| # chatglm-maths |
| chatglm-6b微调/LORA/PPO/推理, 样本为自动生成的整数/小数加减乘除运算, 可gpu/cpu |
|
|
| # Github |
| [https://github.com/yongzhuo/chatglm-maths](https://github.com/yongzhuo/chatglm-maths) |
|
|
| ## 踩坑 |
| ```python |
| 1. eps=1e-5(不要改小), 半精度float16, 以及LN采用的是Post-LN(泛化性更好) + DeepNorm, 【害, Attention前也有LN】目的是大模型为了防止梯度溢出等; |
| 2. 模型输入输出, 默认的tokenization_chatglm.py/modeling_chatglm.py不能用, 因为那是完全为生成generate设置的, 需要自己写好所有缩入参数, 或者机子改成适配的; |
| 2.1 ChatGLMModel中, get_masks()正常, get_position_ids()函数中‘context_length = seq.index(150004) + 1’ 改为 ‘context_length = len(seq)’; |
| 2.2 训练输入input_ids格式暂定为(训练后post-padding, 推理前pre-padding[tokenization_chatglm.py默认pre-padding]) |
| x: prompt_1 + "_" + text_1 + "\n" + prompt_2 + [gMASK] + [BOS] + "_" + text_2 + [PAD]*N |
| 2.3 训练输入label_ids格式暂定为(CrossEntropyLoss默认忽略-100不参与计算loss) |
| y = [-100]*len(text_1) + [BOS] + text_2 + [EOS] + [-100]*N |
| 2.4 注意position/mask(自带的只是推理用的batch_size=1, 所以训练输入还得自己写), 可参考GLM-130的README.md, huozhe 查看GLM-1源码https://github.com/THUDM/GLM/blob/main/tasks/seq2seq/dataset.py |
| 3. 注意chatglm-6b权重是float16的, 不过计算loss时候会转成float32计算, 最后loss再转回float16更新梯度; |
| 4. ChatGLMTokenizer有时候会报奇奇怪怪的错误, 建议生成时候设置max_new_tokens, 最大{"max_new_tokens": 2048}; decode有时候会出现不存在id; |
| 5. 低秩自适应LORA, RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! |
| 尝试 transformers升级到最新, get_peft_model后再.cuda(), device_map={'':torch.cuda.current_device()}, |
| ``` |
|
|
| ## 微调数据 |
| 1. 原始数据来自[https://github.com/LYH-YF/MWPToolkit](https://github.com/LYH-YF/MWPToolkit) |
|
|
| 处理后的微调数据(算式/解方程)-MWP: [https://huggingface.co/datasets/Macropodus/MWP-Instruct](https://huggingface.co/datasets/Macropodus/MWP-Instruct) |
| |
| 3. 大数加减乘除来自: [https://github.com/liutiedong/goat.git ](https://github.com/liutiedong/goat.git ) |
|
|
|
|
| ## LoRA权重 |
| ```shell |
| Baichuan-7B-GPT4ForALL: https://huggingface.co/Macropodus/MWP-Instruct |
| Bloomz-7B-GPT4ForALL: https://huggingface.co/Macropodus/MWP-Instruct |
| ChatGLM-6B-GPT4ForALL: https://huggingface.co/Macropodus/MWP-Instruct |
| LlaMA-7B-GPT4ForALL: https://huggingface.co/Macropodus/MWP-Instruct |
| ChatGLM-6B-MWP: https://huggingface.co/Macropodus/MWP-Instruct |
| ``` |
|
|
| ## 数据集-中文 |
| - [https://github.com/tatsu-lab/stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca) |
| - [https://github.com/LianjiaTech/BELLE](https://github.com/LianjiaTech/BELLE) |
| - [https://github.com/carbonz0/alpaca-chinese-dataset](https://github.com/carbonz0/alpaca-chinese-dataset) |
| |
|
|
| ## 环境配置 |
| ```shell |
| transformers>=4.26.1 |
| cpm_kernels==1.0.11 |
| icetk==0.0.4 |
| torch>=1.10.1 |
| rouge==1.0.1 |
| nltk==3.6.6 |
| peft>=0.2.0 |
| numpy |
| tqdm |
| |
| lion_pytorch |
| macropodus |
| trl>=0.4.1 |
| ``` |
|
|
| ## 微调-计算题 |
| ```shell |
| lora |
| 微调: python c00_toy_lora_train_6b.py |
| 推理: python p00_toy_lora_predict_6b.py |
| |
| ppo |
| 训练: python t10_toy_trl_train_ppo.py |
| 测试: python t10_toy_trl_predict_ppo.py |
| |
| 6b |
| 微调: python c00_toy_cpu_train_6b.py |
| 推理: python p00_toy_cpu_predit_6b.py |
| |
| small-layer |
| 微调: python c01_toy_cpu_train_small.py |
| 推理: python p01_toy_cpu_predict_small.py |
| ``` |
|
|
|
|
| ## 参考/感谢 |
| - [https://github.com/THUDM/ChatGLM-6B](https://github.com/THUDM/ChatGLM-6B) |
| - [https://github.com/THUDM/GLM](https://github.com/THUDM/GLM) |
| - [https://github.com/tatsu-lab/stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca) |
| - [https://github.com/LianjiaTech/BELLE](https://github.com/LianjiaTech/BELLE) |
| - [https://github.com/huggingface/peft](https://github.com/huggingface/peft) |
| - [https://github.com/mymusise/ChatGLM-Tuning](https://github.com/mymusise/ChatGLM-Tuning) |
| - [https://github.com/bojone/bert4keras](https://github.com/bojone/bert4keras) |
| - [trl](https://github.com/lvwerra/trl) |
| - [math23k](https://aclanthology.org/D17-1088) |
|
|
| ## 推理日志toy |
| ```cpu |
| generator_calculate_line: ('13+75=', '13+75=88') |
| tokenizer.vocab_size: 150344 |
| eval: 0%| | 0/1 [00:00<?, ?it/s]batch_query: ['简便运算: 98+83= 剖析: 98+83=181'] |
| batch_qtext_0: 简便运算: 98+83= 剖析: |
| batch_qans_0: 98+83=181 |
| response_0: 98+83=171 |
| {'rouge-1': 0.0, 'rouge-2': 0.0, 'rouge-l': 0.0, 'bleu': 0.0} |
| 请输入: |
| 25.31+86.35= |
| 请稍等... |
| 25.31+86.35=101.66 |
| ``` |
|
|
|
|
| ## 微调日志toy |
| ```cpu |
| generator_calculate_line: ('13+75=', '13+75=88') |
| tokenizer.vocab_size: 150344 |
| Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:10<00:00, 1.31s/it] |
| transformer.word_embeddings.weight False |
| ...... |
| transformer.layers.26.mlp.dense_4h_to_h.bias False |
| transformer.layers.27.input_layernorm.weight True |
| transformer.layers.27.input_layernorm.bias True |
| transformer.layers.27.attention.query_key_value.weight True |
| transformer.layers.27.attention.query_key_value.bias True |
| transformer.layers.27.attention.dense.weight True |
| transformer.layers.27.attention.dense.bias True |
| transformer.layers.27.post_attention_layernorm.weight True |
| transformer.layers.27.post_attention_layernorm.bias True |
| transformer.layers.27.mlp.dense_h_to_4h.weight True |
| transformer.layers.27.mlp.dense_h_to_4h.bias True |
| transformer.layers.27.mlp.dense_4h_to_h.weight True |
| transformer.layers.27.mlp.dense_4h_to_h.bias True |
| transformer.final_layernorm.weight True |
| transformer.final_layernorm.bias True |
| model.chat start |
| 13+75=88, but that's not the correct answer. The correct answer is 13+75=88, which is 90. |
| /anaconda3/envs/py371/lib/python3.7/site-packages/transformers/optimization.py:395: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning |
| FutureWarning, |
| epoch: 0%| |
| |
| |
| --- |
| license: cc-by-nc-4.0 |
| --- |
| |
