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from typing import List, Optional, Union
from vllm.engine.llm_engine import LLMEngine
from vllm.engine.arg_utils import EngineArgs
from vllm.usage.usage_lib import UsageContext
from vllm.utils import Counter
from vllm.outputs import RequestOutput
from vllm import SamplingParams
from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
import gradio as gr
class StreamingLLM:
def __init__(
self,
model: str,
dtype: str = "auto",
quantization: Optional[str] = None,
**kwargs,
) -> None:
engine_args = EngineArgs(model=model, quantization=quantization, dtype=dtype, enforce_eager=True)
self.llm_engine = LLMEngine.from_engine_args(engine_args, usage_context=UsageContext.LLM_CLASS)
self.request_counter = Counter()
def generate(
self,
prompt: Optional[str] = None,
sampling_params: Optional[SamplingParams] = None
) -> List[RequestOutput]:
request_id = str(next(self.request_counter))
self.llm_engine.add_request(request_id, prompt, sampling_params)
while self.llm_engine.has_unfinished_requests():
step_outputs = self.llm_engine.step()
for output in step_outputs:
yield output
class UI:
def __init__(
self,
llm: StreamingLLM,
tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
sampling_params: Optional[SamplingParams] = None,
) -> None:
self.llm = llm
self.tokenizer = tokenizer
self.sampling_params = sampling_params
def _generate(self, message, history):
history_chat_format = []
for human, assistant in history:
history_chat_format.append({"role": "user", "content": human })
history_chat_format.append({"role": "assistant", "content": assistant})
history_chat_format.append({"role": "user", "content": message})
prompt = self.tokenizer.apply_chat_template(history_chat_format, tokenize=False)
for chunk in self.llm.generate(prompt, self.sampling_params):
yield chunk.outputs[0].text
def launch(self):
gr.ChatInterface(self._generate).launch()
if __name__ == "__main__":
llm = StreamingLLM(model="casperhansen/llama-3-70b-instruct-awq", quantization="AWQ", dtype="float16")
tokenizer = llm.llm_engine.tokenizer.tokenizer
sampling_params = SamplingParams(temperature=0.6,
top_p=0.9,
max_tokens=4096,
stop_token_ids=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
)
ui = UI(llm, tokenizer, sampling_params)
ui.launch() |