update
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507
vllm/multimodal/processing/context.py
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507
vllm/multimodal/processing/context.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import time
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from abc import abstractmethod
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from collections.abc import Mapping
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from contextlib import contextmanager
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from dataclasses import dataclass, field
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from functools import cached_property
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from typing import TYPE_CHECKING, Any, overload
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import torch
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from typing_extensions import TypeVar
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from vllm.logger import init_logger
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from vllm.multimodal.inputs import MultiModalDataDict
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from vllm.multimodal.parse import (
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DictEmbeddingItems,
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EmbeddingItems,
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MultiModalDataItems,
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MultiModalDataParser,
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)
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from vllm.renderers import TokenizeParams
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from vllm.tokenizers import TokenizerLike
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from vllm.transformers_utils.processor import cached_processor_from_config
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from vllm.utils.func_utils import get_allowed_kwarg_only_overrides
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from vllm.utils.jsontree import JSONTree, json_map_leaves
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from vllm.utils.mistral import is_mistral_tokenizer
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if TYPE_CHECKING:
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from transformers.configuration_utils import PretrainedConfig
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from transformers.feature_extraction_utils import BatchFeature
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from transformers.processing_utils import ProcessorMixin
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from vllm.config import ModelConfig
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else:
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PretrainedConfig = object
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BatchFeature = object
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ProcessorMixin = object
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ModelConfig = object
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logger = init_logger(__name__)
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@dataclass
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class TimingContext:
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"""Helper class to record execution times during multi-modal processing."""
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enabled: bool = True
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"""If disabled, `TimingContext.record` becomes a no-op."""
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stage_secs: dict[str, float] = field(default_factory=dict)
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"""The execution time (in seconds) for each processing stage."""
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@property
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def total_secs(self) -> float:
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return sum(self.stage_secs.values())
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@contextmanager
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def record(self, stage: str):
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"""Record the execution time for a processing stage."""
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if not self.enabled:
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yield
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return
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start_time = time.perf_counter()
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try:
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yield
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finally:
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elapsed = time.perf_counter() - start_time
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self.stage_secs.setdefault(stage, 0.0)
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self.stage_secs[stage] += elapsed
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def get_stats_dict(self):
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stats_dict = {
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f"{stage}_secs": time_s for stage, time_s in self.stage_secs.items()
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}
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stats_dict["preprocessor_total_secs"] = self.total_secs
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return stats_dict
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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 InputProcessingContext:
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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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tokenizer: TokenizerLike | None
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"""The tokenizer used to tokenize the inputs."""
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def get_tokenizer(self) -> TokenizerLike:
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if self.tokenizer is None:
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raise ValueError(
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"You cannot pass text prompts when `skip_tokenizer_init=True`"
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)
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return self.tokenizer
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@overload
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def get_hf_config(self, /) -> PretrainedConfig: ...
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@overload
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def get_hf_config(
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self,
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typ: type[_C] | tuple[type[_C], ...],
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/,
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) -> _C: ...
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def get_hf_config(
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self,
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typ: type[Any] | tuple[type[Any], ...] | None = None,
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/,
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) -> Any:
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"""
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Get the HuggingFace configuration
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(`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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if typ is None:
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from transformers.configuration_utils import PretrainedConfig
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typ = PretrainedConfig
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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(
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"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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)
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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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@overload
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def get_hf_processor(self, /, **kwargs: object) -> ProcessorMixin: ...
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@overload
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def get_hf_processor(
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self,
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typ: type[_P] | tuple[type[_P], ...],
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/,
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**kwargs: object,
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) -> _P: ...
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def get_hf_processor(
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self,
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typ: type[Any] | tuple[type[Any], ...] | None = None,
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/,
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**kwargs: object,
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) -> Any:
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"""
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Get the HuggingFace processor
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(`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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if typ is None:
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from transformers.processing_utils import ProcessorMixin
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typ = ProcessorMixin
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tokenizer = self.tokenizer
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if is_mistral_tokenizer(tokenizer):
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tokenizer = tokenizer.transformers_tokenizer
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merged_kwargs = self.get_merged_mm_kwargs(kwargs)
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merged_kwargs.pop("tokenizer", None)
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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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tokenizer=tokenizer,
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**merged_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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merged_kwargs = self.get_merged_mm_kwargs(kwargs)
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return typ(**merged_kwargs)
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def _postprocess_output(
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self,
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output: JSONTree,
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) -> JSONTree:
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def _postprocess_one(x: object):
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if isinstance(x, torch.Tensor): # noqa: SIM102
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# This mimics the behavior of transformers.BatchFeature
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if x.is_floating_point():
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x = x.to(dtype=self.model_config.dtype)
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return x
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return json_map_leaves(_postprocess_one, output)
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def get_merged_mm_kwargs(self, kwargs: Mapping[str, object]):
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mm_config = self.model_config.get_multimodal_config()
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return mm_config.merge_mm_processor_kwargs(kwargs)
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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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*,
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num_tries: int = 1,
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max_tries: int = 5,
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) -> BatchFeature | JSONTree:
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"""
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Call `hf_processor` on the prompt `data`
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(text, image, audio...) with configurable options `kwargs`.
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"""
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assert callable(hf_processor)
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merged_kwargs = self.get_merged_mm_kwargs(kwargs)
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allowed_kwargs = get_allowed_kwarg_only_overrides(
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hf_processor,
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merged_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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try:
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output = hf_processor(**data, **allowed_kwargs, return_tensors="pt")
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except Exception as exc:
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# See https://github.com/huggingface/tokenizers/issues/537
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if (
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isinstance(exc, RuntimeError)
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and exc
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and exc.args[0] == "Already borrowed"
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and num_tries < max_tries
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):
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logger.warning(
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"Failed to acquire tokenizer in current thread. "
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"Retrying (%d/%d)...",
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num_tries,
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max_tries,
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)
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time.sleep(0.5)
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return self.call_hf_processor(
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hf_processor,
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data,
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kwargs,
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num_tries=num_tries + 1,
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max_tries=max_tries,
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)
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msg = (
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f"Failed to apply {type(hf_processor).__name__} "
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f"on data={data} with kwargs={allowed_kwargs}"
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)
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raise ValueError(msg) from exc
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# this emulates output.to(dtype=self.model_config.dtype)
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from transformers.feature_extraction_utils import BatchFeature
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if isinstance(output, BatchFeature):
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output_ = self._postprocess_output(output.data)
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return BatchFeature(output_)
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logger.warning_once(
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"%s did not return `BatchFeature`. "
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"Make sure to match the behaviour of `ProcessorMixin` when "
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"implementing custom processors.",
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type(hf_processor).__name__,
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)
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return self._postprocess_output(output)
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class BaseProcessingInfo:
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"""Base class to provide the information necessary for data processing."""
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def __init__(self, ctx: InputProcessingContext) -> None:
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super().__init__()
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self.ctx = ctx
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@property
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def model_id(self) -> str:
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return self.ctx.model_config.model
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def get_tokenizer(self) -> TokenizerLike:
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return self.ctx.get_tokenizer()
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def get_hf_config(self) -> PretrainedConfig:
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return self.ctx.get_hf_config()
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def get_hf_processor(self, **kwargs: object) -> ProcessorMixin:
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"""
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Subclasses can override this method to handle
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specific kwargs from model config or user inputs.
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"""
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return self.ctx.get_hf_processor(**kwargs)
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def get_default_tok_params(self) -> TokenizeParams:
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"""Construct the default parameters for tokenization."""
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model_config = self.ctx.model_config
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encoder_config = model_config.encoder_config or {}
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return TokenizeParams(
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max_total_tokens=model_config.max_model_len,
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do_lower_case=encoder_config.get("do_lower_case", False),
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add_special_tokens=True,
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)
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@cached_property
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def default_tok_params(self) -> TokenizeParams:
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return self.get_default_tok_params()
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def _get_expected_hidden_size(self) -> int | None:
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"""
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Get expected hidden size for embedding validation if `mm_embeds` are enabled.
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This validates hidden dimensions to prevent a vulnerability where embeddings
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with correct `ndim` but wrong `shape` could cause crashes at inference time.
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"""
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model_config = self.ctx.model_config
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mm_config = model_config.get_multimodal_config()
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if mm_config.enable_mm_embeds:
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return model_config.get_inputs_embeds_size()
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return None
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def get_data_parser(self) -> MultiModalDataParser:
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"""
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Constructs a parser to preprocess multi-modal data items
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before passing them to
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[`_get_hf_mm_data`][vllm.multimodal.processing.BaseMultiModalProcessor._get_hf_mm_data].
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You can support additional modalities by creating a subclass
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of [`MultiModalDataParser`][vllm.multimodal.parse.MultiModalDataParser]
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that has additional subparsers.
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"""
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return MultiModalDataParser(
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expected_hidden_size=self._get_expected_hidden_size(),
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)
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@cached_property
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def data_parser(self) -> MultiModalDataParser:
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return self.get_data_parser()
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@property
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def skip_prompt_length_check(self) -> bool:
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return False
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@abstractmethod
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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"""
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Return the maximum supported number of items for each modality.
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A value of `None` means unlimited number of items.
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Omitting a modality from the returned dictionary means that
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it is not supported at all.
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"""
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raise NotImplementedError
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@cached_property
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def supported_mm_limits(self) -> Mapping[str, int | None]:
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"""The maximum supported number of items for each modality."""
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return self.get_supported_mm_limits()
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@cached_property
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def allowed_mm_limits(self) -> Mapping[str, int]:
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"""The maximum allowed number of items for each modality."""
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mm_config = self.ctx.get_mm_config()
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allowed_limits = dict[str, int]()
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for modality, supported_limit in self.supported_mm_limits.items():
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user_limit = mm_config.get_limit_per_prompt(modality)
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allowed_limits[modality] = (
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user_limit
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if supported_limit is None
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else min(user_limit, supported_limit)
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)
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return allowed_limits
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def validate_num_items(self, modality: str, num_items: int) -> None:
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"""
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Raise `ValueError` if the number of input items for the given modality
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is invalid.
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"""
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supported_limit = self.supported_mm_limits.get(modality, 0)
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allowed_limit = self.allowed_mm_limits.get(modality, 0)
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if supported_limit is None:
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supported_limit = allowed_limit
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limit = min(supported_limit, allowed_limit)
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if num_items > limit:
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msg = f"At most {limit} {modality}(s) may be provided in one prompt."
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if num_items <= supported_limit:
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msg += " Set `--limit-mm-per-prompt` to increase this limit."
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raise ValueError(msg)
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def parse_mm_data(
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self,
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mm_data: MultiModalDataDict,
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*,
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validate: bool = True,
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) -> MultiModalDataItems:
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"""
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Normalize
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[`MultiModalDataDict`][vllm.multimodal.inputs.MultiModalDataDict]
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to [`MultiModalDataItems`][vllm.multimodal.parse.MultiModalDataItems]
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before passing them to
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[`_get_hf_mm_data`][vllm.multimodal.processing.BaseMultiModalProcessor._get_hf_mm_data].
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"""
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mm_items = self.data_parser.parse_mm_data(mm_data)
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if validate:
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mm_config = self.ctx.get_mm_config()
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for modality, items in mm_items.items():
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if isinstance(items, (EmbeddingItems, DictEmbeddingItems)):
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if not mm_config.enable_mm_embeds:
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raise ValueError(
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f"You must set `--enable-mm-embeds` to input "
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f"`{modality}_embeds`"
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)
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if mm_config.get_limit_per_prompt(modality) == 0:
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logger.debug(
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"Skipping count validation for modality "
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"'%s' (embeddings with limit=0)",
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modality,
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)
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continue
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self.validate_num_items(modality, len(items))
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return mm_items
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def get_mm_max_tokens_per_item(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> Mapping[str, int] | None:
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"""
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Return the maximum number of tokens per item of for each modality.
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When `None` (the default) is returned, vLLM will generate dummy inputs
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(images/videos) at maximum possible sizes and process them to determine
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the maximum token count per modality.
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This approach works but can be very slow for certain models (e.g.,
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Qwen2.5-VL), leading to very long startup time. For better performance,
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each model can override this method to return pre-computed maximum token
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counts, avoiding the need for dummy input generation and processing.
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Note:
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The maximum number of tokens per item of each modality returned
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from this function should respect the model's maximum sequence
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length and the maximum number of items of each modality allowed,
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and agree with dummy inputs (images/videos) at maximum possible
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sizes.
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"""
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return None
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Reference in New Issue
Block a user