# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project from __future__ import annotations import asyncio import concurrent import contextlib import datetime import enum import gc import getpass import hashlib import importlib import importlib.metadata import importlib.util import inspect import ipaddress import json import multiprocessing import os import pickle import signal import socket import subprocess import sys import tempfile import textwrap import threading import time import traceback import types import uuid import warnings import weakref from argparse import (Action, ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError, RawDescriptionHelpFormatter, _ArgumentGroup) from asyncio import FIRST_COMPLETED, AbstractEventLoop, Task from collections import UserDict, defaultdict from collections.abc import (AsyncGenerator, Awaitable, Collection, Generator, Hashable, Iterable, Iterator, KeysView, Mapping) from concurrent.futures.process import ProcessPoolExecutor from dataclasses import dataclass, field from functools import cache, lru_cache, partial, wraps from types import MappingProxyType from typing import (TYPE_CHECKING, Any, Callable, Generic, Literal, NamedTuple, Optional, Sequence, Tuple, Type, TypeVar, Union, cast, overload) from urllib.parse import urlparse from uuid import uuid4 import cachetools import cloudpickle import numpy as np import numpy.typing as npt import psutil import regex as re import torch import torch.types import yaml import zmq import zmq.asyncio from packaging import version from packaging.version import Version from torch.library import Library from typing_extensions import Never, ParamSpec, TypeIs, assert_never import vllm.envs as envs # NOTE: import triton_utils to make TritonPlaceholderModule work # if triton is unavailable import vllm.triton_utils # noqa: F401 from vllm.logger import enable_trace_function_call, init_logger if TYPE_CHECKING: from argparse import Namespace from vllm.config import ModelConfig, VllmConfig logger = init_logger(__name__) # This value is chosen to have a balance between ITL and TTFT. Note it is # not optimized for throughput. DEFAULT_MAX_NUM_BATCHED_TOKENS = 2048 POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768 MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120 # Exception strings for non-implemented encoder/decoder scenarios # Reminder: Please update docs/features/compatibility_matrix.md # 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 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_DUAL_CHUNK_FLASH_ATTN_VAL: str = "DUAL_CHUNK_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, "int8": torch.int8, } 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') T = TypeVar("T") U = TypeVar("U") _K = TypeVar("_K", bound=Hashable) _V = TypeVar("_V") _T = TypeVar("_T") 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 _MappingOrderCacheView(UserDict[_K, _V]): def __init__(self, data: Mapping[_K, _V], ordered_keys: Mapping[_K, None]): super().__init__(data) self.ordered_keys = ordered_keys def __iter__(self) -> Iterator[_K]: return iter(self.ordered_keys) def keys(self) -> KeysView[_K]: return KeysView(self.ordered_keys) class CacheInfo(NamedTuple): hits: int total: int @property def hit_ratio(self) -> float: if self.total == 0: return 0 return self.hits / self.total def __sub__(self, other: CacheInfo): return CacheInfo( hits=self.hits - other.hits, total=self.total - other.total, ) class LRUCache(cachetools.LRUCache[_K, _V], Generic[_K, _V]): def __init__(self, capacity: float, getsizeof: Optional[Callable[[_V], float]] = None): super().__init__(capacity, getsizeof) self.pinned_items = set[_K]() self._hits = 0 self._total = 0 self._last_info = CacheInfo(hits=0, total=0) def __getitem__(self, key: _K, *, update_info: bool = True) -> _V: value = super().__getitem__(key) if update_info: self._hits += 1 self._total += 1 return value def __delitem__(self, key: _K) -> None: run_on_remove = key in self value = self.__getitem__(key, update_info=False) # type: ignore[call-arg] super().__delitem__(key) if key in self.pinned_items: # Todo: add warning to inform that del pinned item self._unpin(key) if run_on_remove: self._on_remove(key, value) @property def cache(self) -> Mapping[_K, _V]: """Return the internal cache dictionary in order (read-only).""" return _MappingOrderCacheView( self._Cache__data, # type: ignore self.order) @property def order(self) -> Mapping[_K, None]: """Return the internal order dictionary (read-only).""" return MappingProxyType(self._LRUCache__order) # type: ignore @property def capacity(self) -> float: return self.maxsize @property def usage(self) -> float: if self.maxsize == 0: return 0 return self.currsize / self.maxsize def stat(self, *, delta: bool = False) -> CacheInfo: """ Gets the cumulative number of hits and queries against this cache. If `delta=True`, instead gets these statistics since the last call that also passed `delta=True`. """ info = CacheInfo(hits=self._hits, total=self._total) if delta: info_delta = info - self._last_info self._last_info = info info = info_delta return info def touch(self, key: _K) -> None: try: self._LRUCache__order.move_to_end(key) # type: ignore except KeyError: self._LRUCache__order[key] = None # type: ignore @overload def get(self, key: _K, /) -> Optional[_V]: ... @overload def get(self, key: _K, /, default: Union[_V, _T]) -> Union[_V, _T]: ... def get(self, key: _K, /, default: Optional[Union[_V, _T]] = None) -> Optional[Union[_V, _T]]: value: Optional[Union[_V, _T]] if key in self: value = self.__getitem__( key, update_info=False) # type: ignore[call-arg] self._hits += 1 else: value = default self._total += 1 return value @overload def pop(self, key: _K) -> _V: ... @overload def pop(self, key: _K, default: Union[_V, _T]) -> Union[_V, _T]: ... def pop(self, key: _K, default: Optional[Union[_V, _T]] = None) -> Optional[Union[_V, _T]]: value: Optional[Union[_V, _T]] if key not in self: return default value = self.__getitem__(key, update_info=False) # type: ignore[call-arg] self.__delitem__(key) return value def put(self, key: _K, value: _V) -> None: self.__setitem__(key, value) def pin(self, key: _K) -> None: """ Pins a key in the cache preventing it from being evicted in the LRU order. """ if key not in self: raise ValueError(f"Cannot pin key: {key} not in cache.") self.pinned_items.add(key) def _unpin(self, key: _K) -> None: """ Unpins a key in the cache allowing it to be evicted in the LRU order. """ self.pinned_items.remove(key) def _on_remove(self, key: _K, value: Optional[_V]) -> None: pass def remove_oldest(self, *, remove_pinned: bool = False) -> None: if len(self) == 0: return self.popitem(remove_pinned=remove_pinned) def _remove_old_if_needed(self) -> None: while self.currsize > self.capacity: self.remove_oldest() def popitem(self, remove_pinned: bool = False): """Remove and return the `(key, value)` pair least recently used.""" if not remove_pinned: # pop the oldest item in the cache that is not pinned lru_key = next( (key for key in self.order 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.order)) value = self.pop(cast(_K, lru_key)) return (lru_key, value) def clear(self) -> None: while len(self) > 0: self.remove_oldest(remove_pinned=True) self._hits = 0 self._total = 0 self._last_info = CacheInfo(hits=0, total=0) 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 @cache 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 merge_async_iterators( *iterators: AsyncGenerator[T, 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. """ if len(iterators) == 1: # Fast-path single iterator case. async for item in iterators[0]: yield 0, item return loop = asyncio.get_running_loop() awaits = {_next_task(pair[1], loop): pair for pair in enumerate(iterators)} try: while awaits: done, _ = await asyncio.wait(awaits.keys(), return_when=FIRST_COMPLETED) 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: return get_tcp_uri(ip, port) def get_tcp_uri(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_zmq_inproc_path() -> str: return f"inproc://{uuid4()}" def get_open_port() -> int: """ Get an open port for the vLLM process to listen on. An edge case to handle, is when we run data parallel, we need to avoid ports that are potentially used by the data parallel master process. Right now we reserve 10 ports for the data parallel master process. Currently it uses 2 ports. """ if "VLLM_DP_MASTER_PORT" in os.environ: dp_master_port = envs.VLLM_DP_MASTER_PORT reserved_port_range = range(dp_master_port, dp_master_port + 10) while True: candidate_port = _get_open_port() if candidate_port not in reserved_port_range: return candidate_port return _get_open_port() 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]: # TODO: We can not check for running processes with network # port on macOS. Therefore, we can not have a full graceful shutdown # of vLLM. For now, let's not look for processes in this case. # Ref: https://www.florianreinhard.de/accessdenied-in-psutil/ if sys.platform.startswith("darwin"): return None 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 next_power_of_2(n) -> int: """The next power of 2 (inclusive)""" if n < 1: return 1 return 1 << (n - 1).bit_length() def round_up(x: int, y: int) -> int: return ((x + y - 1) // y) * y def round_down(x: int, y: int) -> int: return (x // y) * y 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) and model_dtype in STR_DTYPE_TO_TORCH_DTYPE: 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 STR_DTYPE_TO_TORCH_DTYPE: torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_dtype] 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: Optional[int] = None, device: Optional[str] = "cuda", cache_layout: Optional[str] = "NHD", ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: from vllm.platforms import current_platform current_platform.seed_everything(seed) torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype) generic_kv_cache_shape = (num_blocks, 2, block_size, num_heads, head_size) assert cache_layout in ("NHD", "HND") stride_order = (0, 1, 2, 3, 4) if cache_layout == "NHD" else (0, 1, 3, 2, 4) kv_cache_allocation_shape = tuple(generic_kv_cache_shape[i] for i in stride_order) 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=kv_cache_allocation_shape, dtype=torch_dtype, device=device).permute(*stride_order) 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: Optional[int] = None, 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}" ) from vllm.platforms import current_platform 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 @cache def is_pin_memory_available() -> bool: from vllm.platforms import current_platform return current_platform.is_pin_memory_available() @cache def is_uva_available() -> bool: """Check if Unified Virtual Addressing (UVA) is available.""" # UVA requires pinned memory. # TODO: Add more requirements for UVA if needed. return 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. from vllm.platforms import current_platform return current_platform.get_current_memory_usage(self.device) 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() # bool = 0, int = 1, float = 2, complex = 3 def _get_precision_level(dtype: torch.dtype) -> int: # NOTE: Complex dtypes return `is_floating_point=False` return ((dtype != torch.bool) + dtype.is_floating_point + dtype.is_complex * 2) def is_lossless_cast(src_dtype: torch.dtype, tgt_dtype: torch.dtype): """ Test whether it is lossless to cast a tensor from `src_dtype` to `tgt_dtype`. """ if src_dtype == tgt_dtype: return True src_level = _get_precision_level(src_dtype) tgt_level = _get_precision_level(tgt_dtype) if src_level < tgt_level: return True if src_level > tgt_level: return False # Compare integral types if not src_dtype.is_floating_point and not src_dtype.is_complex: src_info = torch.iinfo(src_dtype) tgt_info = torch.iinfo(tgt_dtype) return src_info.min >= tgt_info.min and src_info.max <= tgt_info.max # Compare floating-point types src_info = torch.finfo(src_dtype) tgt_info = torch.finfo(tgt_dtype) return (src_info.min >= tgt_info.min and src_info.max <= tgt_info.max and src_info.resolution >= tgt_info.resolution) def common_broadcastable_dtype(dtypes: Collection[torch.dtype]): """ Get the common `dtype` where all of the other `dtypes` can be cast to it without losing any information. """ return max( dtypes, key=lambda dtype: sum(is_lossless_cast(dt, dtype) for dt in dtypes), ) # `collections` helpers def is_list_of( value: object, typ: Union[type[T], tuple[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) def flatten_2d_lists(lists: Iterable[Iterable[T]]) -> list[T]: """Flatten a list of lists to a single list.""" return [item for sublist in lists for item in sublist] def full_groupby(values: Iterable[_V], *, key: Callable[[_V], _K]): """ Unlike [`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() @cache 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 prev_set_stream = torch.cuda.set_stream _current_stream = None def _patched_set_stream(stream: torch.cuda.Stream) -> None: global _current_stream _current_stream = stream prev_set_stream(stream) torch.cuda.set_stream = _patched_set_stream def current_stream() -> torch.cuda.Stream: """ replace `torch.cuda.current_stream()` with `vllm.utils.current_stream()`. it turns out that `torch.cuda.current_stream()` is quite expensive, as it will construct a new stream object at each call. here we patch `torch.cuda.set_stream` to keep track of the current stream directly, so that we can avoid calling `torch.cuda.current_stream()`. the underlying hypothesis is that we do not call `torch._C._cuda_setStream` from C/C++ code. """ from vllm.platforms import current_platform global _current_stream if _current_stream is None: # when this function is called before any stream is set, # we return the default stream. # On ROCm using the default 0 stream in combination with RCCL # is hurting performance. Therefore creating a dedicated stream # per process _current_stream = torch.cuda.Stream() if current_platform.is_rocm( ) else torch.cuda.current_stream() return _current_stream 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 from vllm.platforms import current_platform 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 cuda_get_device_properties(device, names: Sequence[str], init_cuda=False) -> tuple[Any, ...]: """Get specified CUDA device property values without initializing CUDA in the current process.""" if init_cuda or cuda_is_initialized(): props = torch.cuda.get_device_properties(device) return tuple(getattr(props, name) for name in names) # Run in subprocess to avoid initializing CUDA as a side effect. mp_ctx = multiprocessing.get_context("fork") with ProcessPoolExecutor(max_workers=1, mp_context=mp_ctx) as executor: return executor.submit(cuda_get_device_properties, device, names, True).result() 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(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(ArgumentDefaultsHelpFormatter, RawDescriptionHelpFormatter): """SortedHelpFormatter that sorts arguments by their option strings.""" def _split_lines(self, text, width): """ 1. Sentences split across lines have their single newlines removed. 2. Paragraphs and explicit newlines are split into separate lines. 3. Each line is wrapped to the specified width (width of terminal). """ # The patterns also include whitespace after the newline single_newline = re.compile(r"(? str: """Replaces underscores with dashes in the matched string.""" return match.group(0).replace("_", "-") # Everything between the first -- and the first . pattern = re.compile(r"(?<=--)[^\.]*") # 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 = pattern.sub(repl, key, count=1) processed_args.append(f'{key}={value}') else: key = pattern.sub(repl, arg, count=1) processed_args.append(key) 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) def create_nested_dict(keys: list[str], value: str): """Creates a nested dictionary from a list of keys and a value. For example, `keys = ["a", "b", "c"]` and `value = 1` will create: `{"a": {"b": {"c": 1}}}` """ nested_dict: Any = value for key in reversed(keys): nested_dict = {key: nested_dict} return nested_dict def recursive_dict_update(original: dict, update: dict): """Recursively updates a dictionary with another dictionary.""" for k, v in update.items(): if isinstance(v, dict) and isinstance(original.get(k), dict): recursive_dict_update(original[k], v) else: original[k] = v delete = set() dict_args: dict[str, dict] = defaultdict(dict) for i, processed_arg in enumerate(processed_args): if processed_arg.startswith("--") and "." in processed_arg: if "=" in processed_arg: processed_arg, value = processed_arg.split("=", 1) if "." not in processed_arg: # False positive, . was only in the value continue else: value = processed_args[i + 1] delete.add(i + 1) key, *keys = processed_arg.split(".") # Merge all values with the same key into a single dict arg_dict = create_nested_dict(keys, value) recursive_dict_update(dict_args[key], arg_dict) delete.add(i) # Filter out the dict args we set to None processed_args = [ a for i, a in enumerate(processed_args) if i not in delete ] # Add the dict args back as if they were originally passed as JSON for dict_arg, dict_value in dict_args.items(): processed_args.append(dict_arg) processed_args.append(json.dumps(dict_value)) return super().parse_args(processed_args, namespace) def check_port(self, value): try: value = int(value) except ValueError: msg = "Port must be an integer" raise ArgumentTypeError(msg) from None if not (1024 <= value <= 65535): raise ArgumentTypeError("Port must be between 1024 and 65535") return value 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} # optionally 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": model_in_cli = len(args) > 1 and not args[1].startswith('-') model_in_config = any(arg == '--model' for arg in config_args) if not model_in_cli and not model_in_config: raise ValueError( "No model specified! Please specify model either " "as a positional argument or in a config file.") if model_in_cli: # Model specified as positional arg, keep CLI version args = [args[0]] + [ args[1] ] + config_args + args[2:index] + args[index + 2:] else: # No model in CLI, use config if available args = [args[0] ] + config_args + args[1: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], *, requires_kw_only: bool = True, 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, requires_kw_only=requires_kw_only, 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, requires_kw_only=requires_kw_only, 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]], *, requires_kw_only: bool = True, 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=requires_kw_only, allow_var_kwargs=allow_var_kwargs) } # If anything is dropped, log a warning dropped_keys = overrides.keys() - filtered_overrides.keys() if dropped_keys: if requires_kw_only: logger.warning( "The following intended overrides are not keyword-only args " "and will be dropped: %s", dropped_keys) else: logger.warning( "The following intended overrides are not keyword args " "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: Any) -> Any: """ 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. """ if isinstance(tensor, torch.Tensor): return torch.ops._C.weak_ref_tensor(tensor) else: return tensor def weak_ref_tensors( tensors: Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]] ) -> Union[torch.Tensor, list[Any], tuple[Any], Any]: """ 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 get_cuda_view_from_cpu_tensor(cpu_tensor: torch.Tensor) -> torch.Tensor: """ Get a CUDA view of a CPU tensor using Unified Virtual Addressing (UVA). """ assert cpu_tensor.is_pinned(), "CPU tensor must be pinned" return torch.ops._C.get_cuda_view_from_cpu_tensor(cpu_tensor) 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 @cache def get_vllm_optional_dependencies(): metadata = importlib.metadata.metadata("vllm") requirements = metadata.get_all("Requires-Dist", []) extras = metadata.get_all("Provides-Extra", []) return { extra: [ re.split(r";|>=|<=|==", req)[0] for req in requirements if req.endswith(f'extra == "{extra}"') ] for extra in extras } class _PlaceholderBase: """ Disallows downstream usage of placeholder modules. We need to explicitly override each dunder method because [`__getattr__`][vllm.utils._PlaceholderBase.__getattr__] is not called when they are accessed. Info: [Special method lookup](https://docs.python.org/3/reference/datamodel.html#special-lookup) """ def __getattr__(self, key: str) -> Never: """ The main class should implement this to throw an error for attribute accesses representing downstream usage. """ raise NotImplementedError # [Basic customization] def __lt__(self, other: object): return self.__getattr__("__lt__") def __le__(self, other: object): return self.__getattr__("__le__") def __eq__(self, other: object): return self.__getattr__("__eq__") def __ne__(self, other: object): return self.__getattr__("__ne__") def __gt__(self, other: object): return self.__getattr__("__gt__") def __ge__(self, other: object): return self.__getattr__("__ge__") def __hash__(self): return self.__getattr__("__hash__") def __bool__(self): return self.__getattr__("__bool__") # [Callable objects] def __call__(self, *args: object, **kwargs: object): return self.__getattr__("__call__") # [Container types] def __len__(self): return self.__getattr__("__len__") def __getitem__(self, key: object): return self.__getattr__("__getitem__") def __setitem__(self, key: object, value: object): return self.__getattr__("__setitem__") def __delitem__(self, key: object): return self.__getattr__("__delitem__") # __missing__ is optional according to __getitem__ specification, # so it is skipped # __iter__ and __reversed__ have a default implementation # based on __len__ and __getitem__, so they are skipped. # [Numeric Types] def __add__(self, other: object): return self.__getattr__("__add__") def __sub__(self, other: object): return self.__getattr__("__sub__") def __mul__(self, other: object): return self.__getattr__("__mul__") def __matmul__(self, other: object): return self.__getattr__("__matmul__") def __truediv__(self, other: object): return self.__getattr__("__truediv__") def __floordiv__(self, other: object): return self.__getattr__("__floordiv__") def __mod__(self, other: object): return self.__getattr__("__mod__") def __divmod__(self, other: object): return self.__getattr__("__divmod__") def __pow__(self, other: object, modulo: object = ...): return self.__getattr__("__pow__") def __lshift__(self, other: object): return self.__getattr__("__lshift__") def __rshift__(self, other: object): return self.__getattr__("__rshift__") def __and__(self, other: object): return self.__getattr__("__and__") def __xor__(self, other: object): return self.__getattr__("__xor__") def __or__(self, other: object): return self.__getattr__("__or__") # r* and i* methods have lower priority than # the methods for left operand so they are skipped def __neg__(self): return self.__getattr__("__neg__") def __pos__(self): return self.__getattr__("__pos__") def __abs__(self): return self.__getattr__("__abs__") def __invert__(self): return self.__getattr__("__invert__") # __complex__, __int__ and __float__ have a default implementation # based on __index__, so they are skipped. def __index__(self): return self.__getattr__("__index__") def __round__(self, ndigits: object = ...): return self.__getattr__("__round__") def __trunc__(self): return self.__getattr__("__trunc__") def __floor__(self): return self.__getattr__("__floor__") def __ceil__(self): return self.__getattr__("__ceil__") # [Context managers] def __enter__(self): return self.__getattr__("__enter__") def __exit__(self, *args: object, **kwargs: object): return self.__getattr__("__exit__") class PlaceholderModule(_PlaceholderBase): """ A placeholder object to use when a module does not exist. This enables more informative errors when trying to access attributes of a module that does not exists. """ def __init__(self, name: str) -> None: super().__init__() # Apply name mangling to avoid conflicting with module attributes self.__name = name def placeholder_attr(self, attr_path: str): return _PlaceholderModuleAttr(self, attr_path) def __getattr__(self, key: str): name = self.__name try: importlib.import_module(name) except ImportError as exc: for extra, names in get_vllm_optional_dependencies().items(): if name in names: msg = f"Please install vllm[{extra}] for {extra} support" raise ImportError(msg) from exc raise exc raise AssertionError("PlaceholderModule should not be used " "when the original module can be imported") class _PlaceholderModuleAttr(_PlaceholderBase): def __init__(self, module: PlaceholderModule, attr_path: str) -> None: super().__init__() # Apply name mangling to avoid conflicting with module attributes self.__module = module self.__attr_path = attr_path def placeholder_attr(self, attr_path: str): return _PlaceholderModuleAttr(self.__module, f"{self.__attr_path}.{attr_path}") def __getattr__(self, key: str): getattr(self.__module, f"{self.__attr_path}.{key}") raise AssertionError("PlaceholderModule should not be used " "when the original module can be imported") # 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", tags: Tuple[torch.Tag, ...] = (), ): """ `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 not supports_custom_op(): from vllm.platforms import current_platform assert not current_platform.is_cuda_alike(), ( "cuda platform needs torch>=2.4 to support custom op, " "chances are you are using an old version of pytorch " "or a custom build of pytorch. It is recommended to " "use vLLM in a fresh new environment and let it install " "the required dependencies.") 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, tags=tags) 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: int = 0 cuda_memory: int = 0 torch_memory: int = 0 non_torch_memory: int = 0 timestamp: float = 0.0 auto_measure: bool = True def __post_init__(self): if self.auto_measure: self.measure() def measure(self): # we measure the torch peak memory usage via allocated_bytes, # rather than `torch.cuda.memory_reserved()` . # After `torch.cuda.reset_peak_memory_stats()`, # `torch.cuda.memory_reserved()` will keep growing, and only shrink # when we call `torch.cuda.empty_cache()` or OOM happens. self.torch_peak = torch.cuda.memory_stats().get( "allocated_bytes.all.peak", 0) self.cuda_memory = torch.cuda.mem_get_info( )[1] - torch.cuda.mem_get_info()[0] # torch.cuda.memory_reserved() is how many bytes # PyTorch gets from cuda (by calling cudaMalloc, etc.) # this is used to measure the non-torch memory usage self.torch_memory = torch.cuda.memory_reserved() self.non_torch_memory = self.cuda_memory - self.torch_memory self.timestamp = time.time() def __sub__(self, other: MemorySnapshot) -> MemorySnapshot: return MemorySnapshot( torch_peak=self.torch_peak - other.torch_peak, cuda_memory=self.cuda_memory - other.cuda_memory, torch_memory=self.torch_memory - other.torch_memory, non_torch_memory=self.non_torch_memory - other.non_torch_memory, timestamp=self.timestamp - other.timestamp, auto_measure=False, ) @dataclass class MemoryProfilingResult: """Memory profiling result. All numbers are in bytes. """ non_kv_cache_memory: int = 0 torch_peak_increase: int = 0 non_torch_increase: int = 0 weights_memory: float = 0 before_create: MemorySnapshot = field(default_factory=MemorySnapshot) 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_snapshot: MemorySnapshot, weights_memory: int) -> Generator[MemoryProfilingResult, None, None]: """Memory profiling context manager. baseline_snapshot: the memory snapshot before the current vLLM instance. weights_memory: 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 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`. The increase of `torch.cuda.memory_stats()["allocated_bytes.all.peak"]` during profiling gives (b.). The increase of `non_torch_memory` from creating the current vLLM instance until after profiling to get (c.). """ # noqa gc.collect() torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() result = MemoryProfilingResult() result.before_create = baseline_snapshot # the part of memory used for holding the model weights result.weights_memory = weights_memory result.before_profile.measure() yield result gc.collect() torch.cuda.empty_cache() result.after_profile.measure() diff_profile = result.after_profile - result.before_profile diff_from_create = result.after_profile - result.before_create result.torch_peak_increase = diff_profile.torch_peak result.non_torch_increase = diff_from_create.non_torch_memory result.profile_time = diff_profile.timestamp result.non_kv_cache_memory = result.non_torch_increase + result.torch_peak_increase + result.weights_memory # noqa # Adapted from: https://github.com/sgl-project/sglang/blob/v0.4.1/python/sglang/srt/utils.py#L630 # noqa: E501 def set_ulimit(target_soft_limit=65535): if sys.platform.startswith('win'): logger.info("Windows detected, skipping ulimit adjustment.") return import resource 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( "Found ulimit of %s and failed to automatically increase " "with error %s. This can cause fd limit errors like " "`OSError: [Errno 24] Too many open files`. Consider " "increasing with ulimit -n", current_soft, e) # Adapted from: https://github.com/sgl-project/sglang/blob/v0.4.1/python/sglang/utils.py#L28 # noqa: E501 def get_exception_traceback(): etype, value, tb = sys.exc_info() err_str = "".join(traceback.format_exception(etype, value, tb)) return err_str def split_zmq_path(path: str) -> Tuple[str, str, str]: """Split a zmq path into its parts.""" parsed = urlparse(path) if not parsed.scheme: raise ValueError(f"Invalid zmq path: {path}") scheme = parsed.scheme host = parsed.hostname or "" port = str(parsed.port or "") if scheme == "tcp" and not all((host, port)): # The host and port fields are required for tcp raise ValueError(f"Invalid zmq path: {path}") if scheme != "tcp" and port: # port only makes sense with tcp raise ValueError(f"Invalid zmq path: {path}") return scheme, host, port def make_zmq_path(scheme: str, host: str, port: Optional[int] = None) -> str: """Make a ZMQ path from its parts. Args: scheme: The ZMQ transport scheme (e.g. tcp, ipc, inproc). host: The host - can be an IPv4 address, IPv6 address, or hostname. port: Optional port number, only used for TCP sockets. Returns: A properly formatted ZMQ path string. """ if port is None: return f"{scheme}://{host}" if is_valid_ipv6_address(host): return f"{scheme}://[{host}]:{port}" return f"{scheme}://{host}:{port}" # Adapted from: https://github.com/sgl-project/sglang/blob/v0.4.1/python/sglang/srt/utils.py#L783 # noqa: E501 def make_zmq_socket( ctx: Union[zmq.asyncio.Context, zmq.Context], # type: ignore[name-defined] path: str, socket_type: Any, bind: Optional[bool] = None, identity: Optional[bytes] = None, linger: Optional[int] = None, ) -> Union[zmq.Socket, zmq.asyncio.Socket]: # type: ignore[name-defined] """Make a ZMQ socket with the proper bind/connect semantics.""" mem = psutil.virtual_memory() socket = ctx.socket(socket_type) # Calculate buffer size based on system memory total_mem = mem.total / 1024**3 available_mem = mem.available / 1024**3 # For systems with substantial memory (>32GB total, >16GB available): # - Set a large 0.5GB buffer to improve throughput # For systems with less memory: # - Use system default (-1) to avoid excessive memory consumption if total_mem > 32 and available_mem > 16: buf_size = int(0.5 * 1024**3) # 0.5GB in bytes else: buf_size = -1 # Use system default buffer size if bind is None: bind = socket_type not in (zmq.PUSH, zmq.SUB, zmq.XSUB) if socket_type in (zmq.PULL, zmq.DEALER, zmq.ROUTER): socket.setsockopt(zmq.RCVHWM, 0) socket.setsockopt(zmq.RCVBUF, buf_size) if socket_type in (zmq.PUSH, zmq.DEALER, zmq.ROUTER): socket.setsockopt(zmq.SNDHWM, 0) socket.setsockopt(zmq.SNDBUF, buf_size) if identity is not None: socket.setsockopt(zmq.IDENTITY, identity) if linger is not None: socket.setsockopt(zmq.LINGER, linger) # Determine if the path is a TCP socket with an IPv6 address. # Enable IPv6 on the zmq socket if so. scheme, host, _ = split_zmq_path(path) if scheme == "tcp" and is_valid_ipv6_address(host): socket.setsockopt(zmq.IPV6, 1) if bind: socket.bind(path) else: socket.connect(path) return socket @contextlib.contextmanager def zmq_socket_ctx( path: str, socket_type: Any, bind: Optional[bool] = None, linger: int = 0, identity: Optional[bytes] = None, ) -> Iterator[zmq.Socket]: """Context manager for a ZMQ socket""" ctx = zmq.Context() # type: ignore[attr-defined] try: yield make_zmq_socket(ctx, path, socket_type, bind=bind, identity=identity) except KeyboardInterrupt: logger.debug("Got Keyboard Interrupt.") finally: ctx.destroy(linger=linger) def is_in_ray_actor(): """Check if we are in a Ray actor.""" try: import ray return (ray.is_initialized() and ray.get_runtime_context().get_actor_id() is not None) except ImportError: return False def _maybe_force_spawn(): """Check if we need to force the use of the `spawn` multiprocessing start method. """ if os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") == "spawn": return reason = None if cuda_is_initialized(): reason = "CUDA is initialized" elif is_in_ray_actor(): # even if we choose to spawn, we need to pass the ray address # to the subprocess so that it knows how to connect to the ray cluster. # env vars are inherited by subprocesses, even if we use spawn. import ray os.environ["RAY_ADDRESS"] = ray.get_runtime_context().gcs_address reason = "In a Ray actor and can only be spawned" if reason is not None: logger.warning( "We must use the `spawn` multiprocessing start method. " "Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. " "See https://docs.vllm.ai/en/latest/usage/" "troubleshooting.html#python-multiprocessing " "for more information. Reason: %s", reason) os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" def get_mp_context(): """Get a multiprocessing context with a particular method (spawn or fork). By default we follow the value of the VLLM_WORKER_MULTIPROC_METHOD to determine the multiprocessing method (default is fork). However, under certain conditions, we may enforce spawn and override the value of VLLM_WORKER_MULTIPROC_METHOD. """ _maybe_force_spawn() mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD return multiprocessing.get_context(mp_method) def bind_kv_cache( ctx: dict[str, Any], kv_cache: list[list[torch.Tensor]], # [virtual_engine][layer_index] ) -> None: # Bind the kv_cache tensor to Attention modules, similar to # ctx[layer_name].kv_cache[ve]=kv_cache[ve][extract_layer_index(layer_name)] # Special things handled here: # 1. Some models have non-attention layers, e.g., Jamba # 2. Pipeline parallelism, each rank only has a subset of layers # 3. Encoder attention has no kv cache # 4. Encoder-decoder models, encoder-decoder attention and decoder-only # attention of the same layer (e.g., bart's decoder.layers.1.self_attn # and decoder.layers.1.encoder_attn) is mapped to the same kv cache # tensor from vllm.attention import AttentionType from vllm.model_executor.models.utils import extract_layer_index layer_need_kv_cache = [ layer_name for layer_name in ctx if (hasattr(ctx[layer_name], 'attn_type') and ctx[layer_name].attn_type in (AttentionType.DECODER, AttentionType.ENCODER_DECODER)) ] layer_index_sorted = sorted( set( extract_layer_index(layer_name) for layer_name in layer_need_kv_cache)) for layer_name in layer_need_kv_cache: kv_cache_idx = layer_index_sorted.index( extract_layer_index(layer_name)) forward_ctx = ctx[layer_name] assert len(forward_ctx.kv_cache) == len(kv_cache) for ve, ve_kv_cache in enumerate(kv_cache): forward_ctx.kv_cache[ve] = ve_kv_cache[kv_cache_idx] def run_method(obj: Any, method: Union[str, bytes, Callable], args: tuple[Any], kwargs: dict[str, Any]) -> Any: """ Run a method of an object with the given arguments and keyword arguments. If the method is string, it will be converted to a method using getattr. If the method is serialized bytes and will be deserialized using cloudpickle. If the method is a callable, it will be called directly. """ if isinstance(method, bytes): func = partial(cloudpickle.loads(method), obj) elif isinstance(method, str): try: func = getattr(obj, method) except AttributeError: raise NotImplementedError(f"Method {method!r} is not" " implemented.") from None else: func = partial(method, obj) # type: ignore return func(*args, **kwargs) def import_pynvml(): """ Historical comments: libnvml.so is the library behind nvidia-smi, and pynvml is a Python wrapper around it. We use it to get GPU status without initializing CUDA context in the current process. Historically, there are two packages that provide pynvml: - `nvidia-ml-py` (https://pypi.org/project/nvidia-ml-py/): The official wrapper. It is a dependency of vLLM, and is installed when users install vLLM. It provides a Python module named `pynvml`. - `pynvml` (https://pypi.org/project/pynvml/): An unofficial wrapper. Prior to version 12.0, it also provides a Python module `pynvml`, and therefore conflicts with the official one. What's worse, the module is a Python package, and has higher priority than the official one which is a standalone Python file. This causes errors when both of them are installed. Starting from version 12.0, it migrates to a new module named `pynvml_utils` to avoid the conflict. It is so confusing that many packages in the community use the unofficial one by mistake, and we have to handle this case. For example, `nvcr.io/nvidia/pytorch:24.12-py3` uses the unofficial one, and it will cause errors, see the issue https://github.com/vllm-project/vllm/issues/12847 for example. After all the troubles, we decide to copy the official `pynvml` module to our codebase, and use it directly. """ import vllm.third_party.pynvml as pynvml return pynvml def warn_for_unimplemented_methods(cls: type[T]) -> type[T]: """ A replacement for `abc.ABC`. When we use `abc.ABC`, subclasses will fail to instantiate if they do not implement all abstract methods. Here, we only require `raise NotImplementedError` in the base class, and log a warning if the method is not implemented in the subclass. """ original_init = cls.__init__ def find_unimplemented_methods(self: object): unimplemented_methods = [] for attr_name in dir(self): # bypass inner method if attr_name.startswith('_'): continue try: attr = getattr(self, attr_name) # get the func of callable method if callable(attr): attr_func = attr.__func__ except AttributeError: continue src = inspect.getsource(attr_func) if "NotImplementedError" in src: unimplemented_methods.append(attr_name) if unimplemented_methods: method_names = ','.join(unimplemented_methods) msg = (f"Methods {method_names} not implemented in {self}") logger.warning(msg) @wraps(original_init) def wrapped_init(self, *args, **kwargs) -> None: original_init(self, *args, **kwargs) find_unimplemented_methods(self) type.__setattr__(cls, '__init__', wrapped_init) return cls class LazyLoader(types.ModuleType): """ LazyLoader module borrowed from Tensorflow https://github.com/tensorflow/tensorflow/blob/main/tensorflow/python/util/lazy_loader.py with a addition of "module caching". Lazily import a module, mainly to avoid pulling in large dependencies. Modules such as `xgrammar` might do additional side effects, so we only want to use this when it is needed, delaying all eager effects """ def __init__( self, local_name: str, parent_module_globals: dict[str, Any], name: str, ): self._local_name = local_name self._parent_module_globals = parent_module_globals self._module: types.ModuleType | None = None super().__init__(str(name)) def _load(self) -> types.ModuleType: # Import the target module and insert it into the parent's namespace try: module = importlib.import_module(self.__name__) self._parent_module_globals[self._local_name] = module # The additional add to sys.modules # ensures library is actually loaded. sys.modules[self._local_name] = module except ModuleNotFoundError as err: raise err from None # Update this object's dict so that if someone keeps a # reference to the LazyLoader, lookups are efficient # (__getattr__ is only called on lookups that fail). self.__dict__.update(module.__dict__) return module def __getattr__(self, item: Any) -> Any: if self._module is None: self._module = self._load() return getattr(self._module, item) def __dir__(self) -> list[str]: if self._module is None: self._module = self._load() return dir(self._module) def swap_dict_values(obj: dict[_K, _V], key1: _K, key2: _K) -> None: """ Helper function to swap values for two keys """ v1 = obj.get(key1) v2 = obj.get(key2) if v1 is not None: obj[key2] = v1 else: obj.pop(key2, None) if v2 is not None: obj[key1] = v2 else: obj.pop(key1, None) @contextlib.contextmanager def cprofile_context(save_file: Optional[str] = None): """Run a cprofile Args: save_file: path to save the profile result. "1" or None will result in printing to stdout. """ import cProfile prof = cProfile.Profile() prof.enable() try: yield finally: prof.disable() if save_file and save_file != "1": prof.dump_stats(save_file) else: prof.print_stats(sort="cumtime") def cprofile(save_file: Optional[str] = None, enabled: bool = True): """Decorator to profile a Python method using cProfile. Args: save_file: Path to save the profile result. If "1", None, or "", results will be printed to stdout. enabled: Set to false to turn this into a no-op """ def decorator(func: Callable): @wraps(func) def wrapper(*args, **kwargs): if not enabled: # If profiling is disabled, just call the function directly. return func(*args, **kwargs) with cprofile_context(save_file): return func(*args, **kwargs) return wrapper return decorator # Only relevant for models using ALiBi (e.g, MPT) def check_use_alibi(model_config: ModelConfig) -> bool: cfg = model_config.hf_text_config return (getattr(cfg, "alibi", False) # Falcon or ("BloomForCausalLM" in getattr(model_config.hf_config, "architectures", [])) # Bloom or getattr(cfg, "position_encoding_type", "") == "alibi" # codellm_1b_alibi or (hasattr(cfg, "attn_config") # MPT and ((isinstance(cfg.attn_config, dict) and cfg.attn_config.get("alibi", False)) or (not isinstance(cfg.attn_config, dict) and getattr(cfg.attn_config, "alibi", False))))) def sha256(input) -> int: """Hash any picklable Python object using SHA-256. The input is serialized using pickle before hashing, which allows arbitrary Python objects to be used. Note that this function does not use a hash seed—if you need one, prepend it explicitly to the input. Args: input: Any picklable Python object. Returns: An integer representing the SHA-256 hash of the serialized input. """ input_bytes = pickle.dumps(input, protocol=pickle.HIGHEST_PROTOCOL) return int.from_bytes(hashlib.sha256(input_bytes).digest(), byteorder="big") def is_torch_equal_or_newer(target: str) -> bool: """Check if the installed torch version is >= the target version. Args: target: a version string, like "2.6.0". Returns: Whether the condition meets. """ try: torch_version = version.parse(str(torch.__version__)) return torch_version >= version.parse(target) except Exception: # Fallback to PKG-INFO to load the package info, needed by the doc gen. return Version(importlib.metadata.version('torch')) >= Version(target)