488 lines
16 KiB
Python
488 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import functools
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from collections import UserDict
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from collections.abc import Mapping
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from dataclasses import dataclass
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from typing import (TYPE_CHECKING, Any, Callable, NamedTuple, Optional,
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Protocol, Union)
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from torch import nn
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from transformers import BatchFeature, PretrainedConfig, ProcessorMixin
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from typing_extensions import TypeVar, assert_never
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from vllm.logger import init_logger
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from vllm.transformers_utils.processor import cached_processor_from_config
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
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resolve_mm_processor_kwargs)
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from .data import ProcessorInputs, SingletonInputs
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from .parse import split_enc_dec_inputs
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if TYPE_CHECKING:
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from vllm.config import ModelConfig
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from vllm.multimodal import (MultiModalDataDict, MultiModalPlaceholderDict,
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MultiModalRegistry)
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from vllm.sequence import SequenceData
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logger = init_logger(__name__)
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_T = TypeVar("_T")
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_C = TypeVar("_C", bound=PretrainedConfig, default=PretrainedConfig)
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_P = TypeVar("_P", bound=ProcessorMixin, default=ProcessorMixin)
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@dataclass(frozen=True)
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class InputContext:
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"""
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Contains information about the model which may be used to
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modify the inputs.
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"""
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model_config: "ModelConfig"
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"""The configuration of the model."""
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def get_hf_config(
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self,
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typ: Union[type[_C], tuple[type[_C], ...]] = PretrainedConfig,
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/,
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) -> _C:
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"""
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Get the HuggingFace configuration
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(:class:`transformers.PretrainedConfig`) of the model,
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additionally checking its type.
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Raises:
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TypeError: If the configuration is not of the specified type.
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"""
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hf_config = self.model_config.hf_config
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if not isinstance(hf_config, typ):
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raise TypeError("Invalid type of HuggingFace config. "
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f"Expected type: {typ}, but "
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f"found type: {type(hf_config)}")
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return hf_config
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def get_hf_image_processor_config(self) -> dict[str, Any]:
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"""
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Get the HuggingFace image processor configuration of the model.
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"""
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return self.model_config.hf_image_processor_config
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def get_mm_config(self):
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"""
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Get the multimodal config of the model.
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Raises:
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RuntimeError: If the model is not a multimodal model.
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"""
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mm_config = self.model_config.multimodal_config
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if mm_config is None:
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raise RuntimeError("Not a multimodal model")
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return mm_config
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def get_hf_processor(
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self,
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typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
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/,
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**kwargs: object,
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) -> _P:
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"""
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Get the HuggingFace processor
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(:class:`transformers.ProcessorMixin`) of the model,
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additionally checking its type.
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Raises:
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TypeError: If the processor is not of the specified type.
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"""
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return cached_processor_from_config(
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self.model_config,
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processor_cls=typ,
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**kwargs,
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)
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def init_processor(
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self,
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typ: type[_T],
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/,
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**kwargs: object,
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) -> _T:
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"""
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Initialize a HuggingFace-like processor class, merging the
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keyword arguments with those in the model's configuration.
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"""
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base_kwargs = self.model_config.mm_processor_kwargs
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if base_kwargs is None:
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base_kwargs = {}
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merged_kwargs = {**base_kwargs, **kwargs}
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return typ(**merged_kwargs)
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@dataclass(frozen=True)
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class InputProcessingContext(InputContext):
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tokenizer: AnyTokenizer
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"""The tokenizer used to tokenize the inputs."""
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def get_hf_processor(
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self,
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typ: Union[type[_P], tuple[type[_P], ...]] = ProcessorMixin,
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/,
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**kwargs: object,
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) -> _P:
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return super().get_hf_processor(
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typ,
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tokenizer=self.tokenizer,
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**kwargs,
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)
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def call_hf_processor(
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self,
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hf_processor: ProcessorMixin,
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data: Mapping[str, object],
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kwargs: Mapping[str, object] = {},
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) -> BatchFeature:
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"""
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Call :code:`hf_processor` on the prompt :code:`data`
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(text, image, audio...) with configurable options :code:`kwargs`.
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"""
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assert callable(hf_processor)
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base_kwargs = self.model_config.mm_processor_kwargs
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if base_kwargs is None:
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base_kwargs = {}
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merged_kwargs = resolve_mm_processor_kwargs(
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base_kwargs,
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kwargs,
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hf_processor,
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requires_kw_only=False,
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allow_var_kwargs=True,
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)
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try:
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return hf_processor(**data, **merged_kwargs, return_tensors="pt")
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except Exception as exc:
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msg = (f"Failed to apply {type(hf_processor).__name__} "
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f"on data={data} with kwargs={merged_kwargs}")
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raise RuntimeError(msg) from exc
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N = TypeVar("N", bound=type[nn.Module])
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class DummyData(NamedTuple):
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"""Dummy data used for profiling."""
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seq_data: "SequenceData"
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multi_modal_data: Optional["MultiModalDataDict"] = None
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multi_modal_placeholders: Optional["MultiModalPlaceholderDict"] = None
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class DummyDataFactory(Protocol):
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def __call__(
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self,
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ctx: InputContext,
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seq_len: int,
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mm_counts: Mapping[str, int],
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**mm_processor_kwargs: Any,
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) -> DummyData:
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"""
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Create dummy data to be inputted into the model.
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Note:
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:data:`InputProcessor` is not applied to the dummy data.
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The :code:`mm_processor_kwargs` are overrides provided at
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initialization time to values in the config whose values
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may affect the number of tokens per instance.
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"""
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...
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class _MultiModalCounts(UserDict[str, int]):
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"""
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Wraps `mm_counts` for a more informative error message
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when attempting to access a plugin that does not exist.
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"""
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def __getitem__(self, key: str) -> int:
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try:
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return super().__getitem__(key)
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except KeyError as exc:
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msg = (f"There is no multi-modal plugin with the key: {key}. "
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f"Available keys: {set(self.keys())}")
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raise KeyError(msg) from exc
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InputProcessor = Callable[[InputContext, ProcessorInputs], ProcessorInputs]
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"""Preprocess the inputs to the model."""
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class InputRegistry:
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"""
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A registry to dispatch data processing
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according to the target model.
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"""
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def __init__(self) -> None:
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self._dummy_factories_by_model_type = \
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ClassRegistry[nn.Module, DummyDataFactory]()
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self._dummy_encoder_factories_by_model_type = \
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ClassRegistry[nn.Module, DummyDataFactory]()
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self._input_processors_by_model_type = \
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ClassRegistry[nn.Module, InputProcessor]()
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def _default_dummy_data_factory(
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self,
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ctx: InputContext,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> DummyData:
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"""
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The default dummy data factory represents the longest possible text
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that can be inputted to the model.
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Note:
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:data:`InputProcessor` is not applied to the dummy data.
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"""
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# Avoid circular import
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from vllm.sequence import SequenceData
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return DummyData(SequenceData.from_prompt_token_counts((0, seq_len)))
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def register_dummy_data(self, factory: DummyDataFactory):
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"""
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Register a dummy data factory to a model class.
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During memory profiling, the provided function is invoked to create
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dummy data to be inputted into the model. The resulting memory usage
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should be an upper bound of what the model would use at inference time.
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"""
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def wrapper(model_cls: N) -> N:
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if self._dummy_factories_by_model_type.contains(model_cls,
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strict=True):
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logger.warning(
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"Model class %s already has dummy data "
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"registered to %s. It is overwritten by the new one.",
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model_cls, self)
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self._dummy_factories_by_model_type[model_cls] = factory
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return model_cls
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return wrapper
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def _get_dummy_data_factory(self, model_cls: type[nn.Module]):
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return self._dummy_factories_by_model_type \
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.get(model_cls, self._default_dummy_data_factory)
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def register_dummy_encoder_data(self, factory: DummyDataFactory):
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"""
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Register a dummy encoder data factory to a model class
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This is similar to :meth:`~register_dummy_data`, but for encoder input.
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"""
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def wrapper(model_cls: N) -> N:
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if self._dummy_encoder_factories_by_model_type.contains(
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model_cls, strict=True):
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logger.warning(
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"Model class %s already has dummy encoder data "
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"registered to %s. It is overwritten by the new one.",
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model_cls, self)
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self._dummy_encoder_factories_by_model_type[model_cls] = factory
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return model_cls
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return wrapper
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def _get_dummy_encoder_data_factory(self, model_cls: type[nn.Module]):
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return self._dummy_encoder_factories_by_model_type \
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.get(model_cls, self._default_dummy_data_factory)
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def dummy_data_for_profiling(
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self,
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model_config: "ModelConfig",
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seq_len: int,
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mm_registry: "MultiModalRegistry",
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is_encoder_data: bool = False,
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) -> DummyData:
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"""
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Create dummy data for profiling the memory usage of a model.
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The model is identified by ``model_config``.
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Note:
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This should be called after
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:meth:`~MultiModalRegistry.init_mm_limits_per_prompt`.
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"""
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# Avoid circular import
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from vllm.model_executor.model_loader import get_model_architecture
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from vllm.multimodal import MultiModalKwargs
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from vllm.multimodal.profiling import MultiModalProfiler
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from vllm.sequence import SequenceData
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if mm_registry.has_processor(model_config):
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processor = mm_registry.create_processor(model_config,
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disable_cache=True)
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profiler = MultiModalProfiler(processor)
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dummy_data_v1 = (profiler.get_encoder_dummy_data(seq_len)
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if is_encoder_data else
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profiler.get_decoder_dummy_data(seq_len))
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_seq_data = SequenceData.from_seqs(
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dummy_data_v1.prompt_token_ids) # type: ignore[attr-defined]
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dummy_data = DummyData(
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seq_data=_seq_data,
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multi_modal_data=getattr(dummy_data_v1, "multi_modal_data",
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None),
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multi_modal_placeholders=getattr(dummy_data_v1,
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"multi_modal_placeholders",
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None),
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)
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else:
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model_cls, _ = get_model_architecture(model_config)
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if is_encoder_data:
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dummy_factory = self._get_dummy_encoder_data_factory(model_cls)
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else:
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dummy_factory = self._get_dummy_data_factory(model_cls)
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mm_counts = mm_registry.get_mm_limits_per_prompt(model_config)
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mm_processor_kwargs = get_allowed_kwarg_only_overrides(
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dummy_factory,
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overrides=model_config.mm_processor_kwargs,
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requires_kw_only=False,
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allow_var_kwargs=True,
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)
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dummy_data = dummy_factory(InputContext(model_config), seq_len,
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_MultiModalCounts(mm_counts),
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**mm_processor_kwargs)
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# Having more tokens is over-conservative but otherwise fine
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num_tokens = dummy_data.seq_data.prompt_token_ids
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if len(num_tokens) < seq_len:
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if is_encoder_data:
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logger.warning_once(
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f"Expected at least {seq_len} dummy encoder tokens for "
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f"profiling, but found {len(num_tokens)} tokens instead.")
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else:
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raise AssertionError(
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f"Expected at least {seq_len} dummy tokens for profiling, "
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f"but found {len(num_tokens)} tokens instead.")
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if (dummy_data.multi_modal_data is not None and
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not isinstance(dummy_data.multi_modal_data, MultiModalKwargs)):
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for k, v in dummy_data.multi_modal_data.items():
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num_items = len(v) if isinstance(v, list) else 1
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num_expected = mm_counts[k]
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assert num_items >= num_expected, (
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f"Expected at least {num_expected} dummy '{k}' instances "
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f"for profiling, but found {num_items} instances instead.")
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return dummy_data
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def _default_input_processor(
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self,
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ctx: InputContext,
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inputs: ProcessorInputs,
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**kwargs: object,
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) -> ProcessorInputs:
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"""The default input processor is a no-op."""
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return inputs
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def register_input_processor(self, processor: InputProcessor):
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"""
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Register an input processor to a model class.
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The provided function is invoked on each input to the model. This
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happens before
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:meth:`~vllm.multimodal.registry.MultiModalRegistry.map_input`.
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"""
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def wrapper(model_cls: N) -> N:
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if self._input_processors_by_model_type.contains(model_cls,
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strict=True):
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logger.warning(
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"Model class %s already has input processor "
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"registered to %s. It is overwritten by the new one.",
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model_cls, self)
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self._input_processors_by_model_type[model_cls] = processor
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return model_cls
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return wrapper
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def _get_model_input_processor(self, model_cls: type[nn.Module]):
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return self._input_processors_by_model_type \
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.get(model_cls, self._default_input_processor)
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def _ensure_mm_kwargs(
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self,
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inputs: SingletonInputs,
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mm_processor_kwargs: dict[str, Any],
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):
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if inputs["type"] == "token":
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# In case the input processor for that model fails to set it
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if "mm_processor_kwargs" not in inputs:
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inputs["mm_processor_kwargs"] = mm_processor_kwargs
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elif inputs["type"] == "multimodal":
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# Be more strict in V2
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assert "mm_kwargs" in inputs
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else:
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assert_never(inputs["type"]) # type: ignore[arg-type]
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def process_input(self, model_config: "ModelConfig",
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inputs: ProcessorInputs) -> ProcessorInputs:
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"""
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Apply an input processor to an instance of model inputs.
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The model is identified by ``model_config``.
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"""
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# Avoid circular import
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from vllm.model_executor.model_loader import get_model_architecture
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model_cls, _ = get_model_architecture(model_config)
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processor = self._get_model_input_processor(model_cls)
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# Handle multimodal processor kwargs with priority:
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# Inference kwargs -> Init kwargs -> {}
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# If it's empty, it'll fall back to the default kwarg values
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mm_processor_kwargs = resolve_mm_processor_kwargs(
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model_config.mm_processor_kwargs,
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inputs.get("mm_processor_kwargs", {}), # type: ignore
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processor,
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requires_kw_only=False,
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allow_var_kwargs=True,
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)
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processed_inputs = processor(
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InputContext(model_config),
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inputs,
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**mm_processor_kwargs,
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)
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encoder_inputs, decoder_inputs = split_enc_dec_inputs(processed_inputs)
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if encoder_inputs is not None:
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self._ensure_mm_kwargs(encoder_inputs, mm_processor_kwargs)
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if decoder_inputs is not None:
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self._ensure_mm_kwargs(decoder_inputs, mm_processor_kwargs)
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return processed_inputs
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def create_input_processor(self, model_config: "ModelConfig"):
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"""
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Create an input processor (see :meth:`_process_input`) for a
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specific model.
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"""
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return functools.partial(self.process_input, model_config)
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