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from abc import ABC, abstractmethod
from typing import List, Optional
import torch
from PIL import Image
class AbstractMultimodalPipeline(ABC):
@staticmethod
@abstractmethod
def name() -> str:
'name of the pipeline, should be same as in --multimodal-pipeline'
pass
@staticmethod
@abstractmethod
def image_start() -> Optional[str]:
'return image start string, string representation of image start token, or None if not applicable'
pass
@staticmethod
@abstractmethod
def image_end() -> Optional[str]:
'return image end string, string representation of image end token, or None if not applicable'
pass
@staticmethod
@abstractmethod
def placeholder_token_id() -> int:
'return placeholder token id'
pass
@staticmethod
@abstractmethod
def num_image_embeds() -> int:
'return the number of embeds used by a single image (for example: 256 for LLaVA)'
pass
@abstractmethod
def embed_images(self, images: List[Image.Image]) -> torch.Tensor:
'forward the images through vision pipeline, and return their embeddings'
pass
@staticmethod
@abstractmethod
def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor:
'embed tokens, the exact function varies by LLM, for LLaMA it is `shared.model.model.embed_tokens`'
pass
@staticmethod
@abstractmethod
def placeholder_embeddings() -> torch.Tensor:
'get placeholder embeddings if there are multiple images, and `add_all_images_to_prompt` is False'
pass
def _get_device(self, setting_name: str, params: dict):
if params[setting_name] is None:
return torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
return torch.device(params[setting_name])
def _get_dtype(self, setting_name: str, params: dict):
return torch.float32 if int(params[setting_name]) == 32 else torch.float16
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