantized weights: [yellow]{resolved_weights_path}[/yellow]")

    console.print()
    console.print(f"  Server: [green]http://{final_host}:{final_port}[/green]")
    console.print()

    # Step 6: Show or execute
    if dry_run:
        console.print()
        console.print("[bold]Command:[/bold]")
        console.print()
        console.print(f"  [dim]{' '.join(cmd)}[/dim]")
        console.print()
        return

    # Execute with prepared environment variables
    # Don't print "Server started" or API info here - let sglang's logs speak for themselves
    # The actual startup takes time and these messages are misleading

    # Print the command being executed
    console.print()
    console.print("[bold]Launching server with command:[/bold]")
    console.print()
    console.print(f"  [dim]{' '.join(cmd)}[/dim]")
    console.print()

    try:
        # Execute directly without intercepting output or signals
        # This allows direct output to terminal and Ctrl+C to work naturally
        process = subprocess.run(cmd, env=env)
        sys.exit(process.returncode)

    except FileNotFoundError:
        from kt_kernel.cli.utils.sglang_checker import print_sglang_install_instructions

        print_error(t("sglang_not_found"))
        console.print()
        print_sglang_install_instructions()
        raise typer.Exit(1)
    except Exception as e:
        print_error(f"Failed to start server: {e}")
        raise typer.Exit(1)


def _find_model_path(model_info: ModelInfo, settings, max_depth: int = 3) -> Optional[Path]:
    """Find the model path on disk by searching all configured model paths.

    Args:
        model_info: Model information to search for
        settings: Settings instance
        max_depth: Maximum depth to search within each model path (default: 3)

    Returns:
        Path to the model directory, or None if not found
    """
    model_paths = settings.get_model_paths()

    # Generate possible names to search for
    possible_names = [
        model_info.name,
        model_info.name.lower(),
        model_info.name.replace(" ", "-"),
        model_info.hf_repo.split("/")[-1],
        model_info.hf_repo.replace("/", "--"),
    ]

    # Add alias-based names
    for alias in model_info.aliases:
        possible_names.append(alias)
        possible_names.append(alias.lower())

    # Search in all configured model directories
    for models_dir in model_paths:
        if not models_dir.exists():
            continue

        # Search recursively up to max_depth
        for depth in range(max_depth):
            for name in possible_names:
                if depth == 0:
                    # Direct children: models_dir / name
                    search_paths = [models_dir / name]
                else:
                    # Nested: use rglob to find directories matching the name
                    search_paths = list(models_dir.rglob(name))

                for path in search_paths:
                    if path.exists() and (path / "config.json").exists():
                        return path

    return None


def _find_weights_path(model_info: ModelInfo, settings) -> Optional[Path]:
    """Find the quantized weights path on disk by searching all configured paths."""
    model_paths = settings.get_model_paths()
    weights_dir = settings.weights_dir

    # Check common patterns
    base_names = [
        model_info.name,
        model_info.name.lower(),
        model_info.hf_repo.split("/")[-1],
    ]

    suffixes = ["-INT4", "-int4", "_INT4", "_int4", "-quant", "-quantized"]

    # Prepare search directories
    search_dirs = [weights_dir] if weights_dir else []
    search_dirs.extend(model_paths)

    for base in base_names:
        for suffix in suffixes:
            for dir_path in search_dirs:
                if dir_path:
                    path = dir_path / f"{base}{suffix}"
                    if path.exists():
                        return path

    return None


def _build_sglang_command(
    model_path: Path,
    weights_path: Optional[Path],
    model_info: Optional[ModelInfo],
    host: str,
    port: int,
    gpu_experts: int,
    cpu_threads: int,
    numa_nodes: int,
    tensor_parallel_size: int,
    kt_method: str,
    kt_gpu_prefill_threshold: int,
    attention_backend: str,
    max_total_tokens: int,
    max_running_requests: int,
    chunked_prefill_size: int,
    mem_fraction_static: float,
    watchdog_timeout: int,
    served_model_name: str,
    disable_shared_experts_fusion: bool,
    settings,
    extra_model_params: Optional[dict] = None,  # New parameter for additional params
    extra_cli_args: Optional[list[str]] = None,  # Extra args from CLI to pass to sglang
) -> list[str]:
    """Build the SGLang launch command."""
    cmd = [
        sys.executable,
        "-m",
        "sglang.launch_server",
        "--host",
        host,
        "--port",
        str(port),
        "--model",
        str(model_path),
    ]

    # Add kt-kernel options
    # kt-kernel is needed for:
    # 1. Quantized models (when weights_path is provided)
    # 2. MoE models with CPU offloading (when kt-cpuinfer > 0 or kt-num-gpu-experts is configured)
    use_kt_kernel = False

    # Check if we should use kt-kernel
    if weights_path:
        # Quantized model - always use kt-kernel
        use_kt_kernel = True
    elif cpu_threads > 0 or gpu_experts > 1:
        # CPU offloading configured - use kt-kernel
        use_kt_kernel = True
    elif model_info and model_info.type == "moe":
        # MoE model - likely needs kt-kernel for expert offloading
        use_kt_kernel = True

    if use_kt_kernel:
        # Add kt-weight-path: use quantized weights if available, otherwise use model path
        weight_path_to_use = weights_path if weights_path else model_path

        # Add kt-kernel configuration
        cmd.extend(
            [
                "--kt-weight-path",
                str(weight_path_to_use),
                "--kt-cpuinfer",
                str(cpu_threads),
                "--kt-threadpool-count",
                str(numa_nodes),
                "--kt-num-gpu-experts",
                str(gpu_experts),
                "--kt-method",
                kt_method,
                "--kt-gpu-prefill-token-threshold",
                str(kt_gpu_prefill_threshold),
            ]
        )

    # Add SGLang options
    cmd.extend(
        [
            "--attention-backend",
            attention_backend,
            "--trust-remote-code",
            "--mem-fraction-static",
            str(mem_fraction_static),
            "--chunked-prefill-size",
            str(chunked_prefill_size),
            "--max-running-requests",
            str(max_running_requests),
            "--max-total-tokens",
            str(max_total_tokens),
            "--watchdog-timeout",
            str(watchdog_timeout),
            "--enable-mixed-chunk",
            "--tensor-parallel-size",
            str(tensor_parallel_size),
            "--enable-p2p-check",
        ]
    )

    # Add served model name if specified
    if served_model_name:
        cmd.extend(["--served-model-name", served_model_name])

    # Add performance flags
    if disable_shared_experts_fusion:
        cmd.append("--disable-shared-experts-fusion")

    # Add any extra parameters from model defaults that weren't explicitly handled
    if extra_model_params:
        # List of parameters already handled above
        handled_params = {
            "kt-num-gpu-experts",
            "kt-cpuinfer",
            "kt-threadpool-count",
            "kt-method",
            "kt-gpu-prefill-token-threshold",
            "attention-backend",
            "tensor-parallel-size",
            "max-total-tokens",
            "max-running-requests",
            "chunked-prefill-size",
            "mem-fraction-static",
            "watchdog-timeout",
            "served-model-name",
            "disable-shared-experts-fusion",
        }

        for key, value in extra_model_params.items():
            if key not in handled_params:
                # Add unhandled parameters dynamically
                cmd.append(f"--{key}")
                if isinstance(value, bool):
                    # Boolean flags don't need a value
                    if not value:
                        # For False boolean, skip the flag entirely
                        cmd.pop()  # Remove the flag we just added
                else:
                    cmd.append(str(value))

    # Add extra args from settings
    extra_args = settings.get("advanced.sglang_args", [])
    if extra_args:
        cmd.extend(extra_args)

    # Add extra CLI args (user-provided options not defined in kt CLI)
    if extra_cli_args:
        cmd.extend(extra_cli_args)

    return cmd


def _interactive_model_selection(registry, settings) -> Optional[str]:
    """Show interactive model selection interface.

    Returns:
        Selected model name or None if cancelled.
    """
    from rich.panel import Panel
    from rich.table import Table
    from rich.prompt import Prompt

    from kt_kernel.cli.i18n import get_lang

    lang = get_lang()

    # Find local models first
    local_models = registry.find_local_models()

    # Get all registered models
    all_models = registry.list_all()

    console.print()
    console.print(
        Panel.fit(
            t("run_select_model_title"),
            border_style="cyan",
        )
    )
    console.print()

    # Build choices list
    choices = []
    choice_map = {}  # index -> model name

    # Section 1: Local models (downloaded)
    if local_models:
        console.print(f"[bold green]{t('run_local_models')}[/bold green]")
        console.print()

        for i, (model_info, path) in enumerate(local_models, 1):
            desc = model_info.description_zh if lang == "zh" else model_info.description
            short_desc = desc[:50] + "..." if len(desc) > 50 else desc
            console.print(f"  [cyan][{i}][/cyan] [bold]{model_info.name}[/bold]")
            console.print(f"      [dim]{short_desc}[/dim]")
            console.print(f"      [dim]{path}[/dim]")
            choices.append(str(i))
            choice_map[str(i)] = model_info.name

        console.print()

    # Section 2: All registered models (for reference)
    start_idx = len(local_models) + 1
    console.print(f"[bold yellow]{t('run_registered_models')}[/bold yellow]")
    console.print()

    # Filter out already shown local models
    local_model_names = {m.name for m, _ in local_models}

    for i, model_info in enumerate(all_models, start_idx):
        if model_info.name in local_model_names:
            continue

        desc = model_info.description_zh if lang == "zh" else model_info.description
        short_desc = desc[:50] + "..." if len(desc) > 50 else desc
        console.print(f"  [cyan][{i}][/cyan] [bold]{model_info.name}[/bold]")
        console.print(f"      [dim]{short_desc}[/dim]")
        console.print(f"      [dim]{model_info.hf_repo}[/dim]")
        choices.append(str(i))
        choice_map[str(i)] = model_info.name

    console.print()

    # Add cancel option
    cancel_idx = str(len(choices) + 1)
    console.print(f"  [cyan][{cancel_idx}][/cyan] [dim]{t('cancel')}[/dim]")
    choices.append(cancel_idx)
    console.print()

    # Prompt for selection
    try:
        selection = Prompt.ask(
            t("run_select_model_prompt"),
            choices=choices,
            default="1" if choices else cancel_idx,
        )
    except KeyboardInterrupt:
        console.print()
        return None

    if selection == cancel_idx:
        return None

    return choice_map.get(selection)