| 7 | |
| 8 | |
| 9 | class YiChatEvaluator(Evaluator): |
| 10 | |
| 11 | def __init__(self, pretrained_model_name_or_path, cache_dir=None, do_sample=False): |
| 12 | super(YiChatEvaluator, self).__init__() |
| 13 | |
| 14 | self.tokenizer = AutoTokenizer.from_pretrained( |
| 15 | pretrained_model_name_or_path=pretrained_model_name_or_path, |
| 16 | cache_dir=cache_dir, |
| 17 | use_fast=False, |
| 18 | trust_remote_code=True |
| 19 | ) |
| 20 | self.model = AutoModelForCausalLM.from_pretrained( |
| 21 | pretrained_model_name_or_path=pretrained_model_name_or_path, |
| 22 | cache_dir=cache_dir, |
| 23 | device_map='auto', |
| 24 | low_cpu_mem_usage=True, |
| 25 | torch_dtype=torch.float16, |
| 26 | trust_remote_code=True |
| 27 | ) |
| 28 | self.model = self.model.eval() |
| 29 | self.do_sample = do_sample |
| 30 | print(f'Memory footprint: {self.model.get_memory_footprint() / 1e6:.2f} MB') |
| 31 | |
| 32 | def format_prompt(self, prompt): |
| 33 | return [ |
| 34 | { |
| 35 | 'role': 'user', |
| 36 | 'content': prompt |
| 37 | } |
| 38 | ] |
| 39 | |
| 40 | @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) |
| 41 | def generate_text(self, prompt): |
| 42 | prompt = self.format_prompt(prompt) |
| 43 | inputs = self.tokenizer.apply_chat_template(conversation=prompt, tokenize=True, add_generation_prompt=True, return_tensors='pt').to(self.model.device) |
| 44 | outputs = self.model.generate( |
| 45 | inputs, |
| 46 | do_sample=self.do_sample |
| 47 | ) |
| 48 | response = self.tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) |
| 49 | |
| 50 | return response.strip() |
| 51 | |
| 52 | @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) |
| 53 | def count_tokens(self, prompt): |
| 54 | return len(self.tokenizer(prompt)['input_ids']) |