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TimelordRi
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## 快速上手
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### 环境依赖
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> python == 3.8
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- torch == 1.5
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- transformers == 3.4.0
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- hydra-core == 1.0.6
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- deepke
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### 克隆代码
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```
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git clone git@github.com:zjunlp/DeepKE.git
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```
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### 使用pip安装
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首先创建python虚拟环境，再进入虚拟环境
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- 安装依赖: ```pip install -r requirements.txt```
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### 使用数据进行训练预测
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- 存放数据：在 `data` 文件夹下存放训练数据。模型采用的数据集是[SEMEVAL](https://semeval2.fbk.eu/semeval2.php?location=tasks#T11)，SEMEVAL数据集来自于2010年的国际语义评测大会中Task 8："Multi-Way Classification of Semantic Relations Between Pairs of Nominals"。
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- SEMEVAL包含以下数据：
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- `rel2id.json`：关系标签到ID的映射
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- `temp.txt`：关系标签处理
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- `test.txt`： 测试集
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- `train.txt`：训练集
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- `val.txt`：验证集
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- 开始训练：模型加载和保存位置以及配置可以在conf的`.yaml`文件中修改
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- 对数据集SEMEVAL进行few-shot训练：`python run.py`
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- 训练好的模型默认保存在根目录
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- 从上次训练的模型开始训练：设置`.yaml`中的train_from_saved_model为上次保存模型的路径
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- 每次训练的日志保存路径默认保存在根目录，可以通过`.yaml`中的log_dir来配置
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- 进行预测： `python predict.py `
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## 模型内容
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KnowPrompt
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defaults:
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- hydra/output: custom
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- train
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hydra:
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run:
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# Output directory for normal runs
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dir: logs/${now:%Y-%m-%d_%H-%M-%S}
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sweep:
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# Output directory for sweep runs
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dir: logs/${now:%Y-%m-%d_%H-%M-%S}
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# Output sub directory for sweep runs.
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subdir: ${hydra.job.num}_${hydra.job.id}
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accelerator: None
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accumulate_grad_batches: '1'
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amp_backend: 'native'
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amp_level: 'O2'
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auto_lr_find: False
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auto_scale_batch_size: False
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auto_select_gpus: False
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batch_size: 16
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benchmark: False
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check_val_every_n_epoch: '3'
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checkpoint_callback: True
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data_class: 'REDataset'
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data_dir: 'data/k-shot/8-1'
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default_root_dir: None
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deterministic: False
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devices: None
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distributed_backend: None
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fast_dev_run: False
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flush_logs_every_n_steps: 100
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gpus: None
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gradient_accumulation_steps: 1
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gradient_clip_algorithm: 'norm'
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gradient_clip_val: 0.0
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ipus: None
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limit_predict_batches: 1.0
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limit_test_batches: 1.0
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limit_train_batches: 1.0
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limit_val_batches: 1.0
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litmodel_class: 'BertLitModel'
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load_checkpoint: None
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log_dir: './model_bert.log'
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log_every_n_steps: 50
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log_gpu_memory: None
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logger: True
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lr: 3e-05
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lr_2: 3e-05
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max_epochs: '30'
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max_seq_length: 256
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max_steps: None
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max_time: None
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min_epochs: None
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min_steps: None
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model_class: 'BertForMaskedLM'
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model_name_or_path: 'bert-large-uncased'
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move_metrics_to_cpu: False
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multiple_trainloader_mode: 'max_size_cycle'
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num_nodes: 1
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num_processes: 1
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num_sanity_val_steps: 2
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num_train_epochs: 30
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num_workers: 8
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optimizer: 'AdamW'
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overfit_batches: 0.0
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plugins: None
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precision: 32
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prepare_data_per_node: True
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process_position: 0
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profiler: None
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progress_bar_refresh_rate: None
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ptune_k: 7
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reload_dataloaders_every_epoch: False
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reload_dataloaders_every_n_epochs: 0
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replace_sampler_ddp: True
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resume_from_checkpoint: None
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save_path: './model_bert.pt'
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seed: 666
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stochastic_weight_avg: False
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sync_batchnorm: False
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t_lambda: 0.001
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task_name: 'wiki80'
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terminate_on_nan: False
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tpu_cores: None
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track_grad_norm: -1
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train_from_saved_model: ''
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truncated_bptt_steps: None
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two_steps: False
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use_prompt: True
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val_check_interval: 1.0
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wandb: False
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weight_decay: 0.01
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weights_save_path: None
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weights_summary: 'top'
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load_path: './model_bert.pt'
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{"Component-Whole(e2,e1)": 1, "Other": 0, "Instrument-Agency(e2,e1)": 2, "Member-Collection(e1,e2)": 3, "Cause-Effect(e2,e1)": 4, "Entity-Destination(e1,e2)": 5, "Content-Container(e1,e2)": 6, "Message-Topic(e1,e2)": 7, "Product-Producer(e2,e1)": 8, "Member-Collection(e2,e1)": 9, "Entity-Origin(e1,e2)": 10, "Cause-Effect(e1,e2)": 11, "Component-Whole(e1,e2)": 12, "Message-Topic(e2,e1)": 13, "Product-Producer(e1,e2)": 14, "Entity-Origin(e2,e1)": 15, "Content-Container(e2,e1)": 16, "Instrument-Agency(e1,e2)": 17, "Entity-Destination(e2,e1)": 18}
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0 Other nothing has nothing to nothing
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0 Member-Collection(e1,e2) member member of collection collection
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0 Entity-Origin(e1,e2) entity entity of origin origin
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0 Cause-Effect(e1,e2) cause cause of effect effect
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0 Component-Whole(e1,e2) component component of whole whole
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0 Product-Producer(e1,e2) product product of producer producer
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0 Instrument-Agency(e1,e2) instrument instrument of agency agency
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0 Entity-Destination(e1,e2) entity entity of destination destination
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0 Content-Container(e1,e2) content content of container container
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0 Message-Topic(e1,e2) message message of topic topic
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2 Cause-Effect(e2,e1) effect effect of cause cause
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2 Product-Producer(e2,e1) producer producer of product product
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2 Component-Whole(e2,e1) whole whole of component component
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2 Instrument-Agency(e2,e1) agency agency of instrument instrument
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2 Member-Collection(e2,e1) collection collection of member member
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2 Message-Topic(e2,e1) topic topic of message message
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2 Entity-Origin(e2,e1) origin origin of entity entity
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2 Content-Container(e2,e1) container container of content content
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2 Entity-Destination(e2,e1) destination destination of entity entity
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Load Diff
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Load Diff
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Load Diff
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from logging import debug
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import hydra
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from hydra.utils import get_original_cwd
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import numpy as np
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import torch
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from torch.utils.data.dataloader import DataLoader
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import yaml
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import time
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from transformers import AutoConfig, AutoModelForMaskedLM
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from transformers.optimization import get_linear_schedule_with_warmup
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import os
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from tqdm import tqdm
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from deepkerefew import *
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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# In order to ensure reproducible experiments, we must set random seeds.
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def logging(log_dir, s, print_=True, log_=True):
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if print_:
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print(s)
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if log_dir != '' and log_:
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with open(log_dir, 'a+') as f_log:
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f_log.write(s + '\n')
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def test(args, model, lit_model, data):
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model.eval()
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with torch.no_grad():
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test_loss = []
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for test_index, test_batch in enumerate(tqdm(data.test_dataloader())):
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loss = lit_model.test_step(test_batch, test_index)
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test_loss.append(loss)
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f1 = lit_model.test_epoch_end(test_loss)
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logging(args.log_dir,
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'| test_result: {}'.format(f1))
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logging(args.log_dir,'-' * 89)
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@hydra.main(config_path="conf/config.yaml")
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def main(cfg):
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cwd = get_original_cwd()
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os.chdir(cwd)
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if not os.path.exists(f"data/{cfg.model_name_or_path}.pt"):
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get_label_word(cfg)
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if not os.path.exists(cfg.data_dir):
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generate_k_shot(cfg.data_dir)
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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data = REDataset(cfg)
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data_config = data.get_data_config()
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config = AutoConfig.from_pretrained(cfg.model_name_or_path)
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config.num_labels = data_config["num_labels"]
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model = AutoModelForMaskedLM.from_pretrained(cfg.model_name_or_path, config=config)
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# if torch.cuda.device_count() > 1:
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# print("Let's use", torch.cuda.device_count(), "GPUs!")
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# model = torch.nn.DataParallel(model, device_ids = list(range(torch.cuda.device_count())))
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model.to(device)
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lit_model = BertLitModel(args=cfg, model=model, tokenizer=data.tokenizer)
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data.setup()
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model.load_state_dict(torch.load(cfg.load_path)["checkpoint"], False)
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print("load trained model from {}.".format(cfg.load_path))
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test(cfg, model, lit_model, data)
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if __name__ == "__main__":
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main()
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torch==1.5
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transformers==3.4.0
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hydra-core==1.0.6
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from logging import debug
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import hydra
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from hydra.utils import get_original_cwd
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import numpy as np
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import torch
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from torch.utils.data.dataloader import DataLoader
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import yaml
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import time
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from transformers import AutoConfig, AutoModelForMaskedLM
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from transformers.optimization import get_linear_schedule_with_warmup
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import os
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from tqdm import tqdm
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from deepkerefew import *
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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def logging(log_dir, s, print_=True, log_=True):
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if print_:
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print(s)
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if log_dir != '' and log_:
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with open(log_dir, 'a+') as f_log:
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f_log.write(s + '\n')
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@hydra.main(config_path="conf/config.yaml")
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def main(cfg):
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cwd = get_original_cwd()
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os.chdir(cwd)
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if not os.path.exists(f"data/{cfg.model_name_or_path}.pt"):
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get_label_word(cfg)
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if not os.path.exists(cfg.data_dir):
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generate_k_shot(cfg.data_dir)
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device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
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data = REDataset(cfg)
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data_config = data.get_data_config()
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config = AutoConfig.from_pretrained(cfg.model_name_or_path)
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config.num_labels = data_config["num_labels"]
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model = AutoModelForMaskedLM.from_pretrained(cfg.model_name_or_path, config=config)
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# if torch.cuda.device_count() > 1:
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# print("Let's use", torch.cuda.device_count(), "GPUs!")
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# model = torch.nn.DataParallel(model, device_ids = list(range(torch.cuda.device_count())))
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model.to(device)
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lit_model = BertLitModel(args=cfg, model=model, tokenizer=data.tokenizer)
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data.setup()
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if cfg.train_from_saved_model != '':
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model.load_state_dict(torch.load(cfg.train_from_saved_model)["checkpoint"])
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print("load saved model from {}.".format(cfg.train_from_saved_model))
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lit_model.best_f1 = torch.load(cfg.train_from_saved_model)["best_f1"]
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#data.tokenizer.save_pretrained('test')
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optimizer = lit_model.configure_optimizers()
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if cfg.train_from_saved_model != '':
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optimizer.load_state_dict(torch.load(cfg.train_from_saved_model)["optimizer"])
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print("load saved optimizer from {}.".format(cfg.train_from_saved_model))
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num_training_steps = len(data.train_dataloader()) // cfg.gradient_accumulation_steps * cfg.num_train_epochs
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_training_steps * 0.1, num_training_steps=num_training_steps)
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log_step = 100
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logging(cfg.log_dir,'-' * 89, print_=False)
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logging(cfg.log_dir, time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + ' INFO : START TO TRAIN ', print_=False)
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logging(cfg.log_dir,'-' * 89, print_=False)
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for epoch in range(cfg.num_train_epochs):
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model.train()
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num_batch = len(data.train_dataloader())
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total_loss = 0
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log_loss = 0
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for index, train_batch in enumerate(tqdm(data.train_dataloader())):
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loss = lit_model.training_step(train_batch, index)
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total_loss += loss.item()
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log_loss += loss.item()
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loss.backward()
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optimizer.step()
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scheduler.step()
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optimizer.zero_grad()
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if log_step > 0 and (index+1) % log_step == 0:
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cur_loss = log_loss / log_step
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logging(cfg.log_dir,
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'| epoch {:2d} | step {:4d} | lr {} | train loss {:5.3f}'.format(
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epoch, (index+1), scheduler.get_last_lr(), cur_loss * 1000)
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, print_=False)
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log_loss = 0
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avrg_loss = total_loss / num_batch
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logging(cfg.log_dir,
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'| epoch {:2d} | train loss {:5.3f}'.format(
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epoch, avrg_loss * 1000))
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model.eval()
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with torch.no_grad():
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val_loss = []
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for val_index, val_batch in enumerate(tqdm(data.val_dataloader())):
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loss = lit_model.validation_step(val_batch, val_index)
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val_loss.append(loss)
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f1, best, best_f1 = lit_model.validation_epoch_end(val_loss)
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logging(cfg.log_dir,'-' * 89)
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logging(cfg.log_dir,
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'| epoch {:2d} | dev_result: {}'.format(epoch, f1))
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logging(cfg.log_dir,'-' * 89)
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logging(cfg.log_dir,
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'| best_f1: {}'.format(best_f1))
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logging(cfg.log_dir,'-' * 89)
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if cfg.save_path != "" and best != -1:
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save_path = cfg.save_path
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torch.save({
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'epoch': epoch,
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'checkpoint': model.state_dict(),
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'best_f1': best_f1,
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'optimizer': optimizer.state_dict()
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}, save_path
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, _use_new_zipfile_serialization=False)
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logging(cfg.log_dir,
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'| successfully save model at: {}'.format(save_path))
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logging(cfg.log_dir,'-' * 89)
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if __name__ == "__main__":
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main()
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