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RotatE-WN18RR-single-gpu-adv || RotatE-WN18RR-single-gpu-adv-wandb || RotatE-WN18RR-single-gpu-adv-hpo

RotatE-WN18RR-single-gpu-adv

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created by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on May 7, 2023

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updated by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on May 15, 2024

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last run by LuYF-Lemon-love <luyanfeng_nlp@qq.com> on May 15, 2024

这一部分介绍如何用一个 GPU 在 WN18RR 知识图谱上训练 RotatE [SDNT19]

导入数据

pybind11-OpenKE 有 1 个工具用于导入数据: pybind11_ke.data.KGEDataLoader

from pybind11_ke.data import KGEDataLoader, UniSampler, TradTestSampler
from pybind11_ke.module.model import RotatE
from pybind11_ke.module.loss import SigmoidLoss
from pybind11_ke.module.strategy import NegativeSampling
from pybind11_ke.config import Trainer, Tester

pybind11-OpenKE 提供了很多数据集,它们很多都是 KGE 原论文发表时附带的数据集。 pybind11_ke.data.KGEDataLoader 包含 in_path 用于传递数据集目录。

# dataloader for training
dataloader = KGEDataLoader(
    in_path = '../../benchmarks/WN18RR/',
    batch_size = 2000,
    neg_ent = 64,
    test = True,
    test_batch_size = 10,
    num_workers = 16,
    train_sampler = UniSampler,
    test_sampler = TradTestSampler
)

导入模型

pybind11-OpenKE 提供了很多 KGE 模型,它们都是目前最常用的基线模型。我们下面将要导入 pybind11_ke.module.model.RotatE,它将实体表示成复数向量,关系建模为复数向量空间的旋转。

# define the model
rotate = RotatE(
    ent_tol = dataloader.get_ent_tol(),
    rel_tol = dataloader.get_rel_tol(),
    dim = 1024,
    margin = 6.0,
    epsilon = 2.0,
)

损失函数

我们这里使用了逻辑损失函数:pybind11_ke.module.loss.SigmoidLosspybind11_ke.module.strategy.NegativeSamplingpybind11_ke.module.loss.SigmoidLoss 进行了封装,加入权重衰减等额外项。

# define the loss function
model = NegativeSampling(
    model = rotate,
    loss = SigmoidLoss(adv_temperature = 2),
    regul_rate = 0.0,
)

训练模型

pybind11-OpenKE 将训练循环包装成了 pybind11_ke.config.Trainer, 可以运行它的 pybind11_ke.config.Trainer.run() 函数进行模型学习; 也可以通过传入 pybind11_ke.config.Tester, 使得训练器能够在训练过程中评估模型。

# test the model
tester = Tester(model = rotate, data_loader = dataloader, use_tqdm = False,
                use_gpu = True, device = 'cuda:1')

# train the model
trainer = Trainer(model = model, data_loader = dataloader.train_dataloader(), epochs = 6000,
    lr = 2e-5, opt_method = 'adam', use_gpu = True, device = 'cuda:1',
    tester = tester, test = True, valid_interval = 100,
    log_interval = 100, save_interval = 100,
    save_path = '../../checkpoint/rotate.pth', use_wandb = False)
trainer.run()

备注

上述代码的运行日志可以从 此处 下载。


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