# 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 ipaddress
import itertools
import json
import logging
import os
import pickle
import random
import re
import resource
import shutil
import signal
import socket
import subprocess
import tempfile
import time
import warnings
from functools import lru_cache
from importlib.metadata import PackageNotFoundError, version
from io import BytesIO
from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple, Union

import numpy as np
import psutil
import requests
import torch
import torch.distributed
import torch.distributed as dist
import triton
import zmq
from fastapi.responses import ORJSONResponse
from packaging import version as pkg_version
from starlette.routing import Mount
from torch import nn
from torch.func import functional_call
from torch.library import Library
from torch.profiler import ProfilerActivity, profile, record_function
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 is_hip() -> bool:
    """Return whether it is HIP on the AMD ROCm platform."""
    return torch.version.hip is not None


def is_cuda():
    return hasattr(torch, "cuda") and torch.cuda.is_available()


def is_cuda_alike():
    return is_cuda() or is_hip()


def is_hpu() -> bool:
    return hasattr(torch, "hpu") and torch.hpu.is_available()


def is_xpu() -> bool:
    return hasattr(torch, "xpu") and torch.xpu.is_available()


def is_flashinfer_available():
    """
    Check whether flashinfer is available.
    As of Oct. 6, 2024, it is only available on NVIDIA GPUs.
    """
    if not get_bool_env_var("SGLANG_IS_FLASHINFER_AVAILABLE", default="true"):
        return False
    return torch.cuda.is_available() and torch.version.cuda


def is_ipv6(address):
    try:
        ipaddress.IPv6Address(address)
        return True
    except ipaddress.AddressValueError:
        return False


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(device, gpu_id, distributed=False, empty_cache=True):
    """
    Get available memory for cuda:gpu_id device.
    When distributed is True, the available memory is the minimum available memory of all GPUs.
    """
    if device == "cuda":
        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.",
            )

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

    elif device == "xpu":
        num_gpus = torch.xpu.device_count()
        assert gpu_id < num_gpus

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

        if empty_cache:
            torch.xpu.empty_cache()
        used_memory = torch.xpu.memory_allocated()
        total_gpu_memory = torch.xpu.get_device_properties(gpu_id).total_memory
        free_gpu_memory = total_gpu_memory - used_memory

    elif device == "hpu":
        num_gpus = torch.hpu.device_count()
        assert gpu_id < num_gpus

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

        free_gpu_memory, total_gpu_memory = torch.hpu.mem_get_info()

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

    return free_gpu_memory / (1 << 30)


def is_pin_memory_available() -> bool:
    return torch.cuda.is_available()


_CPU_OFFLOAD_BYTES = 0
_CPU_OFFLOAD_MAX_BYTES = 0


def set_cpu_offload_max_bytes(max_bytes: int) -> None:
    global _CPU_OFFLOAD_MAX_BYTES, _CPU_OFFLOAD_BYTES
    _CPU_OFFLOAD_BYTES = 0
    _CPU_OFFLOAD_MAX_BYTES = max_bytes


def maybe_offload_to_cpu(module: torch.nn.Module) -> torch.nn.Module:
    device = next(module.parameters()).device

    if device == torch.device("cpu"):
        return module

    global _CPU_OFFLOAD_MAX_BYTES, _CPU_OFFLOAD_BYTES
    if _CPU_OFFLOAD_BYTES >= _CPU_OFFLOAD_MAX_BYTES:
        return module

    pin_memory = is_pin_memory_available()
    # offload parameters to CPU
    # use pin_memory if possible, which helps cudagraph capture speed
    offloaded_parameters = False
    for p in module.parameters():
        if _CPU_OFFLOAD_BYTES >= _CPU_OFFLOAD_MAX_BYTES:
            # we use per-parameter offloading
            # one module might have some parameters offloaded and some not
            break

        # `torch.empty_like` does not support `pin_memory` argument
        cpu_data = torch.empty_strided(
            size=p.data.size(),
            stride=p.data.stride(),
            dtype=p.data.dtype,
            layout=p.data.layout,
            device="cpu",
            pin_memory=pin_memory,
        )
        cpu_data.copy_(p.data)
        p.data = cpu_data
        _CPU_OFFLOAD_BYTES += p.data.numel() * p.data.element_size()
        offloaded_parameters = True

    if offloaded_parameters:
        original_forward = module.forward

        def forward(*args, **kwargs):
            module.forward = original_forward
            device_state = {
                # here we blindly call `to(device)`
                # if the parameter is already on the device, it will be a no-op
                k: v.to(device, non_blocking=True)
                for k, v in module.state_dict().items()
            }
            output = functional_call(module, device_state, args=args, kwargs=kwargs)
            module.forward = forward
            return output

        module.forward = forward

    return module


class LayerFn(Protocol):

    def __call__(self, layer_id: int, prefix: str) -> torch.nn.Module: ...


def make_layers(
    num_hidden_layers: int,
    layer_fn: LayerFn,
    prefix: str = "",
) -> Tuple[int, int, torch.nn.ModuleList]:
    """Make a list of layers with the given layer function"""
    modules = torch.nn.ModuleList(
        [
            maybe_offload_to_cpu(layer_fn(idx=idx, prefix=f"{prefix}.{idx}"))
            for idx in range(num_hidden_layers)
        ]
    )
    return modules


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 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: Union[str, bytes]):
    from PIL import Image

    image = image_size = None

    if isinstance(image_file, bytes):
        image = Image.open(BytesIO(image_file))
    elif 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)
    elif isinstance(image_file, str):
        image = Image.open(BytesIO(base64.b64decode(image_file)))
    else:
        raise ValueError(f"Invalid image: {image}")

    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.distributed.device_communicators.pynccl").setLevel(
        logging.WARN
    )
    logging.getLogger("vllm.distributed.device_communicators.shm_broadcast").setLevel(
        logging.WARN
    )

    warnings.filterwarnings(
        "ignore", category=UserWarning, message="The given NumPy array is not writable"
    )


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_process_tree(parent_pid, include_parent: bool = True, skip_pid: int = None):
    """Kill the process and all its child processes."""
    if parent_pid is None:
        parent_pid = os.getpid()
        include_parent = False

    try:
        itself = psutil.Process(parent_pid)
    except psutil.NoSuchProcess:
        return

    children = itself.children(recursive=True)
    for child in children:
        if child.pid == skip_pid:
            continue
        try:
            child.kill()
        except psutil.NoSuchProcess:
            pass

    if include_parent:
        try:
            itself.kill()

            # Sometime processes cannot be killed with SIGKILL (e.g, PID=1 launched by kubernetes),
            # so we send an additional signal to kill them.
            itself.send_signal(signal.SIGQUIT)
        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)

    # Suppress the warnings from this delete function when using sglang.bench_one_batch
    from vllm.distributed.device_communicators.custom_all_reduce import CustomAllreduce

    setattr(CustomAllreduce, "__del__", lambda *args, **kwargs: None)


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 monkey_patch_vllm_gguf_config():
    from vllm.model_executor.layers.linear import LinearBase
    from vllm.model_executor.layers.quantization.gguf import (
        GGUFConfig,
        GGUFEmbeddingMethod,
        GGUFLinearMethod,
    )

    from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding

    def get_quant_method_with_embedding_replaced(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:
        if isinstance(layer, LinearBase):
            return GGUFLinearMethod(self)
        elif isinstance(layer, VocabParallelEmbedding):
            # patch to own VocabParallelEmbedding
            return GGUFEmbeddingMethod(self)
        return None

    setattr(GGUFConfig, "get_quant_method", get_quant_method_with_embedding_replaced)


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")


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.warning(f"Fail to set RLIMIT_NOFILE: {e}")


def add_api_key_middleware(app, api_key: str):
    @app.middleware("http")
    async def authentication(request, call_next):
        if request.method == "OPTIONS":
            return await call_next(request)
        if request.url.path.startswith("/health"):
            return await call_next(request)
        if request.headers.get("Authorization") != "Bearer " + api_key:
            return ORJSONResponse(content={"error": "Unauthorized"}, status_code=401)
        return await call_next(request)


def prepare_model_and_tokenizer(model_path: str, tokenizer_path: str):
    if get_bool_env_var("SGLANG_USE_MODELSCOPE"):
        if not os.path.exists(model_path):
            from modelscope import snapshot_download

            model_path = snapshot_download(model_path)
            tokenizer_path = snapshot_download(
                tokenizer_path, ignore_patterns=["*.bin", "*.safetensors"]
            )
    return model_path, tokenizer_path


def configure_logger(server_args, prefix: str = ""):
    format = f"[%(asctime)s{prefix}] %(message)s"
    # format = f"[%(asctime)s.%(msecs)03d{prefix}] %(message)s"
    logging.basicConfig(
        level=getattr(logging, server_args.log_level.upper()),
        format=format,
        datefmt="%Y-%m-%d %H:%M:%S",
        force=True,
    )


# source: https://github.com/vllm-project/vllm/blob/93b38bea5dd03e1b140ca997dfaadef86f8f1855/vllm/lora/utils.py#L9
def replace_submodule(
    model: nn.Module, module_name: str, new_module: nn.Module
) -> nn.Module:
    """Replace a submodule in a model with a new module."""
    parent = model.get_submodule(".".join(module_name.split(".")[:-1]))
    target_name = module_name.split(".")[-1]
    setattr(parent, target_name, new_module)
    return new_module


def set_weight_attrs(
    weight: torch.Tensor,
    weight_attrs: Optional[Dict[str, Any]],
):
    """Set attributes on a weight tensor.

    This method is used to set attributes on a weight tensor. This method
    will not overwrite existing attributes.

    Args:
        weight: The weight tensor.
        weight_attrs: A dictionary of attributes to set on the weight tensor.
    """
    if weight_attrs is None:
        return
    for key, value in weight_attrs.items():
        assert not hasattr(weight, key), f"Overwriting existing tensor attribute: {key}"
        setattr(weight, key, value)


def broadcast_pyobj(
    data: List[Any],
    rank: int,
    dist_group: Optional[torch.distributed.ProcessGroup] = None,
):
    """Broadcast inputs from rank=0 to all other ranks with torch.dist backend."""

    if rank == 0:
        if len(data) == 0:
            tensor_size = torch.tensor([0], dtype=torch.long)
            dist.broadcast(tensor_size, src=0, group=dist_group)
        else:
            serialized_data = pickle.dumps(data)
            size = len(serialized_data)
            tensor_data = torch.ByteTensor(
                np.frombuffer(serialized_data, dtype=np.uint8)
            )
            tensor_size = torch.tensor([size], dtype=torch.long)

            dist.broadcast(tensor_size, src=0, group=dist_group)
            dist.broadcast(tensor_data, src=0, group=dist_group)
        return data
    else:
        tensor_size = torch.tensor([0], dtype=torch.long)
        dist.broadcast(tensor_size, src=0, group=dist_group)
        size = tensor_size.item()

        if size == 0:
            return []

        tensor_data = torch.empty(size, dtype=torch.uint8)
        dist.broadcast(tensor_data, src=0, group=dist_group)

        serialized_data = bytes(tensor_data.cpu().numpy())
        data = pickle.loads(serialized_data)
        return data


step_counter = 0


def pytorch_profile(name, func, *args, data_size=-1):
    """
    Args:
        name (string): the name of recorded function.
        func: the function to be profiled.
        args: the arguments of the profiled function.
        data_size (int): some measurement of the computation complexity.
            Usually, it could be the batch size.
    """
    global step_counter
    os.makedirs("trace", exist_ok=True)
    with profile(
        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
        # schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
        # on_trace_ready=tensorboard_trace_handler('./log_dir'),
        record_shapes=True,
        profile_memory=True,
        with_stack=True,
    ) as prof:
        with record_function(name):
            with open(f"trace/size_{step_counter}.json", "w") as f:
                json.dump({"size": data_size}, f)
            result = func(*args)
    prof.export_chrome_trace(f"trace/{name}_{step_counter}.json")
    step_counter += 1
    return result


def first_rank_print(*args, **kwargs):
    if torch.cuda.current_device() == 0:
        print(*args, **kwargs)
    else:
        pass


def get_zmq_socket(context: zmq.Context, socket_type: zmq.SocketType, endpoint: str):
    mem = psutil.virtual_memory()
    total_mem = mem.total / 1024**3
    available_mem = mem.available / 1024**3
    if total_mem > 32 and available_mem > 16:
        buf_size = int(0.5 * 1024**3)
    else:
        buf_size = -1

    socket = context.socket(socket_type)
    if socket_type == zmq.PUSH:
        socket.setsockopt(zmq.SNDHWM, 0)
        socket.setsockopt(zmq.SNDBUF, buf_size)
        socket.connect(f"ipc://{endpoint}")
    elif socket_type == zmq.PULL:
        socket.setsockopt(zmq.RCVHWM, 0)
        socket.setsockopt(zmq.RCVBUF, buf_size)
        socket.bind(f"ipc://{endpoint}")
    else:
        raise ValueError(f"Unsupported socket type: {socket_type}")

    return socket


def dump_to_file(dirpath, name, value):
    from vllm.distributed import get_tensor_model_parallel_rank

    if get_tensor_model_parallel_rank() != 0:
        return

    os.makedirs(dirpath, exist_ok=True)
    if value.dtype is torch.bfloat16:
        value = value.float()
    value = value.cpu().numpy()
    output_filename = os.path.join(dirpath, f"pytorch_dump_{name}.npy")
    logger.info(f"Dump a tensor to {output_filename}. Shape = {value.shape}")
    np.save(output_filename, value)


def is_triton_3():
    return triton.__version__.startswith("3.")


def maybe_torch_compile(*args, **kwargs):
    """
    torch.compile does not work for triton 2.2.0, which is needed in xlm1's jax.
    Therefore, we disable it here.
    """

    def decorator(func):
        if is_triton_3():
            return torch.compile(*args, **kwargs)(func)
        return func

    return decorator


def delete_directory(dirpath):
    try:
        # This will remove the directory and all its contents
        shutil.rmtree(dirpath)
    except OSError as e:
        print(f"Warning: {dirpath} : {e.strerror}")


# Temporary directory for prometheus multiprocess mode
# Cleaned up automatically when this object is garbage collected
prometheus_multiproc_dir: tempfile.TemporaryDirectory


def set_prometheus_multiproc_dir():
    # Set prometheus multiprocess directory
    # sglang uses prometheus multiprocess mode
    # we need to set this before importing prometheus_client
    # https://prometheus.github.io/client_python/multiprocess/
    global prometheus_multiproc_dir

    if "PROMETHEUS_MULTIPROC_DIR" in os.environ:
        logger.debug("User set PROMETHEUS_MULTIPROC_DIR detected.")
        prometheus_multiproc_dir = tempfile.TemporaryDirectory(
            dir=os.environ["PROMETHEUS_MULTIPROC_DIR"]
        )
    else:
        prometheus_multiproc_dir = tempfile.TemporaryDirectory()
        os.environ["PROMETHEUS_MULTIPROC_DIR"] = prometheus_multiproc_dir.name
    logger.debug(f"PROMETHEUS_MULTIPROC_DIR: {os.environ['PROMETHEUS_MULTIPROC_DIR']}")


def add_prometheus_middleware(app):
    # We need to import prometheus_client after setting the env variable `PROMETHEUS_MULTIPROC_DIR`
    from prometheus_client import CollectorRegistry, make_asgi_app, multiprocess

    registry = CollectorRegistry()
    multiprocess.MultiProcessCollector(registry)
    metrics_route = Mount("/metrics", make_asgi_app(registry=registry))

    # Workaround for 307 Redirect for /metrics
    metrics_route.path_regex = re.compile("^/metrics(?P<path>.*)$")
    app.routes.append(metrics_route)


def bind_port(port):
    """Bind to a specific port, assuming it's available."""
    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)  # Allows address reuse
    sock.bind(("", port))
    sock.listen(1)
    return sock


def get_amdgpu_memory_capacity():
    try:
        # Run rocm-smi and capture the output
        result = subprocess.run(
            [
                "rocminfo | grep 'gfx' -A 100 | grep 'Pool 1' -A 5 | grep 'Size:' | awk '{print $2}'"
            ],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            shell=True,
            text=True,
        )
        if result.returncode != 0:
            raise RuntimeError(f"rocm-smi error: {result.stderr.strip()}")

        # Parse the output to extract memory values in MiB
        memory_values = [
            float(mem.split("(")[0].strip()) / 1024
            for mem in result.stdout.strip().split("\n")
        ]

        if not memory_values:
            raise ValueError("No GPU memory values found.")

        # Return the minimum memory value
        return min(memory_values)

    except FileNotFoundError:
        raise RuntimeError(
            "rocm-smi not found. Ensure AMD ROCm drivers are installed and accessible."
        )


def get_nvgpu_memory_capacity():
    try:
        # Run nvidia-smi and capture the output
        result = subprocess.run(
            ["nvidia-smi", "--query-gpu=memory.total", "--format=csv,noheader,nounits"],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            text=True,
        )

        if result.returncode != 0:
            raise RuntimeError(f"nvidia-smi error: {result.stderr.strip()}")

        # Parse the output to extract memory values
        memory_values = [
            float(mem)
            for mem in result.stdout.strip().split("\n")
            if re.match(r"^\d+(\.\d+)?$", mem.strip())
        ]

        if not memory_values:
            raise ValueError("No GPU memory values found.")

        # Return the minimum memory value
        return min(memory_values)

    except FileNotFoundError:
        raise RuntimeError(
            "nvidia-smi not found. Ensure NVIDIA drivers are installed and accessible."
        )


def get_hpu_memory_capacity():
    try:
        # Run hl-smi and capture the output
        result = subprocess.run(
            ["hl-smi --query | grep 'Total'"],
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            shell=True,
            text=True,
        )

        if result.returncode != 0:
            raise RuntimeError(f"hl-smi error: {result.stderr.strip()}")

        # Parse the output to extract memory values in MiB
        memory_values = [
            float(mem.split(" ")[-2]) for mem in result.stdout.strip().split("\n")
        ]

        if not memory_values:
            raise ValueError("No GPU memory values found.")

        # Return the minimum memory value
        return min(memory_values)

    except FileNotFoundError:
        raise RuntimeError(
            "hl-smi not found. Ensure Habana drivers are installed and accessible."
        )


# Copy from pytorch and OpenRLHF to allow creating multiple main groups.
# https://github.com/pytorch/pytorch/blob/main/torch/distributed/distributed_c10d.py
# https://github.com/OpenRLHF/OpenRLHF/blob/main/openrlhf/utils/distributed_util.py
def init_custom_process_group(
    backend=None,
    init_method=None,
    timeout=None,
    world_size=-1,
    rank=-1,
    store=None,
    group_name=None,
    pg_options=None,
):
    from torch.distributed.distributed_c10d import (
        Backend,
        PrefixStore,
        _new_process_group_helper,
        _world,
        default_pg_timeout,
        rendezvous,
    )

    assert (store is None) or (
        init_method is None
    ), "Cannot specify both init_method and store."

    if store is not None:
        assert world_size > 0, "world_size must be positive if using store"
        assert rank >= 0, "rank must be non-negative if using store"
    elif init_method is None:
        init_method = "env://"

    if backend:
        backend = Backend(backend)
    else:
        backend = Backend("undefined")

    if timeout is None:
        timeout = default_pg_timeout

    # backward compatible API
    if store is None:
        rendezvous_iterator = rendezvous(init_method, rank, world_size, timeout=timeout)
        store, rank, world_size = next(rendezvous_iterator)
        store.set_timeout(timeout)

        # Use a PrefixStore to avoid accidental overrides of keys used by
        # different systems (e.g. RPC) in case the store is multi-tenant.
        store = PrefixStore(group_name, store)

    # NOTE: The pg_options parameter was renamed into backend_options in PyTorch 2.6.0
    # https://github.com/pytorch/pytorch/commit/a0c7029a75628cd5fa8df83c0de0ea98ee7fd844
    # We need to determine the appropriate parameter name based on PyTorch version
    pg_options_param_name = (
        "backend_options" if str(torch.__version__) >= "2.6" else "pg_options"
    )
    pg, _ = _new_process_group_helper(
        world_size,
        rank,
        [],
        backend,
        store,
        group_name=group_name,
        **{pg_options_param_name: pg_options},
        timeout=timeout,
    )

    _world.pg_group_ranks[pg] = {i: i for i in range(world_size)}

    return pg


def crash_on_warnings():
    # Crash on warning if we are running CI tests
    return get_bool_env_var("SGLANG_IS_IN_CI")


def print_warning_once(msg: str) -> None:
    # Set the stacklevel to 2 to print the caller's line info
    logger.warning(msg, stacklevel=2)


def get_device_name(device_id: int = 0) -> str:
    if hasattr(torch, "cuda") and torch.cuda.is_available():
        return torch.cuda.get_device_name(device_id)

    if hasattr(torch, "xpu") and torch.xpu.is_available():
        return torch.xpu.get_device_name(device_id)

    if hasattr(torch, "hpu") and torch.hpu.is_available():
        return torch.hpu.get_device_name(device_id)


def get_device_capability(device_id: int = 0) -> Tuple[int, int]:
    major, minor = None, None
    if hasattr(torch, "cuda") and torch.cuda.is_available():
        major, minor = torch.cuda.get_device_capability(device_id)

    if hasattr(torch, "xpu") and torch.xpu.is_available():
        major, minor, *_ = torch.xpu.get_device_capability(device_id)["version"].split(
            "."
        )
        major, minor = int(major), int(minor)

    # TODO(HandH1998): `get_device_capability` is not supported by `torch.hpu` for now.
    # Update this once the support is available.
    if hasattr(torch, "hpu") and torch.hpu.is_available():
        try:
            major, minor = torch.hpu.get_device_capability(device_id)
        except Exception as e:
            raise RuntimeError(
                f"An error occurred while getting device capability of hpu: {e}."
            ) from e

    return major, minor


def get_compiler_backend() -> str:
    if hasattr(torch, "hpu") and torch.hpu.is_available():
        return "hpu_backend"

    return "inductor"


sglang_lib = Library("sglang", "FRAGMENT")  # noqa


# Some backends use pytorch version < 2.4.0 which doesn't
# support `torch.library.custom_op`.
def supports_custom_op() -> bool:
    return hasattr(torch.library, "custom_op")


def direct_register_custom_op(
    op_name: str,
    op_func: Callable,
    mutates_args: List[str],
    fake_impl: Optional[Callable] = None,
    target_lib: Optional[Library] = None,
):
    """
    `torch.library.custom_op` can have significant overhead because it
    needs to consider complicated dispatching logic. This function
    directly registers a custom op and dispatches it to the CUDA backend.
    See https://gist.github.com/youkaichao/ecbea9ec9fc79a45d2adce1784d7a9a5
    for more details.

    By default, the custom op is registered to the vLLM library. If you
    want to register it to a different library, you can pass the library
    object to the `target_lib` argument.

    IMPORTANT: the lifetime of the operator is tied to the lifetime of the
    library object. If you want to bind the operator to a different library,
    make sure the library object is alive when the operator is used.
    """
    import torch.library

    if hasattr(torch.library, "infer_schema"):
        schema_str = torch.library.infer_schema(op_func, mutates_args=mutates_args)
    else:
        # for pytorch 2.4
        import torch._custom_op.impl

        schema_str = torch._custom_op.impl.infer_schema(op_func, mutates_args)

    my_lib = target_lib or sglang_lib
    my_lib.define(op_name + schema_str)
    my_lib.impl(op_name, op_func, "CUDA")
    if fake_impl is not None:
        my_lib._register_fake(op_name, fake_impl)


def set_gpu_proc_affinity(
    tp_size: int,
    nnodes: int,
    gpu_id: int,
):
    # current process
    pid = os.getpid()
    p = psutil.Process(pid)

    tp_size_per_node = tp_size // nnodes

    # total physical cores
    total_pcores = psutil.cpu_count(logical=False)
    # physical cores per TP (N.B. more Cores than GPUs on node)
    num_cores_bind = total_pcores // tp_size_per_node

    # able to handle multiple DP per node
    start_cpu_id = (gpu_id * num_cores_bind) % total_pcores
    end_cpu_id = start_cpu_id + num_cores_bind

    if psutil.cpu_count() != psutil.cpu_count(logical=False):
        # HT on
        upper_cpu_ids = [id for id in range(start_cpu_id, end_cpu_id)]
        lower_cpu_ids = [id + total_pcores for id in range(start_cpu_id, end_cpu_id)]
        bind_cpu_ids = list(itertools.chain(upper_cpu_ids, lower_cpu_ids))
    else:
        # HT off
        bind_cpu_ids = [id for id in range(start_cpu_id, end_cpu_id)]

    # set cpu_affinity to current process
    p.cpu_affinity(bind_cpu_ids)
    logger.info(f"Process {pid} gpu_id {gpu_id} is running on CPUs: {p.cpu_affinity()}")


def get_bool_env_var(name: str, default: str = "false") -> bool:
    value = os.getenv(name, default)
    return value.lower() in ("true", "1")


@lru_cache(maxsize=8)
def _cuda_device_count_stateless(cuda_visible_devices: Optional[str] = None) -> int:
    # Note: cuda_visible_devices is not used, but we keep it as an argument for
    # LRU Cache purposes.

    # Code below is based on
    # https://github.com/pytorch/pytorch/blob/
    # c1cd946818442aca8c7f812b16d187ce1586c3bc/
    # torch/cuda/__init__.py#L831C1-L831C17
    import torch.cuda
    import torch.version

    if not torch.cuda._is_compiled():
        return 0
    if is_hip():
        # ROCm uses amdsmi instead of nvml for stateless device count
        # This requires a sufficiently modern version of Torch 2.4.0
        raw_count = (
            torch.cuda._device_count_amdsmi()
            if (hasattr(torch.cuda, "_device_count_amdsmi"))
            else -1
        )
    else:
        raw_count = torch.cuda._device_count_nvml()
    r = torch._C._cuda_getDeviceCount() if raw_count < 0 else raw_count
    return r


# Adapted from https://github.com/vllm-project/vllm/blob/a6221a144af772fd1a68fe7e627935dc53e81738/vllm/utils.py
def cuda_device_count_stateless() -> int:
    """Get number of CUDA devices, caching based on the value of
    CUDA_VISIBLE_DEVICES at the time of call.

    This should be used instead of torch.cuda.device_count()
    unless CUDA_VISIBLE_DEVICES has already been set to the desired
    value."""

    # This can be removed and simply replaced with torch.cuda.get_device_count
    # after https://github.com/pytorch/pytorch/pull/122815 is released.
    return _cuda_device_count_stateless(os.environ.get("CUDA_VISIBLE_DEVICES", None))


def should_use_tensor_core(
    kv_cache_dtype: torch.dtype,
    num_attention_heads: int,
    num_kv_heads: int,
) -> bool:
    """
    Determine whether to use tensor cores for attention computation.

    Args:
        kv_cache_dtype: Data type of the KV cache
        num_attention_heads: Number of attention heads
        num_kv_heads: Number of key/value heads

    Returns:
        bool: Whether to use tensor cores
    """
    # Try to use environment variable first
    env_override = os.environ.get("SGLANG_FLASHINFER_USE_TENSOR_CORE")
    if env_override is not None:
        return env_override.lower() == "true"

    # Try to use _grouped_size_compiled_for_decode_kernels if available
    # This is for flashinfer <=0.1.6. Otherwise, there is an accuracy bug
    try:
        from flashinfer.decode import _grouped_size_compiled_for_decode_kernels

        if not _grouped_size_compiled_for_decode_kernels(
            num_attention_heads,
            num_kv_heads,
        ):
            return True
        else:
            return False
    except (ImportError, AttributeError):
        pass

    # Calculate GQA group size
    gqa_group_size = num_attention_heads // num_kv_heads

    # Determine based on dtype and GQA group size
    if kv_cache_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
        return True
    elif kv_cache_dtype in (torch.float16, torch.half, torch.bfloat16):
        return gqa_group_size > 4
    else:
        return False
