| import torch |
| from transformers import AutoModel, AutoTokenizer |
|
|
| from .utils import load_image |
|
|
|
|
| class InternVL: |
| def __init__(self,model_path,args): |
| super().__init__() |
| self.llm = AutoModel.from_pretrained( |
| model_path, |
| torch_dtype=torch.bfloat16, |
| low_cpu_mem_usage=True, |
| trust_remote_code=True, |
| device_map="cuda", |
| attn_implementation="flash_attention_2" |
| ) |
|
|
| self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False) |
| self.generation_config ={ |
| 'max_new_tokens': args.max_new_tokens, |
| 'repetition_penalty': args.repetition_penalty, |
| 'temperature' : args.temperature, |
| 'top_p' : args.top_p |
| } |
| |
|
|
| def process_messages(self,messages): |
| prompt = "" |
| if "system" in messages: |
| prompt = messages["system"] |
|
|
| if "image" in messages: |
| text = messages["prompt"] |
| prompt = prompt + "\n" + "<image>" + '\n' + text |
| image = messages["image"] |
| pixel_value = load_image(image).to(torch.bfloat16).to("cuda") |
| elif "images" in messages: |
| text = messages["prompt"] |
| images = messages["images"] |
| for i,image in enumerate(images): |
| prompt = prompt + f"<image_{i+1}>: <image>" + "\n" |
| prompt = prompt + '\n' + text |
| pixel_value = [load_image(image).to(torch.bfloat16).to("cuda") for image in images] |
| pixel_value = torch.cat(pixel_value,dim=0) |
| else: |
| text = messages["prompt"] |
| prompt = prompt + "\n" + text |
| pixel_value = None |
|
|
| llm_inputs = { |
| "prompt": prompt, |
| "pixel_values": pixel_value |
| } |
| return llm_inputs |
|
|
|
|
| def generate_output(self,messages): |
| llm_inputs = self.process_messages(messages) |
| question = llm_inputs["prompt"] |
| pixel_values = llm_inputs["pixel_values"] |
| response, history = self.llm.chat(self.tokenizer, pixel_values, question, self.generation_config, |
| history=None, return_history=True) |
| return response |
| |
| def generate_outputs(self,messages_list): |
| res = [] |
| for messages in messages_list: |
| result = self.generate_output(messages) |
| res.append(result) |
| return res |
|
|
