Add e2e migration tutorial of mindconverter.

mindinsight/mindconverter/tutorial/pytorch_bert_migration_tutorial.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "headed-output",
+   "metadata": {},
+   "source": [
+    "# PyTorch BERT迁移案例\n",
+    "PyTorch模型转换为MindSpore脚本+权重，首先需要将PyTorch模型导出为ONNX模型，然后使用MindConverter CLI工具进行脚本+权重迁移。\n",
+    "HuggingFace Transformers是PyTorch框架下主流的自然语言处理三方库，我们以Transformer中的BertForMaskedLM为例，演示迁移过程。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "sustained-touch",
+   "metadata": {},
+   "source": [
+    "## 1. ONNX模型导出\n",
+    "\n",
+    "首先实例化HuggingFace中的BertForMaskedLM，以及相应的分词器（首次使用需要下降模型权重、词表、模型配置等数据）。\n",
+    "\n",
+    "关于HuggingFace的使用，本文不做过多介绍，详细使用请参考[HuggingFace使用文档](https://huggingface.co/transformers/model_doc)。\n",
+    "\n",
+    "该模型可对句子中被掩蔽（mask）的词进行预测。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "interpreted-trunk",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "from transformers.models.bert import BertForMaskedLM, BertTokenizer\n",
+    "\n",
+    "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
+    "model = BertForMaskedLM.from_pretrained(\"bert-base-uncased\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bronze-authentication",
+   "metadata": {},
+   "source": [
+    "我们使用该模型进行推理，生成若干组测试用例，以验证模型迁移的正确性。\n",
+    "\n",
+    "这里我们以一条句子为例`china is a poworful country, its capital is beijing.`。\n",
+    "\n",
+    "我们对`beijing`进行掩蔽(mask)，输入`china is a poworful country, its capital is [MASK].`至模型，模型预期输出应为`beijing`。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "legendary-seven",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MASK TOKEN id: 12\n",
+      "Tokens: [[  101  2859  2003  1037 23776 16347  5313  2406  1010  2049  3007  2003\n",
+      "    103  1012   102]]\n",
+      "Attention mask: [[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]]\n",
+      "Token type ids: [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n",
+      "Pred id: 7211\n",
+      "Pred token: beijing\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = \"china is a poworful country, its capital is [MASK].\"\n",
+    "tokenized_sentence = tokenizer(text)\n",
+    "\n",
+    "mask_idx = tokenized_sentence[\"input_ids\"].index(tokenizer.convert_tokens_to_ids(\"[MASK]\"))\n",
+    "input_ids = np.array([tokenized_sentence[\"input_ids\"]])\n",
+    "attention_mask = np.array([tokenized_sentence[\"attention_mask\"]])\n",
+    "token_type_ids = np.array([tokenized_sentence[\"token_type_ids\"]])\n",
+    "\n",
+    "# Get [MASK] token id.\n",
+    "print(f\"MASK TOKEN id: {mask_idx}\")\n",
+    "print(f\"Tokens: {input_ids}\") \n",
+    "print(f\"Attention mask: {attention_mask}\")\n",
+    "print(f\"Token type ids: {token_type_ids}\")\n",
+    "\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    predictions = model(input_ids=torch.tensor(input_ids),\n",
+    "                        attention_mask=torch.tensor(attention_mask),\n",
+    "                        token_type_ids=torch.tensor(token_type_ids))\n",
+    "    predicted_index = torch.argmax(predictions[0][0][mask_idx])\n",
+    "    predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]\n",
+    "    print(f\"Pred id: {predicted_index}\")\n",
+    "    print(f\"Pred token: {predicted_token}\")\n",
+    "    assert predicted_token == \"beijing\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "opponent-validity",
+   "metadata": {},
+   "source": [
+    "HuggingFace提供了导出ONNX模型的工具，可使用如下方法将HuggingFace的预训练模型导出为ONNX模型："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ethical-radiation",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from transformers.convert_graph_to_onnx import convert\n",
+    "\n",
+    "\n",
+    "saved_onnx_path = \"./exported_bert_base_uncased/bert_base_uncased.onnx\"\n",
+    "convert(\"pt\", model, Path(saved_onnx_path), 11, tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "southeast-response",
+   "metadata": {},
+   "source": [
+    "根据打印的信息，我们可以看到导出的ONNX模型输入节点有3个：`input_ids`，`token_type_ids`，`attention_mask`，以及相应的输入轴，\n",
+    "输出节点有一个`output_0`。\n",
+    "\n",
+    "至此ONNX模型导出成功，接下来对导出的ONNX模型精度进行验证。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "historic-business",
+   "metadata": {},
+   "source": [
+    "## 2. ONNX模型验证\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "naval-virgin",
+   "metadata": {},
+   "source": [
+    "我们仍然使用PyTorch模型推理时的句子`china is a poworful country, its capital is [MASK].`作为输入，观测ONNX模型表现是否符合预期。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "satisfactory-embassy",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ONNX Pred id: 7211\n",
+      "ONNX Pred token: beijing\n"
+     ]
+    }
+   ],
+   "source": [
+    "import onnx\n",
+    "import onnxruntime as ort\n",
+    "\n",
+    "model = onnx.load(saved_onnx_path)\n",
+    "sess = ort.InferenceSession(bytes(model.SerializeToString()))\n",
+    "result = sess.run(\n",
+    "    output_names=None,\n",
+    "    input_feed={\"input_ids\": input_ids, \n",
+    "                \"attention_mask\": attention_mask,\n",
+    "                \"token_type_ids\": token_type_ids}\n",
+    ")[0]\n",
+    "predicted_index = np.argmax(result[0][mask_idx])\n",
+    "predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]\n",
+    "\n",
+    "print(f\"ONNX Pred id: {predicted_index}\")\n",
+    "print(f\"ONNX Pred token: {predicted_token}\")\n",
+    "assert predicted_token == \"beijing\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "legal-consensus",
+   "metadata": {},
+   "source": [
+    "可以看到，导出的ONNX模型功能与原PyTorch模型完全一致，接下来可以使用MindConverter进行脚本+权重迁移了！"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adverse-coverage",
+   "metadata": {},
+   "source": [
+    "## 3. MindConverter进行模型脚本+权重迁移"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "vanilla-nature",
+   "metadata": {},
+   "source": [
+    "MindConverter进行模型转换时，需要给定模型路径（`--model_file`）、输入节点（`--input_nodes`）、输入节点尺寸（`--shape`）、输出节点（`--output_nodes`）。\n",
+    "\n",
+    "生成的脚本输出路径（`--output`）、转换报告路径（`--report`）为可选参数，默认为当前路径下的output目录，若输出目录不存在将自动创建。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "metallic-wright",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mindconverter --help"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "horizontal-heater",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!mindconverter --model_file ./exported_bert_base_uncased/bert_base_uncased.onnx --shape 1,128 1,128 1,128  \\\n",
+    "               --input_nodes input_ids，token_type_ids，attention_mask\n",
+    "               --output_nodes output_0\n",
+    "               --output ./converted_bert_base_uncased\n",
+    "               --report ./converted_bert_base_uncased"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "blind-forty",
+   "metadata": {},
+   "source": [
+    "转换完成后，该目录下生成如下文件：\n",
+    "- 模型定义脚本（后缀为.py）\n",
+    "- 权重ckpt文件（后缀为.ckpt）\n",
+    "- 迁移前后权重映射（后缀为.json）\n",
+    "- 转换报告（后缀为.txt）\n",
+    "\n",
+    "通过ls命令检查一下转换结果。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "blocked-teens",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls ./converted_bert_base_uncased"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "improving-difference",
+   "metadata": {},
+   "source": [
+    "可以看到所有文件已生成。\n",
+    "\n",
+    "迁移完成，接下来我们对迁移后模型精度进行验证。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dimensional-driver",
+   "metadata": {},
+   "source": [
+    "## 4. MindSpore模型验证\n",
+    "我们仍然使用`china is a poworful country, its capital is [MASK].`作为输入，观测迁移后模型表现是否符合预期。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "unexpected-permit",
+   "metadata": {},
+   "source": [
+    "由于工具在转换时，需要将模型尺寸冻结，因此在使用MindSpore进行推理验证时，需要将句子补齐（Pad）到固定长度，可通过如下函数实现句子补齐。\n",
+    "\n",
+    "推理时，句子长度需小于转换时的最大句长（这里我们最长句子长度为128）。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "going-fields",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def padding(input_ids, attn_mask, token_type_ids, target_len=128):\n",
+    "    length = len(input_ids)\n",
+    "    for i in range(target_len - length):\n",
+    "        input_ids.append(0)\n",
+    "        attn_mask.append(0)\n",
+    "        token_type_ids.append(0)\n",
+    "    return np.array([input_ids]), np.array([attn_mask]), np.array([token_type_ids])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "intense-carrier",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from converted_bert_base_uncased.bert_base_uncased import Model as MsBert\n",
+    "from mindspore import load_checkpoint, load_param_into_net, context, Tensor\n",
+    "\n",
+    "\n",
+    "context.set_context(mode=context.GRAPH_MODE, device_target=\"GPU\")\n",
+    "padded_input_ids, padded_attention_mask, padded_token_type = padding(tokenized_sentence[\"input_ids\"], \n",
+    "                                                                     tokenized_sentence[\"attention_mask\"], \n",
+    "                                                                     tokenized_sentence[\"token_type_ids\"], \n",
+    "                                                                     target_len=128)\n",
+    "padded_input_ids = Tensor(padded_input_ids)\n",
+    "padded_attention_mask = Tensor(padded_attention_mask)\n",
+    "padded_token_type = Tensor(padded_token_type)\n",
+    "\n",
+    "model = MsBert()\n",
+    "param_dict = load_checkpoint(\"./converted_bert_base_uncased/bert_base_uncased.ckpt\")\n",
+    "not_load_params = load_param_into_net(model, param_dict)\n",
+    "output = model(padded_attention_mask, padded_input_ids, padded_token_type)\n",
+    "\n",
+    "assert not not_load_params\n",
+    "\n",
+    "predicted_index = np.argmax(output.asnumpy()[0][mask_idx])\n",
+    "print(f\"ONNX Pred id: {predicted_index}\")\n",
+    "assert predicted_index == 7211"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "national-norfolk",
+   "metadata": {},
+   "source": [
+    "至此，使用MindConverter进行脚本+权重迁移完成。\n",
+    "\n",
+    "用户可根据使用场景编写训练、推理、部署脚本，实现个人业务逻辑。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "capital-joint",
+   "metadata": {},
+   "source": [
+    "## 5. 其他问题"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "magnetic-collective",
+   "metadata": {},
+   "source": [
+    "1. 如何修改迁移后脚本的批次大小（Batch size）、句子长度（Sequence length），实现模型可支持任意的尺寸的数据推理、训练？\n",
+    "\n",
+    "> 答：迁移后脚本存在shape限制，通常是由于Reshape算子导致，或其他涉及张量排布变化的算子导致。以上述Bert迁移为例，首先创建两个全局变量，作为预期的批次大小、句子长度的表示，而后将Reshape操作的目标尺寸进行修改，相应的替换成批次大小、句子长度的全局变量即可。\n",
+    "> ./converted_bert_base_uncased/modified_bert_base_uncased.py为修改后的可支持任意尺寸数据训练、推理的脚本，该脚本脚本展示了相应的修改。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "proprietary-yugoslavia",
+   "metadata": {},
+   "source": [
+    "2. 生成后的脚本中类名的定义不符合开发者的习惯，如`class Module0(nn.Cell)`，人工修改是否会影响转换后的权重加载？\n",
+    "\n",
+    "> 答：权重的加载仅与变量名、类结构有关，因此类名可以修改，不影响权重加载。若需要调整类的结构，则相应的权重命名需要同步修改以适应迁移后模型的结构。"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "selective-flight",
+   "metadata": {},
+   "source": [
+    "## 6. 其他参考教程\n",
+    "1. [MindConverter高阶使用教程-自定义生成代码结构]()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}