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232 | class HuggingFaceMultiModal(MultiModalLLM):
"""
This class provides a base implementation for interacting with HuggingFace multi-modal models.
It handles model initialization, input preparation, and text/image-based interaction.
"""
model_name: str = Field(
description="The name of the Hugging Face multi-modal model to use."
)
device: str = Field(
default="cuda" if torch.cuda.is_available() else "cpu",
description="The device to run the model on.",
)
torch_dtype: Any = Field(
default=torch.float16 if torch.cuda.is_available() else torch.float32,
description="The torch dtype to use.",
)
trust_remote_code: bool = Field(
default=True, description="Whether to trust remote code when loading the model."
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
)
max_new_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The maximum number of new tokens to generate.",
)
temperature: float = Field(
default=0.0, description="The temperature to use for sampling."
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional kwargs for model initialization and generation.",
)
_model: Any = PrivateAttr()
_processor: Any = PrivateAttr()
_config: Any = PrivateAttr()
def __init__(self, **kwargs: Any) -> None:
"""
Initializes the HuggingFace multi-modal model and processor based on the provided configuration.
"""
super().__init__(**kwargs)
try:
# Load model configuration
self._config = AutoConfig.from_pretrained(
self.model_name, trust_remote_code=True
)
architecture = self._config.architectures[0]
AutoModelClass = AutoModelForCausalLM # Default model class
# Special cases for specific model architectures
if "Qwen2VLForConditionalGeneration" in architecture:
AutoModelClass = Qwen2VLForConditionalGeneration
if "PaliGemmaForConditionalGeneration" in architecture:
AutoModelClass = PaliGemmaForConditionalGeneration
# Load the model based on the architecture
self._model = AutoModelClass.from_pretrained(
self.model_name,
device_map=self.device,
torch_dtype=self.torch_dtype,
trust_remote_code=self.trust_remote_code,
**self.additional_kwargs,
)
# Load the processor (for handling text and image inputs)
self._processor = AutoProcessor.from_pretrained(
self.model_name, trust_remote_code=self.trust_remote_code
)
except Exception as e:
raise ValueError(f"Failed to initialize the model and processor: {e!s}")
@classmethod
def class_name(cls) -> str:
"""Returns the class name for the model."""
return "HuggingFace_multi_modal_llm"
@property
def metadata(self) -> MultiModalLLMMetadata:
"""Multi Modal LLM metadata."""
return MultiModalLLMMetadata(
context_window=self.context_window,
num_output=self.max_new_tokens,
model_name=self.model_name,
)
# each unique model will override it
def _prepare_messages(
self, messages: Sequence[ChatMessage], image_documents: Sequence[ImageDocument]
) -> Dict[str, Any]:
"""
Abstract method: Prepares input messages and image documents for the model.
This must be overridden by subclasses.
"""
raise NotImplementedError
# each unique model will override it
def _generate(self, prepared_inputs: Dict[str, Any]) -> str:
"""
Abstract method: Generates text based on the prepared inputs.
This must be overridden by subclasses.
"""
raise NotImplementedError
# some models will override it, some won't
def complete(
self, prompt: str, image_documents: Sequence[ImageDocument], **kwargs: Any
) -> CompletionResponse:
"""
Completes a task based on a text prompt and optional images.
The method prepares inputs and generates the corresponding text.
"""
prepared_inputs = self._prepare_messages(
[ChatMessage(role="user", content=prompt)], image_documents
)
generated_text = self._generate(prepared_inputs)
return CompletionResponse(text=generated_text)
# some models will override it, some won't
def chat(
self,
messages: Sequence[ChatMessage],
image_documents: Sequence[ImageDocument],
**kwargs: Any,
) -> ChatResponse:
"""
Engages in a chat-style interaction by processing a sequence of messages and optional images.
"""
prepared_inputs = self._prepare_messages(messages, image_documents)
generated_text = self._generate(prepared_inputs)
return ChatResponse(
message=ChatMessage(role="assistant", content=generated_text),
raw={"model_output": generated_text},
)
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError(
"HuggingFaceMultiModal does not support async streaming chat yet."
)
async def astream_complete(
self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError(
"HuggingFaceMultiModal does not support async streaming completion yet."
)
async def acomplete(
self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError(
"HuggingFaceMultiModal does not support async completion yet."
)
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
raise NotImplementedError(
"HuggingFaceMultiModal does not support async chat yet."
)
async def stream_complete(
self, prompt: str, image_documents: Sequence[ImageNode], **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError(
"HuggingFaceMultiModal does not support async completion yet."
)
# we check the model architecture here
@classmethod
def from_model_name(cls, model_name: str, **kwargs: Any) -> "HuggingFaceMultiModal":
"""Checks the model architecture and initializes the model."""
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
# we check the architecture because users would want to use their own finetuned versions of VLMs
architecture = config.architectures[0]
if "Phi3VForCausalLM" in architecture:
return Phi35VisionMultiModal(model_name=model_name, **kwargs)
elif "Florence2ForConditionalGeneration" in architecture:
return Florence2MultiModal(model_name=model_name, **kwargs)
elif "Qwen2VLForConditionalGeneration" in architecture:
return Qwen2VisionMultiModal(model_name=model_name, **kwargs)
elif "PaliGemmaForConditionalGeneration" in architecture:
return PaliGemmaMultiModal(model_name=model_name, **kwargs)
elif "MllamaForConditionalGeneration" in architecture:
return LlamaMultiModal(model_name=model_name, **kwargs)
else:
raise ValueError(
f"Unsupported model architecture: {architecture}. "
f"We currently support: {', '.join(SUPPORTED_VLMS)}"
)
|
