LightRAG/lightrag/llm/lmdeploy.py
2025-05-14 10:57:05 +08:00

148 lines
4.7 KiB
Python

import pipmaster as pm # Pipmaster for dynamic library install
# install specific modules
if not pm.is_installed("lmdeploy"):
pm.install("lmdeploy[all]")
from lightrag.exceptions import (
APIConnectionError,
RateLimitError,
APITimeoutError,
)
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
from functools import lru_cache
@lru_cache(maxsize=1)
def initialize_lmdeploy_pipeline(
model,
tp=1,
chat_template=None,
log_level="WARNING",
model_format="hf",
quant_policy=0,
):
from lmdeploy import pipeline, ChatTemplateConfig, TurbomindEngineConfig
lmdeploy_pipe = pipeline(
model_path=model,
backend_config=TurbomindEngineConfig(
tp=tp, model_format=model_format, quant_policy=quant_policy
),
chat_template_config=(
ChatTemplateConfig(model_name=chat_template) if chat_template else None
),
log_level="WARNING",
)
return lmdeploy_pipe
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
retry=retry_if_exception_type(
(RateLimitError, APIConnectionError, APITimeoutError)
),
)
async def lmdeploy_model_if_cache(
model,
prompt,
system_prompt=None,
history_messages=[],
chat_template=None,
model_format="hf",
quant_policy=0,
**kwargs,
) -> str:
"""
Args:
model (str): The path to the model.
It could be one of the following options:
- i) A local directory path of a turbomind model which is
converted by `lmdeploy convert` command or download
from ii) and iii).
- ii) The model_id of a lmdeploy-quantized model hosted
inside a model repo on huggingface.co, such as
"InternLM/internlm-chat-20b-4bit",
"lmdeploy/llama2-chat-70b-4bit", etc.
- iii) The model_id of a model hosted inside a model repo
on huggingface.co, such as "internlm/internlm-chat-7b",
"Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
and so on.
chat_template (str): needed when model is a pytorch model on
huggingface.co, such as "internlm-chat-7b",
"Qwen-7B-Chat ", "Baichuan2-7B-Chat" and so on,
and when the model name of local path did not match the original model name in HF.
tp (int): tensor parallel
prompt (Union[str, List[str]]): input texts to be completed.
do_preprocess (bool): whether pre-process the messages. Default to
True, which means chat_template will be applied.
skip_special_tokens (bool): Whether or not to remove special tokens
in the decoding. Default to be True.
do_sample (bool): Whether or not to use sampling, use greedy decoding otherwise.
Default to be False, which means greedy decoding will be applied.
"""
try:
import lmdeploy
from lmdeploy import version_info, GenerationConfig
except Exception:
raise ImportError("Please install lmdeploy before initialize lmdeploy backend.")
kwargs.pop("hashing_kv", None)
kwargs.pop("response_format", None)
max_new_tokens = kwargs.pop("max_tokens", 512)
tp = kwargs.pop("tp", 1)
skip_special_tokens = kwargs.pop("skip_special_tokens", True)
do_preprocess = kwargs.pop("do_preprocess", True)
do_sample = kwargs.pop("do_sample", False)
gen_params = kwargs
version = version_info
if do_sample is not None and version < (0, 6, 0):
raise RuntimeError(
"`do_sample` parameter is not supported by lmdeploy until "
f"v0.6.0, but currently using lmdeloy {lmdeploy.__version__}"
)
else:
do_sample = True
gen_params.update(do_sample=do_sample)
lmdeploy_pipe = initialize_lmdeploy_pipeline(
model=model,
tp=tp,
chat_template=chat_template,
model_format=model_format,
quant_policy=quant_policy,
log_level="WARNING",
)
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.extend(history_messages)
messages.append({"role": "user", "content": prompt})
gen_config = GenerationConfig(
skip_special_tokens=skip_special_tokens,
max_new_tokens=max_new_tokens,
**gen_params,
)
response = ""
async for res in lmdeploy_pipe.generate(
messages,
gen_config=gen_config,
do_preprocess=do_preprocess,
stream_response=False,
session_id=1,
):
response += res.response
return response