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import os |
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from .base import BaseModel |
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from ..smp import * |
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from ..dataset import DATASET_TYPE |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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import torch |
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import json |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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def load_model_tokenizer(checkpoint_path): |
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tokenizer = AutoTokenizer.from_pretrained( |
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checkpoint_path, trust_remote_code=True, |
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) |
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device_map = 'auto' |
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model = AutoModelForCausalLM.from_pretrained( |
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checkpoint_path, |
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device_map=device_map, |
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trust_remote_code=True, |
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torch_dtype=torch.bfloat16, |
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) |
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return model, tokenizer |
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class Baichuan(BaseModel): |
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INSTALL_REQ = False |
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INTERLEAVE = False |
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def __init__(self, sft=True, model_path=None): |
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assert model_path is not None |
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self.device = "cuda" |
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self.model_path = model_path |
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self.model, self.tokenizer = load_model_tokenizer(model_path) |
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self.model.bind_processor(self.tokenizer, training=False) |
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torch.cuda.empty_cache() |
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self.use_reserve_qa_prompt = sft |
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self.reserve_qa_start_prompt = "<C_Q>" |
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self.reserve_qa_end_prompt = "<C_A>" |
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self.task_prompt="" |
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self.options_system_prompt = ('Carefully read the following question and select the letter corresponding ' |
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'to the correct answer. Highlight the applicable choices without giving ' |
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'explanations. ') |
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self.wo_options_system_prompt = 'Carefully read the following question Answer the question directly. ' |
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self.detail_system_prompt = 'Answer this question in detail and step by step. ' |
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self.vqa_prompt = 'Answer the question using a single word or phrase. ' |
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def generate_inner(self, message, dataset=None): |
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image_str, question = '', '' |
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for s in message: |
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if s['type'] == 'image': |
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if len(s["value"].split(".")[-1]) > 2: |
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image_dict = {"local": s["value"]} |
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else: |
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image_dict = {"base64": s["value"]} |
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image_str += f"<img_start_baichuan>{json.dumps(image_dict)}<img_end_baichuan>\n" |
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elif s['type'] == 'text': |
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question += s['value'] |
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if self.use_reserve_qa_prompt: |
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prompt = "{}{}{}{}{}".format(self.reserve_qa_start_prompt, image_str, question, self.task_prompt, self.reserve_qa_end_prompt) |
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else: |
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prompt = "{}{}{}".format(image_str, question, self.task_prompt) |
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print("****************************** prompt ******************************") |
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print(prompt) |
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print("********************************************************************") |
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with torch.inference_mode(): |
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ret = self.model.processor(prompt) |
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input_ids = ret.input_ids |
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try: |
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ret = self.model.generate( |
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inputs=torch.LongTensor([input_ids]).cuda(), |
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images=[torch.tensor(img, dtype=torch.float32).cuda() for img in images] if ret.images is not None else None, |
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patch_nums=ret.patch_nums, |
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images_grid=ret.images_grid, |
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max_new_tokens=1024, do_sample=False, top_k=5, top_p=0.85, temperature=0, |
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num_return_sequences=1, repetition_penalty=1.05, |
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use_cache=False |
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) |
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ret = self.tokenizer.batch_decode(ret[:, torch.LongTensor([input_ids]).to(self.device).shape[1]:], skip_special_tokens=True)[0].strip() |
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except Exception as e: |
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print(e) |
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ret = "" |
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response = ret |
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print("=========================================== response ===========================================") |
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print(f"\033[32m{response}\033[0m") |
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print("================================================================================================") |
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return response |
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def use_custom_prompt(self, dataset): |
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if dataset is not None and listinstr(['M3GIA'], dataset): |
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return False |
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if listinstr(['MCQ', 'VQA'], DATASET_TYPE(dataset)): |
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return True |
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elif dataset is not None and listinstr(['HallusionBench'], dataset): |
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return True |
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return False |
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def build_prompt(self, line, dataset=None): |
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if isinstance(line, int): |
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line = self.data.iloc[line] |
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tgt_path = self.dump_image(line, dataset) |
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system_prompt = '' |
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question = line['question'] |
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if DATASET_TYPE(dataset) == 'MCQ': |
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options = { |
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cand: line[cand] |
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for cand in string.ascii_uppercase |
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if cand in line and not pd.isna(line[cand]) |
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} |
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options_prompt = 'Options:\n' |
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for key, item in options.items(): |
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options_prompt += f'{key}. {item}\n' |
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hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None |
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prompt = '' |
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if hint is not None: |
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prompt += f'Hint: {hint}\n' |
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prompt += f'Question: {question}\n' |
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if len(options): |
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prompt += options_prompt |
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if 'MMBench' in dataset: |
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prompt += 'Please select the correct answer from the options above. \n' |
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else: |
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system_prompt = self.options_system_prompt + '\nPlease just indicate your choice.' |
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else: |
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system_prompt = self.wo_options_system_prompt |
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if 'MMMU' in dataset: |
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prompt = system_prompt + '\n' + prompt |
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system_prompt = '' |
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elif dataset is not None and listinstr(['HallusionBench'], dataset): |
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question = line['question'] + ' Yes or No?' |
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prompt = question |
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elif dataset is not None and listinstr(['MME'], dataset): |
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question = line['question'] + ' Yes or No?' |
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prompt = question |
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elif dataset is not None and listinstr(['OCRBench'], dataset): |
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system_prompt = self.vqa_prompt |
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question = line['question'] |
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prompt = question |
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elif DATASET_TYPE(dataset) == 'VQA': |
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if listinstr(['LLaVABench', 'MMLongBench_DOC'], dataset): |
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system_prompt = '' |
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prompt = question |
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elif listinstr(['MMVet'], dataset): |
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system_prompt = self.detail_system_prompt |
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prompt = question |
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elif listinstr(['ChartQA'], dataset): |
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system_prompt = 'Please answer the question using a single word. ' |
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prompt = question |
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else: |
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system_prompt = self.vqa_prompt |
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prompt = question |
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msgs = [] |
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if system_prompt: |
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msgs.append(dict(type='text', value=system_prompt)) |
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if isinstance(tgt_path, list): |
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msgs.extend([dict(type='image', value=p) for p in tgt_path]) |
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else: |
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msgs = [dict(type='image', value=tgt_path)] |
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msgs.append(dict(type='text', value=prompt)) |
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return msgs |
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