forked from EngineX-Cambricon/enginex-mlu370-vllm
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Chranos
321
vllm-v0.6.2/vllm/multimodal/registry.py
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321
vllm-v0.6.2/vllm/multimodal/registry.py
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import functools
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from collections import UserDict
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from typing import (TYPE_CHECKING, Any, Callable, Dict, Mapping, Optional,
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Sequence, Type, TypeVar)
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import torch.nn as nn
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from typing_extensions import TypeAlias
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from vllm.inputs import InputProcessingContext
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from vllm.logger import init_logger
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from .audio import AudioPlugin
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from .base import MultiModalInputMapper, MultiModalPlugin, MultiModalTokensCalc
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from .image import ImagePlugin
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from .inputs import MultiModalDataDict, MultiModalKwargs, NestedTensors
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from .processing import MultiModalProcessor
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from .video import VideoPlugin
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if TYPE_CHECKING:
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from vllm.config import ModelConfig
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logger = init_logger(__name__)
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N = TypeVar("N", bound=Type[nn.Module])
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MultiModalProcessorFactory: TypeAlias = Callable[[InputProcessingContext],
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MultiModalProcessor]
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"""
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Constructs a :class:`MultiModalProcessor` instance from the context.
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The processing metadata should be derived from the context.
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"""
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class _MultiModalLimits(UserDict["ModelConfig", Dict[str, int]]):
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"""
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Wraps `_limits_by_model` for a more informative error message
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when attempting to access a model that does not exist.
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"""
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def __getitem__(self, key: "ModelConfig") -> Dict[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"Cannot find `mm_limits` for model={key.model}. Did you "
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"forget to call `init_mm_limits_per_prompt`?")
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raise KeyError(msg) from exc
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class MultiModalRegistry:
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"""
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A registry that dispatches data processing to the
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:class:`~vllm.multimodal.MultiModalPlugin` for each modality.
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"""
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DEFAULT_PLUGINS = (ImagePlugin(), AudioPlugin(), VideoPlugin())
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def __init__(
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self,
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*,
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plugins: Sequence[MultiModalPlugin] = DEFAULT_PLUGINS) -> None:
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self._plugins = {p.get_data_key(): p for p in plugins}
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self._processor_factories: Dict[Type[nn.Module],
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MultiModalProcessorFactory] = {}
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# This is used for non-multimodal models
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self._disabled_limits_per_plugin = {k: 0 for k in self._plugins}
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self._limits_by_model = _MultiModalLimits()
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def register_plugin(self, plugin: MultiModalPlugin) -> None:
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"""
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Register a multi-modal plugin so it can be recognized by vLLM.
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See also:
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:ref:`adding_multimodal_plugin`
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"""
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data_type_key = plugin.get_data_key()
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if data_type_key in self._plugins:
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logger.warning(
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"A plugin is already registered for data type %s, "
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"and will be overwritten by the new plugin %s.", data_type_key,
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plugin)
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self._plugins[data_type_key] = plugin
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def _get_plugin(self, data_type_key: str):
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plugin = self._plugins.get(data_type_key)
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if plugin is not None:
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return plugin
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msg = f"Unknown multi-modal data type: {data_type_key}"
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raise NotImplementedError(msg)
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def register_input_mapper(
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self,
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data_type_key: str,
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mapper: Optional[MultiModalInputMapper] = None,
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):
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"""
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Register an input mapper for a specific modality to a model class.
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See :meth:`MultiModalPlugin.register_input_mapper` for more details.
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"""
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return self._get_plugin(data_type_key).register_input_mapper(mapper)
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def register_image_input_mapper(
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self,
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mapper: Optional[MultiModalInputMapper] = None,
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):
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"""
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Register an input mapper for image data to a model class.
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See :meth:`MultiModalPlugin.register_input_mapper` for more details.
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"""
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return self.register_input_mapper("image", mapper)
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def map_input(
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self,
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model_config: "ModelConfig",
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data: MultiModalDataDict,
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mm_processor_kwargs: Optional[Dict[str, Any]] = None,
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) -> MultiModalKwargs:
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"""
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Apply an input mapper to the data passed to the model.
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The data belonging to each modality is passed to the corresponding
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plugin which in turn converts the data into into keyword arguments
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via the input mapper registered for that model.
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See :meth:`MultiModalPlugin.map_input` for more details.
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Note:
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This should be called after :meth:`init_mm_limits_per_prompt`.
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"""
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merged_dict: Dict[str, NestedTensors] = {}
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for data_key, data_value in data.items():
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plugin = self._get_plugin(data_key)
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num_items = len(data_value) if isinstance(data_value, list) else 1
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max_items = self._limits_by_model[model_config][data_key]
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if num_items > max_items:
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raise ValueError(
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f"You set {data_key}={max_items} (or defaulted to 1) in "
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f"`--limit-mm-per-prompt`, but found {num_items} items "
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"in the same prompt.")
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input_dict = plugin.map_input(model_config, data_value,
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mm_processor_kwargs)
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for input_key, input_tensor in input_dict.items():
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if input_key in merged_dict:
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raise ValueError(f"The input mappers (keys={set(data)}) "
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f"resulted in a conflicting keyword "
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f"argument to `forward()`: {input_key}")
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merged_dict[input_key] = input_tensor
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return MultiModalKwargs(merged_dict)
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def create_input_mapper(self, model_config: "ModelConfig"):
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"""
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Create an input mapper (see :meth:`map_input`) for a specific model.
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"""
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# NOTE - we currently make the assumption that if a model has multiple
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# supported modalities, they take the same kwargs. For the default,
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# this could be an issue in the future if it falls back to two HF
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# resources and we can't inspect the signature easily since it's
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# getting initialized through the autoclass.
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#
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# If this is a problem in the future, we should revisit it, but since
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# it potentially introduces a lot of complexity for a currently
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# uncommon case, we do not for simplicity of both use & implementation
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return functools.partial(self.map_input, model_config)
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def register_max_multimodal_tokens(
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self,
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data_type_key: str,
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max_mm_tokens: Optional[MultiModalTokensCalc] = None,
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):
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"""
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Register the maximum number of tokens, corresponding to a single
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instance of multimodal data belonging to a specific modality, that are
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passed to the language model for a model class.
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"""
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return self._get_plugin(data_type_key) \
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.register_max_multimodal_tokens(max_mm_tokens)
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def register_max_image_tokens(
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self,
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max_mm_tokens: Optional[MultiModalTokensCalc] = None,
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):
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"""
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Register the maximum number of image tokens, corresponding to a single
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image, that are passed to the language model for a model class.
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"""
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return self.register_max_multimodal_tokens("image", max_mm_tokens)
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def get_max_multimodal_tokens(self, model_config: "ModelConfig") -> int:
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"""
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Get the maximum number of multi-modal tokens
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for profiling the memory usage of a model.
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See :meth:`MultiModalPlugin.get_max_multimodal_tokens` for more details.
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Note:
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This should be called after :meth:`init_mm_limits_per_prompt`.
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"""
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limits_per_plugin = self._limits_by_model[model_config]
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return sum((limits_per_plugin[key] *
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plugin.get_max_multimodal_tokens(model_config))
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for key, plugin in self._plugins.items())
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def init_mm_limits_per_prompt(
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self,
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model_config: "ModelConfig",
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) -> None:
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"""
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Initialize the maximum number of multi-modal input instances for each
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modality that are allowed per prompt for a model class.
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"""
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if model_config in self._limits_by_model:
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logger.warning(
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"`mm_limits` has already been set for model=%s, and will "
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"be overwritten by the new values.", model_config.model)
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multimodal_config = model_config.multimodal_config
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if multimodal_config is None:
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limits_per_plugin = self._disabled_limits_per_plugin
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else:
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config_limits_per_plugin = multimodal_config.limit_per_prompt
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extra_keys = config_limits_per_plugin.keys() - self._plugins.keys()
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if extra_keys:
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logger.warning(
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"Detected extra keys in `--limit-mm-per-prompt` which "
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"are not registered as multi-modal plugins: %s. "
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"They will be ignored.", extra_keys)
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# NOTE: Currently the default is set to 1 for each plugin
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# TODO: Automatically determine the limits based on budget
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# once more models support multi-image inputs
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limits_per_plugin = {
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key: config_limits_per_plugin.get(key, 1)
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for key in self._plugins
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}
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self._limits_by_model[model_config] = limits_per_plugin
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def get_mm_limits_per_prompt(
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self,
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model_config: "ModelConfig",
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) -> Mapping[str, int]:
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"""
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Get the maximum number of multi-modal input instances for each modality
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that are allowed per prompt for a model class.
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Note:
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This should be called after :meth:`init_mm_limits_per_prompt`.
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"""
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return self._limits_by_model[model_config]
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def register_processor(
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self,
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factory: MultiModalProcessorFactory,
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):
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"""
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Register a multi-modal processor to a model class.
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When the model receives multi-modal data, the provided function is
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invoked to transform the data into a dictionary of model inputs.
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See also:
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- :ref:`input_processing_pipeline`
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- :ref:`enabling_multimodal_inputs`
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"""
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def wrapper(model_cls: N) -> N:
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if model_cls in self._processor_factories:
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logger.warning(
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"Model class %s already has an input mapper "
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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._processor_factories[model_cls] = factory
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return model_cls
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return wrapper
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def has_processor(self, model_config: "ModelConfig") -> bool:
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"""
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Test whether a multi-modal processor is defined for a specific model.
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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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return model_cls in self._processor_factories
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def create_processor(
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self,
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model_config: "ModelConfig",
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tokenizer: AnyTokenizer,
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) -> MultiModalProcessor:
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"""
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Create a multi-modal processor for a specific model and tokenizer.
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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_factory = self._processor_factories[model_cls]
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ctx = InputProcessingContext(model_config, tokenizer)
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return processor_factory(ctx)
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