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https://gitee.com/fastnlp/fastNLP.git
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1566 lines
62 KiB
Plaintext
1566 lines
62 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "aec0fde7",
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"metadata": {},
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"source": [
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"# T0. trainer 和 evaluator 的基本使用\n",
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"\n",
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"  1   trainer 和 evaluator 的基本关系\n",
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" \n",
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"    1.1   trainer 和 evaluater 的初始化\n",
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"\n",
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"    1.2   driver 的含义与使用要求\n",
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"\n",
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"    1.3   trainer 内部初始化 evaluater\n",
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"\n",
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"  2   使用 fastNLP 搭建 argmax 模型\n",
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"\n",
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"    2.1   trainer_step 和 evaluator_step\n",
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"\n",
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"    2.2   trainer 和 evaluator 的参数匹配\n",
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"\n",
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"    2.3   示例：argmax 模型的搭建\n",
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"\n",
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"  3   使用 fastNLP 训练 argmax 模型\n",
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" \n",
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"    3.1   trainer 外部初始化的 evaluator\n",
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"\n",
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"    3.2   trainer 内部初始化的 evaluator "
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]
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},
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{
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"cell_type": "markdown",
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"id": "09ea669a",
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"metadata": {},
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"source": [
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"## 1. trainer 和 evaluator 的基本关系\n",
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"\n",
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"### 1.1 trainer 和 evaluator 的初始化\n",
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"\n",
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"在`fastNLP 0.8`中，**`Trainer`模块和`Evaluator`模块分别表示“训练器”和“评测器”**\n",
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"\n",
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"  对应于之前的`fastNLP`版本中的`Trainer`模块和`Tester`模块，其定义方法如下所示\n",
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"\n",
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"在`fastNLP 0.8`中，需要注意，在同个`python`脚本中先使用`Trainer`训练，然后使用`Evaluator`评测\n",
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"\n",
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"  非常关键的问题在于**如何正确设置二者的`driver`**。这就引入了另一个问题：什么是 `driver`？\n",
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"\n",
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"\n",
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"```python\n",
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"trainer = Trainer(\n",
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" model=model, # 模型基于 torch.nn.Module\n",
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" train_dataloader=train_dataloader, # 加载模块基于 torch.utils.data.DataLoader \n",
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" optimizers=optimizer, # 优化模块基于 torch.optim.*\n",
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"\t...\n",
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"\tdriver=\"torch\", # 使用 pytorch 模块进行训练 \n",
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"\tdevice='cuda', # 使用 GPU：0 显卡执行训练\n",
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"\t...\n",
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")\n",
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"...\n",
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"evaluator = Evaluator(\n",
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" model=model, # 模型基于 torch.nn.Module\n",
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" dataloaders=evaluate_dataloader, # 加载模块基于 torch.utils.data.DataLoader\n",
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" metrics={'acc': Accuracy()}, # 测评方法使用 fastNLP.core.metrics.Accuracy \n",
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" ...\n",
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" driver=trainer.driver, # 保持同 trainer 的 driver 一致\n",
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"\tdevice=None,\n",
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" ...\n",
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")\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c11fe1a",
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"metadata": {},
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"source": [
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"### 1.2 driver 的含义与使用要求\n",
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"\n",
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"在`fastNLP 0.8`中，**`driver`**这一概念被用来表示**控制具体训练的各个步骤的最终执行部分**\n",
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"\n",
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"  例如神经网络前向、后向传播的具体执行、网络参数的优化和数据在设备间的迁移等\n",
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"\n",
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"在`fastNLP 0.8`中，**`Trainer`和`Evaluator`都依赖于具体的`driver`来完成整体的工作流程**\n",
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"\n",
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"  具体`driver`与`Trainer`以及`Evaluator`之间的关系请参考`fastNLP 0.8`的框架设计\n",
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"\n",
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"注：这里给出一条建议：**在同一脚本中**，**所有的`Trainer`和`Evaluator`使用的`driver`应当保持一致**\n",
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"\n",
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"  尽量不出现，之前使用单卡的`driver`，后面又使用多卡的`driver`，这是因为，当脚本执行至\n",
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"\n",
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"  多卡`driver`处时，会重启一个进程执行之前所有内容，如此一来可能会造成一些意想不到的麻烦"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2cac4a1a",
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"metadata": {},
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"source": [
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"### 1.3 Trainer 内部初始化 Evaluator\n",
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"\n",
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"在`fastNLP 0.8`中，如果在**初始化`Trainer`时**，**传入参数`evaluator_dataloaders`和`metrics`**\n",
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"\n",
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"  则在`Trainer`内部，也会初始化单独的`Evaluator`来帮助训练过程中对验证集的评测\n",
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"\n",
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"```python\n",
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"trainer = Trainer(\n",
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" model=model,\n",
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" train_dataloader=train_dataloader,\n",
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" optimizers=optimizer,\n",
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"\t...\n",
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"\tdriver=\"torch\",\n",
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"\tdevice='cuda',\n",
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"\t...\n",
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" evaluate_dataloaders=evaluate_dataloader, # 传入参数 evaluator_dataloaders\n",
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" metrics={'acc': Accuracy()}, # 传入参数 metrics\n",
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"\t...\n",
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")\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0c9c7dda",
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"metadata": {},
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"source": [
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"## 2. argmax 模型的搭建实例"
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]
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},
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{
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"cell_type": "markdown",
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"id": "524ac200",
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"metadata": {},
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"source": [
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"### 2.1 trainer_step 和 evaluator_step\n",
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"\n",
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"在`fastNLP 0.8`中，使用`pytorch.nn.Module`搭建需要训练的模型，在搭建模型过程中，除了\n",
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"\n",
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"  添加`pytorch`要求的`forward`方法外，还需要添加 **`train_step`** 和 **`evaluate_step`** 这两个方法\n",
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"\n",
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"```python\n",
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"class Model(torch.nn.Module):\n",
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" def __init__(self):\n",
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" super(Model, self).__init__()\n",
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" self.loss_fn = torch.nn.CrossEntropyLoss()\n",
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" pass\n",
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"\n",
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" def forward(self, x):\n",
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" pass\n",
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"\n",
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" def train_step(self, x, y):\n",
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" pred = self(x)\n",
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" return {\"loss\": self.loss_fn(pred, y)}\n",
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"\n",
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" def evaluate_step(self, x, y):\n",
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" pred = self(x)\n",
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" pred = torch.max(pred, dim=-1)[1]\n",
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" return {\"pred\": pred, \"target\": y}\n",
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"```\n",
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"***\n",
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"在`fastNLP 0.8`中，**函数`train_step`是`Trainer`中参数`train_fn`的默认值**\n",
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"\n",
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"  由于，在`Trainer`训练时，**`Trainer`通过参数`train_fn`对应的模型方法获得当前数据批次的损失值**\n",
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"\n",
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"  因此，在`Trainer`训练时，`Trainer`首先会寻找模型是否定义了`train_step`这一方法\n",
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"\n",
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"    如果没有找到，那么`Trainer`会默认使用模型的`forward`函数来进行训练的前向传播过程\n",
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"\n",
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"注：在`fastNLP 0.8`中，**`Trainer`要求模型通过`train_step`来返回一个字典**，**满足如`{\"loss\": loss}`的形式**\n",
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"\n",
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"  此外，这里也可以通过传入`Trainer`的参数`output_mapping`来实现输出的转换，详见（trainer的详细讲解，待补充）\n",
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"\n",
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"同样，在`fastNLP 0.8`中，**函数`evaluate_step`是`Evaluator`中参数`evaluate_fn`的默认值**\n",
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"\n",
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"  在`Evaluator`测试时，**`Evaluator`通过参数`evaluate_fn`对应的模型方法获得当前数据批次的评测结果**\n",
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"\n",
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"  从用户角度，模型通过`evaluate_step`方法来返回一个字典，内容与传入`Evaluator`的`metrics`一致\n",
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"\n",
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"  从模块角度，该字典的键值和`metric`中的`update`函数的签名一致，这样的机制在传参时被称为“**参数匹配**”\n",
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"\n",
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"<img src=\"./figures/T0-fig-training-structure.png\" width=\"68%\" height=\"68%\" align=\"center\"></img>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fb3272eb",
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||
"metadata": {},
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"source": [
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"### 2.2 trainer 和 evaluator 的参数匹配\n",
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"\n",
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"在`fastNLP 0.8`中，参数匹配涉及到两个方面，分别是在\n",
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"\n",
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"  一方面，**在模型的前向传播中**，**`dataloader`向`train_step`或`evaluate_step`函数传递`batch`**\n",
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"\n",
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"  另方面，**在模型的评测过程中**，**`evaluate_dataloader`向`metric`的`update`函数传递`batch`**\n",
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"\n",
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"对于前者，在`Trainer`和`Evaluator`中的参数`model_wo_auto_param_call`被设置为`False`时\n",
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"\n",
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"    **`fastNLP 0.8`要求`dataloader`生成的每个`batch`**，**满足如`{\"x\": x, \"y\": y}`的形式**\n",
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"\n",
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"  同时，`fastNLP 0.8`会查看模型的`train_step`和`evaluate_step`方法的参数签名，并为对应参数传入对应数值\n",
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"\n",
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"    **字典形式的定义**，**对应在`Dataset`定义的`__getitem__`方法中**，例如下方的`ArgMaxDatset`\n",
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"\n",
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"  而在`Trainer`和`Evaluator`中的参数`model_wo_auto_param_call`被设置为`True`时\n",
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"\n",
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"    `fastNLP 0.8`会将`batch`直接传给模型的`train_step`、`evaluate_step`或`forward`函数\n",
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"\n",
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"```python\n",
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"class Dataset(torch.utils.data.Dataset):\n",
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" def __init__(self, x, y):\n",
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" self.x = x\n",
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" self.y = y\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.x)\n",
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"\n",
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" def __getitem__(self, item):\n",
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" return {\"x\": self.x[item], \"y\": self.y[item]}\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f5f1a6aa",
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"metadata": {},
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"source": [
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"对于后者，首先要明确，在`Trainer`和`Evaluator`中，`metrics`的计算分为`update`和`get_metric`两步\n",
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"\n",
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"    **`update`函数**，**针对一个`batch`的预测结果**，计算其累计的评价指标\n",
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"\n",
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"    **`get_metric`函数**，**统计`update`函数累计的评价指标**，来计算最终的评价结果\n",
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"\n",
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"  例如对于`Accuracy`来说，`update`函数会更新一个`batch`的正例数量`right_num`和负例数量`total_num`\n",
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"\n",
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"    而`get_metric`函数则会返回所有`batch`的评测值`right_num / total_num`\n",
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"\n",
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"  在此基础上，**`fastNLP 0.8`要求`evaluate_dataloader`生成的每个`batch`传递给对应的`metric`**\n",
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"\n",
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"    **以`{\"pred\": y_pred, \"target\": y_true}`的形式**，对应其`update`函数的函数签名\n",
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"\n",
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"<img src=\"./figures/T0-fig-parameter-matching.png\" width=\"75%\" height=\"75%\" align=\"center\"></img>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f62b7bb1",
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"metadata": {},
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"source": [
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"### 2.3 示例：argmax 模型的搭建\n",
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"\n",
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"下文将通过训练`argmax`模型，简单介绍如何`Trainer`模块的使用方式\n",
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"\n",
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"  首先，使用`pytorch.nn.Module`定义`argmax`模型，目标是输入一组固定维度的向量，输出其中数值最大的数的索引"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "5314482b",
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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"\n",
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"class ArgMaxModel(nn.Module):\n",
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" def __init__(self, num_labels, feature_dimension):\n",
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" nn.Module.__init__(self)\n",
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" self.num_labels = num_labels\n",
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"\n",
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" self.linear1 = nn.Linear(in_features=feature_dimension, out_features=10)\n",
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" self.ac1 = nn.ReLU()\n",
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" self.linear2 = nn.Linear(in_features=10, out_features=10)\n",
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" self.ac2 = nn.ReLU()\n",
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" self.output = nn.Linear(in_features=10, out_features=num_labels)\n",
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" self.loss_fn = nn.CrossEntropyLoss()\n",
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"\n",
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" def forward(self, x):\n",
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" pred = self.ac1(self.linear1(x))\n",
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" pred = self.ac2(self.linear2(pred))\n",
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" pred = self.output(pred)\n",
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" return pred\n",
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"\n",
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" def train_step(self, x, y):\n",
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" pred = self(x)\n",
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" return {\"loss\": self.loss_fn(pred, y)}\n",
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"\n",
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" def evaluate_step(self, x, y):\n",
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" pred = self(x)\n",
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" pred = torch.max(pred, dim=-1)[1]\n",
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" return {\"pred\": pred, \"target\": y}"
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||
]
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||
},
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||
{
|
||
"cell_type": "markdown",
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||
"id": "71f3fa6b",
|
||
"metadata": {},
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||
"source": [
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||
"  接着，使用`torch.utils.data.Dataset`定义`ArgMaxDataset`数据集\n",
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"\n",
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"    数据集包含三个参数：维度`feature_dimension`、数据量`data_num`和随机种子`seed`\n",
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"\n",
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"    数据及初始化是，自动生成指定维度的向量，并为每个向量标注出其中最大值的索引作为预测标签"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
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||
"id": "fe612e61",
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||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": false
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||
}
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||
},
|
||
"outputs": [],
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||
"source": [
|
||
"from torch.utils.data import Dataset\n",
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"\n",
|
||
"class ArgMaxDataset(Dataset):\n",
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||
" def __init__(self, feature_dimension, data_num=1000, seed=0):\n",
|
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" self.num_labels = feature_dimension\n",
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||
" self.feature_dimension = feature_dimension\n",
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" self.data_num = data_num\n",
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" self.seed = seed\n",
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"\n",
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" g = torch.Generator()\n",
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||
" g.manual_seed(1000)\n",
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" self.x = torch.randint(low=-100, high=100, size=[data_num, feature_dimension], generator=g).float()\n",
|
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" self.y = torch.max(self.x, dim=-1)[1]\n",
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||
"\n",
|
||
" def __len__(self):\n",
|
||
" return self.data_num\n",
|
||
"\n",
|
||
" def __getitem__(self, item):\n",
|
||
" return {\"x\": self.x[item], \"y\": self.y[item]}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2cb96332",
|
||
"metadata": {},
|
||
"source": [
|
||
"  然后，根据`ArgMaxModel`类初始化模型实例，保持输入维度`feature_dimension`和输出标签数量`num_labels`一致\n",
|
||
"\n",
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||
"    再根据`ArgMaxDataset`类初始化两个数据集实例，分别用来模型测试和模型评测，数据量各1000笔"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "76172ef8",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"model = ArgMaxModel(num_labels=10, feature_dimension=10)\n",
|
||
"\n",
|
||
"train_dataset = ArgMaxDataset(feature_dimension=10, data_num=1000)\n",
|
||
"evaluate_dataset = ArgMaxDataset(feature_dimension=10, data_num=100)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4e7d25ee",
|
||
"metadata": {},
|
||
"source": [
|
||
"  此外，使用`torch.utils.data.DataLoader`初始化两个数据加载模块，批量大小同为8，分别用于训练和测评"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "363b5b09",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from torch.utils.data import DataLoader\n",
|
||
"\n",
|
||
"train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
|
||
"evaluate_dataloader = DataLoader(evaluate_dataset, batch_size=8)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c8d4443f",
|
||
"metadata": {},
|
||
"source": [
|
||
"  最后，使用`torch.optim.SGD`初始化一个优化模块，基于随机梯度下降法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "dc28a2d9",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": false
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from torch.optim import SGD\n",
|
||
"\n",
|
||
"optimizer = SGD(model.parameters(), lr=0.001)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eb8ca6cf",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 3. 使用 fastNLP 0.8 训练 argmax 模型\n",
|
||
"\n",
|
||
"### 3.1 trainer 外部初始化的 evaluator"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "55145553",
|
||
"metadata": {},
|
||
"source": [
|
||
"通过从`fastNLP`库中导入`Trainer`类，初始化`trainer`实例，对模型进行训练\n",
|
||
"\n",
|
||
"  需要导入预先定义好的模型`model`、对应的数据加载模块`train_dataloader`、优化模块`optimizer`\n",
|
||
"\n",
|
||
"  通过`progress_bar`设定进度条格式，默认为`\"auto\"`，此外还有`\"rich\"`、`\"raw\"`和`None`\n",
|
||
"\n",
|
||
"    但对于`\"auto\"`和`\"rich\"`格式，在`jupyter`中，进度条会在训练结束后会被丢弃\n",
|
||
"\n",
|
||
"  通过`n_epochs`设定优化迭代轮数，默认为20；全部`Trainer`的全部变量与函数可以通过`dir(trainer)`查询"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "b51b7a2d",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": false
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import sys\n",
|
||
"sys.path.append('..')\n",
|
||
"\n",
|
||
"from fastNLP import Trainer\n",
|
||
"\n",
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" driver=\"torch\",\n",
|
||
" device='cuda',\n",
|
||
" train_dataloader=train_dataloader,\n",
|
||
" optimizers=optimizer,\n",
|
||
" n_epochs=10, # 设定迭代轮数 \n",
|
||
" progress_bar=\"auto\" # 设定进度条格式\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e202d6e",
|
||
"metadata": {},
|
||
"source": [
|
||
"通过使用`Trainer`类的`run`函数，进行训练\n",
|
||
"\n",
|
||
"  其中，可以通过参数`num_train_batch_per_epoch`决定每个`epoch`运行多少个`batch`后停止，默认全部\n",
|
||
"\n",
|
||
"  `run`函数完成后在`jupyter`中没有输出保留，此外，通过`help(trainer.run)`可以查询`run`函数的详细内容"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "ba047ead",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Output()"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
|
||
],
|
||
"text/plain": []
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"trainer.run()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c16c5fa4",
|
||
"metadata": {},
|
||
"source": [
|
||
"通过从`fastNLP`库中导入`Evaluator`类，初始化`evaluator`实例，对模型进行评测\n",
|
||
"\n",
|
||
"  需要导入预先定义好的模型`model`、对应的数据加载模块`evaluate_dataloader`\n",
|
||
"\n",
|
||
"  需要注意的是评测方法`metrics`，设定为形如`{'acc': fastNLP.core.metrics.Accuracy()}`的字典\n",
|
||
"\n",
|
||
"  类似地，也可以通过`progress_bar`限定进度条格式，默认为`\"auto\"`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "1c6b6b36",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": true
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from fastNLP import Evaluator\n",
|
||
"from fastNLP.core.metrics import Accuracy\n",
|
||
"\n",
|
||
"evaluator = Evaluator(\n",
|
||
" model=model,\n",
|
||
" driver=trainer.driver, # 需要使用 trainer 已经启动的 driver\n",
|
||
" device=None,\n",
|
||
" dataloaders=evaluate_dataloader,\n",
|
||
" metrics={'acc': Accuracy()} # 需要严格使用此种形式的字典\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8157bb9b",
|
||
"metadata": {},
|
||
"source": [
|
||
"通过使用`Evaluator`类的`run`函数，进行训练\n",
|
||
"\n",
|
||
"  其中，可以通过参数`num_eval_batch_per_dl`决定每个`evaluate_dataloader`运行多少个`batch`停止，默认全部\n",
|
||
"\n",
|
||
"  最终，输出形如`{'acc#acc': acc}`的字典，在`jupyter`中，进度条会在评测结束后会被丢弃"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "f7cb0165",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Output()"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
|
||
],
|
||
"text/plain": []
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.31</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'total#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'correct#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">31.0</span><span style=\"font-weight: bold\">}</span>\n",
|
||
"</pre>\n"
|
||
],
|
||
"text/plain": [
|
||
"\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.31\u001b[0m, \u001b[32m'total#acc'\u001b[0m: \u001b[1;36m100.0\u001b[0m, \u001b[32m'correct#acc'\u001b[0m: \u001b[1;36m31.0\u001b[0m\u001b[1m}\u001b[0m\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'acc#acc': 0.31, 'total#acc': 100.0, 'correct#acc': 31.0}"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"evaluator.run()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "dd9f68fa",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 3.2 trainer 内部初始化的 evaluator \n",
|
||
"\n",
|
||
"通过在初始化`trainer`实例时加入`evaluate_dataloaders`和`metrics`，可以实现在训练过程中进行评测\n",
|
||
"\n",
|
||
"  通过`progress_bar`同时设定训练和评估进度条格式，在`jupyter`中，在进度条训练结束后会被丢弃\n",
|
||
"\n",
|
||
"  但是中间的评估结果仍会保留；**通过`evaluate_every`设定评估频率**，可以为负数、正数或者函数：\n",
|
||
"\n",
|
||
"    **为负数时**，**表示每隔几个`epoch`评估一次**；**为正数时**，**则表示每隔几个`batch`评估一次**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "183c7d19",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": true
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" driver=trainer.driver, # 因为是在同个脚本中，这里的 driver 同样需要重用\n",
|
||
" train_dataloader=train_dataloader,\n",
|
||
" evaluate_dataloaders=evaluate_dataloader,\n",
|
||
" metrics={'acc': Accuracy()},\n",
|
||
" optimizers=optimizer,\n",
|
||
" n_epochs=10, \n",
|
||
" evaluate_every=-1, # 表示每个 epoch 的结束进行评估\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "714cc404",
|
||
"metadata": {},
|
||
"source": [
|
||
"通过使用`Trainer`类的`run`函数，进行训练\n",
|
||
"\n",
|
||
"  还可以通过**参数`num_eval_sanity_batch`决定每次训练前运行多少个`evaluate_batch`进行评测**，**默认为`2`**\n",
|
||
"\n",
|
||
"  之所以“先评测后训练”，是为了保证训练很长时间的数据，不会在评测阶段出问题，故作此**试探性评测**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "2e4daa2c",
|
||
"metadata": {
|
||
"pycharm": {
|
||
"is_executing": true
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
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|
||
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|
||
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|
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"</pre>\n"
|
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|
||
"text/plain": [
|
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"----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
|
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|
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|
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|
||
" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"acc#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.31</span>,\n",
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" <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"acc#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.36</span>,\n",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
"trainer.run()"
|
||
]
|
||
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|
||
{
|
||
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|
||
"execution_count": 12,
|
||
"id": "c4e9c619",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
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|
||
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|
||
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|
||
},
|
||
"text/plain": [
|
||
"Output()"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
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|
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|
||
],
|
||
"text/plain": []
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'acc#acc': 0.4, 'total#acc': 100.0, 'correct#acc': 40.0}"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"trainer.evaluator.run()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "db784d5b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"['__annotations__',\n",
|
||
" '__class__',\n",
|
||
" '__delattr__',\n",
|
||
" '__dict__',\n",
|
||
" '__dir__',\n",
|
||
" '__doc__',\n",
|
||
" '__eq__',\n",
|
||
" '__format__',\n",
|
||
" '__ge__',\n",
|
||
" '__getattribute__',\n",
|
||
" '__gt__',\n",
|
||
" '__hash__',\n",
|
||
" '__init__',\n",
|
||
" '__init_subclass__',\n",
|
||
" '__le__',\n",
|
||
" '__lt__',\n",
|
||
" '__module__',\n",
|
||
" '__ne__',\n",
|
||
" '__new__',\n",
|
||
" '__reduce__',\n",
|
||
" '__reduce_ex__',\n",
|
||
" '__repr__',\n",
|
||
" '__setattr__',\n",
|
||
" '__sizeof__',\n",
|
||
" '__str__',\n",
|
||
" '__subclasshook__',\n",
|
||
" '__weakref__',\n",
|
||
" '_check_callback_called_legality',\n",
|
||
" '_check_train_batch_loop_legality',\n",
|
||
" '_custom_callbacks',\n",
|
||
" '_driver',\n",
|
||
" '_evaluate_dataloaders',\n",
|
||
" '_fetch_matched_fn_callbacks',\n",
|
||
" '_set_num_eval_batch_per_dl',\n",
|
||
" '_train_batch_loop',\n",
|
||
" '_train_dataloader',\n",
|
||
" '_train_step',\n",
|
||
" '_train_step_signature_fn',\n",
|
||
" 'accumulation_steps',\n",
|
||
" 'add_callback_fn',\n",
|
||
" 'backward',\n",
|
||
" 'batch_idx_in_epoch',\n",
|
||
" 'batch_step_fn',\n",
|
||
" 'callback_manager',\n",
|
||
" 'check_batch_step_fn',\n",
|
||
" 'cur_epoch_idx',\n",
|
||
" 'data_device',\n",
|
||
" 'dataloader',\n",
|
||
" 'device',\n",
|
||
" 'driver',\n",
|
||
" 'driver_name',\n",
|
||
" 'epoch_evaluate',\n",
|
||
" 'evaluate_batch_step_fn',\n",
|
||
" 'evaluate_dataloaders',\n",
|
||
" 'evaluate_every',\n",
|
||
" 'evaluate_fn',\n",
|
||
" 'evaluator',\n",
|
||
" 'extract_loss_from_outputs',\n",
|
||
" 'fp16',\n",
|
||
" 'get_no_sync_context',\n",
|
||
" 'global_forward_batches',\n",
|
||
" 'has_checked_train_batch_loop',\n",
|
||
" 'input_mapping',\n",
|
||
" 'kwargs',\n",
|
||
" 'larger_better',\n",
|
||
" 'load_checkpoint',\n",
|
||
" 'load_model',\n",
|
||
" 'marker',\n",
|
||
" 'metrics',\n",
|
||
" 'model',\n",
|
||
" 'model_device',\n",
|
||
" 'monitor',\n",
|
||
" 'move_data_to_device',\n",
|
||
" 'n_epochs',\n",
|
||
" 'num_batches_per_epoch',\n",
|
||
" 'on',\n",
|
||
" 'on_after_backward',\n",
|
||
" 'on_after_optimizers_step',\n",
|
||
" 'on_after_trainer_initialized',\n",
|
||
" 'on_after_zero_grad',\n",
|
||
" 'on_before_backward',\n",
|
||
" 'on_before_optimizers_step',\n",
|
||
" 'on_before_zero_grad',\n",
|
||
" 'on_evaluate_begin',\n",
|
||
" 'on_evaluate_end',\n",
|
||
" 'on_exception',\n",
|
||
" 'on_fetch_data_begin',\n",
|
||
" 'on_fetch_data_end',\n",
|
||
" 'on_load_checkpoint',\n",
|
||
" 'on_load_model',\n",
|
||
" 'on_sanity_check_begin',\n",
|
||
" 'on_sanity_check_end',\n",
|
||
" 'on_save_checkpoint',\n",
|
||
" 'on_save_model',\n",
|
||
" 'on_train_batch_begin',\n",
|
||
" 'on_train_batch_end',\n",
|
||
" 'on_train_begin',\n",
|
||
" 'on_train_end',\n",
|
||
" 'on_train_epoch_begin',\n",
|
||
" 'on_train_epoch_end',\n",
|
||
" 'optimizers',\n",
|
||
" 'output_mapping',\n",
|
||
" 'progress_bar',\n",
|
||
" 'run',\n",
|
||
" 'run_evaluate',\n",
|
||
" 'save_checkpoint',\n",
|
||
" 'save_model',\n",
|
||
" 'start_batch_idx_in_epoch',\n",
|
||
" 'state',\n",
|
||
" 'step',\n",
|
||
" 'step_evaluate',\n",
|
||
" 'total_batches',\n",
|
||
" 'train_batch_loop',\n",
|
||
" 'train_dataloader',\n",
|
||
" 'train_fn',\n",
|
||
" 'train_step',\n",
|
||
" 'trainer_state',\n",
|
||
" 'zero_grad']"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"dir(trainer)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "953533c4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Help on method run in module fastNLP.core.controllers.trainer:\n",
|
||
"\n",
|
||
"run(num_train_batch_per_epoch: int = -1, num_eval_batch_per_dl: int = -1, num_eval_sanity_batch: int = 2, resume_from: str = None, resume_training: bool = True, catch_KeyboardInterrupt=None) method of fastNLP.core.controllers.trainer.Trainer instance\n",
|
||
" 该函数是在 ``Trainer`` 初始化后用于真正开始训练的函数；\n",
|
||
" \n",
|
||
" 注意如果是断点重训的第一次训练，即还没有保存任何用于断点重训的文件，那么其应当置 resume_from 为 None，并且使用 ``CheckpointCallback``\n",
|
||
" 去保存断点重训的文件；\n",
|
||
" \n",
|
||
" :param num_train_batch_per_epoch: 每个 epoch 训练多少个 batch 后停止，*-1* 表示使用 train_dataloader 本身的长度；\n",
|
||
" :param num_eval_batch_per_dl: 每个 evaluate_dataloader 验证多少个 batch 停止，*-1* 表示使用 evaluate_dataloader 本身的长度；\n",
|
||
" :param num_eval_sanity_batch: 在训练之前运行多少个 evaluation batch 来检测一下 evaluation 的过程是否有错误。为 0 表示不检测；\n",
|
||
" :param resume_from: 从哪个路径下恢复 trainer 的状态，注意该值需要为一个文件夹，例如使用 ``CheckpointCallback`` 时帮助您创建的保存的子文件夹；\n",
|
||
" :param resume_training: 是否按照 checkpoint 中训练状态恢复。如果为 False，则只恢复 model 和 optimizers 的状态；该参数如果为 ``True``，\n",
|
||
" 在下一次断点重训的时候我们会精确到上次训练截止的具体的 sample 进行训练；否则我们只会恢复 model 和 optimizers 的状态，而 ``Trainer`` 中的\n",
|
||
" 其余状态都是保持初始化时的状态不会改变；\n",
|
||
" :param catch_KeyboardInterrupt: 是否捕获 KeyboardInterrupt；如果该参数为 ``True``，在训练时如果您使用 ``ctrl+c`` 来终止程序，\n",
|
||
" ``Trainer`` 不会抛出异常，但是会提前退出，然后 ``trainer.run()`` 之后的代码会继续运行。注意该参数在您使用分布式训练的 ``Driver``\n",
|
||
" 时无效，例如 ``TorchDDPDriver``；非分布式训练的 ``Driver`` 下该参数默认为 True；\n",
|
||
" \n",
|
||
" .. warning::\n",
|
||
" \n",
|
||
" 注意初始化的 ``Trainer`` 只能调用一次 ``run`` 函数，即之后的调用 ``run`` 函数实际不会运行，因为此时\n",
|
||
" ``trainer.cur_epoch_idx == trainer.n_epochs``；\n",
|
||
" \n",
|
||
" 这意味着如果您需要再次调用 ``run`` 函数，您需要重新再初始化一个 ``Trainer``；\n",
|
||
" \n",
|
||
" .. note::\n",
|
||
" \n",
|
||
" 您可以使用 ``num_train_batch_per_epoch`` 来简单地对您的训练过程进行验证，例如，当您指定 ``num_train_batch_per_epoch=10`` 后，\n",
|
||
" 每一个 epoch 下实际训练的 batch 的数量则会被修改为 10。您可以先使用该值来设定一个较小的训练长度，在验证整体的训练流程没有错误后，再将\n",
|
||
" 该值设定为 **-1** 开始真正的训练；\n",
|
||
" \n",
|
||
" ``num_eval_batch_per_dl`` 的意思和 ``num_train_batch_per_epoch`` 类似，即您可以通过设定 ``num_eval_batch_per_dl`` 来验证\n",
|
||
" 整体的验证流程是否正确；\n",
|
||
" \n",
|
||
" ``num_eval_sanity_batch`` 的作用可能会让人产生迷惑，其本质和 ``num_eval_batch_per_dl`` 作用一致，但是其只被 ``Trainer`` 使用；\n",
|
||
" 并且其只会在训练的一开始使用，意思为：我们在训练的开始时会先使用 ``Evaluator``（如果其不为 ``None``） 进行验证，此时验证的 batch 的\n",
|
||
" 数量只有 ``num_eval_sanity_batch`` 个；但是对于 ``num_eval_batch_per_dl`` 而言，其表示在实际的整体的训练过程中，每次 ``Evaluator``\n",
|
||
" 进行验证时会验证的 batch 的数量。\n",
|
||
" \n",
|
||
" 并且，在实际真正的训练中，``num_train_batch_per_epoch`` 和 ``num_eval_batch_per_dl`` 应当都被设置为 **-1**，但是 ``num_eval_sanity_batch``\n",
|
||
" 应当为一个很小的正整数，例如 2；\n",
|
||
" \n",
|
||
" .. note::\n",
|
||
" \n",
|
||
" 参数 ``resume_from`` 和 ``resume_training`` 的设立是为了支持断点重训功能；仅当 ``resume_from`` 不为 ``None`` 时，``resume_training`` 才有效；\n",
|
||
" \n",
|
||
" 断点重训的意思为将上一次训练过程中的 ``Trainer`` 的状态保存下来，包括模型和优化器的状态、当前训练过的 epoch 的数量、对于当前的 epoch\n",
|
||
" 已经训练过的 batch 的数量、callbacks 的状态等等；然后在下一次训练时直接加载这些状态，从而直接恢复到上一次训练过程的某一个具体时间点的状态开始训练；\n",
|
||
" \n",
|
||
" fastNLP 将断点重训分为了 **保存状态** 和 **恢复断点重训** 两部分：\n",
|
||
" \n",
|
||
" 1. 您需要使用 ``CheckpointCallback`` 来保存训练过程中的 ``Trainer`` 的状态；具体详见 :class:`~fastNLP.core.callbacks.CheckpointCallback`；\n",
|
||
" ``CheckpointCallback`` 会帮助您把 ``Trainer`` 的状态保存到一个具体的文件夹下，这个文件夹的名字由 ``CheckpointCallback`` 自己生成；\n",
|
||
" 2. 在第二次训练开始时，您需要找到您想要加载的 ``Trainer`` 状态所存放的文件夹，然后传入给参数 ``resume_from``；\n",
|
||
" \n",
|
||
" 需要注意的是 **保存状态** 和 **恢复断点重训** 是互不影响的。\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"help(trainer.run)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "1bc7cb4a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.13"
|
||
},
|
||
"pycharm": {
|
||
"stem_cell": {
|
||
"cell_type": "raw",
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"source": []
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|