目錄
- 1. 基本概念
- 2. 準備工作
- 2.1 建立cuda12.2-conda的開發機
- 2.2 環境準備
- 2.3 安裝xtuner
- 2.4 模型準備
- 3. 快速開始
- 3.1 微調前
- 3.2 指令跟隨微調
- 3.3 微調後
1. 基本概念
//todo
2. 準備工作
2.1 建立cuda12.2-conda的開發機
略
2.2 環境準備
# 建立虛擬環境
conda create -n xtuner0121 python=3.10 -y
# 啟用虛擬環境（注意：後續的所有操作都需要在這個虛擬環境中進行）
conda activate xtuner0121
# 安裝一些必要的庫
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
# 安裝其他依賴
pip install transformers==4.39.3
pip install streamlit==1.36.0
2.3 安裝xtuner
# 建立一個目錄，用來存放原始碼
mkdir -p /root/InternLM/code
cd /root/InternLM/code
git clone -b v0.1.21 https://github.com/InternLM/XTuner /root/InternLM/code/XTuner
# 進入到原始碼目錄
cd /root/InternLM/code/XTuner
conda activate xtuner0121
# 執行安裝
pip install -e '.[deepspeed]'
驗證結果
xtuner version
2.4 模型準備
開發機中已經提供了模型本地檔案，不需要再下載。透過以下程式碼一鍵透過符號連結的方式連結到模型檔案，這樣既節省了空間，也便於管理。
# 建立一個目錄，用來存放微調的所有資料，後續的所有操作都在該路徑中進行
mkdir -p /root/InternLM/XTuner
cd /root/InternLM/XTuner
mkdir -p Shanghai_AI_Laboratory
ln -s /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b Shanghai_AI_Laboratory/internlm2-chat-1_8b
安裝tree命令來慣常目錄結構
apt-get install -y tree
tree -l
3. 快速開始
3.1 微調前
準備一個Streamlit程式的指令碼
import copy
import warnings
from dataclasses import asdict, dataclass
from typing import Callable, List, Optional
import streamlit as st
import torch
from torch import nn
from transformers.generation.utils import (LogitsProcessorList,
StoppingCriteriaList)
from transformers.utils import logging
from transformers import AutoTokenizer, AutoModelForCausalLM # isort: skip
logger = logging.get_logger(__name__)
model_name_or_path = "/root/InternLM/XTuner/Shanghai_AI_Laboratory/internlm2-chat-1_8b"
@dataclass
class GenerationConfig:
# this config is used for chat to provide more diversity
max_length: int = 2048
top_p: float = 0.75
temperature: float = 0.1
do_sample: bool = True
repetition_penalty: float = 1.000
@torch.inference_mode()
def generate_interactive(
model,
tokenizer,
prompt,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor],
List[int]]] = None,
additional_eos_token_id: Optional[int] = None,
**kwargs,
):
inputs = tokenizer([prompt], padding=True, return_tensors='pt')
input_length = len(inputs['input_ids'][0])
for k, v in inputs.items():
inputs[k] = v.cuda()
input_ids = inputs['input_ids']
_, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
if generation_config is None:
generation_config = model.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs)
bos_token_id, eos_token_id = ( # noqa: F841 # pylint: disable=W0612
generation_config.bos_token_id,
generation_config.eos_token_id,
)
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
if additional_eos_token_id is not None:
eos_token_id.append(additional_eos_token_id)
has_default_max_length = kwargs.get(
'max_length') is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using 'max_length''s default ({repr(generation_config.max_length)}) \
to control the generation length. "
'This behaviour is deprecated and will be removed from the \
config in v5 of Transformers -- we'
' recommend using `max_new_tokens` to control the maximum \
length of the generation.',
UserWarning,
)
elif generation_config.max_new_tokens is not None:
generation_config.max_length = generation_config.max_new_tokens + \
input_ids_seq_length
if not has_default_max_length:
logger.warn( # pylint: disable=W4902
f"Both 'max_new_tokens' (={generation_config.max_new_tokens}) "
f"and 'max_length'(={generation_config.max_length}) seem to "
"have been set. 'max_new_tokens' will take precedence. "
'Please refer to the documentation for more information. '
'(https://huggingface.co/docs/transformers/main/'
'en/main_classes/text_generation)',
UserWarning,
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = 'input_ids'
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, "
f"but 'max_length' is set to {generation_config.max_length}. "
'This can lead to unexpected behavior. You should consider'
" increasing 'max_new_tokens'.")
# 2. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None \
else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None \
else StoppingCriteriaList()
logits_processor = model._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=input_ids,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
)
stopping_criteria = model._get_stopping_criteria(
generation_config=generation_config,
stopping_criteria=stopping_criteria)
logits_warper = model._get_logits_warper(generation_config)
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
scores = None
while True:
model_inputs = model.prepare_inputs_for_generation(
input_ids, **model_kwargs)
# forward pass to get next token
outputs = model(
**model_inputs,
return_dict=True,
output_attentions=False,
output_hidden_states=False,
)
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_token_scores = logits_processor(input_ids, next_token_logits)
next_token_scores = logits_warper(input_ids, next_token_scores)
# sample
probs = nn.functional.softmax(next_token_scores, dim=-1)
if generation_config.do_sample:
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
else:
next_tokens = torch.argmax(probs, dim=-1)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
model_kwargs = model._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=False)
unfinished_sequences = unfinished_sequences.mul(
(min(next_tokens != i for i in eos_token_id)).long())
output_token_ids = input_ids[0].cpu().tolist()
output_token_ids = output_token_ids[input_length:]
for each_eos_token_id in eos_token_id:
if output_token_ids[-1] == each_eos_token_id:
output_token_ids = output_token_ids[:-1]
response = tokenizer.decode(output_token_ids)
yield response
# stop when each sentence is finished
# or if we exceed the maximum length
if unfinished_sequences.max() == 0 or stopping_criteria(
input_ids, scores):
break
def on_btn_click():
del st.session_state.messages
@st.cache_resource
def load_model():
model = (AutoModelForCausalLM.from_pretrained(model_name_or_path,
trust_remote_code=True).to(
torch.bfloat16).cuda())
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,
trust_remote_code=True)
return model, tokenizer
def prepare_generation_config():
with st.sidebar:
max_length = st.slider('Max Length',
min_value=8,
max_value=32768,
value=2048)
top_p = st.slider('Top P', 0.0, 1.0, 0.75, step=0.01)
temperature = st.slider('Temperature', 0.0, 1.0, 0.1, step=0.01)
st.button('Clear Chat History', on_click=on_btn_click)
generation_config = GenerationConfig(max_length=max_length,
top_p=top_p,
temperature=temperature)
return generation_config
user_prompt = '<|im_start|>user\n{user}<|im_end|>\n'
robot_prompt = '<|im_start|>assistant\n{robot}<|im_end|>\n'
cur_query_prompt = '<|im_start|>user\n{user}<|im_end|>\n\
<|im_start|>assistant\n'
def combine_history(prompt):
messages = st.session_state.messages
meta_instruction = ('')
total_prompt = f"<s><|im_start|>system\n{meta_instruction}<|im_end|>\n"
for message in messages:
cur_content = message['content']
if message['role'] == 'user':
cur_prompt = user_prompt.format(user=cur_content)
elif message['role'] == 'robot':
cur_prompt = robot_prompt.format(robot=cur_content)
else:
raise RuntimeError
total_prompt += cur_prompt
total_prompt = total_prompt + cur_query_prompt.format(user=prompt)
return total_prompt
def main():
# torch.cuda.empty_cache()
print('load model begin.')
model, tokenizer = load_model()
print('load model end.')
st.title('InternLM2-Chat-1.8B')
generation_config = prepare_generation_config()
# Initialize chat history
if 'messages' not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message['role'], avatar=message.get('avatar')):
st.markdown(message['content'])
# Accept user input
if prompt := st.chat_input('What is up?'):
# Display user message in chat message container
with st.chat_message('user'):
st.markdown(prompt)
real_prompt = combine_history(prompt)
# Add user message to chat history
st.session_state.messages.append({
'role': 'user',
'content': prompt,
})
with st.chat_message('robot'):
message_placeholder = st.empty()
for cur_response in generate_interactive(
model=model,
tokenizer=tokenizer,
prompt=real_prompt,
additional_eos_token_id=92542,
**asdict(generation_config),
):
# Display robot response in chat message container
message_placeholder.markdown(cur_response + '▌')
message_placeholder.markdown(cur_response)
# Add robot response to chat history
st.session_state.messages.append({
'role': 'robot',
'content': cur_response, # pylint: disable=undefined-loop-variable
})
torch.cuda.empty_cache()
if __name__ == '__main__':
main()
執行：
conda activate xtuner0121
streamlit run /root/InternLM/Tutorial/tools/xtuner_streamlit_demo.py
埠對映之後再本地訪問：
ssh -CNg -L 8501:127.0.0.1:8501 root@ssh.intern-ai.org.cn -p 43551
3.2 指令跟隨微調
3.3 微調後
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