utils.py

import argparse
import asyncio
import concurrent
import contextlib
import datetime
import enum
import gc
import getpass
import importlib.util
import inspect
import ipaddress
import os
import signal
import socket
import subprocess
import sys
import tempfile
import threading
import time
import uuid
import warnings
import weakref
from asyncio import FIRST_COMPLETED, AbstractEventLoop, Future, Task
from collections import UserDict, defaultdict
from collections.abc import Iterable, Mapping
from dataclasses import dataclass, field
from functools import lru_cache, partial, wraps
from typing import (TYPE_CHECKING, Any, AsyncGenerator, Awaitable, Callable,
                    Dict, Generator, Generic, Hashable, List, Literal,
                    Optional, OrderedDict, Set, Tuple, Type, TypeVar, Union,
                    overload)
from uuid import uuid4

import numpy as np
import numpy.typing as npt
import psutil
import torch
import torch.types
import yaml
from packaging.version import Version
from torch.library import Library
from typing_extensions import ParamSpec, TypeIs, assert_never

import vllm.envs as envs
from vllm.logger import enable_trace_function_call, init_logger
from vllm.platforms import current_platform

if TYPE_CHECKING:
    from vllm.config import VllmConfig

logger = init_logger(__name__)

# Exception strings for non-implemented encoder/decoder scenarios

# Reminder: Please update docs/source/usage/compatibility_matrix.rst
# If the feature combo become valid

STR_NOT_IMPL_ENC_DEC_SWA = \
    "Sliding window attention for encoder/decoder models " + \
                    "is not currently supported."

STR_NOT_IMPL_ENC_DEC_PREFIX_CACHE = \
    "Prefix caching for encoder/decoder models " + \
                    "is not currently supported."

STR_NOT_IMPL_ENC_DEC_CHUNKED_PREFILL = \
    "Chunked prefill for encoder/decoder models " + \
                    "is not currently supported."

STR_NOT_IMPL_ENC_DEC_LOGIT_SOFTCAP = (
    "Models with logits_soft_cap "
    "require FlashInfer backend, which is "
    "currently not supported for encoder/decoder "
    "models.")

STR_NOT_IMPL_ENC_DEC_LORA = ("LoRA is currently not currently "
                             "supported with encoder/decoder "
                             "models.")

STR_NOT_IMPL_ENC_DEC_PP = ("Pipeline parallelism is not "
                           "currently supported with "
                           "encoder/decoder models.")

STR_NOT_IMPL_ENC_DEC_MM = ("Multimodal is not currently "
                           "supported with encoder/decoder "
                           "models.")

STR_NOT_IMPL_ENC_DEC_SPEC_DEC = ("Speculative decoding is not "
                                 "currently supported with encoder/"
                                 "decoder models.")

STR_NOT_IMPL_ENC_DEC_BACKEND = ("XFormers and Flash-Attention are the only "
                                "backends currently supported with encoder/"
                                "decoder models.")

STR_NOT_IMPL_ENC_DEC_PROMPT_ADAPTER = ("Prompt adapters are not "
                                       "currently supported with encoder/"
                                       "decoder models.")

# Efficiently import all enc/dec error strings
# rather than having to import all of the above
STR_NOT_IMPL_ENC_DEC_ERR_STRS = {
    "STR_NOT_IMPL_ENC_DEC_SWA": STR_NOT_IMPL_ENC_DEC_SWA,
    "STR_NOT_IMPL_ENC_DEC_PREFIX_CACHE": STR_NOT_IMPL_ENC_DEC_PREFIX_CACHE,
    "STR_NOT_IMPL_ENC_DEC_CHUNKED_PREFILL":
    STR_NOT_IMPL_ENC_DEC_CHUNKED_PREFILL,
    "STR_NOT_IMPL_ENC_DEC_LOGIT_SOFTCAP": STR_NOT_IMPL_ENC_DEC_LOGIT_SOFTCAP,
    "STR_NOT_IMPL_ENC_DEC_LORA": STR_NOT_IMPL_ENC_DEC_LORA,
    "STR_NOT_IMPL_ENC_DEC_PP": STR_NOT_IMPL_ENC_DEC_PP,
    "STR_NOT_IMPL_ENC_DEC_MM": STR_NOT_IMPL_ENC_DEC_MM,
    "STR_NOT_IMPL_ENC_DEC_SPEC_DEC": STR_NOT_IMPL_ENC_DEC_SPEC_DEC,
    "STR_NOT_IMPL_ENC_DEC_BACKEND": STR_NOT_IMPL_ENC_DEC_BACKEND,
    "STR_NOT_IMPL_ENC_DEC_PROMPT_ADAPTER": STR_NOT_IMPL_ENC_DEC_PROMPT_ADAPTER,
}

# Constants related to forcing the attention backend selection

# String name of register which may be set in order to
# force auto-selection of attention backend by Attention
# wrapper
STR_BACKEND_ENV_VAR: str = "VLLM_ATTENTION_BACKEND"

# Possible string values of STR_BACKEND_ENV_VAR
# register, corresponding to possible backends
STR_FLASHINFER_ATTN_VAL: str = "FLASHINFER"
STR_TORCH_SDPA_ATTN_VAL: str = "TORCH_SDPA"
STR_ROCM_FLASH_ATTN_VAL: str = "ROCM_FLASH"
STR_XFORMERS_ATTN_VAL: str = "XFORMERS"
STR_FLASH_ATTN_VAL: str = "FLASH_ATTN"
STR_INVALID_VAL: str = "INVALID"

GB_bytes = 1_000_000_000
"""The number of bytes in one gigabyte (GB)."""

GiB_bytes = 1 << 30
"""The number of bytes in one gibibyte (GiB)."""

STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.half,
    "bfloat16": torch.bfloat16,
    "float": torch.float,
    "fp8": torch.uint8,
    "fp8_e4m3": torch.uint8,
    "fp8_e5m2": torch.uint8,
}

TORCH_DTYPE_TO_NUMPY_DTYPE = {
    torch.float16: np.float16,
    torch.float32: np.float32,
    torch.float64: np.float64,
    torch.uint8: np.uint8,
    torch.int32: np.int32,
    torch.int64: np.int64,
}

P = ParamSpec('P')
K = TypeVar("K")
T = TypeVar("T")
U = TypeVar("U")


class _Sentinel:
    ...


ALL_PINNED_SENTINEL = _Sentinel()


class Device(enum.Enum):
    GPU = enum.auto()
    CPU = enum.auto()


class LayerBlockType(enum.Enum):
    attention = "attention"
    mamba = "mamba"


class Counter:

    def __init__(self, start: int = 0) -> None:
        self.counter = start

    def __next__(self) -> int:
        i = self.counter
        self.counter += 1
        return i

    def reset(self) -> None:
        self.counter = 0


class LRUCache(Generic[T]):

    def __init__(self, capacity: int):
        self.cache: OrderedDict[Hashable, T] = OrderedDict()
        self.pinned_items: Set[Hashable] = set()
        self.capacity = capacity

    def __contains__(self, key: Hashable) -> bool:
        return key in self.cache

    def __len__(self) -> int:
        return len(self.cache)

    def __getitem__(self, key: Hashable) -> T:
        value = self.cache[key]  # Raise KeyError if not exists
        self.cache.move_to_end(key)
        return value

    def __setitem__(self, key: Hashable, value: T) -> None:
        self.put(key, value)

    def __delitem__(self, key: Hashable) -> None:
        self.pop(key)

    def touch(self, key: Hashable) -> None:
        self.cache.move_to_end(key)

    def get(self,
            key: Hashable,
            default_value: Optional[T] = None) -> Optional[T]:
        value: Optional[T]
        if key in self.cache:
            value = self.cache[key]
            self.cache.move_to_end(key)
        else:
            value = default_value
        return value

    def put(self, key: Hashable, value: T) -> None:
        self.cache[key] = value
        self.cache.move_to_end(key)
        self._remove_old_if_needed()

    def pin(self, key: Hashable) -> None:
        """
        Pins a key in the cache preventing it from being
        evicted in the LRU order.
        """
        if key not in self.cache:
            raise ValueError(f"Cannot pin key: {key} not in cache.")
        self.pinned_items.add(key)

    def _unpin(self, key: Hashable) -> None:
        self.pinned_items.remove(key)

    def _on_remove(self, key: Hashable, value: Optional[T]):
        pass

    def remove_oldest(self, remove_pinned=False):
        if not self.cache:
            return

        if not remove_pinned:
            # pop the oldest item in the cache that is not pinned
            lru_key = next(
                (key for key in self.cache if key not in self.pinned_items),
                ALL_PINNED_SENTINEL)
            if lru_key is ALL_PINNED_SENTINEL:
                raise RuntimeError("All items are pinned, "
                                   "cannot remove oldest from the cache.")
        else:
            lru_key = next(iter(self.cache))
        self.pop(lru_key)

    def _remove_old_if_needed(self) -> None:
        while len(self.cache) > self.capacity:
            self.remove_oldest()

    def pop(self,
            key: Hashable,
            default_value: Optional[T] = None) -> Optional[T]:
        run_on_remove = key in self.cache
        value: Optional[T] = self.cache.pop(key, default_value)
        # remove from pinned items
        if key in self.pinned_items:
            self._unpin(key)
        if run_on_remove:
            self._on_remove(key, value)
        return value

    def clear(self):
        while len(self.cache) > 0:
            self.remove_oldest(remove_pinned=True)
        self.cache.clear()


class PyObjectCache:
    """Used to cache python objects to avoid object allocations
    across scheduler iterations.
    """

    def __init__(self, obj_builder):
        self._obj_builder = obj_builder
        self._index = 0

        self._obj_cache = []
        for _ in range(128):
            self._obj_cache.append(self._obj_builder())

    def _grow_cache(self):
        # Double the size of the cache
        num_objs = len(self._obj_cache)
        for _ in range(num_objs):
            self._obj_cache.append(self._obj_builder())

    def get_object(self):
        """Returns a pre-allocated cached object. If there is not enough
        objects, then the cache size will double.
        """
        if self._index >= len(self._obj_cache):
            self._grow_cache()
            assert self._index < len(self._obj_cache)

        obj = self._obj_cache[self._index]
        self._index += 1

        return obj

    def reset(self):
        """Makes all cached-objects available for the next scheduler iteration.
        """
        self._index = 0


@lru_cache(maxsize=None)
def get_max_shared_memory_bytes(gpu: int = 0) -> int:
    """Returns the maximum shared memory per thread block in bytes."""
    from vllm import _custom_ops as ops
    max_shared_mem = (
        ops.get_max_shared_memory_per_block_device_attribute(gpu))
    # value 0 will cause MAX_SEQ_LEN become negative and test_attention.py
    # will fail
    assert max_shared_mem > 0, "max_shared_mem can not be zero"
    return int(max_shared_mem)


def get_cpu_memory() -> int:
    """Returns the total CPU memory of the node in bytes."""
    return psutil.virtual_memory().total


def random_uuid() -> str:
    return str(uuid.uuid4().hex)


def make_async(
    func: Callable[P, T],
    executor: Optional[concurrent.futures.Executor] = None
) -> Callable[P, Awaitable[T]]:
    """Take a blocking function, and run it on in an executor thread.

    This function prevents the blocking function from blocking the
    asyncio event loop.
    The code in this function needs to be thread safe.
    """

    def _async_wrapper(*args: P.args, **kwargs: P.kwargs) -> asyncio.Future:
        loop = asyncio.get_event_loop()
        p_func = partial(func, *args, **kwargs)
        return loop.run_in_executor(executor=executor, func=p_func)

    return _async_wrapper


def _next_task(iterator: AsyncGenerator[T, None],
               loop: AbstractEventLoop) -> Task:
    # Can use anext() in python >= 3.10
    return loop.create_task(iterator.__anext__())  # type: ignore[arg-type]


async def iterate_with_cancellation(
    iterator: AsyncGenerator[T, None],
    is_cancelled: Callable[[], Awaitable[bool]],
) -> AsyncGenerator[T, None]:
    """Convert async iterator into one that polls the provided function
    at least once per second to check for client cancellation.
    """

    loop = asyncio.get_running_loop()

    awaits: List[Future[T]] = [_next_task(iterator, loop)]
    next_cancel_check: float = 0
    while True:
        done, pending = await asyncio.wait(awaits, timeout=1.5)

        # Check for cancellation at most once per second
        time_now = time.time()
        if time_now >= next_cancel_check:
            if await is_cancelled():
                with contextlib.suppress(BaseException):
                    awaits[0].cancel()
                    await iterator.aclose()
                raise asyncio.CancelledError("client cancelled")
            next_cancel_check = time_now + 1

        if done:
            try:
                item = await awaits[0]
                awaits[0] = _next_task(iterator, loop)
                yield item
            except StopAsyncIteration:
                # we are done
                return


async def merge_async_iterators(
    *iterators: AsyncGenerator[T, None],
    is_cancelled: Optional[Callable[[], Awaitable[bool]]] = None,
) -> AsyncGenerator[Tuple[int, T], None]:
    """Merge multiple asynchronous iterators into a single iterator.

    This method handle the case where some iterators finish before others.
    When it yields, it yields a tuple (i, item) where i is the index of the
    iterator that yields the item.

    It also optionally polls a provided function at least once per second
    to check for client cancellation.
    """

    loop = asyncio.get_running_loop()

    awaits = {_next_task(pair[1], loop): pair for pair in enumerate(iterators)}
    timeout = None if is_cancelled is None else 1.5
    next_cancel_check: float = 0
    try:
        while awaits:
            done, pending = await asyncio.wait(awaits.keys(),
                                               return_when=FIRST_COMPLETED,
                                               timeout=timeout)
            if is_cancelled is not None:
                # Check for cancellation at most once per second
                time_now = time.time()
                if time_now >= next_cancel_check:
                    if await is_cancelled():
                        raise asyncio.CancelledError("client cancelled")
                    next_cancel_check = time_now + 1
            for d in done:
                pair = awaits.pop(d)
                try:
                    item = await d
                    i, it = pair
                    awaits[_next_task(it, loop)] = pair
                    yield i, item
                except StopAsyncIteration:
                    pass
    finally:
        # Cancel any remaining iterators
        for f, (_, it) in awaits.items():
            with contextlib.suppress(BaseException):
                f.cancel()
                await it.aclose()


async def collect_from_async_generator(
        iterator: AsyncGenerator[T, None]) -> List[T]:
    """Collect all items from an async generator into a list."""
    items = []
    async for item in iterator:
        items.append(item)
    return items


def get_ip() -> str:
    host_ip = envs.VLLM_HOST_IP
    if "HOST_IP" in os.environ and "VLLM_HOST_IP" not in os.environ:
        logger.warning(
            "The environment variable HOST_IP is deprecated and ignored, as"
            " it is often used by Docker and other software to"
            "interact with the container's network stack. Please"
            "use VLLM_HOST_IP instead to set the IP address for vLLM processes"
            " to communicate with each other.")
    if host_ip:
        return host_ip

    # IP is not set, try to get it from the network interface

    # try ipv4
    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
    try:
        s.connect(("8.8.8.8", 80))  # Doesn't need to be reachable
        return s.getsockname()[0]
    except Exception:
        pass

    # try ipv6
    try:
        s = socket.socket(socket.AF_INET6, socket.SOCK_DGRAM)
        # Google's public DNS server, see
        # https://developers.google.com/speed/public-dns/docs/using#addresses
        s.connect(("2001:4860:4860::8888", 80))  # Doesn't need to be reachable
        return s.getsockname()[0]
    except Exception:
        pass

    warnings.warn(
        "Failed to get the IP address, using 0.0.0.0 by default."
        "The value can be set by the environment variable"
        " VLLM_HOST_IP or HOST_IP.",
        stacklevel=2)
    return "0.0.0.0"


def is_valid_ipv6_address(address: str) -> bool:
    try:
        ipaddress.IPv6Address(address)
        return True
    except ValueError:
        return False


def get_distributed_init_method(ip: str, port: int) -> str:
    # Brackets are not permitted in ipv4 addresses,
    # see https://github.com/python/cpython/issues/103848
    return f"tcp://[{ip}]:{port}" if ":" in ip else f"tcp://{ip}:{port}"


def get_open_zmq_ipc_path() -> str:
    base_rpc_path = envs.VLLM_RPC_BASE_PATH
    return f"ipc://{base_rpc_path}/{uuid4()}"


def get_open_port() -> int:
    port = envs.VLLM_PORT
    if port is not None:
        while True:
            try:
                with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
                    s.bind(("", port))
                    return port
            except OSError:
                port += 1  # Increment port number if already in use
                logger.info("Port %d is already in use, trying port %d",
                            port - 1, port)
    # try ipv4
    try:
        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
            s.bind(("", 0))
            return s.getsockname()[1]
    except OSError:
        # try ipv6
        with socket.socket(socket.AF_INET6, socket.SOCK_STREAM) as s:
            s.bind(("", 0))
            return s.getsockname()[1]


def find_process_using_port(port: int) -> Optional[psutil.Process]:
    for conn in psutil.net_connections():
        if conn.laddr.port == port:
            try:
                return psutil.Process(conn.pid)
            except psutil.NoSuchProcess:
                return None
    return None


def update_environment_variables(envs: Dict[str, str]):
    for k, v in envs.items():
        if k in os.environ and os.environ[k] != v:
            logger.warning(
                "Overwriting environment variable %s "
                "from '%s' to '%s'", k, os.environ[k], v)
        os.environ[k] = v


def chunk_list(lst: List[T], chunk_size: int):
    """Yield successive chunk_size chunks from lst."""
    for i in range(0, len(lst), chunk_size):
        yield lst[i:i + chunk_size]


def cdiv(a: int, b: int) -> int:
    """Ceiling division."""
    return -(a // -b)


def _generate_random_fp8(
    tensor: torch.Tensor,
    low: float,
    high: float,
) -> None:
    # NOTE(zhaoyang): Due to NaN and Inf representation for fp8 data type,
    # it may occur Inf or NaN if we directly use torch.randint
    # to generate random data for fp8 data.
    # For example, s.11111.00 in fp8e5m2 format represents Inf.
    #     | E4M3        | E5M2
    #-----|-------------|-------------------
    # Inf | N/A         | s.11111.00
    # NaN | s.1111.111  | s.11111.{01,10,11}
    from vllm import _custom_ops as ops
    tensor_tmp = torch.empty_like(tensor, dtype=torch.float16)
    tensor_tmp.uniform_(low, high)
    ops.convert_fp8(tensor, tensor_tmp)
    del tensor_tmp


def get_kv_cache_torch_dtype(
        cache_dtype: Optional[Union[str, torch.dtype]],
        model_dtype: Optional[Union[str, torch.dtype]] = None) -> torch.dtype:
    if isinstance(cache_dtype, str):
        if cache_dtype == "auto":
            if isinstance(model_dtype, str):
                torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
            elif isinstance(model_dtype, torch.dtype):
                torch_dtype = model_dtype
            else:
                raise ValueError(f"Invalid model dtype: {model_dtype}")
        elif cache_dtype in ["half", "bfloat16", "float"]:
            torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_dtype]
        elif cache_dtype == "fp8":
            torch_dtype = torch.uint8
        else:
            raise ValueError(f"Invalid kv cache dtype: {cache_dtype}")
    elif isinstance(cache_dtype, torch.dtype):
        torch_dtype = cache_dtype
    else:
        raise ValueError(f"Invalid kv cache dtype: {cache_dtype}")
    return torch_dtype


def create_kv_caches_with_random_flash(
    num_blocks: int,
    block_size: int,
    num_layers: int,
    num_heads: int,
    head_size: int,
    cache_dtype: Optional[Union[str, torch.dtype]],
    model_dtype: Optional[Union[str, torch.dtype]] = None,
    seed: int = 0,
    device: Optional[str] = "cuda",
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
    current_platform.seed_everything(seed)

    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
    key_value_cache_shape = (num_blocks, 2, block_size, num_heads, head_size)
    scale = head_size**-0.5

    key_caches: List[torch.Tensor] = []
    value_caches: List[torch.Tensor] = []

    for _ in range(num_layers):
        key_value_cache = torch.empty(size=key_value_cache_shape,
                                      dtype=torch_dtype,
                                      device=device)
        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
            key_value_cache.uniform_(-scale, scale)
        elif cache_dtype == 'fp8':
            _generate_random_fp8(key_value_cache, -scale, scale)
        else:
            raise ValueError(
                f"Does not support key cache of type {cache_dtype}")
        key_caches.append(key_value_cache[:, 0])
        value_caches.append(key_value_cache[:, 1])
    return key_caches, value_caches


def create_kv_caches_with_random(
    num_blocks: int,
    block_size: int,
    num_layers: int,
    num_heads: int,
    head_size: int,
    cache_dtype: Optional[Union[str, torch.dtype]],
    model_dtype: Optional[Union[str, torch.dtype]] = None,
    seed: int = 0,
    device: Optional[str] = "cuda",
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:

    if cache_dtype == "fp8" and head_size % 16:
        raise ValueError(
            f"Does not support key cache of type fp8 with head_size {head_size}"
        )

    current_platform.seed_everything(seed)

    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)

    scale = head_size**-0.5
    x = 16 // torch.tensor([], dtype=torch_dtype).element_size()
    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
    key_caches: List[torch.Tensor] = []
    for _ in range(num_layers):
        key_cache = torch.empty(size=key_cache_shape,
                                dtype=torch_dtype,
                                device=device)
        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
            key_cache.uniform_(-scale, scale)
        elif cache_dtype == 'fp8':
            _generate_random_fp8(key_cache, -scale, scale)
        else:
            raise ValueError(
                f"Does not support key cache of type {cache_dtype}")
        key_caches.append(key_cache)

    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
    value_caches: List[torch.Tensor] = []
    for _ in range(num_layers):
        value_cache = torch.empty(size=value_cache_shape,
                                  dtype=torch_dtype,
                                  device=device)
        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
            value_cache.uniform_(-scale, scale)
        elif cache_dtype == 'fp8':
            _generate_random_fp8(value_cache, -scale, scale)
        else:
            raise ValueError(
                f"Does not support value cache of type {cache_dtype}")
        value_caches.append(value_cache)
    return key_caches, value_caches


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


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


@lru_cache(maxsize=None)
def is_pin_memory_available() -> bool:
    return current_platform.is_pin_memory_available()


class DeviceMemoryProfiler:

    def __init__(self, device: Optional[torch.types.Device] = None):
        self.device = device

    def current_memory_usage(self) -> float:
        # Return the memory usage in bytes.
        if current_platform.is_cuda_alike():
            torch.cuda.reset_peak_memory_stats(self.device)
            mem = torch.cuda.max_memory_allocated(self.device)
        elif current_platform.is_xpu():
            torch.xpu.reset_peak_memory_stats(self.device)  # type: ignore
            mem = torch.xpu.max_memory_allocated(self.device)  # type: ignore
        return mem

    def __enter__(self):
        self.initial_memory = self.current_memory_usage()
        # This allows us to call methods of the context manager if needed
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.final_memory = self.current_memory_usage()
        self.consumed_memory = self.final_memory - self.initial_memory

        # Force garbage collection
        gc.collect()


def make_ndarray_with_pad(
    x: List[List[T]],
    pad: T,
    dtype: npt.DTypeLike,
    *,
    max_len: Optional[int] = None,
) -> npt.NDArray:
    """
    Make a padded array from 2D inputs.

    The padding is applied to the end of each inner list until it reaches
    `max_len`.
    """
    if max_len is None:
        # Unlike for most functions, map is faster than a genexpr over `len`
        max_len = max(map(len, x), default=0)

    padded_x = np.full((len(x), max_len), pad, dtype=dtype)
    for ind, blocktb in enumerate(x):
        assert len(blocktb) <= max_len
        padded_x[ind, :len(blocktb)] = blocktb

    return padded_x


def make_tensor_with_pad(
    x: List[List[T]],
    pad: T,
    dtype: torch.dtype,
    *,
    max_len: Optional[int] = None,
    device: Optional[Union[str, torch.device]] = None,
    pin_memory: bool = False,
) -> torch.Tensor:
    """
    Make a padded tensor from 2D inputs.

    The padding is applied to the end of each inner list until it reaches
    `max_len`.
    """
    np_dtype = TORCH_DTYPE_TO_NUMPY_DTYPE[dtype]
    padded_x = make_ndarray_with_pad(x, pad, np_dtype, max_len=max_len)

    tensor = torch.from_numpy(padded_x).to(device)
    if pin_memory:
        tensor = tensor.pin_memory()

    return tensor


def async_tensor_h2d(
    data: list,
    dtype: torch.dtype,
    target_device: Union[str, torch.device],
    pin_memory: bool,
) -> torch.Tensor:
    """Asynchronously create a tensor and copy it from host to device."""
    t = torch.tensor(data, dtype=dtype, pin_memory=pin_memory, device="cpu")
    return t.to(device=target_device, non_blocking=True)


def get_dtype_size(dtype: torch.dtype) -> int:
    """Get the size of the data type in bytes."""
    return torch.tensor([], dtype=dtype).element_size()


# `collections` helpers
def is_list_of(
    value: object,
    typ: Type[T],
    *,
    check: Literal["first", "all"] = "first",
) -> TypeIs[List[T]]:
    if not isinstance(value, list):
        return False

    if check == "first":
        return len(value) == 0 or isinstance(value[0], typ)
    elif check == "all":
        return all(isinstance(v, typ) for v in value)

    assert_never(check)


JSONTree = Union[Dict[str, "JSONTree[T]"], List["JSONTree[T]"],
                 Tuple["JSONTree[T]", ...], T]
"""A nested JSON structure where the leaves need not be JSON-serializable."""


@overload
def json_map_leaves(
    func: Callable[[T], U],
    value: Dict[str, JSONTree[T]],
) -> Dict[str, JSONTree[U]]:
    ...


@overload
def json_map_leaves(
    func: Callable[[T], U],
    value: List[JSONTree[T]],
) -> List[JSONTree[U]]:
    ...


@overload
def json_map_leaves(
    func: Callable[[T], U],
    value: Tuple[JSONTree[T], ...],
) -> Tuple[JSONTree[U], ...]:
    ...


@overload
def json_map_leaves(
    func: Callable[[T], U],
    value: JSONTree[T],
) -> JSONTree[U]:
    ...


def json_map_leaves(func: Callable[[T], U], value: JSONTree[T]) -> JSONTree[U]:
    if isinstance(value, dict):
        return {k: json_map_leaves(func, v) for k, v in value.items()}
    elif isinstance(value, list):
        return [json_map_leaves(func, v) for v in value]
    elif isinstance(value, tuple):
        return tuple(json_map_leaves(func, v) for v in value)
    else:
        return func(value)


def flatten_2d_lists(lists: List[List[T]]) -> List[T]:
    """Flatten a list of lists to a single list."""
    return [item for sublist in lists for item in sublist]


_K = TypeVar("_K", bound=Hashable)
_V = TypeVar("_V")


def full_groupby(values: Iterable[_V], *, key: Callable[[_V], _K]):
    """
    Unlike :class:`itertools.groupby`, groups are not broken by
    non-contiguous data.
    """
    groups = defaultdict[_K, list[_V]](list)

    for value in values:
        groups[key(value)].append(value)

    return groups.items()


# TODO: This function can be removed if transformer_modules classes are
# serialized by value when communicating between processes
def init_cached_hf_modules() -> None:
    """
    Lazy initialization of the Hugging Face modules.
    """
    from transformers.dynamic_module_utils import init_hf_modules
    init_hf_modules()


@lru_cache(maxsize=None)
def find_library(lib_name: str) -> str:
    """
    Find the library file in the system.
    `lib_name` is full filename, with both prefix and suffix.
    This function resolves `lib_name` to the full path of the library.
    """
    # Adapted from https://github.com/openai/triton/blob/main/third_party/nvidia/backend/driver.py#L19 # noqa
    # According to https://en.wikipedia.org/wiki/Filesystem_Hierarchy_Standard
    # `/sbin/ldconfig` should exist in all Linux systems.
    # `/sbin/ldconfig` searches the library in the system
    libs = subprocess.check_output(["/sbin/ldconfig", "-p"]).decode()
    # each line looks like the following:
    # libcuda.so.1 (libc6,x86-64) => /lib/x86_64-linux-gnu/libcuda.so.1
    locs = [line.split()[-1] for line in libs.splitlines() if lib_name in line]
    # `LD_LIBRARY_PATH` searches the library in the user-defined paths
    env_ld_library_path = envs.LD_LIBRARY_PATH
    if not locs and env_ld_library_path:
        locs = [
            os.path.join(dir, lib_name)
            for dir in env_ld_library_path.split(":")
            if os.path.exists(os.path.join(dir, lib_name))
        ]
    if not locs:
        raise ValueError(f"Cannot find {lib_name} in the system.")
    return locs[0]


def find_nccl_library() -> str:
    """
    We either use the library file specified by the `VLLM_NCCL_SO_PATH`
    environment variable, or we find the library file brought by PyTorch.
    After importing `torch`, `libnccl.so.2` or `librccl.so.1` can be
    found by `ctypes` automatically.
    """
    so_file = envs.VLLM_NCCL_SO_PATH

    # manually load the nccl library
    if so_file:
        logger.info(
            "Found nccl from environment variable VLLM_NCCL_SO_PATH=%s",
            so_file)
    else:
        if torch.version.cuda is not None:
            so_file = "libnccl.so.2"
        elif torch.version.hip is not None:
            so_file = "librccl.so.1"
        else:
            raise ValueError("NCCL only supports CUDA and ROCm backends.")
        logger.info("Found nccl from library %s", so_file)
    return so_file


def enable_trace_function_call_for_thread(vllm_config: "VllmConfig") -> None:
    """Set up function tracing for the current thread,
    if enabled via the VLLM_TRACE_FUNCTION environment variable
    """

    if envs.VLLM_TRACE_FUNCTION:
        tmp_dir = tempfile.gettempdir()
        # add username to tmp_dir to avoid permission issues
        tmp_dir = os.path.join(tmp_dir, getpass.getuser())
        filename = (f"VLLM_TRACE_FUNCTION_for_process_{os.getpid()}"
                    f"_thread_{threading.get_ident()}_"
                    f"at_{datetime.datetime.now()}.log").replace(" ", "_")
        log_path = os.path.join(tmp_dir, "vllm",
                                f"vllm-instance-{vllm_config.instance_id}",
                                filename)
        os.makedirs(os.path.dirname(log_path), exist_ok=True)
        enable_trace_function_call(log_path)


# `functools` helpers
def identity(value: T, **kwargs) -> T:
    """Returns the first provided value."""
    return value


F = TypeVar('F', bound=Callable[..., Any])


def deprecate_args(
    start_index: int,
    is_deprecated: Union[bool, Callable[[], bool]] = True,
    additional_message: Optional[str] = None,
) -> Callable[[F], F]:

    if not callable(is_deprecated):
        is_deprecated = partial(identity, is_deprecated)

    def wrapper(fn: F) -> F:

        params = inspect.signature(fn).parameters
        pos_types = (
            inspect.Parameter.POSITIONAL_ONLY,
            inspect.Parameter.POSITIONAL_OR_KEYWORD,
        )
        pos_kws = [
            kw for kw, param in params.items() if param.kind in pos_types
        ]

        @wraps(fn)
        def inner(*args, **kwargs):
            if is_deprecated():
                deprecated_args = pos_kws[start_index:len(args)]
                if deprecated_args:
                    msg = (
                        f"The positional arguments {deprecated_args} are "
                        "deprecated and will be removed in a future update.")
                    if additional_message is not None:
                        msg += f" {additional_message}"

                    warnings.warn(
                        DeprecationWarning(msg),
                        stacklevel=3,  # The inner function takes up one level
                    )

            return fn(*args, **kwargs)

        return inner  # type: ignore

    return wrapper


def deprecate_kwargs(
    *kws: str,
    is_deprecated: Union[bool, Callable[[], bool]] = True,
    additional_message: Optional[str] = None,
) -> Callable[[F], F]:
    deprecated_kws = set(kws)

    if not callable(is_deprecated):
        is_deprecated = partial(identity, is_deprecated)

    def wrapper(fn: F) -> F:

        @wraps(fn)
        def inner(*args, **kwargs):
            if is_deprecated():
                deprecated_kwargs = kwargs.keys() & deprecated_kws
                if deprecated_kwargs:
                    msg = (
                        f"The keyword arguments {deprecated_kwargs} are "
                        "deprecated and will be removed in a future update.")
                    if additional_message is not None:
                        msg += f" {additional_message}"

                    warnings.warn(
                        DeprecationWarning(msg),
                        stacklevel=3,  # The inner function takes up one level
                    )

            return fn(*args, **kwargs)

        return inner  # type: ignore

    return wrapper


@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 current_platform.is_rocm():
        # 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


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(envs.CUDA_VISIBLE_DEVICES)


def cuda_is_initialized() -> bool:
    """Check if CUDA is initialized."""
    if not torch.cuda._is_compiled():
        return False
    return torch.cuda.is_initialized()


def weak_bind(bound_method: Callable[..., Any], ) -> Callable[..., None]:
    """Make an instance method that weakly references
    its associated instance and no-ops once that
    instance is collected."""
    ref = weakref.ref(bound_method.__self__)  # type: ignore[attr-defined]
    unbound = bound_method.__func__  # type: ignore[attr-defined]

    def weak_bound(*args, **kwargs) -> None:
        if inst := ref():
            unbound(inst, *args, **kwargs)

    return weak_bound


#From: https://stackoverflow.com/a/4104188/2749989
def run_once(f: Callable[P, None]) -> Callable[P, None]:

    def wrapper(*args: P.args, **kwargs: P.kwargs) -> None:
        if not wrapper.has_run:  # type: ignore[attr-defined]
            wrapper.has_run = True  # type: ignore[attr-defined]
            return f(*args, **kwargs)

    wrapper.has_run = False  # type: ignore[attr-defined]
    return wrapper


class StoreBoolean(argparse.Action):

    def __call__(self, parser, namespace, values, option_string=None):
        if values.lower() == "true":
            setattr(namespace, self.dest, True)
        elif values.lower() == "false":
            setattr(namespace, self.dest, False)
        else:
            raise ValueError(f"Invalid boolean value: {values}. "
                             "Expected 'true' or 'false'.")


class SortedHelpFormatter(argparse.HelpFormatter):
    """SortedHelpFormatter that sorts arguments by their option strings."""

    def add_arguments(self, actions):
        actions = sorted(actions, key=lambda x: x.option_strings)
        super().add_arguments(actions)


class FlexibleArgumentParser(argparse.ArgumentParser):
    """ArgumentParser that allows both underscore and dash in names."""

    def __init__(self, *args, **kwargs):
        # Set the default 'formatter_class' to SortedHelpFormatter
        if 'formatter_class' not in kwargs:
            kwargs['formatter_class'] = SortedHelpFormatter
        super().__init__(*args, **kwargs)

    def parse_args(self, args=None, namespace=None):
        if args is None:
            args = sys.argv[1:]

        if '--config' in args:
            args = self._pull_args_from_config(args)

        # Convert underscores to dashes and vice versa in argument names
        processed_args = []
        for arg in args:
            if arg.startswith('--'):
                if '=' in arg:
                    key, value = arg.split('=', 1)
                    key = '--' + key[len('--'):].replace('_', '-')
                    processed_args.append(f'{key}={value}')
                else:
                    processed_args.append('--' +
                                          arg[len('--'):].replace('_', '-'))
            elif arg.startswith('-O') and arg != '-O' and len(arg) == 2:
                # allow -O flag to be used without space, e.g. -O3
                processed_args.append('-O')
                processed_args.append(arg[2:])
            else:
                processed_args.append(arg)

        return super().parse_args(processed_args, namespace)

    def _pull_args_from_config(self, args: List[str]) -> List[str]:
        """Method to pull arguments specified in the config file
        into the command-line args variable.

        The arguments in config file will be inserted between
        the argument list.

        example:
        ```yaml
            port: 12323
            tensor-parallel-size: 4
        ```
        ```python
        $: vllm {serve,chat,complete} "facebook/opt-12B" \
            --config config.yaml -tp 2
        $: args = [
            "serve,chat,complete",
            "facebook/opt-12B",
            '--config', 'config.yaml',
            '-tp', '2'
        ]
        $: args = [
            "serve,chat,complete",
            "facebook/opt-12B",
            '--port', '12323',
            '--tensor-parallel-size', '4',
            '-tp', '2'
            ]
        ```

        Please note how the config args are inserted after the sub command.
        this way the order of priorities is maintained when these are args
        parsed by super().
        """
        assert args.count(
            '--config') <= 1, "More than one config file specified!"

        index = args.index('--config')
        if index == len(args) - 1:
            raise ValueError("No config file specified! \
                             Please check your command-line arguments.")

        file_path = args[index + 1]

        config_args = self._load_config_file(file_path)

        # 0th index is for {serve,chat,complete}
        # followed by model_tag (only for serve)
        # followed by config args
        # followed by rest of cli args.
        # maintaining this order will enforce the precedence
        # of cli > config > defaults
        if args[0] == "serve":
            if index == 1:
                raise ValueError(
                    "No model_tag specified! Please check your command-line"
                    " arguments.")
            args = [args[0]] + [
                args[1]
            ] + config_args + args[2:index] + args[index + 2:]
        else:
            args = [args[0]] + config_args + args[1:index] + args[index + 2:]

        return args

    def _load_config_file(self, file_path: str) -> List[str]:
        """Loads a yaml file and returns the key value pairs as a
        flattened list with argparse like pattern
        ```yaml
            port: 12323
            tensor-parallel-size: 4
        ```
        returns:
            processed_args: list[str] = [
                '--port': '12323',
                '--tensor-parallel-size': '4'
            ]

        """

        extension: str = file_path.split('.')[-1]
        if extension not in ('yaml', 'yml'):
            raise ValueError(
                "Config file must be of a yaml/yml type.\
                              %s supplied", extension)

        # only expecting a flat dictionary of atomic types
        processed_args: List[str] = []

        config: Dict[str, Union[int, str]] = {}
        try:
            with open(file_path) as config_file:
                config = yaml.safe_load(config_file)
        except Exception as ex:
            logger.error(
                "Unable to read the config file at %s. \
                Make sure path is correct", file_path)
            raise ex

        store_boolean_arguments = [
            action.dest for action in self._actions
            if isinstance(action, StoreBoolean)
        ]

        for key, value in config.items():
            if isinstance(value, bool) and key not in store_boolean_arguments:
                if value:
                    processed_args.append('--' + key)
            else:
                processed_args.append('--' + key)
                processed_args.append(str(value))

        return processed_args


async def _run_task_with_lock(task: Callable, lock: asyncio.Lock, *args,
                              **kwargs):
    """Utility function to run async task in a lock"""
    async with lock:
        return await task(*args, **kwargs)


def supports_kw(
    callable: Callable[..., object],
    kw_name: str,
    requires_kw_only: bool = False,
    allow_var_kwargs: bool = True,
) -> bool:
    """Check if a keyword is a valid kwarg for a callable; if requires_kw_only
    disallows kwargs names that can also be positional arguments.
    """
    params = inspect.signature(callable).parameters
    if not params:
        return False

    param_val = params.get(kw_name)

    # Types where the it may be valid, i.e., explicitly defined & nonvariadic
    passable_kw_types = set((inspect.Parameter.POSITIONAL_ONLY,
                             inspect.Parameter.POSITIONAL_OR_KEYWORD,
                             inspect.Parameter.KEYWORD_ONLY))

    if param_val:
        is_sig_param = param_val.kind in passable_kw_types
        # We want kwargs only, but this is passable as a positional arg
        if (requires_kw_only and is_sig_param
                and param_val.kind != inspect.Parameter.KEYWORD_ONLY):
            return False
        if ((requires_kw_only
             and param_val.kind == inspect.Parameter.KEYWORD_ONLY)
                or (not requires_kw_only and is_sig_param)):
            return True

    # If we're okay with var-kwargs, it's supported as long as
    # the kw_name isn't something like *args, **kwargs
    if allow_var_kwargs:
        # Get the last param; type is ignored here because params is a proxy
        # mapping, but it wraps an ordered dict, and they appear in order.
        # Ref: https://docs.python.org/3/library/inspect.html#inspect.Signature.parameters
        last_param = params[next(reversed(params))]  # type: ignore
        return (last_param.kind == inspect.Parameter.VAR_KEYWORD
                and last_param.name != kw_name)
    return False


def resolve_mm_processor_kwargs(
    init_kwargs: Optional[Mapping[str, object]],
    inference_kwargs: Optional[Mapping[str, object]],
    callable: Callable[..., object],
    allow_var_kwargs: bool = False,
) -> Dict[str, Any]:
    """Applies filtering to eliminate invalid mm_processor_kwargs, i.e.,
    those who are not explicit keywords to the given callable (of one is
    given; otherwise no filtering is done), then merges the kwarg dicts,
    giving priority to inference_kwargs if there are any collisions.

    In the case that no kwarg overrides are provided, returns an empty
    dict so that it can still be kwarg expanded into the callable later on.

    If allow_var_kwargs=True, allows for things that can be expanded into
    kwargs as long as they aren't naming collision for var_kwargs or potential
    positional arguments.
    """
    # Filter inference time multimodal processor kwargs provided
    runtime_mm_kwargs = get_allowed_kwarg_only_overrides(
        callable,
        overrides=inference_kwargs,
        allow_var_kwargs=allow_var_kwargs)

    # Filter init time multimodal processor kwargs provided
    init_mm_kwargs = get_allowed_kwarg_only_overrides(
        callable, overrides=init_kwargs, allow_var_kwargs=allow_var_kwargs)

    # Merge the final processor kwargs, prioritizing inference
    # time values over the initialization time values.
    mm_processor_kwargs = {**init_mm_kwargs, **runtime_mm_kwargs}
    return mm_processor_kwargs


def get_allowed_kwarg_only_overrides(
    callable: Callable[..., object],
    overrides: Optional[Mapping[str, object]],
    allow_var_kwargs: bool = False,
) -> Dict[str, Any]:
    """
    Given a callable which has one or more keyword only params and a dict
    mapping param names to values, drop values that can be not be kwarg
    expanded to overwrite one or more keyword-only args. This is used in a
    few places to handle custom processor overrides for multimodal models,
    e.g., for profiling when processor options provided by the user
    may affect the number of mm tokens per instance.

    Args:
        callable: Callable which takes 0 or more keyword only arguments.
                  If None is provided, all overrides names are allowed.
        overrides: Potential overrides to be used when invoking the callable.
        allow_var_kwargs: Allows overrides that are expandable for var kwargs.

    Returns:
        Dictionary containing the kwargs to be leveraged which may be used
        to overwrite one or more keyword only arguments when invoking the
        callable.
    """
    if not overrides:
        return {}

    # Drop any mm_processor_kwargs provided by the user that
    # are not kwargs, unless it can fit it var_kwargs param
    filtered_overrides = {
        kwarg_name: val
        for kwarg_name, val in overrides.items()
        if supports_kw(callable,
                       kwarg_name,
                       requires_kw_only=True,
                       allow_var_kwargs=allow_var_kwargs)
    }

    # If anything is dropped, log a warning
    dropped_keys = overrides.keys() - filtered_overrides.keys()
    if dropped_keys:
        logger.warning(
            "The following intended overrides are not keyword-only args "
            "and and will be dropped: %s", dropped_keys)

    return filtered_overrides


# Using dynamo with vLLM doesn't really work well with PyTorch versions < 2.4.0.
# In particular, the FakeScalarType is not supported for earlier versions of
# PyTorch which breaks dynamo for any ops registered using ScalarType.
def supports_dynamo() -> bool:
    base_torch_version = Version(Version(torch.__version__).base_version)
    return base_torch_version >= Version("2.4.0")


# 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")


class AtomicCounter:
    """An atomic, thread-safe counter"""

    def __init__(self, initial=0):
        """Initialize a new atomic counter to given initial value"""
        self._value = initial
        self._lock = threading.Lock()

    def inc(self, num=1):
        """Atomically increment the counter by num and return the new value"""
        with self._lock:
            self._value += num
            return self._value

    def dec(self, num=1):
        """Atomically decrement the counter by num and return the new value"""
        with self._lock:
            self._value -= num
            return self._value

    @property
    def value(self):
        return self._value


# Adapted from: https://stackoverflow.com/a/47212782/5082708
class LazyDict(Mapping[str, T], Generic[T]):

    def __init__(self, factory: Dict[str, Callable[[], T]]):
        self._factory = factory
        self._dict: Dict[str, T] = {}

    def __getitem__(self, key: str) -> T:
        if key not in self._dict:
            if key not in self._factory:
                raise KeyError(key)
            self._dict[key] = self._factory[key]()
        return self._dict[key]

    def __setitem__(self, key: str, value: Callable[[], T]):
        self._factory[key] = value

    def __iter__(self):
        return iter(self._factory)

    def __len__(self):
        return len(self._factory)


class ClassRegistry(UserDict[Type[T], _V]):

    def __getitem__(self, key: Type[T]) -> _V:
        for cls in key.mro():
            if cls in self.data:
                return self.data[cls]

        raise KeyError(key)

    def __contains__(self, key: object) -> bool:
        return self.contains(key)

    def contains(self, key: object, *, strict: bool = False) -> bool:
        if not isinstance(key, type):
            return False

        if strict:
            return key in self.data

        return any(cls in self.data for cls in key.mro())


def weak_ref_tensor(tensor: torch.Tensor) -> torch.Tensor:
    """
    Create a weak reference to a tensor.
    The new tensor will share the same data as the original tensor,
    but will not keep the original tensor alive.
    """
    return torch.ops._C.weak_ref_tensor(tensor)


def weak_ref_tensors(
    tensors: Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]]
) -> Union[torch.Tensor, List[torch.Tensor], Tuple[torch.Tensor]]:
    """
    Convenience function to create weak references to tensors,
    for single tensor, list of tensors or tuple of tensors.
    """
    if isinstance(tensors, torch.Tensor):
        return weak_ref_tensor(tensors)
    if isinstance(tensors, list):
        return [weak_ref_tensor(t) for t in tensors]
    if isinstance(tensors, tuple):
        return tuple(weak_ref_tensor(t) for t in tensors)
    raise ValueError("Invalid type for tensors")


def is_in_doc_build() -> bool:
    try:
        from sphinx.ext.autodoc.mock import _MockModule
        return isinstance(torch, _MockModule)
    except ModuleNotFoundError:
        return False


def import_from_path(module_name: str, file_path: Union[str, os.PathLike]):
    """
    Import a Python file according to its file path.

    Based on the official recipe:
    https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly
    """
    spec = importlib.util.spec_from_file_location(module_name, file_path)
    if spec is None:
        raise ModuleNotFoundError(f"No module named '{module_name}'")

    assert spec.loader is not None

    module = importlib.util.module_from_spec(spec)
    sys.modules[module_name] = module
    spec.loader.exec_module(module)
    return module


# create a library to hold the custom op
vllm_lib = Library("vllm", "FRAGMENT")  # noqa


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,
    dispatch_key: str = "CUDA",
):
    """
    `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.
    """
    if is_in_doc_build() or not supports_custom_op():
        return
    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 vllm_lib
    my_lib.define(op_name + schema_str)
    my_lib.impl(op_name, op_func, dispatch_key=dispatch_key)
    if fake_impl is not None:
        my_lib._register_fake(op_name, fake_impl)


def resolve_obj_by_qualname(qualname: str) -> Any:
    """
    Resolve an object by its fully qualified name.
    """
    module_name, obj_name = qualname.rsplit(".", 1)
    module = importlib.import_module(module_name)
    return getattr(module, obj_name)


def kill_process_tree(pid: int):
    """
    Kills all descendant processes of the given pid by sending SIGKILL.

    Args:
        pid (int): Process ID of the parent process
    """
    try:
        parent = psutil.Process(pid)
    except psutil.NoSuchProcess:
        return

    # Get all children recursively
    children = parent.children(recursive=True)

    # Send SIGKILL to all children first
    for child in children:
        with contextlib.suppress(ProcessLookupError):
            os.kill(child.pid, signal.SIGKILL)

    # Finally kill the parent
    with contextlib.suppress(ProcessLookupError):
        os.kill(pid, signal.SIGKILL)


@dataclass
class MemorySnapshot:
    """Memory snapshot."""
    torch_peak_in_bytes: int = 0
    torch_memory_in_bytes: int = 0
    timestamp: float = 0.0

    def measure(self):
        self.torch_peak_in_bytes = torch.cuda.memory_stats(
        )["allocated_bytes.all.peak"]
        self.torch_memory_in_bytes = torch.cuda.memory_stats(
        )["allocated_bytes.all.current"]
        self.timestamp = time.time()

    def __sub__(self, other: "MemorySnapshot") -> "MemorySnapshot":
        """support a - b"""
        return MemorySnapshot(
            torch_peak_in_bytes=self.torch_peak_in_bytes -
            other.torch_peak_in_bytes,
            torch_memory_in_bytes=self.torch_memory_in_bytes -
            other.torch_memory_in_bytes,
            timestamp=self.timestamp - other.timestamp)


@dataclass
class MemoryProfilingResult:
    """Memory profiling result.
    """  # noqa
    baseline_memory_in_bytes: int = 0
    non_kv_cache_memory_in_bytes: int = 0
    torch_peak_increase_in_bytes: int = 0
    non_torch_increase_in_bytes: int = 0
    weights_memory_in_bytes: float = 0
    before_profile: MemorySnapshot = field(default_factory=MemorySnapshot)
    after_profile: MemorySnapshot = field(default_factory=MemorySnapshot)
    profile_time: float = 0.0


@contextlib.contextmanager
def memory_profiling(
    baseline_memory_in_bytes: int, weights_memory_in_bytes: int
) -> Generator[MemoryProfilingResult, None, None]:
    """Memory profiling context manager.
    baseline_memory_in_bytes: memory used by all the components other than
        the current vLLM instance. It contains: memory used by other processes, memory
        used by another vLLM instance in the same process, etc. It is usually measured
        before the current vLLM instance initialize the device. And we assume it is
        constant during the profiling of the current vLLM instance.
    weights_memory_in_bytes: memory used by PyTorch when loading the model weights.
        Note that, before loading the model weights, we also initialize the device
        and distributed environment, which may consume some memory. This part is not
        included in the weights_memory_in_bytes because PyTorch does not control it.

    The memory in one GPU can be classified into 3 categories:
    1. memory used by anything other than the current vLLM instance.
    2. memory used by torch in the current vLLM instance.
    3. memory used in the current vLLM instance, but not by torch.

    A quantitive example:

    Before creating the current vLLM instance:
        category 1: 1 GiB
        category 2: 0 GiB
        category 3: 0 GiB

    After creating the current vLLM instance and loading the model,
    (i.e. before profiling):
        category 1: 1 GiB
        category 2: 2 GiB (model weights take 2 GiB)
        category 3: 0.5 GiB (memory used by NCCL)

    During profiling (peak):
        category 1: 1 GiB
        category 2: 4 GiB (peak activation tensors take 2 GiB)
        category 3: 1 GiB (memory used by NCCL + buffers for some attention backends)

    After profiling:
        category 1: 1 GiB
        category 2: 3 GiB (after garbage-collecting activation tensors)
        category 3: 1 GiB (memory used by NCCL + buffers for some attention backends)

    In this case, non-kv cache takes 5 GiB in total, including:
    a. 2 GiB used by the model weights (category 2)
    b. 2 GiB reserved for the peak activation tensors (category 2)
    c. 1 GiB used by non-torch components (category 3)

    The memory used for loading weights (a.) is directly given from the argument `weights_memory_in_bytes`.

    The increase of ``torch.cuda.memory_stats()["allocated_bytes.all.peak"]` after profiling gives (b.).

    (c.) is tricky. We measure the total memory used in this GPU (`torch.cuda.mem_get_info()[1] - torch.cuda.mem_get_info()[0]`),
    subtract the baseline memory, the memory used by the model weights, and diff of `torch.cuda.memory_stats()["allocated_bytes.all.current"]`.
    """ # noqa
    torch.cuda.reset_peak_memory_stats()

    result = MemoryProfilingResult()

    result.baseline_memory_in_bytes = baseline_memory_in_bytes
    # the part of memory used for holding the model weights
    result.weights_memory_in_bytes = weights_memory_in_bytes

    result.before_profile.measure()

    yield result

    gc.collect()
    torch.cuda.empty_cache()

    result.after_profile.measure()

    diff = result.after_profile - result.before_profile
    result.torch_peak_increase_in_bytes = diff.torch_peak_in_bytes
    current_cuda_memory_bytes = torch.cuda.mem_get_info(
    )[1] - torch.cuda.mem_get_info()[0]
    result.non_torch_increase_in_bytes = current_cuda_memory_bytes - baseline_memory_in_bytes - weights_memory_in_bytes - diff.torch_memory_in_bytes  # noqa
    result.profile_time = diff.timestamp
    result.non_kv_cache_memory_in_bytes = result.non_torch_increase_in_bytes + result.torch_peak_increase_in_bytes + result.weights_memory_in_bytes  # noqa