ChatGLM2-6B微调实践-P-Tuning方案_master_addr is only used for static rdzv_backend a

环境准备

申请阿里云GPU服务器：

• CentOS 7.6 64
• Anaconda3-2023.07-1-Linux-x86_64
• Python 3.11.5
• GPU NVIDIA A10（显存24 G/1 core）
• CPU 8 vCore/30G

安装部署

1、安装 Anaconda

wget https://repo.anaconda.com/archive/Anaconda3-2023.07-1-Linux-x86_64.sh
sh Anaconda3-2023.07-1-Linux-x86_64.sh
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根据提示一路安装即可。

2、安装CUDA

wget https://developer.download.nvidia.com/compute/cuda/11.2.0/local_installers/cuda_11.2.0_460.27.04_linux.run
sh cuda_11.2.0_460.27.04_linux.run
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根据提示安装即可

3、安装PyTorch

conda install pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia
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如提示找不到conda命令，需配置Anaconda环境变量。

4、安装 ChatGLM2-6B

mkdir ChatGLM
cd ChatGLM
git clone https://github.com/THUDM/ChatGLM2-6B.git
cd ChatGLM2-6B
pip install -r requirements.txt
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加载模型，需要从网上下载模型的7个分片文件，总共大约10几个G大小，可提前下载。

模型下载地址：https://huggingface.co/THUDM/chatglm2-6b/tree/main

微调实践

1、准备数据集

准备我们自己的数据集，分别生成训练文件和测试文件这两个文件，放在目录 ChatGLM2-6B/ptuning/myDataset/ 下面。

训练集文件： train.json
测试集文件： dev.json

2、安装python依赖

后面微调训练，需要依赖一些 Python 模块，提前安装一下：

conda install rouge_chinese nltk jieba datasets
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3、微调并训练新模型

修改 train.sh 脚本文件，根据自己实际情况配置即可，修改后的配置为：

PRE_SEQ_LEN=128
LR=2e-2
NUM_GPUS=1
 
torchrun --standalone --nnodes=1 --nproc-per-node=$NUM_GPUS main.py \
    --do_train \
    --train_file myDataset/train.json \
    --validation_file myDataset/dev.json \
    --preprocessing_num_workers 6 \
    --prompt_column content \
    --response_column summary \
    --overwrite_cache \
    --model_name_or_path /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b \
    --output_dir output/zhbr-chatglm2-6b-checkpoint \
    --overwrite_output_dir \
    --max_source_length 64 \
    --max_target_length 128 \
    --per_device_train_batch_size 6 \
    --per_device_eval_batch_size 6 \
    --gradient_accumulation_steps 16 \
    --predict_with_generate \
    --max_steps 20 \
    --logging_steps 5 \
    --save_steps 5 \
    --learning_rate $LR \
    --pre_seq_len $PRE_SEQ_LEN \
    --quantization_bit 4
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修改完即可进行微调：

cd /root/ChatGLM/ChatGLM2-6B/ptuning/
sh train.sh
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运行结果如下：

(base) [root@iZbp178u8rw9n9ko94ubbyZ ptuning]# sh train.sh 
[2023-10-08 13:09:12,312] torch.distributed.run: [WARNING] master_addr is only used for static rdzv_backend and when rdzv_endpoint is not specified.
10/08/2023 13:09:15 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
10/08/2023 13:09:15 - INFO - __main__ - Training/evaluation parameters Seq2SeqTrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=False,
do_predict=False,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=IntervalStrategy.NO,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={
   'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
generation_config=None,
generation_max_length=None,
generation_num_beams=None,
gradient_accumulation_steps=16,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=HubStrategy.EVERY_SAVE,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=0.02,
length_column_name=length,
load_best_model_at_end=False,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=output/zhbr-chatglm2-6b-checkpoint/runs/Oct08_13-09-15_iZbp178u8rw9n9ko94ubbyZ,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=5,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=20,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=OptimizerNames.ADAMW_TORCH,
optim_args=None,
output_dir=output/zhbr-chatglm2-6b-checkpoint,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=6,
per_device_train_batch_size=6,
predict_with_generate=True,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=output/zhbr-chatglm2-6b-checkpoint,
save_on_each_node=False,
save_safetensors=False,
save_steps=5,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
sortish_sampler=False,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
10/08/2023 13:09:16 - WARNING - datasets.builder - Found cached dataset json (/root/.cache/huggingface/datasets/json/default-8e52c57dfec9ef61/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4)
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 1379.71it/s]
[INFO|configuration_utils.py:713] 2023-10-08 13:09:16,749 >> loading configuration file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/config.json
[INFO|configuration_utils.py:713] 2023-10-08 13:09:16,751 >> loading configuration file /root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b/config.json
[INFO|configuration_utils.py:775] 2023-10-08 13:09:16,751 >> Model config ChatGLMConfig {
   
  "_name_or_path": "/root/ChatGLM/ChatGLM2-6B-main/zhbr/chatglm2-6b",
  "add_bias_linear": false,
  "add_qkv_bias": true,
  "apply_query_key_layer_scaling": true,
  "apply_residual_connection_post_layernorm": false,
  "architectures": [
    "ChatGLMModel"
  ],
  "attention_dropout": 0.0,
  "attention_softmax_in_fp32": true,
  "auto_map": {
   
    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
    "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
  },
  "bias_dropout_fusion": true,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "ffn_hidden_size": 13696,
  "fp32_residual_connection": false,
  "hidden_dropout": 0.0,
  "hidden_size": 4096,
  "kv_channels": 128,
  "layernorm_epsilon": 1e-05,
  "model_type": "chatglm",
  "multi_query_attention": true,
  "multi_query_group_num": 2,
  "num_attention_heads": 32,
  "num_layers": 28,
  "original_rope": true,
  "pad_token_id": 0,
  "padded_vocab_size": 65024,
  "post_layer_norm": true,
  "pre_seq_len": null,
  "prefix_projection": false,
  "quantization_bit": 0,
  
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