finish tutorial-3456 lxr 220604

tutorials/fastnlp_tutorial_3.ipynb

@@ -17,7 +17,7 @@
     "\n",
     "    2.1   collator 的概念与使用\n",
     "\n",
-    "    2.2   sampler 的概念与使用"
+    "    2.2   结合 datasets 框架"
    ]
   },
   {
@@ -71,8 +71,8 @@
     "| `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",
+    "| `sampler` | √ | √ | 指定`dataloader`的`__len__`和`__iter__`函数的实现 | 默认`None` |\n",
+    "| `batch_sampler` | √ | √ | 指定`dataloader`的`__len__`和`__iter__`函数的实现 | 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",
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "aca72b49",
    "metadata": {
     "pycharm": {
@@ -103,6 +103,26 @@
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[38;5;2m[i 0604 15:44:29.773860 92 log.cc:351] Load log_sync: 1\u001b[m\n"
+     ]
+    },
+    {
+     "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"
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
@@ -149,14 +169,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+------------+------------------+-----------+------------------+--------------------+--------------------+\n",
-      "| SentenceId | Sentence         | Sentiment | input_ids        | token_type_ids     | attention_mask     |\n",
-      "+------------+------------------+-----------+------------------+--------------------+--------------------+\n",
-      "| 5          | A comedy-dram... | positive  | [101, 1037, 4... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... |\n",
-      "| 2          | This quiet , ... | positive  | [101, 2023, 4... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... |\n",
-      "| 1          | A series of e... | negative  | [101, 1037, 2... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... |\n",
-      "| 6          | The Importanc... | neutral   | [101, 1996, 5... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... |\n",
-      "+------------+------------------+-----------+------------------+--------------------+--------------------+\n"
+      "+------------+----------------+-----------+----------------+--------------------+--------------------+--------+\n",
+      "| SentenceId | Sentence       | Sentiment | input_ids      | token_type_ids     | attention_mask     | target |\n",
+      "+------------+----------------+-----------+----------------+--------------------+--------------------+--------+\n",
+      "| 1          | A series of... | negative  | [101, 1037,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 1      |\n",
+      "| 4          | A positivel... | neutral   | [101, 1037,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 2      |\n",
+      "| 3          | Even fans o... | negative  | [101, 2130,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 1      |\n",
+      "| 5          | A comedy-dr... | positive  | [101, 1037,... | [0, 0, 0, 0, 0,... | [1, 1, 1, 1, 1,... | 0      |\n",
+      "+------------+----------------+-----------+----------------+--------------------+--------------------+--------+\n"
      ]
     }
    ],
@@ -200,7 +220,9 @@
     "    \n",
     "pipe = PipeDemo(tokenizer='bert-base-uncased')\n",
     "\n",
-    "data_bundle = pipe.process_from_file('./data/test4dataset.tsv')"
+    "data_bundle = pipe.process_from_file('./data/test4dataset.tsv')\n",
+    "\n",
+    "print(data_bundle.get_dataset('train'))"
    ]
   },
   {
@@ -214,15 +236,65 @@
     "\n",
     "&emsp; 例如下方的`prepare_torch_dataloader`函数，指定必要参数，读取数据集，生成对应`dataloader`\n",
     "\n",
-    "&emsp; 类型为`TorchDataLoader`，只能适用于`pytorch`框架，因此对应`trainer`初始化时`driver='torch'`"
+    "&emsp; 类型为`TorchDataLoader`，只能适用于`pytorch`框架，因此对应`trainer`初始化时`driver='torch'`\n",
+    "\n",
+    "同时我们看还可以发现，在`fastNLP 0.8`中，**`batch`表示为字典`dict`类型**，**`key`值就是原先数据集中各个字段**\n",
+    "\n",
+    "&emsp; **除去经过`DataBundle.set_ignore`函数隐去的部分**，而`value`值为`pytorch`框架对应的`torch.Tensor`类型"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "id": "5fd60e42",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'fastNLP.core.dataloaders.torch_dataloader.fdl.TorchDataLoader'>\n",
+      "<class 'dict'> <class 'torch.Tensor'> ['input_ids', 'token_type_ids', 'attention_mask', 'target']\n",
+      "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
+      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "         1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]),\n",
+      " 'input_ids': tensor([[  101,  1037,  4038,  1011,  3689,  1997,  3053,  8680, 19173, 15685,\n",
+      "          1999,  1037, 18006,  2836,  2011,  1996,  2516,  2839, 14996,  3054,\n",
+      "         15509,  5325,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0],\n",
+      "        [  101,  1037,  2186,  1997,  9686, 17695, 18673, 14313,  1996, 15262,\n",
+      "          3351,  2008,  2054,  2003,  2204,  2005,  1996, 13020,  2003,  2036,\n",
+      "          2204,  2005,  1996, 25957,  4063,  1010,  2070,  1997,  2029,  5681,\n",
+      "          2572, 25581,  2021,  3904,  1997,  2029,  8310,  2000,  2172,  1997,\n",
+      "          1037,  2466,  1012,   102],\n",
+      "        [  101,  2130,  4599,  1997, 19214,  6432,  1005,  1055,  2147,  1010,\n",
+      "          1045,  8343,  1010,  2052,  2031,  1037,  2524,  2051,  3564,  2083,\n",
+      "          2023,  2028,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0],\n",
+      "        [  101,  1037, 13567, 26162,  5257,  1997,  3802,  7295,  9888,  1998,\n",
+      "          2035,  1996, 20014, 27611,  1010, 14583,  1010, 11703, 20175,  1998,\n",
+      "          4028,  1997,  1037,  8101,  2319, 10576,  2030,  1037, 28900,  7815,\n",
+      "          3850,  1012,   102,     0,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0]]),\n",
+      " 'target': tensor([0, 1, 1, 2]),\n",
+      " 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
+     ]
+    }
+   ],
    "source": [
     "from fastNLP import prepare_torch_dataloader\n",
     "\n",
@@ -230,28 +302,15 @@
     "evaluate_dataset = data_bundle.get_dataset('dev')\n",
     "\n",
     "train_dataloader = prepare_torch_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
-    "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c53f181",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "trainer = Trainer(\n",
-    "    model=model,\n",
-    "    train_dataloader=train_dataloader,\n",
-    "    optimizers=optimizer,\n",
-    "\t...\n",
-    "\tdriver='torch',\n",
-    "\tdevice='cuda',\n",
-    "\t...\n",
-    "    evaluate_dataloaders=evaluate_dataloader,     \n",
-    "    metrics={'acc': Accuracy()},\n",
-    "\t...\n",
-    ")\n",
-    "```"
+    "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)\n",
+    "\n",
+    "print(type(train_dataloader))\n",
+    "\n",
+    "import pprint\n",
+    "\n",
+    "for batch in train_dataloader:\n",
+    "    print(type(batch), type(batch['input_ids']), list(batch))\n",
+    "    pprint.pprint(batch, width=1)"
    ]
   },
   {
@@ -259,27 +318,33 @@
    "id": "9f457a6e",
    "metadata": {},
    "source": [
-    "之所以称`prepare_xx_dataloader`函数更方便，是因为其**导入对象不仅可也是`DataSet`类型**，**还可以**\n",
+    "之所以说`prepare_xx_dataloader`函数更方便，是因为其**导入对象不仅可也是`DataSet`类型**，**还可以**\n",
     "\n",
     "&emsp; **是`DataBundle`类型**，不过数据集名称需要是`'train'`、`'dev'`、`'test'`供`fastNLP`识别\n",
     "\n",
-    "&emsp; 例如下方就是**直接通过`prepare_paddle_dataloader`函数生成基于`PaddleDataLoader`的字典**\n",
-    "\n",
-    "&emsp; 在接下来`trainer`的初始化过程中，按如下方式使用即可，除了初始化时`driver='paddle'`外\n",
-    "\n",
-    "&emsp; &emsp; 这里也可以看出 **`evaluate_dataloaders`的妙处**，一次评测可以针对多个数据集"
+    "例如下方就是**直接通过`prepare_paddle_dataloader`函数生成基于`PaddleDataLoader`的字典**\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "7827557d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'fastNLP.core.dataloaders.paddle_dataloader.fdl.PaddleDataLoader'>\n"
+     ]
+    }
+   ],
    "source": [
     "from fastNLP import prepare_paddle_dataloader\n",
     "\n",
-    "dl_bundle = prepare_paddle_dataloader(data_bundle, batch_size=16, shuffle=True)"
+    "dl_bundle = prepare_paddle_dataloader(data_bundle, batch_size=16, shuffle=True)\n",
+    "\n",
+    "print(type(dl_bundle['train']))"
    ]
   },
   {
@@ -287,6 +352,10 @@
    "id": "d898cf40",
    "metadata": {},
    "source": [
+    "&emsp; 而在接下来`trainer`的初始化过程中，按如下方式使用即可，除了初始化时`driver='paddle'`外\n",
+    "\n",
+    "&emsp; 这里也可以看出`trainer`模块中，**`evaluate_dataloaders`的设计允许评测可以针对多个数据集**\n",
+    "\n",
     "```python\n",
     "trainer = Trainer(\n",
     "    model=model,\n",
@@ -312,31 +381,45 @@
     "\n",
     "### 2.1 collator 的概念与使用\n",
     "\n",
-    "在`fastNLP 0.8`中，在数据加载模块`DataLoader`之前，还存在其他的一些模块，负责例如对文本数据\n",
+    "在`fastNLP 0.8`中，在数据加载模块`dataloader`内部，如之前表格所列举的，还存在其他的一些模块\n",
     "\n",
-    "&emsp; 进行补零对齐，即 **核对器`collator`模块**，进行分词标注，即 **分词器`tokenizer`模块**\n",
+    "&emsp; 例如，**实现序列的补零对齐的核对器`collator`模块**；注：`collate vt. 整理（文件或书等）；核对，校勘`\n",
     "\n",
-    "&emsp; 本节将对`fastNLP`中的核对器`collator`等展开介绍，分词器`tokenizer`将在下一节中详细介绍\n",
+    "在`fastNLP 0.8`中，虽然`dataloader`随框架不同，但`collator`模块却是统一的，主要属性、方法如下表所示\n",
     "\n",
-    "在`fastNLP 0.8`中，**核对器`collator`模块负责文本序列的补零对齐**，通过"
+    "| <div align=\"center\">名称</div> | <div align=\"center\">属性</div> | <div align=\"center\">方法</div> | <div align=\"center\">功能</div> | <div align=\"center\">内容</div> |\n",
+    "|:--|:--:|:--:|:--|:--|\n",
+    "| `backend` | √ |  | 记录`collator`对应框架 | 字符串型，如`'torch'` |\n",
+    "| `padders` | √ |  | 记录各字段对应的`padder`，每个负责具体补零对齐&emsp; | 字典类型 |\n",
+    "| `ignore_fields` | √ |  | 记录`dataloader`采样`batch`时不予考虑的字段 | 集合类型 |\n",
+    "| `input_fields` | √ |  | 记录`collator`每个字段的补零值、数据类型等 | 字典类型 |\n",
+    "| `set_backend` |  | √ | 设置`collator`对应框架 | 字符串型，如`'torch'` |\n",
+    "| `set_ignore` |  | √ | 设置`dataloader`采样`batch`时不予考虑的字段 | 字符串型，表示`field_name`&emsp; |\n",
+    "| `set_pad` |  | √ | 设置`collator`每个字段的补零值、数据类型等 |  |"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "651baef6",
+   "execution_count": 4,
+   "id": "d0795b3e",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'function'>\n"
+     ]
+    }
+   ],
    "source": [
-    "from fastNLP import prepare_torch_dataloader\n",
+    "train_dataloader.collate_fn\n",
     "\n",
-    "dl_bundle = prepare_torch_dataloader(data_bundle, train_batch_size=2)\n",
-    "\n",
-    "print(type(dl_bundle), type(dl_bundle['train']))"
+    "print(type(train_dataloader.collate_fn))"
    ]
   },
   {
@@ -344,80 +427,165 @@
    "id": "5f816ef5",
    "metadata": {},
    "source": [
-    "&emsp; "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "726ba357",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "dataloader = prepare_torch_dataloader(datasets['train'], train_batch_size=2)\n",
-    "print(type(dataloader))\n",
-    "print(dir(dataloader))"
+    "此外，还可以**手动定义`dataloader`中的`collate_fn`**，而不是使用`fastNLP 0.8`中自带的`collator`模块\n",
+    "\n",
+    "&emsp; 该函数的定义可以大致如下，需要注意的是，**定义`collate_fn`之前需要了解`batch`作为字典的格式**\n",
+    "\n",
+    "&emsp; 该函数通过`collate_fn`参数传入`dataloader`，**在`batch`分发**（**而不是`batch`划分**）**时调用**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "d0795b3e",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "execution_count": 5,
+   "id": "ff8e405e",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "dataloader.collate_fn"
+    "import torch\n",
+    "\n",
+    "def collate_fn(batch):\n",
+    "    input_ids, atten_mask, labels = [], [], []\n",
+    "    max_length = [0] * 3\n",
+    "    for each_item in batch:\n",
+    "        input_ids.append(each_item['input_ids'])\n",
+    "        max_length[0] = max(len(each_item['input_ids']), max_length[0])\n",
+    "        atten_mask.append(each_item['token_type_ids'])\n",
+    "        max_length[1] = max(len(each_item['token_type_ids']), max_length[1])\n",
+    "        labels.append(each_item['attention_mask'])\n",
+    "        max_length[2] = max(len(each_item['attention_mask']), max_length[2])\n",
+    "\n",
+    "    for i in range(3):\n",
+    "        each = (input_ids, atten_mask, labels)[i]\n",
+    "        for item in each:\n",
+    "            item.extend([0] * (max_length[i] - len(item)))\n",
+    "    return {'input_ids': torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n",
+    "            'token_type_ids': torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n",
+    "            'attention_mask': torch.cat([torch.tensor(item) for item in labels], dim=0)}"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f9bbd9a7",
+   "id": "487b75fb",
    "metadata": {},
    "source": [
-    "### 2.2 sampler 的概念与使用"
+    "注意：使用自定义的`collate_fn`函数，`trainer`的`collate_fn`变量也会自动调整为`function`类型"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b0c3c58d",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
+   "execution_count": 6,
+   "id": "e916d1ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'fastNLP.core.dataloaders.torch_dataloader.fdl.TorchDataLoader'>\n",
+      "<class 'function'>\n",
+      "{'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,\n",
+      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+      "        0, 0, 0, 0, 0, 0, 0, 0]),\n",
+      " 'input_ids': tensor([[  101,  1037,  4038,  1011,  3689,  1997,  3053,  8680, 19173, 15685,\n",
+      "          1999,  1037, 18006,  2836,  2011,  1996,  2516,  2839, 14996,  3054,\n",
+      "         15509,  5325,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0],\n",
+      "        [  101,  1037,  2186,  1997,  9686, 17695, 18673, 14313,  1996, 15262,\n",
+      "          3351,  2008,  2054,  2003,  2204,  2005,  1996, 13020,  2003,  2036,\n",
+      "          2204,  2005,  1996, 25957,  4063,  1010,  2070,  1997,  2029,  5681,\n",
+      "          2572, 25581,  2021,  3904,  1997,  2029,  8310,  2000,  2172,  1997,\n",
+      "          1037,  2466,  1012,   102],\n",
+      "        [  101,  2130,  4599,  1997, 19214,  6432,  1005,  1055,  2147,  1010,\n",
+      "          1045,  8343,  1010,  2052,  2031,  1037,  2524,  2051,  3564,  2083,\n",
+      "          2023,  2028,  1012,   102,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0],\n",
+      "        [  101,  1037, 13567, 26162,  5257,  1997,  3802,  7295,  9888,  1998,\n",
+      "          2035,  1996, 20014, 27611,  1010, 14583,  1010, 11703, 20175,  1998,\n",
+      "          4028,  1997,  1037,  8101,  2319, 10576,  2030,  1037, 28900,  7815,\n",
+      "          3850,  1012,   102,     0,     0,     0,     0,     0,     0,     0,\n",
+      "             0,     0,     0,     0]]),\n",
+      " 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+      "        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}\n"
+     ]
     }
-   },
-   "outputs": [],
+   ],
    "source": [
-    "dataloader.batch_sampler"
+    "train_dataloader = prepare_torch_dataloader(train_dataset, collate_fn=collate_fn, shuffle=True)\n",
+    "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, collate_fn=collate_fn, shuffle=True)\n",
+    "\n",
+    "print(type(train_dataloader))\n",
+    "print(type(train_dataloader.collate_fn))\n",
+    "\n",
+    "for batch in train_dataloader:\n",
+    "    pprint.pprint(batch, width=1)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "51bf0878",
+   "id": "0bd98365",
    "metadata": {},
    "source": [
-    "&emsp; "
+    "### 2.2  fastNLP 与 datasets 的结合\n",
+    "\n",
+    "从`tutorial-1`至`tutorial-3`，我们已经完成了对`fastNLP v0.8`数据读取、预处理、加载，整个流程的介绍\n",
+    "\n",
+    "&emsp; 不过在实际使用中，我们往往也会采取更为简便的方法读取数据，例如使用`huggingface`的`datasets`模块\n",
+    "\n",
+    "**使用`datasets`模块中的`load_dataset`函数**，通过指定数据集两级的名称，示例中即是**`GLUE`标准中的`SST-2`数据集**\n",
+    "\n",
+    "&emsp; 即可以快速从网上下载好`SST-2`数据集读入，之后以`pandas.DataFrame`作为中介，再转化成`fastNLP.DataSet`\n",
+    "\n",
+    "&emsp; 之后的步骤就和其他关于`dataset`、`databundle`、`vocabulary`、`dataloader`中介绍的相关使用相同了"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "3fd2486f",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
+   "execution_count": 7,
+   "id": "91879c30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "639a0ad3c63944c6abef4e8ee1f7bf7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-   },
-   "outputs": [],
-   "source": []
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "sst2data = load_dataset('glue', 'sst2')\n",
+    "\n",
+    "dataset = DataSet.from_pandas(sst2data['train'].to_pandas())"
+   ]
   }
  ],
  "metadata": {

tutorials/fastnlp_tutorial_5.ipynb

@@ -296,7 +296,7 @@
     "\n",
     "    在`fastNLP v0.8`中，`update`函数的默认输入参数：`pred`，对应预测值；`target`，对应真实值\n",
     "\n",
-    "    此处刻意调整为：`pred`，对应预测值，和模型输出一致；`true`，对应真实值，数据集字段需要调整\n",
+    "    此处仍然沿用，因为接下来会需要使用`fastNLP`函数的与定义模型，其输入参数格式即使如此\n",
     "\n",
     "  在`get_metric`函数中，根据需求定义评测指标最终的计算，此处直接计算准确率，该函数必须返回一个字典\n",
     "\n",
@@ -307,10 +307,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "08a872e9",
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -320,16 +334,16 @@
     "class MyMetric(Metric):\n",
     "\n",
     "    def __init__(self):\n",
-    "        MyMetric.__init__(self)\n",
+    "        Metric.__init__(self)\n",
     "        self.total_num = 0\n",
     "        self.right_num = 0\n",
     "\n",
-    "    def update(self, pred, true):\n",
+    "    def update(self, pred, target):\n",
     "        self.total_num += target.size(0)\n",
     "        self.right_num += target.eq(pred).sum().item()\n",
     "\n",
     "    def get_metric(self, reset=True):\n",
-    "        acc = self.acc_count / self.total_num\n",
+    "        acc = self.right_num / self.total_num\n",
     "        if reset:\n",
     "            self.total_num = 0\n",
     "            self.right_num = 0\n",
@@ -346,14 +360,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "5ad81ac7",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef923b90b19847f4916cccda5d33fc36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from datasets import load_dataset\n",
     "\n",
@@ -365,30 +401,43 @@
    "id": "e9d81760",
    "metadata": {},
    "source": [
-    "&emsp; 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",
+    "&emsp; 在数据预处理中，需要注意的是，这里原本应该根据`metric`和`model`的输入参数格式，调整\n",
     "\n",
-    "&emsp; &emsp; 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`"
+    "&emsp; &emsp; 数据集中表示预测目标的字段，调整为`target`，在后文中会揭晓为什么，以及如何补救"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "cfb28b1b",
    "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%|          | 0/6000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from fastNLP import DataSet\n",
     "\n",
     "dataset = DataSet.from_pandas(sst2data['train'].to_pandas())[:6000]\n",
     "\n",
-    "dataset.apply_more(lambda ins:{'words': ins['sentence'].lower().split(), 'true': ins['label']}, \n",
-    "                   progress_bar=\"tqdm\")\n",
+    "dataset.apply_more(lambda ins:{'words': ins['sentence'].lower().split()}, progress_bar=\"tqdm\")\n",
     "dataset.delete_field('sentence')\n",
-    "dataset.delete_field('label')\n",
     "dataset.delete_field('idx')\n",
     "\n",
     "from fastNLP import Vocabulary\n",
@@ -415,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "2fd210c5",
    "metadata": {},
    "outputs": [],
@@ -445,10 +494,10 @@
     "| <div align=\"center\">名称</div> | <div align=\"center\">参数</div> | <div align=\"center\">属性</div> | <div align=\"center\">功能</div> | <div align=\"center\">内容</div> |\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",
+    "| **`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",
     "| `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",
@@ -473,12 +522,34 @@
     "| `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": "9e13ee08",
+   "metadata": {},
+   "source": [
+    "其中，**`input_mapping`和`output_mapping`** 定义形式如下：输入字典形式的数据，根据参数匹配要求\n",
+    "\n",
+    "&emsp; 调整数据格式，这里就回应了前文未在数据集预处理时调整格式的问题，**总之参数匹配一定要求**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "de96c1d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def input_mapping(data):\n",
+    "    data['target'] = data['label']\n",
+    "    return data"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "2fc8b9f3",
    "metadata": {},
    "source": [
-    "&emsp; 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",
+    "&emsp; 而`trainer`模块的基础方法列表如下，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",
     "| <div align=\"center\">名称</div> |<div align=\"center\">功能</div> | <div align=\"center\">主要参数</div> |\n",
     "|:--|:--|:--|\n",
@@ -539,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "926a9c50",
    "metadata": {},
    "outputs": [],
@@ -552,6 +623,7 @@
     "    device=0,  # 'cuda'\n",
     "    n_epochs=10,\n",
     "    optimizers=optimizers,\n",
+    "    input_mapping=input_mapping,\n",
     "    train_dataloader=train_dataloader,\n",
     "    evaluate_dataloaders=evaluate_dataloader,\n",
     "    metrics={'suffix': MyMetric()}\n",
@@ -580,14 +652,557 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "43be274f",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     }
    },
-   "outputs": [],
+   "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\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:30:35] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Running evaluator sanity check for <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> batches.              <a href=\"file://../fastNLP/core/controllers/trainer.py\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">trainer.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://../fastNLP/core/controllers/trainer.py#596\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">596</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[09:30:35]\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=954293;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=366534;file://../fastNLP/core/controllers/trainer.py#596\u001b\\\u001b[2m596\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": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+       "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+       ".get_parent()\n",
+       "  if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+       "output.py:111: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+       ".get_parent()\n",
+       "  if ip and hasattr(ip, 'kernel') and hasattr(ip.kernel, '_parent_header'):\n"
+      ]
+     },
+     "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\">/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+       "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+       ".get_parent()\n",
+       "  self.msg_id = ip.kernel._parent_header['header']['msg_id']\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "/remote-home/xrliu/anaconda3/envs/demo/lib/python3.7/site-packages/ipywidgets/widgets/widget_\n",
+       "output.py:112: DeprecationWarning: Kernel._parent_header is deprecated in ipykernel 6. Use \n",
+       ".get_parent()\n",
+       "  self.msg_id = ip.kernel._parent_header['header']['msg_id']\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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tutorials/fastnlp_tutorial_7.ipynb

@@ -1,59 +0,0 @@
-{
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-   "metadata": {},
-   "source": [
-    "# T7. callback 自定义训练过程\n",
-    "\n",
-    "  1   \n",
-    " \n",
-    "    1.1   \n",
-    "\n",
-    "    1.2   \n",
-    "\n",
-    "  2   \n",
-    "\n",
-    "    2.1   \n",
-    "\n",
-    "    2.2   \n",
-    "\n",
-    "  3   \n",
-    "\n",
-    "    3.1   \n",
-    "\n",
-    "    3.2   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08752c5a",
-   "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

tutorials/fastnlp_tutorial_8.ipynb

@@ -1,59 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fdd7ff16",
-   "metadata": {},
-   "source": [
-    "# T8. fastNLP 中的文件读取模块\n",
-    "\n",
-    "  1   fastNLP 中的 EmbedLoader 模块\n",
-    " \n",
-    "    1.1   \n",
-    "\n",
-    "    1.2   \n",
-    "\n",
-    "  2   fastNLP 中的 Loader 模块\n",
-    "\n",
-    "    2.1   \n",
-    "\n",
-    "    2.2   \n",
-    "\n",
-    "  3   fastNLP 中的 Pipe 模块\n",
-    "\n",
-    "    3.1   \n",
-    "\n",
-    "    3.2   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "08752c5a",
-   "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"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}