From 19a48c7101d8c95ade89bdf09ad907ee13d7e78b Mon Sep 17 00:00:00 2001
From: lxr-tech <1838593642@qq.com>
Date: Fri, 27 May 2022 22:47:11 +0800
Subject: [PATCH] update example-12 lxr 220527

---
 tutorials/fastnlp_tutorial_e1.ipynb |  11 +-
 tutorials/fastnlp_tutorial_e2.ipynb | 815 +++++++---------------------
 2 files changed, 218 insertions(+), 608 deletions(-)

diff --git a/tutorials/fastnlp_tutorial_e1.ipynb b/tutorials/fastnlp_tutorial_e1.ipynb
index 92a49925..628dd7ae 100644
--- a/tutorials/fastnlp_tutorial_e1.ipynb
+++ b/tutorials/fastnlp_tutorial_e1.ipynb
@@ -233,7 +233,7 @@
     }
    ],
    "source": [
-    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n",
+    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\n",
     "\n",
     "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
     "\n",
@@ -881,6 +881,15 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.13"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorials/fastnlp_tutorial_e2.ipynb b/tutorials/fastnlp_tutorial_e2.ipynb
index 8e734f01..9185102f 100644
--- a/tutorials/fastnlp_tutorial_e2.ipynb
+++ b/tutorials/fastnlp_tutorial_e2.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# E2. 使用 PrefixTuning 完成 SST2 分类"
+    "# E2. 使用 continuous prompt 完成 SST2 分类"
    ]
   },
   {
@@ -35,10 +35,12 @@
    ],
    "source": [
     "import torch\n",
-    "import torch.nn as nn\n",
     "from torch.optim import AdamW\n",
     "from torch.utils.data import DataLoader, Dataset\n",
     "\n",
+    "import torch.nn as nn\n",
+    "from torch.nn.utils.rnn import pad_sequence\n",
+    "\n",
     "import transformers\n",
     "from transformers import AutoTokenizer\n",
     "from transformers import AutoModelForSequenceClassification\n",
@@ -69,180 +71,226 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using the latest cached version of the module from /remote-home/xrliu/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad (last modified on Thu May 26 15:30:15 2022) since it couldn't be found locally at glue., or remotely on the Hugging Face Hub.\n",
-      "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": "253d79d7a67e4dc88338448b5bcb3fb9",
-       "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, load_metric\n",
-    "\n",
-    "dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n",
+    "class PromptEncoder(nn.Module):\n",
+    "    def __init__(self, template, hidden_size):\n",
+    "        nn.Module.__init__(self)\n",
+    "        self.template = template\n",
+    "        self.hidden_size = hidden_size\n",
+    "        self.cloze_mask = [[1] * self.template[0] + [1] * self.template[1]]\n",
+    "        self.cloze_mask = torch.LongTensor(self.cloze_mask).bool()\n",
+    "\n",
+    "        self.seq_indices = torch.LongTensor(list(range(len(self.cloze_mask[0]))))\n",
+    "        # embed\n",
+    "        self.embedding = torch.nn.Embedding(len(self.cloze_mask[0]), hidden_size)\n",
+    "        # LSTM\n",
+    "        self.lstm_head = torch.nn.LSTM(input_size=hidden_size,\n",
+    "                                       hidden_size=hidden_size // 2,\n",
+    "                                       num_layers=2, dropout=0.0,\n",
+    "                                       bidirectional=True, batch_first=True)\n",
+    "        # MLP\n",
+    "        self.mlp_head = nn.Sequential(nn.Linear(hidden_size, hidden_size),\n",
+    "                                      nn.ReLU(),\n",
+    "                                      nn.Linear(hidden_size, hidden_size))\n",
+    "        print(\"init prompt encoder...\")\n",
     "\n",
-    "print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))"
+    "    def forward(self, device):\n",
+    "        input_embeds = self.embedding(self.seq_indices.to(device)).unsqueeze(0)\n",
+    "        output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0]).squeeze()\n",
+    "        return output_embeds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "task_to_keys = {\n",
-    "    \"cola\": (\"sentence\", None),\n",
-    "    \"mnli\": (\"premise\", \"hypothesis\"),\n",
-    "    \"mnli-mm\": (\"premise\", \"hypothesis\"),\n",
-    "    \"mrpc\": (\"sentence1\", \"sentence2\"),\n",
-    "    \"qnli\": (\"question\", \"sentence\"),\n",
-    "    \"qqp\": (\"question1\", \"question2\"),\n",
-    "    \"rte\": (\"sentence1\", \"sentence2\"),\n",
-    "    \"sst2\": (\"sentence\", None),\n",
-    "    \"stsb\": (\"sentence1\", \"sentence2\"),\n",
-    "    \"wnli\": (\"sentence1\", \"sentence2\"),\n",
-    "}\n",
+    "class ClassModel(nn.Module):\n",
+    "    def __init__(self, num_labels, model_checkpoint, pseudo_token='[PROMPT]', template=(3, 3)):\n",
+    "        nn.Module.__init__(self)\n",
+    "        self.template = template\n",
+    "        self.num_labels = num_labels\n",
+    "        self.spell_length = sum(template)\n",
+    "        self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n",
+    "        self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
+    "                                                                            num_labels=num_labels)\n",
+    "        for param in self.back_bone.parameters():\n",
+    "            param.requires_grad = False\n",
+    "        self.embeddings = self.back_bone.get_input_embeddings()\n",
+    "        \n",
+    "        self.hidden_size = self.embeddings.embedding_dim\n",
+    "        self.tokenizer.add_special_tokens({'additional_special_tokens': [pseudo_token]})\n",
+    "        self.pseudo_token_id = self.tokenizer.get_vocab()[pseudo_token]\n",
+    "        self.pad_token_id = self.tokenizer.pad_token_id\n",
+    "        \n",
+    "        self.prompt_encoder = PromptEncoder(self.template, self.hidden_size)\n",
+    "\n",
+    "        self.loss_fn = nn.CrossEntropyLoss()\n",
+    "\n",
+    "    def get_query(self, query):\n",
+    "        device = query.device\n",
+    "        return torch.cat([torch.tensor([self.tokenizer.cls_token_id]).to(device),               # [CLS]\n",
+    "                          torch.tensor([self.pseudo_token_id] * self.template[0]).to(device),   # [PROMPT]\n",
+    "                          torch.tensor([self.tokenizer.mask_token_id]).to(device),              # [MASK]  \n",
+    "                          torch.tensor([self.pseudo_token_id] * self.template[1]).to(device),   # [PROMPT]\n",
+    "                          query,   \n",
+    "                          torch.tensor([self.tokenizer.sep_token_id]).to(device)], dim=0)       # [SEP]\n",
+    "\n",
+    "    def forward(self, input_ids):\n",
+    "        input_ids = torch.stack([self.get_query(input_ids[i]) for i in range(len(input_ids))])\n",
+    "        attention_mask = input_ids != self.pad_token_id\n",
+    "        \n",
+    "        bz = input_ids.shape[0]\n",
+    "        inputs_embeds = input_ids.clone()\n",
+    "        inputs_embeds[(input_ids == self.pseudo_token_id)] = self.tokenizer.unk_token_id\n",
+    "        inputs_embeds = self.embeddings(inputs_embeds)\n",
+    "\n",
+    "        blocked_indices = (input_ids == self.pseudo_token_id).nonzero().reshape((bz, self.spell_length, 2))[:, :, 1]  # bz\n",
+    "        replace_embeds = self.prompt_encoder(input_ids.device)\n",
+    "        for bidx in range(bz):\n",
+    "            for i in range(self.spell_length):\n",
+    "                inputs_embeds[bidx, blocked_indices[bidx, i], :] = replace_embeds[i, :]\n",
+    "        \n",
+    "        return self.back_bone(inputs_embeds=inputs_embeds, attention_mask=attention_mask)\n",
     "\n",
-    "sentence1_key, sentence2_key = task_to_keys[task]"
+    "    def train_step(self, input_ids, attention_mask, labels):\n",
+    "        pred = self(input_ids).logits\n",
+    "        return {\"loss\": self.loss_fn(pred, labels)}\n",
+    "\n",
+    "    def evaluate_step(self, input_ids, attention_mask, labels):\n",
+    "        pred = self(input_ids).logits\n",
+    "        pred = torch.max(pred, dim=-1)[1]\n",
+    "        return {\"pred\": pred, \"target\": labels}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_transform.weight']\n",
+      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.weight', 'pre_classifier.weight', 'classifier.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sentence: hide new secretions from the parental units \n"
+      "init prompt encoder...\n"
      ]
     }
    ],
    "source": [
-    "if sentence2_key is None:\n",
-    "    print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\n",
-    "else:\n",
-    "    print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\n",
-    "    print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")"
+    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\n",
+    "\n",
+    "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
+    "\n",
+    "optimizers = AdamW(params=model.parameters(), lr=5e-4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ca1fbe5e8eb059f3.arrow\n",
-      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-03661263fbf302f5.arrow\n",
-      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-fbe8e7a4e4f18f45.arrow\n"
+      "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": "f82d2ccee863492582f94552654482f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "def preprocess_function(examples):\n",
-    "    if sentence2_key is None:\n",
-    "        return tokenizer(examples[sentence1_key], truncation=True)\n",
-    "    return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\n",
-    "\n",
-    "encoded_dataset = dataset.map(preprocess_function, batched=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ClassModel(nn.Module):\n",
-    "    def __init__(self, num_labels, model_checkpoint):\n",
-    "        nn.Module.__init__(self)\n",
-    "        self.num_labels = num_labels\n",
-    "        self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
-    "                                                                            num_labels=num_labels)\n",
-    "        self.loss_fn = nn.CrossEntropyLoss()\n",
-    "\n",
-    "    def forward(self, input_ids, attention_mask):\n",
-    "        return self.back_bone(input_ids, attention_mask)\n",
-    "\n",
-    "    def train_step(self, input_ids, attention_mask, labels):\n",
-    "        pred = self(input_ids, attention_mask).logits\n",
-    "        return {\"loss\": self.loss_fn(pred, labels)}\n",
+    "from datasets import load_dataset, load_metric\n",
     "\n",
-    "    def evaluate_step(self, input_ids, attention_mask, labels):\n",
-    "        pred = self(input_ids, attention_mask).logits\n",
-    "        pred = torch.max(pred, dim=-1)[1]\n",
-    "        return {\"pred\": pred, \"target\": labels}"
+    "dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight']\n",
-      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf324902e7b94ea9be709b979b425c96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/68 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "21eb6203ec6f4592b8cb8530a59eda49",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05b83c4b1a9f44aea805788e1e52db78",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n",
-    "\n",
-    "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
+    "def preprocess_function(examples):\n",
+    "    return model.tokenizer(examples['sentence'], truncation=True)\n",
     "\n",
-    "optimizers = AdamW(params=model.parameters(), lr=5e-5)"
+    "encoded_dataset = dataset.map(preprocess_function, batched=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,54 +367,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# help(model.back_bone.forward)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-      ]
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-       "Output()"
-      ]
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-     "output_type": "display_data"
-    },
-    {
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-       "  <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"correct#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">284.0</span>\n",
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
-       "</pre>\n"
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-      "text/plain": [
-       "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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-       "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\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\">0.890625</span>,\n",
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-       "\u001b[1m}\u001b[0m\n"
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-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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-      "text/plain": []
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "trainer.run(num_eval_batch_per_dl=10)"
+    "trainer.evaluator.run()"
    ]
   },
   {
@@ -881,6 +473,15 @@
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  "nbformat": 4,