# from http import HTTPStatus import os import requests from ..dataset import DATASET_TYPE, DATASET_MODALITY from vlmeval.api.base import BaseAPI from vlmeval.smp import * class InternVL2_PromptUtil: def __init__(self, use_mpo_prompt=False): self.use_mpo_prompt = use_mpo_prompt def dump_image(self, line, dataset): return self.dump_image_func(line) def use_custom_prompt(self, dataset): assert dataset is not None assert DATASET_MODALITY(dataset) != 'VIDEO', 'not supported' if listinstr(['MMDU', 'MME-RealWorld', 'MME-RealWorld-CN'], dataset): # For Multi-Turn we don't have custom prompt return False if DATASET_MODALITY(dataset) == 'VIDEO': # For Video benchmarks we don't have custom prompt at here return False else: return True def build_prompt(self, line, dataset=None): assert self.use_custom_prompt(dataset) assert dataset is None or isinstance(dataset, str) from ..vlm.internvl.utils import (build_multi_choice_prompt, build_mcq_cot_prompt, build_qa_cot_prompt, build_mpo_prompt, reorganize_prompt) tgt_path = self.dump_image(line, dataset) max_num = self.get_max_num(dataset) if dataset is not None and DATASET_TYPE(dataset) == 'Y/N': question = line['question'] if listinstr(['MME'], dataset): prompt = question + ' Answer the question using a single word or phrase.' elif listinstr(['HallusionBench', 'AMBER'], dataset): prompt = question + ' Please answer yes or no. Answer the question using a single word or phrase.' else: prompt = question elif dataset is not None and DATASET_TYPE(dataset) == 'MCQ': prompt = build_multi_choice_prompt(line, dataset) if os.getenv('USE_COT') == '1': prompt = build_mcq_cot_prompt(line, prompt) elif dataset is not None and DATASET_TYPE(dataset) == 'VQA': question = line['question'] if listinstr(['LLaVABench', 'WildVision'], dataset): prompt = question + '\nAnswer this question in detail.' elif listinstr(['OCRVQA', 'TextVQA', 'ChartQA', 'DocVQA', 'InfoVQA', 'OCRBench', 'DUDE', 'SLIDEVQA', 'GQA', 'MMLongBench_DOC'], dataset): prompt = question + '\nAnswer the question using a single word or phrase.' elif listinstr(['MathVista', 'MathVision', 'VCR', 'MTVQA', 'MMVet', 'MathVerse', 'MMDU', 'CRPE', 'MIA-Bench', 'MM-Math', 'DynaMath', 'QSpatial', 'WeMath', 'LogicVista'], dataset): prompt = question if os.getenv('USE_COT') == '1': prompt = build_qa_cot_prompt(line, prompt) else: prompt = question + '\nAnswer the question using a single word or phrase.' else: # VQA_ex_prompt: OlympiadBench, VizWiz prompt = line['question'] if os.getenv('USE_COT') == '1': prompt = build_qa_cot_prompt(line, prompt) message = [dict(type='text', value=prompt)] image_num = len(tgt_path) max_num = max(1, min(max_num, 64 // image_num)) # TODO：support upscale_flag message.extend([dict(type='image', value=s, max_dynamic_patch=max_num) for s in tgt_path]) if self.use_mpo_prompt: message = build_mpo_prompt(message, line, dataset) # reorganize_prompt prompt = reorganize_prompt(message, image_num, dataset=dataset) prompt.replace('', '') message[0] = dict(type='text', value=prompt) return message def get_max_num(self, dataset): assert dataset is not None res_1_datasets = ['MMBench-Video', 'Video-MME', 'MVBench', 'Video', 'WorldSense'] res_12_datasets = ['ChartQA_TEST', 'MMMU_DEV_VAL', 'MMMU_TEST', 'MME-RealWorld', 'VCR_EN', 'VCR_ZH', 'OCRVQA'] res_18_datasets = ['DocVQA_VAL', 'DocVQA_TEST', 'DUDE', 'MMLongBench_DOC', 'SLIDEVQA'] res_24_datasets = ['InfoVQA_VAL', 'InfoVQA_TEST', 'OCRBench', 'HRBench4K', 'HRBench8K'] if listinstr(res_1_datasets, dataset): return 1 elif listinstr(res_12_datasets, dataset): return 12 elif listinstr(res_18_datasets, dataset): return 18 elif listinstr(res_24_datasets, dataset): return 24 else: return 6 class CogVLM2_PromptUtil: def dump_image(self, line, dataset): return self.dump_image_func(line) def use_custom_prompt(self, dataset): assert dataset is not None if DATASET_TYPE(dataset) in 'MCQ': return True return False def build_prompt(self, line, dataset=None): assert dataset is None or isinstance(dataset, str) assert self.use_custom_prompt(dataset) tgt_path = self.dump_image(line, dataset) if dataset is not None and DATASET_TYPE(dataset) == 'MCQ': question = line['question'] hint = line['hint'] if ('hint' in line and not pd.isna(line['hint'])) else None if hint is not None: question = hint + '\n' + question option_candidate = string.ascii_uppercase options = { cand: line[cand] for cand in option_candidate if cand in line and not pd.isna(line[cand]) } for key, item in options.items(): question += f'\n{key}. {item}' prompt = question if not cn_string(prompt): prompt = prompt + '\n' + "Answer with the option's letter from the given choices directly." else: prompt = prompt + '\n' + '请直接回答选项字母。' else: prompt = line['question'] message = [dict(type='text', value=prompt)] message.extend([dict(type='image', value=p) for p in tgt_path]) return message class LMDeployWrapper(BaseAPI): is_api: bool = True custom_prompt: str = None prompt_map = { 'cogvlm2': CogVLM2_PromptUtil(), 'internvl2': InternVL2_PromptUtil(), 'internvl2-mpo-cot': InternVL2_PromptUtil(use_mpo_prompt=True), } def __init__(self, retry: int = 5, wait: int = 5, key: str = 'sk-123456', verbose: bool = True, temperature: float = 0.0, timeout: int = 60, api_base: str = None, system_prompt: str = None, max_tokens: int = 1024, **kwargs): self.fail_msg = 'Failed to obtain answer via API. ' self.max_tokens = max_tokens self.timeout = timeout key = os.environ.get('LMDEPLOY_API_KEY', key) api_base = os.environ.get('LMDEPLOY_API_BASE', api_base) assert key is not None, 'Please set the environment variable LMDEPLOY_API_KEY.' assert api_base is not None, 'Please set the environment variable LMDEPLOY_API_BASE.' self.key = key self.api_base = api_base super().__init__(wait=wait, retry=retry, system_prompt=system_prompt, verbose=verbose, **kwargs) model_url = ''.join([api_base.split('v1')[0], 'v1/models']) resp = requests.get(model_url) self.model = resp.json()['data'][0]['id'] self.logger.info(f'lmdeploy evaluate model: {self.model}') self.set_prompt_pattern(self.model) if hasattr(self, 'custom_prompt'): self.logger.info(f'using custom prompt {self.custom_prompt}') self.temperature = temperature self.logger.info(f'Init temperature: {self.temperature}') def set_dump_image(self, dump_image_func): if self.custom_prompt in self.prompt_map: self.prompt_map[self.custom_prompt].dump_image_func = dump_image_func self.dump_image_func = dump_image_func def use_custom_prompt(self, dataset): if self.custom_prompt in self.prompt_map: return self.prompt_map[self.custom_prompt].use_custom_prompt(dataset) return False def build_prompt(self, line, dataset=None): if self.custom_prompt in self.prompt_map: return self.prompt_map[self.custom_prompt].build_prompt(line, dataset) raise NotImplementedError def set_prompt_pattern(self, model_name): if 'Phi-3.5-Vision'.lower() in model_name.lower(): self.max_tokens = 1000 self.temperature = 0.0 if 'cogvlm2-llama3-chat-19B'.lower() in model_name.lower(): self.max_tokens = 2048 self.temperature = 0.0 self.custom_prompt = 'cogvlm2' if 'InternVL2'.lower() in model_name.lower(): self.max_tokens = 1024 self.temperature = 0.0 if 'mpo' in model_name.lower(): self.max_tokens = 4096 self.logger.info('Use custom prompt internvl2-mpo-cot') self.custom_prompt = 'internvl2-mpo-cot' else: self.logger.info('Use custom prompt internvl2') self.custom_prompt = 'internvl2' if 'internvl2-8b-mpo-cot'.lower() in model_name.lower(): self.use_mpo_prompt = True self.max_tokens = 1024 self.temperature = 0.0 self.logger.info('Use custom prompt internvl2-mpo-cot') self.custom_prompt = 'internvl2-mpo-cot' if 'qvq'.lower() in model_name.lower(): self.max_tokens = 4096 self.temperature = 0.0 self.logger.info('QVQ model detected, do not use custom prompt') def prepare_itlist(self, inputs): assert np.all([isinstance(x, dict) for x in inputs]) has_images = np.sum([x['type'] == 'image' for x in inputs]) if has_images: content_list = [] for msg in inputs: if msg['type'] == 'text': content_list.append(dict(type='text', text=msg['value'])) elif msg['type'] == 'image': from PIL import Image img = Image.open(msg['value']) b64 = encode_image_to_base64(img) extra_args = msg.copy() extra_args.pop('type') extra_args.pop('value') img_struct = dict(url=f'data:image/jpeg;base64,{b64}', **extra_args) content_list.append(dict(type='image_url', image_url=img_struct)) else: assert all([x['type'] == 'text' for x in inputs]) text = '\n'.join([x['value'] for x in inputs]) content_list = [dict(type='text', text=text)] return content_list def prepare_inputs(self, inputs): input_msgs = [] if self.system_prompt is not None: input_msgs.append(dict(role='system', content=self.system_prompt)) assert isinstance(inputs, list) and isinstance(inputs[0], dict) assert np.all(['type' in x for x in inputs]) or np.all(['role' in x for x in inputs]), inputs if 'role' in inputs[0]: assert inputs[-1]['role'] == 'user', inputs[-1] for item in inputs: input_msgs.append(dict(role=item['role'], content=self.prepare_itlist(item['content']))) else: input_msgs.append(dict(role='user', content=self.prepare_itlist(inputs))) return input_msgs def generate_inner(self, inputs, **kwargs) -> str: input_msgs = self.prepare_inputs(inputs) temperature = kwargs.pop('temperature', self.temperature) self.logger.info(f'Generate temperature: {temperature}') max_tokens = kwargs.pop('max_tokens', self.max_tokens) headers = {'Content-Type': 'application/json', 'Authorization': f'Bearer {self.key}'} payload = dict( model=self.model, messages=input_msgs, max_tokens=max_tokens, n=1, temperature=temperature, **kwargs) response = requests.post( self.api_base, headers=headers, data=json.dumps(payload), timeout=self.timeout * 1.1) ret_code = response.status_code ret_code = 0 if (200 <= int(ret_code) < 300) else ret_code answer = self.fail_msg try: resp_struct = json.loads(response.text) answer = resp_struct['choices'][0]['message']['content'].strip() # for internvl2-8b-mpo-cot if getattr(self, 'use_mpo_prompt', False): from ..vlm.internvl.utils import mpo_post_processing answer = mpo_post_processing(answer, kwargs.get('dataset')) except: pass return ret_code, answer, response class LMDeployAPI(LMDeployWrapper): def __init__(self, **kwargs): super().__init__(**kwargs) def generate(self, message, dataset=None): return super(LMDeployAPI, self).generate(message, dataset=dataset)