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deep_training's Introduction

transformer is all you need.

  • deep training framework based on transformers

install and download

  • pip install -U deep_training
  • 源码安装
pip uninstall deep_training
pip install -U git+https://github.com/ssbuild/deep_training.git
  • 源码重装
pip install -U git+https://github.com/ssbuild/deep_training.git --no-deps --force-reinstall

update

  • 2024-02-15

    • 0.2.11 support internlm2
  • 2023-12-02

    • 0.2.10 update qwen model for 1.8b 7b 14b 72b
    • 0.2.10.post0 fix qwen attention_mask
  • 2023-11-13

    • 0.2.9 release
    • 0.2.9.post0 support chatglm3-6b-32k
  • 2023-10-22

  • 2023-10-16

  • 2023-10-07

    • 0.2.5
      • support colossalai 训练 ,策略 ddp ,gemini,gemini_auto,zero2,zero2_cpu,3d
    • 0.2.5.post2
      • support accelerator 训练 , fix some bug in accelerator and hf trainer
    • 0.2.5.post4
      • fix trainer some bug
  • 2023-09-26

    • 0.2.4
      • support transformers trainer and qwen-7b 新版 和 qwen-14b , 旧版不再支持,旧版可以安装 deep_training <= 0.2.3
    • 0.2.4.post3
      • support ia3 finetuning
  • 2023-09-21

  • 2023-09-06

    • 0.2.2
    • 0.2.2.post0
      • fix baichuan ptv2
    • 0.2.2.post1
      • fix rwkv4 a bug
    • 0.2.2.post4
      • fix llama and baichuan mask bug
  • 2023-09-02

    • 0.2.1
      • fix llama model
  • 2023-08-23

    • 0.2.0
      • release lora内部调整
    • 0.2.0.post1
      • add xverse-13b chat and fix muti lora
  • 2023-08-16

    • 0.1.21
      • release 增加 5种 rope scale 方法 , fix chatglm2-6b-32k 推理 rope_ratio
    • 0.1.21.post1
      • fix moss rope
  • 2023-08-09

    • 0.1.17
      • update qwen model
    • 0.1.17.post0
      • update qwen config
  • 2023-08-08

  • 2023-08-05

    • 0.1.13
    • 0.1.13.post2
      • fix quantization bug
    • 0.1.14
      • release fix qwen stream
  • 2023-07-18

  • 2023-07-04

    • 0.1.11 rc1
    • 0.1.11
      • fix baichuan and chatglm2 some bugs
      • support conv2d for lora
      • support arrow parquet dataset
  • 2023-06-06

  • 2023-06-06

    • 0.1.10
      • release add qlora and support more optimizer and scheduler
      • support lora prompt for deepspeed training
      • support rwkv4 完整训练 rwkv_finetuning
    • 0.1.10.post0
      • fix package setup for cpp and cu code for rwkv4
    • 0.1.10.post1
      • fix infer for rwkv4
  • 2023-05-24

    • 0.1.8
      • fix load weight in prompt_tuning,p_tuning,prefix_tuning,adaption_prompt
  • 2023-05-19

    • 0.1.7
      • fix 0.1.5 rl bugs
    • 0.1.7.post1
      • fix chatglm-6b-int4,chatglm-6b-int4 p-tuning-v2 training , fix ilql lightning import
      • fix load weight in prompt_tuning,p_tuning,prefix_tuning,adaption_prompt
  • 2023-05-10

    • 0.1.5
      • fix lora v2 modules_to_save 自定义额外训练模块
      • support reward ppo llm 完整训练 rlhf_llm
      • support reward ppo chatglm 完整训练 rlhf_chatglm
      • support reward ppo chatyuan 完整训练 rlhf_chatyuan
    • 0.1.5.post2 release
      • fix prompt modules_to_save 自定义额外训练模块
      • support ilql 离线模式训练 ilql 完整训练 rlhf_llm
    • 0.1.5.post4 release
      • fix opt model hidden_size for ppo ilql
      • fix ppotrainer ilqltrainer deepspeed save weight
      • import AdmaW from transformers or but torch firstly
  • 2023-05-02

    • 0.1.4
      • support prompt_tuning,p_tuning,prefix_tuning,adaption_prompt
  • 2023-04-21

    • 0.1.3rc0
      • support moss chat模型 完整训练参考 moss_finetuning
      • moss 量化int4 int8推理
    • 0.1.3.post0
      • 新版本基于lightning, pytorch-lightning 更名 lightning,分离numpy-io模块
  • 2023-04-11

    • 0.1.2
      • 重构lora v2, 增加adalora
    • 0.1.2.post0
      • fix lova v1,lova v2 load_in_8bit
  • 2023-04-07

    • deep_training 0.1.1
      • update chatglm config
  • 2023-04-02

    • release 0.1.0 and lightning >= 2
  • 2023-03-15

    • 0.0.18
    • fix deepspeed进程数据平衡
    • 0.0.18.post9
      • 增加流式输出接口stream_chat接口
    • 0.0.20 ChatGLM lora
      • 加载权重继续训练 , 修改数据数据编码 ,权重自适应
    • 0.0.21.post0
      • fix ChatGLM deepspeed stage 3 权重加载
  • 2023-03-09

  • 2023-03-08

    • 增加LLaMA 模型(非模型并行版) 完整训练参考 poetry_training
  • 2023-03-02

  • 2023-02-15

    • 增加诗歌PaLM预训练模型
  • 2023-02-13

    • 增加中文语法纠错模型gector, seq2seq语法纠错模型
  • 2023-02-09

    • 增加诗歌t5decoder预训练, 诗歌laMDA预训练模型 , t5encoder 预训练模型
  • 2023-02-07

    • 增加层次分解位置编码选项,让transformer可以处理超长文本
  • 2023-01-24

    • 增加诗歌gpt2预训练,诗歌t5预训练,诗歌unilm预训练
  • 2023-01-20

    • 增加对抗训练 FGM, FGSM_Local,FreeAT, PGD, FGSM,FreeAT_Local, 其中FreeAT推荐使用FreeAT_Local,FGSM 推荐使用 FGSM_Local
  • 2023-01-19

    • 增加promptbertcse监督和非监督模型
  • 2023-01-16

    • 增加diffcse 监督和非监督模型
  • 2023-01-13

    • 增加ESimcse 模型
  • 2023-01-11

    • 增加TSDAE句向量模型
  • 2023-01-09

    • 增加infonce监督和非监督,simcse监督和非监督,SPN4RE关系模型抽取
  • 2023-01-06

    • 增加onerel关系模型抽取,prgc关系模型抽取,pure实体模型提取
  • 2022-12-24

    • 增加unilm模型蒸馏和事件抽取模型
  • 2022-12-16

    • crf_cascad crf级联抽取实体
    • span ner 可重叠多标签,非重叠多标签两种实现方式抽取实体
    • mhs_ner 多头选择实体抽取模型
    • w2ner 实体抽取模型
    • tplinkerplus 实体抽取
    • tpliner 关系抽取模型
    • tplinkerplus 关系抽取模型
    • mhslinker 多头选择关系抽取模型
  • 2022-11-17:

    • simcse-unilm 系列
    • simcse-bert-wwm 系列
    • tnews circle loss
    • afqmc siamese net similar
  • 2022-11-15:

    • unilm autotitle seq2seq autotitle
    • 普通分类,指针提取命名实体,crf提取命名实体
    • prefixtuning 分类 , prefixtuning 分类 , prefixtuning 指针提取命名实体 , prefixtuning crf 提取命名实体
  • 2022-11-12:

    • gplinker (全局指针提取)
    • casrel (A Novel Cascade Binary Tagging Framework for Relational Triple Extraction 参考 https://github.com/weizhepei/CasRel)
    • spliner (指针提取关系 sigmoid pointer or simple pointer)
  • 2022-11-11:

    • cluener_pointer 中文命名实体提取 和 cluener crf 中文命名实体提取
    • tnews 中文分类
  • 2022-11-06:

    • mlm,gpt2,t5等模型预训练任务

tasks

  • 预训练:
    • 数据参考 THUCNews新闻文本分类数据集的子集
    • mlm预训练例子 bert roberta等一些列中文预训练
    • lm预训练例子 gpt2等一些列中文预训练
    • seq2seq 预训练例子 t5 small等一些列中文预训练   
    • unilm 预训练例子 unilm bert roberta 等一些列中文预训练  &nbsp
  • 中文分类:
    • 例子 tnews 中文分类
  • 命名实体提取:
    • 参考数据 cluner
    • cluener 全局指针提取
    • cluener crf提取
    • cluener crf prompt提取
    • cluener mhs ner多头选择提取
    • cluener span指针提取
    • cluener crf 级联提取
    • cluener tplinkerplus 提取
    • pure 提取
    • cluener w2ner 提取
  • 关系提取
    • 参考数据 duie和法研杯第一阶段数据
    • gplinker 关系提取
    • casrel 关系提取
    • spliner 关系提取
    • mhslinker 关系提取
    • tplinker 关系提取
    • tplinkerplus 关系提取
    • onerel 关系抽取
    • prgc 关系提取
    • spn4re 关系提取
  • 事件提取
  • prompt 系列:
    • 例子 prefixprompt tnews中文分类
    • 例子 prefixtuning tnews 中文分类
    • 例子 prefixtuning cluener 命名实体全局指针提取
    • 例子 prefixtuning cluener 命名实体crf提取
    • 例子 prompt mlm 自行构建数据模板集,训练参考 pretrain/mlm_pretrain
    • 例子 prompt lm 自行构建数据模板集,训练参考 pretrain/seq2seq_pretrain , pretrain/lm_pretrain
  • simcse 系列:
  • sentense embeding:
    • circle loss 例子 tnews circle loss
    • siamese net 例子 afqmc siamese net similar

optimizer

   lamb,adma,adamw_hf,adam,adamw,adamw_torch,adamw_torch_fused,adamw_torch_xla,adamw_apex_fused,
   adafactor,adamw_anyprecision,sgd,adagrad,adamw_bnb_8bit,adamw_8bit,lion,lion_8bit,lion_32bit,
   paged_adamw_32bit,paged_adamw_8bit,paged_lion_32bit,paged_lion_8bit,
   lamb_fused_dp adagrad_cpu_dp adam_cpu_dp adam_fused_dp

scheduler

  linear,WarmupCosine,CAWR,CAL,Step,ReduceLROnPlateau, cosine,cosine_with_restarts,polynomial,
  constant,constant_with_warmup,inverse_sqrt,reduce_lr_on_plateau

works

Create a model factory, lightweight and efficient training program and make it easier, training model easier to get started.

友情链接

纯粹而干净的代码

协议

本仓库的代码依照 Apache-2.0 协议开源

discuss

QQ group:185144988

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deep_training's Issues

Cannot install deep_training package

python: 3.10

command used: pip install deep_training

console output:

Collecting deep_training
  Using cached deep_training-0.1.12-py3-none-any.whl (483 kB)
Collecting lightning>=2 (from deep_training)
  Using cached lightning-2.0.6-py3-none-any.whl (1.9 MB)
Collecting numpy-io<0.1.0,>=0.0.7 (from deep_training)
  Using cached numpy_io-0.0.7-py3-none-any.whl (27 kB)
Requirement already satisfied: sentencepiece in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from deep_training) (0.1.98)
Requirement already satisfied: numpy in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from deep_training) (1.24.0)
Requirement already satisfied: transformers>=4.22 in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from deep_training) (4.31.0)
Collecting seqmetric (from deep_training)
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Requirement already satisfied: scipy in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from deep_training) (1.11.1)
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Requirement already satisfied: tqdm in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from deep_training) (4.65.0)
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Collecting fastdatasets<=0.9.20,>=0.9.14 (from numpy-io<0.1.0,>=0.0.7->deep_training)
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Collecting werkzeug>=1.0.1 (from tensorboard->deep_training)
  Using cached Werkzeug-2.3.6-py3-none-any.whl (242 kB)
Requirement already satisfied: wheel>=0.26 in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from tensorboard->deep_training) (0.38.4)
Requirement already satisfied: python-dateutil>=2.7.0 in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from arrow<3.0,>=1.2.0->lightning>=2->deep_training) (2.8.2)
Requirement already satisfied: soupsieve>1.2 in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from beautifulsoup4<6.0,>=4.8.0->lightning>=2->deep_training) (2.4.1)
Requirement already satisfied: pytz in /opt/homebrew/Caskroom/miniconda/base/envs/gpt/lib/python3.10/site-packages (from dateutils<2.0->lightning>=2->deep_training) (2023.3)
Collecting ordered-set<4.2.0,>=4.0.2 (from deepdiff<8.0,>=5.7.0->lightning>=2->deep_training)
  Using cached ordered_set-4.1.0-py3-none-any.whl (7.6 kB)
INFO: pip is looking at multiple versions of fastdatasets to determine which version is compatible with other requirements. This could take a while.
Collecting fastdatasets<=0.9.20,>=0.9.14 (from numpy-io<0.1.0,>=0.0.7->deep_training)
  Using cached fastdatasets-0.9.14-py3-none-any.whl (59 kB)
ERROR: Cannot install numpy-io because these package versions have conflicting dependencies.

The conflict is caused by:
    fastdatasets 0.9.15 depends on tfrecords<0.3 and >=0.2.12
    fastdatasets 0.9.14 depends on tfrecords<0.3 and >=0.2.12

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

Import error

请问为什么在已经安装deep_training库的情况下仍然会有以下的报错?

ModuleNotFoundError: No module named 'deep_training'

image

Import data_helper and process get killed by itself

image
image
我在windows上运行chatglm-finetune没有问题,但是num_layers设置成一都会GPU爆内存,在linux上运行的时候这个deep_training的包引入有些问题,会自动把自己kill掉。
有什么解决办法吗?

example

看作者这块算法集成了很多,但是没有看到具体训练的例子

AttributeError: module 'inspect' has no attribute 'ArgSpec'

(gh_chatglm_finetuning) ub2004@ub2004-B85M-A0:/llm_dev/chatglm_finetuning$ python data_utils.py
Traceback (most recent call last):
File "/home/ub2004/llm_dev/chatglm_finetuning/data_utils.py", line 10, in
from deep_training.data_helper import DataHelper, ModelArguments, TrainingArguments, DataArguments
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/deep_training/data_helper/init.py", line 4, in
from .data_helper import DataHelper,load_tokenizer, load_configure
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/deep_training/data_helper/data_helper.py", line 8, in
from fastdatasets import memory as MEMORY
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/fastdatasets/init.py", line 5, in
from tfrecords.python.io import gfile
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/tfrecords/init.py", line 4, in
from .python import *
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/tfrecords/python/init.py", line 6, in
from .framework import *
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/tfrecords/python/framework/init.py", line 5, in
from .errors import *
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/tfrecords/python/framework/errors.py", line 22, in
from tfrecords.python.framework.errors_impl import *
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/tfrecords/python/framework/errors_impl.py", line 21, in
from tfrecords.python.util import tf_inspect
File "/home/ub2004/anaconda3/envs/gh_chatglm_finetuning/lib/python3.11/site-packages/tfrecords/python/util/tf_inspect.py", line 38, in
ArgSpec = _inspect.ArgSpec
^^^^^^^^^^^^^^^^
AttributeError: module 'inspect' has no attribute 'ArgSpec'
(gh_chatglm_finetuning) ub2004@ub2004-B85M-A0:
/llm_dev/chatglm_finetuning$

加载模型保存为HF格式报错

File "/root/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/data/persist/code_szy/chatglmV2_finetuning/infer_finetuning.py", line 50, in
pl_model.save_sft_weight('convert/pytorch_model_sft.bin')
File "/root/miniconda3/envs/chatglm-V2/lib/python3.10/site-packages/deep_training/trainer/pl/modelweighter.py", line 150, in save_sft_weight
self.save_pretrained(sft_weight_path)
File "/root/miniconda3/envs/chatglm-V2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1614, in getattr
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'MyTransformer' object has no attribute 'save_pretrained'

#保存sft权重
pl_model.save_sft_weight('convert/pytorch_model_sft.bin'

PushToHubMixin找不到

一运行就报这里找不到PushToHubMixin的错误:from transformers.utils import PushToHubMixin。
猜就是transformers的版本问题。你这个requirement里transformers >=4.16,一开始我的是4.18就报错,然后我又换成4.16、4.17、4.11都是这个错。最后改成4.22就好了。

建议修改下transformers的版本号。

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