bakaEC/OliveSensorAPI
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #8 from aJupyter/dev

Browse Source 
Dev
This commit is contained in:
xzwang 2024-01-20 23:49:01 +08:00 committed by GitHub
commit f156a9c42c
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
5 changed files with 237 additions and 39 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
.gitignore vendored
View File

@ -1,4 +1,5 @@
ESConv.json
.DS_Store
__pycache__/
tmp/
tmp/
data/zhipuai/

3
README.md
View File

@ -1,5 +1,8 @@
# EmoLLM
## 🌟 Contributors
[![EmoLLM contributors](https://contrib.rocks/image?repo=aJupyter/EmoLLM&max=2000)](https://github.com/aJupyter/EmoLLM/graphs/contributors)

36
data/merge_json.py
View File

@ -3,38 +3,38 @@ import os
def save_merge_json(data_lis, file_path):
import json
with open(file_path, 'wt', encoding='utf-8') as file:
json.dump(data_lis, file, ensure_ascii=False)
json.dump(data_lis, file, indent=4, ensure_ascii=False)
def get_all_file_paths(folder_path):
# 确保传入的是一个目录
if not os.path.isdir(folder_path):
raise ValueError(f"{folder_path} is not a valid directory")
# 获取文件夹下所有文件的路径
file_paths = [os.path.join(folder_path, file) for file in os.listdir(
folder_path) if os.path.isfile(os.path.join(folder_path, file))]
return file_paths
files = os.listdir(folder_path)
path = []
for file in files:
file_path = os.path.join(folder_path, file)
if os.path.isdir(file_path):
path.extend(get_all_file_paths(file_path))
else:
path.append(file_path)
return path
if __name__ == '__main__':
conversion_lis = []
folder_path = '' # input
merge_path = '' # input
paths = get_all_file_paths(folder_path=folder_path)
for path in get_all_file_paths('res/'):
for path in paths:
print(path)
with open(path, 'rt', encoding='utf-8') as file:
for line in file:
with open(path, 'rt', encoding='utf-8') as lines:
for line in lines:
# 移除行尾的换行符
line = line.rstrip('\n')
line.rstrip('\n')
# 解析JSON
try:
data = json.loads(line)
conversion_lis.append(data)
except json.JSONDecodeError as e:
print(f"Error decoding JSON: {e}")
save_merge_json(data_lis=conversion_lis, file_path='merge.json')
save_merge_json(data_lis=conversion_lis, file_path=merge_path)

40
data/zhipuai_gen_data.py
View File

@ -1,4 +1,5 @@
import os
import random
import json
from tqdm import tqdm
from dotenv import load_dotenv
@ -22,10 +23,12 @@ def zhipu_api(data, emo):
医生：医生的安抚和建议
'''
top_p = round(random.uniform(0.1, 0.9), 2)
messages = getText('user', prompt)
response = client.chat.completions.create(
model='glm-4',
messages=messages,
top_p=top_p,
)
return response.choices[0].message.content
@ -47,6 +50,8 @@ def convert(conversation):
def save_jsonl(data_lis, file_path):
if not os.path.exists(os.path.dirname(file_path)):
os.makedirs(os.path.dirname(file_path))
with open(file_path, 'w', encoding='utf-8') as f:
for item in data_lis:
f.write(json.dumps(item, ensure_ascii=False) + '\n')
@ -67,7 +72,7 @@ if __name__ == '__main__':
"渴望",
"厌恶",
"同情",
"痛苦"
"痛苦",
"着迷",
"嫉妒",
"兴奋",
@ -80,7 +85,6 @@ if __name__ == '__main__':
"悲伤",
"满意",
"性欲",
"同情",
"满足"
]
areas_of_life = [
@ -103,22 +107,18 @@ if __name__ == '__main__':
]
conversation_lis = []
idx = 0
for area in areas_of_life:
j = 0
for idx in tqdm(range(len(emotions_lis)), desc=f'data:{area}, emo:{emotions_lis[j]}'):
emo = emotions_lis[j]
res = zhipu_api(area, emo)
print(res)
if res == 'null':
print(area, emo, 'error')
for emo in emotions_lis:
for area in areas_of_life:
if os.path.exists(f'./zhipuai/{area}/{emo}.jsonl'):
print(f'./zhipuai/{area}/{emo}.jsonl exists')
continue
conversation_lis.append(convert(res))
if idx % 2 == 1:
save_jsonl(conversation_lis, f'./zhipuai_{idx}.jsonl')
conversation_lis = []
idx += 1
j += 1
if len(conversation_lis) > 0:
save_jsonl(conversation_lis, f'./zhipuai.jsonl')
conversation_lis = []
for i in tqdm(range(5), desc='{emo}, {area}'.format(emo=emo, area=area)):
res = zhipu_api(area, emo)
print(res)
if res == 'null':
print(area, emo, 'error')
continue
conversation_lis.append(convert(res))
save_jsonl(conversation_lis, f'./zhipuai/{area}/{emo}.jsonl')
print(f'generate ./zhipuai/{area}/{emo}.jsonl')
conversation_lis = []

194
finetune/ft_config.py Normal file
View File

@ -0,0 +1,194 @@
# 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 template_map_fn_factory
from xtuner.engine import DatasetInfoHook, EvaluateChatHook
from xtuner.model import SupervisedFinetune
from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE
#######################################################################
# PART 1 Settings #
#######################################################################
# Model
pretrained_model_name_or_path = '/root/share/model_repos/internlm2-chat-7b'
# Data
data_path = 'merge.json'
prompt_template = PROMPT_TEMPLATE.internlm2_chat
max_length = 2048
pack_to_max_length = True
# Scheduler & Optimizer
batch_size = 8 # per_device
accumulative_counts = 2
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
# Evaluate the generation performance during the training
evaluation_freq = 500
SYSTEM = "现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。"
evaluation_inputs = [
'我最近总是感到很焦虑，尤其是在学业上。我有个特别崇拜的同学，他好像在各方面都比我优秀，我总觉得自己怎么努力也追不上他，这让我压力特别大。', '我知道应该理性看待，但就是忍不住会去比较。我甚至晚上会因为这个睡不着觉，总想着怎样才能像他那样出色。'
]
#######################################################################
# 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,
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=64,
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='json', data_files=dict(train=data_path)),
tokenizer=tokenizer,
max_length=max_length,
dataset_map_fn=None,
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)
train_dataloader = dict(
batch_size=batch_size,
num_workers=dataloader_num_workers,
dataset=alpaca_en,
sampler=dict(type=DefaultSampler, shuffle=True),
collate_fn=dict(type=default_collate_fn))
#######################################################################
# 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,
T_max=max_epochs,
convert_to_iter_based=True)
]
# train, val, test setting
train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1)
#######################################################################
# 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)
]
# configure default hooks
default_hooks = dict(
# record the time of every iteration.
timer=dict(type=IterTimerHook),
# print log every 100 iterations.
logger=dict(type=LoggerHook, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per epoch.
checkpoint=dict(type=CheckpointHook, interval=1),
# 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)