utils.py

"""
Copyright 2023-2024 SGLang Team
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

"""Common utilities."""

import base64
import fcntl
import logging
import os
import random
import resource
import socket
import struct
import time
from importlib.metadata import PackageNotFoundError, version
from io import BytesIO
from typing import List, Optional

import numpy as np
import psutil
import requests
import torch
import torch.distributed as dist
from fastapi.responses import JSONResponse
from packaging import version as pkg_version
from starlette.middleware.base import BaseHTTPMiddleware
from torch.nn.parameter import Parameter
from triton.runtime.cache import (
    FileCacheManager,
    default_cache_dir,
    default_dump_dir,
    default_override_dir,
)

logger = logging.getLogger(__name__)


show_time_cost = False
time_infos = {}


def enable_show_time_cost():
    global show_time_cost
    show_time_cost = True


class TimeInfo:
    def __init__(self, name, interval=0.1, color=0, indent=0):
        self.name = name
        self.interval = interval
        self.color = color
        self.indent = indent

        self.acc_time = 0
        self.last_acc_time = 0

    def check(self):
        if self.acc_time - self.last_acc_time > self.interval:
            self.last_acc_time = self.acc_time
            return True
        return False

    def pretty_print(self):
        print(f"\x1b[{self.color}m", end="")
        print("-" * self.indent * 2, end="")
        print(f"{self.name}: {self.acc_time:.3f}s\x1b[0m")


def mark_start(name, interval=0.1, color=0, indent=0):
    global time_infos, show_time_cost
    if not show_time_cost:
        return
    torch.cuda.synchronize()
    if time_infos.get(name, None) is None:
        time_infos[name] = TimeInfo(name, interval, color, indent)
    time_infos[name].acc_time -= time.time()


def mark_end(name):
    global time_infos, show_time_cost
    if not show_time_cost:
        return
    torch.cuda.synchronize()
    time_infos[name].acc_time += time.time()
    if time_infos[name].check():
        time_infos[name].pretty_print()


def calculate_time(show=False, min_cost_ms=0.0):
    def wrapper(func):
        def inner_func(*args, **kwargs):
            torch.cuda.synchronize()
            if show:
                start_time = time.time()
            result = func(*args, **kwargs)
            torch.cuda.synchronize()
            if show:
                cost_time = (time.time() - start_time) * 1000
                if cost_time > min_cost_ms:
                    print(f"Function {func.__name__} took {cost_time} ms to run.")
            return result

        return inner_func

    return wrapper


def get_available_gpu_memory(gpu_id, distributed=False):
    """
    Get available memory for cuda:gpu_id device.
    When distributed is True, the available memory is the minimum available memory of all GPUs.
    """
    num_gpus = torch.cuda.device_count()
    assert gpu_id < num_gpus

    if torch.cuda.current_device() != gpu_id:
        print(
            f"WARNING: current device is not {gpu_id}, but {torch.cuda.current_device()}, ",
            "which may cause useless memory allocation for torch CUDA context.",
        )

    torch.cuda.empty_cache()
    free_gpu_memory, _ = torch.cuda.mem_get_info(gpu_id)

    if distributed:
        tensor = torch.tensor(free_gpu_memory, dtype=torch.float32).to(
            torch.device("cuda", gpu_id)
        )
        torch.distributed.all_reduce(tensor, op=torch.distributed.ReduceOp.MIN)
        free_gpu_memory = tensor.item()

    return free_gpu_memory / (1 << 30)


def set_random_seed(seed: int) -> None:
    """Set the random seed for all libraries."""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)


def is_port_available(port):
    """Return whether a port is available."""
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        try:
            s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
            s.bind(("", port))
            s.listen(1)
            return True
        except socket.error:
            return False


def allocate_init_ports(
    port: Optional[int] = None,
    additional_ports: Optional[List[int]] = None,
    dp_size: int = 1,
):
    """Allocate ports for all connections."""
    if additional_ports:
        ret_ports = [port] + additional_ports
    else:
        ret_ports = [port]

    ret_ports = list(set(x for x in ret_ports if is_port_available(x)))
    cur_port = ret_ports[-1] + 1 if len(ret_ports) > 0 else 10000

    # HTTP + Tokenizer + Controller + Detokenizer + dp_size * 1 (nccl)
    num_ports_needed = 4 + dp_size
    while len(ret_ports) < num_ports_needed:
        if cur_port not in ret_ports and is_port_available(cur_port):
            ret_ports.append(cur_port)
        cur_port += 1

    if port is not None and ret_ports[0] != port:
        logger.warn(
            f"WARNING: Port {port} is not available. Use port {ret_ports[0]} instead."
        )

    return ret_ports[0], ret_ports[1:num_ports_needed]


def get_int_token_logit_bias(tokenizer, vocab_size):
    """Get the logit bias for integer-only tokens."""
    # a bug when model's vocab size > tokenizer.vocab_size
    vocab_size = tokenizer.vocab_size
    logit_bias = np.zeros(vocab_size, dtype=np.float32)
    for t_id in range(vocab_size):
        ss = tokenizer.decode([t_id]).strip()
        if not (ss.isdigit() or len(ss) == 0 or t_id == tokenizer.eos_token_id):
            logit_bias[t_id] = -1e5

    return logit_bias


def is_multimodal_model(model):
    from sglang.srt.model_config import ModelConfig

    if isinstance(model, str):
        model = model.lower()
        return "llava" in model or "yi-vl" in model or "llava-next" in model

    if isinstance(model, ModelConfig):
        model_path = model.path.lower()
        return (
            "llava" in model_path or "yi-vl" in model_path or "llava-next" in model_path
        )

    raise ValueError("unrecognized type")


def decode_video_base64(video_base64):
    from PIL import Image

    # Decode the base64 string
    video_bytes = base64.b64decode(video_base64)

    # Placeholder for the start indices of each PNG image
    img_starts = []

    frame_format = "PNG"  # str(os.getenv('FRAME_FORMAT', "JPEG"))

    assert frame_format in [
        "PNG",
        "JPEG",
    ], "FRAME_FORMAT must be either 'PNG' or 'JPEG'"

    if frame_format == "PNG":
        # Find each PNG start signature to isolate images
        i = 0
        while i < len(video_bytes) - 7:  # Adjusted for the length of the PNG signature
            # Check if we found the start of a PNG file
            if (
                video_bytes[i] == 0x89
                and video_bytes[i + 1] == 0x50
                and video_bytes[i + 2] == 0x4E
                and video_bytes[i + 3] == 0x47
                and video_bytes[i + 4] == 0x0D
                and video_bytes[i + 5] == 0x0A
                and video_bytes[i + 6] == 0x1A
                and video_bytes[i + 7] == 0x0A
            ):
                img_starts.append(i)
                i += 8  # Skip the PNG signature
            else:
                i += 1
    else:
        # Find each JPEG start (0xFFD8) to isolate images
        i = 0
        while (
            i < len(video_bytes) - 1
        ):  # Adjusted for the length of the JPEG SOI signature
            # Check if we found the start of a JPEG file
            if video_bytes[i] == 0xFF and video_bytes[i + 1] == 0xD8:
                img_starts.append(i)
                # Move to the next byte to continue searching for the next image start
                i += 2
            else:
                i += 1

    frames = []
    for start_idx in img_starts:
        # Assuming each image is back-to-back, the end of one image is the start of another
        # The last image goes until the end of the byte string
        end_idx = (
            img_starts[img_starts.index(start_idx) + 1]
            if img_starts.index(start_idx) + 1 < len(img_starts)
            else len(video_bytes)
        )
        img_bytes = video_bytes[start_idx:end_idx]

        # Convert bytes to a PIL Image
        img = Image.open(BytesIO(img_bytes))

        # Convert PIL Image to a NumPy array
        frame = np.array(img)

        # Append the frame to the list of frames
        frames.append(frame)

    # Ensure there's at least one frame to avoid errors with np.stack
    if frames:
        return np.stack(frames, axis=0), img.size
    else:
        return np.array([]), (
            0,
            0,
        )  # Return an empty array and size tuple if no frames were found


def load_image(image_file):
    from PIL import Image

    image = image_size = None

    if image_file.startswith("http://") or image_file.startswith("https://"):
        timeout = int(os.getenv("REQUEST_TIMEOUT", "3"))
        response = requests.get(image_file, timeout=timeout)
        image = Image.open(BytesIO(response.content))
    elif image_file.lower().endswith(("png", "jpg", "jpeg", "webp", "gif")):
        image = Image.open(image_file)
    elif image_file.startswith("data:"):
        image_file = image_file.split(",")[1]
        image = Image.open(BytesIO(base64.b64decode(image_file)))
    elif image_file.startswith("video:"):
        image_file = image_file.replace("video:", "")
        image, image_size = decode_video_base64(image_file)
    else:
        image = Image.open(BytesIO(base64.b64decode(image_file)))

    return image, image_size


def suppress_other_loggers():
    from vllm.logger import logger as vllm_default_logger

    vllm_default_logger.setLevel(logging.WARN)
    logging.getLogger("vllm.config").setLevel(logging.ERROR)
    logging.getLogger("vllm.distributed.device_communicators.pynccl").setLevel(
        logging.WARN
    )
    logging.getLogger("vllm.distributed.device_communicators.shm_broadcast").setLevel(
        logging.WARN
    )
    logging.getLogger("vllm.selector").setLevel(logging.WARN)
    logging.getLogger("vllm.utils").setLevel(logging.WARN)


def assert_pkg_version(pkg: str, min_version: str, message: str):
    try:
        installed_version = version(pkg)
        if pkg_version.parse(installed_version) < pkg_version.parse(min_version):
            raise Exception(
                f"{pkg} is installed with version {installed_version}, which "
                f"is less than the minimum required version {min_version}. " + message
            )
    except PackageNotFoundError:
        raise Exception(
            f"{pkg} with minimum required version {min_version} is not installed. "
            + message
        )


def kill_parent_process():
    """Kill the parent process and all children of the parent process."""
    current_process = psutil.Process()
    parent_process = current_process.parent()
    children = parent_process.children(recursive=True)
    for child in children:
        if child.pid != current_process.pid:
            os.kill(child.pid, 9)
    os.kill(parent_process.pid, 9)


def kill_child_process(pid, including_parent=True):
    try:
        parent = psutil.Process(pid)
    except psutil.NoSuchProcess:
        return

    children = parent.children(recursive=True)
    for child in children:
        try:
            child.kill()
        except psutil.NoSuchProcess:
            pass

    if including_parent:
        try:
            parent.kill()
        except psutil.NoSuchProcess:
            pass


def monkey_patch_vllm_p2p_access_check(gpu_id: int):
    """
    Monkey patch the slow p2p access check in vllm.
    NOTE: We assume the p2p access is always allowed, which can be wrong for some setups.
    """

    import vllm.distributed.device_communicators.custom_all_reduce_utils as tgt

    setattr(tgt, "gpu_p2p_access_check", lambda *arg, **kwargs: True)


def monkey_patch_vllm_dummy_weight_loader():
    """
    Monkey patch the dummy weight loader in vllm to call process_weights_after_loading.
    """

    from vllm.model_executor.model_loader.loader import (
        CacheConfig,
        DeviceConfig,
        DummyModelLoader,
        LoRAConfig,
        ModelConfig,
        MultiModalConfig,
        ParallelConfig,
        SchedulerConfig,
        _initialize_model,
        initialize_dummy_weights,
        nn,
        set_default_torch_dtype,
    )

    def load_model(
        self,
        *,
        model_config: ModelConfig,
        device_config: DeviceConfig,
        lora_config: Optional[LoRAConfig],
        multimodal_config: Optional[MultiModalConfig],
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
        cache_config: CacheConfig,
    ) -> nn.Module:
        with set_default_torch_dtype(model_config.dtype):
            with torch.device(device_config.device):
                model = _initialize_model(
                    model_config,
                    self.load_config,
                    lora_config,
                    multimodal_config,
                    cache_config,
                )

            for _, module in model.named_modules():
                quant_method = getattr(module, "quant_method", None)
                if quant_method is not None:
                    quant_method.process_weights_after_loading(module)
                # FIXME: Remove this after Mixtral is updated
                # to use quant_method.
                if hasattr(module, "process_weights_after_loading"):
                    module.process_weights_after_loading()

            # NOTE(woosuk): For accurate performance evaluation, we assign
            # random values to the weights.
            initialize_dummy_weights(model)
        return model.eval()

    setattr(DummyModelLoader, "load_model", load_model)


vllm_all_gather_backup = None


def monkey_patch_vllm_all_gather(reverse: bool = False):
    """Monkey patch all-gather to remove in-place operations."""
    from torch.distributed import _functional_collectives as funcol
    from vllm.distributed.parallel_state import GroupCoordinator

    global vllm_all_gather_backup
    if vllm_all_gather_backup is None:
        vllm_all_gather_backup = GroupCoordinator.all_gather

    def all_gather(self, input_: torch.Tensor, dim: int = -1) -> torch.Tensor:
        world_size = self.world_size
        # Bypass the function if we are using only 1 GPU.
        if world_size == 1:
            return input_
        assert (
            -input_.dim() <= dim < input_.dim()
        ), f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
        if dim < 0:
            # Convert negative dim to positive.
            dim += input_.dim()
        input_size = input_.size()
        # Allocate output tensor.
        output_tensor = torch.empty(
            (world_size,) + input_size, dtype=input_.dtype, device=input_.device
        )

        output_tensor = funcol.all_gather_tensor(
            input_, gather_dim=0, group=self.device_group
        ).view((world_size,) + input_size)

        # Reshape
        output_tensor = output_tensor.movedim(0, dim)
        output_tensor = output_tensor.reshape(
            input_size[:dim] + (world_size * input_size[dim],) + input_size[dim + 1 :]
        )
        return output_tensor

    if reverse:
        setattr(GroupCoordinator, "all_gather", vllm_all_gather_backup)
    else:
        setattr(GroupCoordinator, "all_gather", all_gather)


def maybe_set_triton_cache_manager() -> None:
    """Set environment variable to tell Triton to use a
    custom cache manager"""
    cache_manger = os.environ.get("TRITON_CACHE_MANAGER", None)
    if cache_manger is None:
        manager = "sglang.srt.utils:CustomCacheManager"
        logger.debug("Setting Triton cache manager to: %s", manager)
        os.environ["TRITON_CACHE_MANAGER"] = manager


class CustomCacheManager(FileCacheManager):
    # Adapted from: https://github.com/tdoublep/vllm/blob/3307522289fdfefe323b6c00d0db696651989a2f/vllm/triton_utils/custom_cache_manager.py
    def __init__(self, key, override=False, dump=False):

        self.key = key
        self.lock_path = None
        if dump:
            self.cache_dir = default_dump_dir()
            self.cache_dir = os.path.join(self.cache_dir, self.key)
            self.lock_path = os.path.join(self.cache_dir, "lock")
            os.makedirs(self.cache_dir, exist_ok=True)
        elif override:
            self.cache_dir = default_override_dir()
            self.cache_dir = os.path.join(self.cache_dir, self.key)
        else:
            # create cache directory if it doesn't exist
            self.cache_dir = (
                os.getenv("TRITON_CACHE_DIR", "").strip() or default_cache_dir()
            )
            if self.cache_dir:
                self.cache_dir = f"{self.cache_dir}_{os.getpid()}"
                self.cache_dir = os.path.join(self.cache_dir, self.key)
                self.lock_path = os.path.join(self.cache_dir, "lock")
                os.makedirs(self.cache_dir, exist_ok=True)
            else:
                raise RuntimeError("Could not create or locate cache dir")


API_KEY_HEADER_NAME = "X-API-Key"


class APIKeyValidatorMiddleware(BaseHTTPMiddleware):
    def __init__(self, app, api_key: str):
        super().__init__(app)
        self.api_key = api_key

    async def dispatch(self, request, call_next):
        # extract API key from the request headers
        api_key_header = request.headers.get(API_KEY_HEADER_NAME)
        if not api_key_header or api_key_header != self.api_key:
            return JSONResponse(
                status_code=403,
                content={"detail": "Invalid API Key"},
            )
        response = await call_next(request)
        return response


def get_ip_address(ifname):
    """
    Get the IP address of a network interface.

    :param ifname: Name of the network interface (e.g., 'eth0')
    :return: IP address of the network interface
    """
    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
    ip_address = fcntl.ioctl(
        s.fileno(),
        0x8915,  # SIOCGIFADDR
        struct.pack("256s", bytes(ifname[:15], "utf-8")),
    )[20:24]
    return socket.inet_ntoa(ip_address)


def send_addrs_to_rank_0(model_port_args, server_args):
    assert server_args.node_rank != 0 and server_args.dp_size == 1

    ifname = os.environ.get(
        "SGLANG_SOCKET_IFNAME", os.environ.get("NCCL_SOCKET_IFNAME", "eth0")
    )
    ip_addr = get_ip_address(ifname)

    num_tp_ports = server_args.tp_size // server_args.nnodes
    model_port_args.model_tp_ips[:num_tp_ports] = [ip_addr] * num_tp_ports
    ip_addr = [int(x) for x in ip_addr.split(".")]
    addrs_tensor = torch.tensor(
        ip_addr + model_port_args.model_tp_ports, dtype=torch.int
    )

    init_method = f"tcp://{server_args.nccl_init_addr}"
    dist.init_process_group(
        backend="gloo",
        init_method=init_method,
        rank=server_args.node_rank,
        world_size=server_args.nnodes,
    )
    dist.send(addrs_tensor, dst=0)
    print(
        f"Node {server_args.node_rank} sent: ip_address {ip_addr} and ports {model_port_args.model_tp_ports}"
    )

    dist.barrier()
    dist.destroy_process_group()


def receive_addrs(model_port_args, server_args):
    assert server_args.node_rank == 0 and server_args.dp_size == 1

    ifname = os.environ.get(
        "SGLANG_SOCKET_IFNAME", os.environ.get("NCCL_SOCKET_IFNAME", "eth0")
    )
    ip_addr = get_ip_address(ifname)

    num_tp_ports = server_args.tp_size // server_args.nnodes
    model_port_args.model_tp_ips[:num_tp_ports] = [ip_addr] * num_tp_ports

    init_method = f"tcp://{server_args.nccl_init_addr}"
    dist.init_process_group(
        backend="gloo",
        init_method=init_method,
        rank=server_args.node_rank,
        world_size=server_args.nnodes,
    )

    for src_rank in range(1, server_args.nnodes):
        tensor = torch.zeros(4 + num_tp_ports, dtype=torch.int)
        dist.recv(tensor, src=src_rank)
        ip = ".".join([str(x) for x in tensor[:4].tolist()])
        ports = tensor[4:].tolist()
        model_port_args.model_tp_ips[
            num_tp_ports * src_rank : num_tp_ports * (src_rank + 1)
        ] = [ip] * num_tp_ports
        model_port_args.model_tp_ports[
            num_tp_ports * src_rank : num_tp_ports * (src_rank + 1)
        ] = ports
        print(f"Node 0 received from rank {src_rank}: {tensor.tolist()}")

    dist.barrier()
    dist.destroy_process_group()


def set_ulimit(target_soft_limit=65535):
    resource_type = resource.RLIMIT_NOFILE
    current_soft, current_hard = resource.getrlimit(resource_type)

    if current_soft < target_soft_limit:
        try:
            resource.setrlimit(resource_type, (target_soft_limit, current_hard))
        except ValueError as e:
            logger.warn(f"Fail to set RLIMIT_NOFILE: {e}")


def is_llama3_405b_fp8(model_config):
    """Return whether the model is meta-llama/Meta-Llama-3.1-405B-FP8 with 16 kv heads."""
    if (
        model_config.hf_config.architectures[0] == "LlamaForCausalLM"
        and model_config.hf_config.hidden_size == 16384
        and model_config.hf_config.intermediate_size == 53248
        and model_config.hf_config.num_hidden_layers == 126
        and model_config.hf_config.num_key_value_heads == 16
        and hasattr(model_config.hf_config, "quantization_config")
        and model_config.hf_config.quantization_config["quant_method"] == "fbgemm_fp8"
    ):
        return True
    return False


def monkey_patch_vllm_qvk_linear_loader():
    """A temporary hack to fix the num_heads for meta-llama/Meta-Llama-3.1-405B-FP8 checkpoints."""
    from vllm.model_executor.layers.linear import QKVParallelLinear

    origin_weight_loader = QKVParallelLinear.weight_loader

    def get_original_weight(loaded_weight, head_dim):
        n_kv_head = loaded_weight.shape[0] // (2 * head_dim)
        dim = loaded_weight.shape[1]
        for i in range(n_kv_head):
            loaded_weight[i * head_dim : (i + 1) * head_dim, :] = loaded_weight[
                2 * i * head_dim : (2 * i + 1) * head_dim, :
            ]
        original_kv_weight = loaded_weight[: n_kv_head * head_dim, :]
        assert original_kv_weight.shape == (n_kv_head * head_dim, dim)
        return original_kv_weight

    def weight_loader_srt(
        self,
        param: Parameter,
        loaded_weight: torch.Tensor,
        loaded_shard_id: Optional[str] = None,
    ):
        if (
            loaded_shard_id in ["k", "v"]
            and loaded_weight.shape[0] == self.head_size * self.total_num_kv_heads * 2
        ):
            loaded_weight = get_original_weight(loaded_weight, self.head_size)

        origin_weight_loader(self, param, loaded_weight, loaded_shard_id)

    setattr(QKVParallelLinear, "weight_loader", weight_loader_srt)