forked from EngineX-Cambricon/enginex-mlu370-vllm
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367
vllm-v0.6.2/vllm/inputs/registry.py
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367
vllm-v0.6.2/vllm/inputs/registry.py
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import functools
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from collections import UserDict
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from dataclasses import dataclass
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from typing import (TYPE_CHECKING, Any, Callable, Dict, Mapping, NamedTuple,
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Optional, Protocol, Type, cast)
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from torch import nn
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from transformers import 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_get_processor
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import (get_allowed_kwarg_only_overrides, print_warning_once,
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resolve_mm_processor_kwargs)
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from .data import ProcessorInputs, SingletonInputs
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from .parse import is_encoder_decoder_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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C = TypeVar("C", bound=PretrainedConfig, default=PretrainedConfig)
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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(self, hf_config_type: Type[C] = PretrainedConfig) -> 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 model 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, hf_config_type):
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raise TypeError("Invalid type of HuggingFace config. "
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f"Expected type: {hf_config_type}, 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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@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(self) -> ProcessorMixin:
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return cached_get_processor(
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self.model_config.tokenizer,
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tokenizer=self.tokenizer, # Override the tokenizer with ours
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trust_remote_code=self.model_config.trust_remote_code,
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)
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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: Dict[Type[nn.Module],
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DummyDataFactory] = {}
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self._dummy_encoder_factories_by_model_type: Dict[
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Type[nn.Module], DummyDataFactory] = {}
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self._input_processors_by_model_type: Dict[Type[nn.Module],
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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 model_cls in self._dummy_factories_by_model_type:
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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 model_cls in self._dummy_encoder_factories_by_model_type:
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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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See also:
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:ref:`enabling_multimodal_inputs`
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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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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, overrides=model_config.mm_processor_kwargs)
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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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print_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:
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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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) -> 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 :meth:`~vllm.multimodal.MultiModalRegistry.map_input`.
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See also:
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:ref:`input_processing_pipeline`
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"""
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def wrapper(model_cls: N) -> N:
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if model_cls in self._input_processors_by_model_type:
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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"])
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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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See also:
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:ref:`input_processing_pipeline`
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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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cast(Dict[str, Any], inputs.get("mm_processor_kwargs")),
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processor,
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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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if is_encoder_decoder_inputs(processed_inputs):
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self._ensure_mm_kwargs(processed_inputs["encoder"],
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mm_processor_kwargs)
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self._ensure_mm_kwargs(processed_inputs["decoder"],
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mm_processor_kwargs)
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else:
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self._ensure_mm_kwargs(processed_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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