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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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- tokenizers == 0.10.3
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- torch == 1.8.0
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- regex == 2021.4.4
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- transformers == 4.7.0
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- tqdm == 4.49.0
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- activations == 0.1.0
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- dataclasses == 0.6
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- file_utils == 0.0.1
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- flax == 0.3.4
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- utils == 1.0.1
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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[DocRED](https://github.com/thunlp/DocRED/tree/master/)，DocRED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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- DocRED包含以下数据：
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- `dev.json`：验证集
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- `rel_info.json`：关系集
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- `rel2id.json`：关系标签到ID的映射
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- `test.json`：测试集
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- `train_annotated.json`：训练集
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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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- 在数据集DocRED中训练：`python run.py`
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- 每次训练的日志保存路径可以通过`.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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DocuNet
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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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adam_epsilon: 1e-06
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bert_lr: 3e-05
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channel_type: 'context-based'
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config_name: ''
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data_dir: './data'
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dataset: 'docred'
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dev_file: 'dev.json'
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down_dim: 256
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evaluation_steps: -1
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gradient_accumulation_steps: 2
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learning_rate: 0.0004
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log_dir: './train_roberta.log'
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max_grad_norm: 1.0
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max_height: 42
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max_seq_length: 1024
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model_name_or_path: 'roberta-base'
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num_class: 97
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num_labels: 4
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num_train_epochs: 30
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save_path: './model_roberta.pt'
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seed: 111
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test_batch_size: 2
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test_file: 'test.json'
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tokenizer_name: ''
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train_batch_size: 2
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train_file: 'train_annotated.json'
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train_from_saved_model: ''
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transformer_type: 'roberta'
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unet_in_dim: 3
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unet_out_dim: 256
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warmup_ratio: 0.06
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load_path: './model_roberta.pt'
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{
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"P1376": 79,
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"P607": 27,
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"P136": 73,
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"P137": 63,
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"P131": 2,
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"P527": 11,
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"P1412": 38,
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"P206": 33,
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"P205": 77,
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"P449": 52,
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"P127": 34,
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"P123": 49,
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"P86": 66,
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"P840": 85,
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"P355": 72,
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"P737": 93,
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"P740": 84,
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"P190": 94,
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"P576": 71,
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"P749": 68,
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"P112": 65,
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"P118": 40,
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"P17": 1,
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"P19": 14,
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"P3373": 19,
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"P6": 42,
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"P276": 44,
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"P1001": 24,
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"P580": 62,
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"P582": 83,
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"P585": 64,
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"P463": 18,
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"P676": 87,
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"P674": 46,
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"P264": 10,
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"P108": 43,
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"P102": 17,
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"P25": 81,
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"P27": 3,
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"P26": 26,
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"P20": 37,
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"P22": 30,
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"Na": 0,
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"P807": 95,
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"P800": 51,
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"P279": 78,
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"P1336": 88,
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"P577": 5,
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"P570": 8,
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"P571": 15,
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"P178": 36,
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"P179": 55,
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"P272": 75,
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"P170": 35,
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"P171": 80,
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"P172": 76,
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"P175": 6,
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"P176": 67,
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"P39": 91,
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"P30": 21,
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"P31": 60,
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"P36": 70,
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"P37": 58,
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"P35": 54,
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"P400": 31,
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"P403": 61,
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"P361": 12,
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"P364": 74,
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"P569": 7,
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"P710": 41,
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"P1344": 32,
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"P488": 82,
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"P241": 59,
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"P162": 57,
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"P161": 9,
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"P166": 47,
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"P40": 20,
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"P1441": 23,
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"P156": 45,
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"P155": 39,
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"P150": 4,
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"P551": 90,
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"P706": 56,
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"P159": 29,
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"P495": 13,
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"P58": 53,
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"P194": 48,
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"P54": 16,
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"P57": 28,
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"P50": 22,
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"P1366": 86,
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"P1365": 92,
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"P937": 69,
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"P140": 50,
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"P69": 25,
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"P1198": 96,
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"P1056": 89
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}
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{"P6": "head of government", "P17": "country", "P19": "place of birth", "P20": "place of death", "P22": "father", "P25": "mother", "P26": "spouse", "P27": "country of citizenship", "P30": "continent", "P31": "instance of", "P35": "head of state", "P36": "capital", "P37": "official language", "P39": "position held", "P40": "child", "P50": "author", "P54": "member of sports team", "P57": "director", "P58": "screenwriter", "P69": "educated at", "P86": "composer", "P102": "member of political party", "P108": "employer", "P112": "founded by", "P118": "league", "P123": "publisher", "P127": "owned by", "P131": "located in the administrative territorial entity", "P136": "genre", "P137": "operator", "P140": "religion", "P150": "contains administrative territorial entity", "P155": "follows", "P156": "followed by", "P159": "headquarters location", "P161": "cast member", "P162": "producer", "P166": "award received", "P170": "creator", "P171": "parent taxon", "P172": "ethnic group", "P175": "performer", "P176": "manufacturer", "P178": "developer", "P179": "series", "P190": "sister city", "P194": "legislative body", "P205": "basin country", "P206": "located in or next to body of water", "P241": "military branch", "P264": "record label", "P272": "production company", "P276": "location", "P279": "subclass of", "P355": "subsidiary", "P361": "part of", "P364": "original language of work", "P400": "platform", "P403": "mouth of the watercourse", "P449": "original network", "P463": "member of", "P488": "chairperson", "P495": "country of origin", "P527": "has part", "P551": "residence", "P569": "date of birth", "P570": "date of death", "P571": "inception", "P576": "dissolved, abolished or demolished", "P577": "publication date", "P580": "start time", "P582": "end time", "P585": "point in time", "P607": "conflict", "P674": "characters", "P676": "lyrics by", "P706": "located on terrain feature", "P710": "participant", "P737": "influenced by", "P740": "location of formation", "P749": "parent organization", "P800": "notable work", "P807": "separated from", "P840": "narrative location", "P937": "work location", "P1001": "applies to jurisdiction", "P1056": "product or material produced", "P1198": "unemployment rate", "P1336": "territory claimed by", "P1344": "participant of", "P1365": "replaces", "P1366": "replaced by", "P1376": "capital of", "P1412": "languages spoken, written or signed", "P1441": "present in work", "P3373": "sibling"}
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Load Diff
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import os
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import time
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import hydra
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import numpy as np
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import torch
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import ujson as json
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from torch.utils.data import DataLoader
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from transformers import AutoConfig, AutoModel, AutoTokenizer
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from transformers.optimization import AdamW, get_linear_schedule_with_warmup
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from deepkeredoc import *
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def evaluate(args, model, features, tag="dev"):
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dataloader = DataLoader(features, batch_size=args.test_batch_size, shuffle=False, collate_fn=collate_fn, drop_last=False)
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preds = []
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total_loss = 0
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for i, batch in enumerate(dataloader):
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model.eval()
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inputs = {'input_ids': batch[0].to(args.device),
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'attention_mask': batch[1].to(args.device),
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'labels': batch[2],
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'entity_pos': batch[3],
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'hts': batch[4],
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}
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with torch.no_grad():
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output = model(**inputs)
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loss = output[0]
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pred = output[1].cpu().numpy()
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pred[np.isnan(pred)] = 0
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preds.append(pred)
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total_loss += loss.item()
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average_loss = total_loss / (i + 1)
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preds = np.concatenate(preds, axis=0).astype(np.float32)
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ans = to_official(preds, features)
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if len(ans) > 0:
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best_f1, _, best_f1_ign, _, re_p, re_r = official_evaluate(ans, args.data_dir)
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output = {
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tag + "_F1": best_f1 * 100,
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tag + "_F1_ign": best_f1_ign * 100,
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tag + "_re_p": re_p * 100,
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tag + "_re_r": re_r * 100,
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tag + "_average_loss": average_loss
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}
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return best_f1, output
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def report(args, model, features):
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dataloader = DataLoader(features, batch_size=args.test_batch_size, shuffle=False, collate_fn=collate_fn, drop_last=False)
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preds = []
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for batch in dataloader:
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model.eval()
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inputs = {'input_ids': batch[0].to(args.device),
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'attention_mask': batch[1].to(args.device),
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'entity_pos': batch[3],
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'hts': batch[4],
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}
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with torch.no_grad():
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pred = model(**inputs)
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pred = pred.cpu().numpy()
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pred[np.isnan(pred)] = 0
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preds.append(pred)
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preds = np.concatenate(preds, axis=0).astype(np.float32)
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preds = to_official(args, preds, features)
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return preds
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@hydra.main(config_path="conf/config.yaml")
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def main(cfg):
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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cfg.n_gpu = torch.cuda.device_count()
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cfg.device = device
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config = AutoConfig.from_pretrained(
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cfg.config_name if cfg.config_name else cfg.model_name_or_path,
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num_labels=cfg.num_class,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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cfg.tokenizer_name if cfg.tokenizer_name else cfg.model_name_or_path,
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)
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Dataset = ReadDataset(cfg.dataset, tokenizer, cfg.max_seq_length)
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train_file = os.path.join(cfg.data_dir, cfg.train_file)
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dev_file = os.path.join(cfg.data_dir, cfg.dev_file)
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test_file = os.path.join(cfg.data_dir, cfg.test_file)
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train_features = Dataset.read(train_file)
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dev_features = Dataset.read(dev_file)
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test_features = Dataset.read(test_file)
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model = AutoModel.from_pretrained(
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cfg.model_name_or_path,
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from_tf=bool(".ckpt" in cfg.model_name_or_path),
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config=config,
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)
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config.cls_token_id = tokenizer.cls_token_id
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config.sep_token_id = tokenizer.sep_token_id
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config.transformer_type = cfg.transformer_type
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set_seed(cfg)
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model = DocREModel(config, cfg, model, num_labels=cfg.num_labels)
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model.load_state_dict(torch.load(cfg.load_path)['checkpoint'])
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model.to(device)
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T_features = test_features # Testing on the test set
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T_score, T_output = evaluate(cfg, model, T_features, tag="test")
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pred = report(cfg, model, T_features)
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with open("./result.json", "w") as fh:
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json.dump(pred, fh)
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if __name__ == "__main__":
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main()
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python==3.7
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cuda==10.2
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torch==1.5.0
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transformers==3.0.4
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opt-einsum==3.3.0
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ujson
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tqdm
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allennlp
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import os
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import time
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import hydra
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import numpy as np
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import torch
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import ujson as json
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from torch.utils.data import DataLoader
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from transformers import AutoConfig, AutoModel, AutoTokenizer
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from transformers.optimization import AdamW, get_linear_schedule_with_warmup
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from deepkeredoc import *
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def train(args, model, train_features, dev_features, test_features):
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def logging(s, print_=True, log_=True):
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if print_:
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print(s)
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if log_:
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with open(args.log_dir, 'a+') as f_log:
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f_log.write(s + '\n')
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def finetune(features, optimizer, num_epoch, num_steps, model):
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cur_model = model.module if hasattr(model, 'module') else model
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if args.train_from_saved_model != '':
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best_score = torch.load(args.train_from_saved_model)["best_f1"]
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epoch_delta = torch.load(args.train_from_saved_model)["epoch"] + 1
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else:
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epoch_delta = 0
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best_score = -1
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train_dataloader = DataLoader(features, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, drop_last=True)
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train_iterator = [epoch + epoch_delta for epoch in range(num_epoch)]
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total_steps = int(len(train_dataloader) * num_epoch // args.gradient_accumulation_steps)
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warmup_steps = int(total_steps * args.warmup_ratio)
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps, num_training_steps=total_steps)
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print("Total steps: {}".format(total_steps))
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print("Warmup steps: {}".format(warmup_steps))
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global_step = 0
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log_step = 100
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total_loss = 0
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#scaler = GradScaler()
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for epoch in train_iterator:
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start_time = time.time()
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optimizer.zero_grad()
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for step, batch in enumerate(train_dataloader):
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model.train()
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inputs = {'input_ids': batch[0].to(args.device),
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'attention_mask': batch[1].to(args.device),
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'labels': batch[2],
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'entity_pos': batch[3],
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'hts': batch[4],
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}
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#with autocast():
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outputs = model(**inputs)
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loss = outputs[0] / args.gradient_accumulation_steps
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total_loss += loss.item()
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# scaler.scale(loss).backward()
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loss.backward()
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if step % args.gradient_accumulation_steps == 0:
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#scaler.unscale_(optimizer)
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if args.max_grad_norm > 0:
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# torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
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torch.nn.utils.clip_grad_norm_(cur_model.parameters(), args.max_grad_norm)
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#scaler.step(optimizer)
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#scaler.update()
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#scheduler.step()
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optimizer.step()
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scheduler.step()
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optimizer.zero_grad()
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global_step += 1
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num_steps += 1
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if global_step % log_step == 0:
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cur_loss = total_loss / log_step
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elapsed = time.time() - start_time
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logging(
|
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'| epoch {:2d} | step {:4d} | min/b {:5.2f} | lr {} | train loss {:5.3f}'.format(
|
||||
epoch, global_step, elapsed / 60, scheduler.get_last_lr(), cur_loss * 1000))
|
||||
total_loss = 0
|
||||
start_time = time.time()
|
||||
|
||||
if (step + 1) == len(train_dataloader) - 1 or (args.evaluation_steps > 0 and num_steps % args.evaluation_steps == 0 and step % args.gradient_accumulation_steps == 0):
|
||||
# if step ==0:
|
||||
logging('-' * 89)
|
||||
eval_start_time = time.time()
|
||||
dev_score, dev_output = evaluate(args, model, dev_features, tag="dev")
|
||||
|
||||
logging(
|
||||
'| epoch {:3d} | time: {:5.2f}s | dev_result:{}'.format(epoch, time.time() - eval_start_time,
|
||||
dev_output))
|
||||
logging('-' * 89)
|
||||
if dev_score > best_score:
|
||||
best_score = dev_score
|
||||
logging(
|
||||
'| epoch {:3d} | best_f1:{}'.format(epoch, best_score))
|
||||
pred = report(args, model, test_features)
|
||||
with open("./submit_result/best_result.json", "w") as fh:
|
||||
json.dump(pred, fh)
|
||||
if args.save_path != "":
|
||||
torch.save({
|
||||
'epoch': epoch,
|
||||
'checkpoint': cur_model.state_dict(),
|
||||
'best_f1': best_score,
|
||||
'optimizer': optimizer.state_dict()
|
||||
}, args.save_path
|
||||
, _use_new_zipfile_serialization=False)
|
||||
return num_steps
|
||||
|
||||
cur_model = model.module if hasattr(model, 'module') else model
|
||||
extract_layer = ["extractor", "bilinear"]
|
||||
bert_layer = ['bert_model']
|
||||
optimizer_grouped_parameters = [
|
||||
{"params": [p for n, p in cur_model.named_parameters() if any(nd in n for nd in bert_layer)], "lr": args.bert_lr},
|
||||
{"params": [p for n, p in cur_model.named_parameters() if any(nd in n for nd in extract_layer)], "lr": 1e-4},
|
||||
{"params": [p for n, p in cur_model.named_parameters() if not any(nd in n for nd in extract_layer + bert_layer)]},
|
||||
]
|
||||
|
||||
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
|
||||
if args.train_from_saved_model != '':
|
||||
optimizer.load_state_dict(torch.load(args.train_from_saved_model)["optimizer"])
|
||||
print("load saved optimizer from {}.".format(args.train_from_saved_model))
|
||||
|
||||
|
||||
num_steps = 0
|
||||
set_seed(args)
|
||||
model.zero_grad()
|
||||
finetune(train_features, optimizer, args.num_train_epochs, num_steps, model)
|
||||
|
||||
|
||||
def evaluate(args, model, features, tag="dev"):
|
||||
|
||||
dataloader = DataLoader(features, batch_size=args.test_batch_size, shuffle=False, collate_fn=collate_fn, drop_last=False)
|
||||
preds = []
|
||||
total_loss = 0
|
||||
for i, batch in enumerate(dataloader):
|
||||
model.eval()
|
||||
|
||||
inputs = {'input_ids': batch[0].to(args.device),
|
||||
'attention_mask': batch[1].to(args.device),
|
||||
'labels': batch[2],
|
||||
'entity_pos': batch[3],
|
||||
'hts': batch[4],
|
||||
}
|
||||
|
||||
with torch.no_grad():
|
||||
output = model(**inputs)
|
||||
loss = output[0]
|
||||
pred = output[1].cpu().numpy()
|
||||
pred[np.isnan(pred)] = 0
|
||||
preds.append(pred)
|
||||
total_loss += loss.item()
|
||||
|
||||
average_loss = total_loss / (i + 1)
|
||||
preds = np.concatenate(preds, axis=0).astype(np.float32)
|
||||
ans = to_official(preds, features)
|
||||
if len(ans) > 0:
|
||||
best_f1, _, best_f1_ign, _, re_p, re_r = official_evaluate(ans, args.data_dir)
|
||||
output = {
|
||||
tag + "_F1": best_f1 * 100,
|
||||
tag + "_F1_ign": best_f1_ign * 100,
|
||||
tag + "_re_p": re_p * 100,
|
||||
tag + "_re_r": re_r * 100,
|
||||
tag + "_average_loss": average_loss
|
||||
}
|
||||
return best_f1, output
|
||||
|
||||
|
||||
|
||||
@hydra.main(config_path="conf/config.yaml")
|
||||
def main(cfg):
|
||||
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
cfg.n_gpu = torch.cuda.device_count()
|
||||
cfg.device = device
|
||||
|
||||
config = AutoConfig.from_pretrained(
|
||||
cfg.config_name if cfg.config_name else cfg.model_name_or_path,
|
||||
num_labels=cfg.num_class,
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
cfg.tokenizer_name if cfg.tokenizer_name else cfg.model_name_or_path,
|
||||
)
|
||||
|
||||
Dataset = ReadDataset(cfg, cfg.dataset, tokenizer, cfg.max_seq_length)
|
||||
|
||||
train_file = os.path.join(cfg.data_dir, cfg.train_file)
|
||||
dev_file = os.path.join(cfg.data_dir, cfg.dev_file)
|
||||
test_file = os.path.join(cfg.data_dir, cfg.test_file)
|
||||
train_features = Dataset.read(train_file)
|
||||
dev_features = Dataset.read(dev_file)
|
||||
test_features = Dataset.read(test_file)
|
||||
|
||||
model = AutoModel.from_pretrained(
|
||||
cfg.model_name_or_path,
|
||||
from_tf=bool(".ckpt" in cfg.model_name_or_path),
|
||||
config=config,
|
||||
)
|
||||
|
||||
config.cls_token_id = tokenizer.cls_token_id
|
||||
config.sep_token_id = tokenizer.sep_token_id
|
||||
config.transformer_type = cfg.transformer_type
|
||||
|
||||
set_seed(cfg)
|
||||
model = DocREModel(config, cfg, model, num_labels=cfg.num_labels)
|
||||
if cfg.train_from_saved_model != '':
|
||||
model.load_state_dict(torch.load(cfg.train_from_saved_model)["checkpoint"])
|
||||
print("load saved model from {}.".format(cfg.train_from_saved_model))
|
||||
|
||||
#if torch.cuda.device_count() > 1:
|
||||
# print("Let's use", torch.cuda.device_count(), "GPUs!")
|
||||
# model = torch.nn.DataParallel(model, device_ids = list(range(torch.cuda.device_count())))
|
||||
model.to(device)
|
||||
|
||||
train(cfg, model, train_features, dev_features, test_features)
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue