squeezebert-paddle

权重转换 && 权重下载

1. 从https://huggingface.co/squeezebert 下载hg的权重到models对应的目录下
2. python convert_torch_to_paddle.py

转好的模型链接: https://pan.baidu.com/s/1Jis7In0veo4ODae5OR_FqA 提取码: p5bk

前向传播精度和速度对比

推理速度对比

• 为了排除其他因素，预处理直接把batch的数据放在list里，不用dataset和dataloader
• cpu推理只取前1000条

  
  # paddle在gpu上预测
  python run_qqp_paddle.py \
  --model_path ./models/squeezebert-mnli-headless \
  --device gpu

paddle在cpu上预测

python run_qqp_paddle.py \ --model_path ./models/squeezebert-mnli-headless \ --device cpu

pytorch在gpu上预测

python run_qqp_torch.py \ --model_path ./models/squeezebert-mnli-headless \ --device gpu

pytorch在cpu上预测

python run_qqp_torch.py \ --model_path ./models/squeezebert-mnli-headless \ --device cpu

paddle bert在gpu上预测

python run_qqp_paddle.py \ --model_path bert-base-uncased \ --device gpu \ --model_type bert

pytoch bert在gpu上预测

python run_qqp_torch.py \ --model_path bert-base-uncased \ --device gpu \ --model_type bert

#### squeezebert在gpu上加速比
- paddle： 186 / 137 = 1.36
- pytorch: 172 / 112 = 1.54  

#### 推理时间
| - |paddle-squeeze|pytorch-squeeze|paddle-bert|pytorch-bert|
| :----:| :----:| :----:| :----:| :----:|
|cpu|89s|41s|-|-|
|gpu|137s|112s|186s|172s|

### 模型精度对比(没有要求，可忽略)

python compare.py

model_name: squeezebert-uncased

mean difference: 8.8708525e-08

max difference: 6.556511e-07

耗时对比 squeeze paddle cost 43.851375579833984, squeeze torch cotst

48.86937141418457, bert cost 51.83529853820801

model_name: squeezebert-mnli

mean difference: 1.12165566e-07

max difference: 7.4505806e-07

model_name: squeezebert-mnli-headless

mean difference: 1.12165566e-07

max difference: 7.4505806e-07

## QQP数据集合效果 
### 运行步骤
在models/squeezebert-mnli-headles复制一份config.json,改名为model_config.json

export CUDA_VISIBLE_DEVICES=0 export TASK_NAME="QQP"

nohup python -u ./run_glue.py --model_type squeezebert --model_name_or_path ./models/squeezebert-mnli-headless --task_name QQP --batch_size 16 --learning_rate 4e-5 --num_train_epochs 5 --logging_steps 10 --save_steps 2000 --output_dir ./tmp/QQP/ --device gpu --lr_scheduler 1 --seed 5

### *运行结果*

acc and f1: 0.8936136479314183, eval done total : 196.82215237617493 s

|acc and f1|
| :----:|
|0.8936|
### 训练日志
见train_log.txt