1038 lines
46 KiB
Python
1038 lines
46 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# ruff: noqa: F401
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import ast
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import copy
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import hashlib
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import inspect
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import json
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import os
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import textwrap
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from contextlib import contextmanager
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from dataclasses import field, fields, is_dataclass, replace
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from functools import cached_property, lru_cache
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from typing import (TYPE_CHECKING, Any, Literal, Optional, Protocol, TypeVar,
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Union, cast)
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import regex as re
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import torch
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from pydantic import ConfigDict, SkipValidation
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from pydantic.dataclasses import dataclass
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from typing_extensions import runtime_checkable
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import vllm.envs as envs
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from vllm import version
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from vllm.config.cache import (BlockSize, CacheConfig, CacheDType, MambaDType,
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PrefixCachingHashAlgo)
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from vllm.config.compilation import (CompilationConfig, CompilationLevel,
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CUDAGraphMode, PassConfig)
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from vllm.config.kv_events import KVEventsConfig
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from vllm.config.kv_transfer import KVTransferConfig
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from vllm.config.load import LoadConfig
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from vllm.config.lora import LoRAConfig
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from vllm.config.model import (ConvertOption, HfOverrides, LogprobsMode,
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ModelConfig, ModelDType, ModelImpl,
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RunnerOption, TaskOption, TokenizerMode,
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iter_architecture_defaults,
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try_match_architecture_defaults)
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from vllm.config.multimodal import (MMCacheType, MMEncoderTPMode,
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MultiModalConfig)
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from vllm.config.parallel import (DistributedExecutorBackend, EPLBConfig,
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ParallelConfig)
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from vllm.config.pooler import PoolerConfig
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from vllm.config.scheduler import RunnerType, SchedulerConfig, SchedulerPolicy
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from vllm.config.speculative import SpeculativeConfig
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from vllm.config.structured_outputs import StructuredOutputsConfig
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from vllm.config.utils import ConfigType, config, get_attr_docs, is_init_field
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from vllm.logger import init_logger
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.transformers_utils.runai_utils import is_runai_obj_uri
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from vllm.utils import random_uuid
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if TYPE_CHECKING:
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from _typeshed import DataclassInstance
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from transformers.configuration_utils import PretrainedConfig
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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else:
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DataclassInstance = Any
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PretrainedConfig = Any
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QuantizationConfig = Any
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QuantizationMethods = Any
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BaseModelLoader = Any
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LogitsProcessor = Any
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logger = init_logger(__name__)
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DataclassInstanceT = TypeVar("DataclassInstanceT", bound=DataclassInstance)
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@runtime_checkable
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class SupportsHash(Protocol):
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def compute_hash(self) -> str:
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...
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class SupportsMetricsInfo(Protocol):
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def metrics_info(self) -> dict[str, str]:
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...
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Device = Literal["auto", "cuda", "cpu", "tpu", "xpu"]
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@config
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@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
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class DeviceConfig:
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"""Configuration for the device to use for vLLM execution."""
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device: SkipValidation[Optional[Union[Device, torch.device]]] = "auto"
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"""Device type for vLLM execution.
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This parameter is deprecated and will be
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removed in a future release.
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It will now be set automatically based
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on the current platform."""
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device_type: str = field(init=False)
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"""Device type from the current platform. This is set in
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`__post_init__`."""
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def compute_hash(self) -> str:
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"""
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WARNING: Whenever a new field is added to this config,
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ensure that it is included in the factors list if
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it affects the computation graph.
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Provide a hash that uniquely identifies all the configs
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that affect the structure of the computation
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graph from input ids/embeddings to the final hidden states,
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excluding anything before input ids/embeddings and after
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the final hidden states.
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"""
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# no factors to consider.
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# the device/platform information will be summarized
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# by torch/vllm automatically.
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factors: list[Any] = []
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hash_str = hashlib.md5(str(factors).encode(),
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usedforsecurity=False).hexdigest()
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return hash_str
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def __post_init__(self):
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if self.device == "auto":
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# Automated device type detection
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from vllm.platforms import current_platform
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self.device_type = current_platform.device_type
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if not self.device_type:
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raise RuntimeError(
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"Failed to infer device type, please set "
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"the environment variable `VLLM_LOGGING_LEVEL=DEBUG` "
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"to turn on verbose logging to help debug the issue.")
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else:
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# Device type is assigned explicitly
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if isinstance(self.device, str):
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self.device_type = self.device
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elif isinstance(self.device, torch.device):
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self.device_type = self.device.type
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# Some device types require processing inputs on CPU
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if self.device_type in ["tpu"]:
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self.device = None
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else:
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# Set device with device type
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self.device = torch.device(self.device_type)
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DetailedTraceModules = Literal["model", "worker", "all"]
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@config
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@dataclass
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class ObservabilityConfig:
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"""Configuration for observability - metrics and tracing."""
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show_hidden_metrics_for_version: Optional[str] = None
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"""Enable deprecated Prometheus metrics that have been hidden since the
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specified version. For example, if a previously deprecated metric has been
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hidden since the v0.7.0 release, you use
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`--show-hidden-metrics-for-version=0.7` as a temporary escape hatch while
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you migrate to new metrics. The metric is likely to be removed completely
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in an upcoming release."""
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@cached_property
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def show_hidden_metrics(self) -> bool:
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"""Check if the hidden metrics should be shown."""
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if self.show_hidden_metrics_for_version is None:
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return False
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return version._prev_minor_version_was(
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self.show_hidden_metrics_for_version)
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otlp_traces_endpoint: Optional[str] = None
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"""Target URL to which OpenTelemetry traces will be sent."""
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collect_detailed_traces: Optional[list[DetailedTraceModules]] = None
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"""It makes sense to set this only if `--otlp-traces-endpoint` is set. If
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set, it will collect detailed traces for the specified modules. This
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involves use of possibly costly and or blocking operations and hence might
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have a performance impact.
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Note that collecting detailed timing information for each request can be
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expensive."""
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@cached_property
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def collect_model_forward_time(self) -> bool:
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"""Whether to collect model forward time for the request."""
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return (self.collect_detailed_traces is not None
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and ("model" in self.collect_detailed_traces
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or "all" in self.collect_detailed_traces))
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@cached_property
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def collect_model_execute_time(self) -> bool:
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"""Whether to collect model execute time for the request."""
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return (self.collect_detailed_traces is not None
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and ("worker" in self.collect_detailed_traces
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or "all" in self.collect_detailed_traces))
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def compute_hash(self) -> str:
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"""
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WARNING: Whenever a new field is added to this config,
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ensure that it is included in the factors list if
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it affects the computation graph.
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Provide a hash that uniquely identifies all the configs
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that affect the structure of the computation
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graph from input ids/embeddings to the final hidden states,
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excluding anything before input ids/embeddings and after
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the final hidden states.
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"""
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# no factors to consider.
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# this config will not affect the computation graph.
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factors: list[Any] = []
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hash_str = hashlib.md5(str(factors).encode(),
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usedforsecurity=False).hexdigest()
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return hash_str
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def __post_init__(self):
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if (self.collect_detailed_traces is not None
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and len(self.collect_detailed_traces) == 1
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and "," in self.collect_detailed_traces[0]):
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self._parse_collect_detailed_traces()
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from vllm.tracing import is_otel_available, otel_import_error_traceback
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if not is_otel_available() and self.otlp_traces_endpoint is not None:
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raise ValueError(
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"OpenTelemetry is not available. Unable to configure "
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"'otlp_traces_endpoint'. Ensure OpenTelemetry packages are "
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f"installed. Original error:\n{otel_import_error_traceback}")
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def _parse_collect_detailed_traces(self):
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assert isinstance(self.collect_detailed_traces, list)
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self.collect_detailed_traces = cast(
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list[DetailedTraceModules],
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self.collect_detailed_traces[0].split(","))
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@config
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@dataclass(config=ConfigDict(arbitrary_types_allowed=True))
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class VllmConfig:
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"""Dataclass which contains all vllm-related configuration. This
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simplifies passing around the distinct configurations in the codebase.
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"""
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# TODO: use default_factory once default constructing ModelConfig doesn't
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# try to download a model
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model_config: ModelConfig = None # type: ignore
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"""Model configuration."""
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cache_config: CacheConfig = field(default_factory=CacheConfig)
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"""Cache configuration."""
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parallel_config: ParallelConfig = field(default_factory=ParallelConfig)
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"""Parallel configuration."""
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scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig)
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"""Scheduler configuration."""
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device_config: DeviceConfig = field(default_factory=DeviceConfig)
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"""Device configuration."""
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load_config: LoadConfig = field(default_factory=LoadConfig)
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"""Load configuration."""
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lora_config: Optional[LoRAConfig] = None
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"""LoRA configuration."""
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speculative_config: Optional[SpeculativeConfig] = None
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"""Speculative decoding configuration."""
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structured_outputs_config: StructuredOutputsConfig = field(
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default_factory=StructuredOutputsConfig)
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"""Structured outputs configuration."""
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observability_config: Optional[ObservabilityConfig] = None
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"""Observability configuration."""
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quant_config: Optional[QuantizationConfig] = None
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"""Quantization configuration."""
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compilation_config: CompilationConfig = field(
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default_factory=CompilationConfig)
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"""`torch.compile` and cudagraph capture configuration for the model.
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As a shorthand, `-O<n>` can be used to directly specify the compilation
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level `n`: `-O3` is equivalent to `-O.level=3` (same as `-O='{"level":3}'`).
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Currently, -O <n> and -O=<n> are supported as well but this will likely be
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removed in favor of clearer -O<n> syntax in the future.
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NOTE: level 0 is the default level without any optimization. level 1 and 2
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are for internal testing only. level 3 is the recommended level for
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production, also default in V1.
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You can specify the full compilation config like so:
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`{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}`
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"""
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kv_transfer_config: Optional[KVTransferConfig] = None
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"""The configurations for distributed KV cache transfer."""
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kv_events_config: Optional[KVEventsConfig] = None
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"""The configurations for event publishing."""
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# some opaque config, only used to provide additional information
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# for the hash computation, mainly used for testing, debugging or out of
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# tree config registration.
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additional_config: Union[dict, SupportsHash] = field(default_factory=dict)
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"""Additional config for specified platform. Different platforms may
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support different configs. Make sure the configs are valid for the platform
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you are using. Contents must be hashable."""
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instance_id: str = ""
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"""The ID of the vLLM instance."""
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def compute_hash(self) -> str:
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"""
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WARNING: Whenever a new field is added to this config,
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ensure that it is included in the factors list if
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it affects the computation graph.
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Provide a hash that uniquely identifies all the configs
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that affect the structure of the computation
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graph from input ids/embeddings to the final hidden states,
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excluding anything before input ids/embeddings and after
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the final hidden states.
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"""
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factors: list[Any] = []
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# summarize vllm config
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vllm_factors: list[Any] = []
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from vllm import __version__
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vllm_factors.append(__version__)
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vllm_factors.append(envs.VLLM_USE_V1)
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if self.model_config:
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vllm_factors.append(self.model_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.cache_config:
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vllm_factors.append(self.cache_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.parallel_config:
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vllm_factors.append(self.parallel_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.scheduler_config:
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vllm_factors.append(self.scheduler_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.device_config:
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vllm_factors.append(self.device_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.load_config:
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vllm_factors.append(self.load_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.lora_config:
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vllm_factors.append(self.lora_config.compute_hash())
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# LoRA creates static buffers based on max_num_batched_tokens.
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# The tensor sizes and strides get captured in the torch.compile
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# graph explicitly.
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vllm_factors.append(
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str(self.scheduler_config.max_num_batched_tokens))
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else:
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vllm_factors.append("None")
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if self.speculative_config:
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vllm_factors.append(self.speculative_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.structured_outputs_config:
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vllm_factors.append(self.structured_outputs_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.observability_config:
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vllm_factors.append(self.observability_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.quant_config:
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pass # should be captured by model_config.quantization
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if self.compilation_config:
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vllm_factors.append(self.compilation_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.kv_transfer_config:
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vllm_factors.append(self.kv_transfer_config.compute_hash())
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else:
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vllm_factors.append("None")
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if self.additional_config:
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if isinstance(additional_config := self.additional_config, dict):
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additional_config_hash = hashlib.md5(
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json.dumps(additional_config, sort_keys=True).encode(),
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usedforsecurity=False,
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).hexdigest()
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else:
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additional_config_hash = additional_config.compute_hash()
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vllm_factors.append(additional_config_hash)
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else:
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vllm_factors.append("None")
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factors.append(vllm_factors)
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hash_str = hashlib.md5(str(factors).encode(),
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usedforsecurity=False).hexdigest()[:10]
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return hash_str
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def pad_for_cudagraph(self, batch_size: int) -> int:
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# if batch_size > self.compilation_config.max_capture_size,
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# it should raise an IndexError.
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# the caller should make sure the batch_size is within the range,
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# i.e., batch_size <= self.compilation_config.max_capture_size
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return self.compilation_config.bs_to_padded_graph_size[batch_size]
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@staticmethod
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def _get_quantization_config(
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model_config: ModelConfig,
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load_config: LoadConfig) -> Optional[QuantizationConfig]:
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"""Get the quantization config."""
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from vllm.platforms import current_platform
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if model_config.quantization is not None:
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from vllm.model_executor.model_loader.weight_utils import (
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get_quant_config)
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quant_config = get_quant_config(model_config, load_config)
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capability_tuple = current_platform.get_device_capability()
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if capability_tuple is not None:
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capability = capability_tuple.to_int()
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if capability < quant_config.get_min_capability():
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raise ValueError(
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f"The quantization method {model_config.quantization} "
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"is not supported for the current GPU. Minimum "
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f"capability: {quant_config.get_min_capability()}. "
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f"Current capability: {capability}.")
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supported_dtypes = quant_config.get_supported_act_dtypes()
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if model_config.dtype not in supported_dtypes:
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raise ValueError(
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f"{model_config.dtype} is not supported for quantization "
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f"method {model_config.quantization}. Supported dtypes: "
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f"{supported_dtypes}")
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return quant_config
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return None
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@staticmethod
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def get_quantization_config(
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model_config: ModelConfig,
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load_config: LoadConfig) -> Optional[QuantizationConfig]:
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import copy
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# For some reason, the _ version of this modifies the model_config
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# object, so using deepcopy to avoid this problem.
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return VllmConfig._get_quantization_config(copy.deepcopy(model_config),
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load_config)
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def with_hf_config(
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self,
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hf_config: PretrainedConfig,
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architectures: Optional[list[str]] = None,
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) -> "VllmConfig":
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if architectures is not None:
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hf_config = copy.deepcopy(hf_config)
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hf_config.architectures = architectures
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model_config = copy.deepcopy(self.model_config)
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model_config.hf_config = hf_config
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return replace(self, model_config=model_config)
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def __post_init__(self):
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"""Verify configs are valid & consistent with each other.
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"""
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self.try_verify_and_update_config()
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if self.model_config is not None:
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self.model_config.verify_async_output_proc(self.parallel_config,
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self.speculative_config,
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self.device_config)
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self.model_config.verify_with_parallel_config(self.parallel_config)
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self.model_config.verify_dual_chunk_attention_config(
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self.load_config)
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self.cache_config.verify_with_parallel_config(self.parallel_config)
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if self.lora_config is not None:
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self.lora_config.verify_with_cache_config(self.cache_config)
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self.lora_config.verify_with_model_config(self.model_config)
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if self.quant_config is None and self.model_config is not None:
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self.quant_config = VllmConfig._get_quantization_config(
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self.model_config, self.load_config)
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from vllm.platforms import current_platform
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if self.model_config is not None and \
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self.scheduler_config.chunked_prefill_enabled and \
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self.model_config.dtype == torch.float32 and \
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current_platform.get_device_capability() == (7, 5):
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logger.warning_once(
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"Turing devices tensor cores do not support float32 matmul. "
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|
"To workaround this limitation, vLLM will set 'ieee' input "
|
|
"precision for chunked prefill triton kernels.")
|
|
|
|
# If the user does not explicitly set a compilation level, then
|
|
# we use the default level. The default level depends on other
|
|
# settings (see the below code).
|
|
if self.compilation_config.level is None:
|
|
if envs.VLLM_USE_V1:
|
|
if (self.model_config is not None
|
|
and not self.model_config.enforce_eager):
|
|
self.compilation_config.level = CompilationLevel.PIECEWISE
|
|
else:
|
|
self.compilation_config.level = \
|
|
CompilationLevel.NO_COMPILATION
|
|
|
|
else:
|
|
# NB: Passing both --enforce-eager and a compilation level
|
|
# in V0 means the compilation level wins out.
|
|
self.compilation_config.level = CompilationLevel.NO_COMPILATION
|
|
|
|
# async tp is built on top of sequence parallelism
|
|
# and requires it to be enabled.
|
|
if self.compilation_config.pass_config.enable_async_tp:
|
|
self.compilation_config.pass_config.enable_sequence_parallelism = \
|
|
True
|
|
if self.compilation_config.pass_config.enable_sequence_parallelism:
|
|
self.compilation_config.custom_ops.append("+rms_norm")
|
|
|
|
if current_platform.is_cuda_alike() or current_platform.is_xpu():
|
|
# if cudagraph_mode is not explicitly set by users, set default
|
|
# value
|
|
if self.compilation_config.cudagraph_mode is None:
|
|
if envs.VLLM_USE_V1 and self.compilation_config.level \
|
|
== CompilationLevel.PIECEWISE:
|
|
self.compilation_config.cudagraph_mode = \
|
|
CUDAGraphMode.PIECEWISE
|
|
else:
|
|
self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
|
|
|
|
# disable cudagraph when enforce eager execution
|
|
if self.model_config is not None and \
|
|
self.model_config.enforce_eager:
|
|
logger.info("Cudagraph is disabled under eager mode")
|
|
self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
|
|
elif envs.VLLM_USE_V1:
|
|
self.compilation_config.cudagraph_num_of_warmups = 1
|
|
|
|
self._set_cudagraph_sizes()
|
|
else:
|
|
self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
|
|
|
|
if self.cache_config.cpu_offload_gb > 0 and \
|
|
self.compilation_config.level != CompilationLevel.NO_COMPILATION \
|
|
and not envs.VLLM_USE_V1:
|
|
logger.warning(
|
|
"CPU offload is not supported with `torch.compile` in v0 yet."
|
|
" Disabling `torch.compile`.")
|
|
self.compilation_config.level = CompilationLevel.NO_COMPILATION
|
|
|
|
if self.cache_config.kv_sharing_fast_prefill:
|
|
if not envs.VLLM_USE_V1:
|
|
raise NotImplementedError(
|
|
"Fast prefill optimization for KV sharing is not supported "
|
|
"in V0 currently.")
|
|
|
|
if self.speculative_config is not None and \
|
|
self.speculative_config.use_eagle():
|
|
raise NotImplementedError(
|
|
"Fast prefill optimization for KV sharing is not "
|
|
"compatible with EAGLE as EAGLE requires correct logits "
|
|
"for all tokens while fast prefill gives incorrect logits "
|
|
"for prompt tokens.")
|
|
|
|
logger.warning_once(
|
|
"--kv-sharing-fast-prefill requires changes on model side for "
|
|
"correctness and to realize prefill savings. ")
|
|
|
|
if ((not envs.VLLM_USE_V1) and self.lora_config is not None
|
|
and self.compilation_config.level
|
|
!= CompilationLevel.NO_COMPILATION):
|
|
logger.warning(
|
|
"LoRA for V0 is not supported with `torch.compile` yet. "
|
|
"Disabling `torch.compile`.")
|
|
self.compilation_config.level = CompilationLevel.NO_COMPILATION
|
|
|
|
disable_chunked_prefill_reasons: list[str] = []
|
|
|
|
if self.model_config:
|
|
if self.model_config.pooler_config:
|
|
pooling_type = self.model_config.pooler_config.pooling_type
|
|
if pooling_type is None or pooling_type.lower() != "last":
|
|
disable_chunked_prefill_reasons.append(
|
|
"Only \"last\" pooling supports chunked "
|
|
"prefill and prefix caching; disabling both.")
|
|
if not getattr(self.model_config.hf_config, "is_causal", True):
|
|
disable_chunked_prefill_reasons.append(
|
|
"Only models using causal attention supports chunked "
|
|
"prefill and prefix caching; disabling both.")
|
|
elif self.model_config.is_encoder_decoder:
|
|
self.scheduler_config.max_num_encoder_input_tokens = \
|
|
MULTIMODAL_REGISTRY.get_encdec_max_encoder_len(self.model_config)
|
|
logger.debug(
|
|
"Encoder-decoder model detected: setting "
|
|
"`max_num_encoder_input_tokens` to encoder length (%s)",
|
|
self.scheduler_config.max_num_encoder_input_tokens)
|
|
self.scheduler_config.disable_chunked_mm_input = True
|
|
disable_chunked_prefill_reasons.append(
|
|
"Encoder-decoder models do not support chunked prefill nor"
|
|
" prefix caching; disabling both.")
|
|
if (self.model_config.architecture
|
|
== "WhisperForConditionalGeneration"
|
|
and os.environ.get("VLLM_WORKER_MULTIPROC_METHOD")
|
|
!= "spawn"):
|
|
logger.warning(
|
|
"Whisper is known to have issues with "
|
|
"forked workers. If startup is hanging, "
|
|
"try setting 'VLLM_WORKER_MULTIPROC_METHOD' "
|
|
"to 'spawn'.")
|
|
|
|
if disable_chunked_prefill_reasons:
|
|
for reason in disable_chunked_prefill_reasons:
|
|
logger.info(reason)
|
|
self.scheduler_config.chunked_prefill_enabled = False
|
|
self.scheduler_config.long_prefill_token_threshold = 0
|
|
|
|
if self.cache_config is not None:
|
|
self.cache_config.enable_prefix_caching = False
|
|
|
|
if (self.kv_events_config is not None
|
|
and self.kv_events_config.enable_kv_cache_events
|
|
and not self.cache_config.enable_prefix_caching):
|
|
logger.warning(
|
|
"KV cache events are on, but prefix caching is not enabled."
|
|
"Use --enable-prefix-caching to enable.")
|
|
if (self.kv_events_config is not None
|
|
and self.kv_events_config.publisher != "null"
|
|
and not self.kv_events_config.enable_kv_cache_events):
|
|
logger.warning("KV cache events are disabled,"
|
|
"but the scheduler is configured to publish them."
|
|
"Modify KVEventsConfig.enable_kv_cache_events"
|
|
"to True to enable.")
|
|
current_platform.check_and_update_config(self)
|
|
|
|
# final check of cudagraph mode after platform-specific update
|
|
if envs.VLLM_USE_V1 and current_platform.is_cuda_alike():
|
|
if self.compilation_config.cudagraph_mode == CUDAGraphMode.FULL \
|
|
and self.model_config is not None and \
|
|
not self.model_config.disable_cascade_attn:
|
|
logger.info("CUDAGraphMode.FULL is not supported with "
|
|
"cascade attention currently. Disabling cascade"
|
|
"attention.")
|
|
self.model_config.disable_cascade_attn = True
|
|
|
|
if self.compilation_config.cudagraph_mode\
|
|
.requires_piecewise_compilation():
|
|
assert self.compilation_config.level == \
|
|
CompilationLevel.PIECEWISE, \
|
|
"Compilation level should be CompilationLevel.PIECEWISE "\
|
|
"when cudagraph_mode piecewise cudagraphs is used, "\
|
|
f"cudagraph_mode={self.compilation_config.cudagraph_mode}"
|
|
|
|
if self.parallel_config.enable_dbo:
|
|
a2a_backend = envs.VLLM_ALL2ALL_BACKEND
|
|
assert a2a_backend == "deepep_low_latency", \
|
|
"Microbatching currently only supports the deepep_low_latency "\
|
|
f"all2all backend. {a2a_backend} is not supported. To fix set "\
|
|
"the VLLM_ALL2ALL_BACKEND environment variable to "\
|
|
"deepep_low_latency and install the DeepEP kerenls."
|
|
|
|
if not self.instance_id:
|
|
self.instance_id = random_uuid()[:5]
|
|
|
|
# Do this after all the updates to compilation_config.level
|
|
if envs.VLLM_USE_V1 and \
|
|
self.compilation_config.level == CompilationLevel.PIECEWISE:
|
|
self.compilation_config.set_splitting_ops_for_v1()
|
|
|
|
if (envs.VLLM_USE_V1
|
|
and not self.scheduler_config.disable_hybrid_kv_cache_manager):
|
|
# logger should only print warning message for hybrid models. As we
|
|
# can't know whether the model is hybrid or not now, so we don't log
|
|
# warning message here and will log it later.
|
|
if not current_platform.support_hybrid_kv_cache():
|
|
# Hybrid KV cache manager is not supported on non-GPU platforms.
|
|
self.scheduler_config.disable_hybrid_kv_cache_manager = True
|
|
if self.kv_transfer_config is not None:
|
|
# Hybrid KV cache manager is not compatible with KV transfer.
|
|
self.scheduler_config.disable_hybrid_kv_cache_manager = True
|
|
if self.kv_events_config is not None:
|
|
# Hybrid KV cache manager is not compatible with KV events.
|
|
self.scheduler_config.disable_hybrid_kv_cache_manager = True
|
|
if self.model_config is not None and \
|
|
self.model_config.attention_chunk_size is not None:
|
|
if self.speculative_config is not None and \
|
|
self.speculative_config.use_eagle():
|
|
# Hybrid KV cache manager is not yet supported with chunked
|
|
# local attention + eagle.
|
|
self.scheduler_config.disable_hybrid_kv_cache_manager = True
|
|
elif \
|
|
not envs.VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE:
|
|
logger.warning(
|
|
"There is a latency regression when using chunked local"
|
|
" attention with the hybrid KV cache manager. Disabling"
|
|
" it, by default. To enable it, set the environment "
|
|
"VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE=1."
|
|
)
|
|
# Hybrid KV cache manager is not yet supported with chunked
|
|
# local attention.
|
|
self.scheduler_config.disable_hybrid_kv_cache_manager = True
|
|
|
|
def update_sizes_for_sequence_parallelism(self,
|
|
possible_sizes: list) -> list:
|
|
# remove the sizes that not multiple of tp_size when
|
|
# enable sequence parallelism
|
|
removed_sizes = [
|
|
size for size in possible_sizes
|
|
if size % self.parallel_config.tensor_parallel_size != 0
|
|
]
|
|
if removed_sizes:
|
|
logger.warning(
|
|
"Batch sizes %s are removed because they are not "
|
|
"multiple of tp_size %d when "
|
|
"sequence parallelism is enabled", removed_sizes,
|
|
self.parallel_config.tensor_parallel_size)
|
|
|
|
return [
|
|
size for size in possible_sizes
|
|
if size % self.parallel_config.tensor_parallel_size == 0
|
|
]
|
|
|
|
def _set_cudagraph_sizes(self):
|
|
"""
|
|
vLLM defines the default candidate list of batch sizes for CUDA graph
|
|
capture as:
|
|
|
|
```python
|
|
max_graph_size = min(max_num_seqs * 2, 512)
|
|
# 1, 2, 4, then multiples of 8 up to max_graph_size
|
|
cuda_graph_sizes = [1, 2, 4, 8, 16, 24, 32, 40, ..., max_graph_size]
|
|
|
|
In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
|
|
will be the final sizes to capture cudagraph (in descending order).
|
|
|
|
These sizes are used to capture and reuse CUDA graphs for
|
|
performance-critical paths (e.g., decoding). Capturing enables
|
|
significantly faster kernel dispatch by avoiding Python overhead. The
|
|
list is then filtered based on `max_num_batched_tokens` (e.g., 8192 on
|
|
most GPUs), which controls the total allowed number of tokens in a
|
|
batch. Since each sequence may have a variable number of tokens, the
|
|
maximum usable batch size will depend on actual sequence lengths.
|
|
|
|
Example:
|
|
With `max_num_batched_tokens = 8192`, and typical sequences
|
|
averaging ~32 tokens, most practical batch sizes fall below 256.
|
|
However, the system will still allow capture sizes up to 512 if
|
|
shape and memory permit.
|
|
|
|
Note:
|
|
If users explicitly specify cudagraph capture sizes in the
|
|
compilation config, those will override this default logic.
|
|
At runtime:
|
|
|
|
- If batch size <= one of the `cudagraph_capture_sizes`, the closest
|
|
padded CUDA graph will be used.
|
|
- If batch size > largest `cudagraph_capture_sizes`, cudagraph will
|
|
not be used.
|
|
"""
|
|
|
|
# calculate the default `batch_size_capture_list`
|
|
if not envs.VLLM_USE_V1:
|
|
batch_size_capture_list = []
|
|
if self.scheduler_config is not None and \
|
|
self.model_config is not None and \
|
|
not self.model_config.enforce_eager:
|
|
|
|
possible_sizes = [1, 2, 4] + [8 * i for i in range(1, 1025)]
|
|
if self.parallel_config.tensor_parallel_size > 1 and \
|
|
self.compilation_config.pass_config.enable_sequence_parallelism:
|
|
possible_sizes = self.update_sizes_for_sequence_parallelism(
|
|
possible_sizes)
|
|
|
|
# find the minimum size that is larger than max_num_seqs,
|
|
# which then becomes the max_batchsize_to_capture
|
|
larger_sizes = [
|
|
x for x in possible_sizes
|
|
if x >= self.scheduler_config.max_num_seqs
|
|
]
|
|
if larger_sizes:
|
|
max_batchsize_to_capture = larger_sizes[0]
|
|
else:
|
|
max_batchsize_to_capture = possible_sizes[-1]
|
|
|
|
# filter out the sizes that are
|
|
# larger than max_batchsize_to_capture
|
|
batch_size_capture_list = [
|
|
size for size in possible_sizes
|
|
if size <= max_batchsize_to_capture
|
|
]
|
|
else:
|
|
batch_size_capture_list = []
|
|
if self.model_config is not None and \
|
|
not self.model_config.enforce_eager:
|
|
cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes
|
|
if len(cuda_graph_sizes) == 1:
|
|
batch_size_capture_list = [1, 2, 4] + [
|
|
i for i in range(8, cuda_graph_sizes[0] + 1, 8)
|
|
]
|
|
elif len(cuda_graph_sizes) > 1:
|
|
batch_size_capture_list = sorted(cuda_graph_sizes)
|
|
else:
|
|
raise TypeError(f"Invalid value for {cuda_graph_sizes=}.")
|
|
if self.parallel_config.tensor_parallel_size > 1 and \
|
|
self.compilation_config.pass_config.enable_sequence_parallelism:
|
|
batch_size_capture_list = \
|
|
self.update_sizes_for_sequence_parallelism(batch_size_capture_list)
|
|
max_num_tokens = self.scheduler_config.max_num_batched_tokens
|
|
batch_size_capture_list = [
|
|
size for size in batch_size_capture_list
|
|
if size <= max_num_tokens
|
|
]
|
|
|
|
self.compilation_config.init_with_cudagraph_sizes(
|
|
batch_size_capture_list)
|
|
|
|
def recalculate_max_model_len(self, max_model_len: int):
|
|
# Can only be called in try_verify_and_update_config
|
|
model_config = self.model_config
|
|
max_model_len = model_config.get_and_verify_max_len(max_model_len)
|
|
self.model_config.max_model_len = max_model_len
|
|
self.scheduler_config.max_model_len = max_model_len
|
|
|
|
def try_verify_and_update_config(self):
|
|
if self.model_config is None:
|
|
return
|
|
|
|
# Avoid running try_verify_and_update_config multiple times
|
|
if getattr(self.model_config, "config_updated", False):
|
|
return
|
|
self.model_config.config_updated = True
|
|
|
|
architecture = self.model_config.architecture
|
|
if architecture is None:
|
|
return
|
|
|
|
from vllm.model_executor.models.config import (
|
|
MODELS_CONFIG_MAP, HybridAttentionMambaModelConfig)
|
|
cls = MODELS_CONFIG_MAP.get(architecture, None)
|
|
if cls is not None:
|
|
cls.verify_and_update_config(self)
|
|
|
|
if self.model_config.is_hybrid:
|
|
HybridAttentionMambaModelConfig.verify_and_update_config(self)
|
|
|
|
if self.model_config.convert_type == "classify":
|
|
# Maybe convert ForCausalLM into ForSequenceClassification model.
|
|
from vllm.model_executor.models.adapters import (
|
|
SequenceClassificationConfig)
|
|
SequenceClassificationConfig.verify_and_update_config(self)
|
|
|
|
if hasattr(self.model_config, "model_weights") and is_runai_obj_uri(
|
|
self.model_config.model_weights):
|
|
if self.load_config.load_format == "auto":
|
|
logger.info("Detected Run:ai model config. "
|
|
"Overriding `load_format` to 'runai_streamer'")
|
|
self.load_config.load_format = "runai_streamer"
|
|
elif self.load_config.load_format != "runai_streamer":
|
|
raise ValueError(f"To load a model from S3, 'load_format' "
|
|
f"must be 'runai_streamer', "
|
|
f"but got '{self.load_config.load_format}'. "
|
|
f"Model: {self.model_config.model}")
|
|
|
|
def __str__(self):
|
|
return (
|
|
f"model={self.model_config.model!r}, "
|
|
f"speculative_config={self.speculative_config!r}, "
|
|
f"tokenizer={self.model_config.tokenizer!r}, "
|
|
f"skip_tokenizer_init={self.model_config.skip_tokenizer_init}, "
|
|
f"tokenizer_mode={self.model_config.tokenizer_mode}, "
|
|
f"revision={self.model_config.revision}, "
|
|
f"tokenizer_revision={self.model_config.tokenizer_revision}, "
|
|
f"trust_remote_code={self.model_config.trust_remote_code}, "
|
|
f"dtype={self.model_config.dtype}, "
|
|
f"max_seq_len={self.model_config.max_model_len}, "
|
|
f"download_dir={self.load_config.download_dir!r}, "
|
|
f"load_format={self.load_config.load_format}, "
|
|
f"tensor_parallel_size={self.parallel_config.tensor_parallel_size}, " # noqa
|
|
f"pipeline_parallel_size={self.parallel_config.pipeline_parallel_size}, " # noqa
|
|
f"data_parallel_size={self.parallel_config.data_parallel_size}, " # noqa
|
|
f"disable_custom_all_reduce={self.parallel_config.disable_custom_all_reduce}, " # noqa
|
|
f"quantization={self.model_config.quantization}, "
|
|
f"enforce_eager={self.model_config.enforce_eager}, "
|
|
f"kv_cache_dtype={self.cache_config.cache_dtype}, "
|
|
f"device_config={self.device_config.device}, "
|
|
f"structured_outputs_config={self.structured_outputs_config!r}, "
|
|
f"observability_config={self.observability_config!r}, "
|
|
f"seed={self.model_config.seed}, "
|
|
f"served_model_name={self.model_config.served_model_name}, "
|
|
f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
|
|
f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, " # noqa
|
|
f"use_async_output_proc={self.model_config.use_async_output_proc}, "
|
|
f"pooler_config={self.model_config.pooler_config!r}, "
|
|
f"compilation_config={self.compilation_config!r}")
|
|
|
|
|
|
_current_vllm_config: Optional[VllmConfig] = None
|
|
_current_prefix: Optional[str] = None
|
|
|
|
|
|
@contextmanager
|
|
def set_current_vllm_config(vllm_config: VllmConfig,
|
|
check_compile=False,
|
|
prefix: Optional[str] = None):
|
|
"""
|
|
Temporarily set the current vLLM config.
|
|
Used during model initialization.
|
|
We save the current vLLM config in a global variable,
|
|
so that all modules can access it, e.g. custom ops
|
|
can access the vLLM config to determine how to dispatch.
|
|
"""
|
|
global _current_vllm_config, _current_prefix
|
|
old_vllm_config = _current_vllm_config
|
|
old_prefix = _current_prefix
|
|
from vllm.compilation.counter import compilation_counter
|
|
num_models_seen = compilation_counter.num_models_seen
|
|
try:
|
|
_current_vllm_config = vllm_config
|
|
_current_prefix = prefix
|
|
yield
|
|
except Exception:
|
|
raise
|
|
else:
|
|
logger.debug("enabled custom ops: %s",
|
|
vllm_config.compilation_config.enabled_custom_ops)
|
|
logger.debug("disabled custom ops: %s",
|
|
vllm_config.compilation_config.disabled_custom_ops)
|
|
if check_compile and \
|
|
vllm_config.compilation_config.level == CompilationLevel.PIECEWISE \
|
|
and compilation_counter.num_models_seen == num_models_seen:
|
|
# If the model supports compilation,
|
|
# compilation_counter.num_models_seen should be increased
|
|
# by at least 1.
|
|
# If it is not increased, it means the model does not support
|
|
# compilation (does not have @support_torch_compile decorator).
|
|
logger.warning(
|
|
"`torch.compile` is turned on, but the model %s"
|
|
" does not support it. Please open an issue on GitHub"
|
|
" if you want it to be supported.",
|
|
vllm_config.model_config.model)
|
|
finally:
|
|
_current_vllm_config = old_vllm_config
|
|
_current_prefix = old_prefix
|
|
# Clear the compilation config cache when context changes
|
|
get_cached_compilation_config.cache_clear()
|
|
|
|
|
|
@lru_cache(maxsize=1)
|
|
def get_cached_compilation_config():
|
|
"""Cache config to avoid repeated calls to get_current_vllm_config()"""
|
|
return get_current_vllm_config().compilation_config
|
|
|
|
|
|
def get_current_vllm_config() -> VllmConfig:
|
|
if _current_vllm_config is None:
|
|
# in ci, usually when we test custom ops/modules directly,
|
|
# we don't set the vllm config. In that case, we set a default
|
|
# config.
|
|
logger.warning("Current vLLM config is not set.")
|
|
from vllm.config import VllmConfig
|
|
return VllmConfig()
|
|
return _current_vllm_config
|
|
|
|
|
|
def get_current_model_prefix() -> str:
|
|
"""
|
|
Get the prefix of the model that's currently being initialized.
|
|
"""
|
|
assert _current_prefix is not None, \
|
|
"Current model prefix is not set. "
|
|
return _current_prefix
|
|
|
|
|
|
T = TypeVar("T")
|
|
|
|
|
|
def get_layers_from_vllm_config(
|
|
vllm_config: VllmConfig,
|
|
layer_type: type[T],
|
|
layer_names: Optional[list[str]] = None) -> dict[str, T]:
|
|
"""
|
|
Get layers from the vLLM config.
|
|
|
|
Args:
|
|
vllm_config: The vLLM config.
|
|
layer_type: The type of the layer to get.
|
|
layer_names: The names of the layers to get. If None, return all layers.
|
|
"""
|
|
|
|
if layer_names is None:
|
|
layer_names = list(
|
|
vllm_config.compilation_config.static_forward_context.keys())
|
|
|
|
forward_context = vllm_config.compilation_config.static_forward_context
|
|
|
|
return {
|
|
layer_name: forward_context[layer_name]
|
|
for layer_name in layer_names
|
|
if isinstance(forward_context[layer_name], layer_type)
|
|
}
|
|
|
|
|
|
@config
|
|
@dataclass
|
|
class SpeechToTextConfig:
|
|
"""Configuration for speech-to-text models."""
|
|
|
|
sample_rate: float = 16_000
|
|
"""Sample rate (Hz) to resample input audio to. Most speech models expect
|
|
16kHz audio input. The input audio will be automatically resampled to this
|
|
rate before processing."""
|
|
|
|
max_audio_clip_s: int = 30
|
|
"""Maximum duration in seconds for a single audio clip without chunking.
|
|
Audio longer than this will be split into smaller chunks if
|
|
`allow_audio_chunking` evaluates to True, otherwise it will be rejected."""
|
|
|
|
overlap_chunk_second: int = 1
|
|
"""Overlap duration in seconds between consecutive audio chunks when
|
|
splitting long audio. This helps maintain context across chunk boundaries
|
|
and improves transcription quality at split points."""
|
|
|
|
min_energy_split_window_size: Optional[int] = 1600
|
|
"""Window size in samples for finding low-energy (quiet) regions to split
|
|
audio chunks. The algorithm looks for the quietest moment within this
|
|
window to minimize cutting through speech. Default 1600 samples ≈ 100ms
|
|
at 16kHz. If None, no chunking will be done."""
|
|
|
|
@property
|
|
def allow_audio_chunking(self) -> bool:
|
|
return self.min_energy_split_window_size is not None
|
|
|
|
|
|
def update_config(config: DataclassInstanceT,
|
|
overrides: dict[str, Any]) -> DataclassInstanceT:
|
|
processed_overrides = {}
|
|
for field_name, value in overrides.items():
|
|
assert hasattr(
|
|
config, field_name), f"{type(config)} has no field `{field_name}`"
|
|
current_value = getattr(config, field_name)
|
|
if is_dataclass(current_value) and not is_dataclass(value):
|
|
assert isinstance(value, dict), (
|
|
f"Overrides to {type(config)}.{field_name} must be a dict"
|
|
f" or {type(current_value)}, but got {type(value)}")
|
|
value = update_config(
|
|
current_value, # type: ignore[type-var]
|
|
value)
|
|
processed_overrides[field_name] = value
|
|
return replace(config, **processed_overrides)
|