TwT-6's picture
Upload 2667 files
256a159 verified
raw
history blame
4.76 kB
import logging
import threading
from concurrent.futures import ThreadPoolExecutor
from typing import Dict, List, Optional, Union
from opencompass.models.base import BaseModel, LMTemplateParser
from opencompass.utils.logging import get_logger
from opencompass.utils.prompt import PromptList
PromptType = Union[PromptList, str]
def valid_str(string, coding='utf-8'):
"""decode text according to its encoding type."""
invalid_chars = [b'\xef\xbf\xbd']
bstr = bytes(string, coding)
for invalid_char in invalid_chars:
bstr = bstr.replace(invalid_char, b'')
ret = bstr.decode(encoding=coding, errors='ignore')
return ret
class TurboMindTisModel(BaseModel):
"""Model wrapper for TurboMind Triton Inference Server gRPC API.
Args:
path (str): The name of OpenAI's model.
tis_addr (str): The address (ip:port format) of turbomind's
triton inference server
max_seq_len (int): The maximum allowed sequence length of a model.
Note that the length of prompt + generated tokens shall not exceed
this value. Defaults to 2048.
meta_template (Dict, optional): The model's meta prompt
template if needed, in case the requirement of injecting or
wrapping of any meta instructions.
"""
is_api: bool = True
def __init__(
self,
path: str,
tis_addr: str = '0.0.0.0:33337',
max_seq_len: int = 2048,
meta_template: Optional[Dict] = None,
):
super().__init__(path=path,
max_seq_len=max_seq_len,
meta_template=meta_template)
self.logger = get_logger()
self.template_parser = LMTemplateParser(meta_template)
self.eos_token_id = None
if meta_template and 'eos_token_id' in meta_template:
self.eos_token_id = meta_template['eos_token_id']
self.tis_addr = tis_addr
def generate(
self,
inputs: List[str or PromptList],
max_out_len: int = 512,
temperature: float = 1.0,
) -> List[str]:
"""Generate results given a list of inputs.
Args:
inputs (List[str or PromptList]): A list of strings or PromptDicts.
The PromptDict should be organized in OpenCompass'
API format.
max_out_len (int): The maximum length of the output.
temperature (float): What sampling temperature to use,
between 0 and 2. Higher values like 0.8 will make the output
more random, while lower values like 0.2 will make it more
focused and deterministic. Defaults to 0.7.
Returns:
List[str]: A list of generated strings.
"""
with ThreadPoolExecutor() as executor:
results = list(
executor.map(self._generate, inputs,
[max_out_len] * len(inputs),
[temperature] * len(inputs)))
return results
def wait(self):
"""Wait till the next query can be sent.
Applicable in both single-thread and multi-thread environments.
"""
return self.token_bucket.get_token()
def _generate(self, prompt: str or PromptList, max_out_len: int,
temperature: float) -> str:
"""Generate results given a list of inputs.
Args:
prompt (str or PromptList): A string or PromptDict.
The PromptDict should be organized in OpenCompass'
API format.
max_out_len (int): The maximum length of the output.
temperature (float): What sampling temperature to use,
between 0 and 2. Higher values like 0.8 will make the output
more random, while lower values like 0.2 will make it more
focused and deterministic.
Returns:
str: The generated string.
"""
assert type(
prompt) is str, 'We only support string for TurboMind RPC API'
from lmdeploy.serve.turbomind.chatbot import Chatbot
chatbot = Chatbot(self.tis_addr,
temperature=temperature,
capability='completion',
top_k=1,
log_level=logging.ERROR)
for status, text, n_token in chatbot.stream_infer(
session_id=threading.currentThread().ident,
prompt=prompt,
request_output_len=max_out_len,
sequence_start=True,
sequence_end=True):
continue
response = valid_str(text)
response = response.replace('<eoa>', '')
return response