From 82b06767f55307143f6ed0e1a1400a8ee339a3cf Mon Sep 17 00:00:00 2001 From: yhcc Date: Fri, 3 Jun 2022 22:35:46 +0800 Subject: [PATCH 1/2] =?UTF-8?q?=E4=BF=AE=E5=A4=8Dn=5Fbatches=E5=AF=BC?= =?UTF-8?q?=E8=87=B4=E7=9A=84bug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- fastNLP/core/controllers/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fastNLP/core/controllers/trainer.py b/fastNLP/core/controllers/trainer.py index 7c6bba53..41fca6ba 100644 --- a/fastNLP/core/controllers/trainer.py +++ b/fastNLP/core/controllers/trainer.py @@ -448,7 +448,7 @@ class Trainer(TrainerEventTrigger): # 初始化 state，包括提供给用户的接口和我们自己使用的接口； self.state = State() self.trainer_state = TrainerState( - n_epochs=n_epochs if n_batches!=-1 else None, + n_epochs=n_epochs if n_batches==-1 else None, cur_epoch_idx=0, global_forward_batches=0, batch_idx_in_epoch=0, From 3797f91434930d869eff2849d79f2d13e7409642 Mon Sep 17 00:00:00 2001 From: lxr-tech <1838593642@qq.com> Date: Sat, 4 Jun 2022 00:03:40 +0800 Subject: [PATCH 2/2] update tutorial-3456 lxr 220603 --- tutorials/fastnlp_tutorial_2.ipynb | 10 +- tutorials/fastnlp_tutorial_3.ipynb | 226 ++++- tutorials/fastnlp_tutorial_4.ipynb | 1323 +--------------------------- tutorials/fastnlp_tutorial_5.ipynb | 218 +++-- tutorials/fastnlp_tutorial_6.ipynb | 302 ++++++- 5 files changed, 676 insertions(+), 1403 deletions(-) diff --git a/tutorials/fastnlp_tutorial_2.ipynb b/tutorials/fastnlp_tutorial_2.ipynb index 64c4bc8b..33f74b8b 100644 --- a/tutorials/fastnlp_tutorial_2.ipynb +++ b/tutorials/fastnlp_tutorial_2.ipynb @@ -801,24 +801,24 @@ "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", "\n", "# 接着，导入数据，先生成为 dataset 形式，再变成 dataset-dict，并转为 databundle 形式\n", - "datasets = DataSet.from_pandas(pd.read_csv('./data/test4dataset.tsv'))\n", + "datasets = DataSet.from_pandas(pd.read_csv('./data/test4dataset.tsv', sep='\\t'))\n", "train_ds, test_ds = datasets.split(ratio=0.7)\n", "data_bundle = DataBundle(datasets={'train': train_ds, 'test': test_ds})\n", "\n", "# 然后，通过 tokenizer.encode_plus 函数，进行文本分词标注、修改并补充数据包内容\n", "encode = partial(tokenizer.encode_plus, max_length=100, truncation=True,\n", " return_attention_mask=True)\n", - "data_bundle.apply_field_more(encode, field_name='text', progress_bar='tqdm')\n", + "data_bundle.apply_field_more(encode, field_name='Sentence', progress_bar='tqdm')\n", "\n", "# 在修改好 'text' 字段的文本信息后，接着处理 'label' 字段的预测信息\n", "target_vocab = Vocabulary(padding=None, unknown=None)\n", - "target_vocab.from_dataset(*[ds for _, ds in data_bundle.iter_datasets()], field_name='label')\n", - "target_vocab.index_dataset(*[ds for _, ds in data_bundle.iter_datasets()], field_name='label',\n", + "target_vocab.from_dataset(*[ds for _, ds in data_bundle.iter_datasets()], field_name='Sentiment')\n", + "target_vocab.index_dataset(*[ds for _, ds in data_bundle.iter_datasets()], field_name='Sentiment',\n", " new_field_name='target')\n", "\n", "# 最后，通过 data_bundle 的其他一些函数，完成善后内容\n", "data_bundle.set_pad('input_ids', pad_val=tokenizer.pad_token_id)\n", - "data_bundle.set_ignore('label', 'text') \n", + "data_bundle.set_ignore('SentenceId', 'Sentiment', 'Sentence') \n", "```" ] }, diff --git a/tutorials/fastnlp_tutorial_3.ipynb b/tutorials/fastnlp_tutorial_3.ipynb index 172e1232..7566c02a 100644 --- a/tutorials/fastnlp_tutorial_3.ipynb +++ b/tutorials/fastnlp_tutorial_3.ipynb @@ -9,9 +9,9 @@ "\n", "  1   fastNLP 中的 dataloader\n", " \n", - "    1.1   dataloader 的职责描述\n", + "    1.1   dataloader 的基本介绍\n", "\n", - "    1.2   dataloader 的基本使用\n", + "    1.2   dataloader 的函数创建\n", "\n", "  2   fastNLP 中 dataloader 的延伸\n", "\n", @@ -27,32 +27,143 @@ "source": [ "## 1. fastNLP 中的 dataloader\n", "\n", - "### 1.1 dataloader 的职责描述\n", + "### 1.1 dataloader 的基本介绍\n", "\n", - "在`fastNLP 0.8`中，在数据加载模块`DataLoader`之前" + "在`fastNLP 0.8`的开发中，最关键的开发目标就是**实现`fastNLP`对当前主流机器学习框架**，例如\n", + "\n", + "  **较为火热的`pytorch`**，以及**国产的`paddle`和`jittor`的兼容**，扩大受众的同时，也是助力国产\n", + "\n", + "本着分而治之的思想，我们可以将`fastNLP 0.8`对`pytorch`、`paddle`、`jittor`框架的兼容，划分为\n", + "\n", + "    **对数据预处理**、**批量`batch`的划分与补齐**、**模型训练**、**模型评测**，**四个部分的兼容**\n", + "\n", + "  针对数据预处理，我们已经在`tutorial-1`中介绍了`dataset`和`vocabulary`的使用\n", + "\n", + "    而结合`tutorial-0`，我们可以发现**数据预处理环节本质上是框架无关的**\n", + "\n", + "    因为在不同框架下，读取的原始数据格式都差异不大，彼此也很容易转换\n", + "\n", + "只有涉及到张量、模型，不同框架才展现出其各自的特色：**`pytorch`中的`tensor`和`nn.Module`**\n", + "\n", + "    **在`paddle`中称为`tensor`和`nn.Layer`**，**在`jittor`中则称为`Var`和`Module`**\n", + "\n", + "    因此，**模型训练、模型评测**，**是兼容的重难点**，我们将会在`tutorial-5`中详细介绍\n", + "\n", + "  针对批量`batch`的处理，作为`fastNLP 0.8`中框架无关部分想框架相关部分的过渡\n", + "\n", + "    就是`dataloader`模块的职责，这也是本篇教程`tutorial-3`讲解的重点\n", + "\n", + "**`dataloader`模块的职责**，详细划分可以包含以下三部分，**采样划分、补零对齐、框架匹配**\n", + "\n", + "    第一，确定`batch`大小，确定采样方式，划分后通过迭代器即可得到`batch`序列\n", + "\n", + "    第二，对于序列处理，这也是`fastNLP`主要针对的，将同个`batch`内的数据对齐\n", + "\n", + "    第三，**`batch`内数据格式要匹配框架**，**但`batch`结构需保持一致**，**参数匹配机制**\n", + "\n", + "  对此，`fastNLP 0.8`给出了 **`TorchDataLoader`、`PaddleDataLoader`和`JittorDataLoader`**\n", + "\n", + "    分别针对并匹配不同框架，但彼此之间参数名、属性、方法仍然类似，前两者大致如下表所示\n", + "\n", + "|
名称
|
参数
|
属性
|
功能
|
内容
|\n", + "|:--|:--:|:--:|:--|:--|\n", + "| **`dataset`** | √ | √ | 指定`dataloader`的数据内容 | |\n", + "| `batch_size` | √ | √ | 指定`dataloader`的`batch`大小 | 默认`16` |\n", + "| `shuffle` | √ | √ | 指定`dataloader`的数据是否打乱 | 默认`False` |\n", + "| `collate_fn` | √ | √ | 指定`dataloader`的`batch`打包方法 | 视框架而定 |\n", + "| `sampler` | √ | √ | ？ | 默认`None` |\n", + "| `batch_sampler` | √ | √ | ？ | 默认`None` |\n", + "| `drop_last` | √ | √ | 指定`dataloader`划分`batch`时是否丢弃剩余的 | 默认`False` |\n", + "| `cur_batch_indices` | | √ | 记录`dataloader`当前遍历批量序号 | |\n", + "| `num_workers` | √ | √ | 指定`dataloader`开启子进程数量 | 默认`0` |\n", + "| `worker_init_fn` | √ | √ | 指定`dataloader`子进程初始方法 | 默认`None` |\n", + "| `generator` | √ | √ | 指定`dataloader`子进程随机种子 | 默认`None` |\n", + "| `prefetch_factor` | | √ | 指定为每个`worker`装载的`sampler`数量 | 默认`2` |" ] }, { "cell_type": "markdown", - "id": "eb8fb51c", + "id": "60a8a224", "metadata": {}, "source": [ - "### 1.2 dataloader 的基本使用\n", + "  论及`dataloader`的函数，其中，`get_batch_indices`用来获取当前遍历到的`batch`序号，其他函数\n", "\n", - "在`fastNLP 0.8`中，在数据加载模块`DataLoader`之前，" + "    包括`set_ignore`、`set_pad`和`databundle`类似，请参考`tutorial-2`，此处不做更多介绍\n", + "\n", + "    以下是`tutorial-2`中已经介绍过的数据预处理流程，接下来是对相关数据进行`dataloader`处理" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "aca72b49", "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Processing: 0%| | 0/4 [00:00\n", - "\n" - ], - "text/plain": [ - "\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing: 0%| | 0/6000 [00:00[17:45:59] INFO Running evaluator sanity check for 2 batches. trainer.py:592\n", - "\n" - ], - "text/plain": [ - "\u001b[2;36m[17:45:59]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches. \u001b]8;id=147745;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=708408;file://../fastNLP/core/controllers/trainer.py#592\u001b\\\u001b[2m592\u001b[0m\u001b]8;;\u001b\\\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "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": [ - "
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+   "outputs": [],
    "source": [
-    "trainer.run(num_eval_batch_per_dl=10)"
+    "trainer.run()"
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   },
   {
    "cell_type": "code",
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+   "execution_count": null,
    "id": "f47a6a35",
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+   "outputs": [],
    "source": [
     "trainer.evaluator.run()"
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@@ -1082,21 +451,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
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-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
+   "outputs": [],
    "source": [
     "import gc\n",
     "\n",
@@ -1144,32 +502,10 @@
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   {
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+   "execution_count": null,
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-       "
----------------------------- Eval. results on Epoch:8, Batch:0 -----------------------------\n",
-       "
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-       "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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-       "
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-      ],
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-       "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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-       "
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-       "
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-       "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.run(num_eval_batch_per_dl=10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "37871d6b",
    "metadata": {},
-   "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": [
-       "
\n"
-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'f#F1': 0.75283, 'pre#F1': 0.727438, 'rec#F1': 0.780059}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.evaluator.run()"
    ]
diff --git a/tutorials/fastnlp_tutorial_5.ipynb b/tutorials/fastnlp_tutorial_5.ipynb
index 0669a60a..63410113 100644
--- a/tutorials/fastnlp_tutorial_5.ipynb
+++ b/tutorials/fastnlp_tutorial_5.ipynb
@@ -312,6 +312,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
     "from fastNLP import Metric\n",
     "\n",
     "class MyMetric(Metric):\n",
@@ -333,33 +336,6 @@
     "        return {'prefix': acc}"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "af3f8c63",
-   "metadata": {},
-   "source": [
-    "  模型使用方面，此处仍然使用`tutorial-4`中介绍过的预定义`CNNText`模型，实现`SST-2`二分类"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2fd210c5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('..')\n",
-    "\n",
-    "from fastNLP.models.torch import CNNText\n",
-    "\n",
-    "model = CNNText(embed=(len(vocab), 100), num_classes=2, dropout=0.1)\n",
-    "\n",
-    "from torch.optim import AdamW\n",
-    "\n",
-    "optimizers = AdamW(params=model.parameters(), lr=5e-4)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0155f447",
@@ -389,9 +365,9 @@
    "id": "e9d81760",
    "metadata": {},
    "source": [
-    "接着是数据预处理，需要注意的是，由于`MyMetric`的`update`函数中，输入参数名称为`pred`和`true`\n",
+    "  在数据预处理中，需要注意的是，由于`MyMetric`的`update`函数中，输入参数名称为`pred`和`true`\n",
     "\n",
-    "  对应地，需要将数据集中表示预测目标的字段，调整为`true`（预定义的`metric`，应调整为`target`"
+    "    对应地，需要将数据集中表示预测目标的字段，调整为`true`（预定义的`metric`，应调整为`target`"
    ]
   },
   {
@@ -429,14 +405,136 @@
     "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "af3f8c63",
+   "metadata": {},
+   "source": [
+    "  模型使用方面，此处仍然使用`tutorial-4`中介绍过的预定义`CNNText`模型，实现`SST-2`二分类"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fd210c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP.models.torch import CNNText\n",
+    "\n",
+    "model = CNNText(embed=(len(vocab), 100), num_classes=2, dropout=0.1)\n",
+    "\n",
+    "from torch.optim import AdamW\n",
+    "\n",
+    "optimizers = AdamW(params=model.parameters(), lr=5e-4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e723b87",
+   "metadata": {},
+   "source": [
+    "## 3. fastNLP 中 trainer 的补充介绍\n",
+    "\n",
+    "### 3.1  trainer 的内部结构\n",
+    "\n",
+    "在`tutorial-0`中，我们已经介绍了`trainer`的基本使用，从`tutorial-1`到`tutorial-4`，我们也已经展示了\n",
+    "\n",
+    "  很多`trainer`的使用案例，这里通过表格，相对完整地介绍`trainer`模块的属性和初始化参数（标粗为必选参数\n",
+    "\n",
+    "| 
名称
 | 
参数
 | 
属性
 | 
功能
 | 
内容
 |\n",
+    "|:--|:--:|:--:|:--|:--|\n",
+    "| **`model`** | √ | √ | 指定`trainer`控制的模型 | 视框架而定，如`torch.nn.Module` |\n",
+    "| **`driver`** | √ |  | 指定`trainer`驱动的框架 | 包括`'torch'`、`'paddle'`、`'jittor'` |\n",
+    "|  |  | √ | 记录`trainer`驱动的框架 | `Driver`类型，在初始化阶段生成 |\n",
+    "| `device` | √ |  | 指定`trainer`运行的卡位 | 例如`'cpu'`、`'cuda'`、`0`、`[0, 1]`等 |\n",
+    "|  |  | √ | 记录`trainer`运行的卡位 | `Device`类型，在初始化阶段生成 |\n",
+    "| `n_epochs` | √ | - | 指定`trainer`迭代的轮数 | 默认`20`，记录在`driver.n_epochs`中 |\n",
+    "| **`optimizers`** | √ | √ | 指定`trainer`优化的方法 | 视框架而定，如`torch.optim.Adam` |\n",
+    "| `metrics` | √ | √ | 指定`trainer`评测的方法 | 字典类型，如`{'acc': Metric()}` |\n",
+    "| `evaluator` |  | √ | 内置的`trainer`评测模块 | `Evaluator`类型，在初始化阶段生成 |\n",
+    "| `input_mapping` | √ | √ | 调整`dataloader`的参数不匹配 | 函数类型，输出字典匹配`forward`输入参数 |\n",
+    "| `output_mapping` | √ | √ | 调整`forward`输出的参数不匹配 | 函数类型，输出字典匹配`xx_step`输入参数 |\n",
+    "| **`train_dataloader`** | √ | √ | 指定`trainer`训练的数据 | `DataLoader`类型，生成视框架而定 |\n",
+    "| `evaluate_dataloaders` | √ | √ | 指定`trainer`评测的数据 | `DataLoader`类型，生成视框架而定 |\n",
+    "| `train_fn` | √ | √ | 指定`trainer`获取某个批次的损失值 | 函数类型，默认为`model.train_step` |\n",
+    "| `evaluate_fn` | √ | √ | 指定`trainer`获取某个批次的评估量 | 函数类型，默认为`model.evaluate_step` |\n",
+    "| `batch_step_fn` | √ | √ | 指定`trainer`训练时前向传输一个批次的方式 | 函数类型，默认为`TrainBatchLoop.batch_step_fn` |\n",
+    "| `evaluate_batch_step_fn` | √ | √ | 指定`trainer`评测时前向传输一个批次的方式 | 函数类型，默认为`EvaluateBatchLoop.batch_step_fn` |\n",
+    "| `accumulation_steps` | √ | √ | 指定`trainer`训练时反向传播的频率 | 默认为`1`，即每个批次都反向传播 |\n",
+    "| `evaluate_every` | √ | √ | 指定`evaluator`评测时计算的频率 | 默认`-1`表示每个循环一次，相反`1`表示每个批次一次 |\n",
+    "| `progress_bar` | √ | √ | 指定`trainer`训练和评测时的进度条样式 | 包括`'auto'`、`'tqdm'`、`'raw'`、`'rich'` |\n",
+    "| `callbacks` | √ |  | 指定`trainer`训练时需要触发的函数 | `Callback`列表类型，详见`tutorial-7` |\n",
+    "| `callback_manager` |  | √ | 记录与管理`callbacks`相关内容 | `CallbackManager`类型，详见`tutorial-7` |\n",
+    "| `monitor` | √ | √ | 辅助部分的`callbacks`相关内容 | 字符串/函数类型，详见`tutorial-7` |\n",
+    "| `marker` | √ | √ | 标记`trainer`实例，辅助`callbacks`相关内容 | 字符串型，详见`tutorial-7` |\n",
+    "| `trainer_state` |  | √ | 记录`trainer`状态，辅助`callbacks`相关内容 | `TrainerState`类型，详见`tutorial-7` |\n",
+    "| `state` |  | √ | 记录`trainer`状态，辅助`callbacks`相关内容 | `State`类型，详见`tutorial-7` |\n",
+    "| `fp16` | √ | √ | 指定`trainer`是否进行混合精度训练 | 布尔类型，默认`False` |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fc8b9f3",
+   "metadata": {},
+   "source": [
+    "  以及`trainer`模块内部的基础方法，相关进阶操作，如“`on`系列函数”、`callback`控制，请参考后续的`tutorial-7`\n",
+    "\n",
+    "| 
名称
 |
功能
 | 
主要参数
 |\n",
+    "|:--|:--|:--|\n",
+    "| `run` | 控制`trainer`中模型的训练和评测 | 详见后文 |\n",
+    "| `train_step` | 实现`trainer`训练中一个批数据的前向传播过程 | 输入`batch` |\n",
+    "| `backward` | 实现`trainer`训练中一次损失的反向传播过程 | 输入`output` |\n",
+    "| `zero_grad` | 实现`trainer`训练中`optimizers`的梯度置零 | 无输入 |\n",
+    "| `step` | 实现`trainer`训练中`optimizers`的参数更新 | 无输入 |\n",
+    "| `epoch_evaluate` | 实现`trainer`训练中每个循环的评测，实际是否执行取决于评测频率 | 无输入 |\n",
+    "| `step_evaluate` | 实现`trainer`训练中每个批次的评测，实际是否执行取决于评测频率 | 无输入 |\n",
+    "| `save_model` | 保存`trainer`中的模型参数/状态字典至`fastnlp_model.pkl.tar` | `folder`指明路径，`only_state_dict`指明是否只保存状态字典，默认`False` |\n",
+    "| `load_model` | 加载`trainer`中的模型参数/状态字典自`fastnlp_model.pkl.tar` | `folder`指明路径，`only_state_dict`指明是否只加载状态字典，默认`True` |\n",
+    "| `save_checkpoint` | 
保存`trainer`中模型参数/状态字典 以及 `callback`、`sampler`
和`optimizer`的状态至`fastnlp_model/checkpoint.pkl.tar`
 | `folder`指明路径，`only_state_dict`指明是否只保存状态字典，默认`True` |\n",
+    "| `load_checkpoint` | 
加载`trainer`中模型参数/状态字典 以及 `callback`、`sampler`
和`optimizer`的状态自`fastnlp_model/checkpoint.pkl.tar`
 | 
`folder`指明路径，`only_state_dict`指明是否只保存状态字典，默认`True`
`resume_training`指明是否只精确到上次训练的批量，默认`True`
 |\n",
+    "| `add_callback_fn` | 在`trainer`初始化后添加`callback`函数 | 输入`event`指明回调时机，`fn`指明回调函数 |\n",
+    "| `on` | 函数修饰器，将一个函数转变为`callback`函数 | 详见`tutorial-7` |\n",
+    "\n",
+    ""
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1e21df35",
    "metadata": {},
    "source": [
-    "然后就是初始化`trainer`实例，其中`metrics`变量输入的键值对，字串`'suffix'`和之前定义的字串`'prefix'`\n",
+    "紧接着，初始化`trainer`实例，继续完成`SST-2`分类，其中`metrics`输入的键值对，字串`'suffix'`和之前定义的\n",
     "\n",
-    "  将拼接在一起显示到`trainer`的`progress bar`中，故完整的输出形式为`{'prefix#suffix': float}`"
+    "  字串`'prefix'`将拼接在一起显示到`progress bar`中，故完整的输出形式为`{'prefix#suffix': float}`"
    ]
   },
   {
@@ -462,61 +560,43 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e723b87",
-   "metadata": {},
+   "id": "b1b2e8b7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "source": [
-    "## 3. fastNLP 中 trainer 的补充介绍\n",
-    "\n",
-    "### 3.1  trainer 的内部结构\n",
-    "\n",
-    "在`tutorial-0`中，我们已经介绍了`trainer`的基本使用，从`tutorial-1`到`tutorial-4`，我们也已经\n",
-    "\n",
-    "  展示了很多关于`trainer`的使用案例，以下我们先补充介绍训练模块`trainer`的一些内部结构\n",
-    "\n",
-    "\n",
-    "\n",
-    "'accumulation_steps', 'add_callback_fn', 'backward', 'batch_idx_in_epoch', 'batch_step_fn',\n",
-    "'callback_manager', 'check_batch_step_fn', 'cur_epoch_idx', 'data_device', 'dataloader',\n",
-    "'device', 'driver', 'driver_name', 'epoch_evaluate', 'evaluate_batch_step_fn', 'evaluate_dataloaders',\n",
-    "'evaluate_every', 'evaluate_fn', 'evaluator', 'extract_loss_from_outputs', 'fp16',\n",
-    "'get_no_sync_context', 'global_forward_batches', 'has_checked_train_batch_loop',\n",
-    "'input_mapping', 'kwargs', 'larger_better', 'load_checkpoint', 'load_model', 'marker',\n",
-    "'metrics', 'model', 'model_device', 'monitor', 'move_data_to_device', 'n_epochs', 'num_batches_per_epoch',\n",
-    "'on', 'on_after_backward', 'on_after_optimizers_step', 'on_after_trainer_initialized',\n",
-    "'on_after_zero_grad', 'on_before_backward', 'on_before_optimizers_step', 'on_before_zero_grad',\n",
-    "'on_evaluate_begin', 'on_evaluate_end', 'on_exception', 'on_fetch_data_begin', 'on_fetch_data_end',\n",
-    "'on_load_checkpoint', 'on_load_model', 'on_sanity_check_begin', 'on_sanity_check_end',\n",
-    "'on_save_checkpoint', 'on_save_model', 'on_train_batch_begin', 'on_train_batch_end',\n",
-    "'on_train_begin', 'on_train_end', 'on_train_epoch_begin', 'on_train_epoch_end',\n",
-    "'optimizers', 'output_mapping', 'progress_bar', 'run', 'run_evaluate',\n",
-    "'save_checkpoint', 'save_model', 'start_batch_idx_in_epoch', 'state',\n",
-    "'step', 'step_evaluate', 'total_batches', 'train_batch_loop', 'train_dataloader', 'train_fn', 'train_step',\n",
-    "'trainer_state', 'zero_grad'\n",
+    "最后就是`run`函数的使用，关于其参数，这里也以表格形式列出，由此就解答了`num_eval_batch_per_dl=10`的含义\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)"
+    "| 
名称
 | 
功能
 | 
默认值
 |\n",
+    "|:--|:--|:--|\n",
+    "| `num_train_batch_per_epoch` | 指定`trainer`训练时，每个循环计算批量数目 | 整数类型，默认`-1`，表示训练时，每个循环计算所有批量 |\n",
+    "| `num_eval_batch_per_dl` | 指定`trainer`评测时，每个循环计算批量数目 | 整数类型，默认`-1`，表示评测时，每个循环计算所有批量 |\n",
+    "| `num_eval_sanity_batch` | 指定`trainer`训练开始前，试探性评测批量数目 | 整数类型，默认`2`，表示训练开始前评估两个批量 |\n",
+    "| `resume_from` | 指定`trainer`恢复状态的路径，需要是文件夹 | 字符串型，默认`None`，使用可参考`CheckpointCallback` |\n",
+    "| `resume_training` | 指定`trainer`恢复状态的程度 | 布尔类型，默认`True`恢复所有状态，`False`仅恢复`model`和`optimizers`状态 |"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c348864c",
+   "id": "43be274f",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "trainer.run(num_eval_batch_per_dl=10)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "43be274f",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "id": "f1abfa0a",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/tutorials/fastnlp_tutorial_6.ipynb b/tutorials/fastnlp_tutorial_6.ipynb
index 2052189e..552f73d9 100644
--- a/tutorials/fastnlp_tutorial_6.ipynb
+++ b/tutorials/fastnlp_tutorial_6.ipynb
@@ -19,20 +19,312 @@
     "\n",
     "    2.2   使用 jittor 搭建并训练模型\n",
     "\n",
-    "  3   fastNLP 实现 paddle 与 pytorch 互转\n",
+    "  3   fastNLP 实现 paddle 与 pytorch 互转"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08752c5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "sst2data = load_dataset('glue', 'sst2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e8cc210",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "from fastNLP import DataSet\n",
+    "\n",
+    "dataset = DataSet.from_pandas(sst2data['train'].to_pandas())[:6000]\n",
     "\n",
-    "    3.1   \n",
+    "dataset.apply_more(lambda ins:{'words': ins['sentence'].lower().split(), 'target': ins['label']}, \n",
+    "                   progress_bar=\"tqdm\")\n",
+    "dataset.delete_field('sentence')\n",
+    "dataset.delete_field('label')\n",
+    "dataset.delete_field('idx')\n",
     "\n",
-    "    3.2   "
+    "from fastNLP import Vocabulary\n",
+    "\n",
+    "vocab = Vocabulary()\n",
+    "vocab.from_dataset(dataset, field_name='words')\n",
+    "vocab.index_dataset(dataset, field_name='words')\n",
+    "\n",
+    "train_dataset, evaluate_dataset = dataset.split(ratio=0.85)\n",
+    "print(type(train_dataset), isinstance(train_dataset, DataSet))\n",
+    "\n",
+    "from fastNLP.io import DataBundle\n",
+    "\n",
+    "data_bundle = DataBundle(datasets={'train': train_dataset, 'dev': evaluate_dataset})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57a3272f",
+   "metadata": {},
+   "source": [
+    "## 1. fastNLP 结合 paddle 训练模型\n",
+    "\n",
+    "```python\n",
+    "import paddle\n",
+    "\n",
+    "lstm = paddle.nn.LSTM(16, 32, 2)\n",
+    "\n",
+    "x = paddle.randn((4, 23, 16))\n",
+    "h = paddle.randn((2, 4, 32))\n",
+    "c = paddle.randn((2, 4, 32))\n",
+    "\n",
+    "y, (h, c) = lstm(x, (h, c))\n",
+    "\n",
+    "print(y.shape)  # [4, 23, 32]\n",
+    "print(h.shape)  # [2, 4, 32]\n",
+    "print(c.shape)  # [2, 4, 32]\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "08752c5a",
+   "id": "e31b3198",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import paddle\n",
+    "import paddle.nn as nn\n",
+    "\n",
+    "\n",
+    "class ClsByPaddle(nn.Layer):\n",
+    "    def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5):\n",
+    "        nn.Layer.__init__(self)\n",
+    "        self.hidden_dim = hidden_dim\n",
+    "\n",
+    "        self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim)\n",
+    "        # self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_dim,  \n",
+    "        #                     num_layers=num_layers, direction='bidirectional', dropout=dropout)\n",
+    "        self.mlp = nn.Sequential(('linear_1', nn.Linear(hidden_dim * 2, hidden_dim * 2)),\n",
+    "                                 ('activate', nn.ReLU()),\n",
+    "                                 ('linear_2', nn.Linear(hidden_dim * 2, output_dim)))\n",
+    "        \n",
+    "        self.loss_fn = nn.CrossEntropyLoss()\n",
+    "\n",
+    "    def forward(self, words):\n",
+    "        output = self.embedding(words)\n",
+    "        # output, (hidden, cell) = self.lstm(output)\n",
+    "        hidden = paddle.randn((2, words.shape[0], self.hidden_dim))\n",
+    "        output = self.mlp(paddle.concat((hidden[-1], hidden[-2]), axis=1))\n",
+    "        return output\n",
+    "    \n",
+    "    def train_step(self, words, target):\n",
+    "        pred = self(words)\n",
+    "        return {\"loss\": self.loss_fn(pred, target)}\n",
+    "\n",
+    "    def evaluate_step(self, words, target):\n",
+    "        pred = self(words)\n",
+    "        pred = paddle.max(pred, axis=-1)[1]\n",
+    "        return {\"pred\": pred, \"target\": target}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c63b030f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ClsByPaddle(vocab_size=len(vocab), embedding_dim=100, output_dim=2)\n",
+    "\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2997c0aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from paddle.optimizer import AdamW\n",
+    "\n",
+    "optimizers = AdamW(parameters=model.parameters(), learning_rate=1e-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ead35fb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP import prepare_paddle_dataloader\n",
+    "\n",
+    "# train_dataloader = prepare_paddle_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
+    "# evaluate_dataloader = prepare_paddle_dataloader(evaluate_dataset, batch_size=16)\n",
+    "\n",
+    "dl_bundle = prepare_paddle_dataloader(data_bundle, batch_size=16, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25e8da83",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from fastNLP import Trainer, Accuracy\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    driver='paddle',\n",
+    "    device='gpu',                           # 'cpu', 'gpu', 'gpu:x'\n",
+    "    n_epochs=10,\n",
+    "    optimizers=optimizers,\n",
+    "    train_dataloader=dl_bundle['train'],    # train_dataloader,\n",
+    "    evaluate_dataloaders=dl_bundle['dev'],  # evaluate_dataloader,\n",
+    "    metrics={'acc': Accuracy()}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d63c5d74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.run(num_eval_batch_per_dl=10)  # 然后卡了？"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb9a0b3c",
+   "metadata": {},
+   "source": [
+    "## 2. fastNLP 结合 jittor 训练模型"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c600191d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jittor\n",
+    "import jittor.nn as nn\n",
+    "\n",
+    "from jittor import Module\n",
+    "\n",
+    "\n",
+    "class ClsByJittor(Module):\n",
+    "    def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5):\n",
+    "        Module.__init__(self)\n",
+    "        self.hidden_dim = hidden_dim\n",
+    "\n",
+    "        self.embedding = nn.Embedding(num=vocab_size, dim=embedding_dim)\n",
+    "        self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_dim,  \n",
+    "                            num_layers=num_layers, bidirectional=True, dropout=dropout)\n",
+    "        self.mlp = nn.Sequential([nn.Linear(hidden_dim * 2, hidden_dim * 2),\n",
+    "                                  nn.ReLU(),\n",
+    "                                  nn.Linear(hidden_dim * 2, output_dim)])\n",
+    "\n",
+    "        self.loss_fn = nn.BCELoss()\n",
+    "\n",
+    "    def execute(self, words):\n",
+    "        output = self.embedding(words)\n",
+    "        output, (hidden, cell) = self.lstm(output)\n",
+    "        # hidden = jittor.randn((2, words.shape[0], self.hidden_dim))\n",
+    "        output = self.mlp(jittor.concat((hidden[-1], hidden[-2]), axis=1))\n",
+    "        return output\n",
+    "    \n",
+    "    def train_step(self, words, target):\n",
+    "        pred = self(words)\n",
+    "        return {\"loss\": self.loss_fn(pred, target)}\n",
+    "\n",
+    "    def evaluate_step(self, words, target):\n",
+    "        pred = self(words)\n",
+    "        pred = jittor.max(pred, axis=-1)[1]\n",
+    "        return {\"pred\": pred, \"target\": target}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a94ed8c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ClsByJittor(vocab_size=len(vocab), embedding_dim=100, output_dim=2)\n",
+    "\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d15ebc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from jittor.optim import AdamW\n",
+    "\n",
+    "optimizers = AdamW(params=model.parameters(), lr=1e-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95d8d09e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP import prepare_jittor_dataloader\n",
+    "\n",
+    "# train_dataloader = prepare_jittor_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
+    "# evaluate_dataloader = prepare_jittor_dataloader(evaluate_dataset, batch_size=16)\n",
+    "\n",
+    "dl_bundle = prepare_jittor_dataloader(data_bundle, batch_size=16, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "917eab81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP import Trainer, Accuracy\n",
+    "\n",
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    driver='jittor',\n",
+    "    device='gpu',                           # 'cpu', 'gpu', 'cuda'\n",
+    "    n_epochs=10,\n",
+    "    optimizers=optimizers,\n",
+    "    train_dataloader=dl_bundle['train'],    # train_dataloader,\n",
+    "    evaluate_dataloaders=dl_bundle['dev'],  # evaluate_dataloader,\n",
+    "    metrics={'acc': Accuracy()}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7c4ac5a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer.run(num_eval_batch_per_dl=10)"
+   ]
   }
  ],
  "metadata": {