| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import warnings | |
| from .base import BaseModel | |
| from ..dataset import DATASET_TYPE | |
| class Monkey(BaseModel): | |
| INSTALL_REQ = False | |
| INTERLEAVE = False | |
| def __init__(self, model_path='echo840/Monkey', **kwargs): | |
| assert model_path is not None | |
| self.model_path = model_path | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained(model_path, device_map='cpu', trust_remote_code=True).eval() | |
| self.model = model.cuda() | |
| self.kwargs = kwargs | |
| warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ') | |
| torch.cuda.empty_cache() | |
| def generate_vanilla(self, image_path, prompt): | |
| cur_prompt = f'<img>{image_path}</img> {prompt} Answer: ' | |
| input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest') | |
| attention_mask = input_ids.attention_mask | |
| input_ids = input_ids.input_ids | |
| output_ids = self.model.generate( | |
| input_ids=input_ids.cuda(), | |
| attention_mask=attention_mask.cuda(), | |
| do_sample=False, | |
| num_beams=1, | |
| max_new_tokens=512, | |
| min_new_tokens=1, | |
| length_penalty=1, | |
| num_return_sequences=1, | |
| output_hidden_states=True, | |
| use_cache=True, | |
| pad_token_id=self.tokenizer.eod_id, | |
| eos_token_id=self.tokenizer.eod_id, | |
| ) | |
| response = self.tokenizer.decode( | |
| output_ids[0][input_ids.size(1):].cpu(), | |
| skip_special_tokens=True | |
| ).strip() | |
| return response | |
| def generate_multichoice(self, image_path, prompt): | |
| cur_prompt = f'<img>{image_path}</img> \n {prompt} Answer: ' | |
| input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest') | |
| attention_mask = input_ids.attention_mask | |
| input_ids = input_ids.input_ids | |
| output_ids = self.model.generate( | |
| input_ids=input_ids.cuda(), | |
| attention_mask=attention_mask.cuda(), | |
| do_sample=False, | |
| num_beams=1, | |
| max_new_tokens=10, | |
| min_new_tokens=1, | |
| length_penalty=1, | |
| num_return_sequences=1, | |
| output_hidden_states=True, | |
| use_cache=True, | |
| pad_token_id=self.tokenizer.eod_id, | |
| eos_token_id=self.tokenizer.eod_id, | |
| ) | |
| response = self.tokenizer.decode( | |
| output_ids[0][input_ids.size(1):].cpu(), | |
| skip_special_tokens=True | |
| ).strip() | |
| return response | |
| def generate_inner(self, message, dataset=None): | |
| prompt, image_path = self.message_to_promptimg(message, dataset=dataset) | |
| if dataset is None: | |
| return self.generate_vanilla(image_path, prompt) | |
| assert isinstance(dataset, str) | |
| if DATASET_TYPE(dataset) == 'MCQ' or DATASET_TYPE(dataset) == 'Y/N' or dataset == 'HallusionBench': | |
| return self.generate_multichoice(image_path, prompt) | |
| else: | |
| return self.generate_vanilla(image_path, prompt) | |
| class MonkeyChat(BaseModel): | |
| INSTALL_REQ = False | |
| INTERLEAVE = False | |
| def __init__(self, model_path='echo840/Monkey-Chat', **kwargs): | |
| assert model_path is not None | |
| self.model_path = model_path | |
| self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | |
| model = AutoModelForCausalLM.from_pretrained(model_path, device_map='cpu', trust_remote_code=True).eval() | |
| self.model = model.cuda() | |
| self.kwargs = kwargs | |
| self.tokenizer.padding_side = 'left' | |
| self.tokenizer.pad_token_id = self.tokenizer.eod_id | |
| warnings.warn(f'Following kwargs received: {self.kwargs}, will use as generation config. ') | |
| torch.cuda.empty_cache() | |
| def generate_vanilla(self, image_path, prompt): | |
| cur_prompt = f'<img>{image_path}</img> {prompt} Answer: ' | |
| input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest') | |
| attention_mask = input_ids.attention_mask | |
| input_ids = input_ids.input_ids | |
| output_ids = self.model.generate( | |
| input_ids=input_ids.cuda(), | |
| attention_mask=attention_mask.cuda(), | |
| do_sample=False, | |
| num_beams=1, | |
| max_new_tokens=512, | |
| min_new_tokens=1, | |
| length_penalty=1, | |
| num_return_sequences=1, | |
| output_hidden_states=True, | |
| use_cache=True, | |
| pad_token_id=self.tokenizer.eod_id, | |
| eos_token_id=self.tokenizer.eod_id, | |
| ) | |
| response = self.tokenizer.decode( | |
| output_ids[0][input_ids.size(1):].cpu(), | |
| skip_special_tokens=True | |
| ).strip() | |
| return response | |
| def generate_multichoice(self, image_path, prompt): | |
| cur_prompt = f'<img>{image_path}</img> \n {prompt} Answer: ' | |
| input_ids = self.tokenizer(cur_prompt, return_tensors='pt', padding='longest') | |
| attention_mask = input_ids.attention_mask | |
| input_ids = input_ids.input_ids | |
| output_ids = self.model.generate( | |
| input_ids=input_ids.cuda(), | |
| attention_mask=attention_mask.cuda(), | |
| do_sample=False, | |
| num_beams=1, | |
| max_new_tokens=10, | |
| min_new_tokens=1, | |
| length_penalty=1, | |
| num_return_sequences=1, | |
| output_hidden_states=True, | |
| use_cache=True, | |
| pad_token_id=self.tokenizer.eod_id, | |
| eos_token_id=self.tokenizer.eod_id, | |
| ) | |
| response = self.tokenizer.decode( | |
| output_ids[0][input_ids.size(1):].cpu(), | |
| skip_special_tokens=True | |
| ).strip() | |
| return response | |
| def generate_inner(self, message, dataset=None): | |
| prompt, image_path = self.message_to_promptimg(message, dataset=dataset) | |
| if dataset is None: | |
| return self.generate_vanilla(image_path, prompt) | |
| assert isinstance(dataset, str) | |
| if DATASET_TYPE(dataset) == 'MCQ' or DATASET_TYPE(dataset) == 'Y/N' or dataset == 'HallusionBench': | |
| return self.generate_multichoice(image_path, prompt) | |
| else: | |
| return self.generate_vanilla(image_path, prompt) | |