| from transformers import AutoProcessor,AutoTokenizer |
| from vllm import LLM, SamplingParams |
| import os |
|
|
|
|
| class Vllm_Text: |
| def __init__(self,model_path,args): |
| super().__init__() |
| self.llm = LLM( |
| model=model_path, |
| tensor_parallel_size=int(os.environ.get("tensor_parallel_size", 1)), |
| enforce_eager=True, |
| trust_remote_code=True |
| ) |
|
|
| self.processor = AutoProcessor.from_pretrained(model_path,trust_remote_code=True) |
|
|
| self.sampling_params = SamplingParams( |
| temperature=args.temperature, |
| top_p=args.top_p, |
| repetition_penalty=args.repetition_penalty, |
| max_tokens= args.max_new_tokens, |
| stop_token_ids=[], |
| ) |
|
|
| def process_messages(self,messages): |
| current_messages = [] |
| prompt = messages["prompt"] |
| if "system" in messages: |
| system_prompt = messages["system"] |
| prompt = system_prompt + "\n" + prompt |
| current_messages.append({"role":"user","content":prompt}) |
| messages = current_messages |
| prompt = self.processor.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True, |
| enable_thinking = False |
| ) |
| return prompt |
|
|
|
|
| def generate_output(self,messages): |
| llm_inputs = self.process_messages(messages) |
| outputs = self.llm.generate([llm_inputs], sampling_params=self.sampling_params) |
| return outputs[0].outputs[0].text |
| |
| def generate_outputs(self,messages_list): |
| llm_inputs_list = [self.process_messages(messages) for messages in messages_list] |
| outputs = self.llm.generate(llm_inputs_list, sampling_params=self.sampling_params) |
| res = [] |
| for output in outputs: |
| generated_text = output.outputs[0].text |
| res.append(generated_text) |
| return res |
|
|
