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from transformers import AutoTokenizer, AutoConfig, AddedToken, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
from dataclasses import dataclass
from typing import Dict
import torch
import copy
import warnings
warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore", category=DeprecationWarning)
## 定义聊天模板
@dataclass
class Template:
template_name:str
system_format: str
user_format: str
assistant_format: str
system: str
stop_word: str
template_dict: Dict[str, Template] = dict()
def register_template(template_name, system_format, user_format, assistant_format, system, stop_word=None):
template_dict[template_name] = Template(
template_name=template_name,
system_format=system_format,
user_format=user_format,
assistant_format=assistant_format,
system=system,
stop_word=stop_word,
)
# 这里的系统提示词是训练时使用的,推理时可以自行尝试修改效果
register_template(
template_name='llama3',
system_format='<|begin_of_text|><system>\n{content}\n<system>\n\n<|eot_id|>',
user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|>',
assistant_format='<|start_header_id|>assistant<|end_header_id|>\n\n{content}\n', # \n\n{content}<|eot_id|>\n
system="你是心理健康助手EmoLLM, 由EmoLLM团队打造, 是一个研究过无数具有心理健康问题的病人与心理健康医生对话的心理专家, 在心理方面拥有广博的知识储备和丰富的研究咨询经验。你旨在通过专业心理咨询, 协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术, 一步步帮助来访者解决心理问题。",
stop_word='<|eot_id|>'
)
## 加载模型
def load_model(model_name_or_path, load_in_4bit=False, adapter_name_or_path=None):
if load_in_4bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
)
else:
quantization_config = None
# 加载base model
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
# load_in_4bit=load_in_4bit,
# # ValueError: You can't pass `load_in_4bit`or `load_in_8bit` as a kwarg when passing `quantization_config` argument at the same time.
trust_remote_code=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
device_map='auto',
quantization_config=quantization_config
)
# 加载adapter
if adapter_name_or_path is not None:
model = PeftModel.from_pretrained(model, adapter_name_or_path)
return model
## 加载tokenzier
def load_tokenizer(model_name_or_path):
tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path,
trust_remote_code=True,
use_fast=False
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
return tokenizer
## 构建prompt
def build_prompt(tokenizer, template, query, history, system=None):
template_name = template.template_name
system_format = template.system_format
user_format = template.user_format
assistant_format = template.assistant_format
system = system if system is not None else template.system
history.append({"role": 'user', 'message': query})
input_ids = []
# 添加系统信息
if system_format is not None:
if system is not None:
system_text = system_format.format(content=system)
input_ids = tokenizer.encode(system_text, add_special_tokens=False)
# 拼接历史对话
for item in history:
role, message = item['role'], item['message']
if role == 'user':
message = user_format.format(content=message, stop_token=tokenizer.eos_token)
else:
message = assistant_format.format(content=message, stop_token=tokenizer.eos_token)
tokens = tokenizer.encode(message, add_special_tokens=False)
input_ids += tokens
input_ids = torch.tensor([input_ids], dtype=torch.long)
return input_ids
def main():
# download model in openxlab
# download(model_repo='MrCat/Meta-Llama-3-8B-Instruct',
# output='MrCat/Meta-Llama-3-8B-Instruct')
# model_name_or_path = 'MrCat/Meta-Llama-3-8B-Instruct'
# # download model in modelscope
# model_name_or_path = snapshot_download('LLM-Research/Meta-Llama-3-8B-Instruct',
# cache_dir='LLM-Research/Meta-Llama-3-8B-Instruct')
# # offline model
# model_name_or_path = '/root/share/new_models/meta-llama/Meta-Llama-3-8B-Instruct'
# adapter_name_or_path = None
# model_name_or_path = "xtuner_config/merged_Llama3_8b_instruct_e3"
# adapter_name_or_path = 'xtuner_config/hf_llama3_e1_sc2'
# model_name_or_path = "xtuner_config/merged_Llama3_8b_instruct_e1_sc"
# adapter_name_or_path = None
print_user = False # 控制是否输入提示输入框用于notebook时改为True
template_name = 'llama3'
template = template_dict[template_name]
# 若开启4bit推理能够节省很多显存但效果可能下降
load_in_4bit = False # True # 6291MiB
# 生成超参配置,可修改以取得更好的效果
max_new_tokens = 500 # 每次回复时AI生成文本的最大长度
top_p = 0.9
temperature = 0.6 # 越大越有创造性,越小越保守
repetition_penalty = 1.1 # 越大越能避免吐字重复
# 加载模型
print(f'Loading model from: {model_name_or_path}')
print(f'adapter_name_or_path: {adapter_name_or_path}')
model = load_model(
model_name_or_path,
load_in_4bit=load_in_4bit,
adapter_name_or_path=adapter_name_or_path
).eval()
tokenizer = load_tokenizer(model_name_or_path if adapter_name_or_path is None else adapter_name_or_path)
if template.stop_word is None:
template.stop_word = tokenizer.eos_token
stop_token_id = tokenizer.encode(template.stop_word, add_special_tokens=True)
assert len(stop_token_id) == 1
stop_token_id = stop_token_id[0]
print("================================================================================")
print("=============欢迎来到Llama3 EmoLLM 心理咨询室, 输入'exit'退出程序===============")
print("================================================================================")
history = []
print("============请输入聊天内容, 按回车键结束输入, 输入'clear'清空聊天信息==============")
print("================================================================================")
print("================================================================================")
print("===============================让我们开启对话吧=================================\n\n")
if print_user:
query = input('用户:')
print("# 用户:{}".format(query))
else:
query = input('# 用户: ')
while True:
if query=='exit':
break
if query.strip() == "clear":
history = []
print("\n\n=============欢迎来到Llama3 EmoLLM 心理咨询室, 输入'exit'退出程序===============")
print("============请输入聊天内容, 按回车键结束输入, 输入'clear'清空聊天信息===========")
print("================================================================================")
print("================================================================================")
if print_user:
query = input('用户:')
print("# 用户:{}".format(query))
else:
query = input('# 用户: ')
continue
query = query.strip()
input_ids = build_prompt(tokenizer, template, query, copy.deepcopy(history), system=None).to(model.device)
outputs = model.generate(
input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True,
top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty,
eos_token_id=stop_token_id, pad_token_id=tokenizer.eos_token_id
)
outputs = outputs.tolist()[0][len(input_ids[0]):]
response = tokenizer.decode(outputs)
response = response.strip().replace(template.stop_word, "").strip()
# 存储对话历史
history.append({"role": 'user', 'message': query})
history.append({"role": 'assistant', 'message': response})
# 当对话长度超过6轮时清空最早的对话可自行修改
if len(history) > 12:
history = history[:-12]
print("# Llama3 EmoLLM 心理咨询师:{}".format(response.replace('\n','').replace('<|start_header_id|>','').replace('assistant<|end_header_id|>','').replace('>','')))
print()
query = input('# 用户:')
if print_user:
print("# 用户:{}".format(query))
print("\n\n===============感谢使用Llama3 EmoLLM 心理咨询室, 祝您生活愉快~===============\n\n")
if __name__ == '__main__':
main()

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@ -72,8 +72,12 @@ OpenXLab浦源 内容平台 是面向 AI 研究员和开发者提供 AI 领域
### 1. 安装git lfs
```bash
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh
apt install git-lfs
# 更好的方法
conda install git-lfs
# 旧方法
# curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh
# apt install git-lfs
```
### 2. 配置git和lfs

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# isort: skip_file
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, BitsAndBytesConfig # isort: skip
from peft import PeftModel
import warnings
warnings.filterwarnings("ignore")
warnings.filterwarnings("ignore", category=DeprecationWarning)
logger = logging.get_logger(__name__)
online = True
if online:
from openxlab.model import download
download(model_repo='chg0901/EmoLLM-Llama3-8B-Instruct2.0',
output='model')
@dataclass
class GenerationConfig:
# this config is used for chat to provide more diversity
max_length: int = 500
top_p: float = 0.9
temperature: float = 0.6
do_sample: bool = True
repetition_penalty: float = 1.1
@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], 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(arg1):
# # model = AutoModelForCausalLM.from_pretrained(args.m).cuda()
# # tokenizer = AutoTokenizer.from_pretrained(args.m, trust_remote_code=True)
# model = AutoModelForCausalLM.from_pretrained(arg1, torch_dtype=torch.float16).cuda()
# tokenizer = AutoTokenizer.from_pretrained(arg1, trust_remote_code=True)
# return model, tokenizer
@st.cache_resource
def load_model(model_name_or_path, load_in_4bit=False, adapter_name_or_path=None):
if load_in_4bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
)
else:
quantization_config = None
# 加载base model
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
# load_in_4bit=load_in_4bit,
# # ValueError: You can't pass `load_in_4bit`or `load_in_8bit` as a kwarg when passing `quantization_config` argument at the same time.
trust_remote_code=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
device_map='auto',
quantization_config=quantization_config
)
# 加载adapter
if adapter_name_or_path is not None:
model = PeftModel.from_pretrained(model, adapter_name_or_path)
## 加载tokenzier
tokenizer = AutoTokenizer.from_pretrained(
model_name_or_path if adapter_name_or_path is None else adapter_name_or_path,
trust_remote_code=True,
use_fast=False
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
return model, tokenizer
def prepare_generation_config():
with st.sidebar:
# 使用 Streamlit 的 markdown 函数添加 Markdown 文本
st.image('assets/EmoLLM_logo_L.png', width=1, caption='EmoLLM Logo', use_column_width=True)
st.markdown("[访问 **EmoLLM** 官方repo: **SmartFlowAI/EmoLLM**](https://github.com/SmartFlowAI/EmoLLM)")
max_length = st.slider('Max Length',
min_value=8,
max_value=8192,
value=500)
top_p = st.slider('Top P', 0.0, 1.0, 0.9, step=0.01)
temperature = st.slider('Temperature', 0.0, 1.0, 0.6, step=0.01)
repetition_penalty = st.slider('Repetition penalty', 0.0, 1.5, 1.1, 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,
repetition_penalty=repetition_penalty,
do_sample=True)
return generation_config
user_prompt = '<|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|>'
robot_prompt = '<|start_header_id|>assistant<|end_header_id|>\n\n{robot}<|eot_id|>'
cur_query_prompt = '<|start_header_id|>user<|end_header_id|>\n\n{user}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
def combine_history(prompt):
messages = st.session_state.messages
meta_instruction = (
"你是心理健康助手EmoLLM, 由EmoLLM团队打造, 是一个研究过无数具有心理健康问题的病人与心理健康医生对话的心理专家, 在心理方面拥有广博的知识储备和丰富的研究咨询经验。你旨在通过专业心理咨询, 协助来访者完成心理诊断。请充分利用专业心理学知识与咨询技术, 一步步帮助来访者解决心理问题。。"
)
total_prompt = f"<|start_header_id|>system<|end_header_id|>\n{meta_instruction}<|eot_id|>\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(arg1):
if online:
model_name_or_path = 'model'
adapter_name_or_path = None
else:
# model_name_or_path = "/root/StableCascade/emollm2/EmoLLM/xtuner_config/merged_Llama3_8b_instruct_e3"
# adapter_name_or_path = '/root/StableCascade/emollm2/EmoLLM/xtuner_config/hf_llama3_e1_sc2'
model_name_or_path = "/root/StableCascade/emollm2/EmoLLM/xtuner_config/merged_Llama3_8b_instruct_e1_sc"
adapter_name_or_path = None
# 若开启4bit推理能够节省很多显存但效果可能下降
load_in_4bit = False # True # 6291MiB
# torch.cuda.empty_cache()
print('load model begin.')
# 加载模型
print(f'Loading model from: {model_name_or_path}')
print(f'adapter_name_or_path: {adapter_name_or_path}')
# model, tokenizer = load_model(arg1)
model, tokenizer = load_model(
arg1 if arg1 is not None else model_name_or_path,
load_in_4bit=load_in_4bit,
adapter_name_or_path=adapter_name_or_path
)
model.eval()
print('load model end.')
user_avator = "assets/user.png"
robot_avator = "assets/EmoLLM.png"
st.title('EmoLLM Llama3心理咨询室V2.0')
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('你好欢迎来到Llama3 EmoLLM 心理咨询室'):
# Display user message in chat message container
with st.chat_message('user', avatar=user_avator):
st.markdown(prompt)
real_prompt = combine_history(prompt)
# Add user message to chat history
st.session_state.messages.append({
'role': 'user',
'content': prompt,
'avatar': user_avator
})
# stop_token_id = tokenizer.encode('<|eot_id|>', add_special_tokens=True)
# assert len(stop_token_id) == 1
# stop_token_id = stop_token_id[0]
with st.chat_message('robot', avatar=robot_avator):
message_placeholder = st.empty()
for cur_response in generate_interactive(
model=model,
tokenizer=tokenizer,
prompt=real_prompt,
additional_eos_token_id=128009, # <|eot_id|>
eos_token_id=128009,
pad_token_id=128009,
**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
"avatar": robot_avator,
})
torch.cuda.empty_cache()
if __name__ == '__main__':
import sys
arg1 = sys.argv[1]
main(arg1)

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@ -1,219 +0,0 @@
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from datasets import load_dataset
from mmengine.dataset import DefaultSampler
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
from peft import LoraConfig
from torch.optim import AdamW
from transformers import (AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig)
from xtuner.dataset import process_hf_dataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.map_fns import alpaca_map_fn, template_map_fn_factory
from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook,
VarlenAttnArgsToMessageHubHook)
from xtuner.engine.runner import TrainLoop
from xtuner.model import SupervisedFinetune
from xtuner.parallel.sequence import SequenceParallelSampler
from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
#######################################################################
# PART 1 Settings #
#######################################################################
# Model
pretrained_model_name_or_path = 'meta-llama/Meta-Llama-3-8B-Instruct'
use_varlen_attn = False
# Data
alpaca_en_path = 'tatsu-lab/alpaca'
prompt_template = PROMPT_TEMPLATE.llama3_chat
max_length = 512
pack_to_max_length = True
# parallel
sequence_parallel_size = 1
# Scheduler & Optimizer
batch_size = 1 # per_device
accumulative_counts = 16
accumulative_counts *= sequence_parallel_size
dataloader_num_workers = 0
max_epochs = 3
optim_type = AdamW
lr = 2e-4
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1 # grad clip
warmup_ratio = 0.03
# Save
save_steps = 500
save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
# Evaluate the generation performance during the training
evaluation_freq = 500
SYSTEM = SYSTEM_TEMPLATE.alpaca
evaluation_inputs = [
'请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai'
]
#######################################################################
# PART 2 Model & Tokenizer #
#######################################################################
tokenizer = dict(
type=AutoTokenizer.from_pretrained,
pretrained_model_name_or_path=pretrained_model_name_or_path,
trust_remote_code=True,
padding_side='right')
model = dict(
type=SupervisedFinetune,
use_varlen_attn=use_varlen_attn,
llm=dict(
type=AutoModelForCausalLM.from_pretrained,
pretrained_model_name_or_path=pretrained_model_name_or_path,
trust_remote_code=True,
torch_dtype=torch.float16,
quantization_config=dict(
type=BitsAndBytesConfig,
load_in_4bit=True,
load_in_8bit=False,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4')),
lora=dict(
type=LoraConfig,
r=16,
lora_alpha=16,
lora_dropout=0.1,
bias='none',
task_type='CAUSAL_LM'))
#######################################################################
# PART 3 Dataset & Dataloader #
#######################################################################
alpaca_en = dict(
type=process_hf_dataset,
dataset=dict(type=load_dataset, path=alpaca_en_path),
tokenizer=tokenizer,
max_length=max_length,
dataset_map_fn=alpaca_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
remove_unused_columns=True,
shuffle_before_pack=True,
pack_to_max_length=pack_to_max_length,
use_varlen_attn=use_varlen_attn)
sampler = SequenceParallelSampler \
if sequence_parallel_size > 1 else DefaultSampler
train_dataloader = dict(
batch_size=batch_size,
num_workers=dataloader_num_workers,
dataset=alpaca_en,
sampler=dict(type=sampler, shuffle=True),
collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))
#######################################################################
# PART 4 Scheduler & Optimizer #
#######################################################################
# optimizer
optim_wrapper = dict(
type=AmpOptimWrapper,
optimizer=dict(
type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
accumulative_counts=accumulative_counts,
loss_scale='dynamic',
dtype='float16')
# learning policy
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
param_scheduler = [
dict(
type=LinearLR,
start_factor=1e-5,
by_epoch=True,
begin=0,
end=warmup_ratio * max_epochs,
convert_to_iter_based=True),
dict(
type=CosineAnnealingLR,
eta_min=0.0,
by_epoch=True,
begin=warmup_ratio * max_epochs,
end=max_epochs,
convert_to_iter_based=True)
]
# train, val, test setting
train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
#######################################################################
# PART 5 Runtime #
#######################################################################
# Log the dialogue periodically during the training process, optional
custom_hooks = [
dict(type=DatasetInfoHook, tokenizer=tokenizer),
dict(
type=EvaluateChatHook,
tokenizer=tokenizer,
every_n_iters=evaluation_freq,
evaluation_inputs=evaluation_inputs,
system=SYSTEM,
prompt_template=prompt_template)
]
if use_varlen_attn:
custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]
# configure default hooks
default_hooks = dict(
# record the time of every iteration.
timer=dict(type=IterTimerHook),
# print log every 10 iterations.
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per `save_steps`.
checkpoint=dict(
type=CheckpointHook,
by_epoch=False,
interval=save_steps,
max_keep_ckpts=save_total_limit),
# set sampler seed in distributed evrionment.
sampler_seed=dict(type=DistSamplerSeedHook),
)
# configure environment
env_cfg = dict(
# whether to enable cudnn benchmark
cudnn_benchmark=False,
# set multi process parameters
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
# set distributed parameters
dist_cfg=dict(backend='nccl'),
)
# set visualizer
visualizer = None
# set log level
log_level = 'INFO'
# load from which checkpoint
load_from = None
# whether to resume training from the loaded checkpoint
resume = False
# Defaults to use random seed and disable `deterministic`
randomness = dict(seed=None, deterministic=False)
# set log processor
log_processor = dict(by_epoch=False)

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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from datasets import load_dataset
from mmengine.dataset import DefaultSampler
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
from peft import LoraConfig
from torch.optim import AdamW
from transformers import (AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig)
from xtuner.dataset import process_hf_dataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.map_fns import alpaca_map_fn, template_map_fn_factory
from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook,
VarlenAttnArgsToMessageHubHook)
from xtuner.engine.runner import TrainLoop
from xtuner.model import SupervisedFinetune
from xtuner.parallel.sequence import SequenceParallelSampler
from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
#######################################################################
# PART 1 Settings #
#######################################################################
# Model
pretrained_model_name_or_path = 'meta-llama/Meta-Llama-3-8B-Instruct'
use_varlen_attn = False
# Data
alpaca_en_path = 'tatsu-lab/alpaca'
prompt_template = PROMPT_TEMPLATE.llama3_chat
max_length = 8192
pack_to_max_length = True
# parallel
sequence_parallel_size = 1
# Scheduler & Optimizer
batch_size = 1 # per_device
accumulative_counts = 16
accumulative_counts *= sequence_parallel_size
dataloader_num_workers = 0
max_epochs = 3
optim_type = AdamW
lr = 2e-4
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1 # grad clip
warmup_ratio = 0.03
# Save
save_steps = 500
save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)
# Evaluate the generation performance during the training
evaluation_freq = 500
SYSTEM = SYSTEM_TEMPLATE.alpaca
evaluation_inputs = [
'请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai'
]
#######################################################################
# PART 2 Model & Tokenizer #
#######################################################################
tokenizer = dict(
type=AutoTokenizer.from_pretrained,
pretrained_model_name_or_path=pretrained_model_name_or_path,
trust_remote_code=True,
padding_side='right')
model = dict(
type=SupervisedFinetune,
use_varlen_attn=use_varlen_attn,
llm=dict(
type=AutoModelForCausalLM.from_pretrained,
pretrained_model_name_or_path=pretrained_model_name_or_path,
trust_remote_code=True,
torch_dtype=torch.float16,
quantization_config=dict(
type=BitsAndBytesConfig,
load_in_4bit=True,
load_in_8bit=False,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4')),
lora=dict(
type=LoraConfig,
r=16,
lora_alpha=16,
lora_dropout=0.1,
bias='none',
task_type='CAUSAL_LM'))
#######################################################################
# PART 3 Dataset & Dataloader #
#######################################################################
alpaca_en = dict(
type=process_hf_dataset,
dataset=dict(type=load_dataset, path=alpaca_en_path),
tokenizer=tokenizer,
max_length=max_length,
dataset_map_fn=alpaca_map_fn,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
remove_unused_columns=True,
shuffle_before_pack=True,
pack_to_max_length=pack_to_max_length,
use_varlen_attn=use_varlen_attn)
sampler = SequenceParallelSampler \
if sequence_parallel_size > 1 else DefaultSampler
train_dataloader = dict(
batch_size=batch_size,
num_workers=dataloader_num_workers,
dataset=alpaca_en,
sampler=dict(type=sampler, shuffle=True),
collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))
#######################################################################
# PART 4 Scheduler & Optimizer #
#######################################################################
# optimizer
optim_wrapper = dict(
type=AmpOptimWrapper,
optimizer=dict(
type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
accumulative_counts=accumulative_counts,
loss_scale='dynamic',
dtype='float16')
# learning policy
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501
param_scheduler = [
dict(
type=LinearLR,
start_factor=1e-5,
by_epoch=True,
begin=0,
end=warmup_ratio * max_epochs,
convert_to_iter_based=True),
dict(
type=CosineAnnealingLR,
eta_min=0.0,
by_epoch=True,
begin=warmup_ratio * max_epochs,
end=max_epochs,
convert_to_iter_based=True)
]
# train, val, test setting
train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)
#######################################################################
# PART 5 Runtime #
#######################################################################
# Log the dialogue periodically during the training process, optional
custom_hooks = [
dict(type=DatasetInfoHook, tokenizer=tokenizer),
dict(
type=EvaluateChatHook,
tokenizer=tokenizer,
every_n_iters=evaluation_freq,
evaluation_inputs=evaluation_inputs,
system=SYSTEM,
prompt_template=prompt_template)
]
if use_varlen_attn:
custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]
# configure default hooks
default_hooks = dict(
# record the time of every iteration.
timer=dict(type=IterTimerHook),
# print log every 10 iterations.
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per `save_steps`.
checkpoint=dict(
type=CheckpointHook,
by_epoch=False,
interval=save_steps,
max_keep_ckpts=save_total_limit),
# set sampler seed in distributed evrionment.
sampler_seed=dict(type=DistSamplerSeedHook),
)
# configure environment
env_cfg = dict(
# whether to enable cudnn benchmark
cudnn_benchmark=False,
# set multi process parameters
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
# set distributed parameters
dist_cfg=dict(backend='nccl'),
)
# set visualizer
visualizer = None
# set log level
log_level = 'INFO'
# load from which checkpoint
load_from = None
# whether to resume training from the loaded checkpoint
resume = False
# Defaults to use random seed and disable `deterministic`
randomness = dict(seed=None, deterministic=False)
# set log processor
log_processor = dict(by_epoch=False)