mirror of
https://gitee.com/fastnlp/fastNLP.git
synced 2024-11-29 18:59:01 +08:00
update example-12 lxr 220527
This commit is contained in:
lxr-tech
parent
49e8ae2daa
commit
19a48c7101
@ -233,7 +233,7 @@
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}
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],
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"source": [
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"num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n",
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"num_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\n",
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"\n",
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"model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
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"\n",
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@ -881,6 +881,15 @@
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.13"
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},
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"pycharm": {
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"stem_cell": {
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"cell_type": "raw",
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"metadata": {
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"collapsed": false
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},
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"source": []
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}
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}
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},
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"nbformat": 4,
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@ -4,7 +4,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# E2. 使用 PrefixTuning 完成 SST2 分类"
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"# E2. 使用 continuous prompt 完成 SST2 分类"
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]
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},
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{
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@ -35,10 +35,12 @@
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],
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"source": [
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"import torch\n",
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"import torch.nn as nn\n",
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"from torch.optim import AdamW\n",
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"from torch.utils.data import DataLoader, Dataset\n",
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"\n",
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"import torch.nn as nn\n",
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"from torch.nn.utils.rnn import pad_sequence\n",
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"\n",
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"import transformers\n",
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"from transformers import AutoTokenizer\n",
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"from transformers import AutoModelForSequenceClassification\n",
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@ -69,6 +71,136 @@
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"class PromptEncoder(nn.Module):\n",
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" def __init__(self, template, hidden_size):\n",
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" nn.Module.__init__(self)\n",
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" self.template = template\n",
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" self.hidden_size = hidden_size\n",
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" self.cloze_mask = [[1] * self.template[0] + [1] * self.template[1]]\n",
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" self.cloze_mask = torch.LongTensor(self.cloze_mask).bool()\n",
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"\n",
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" self.seq_indices = torch.LongTensor(list(range(len(self.cloze_mask[0]))))\n",
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" # embed\n",
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" self.embedding = torch.nn.Embedding(len(self.cloze_mask[0]), hidden_size)\n",
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" # LSTM\n",
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" self.lstm_head = torch.nn.LSTM(input_size=hidden_size,\n",
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" hidden_size=hidden_size // 2,\n",
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" num_layers=2, dropout=0.0,\n",
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" bidirectional=True, batch_first=True)\n",
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" # MLP\n",
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" self.mlp_head = nn.Sequential(nn.Linear(hidden_size, hidden_size),\n",
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" nn.ReLU(),\n",
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" nn.Linear(hidden_size, hidden_size))\n",
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" print(\"init prompt encoder...\")\n",
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"\n",
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" def forward(self, device):\n",
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" input_embeds = self.embedding(self.seq_indices.to(device)).unsqueeze(0)\n",
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" output_embeds = self.mlp_head(self.lstm_head(input_embeds)[0]).squeeze()\n",
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" return output_embeds"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"class ClassModel(nn.Module):\n",
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" def __init__(self, num_labels, model_checkpoint, pseudo_token='[PROMPT]', template=(3, 3)):\n",
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" nn.Module.__init__(self)\n",
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" self.template = template\n",
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" self.num_labels = num_labels\n",
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" self.spell_length = sum(template)\n",
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" self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n",
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" self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
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" num_labels=num_labels)\n",
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" for param in self.back_bone.parameters():\n",
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" param.requires_grad = False\n",
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" self.embeddings = self.back_bone.get_input_embeddings()\n",
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" \n",
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" self.hidden_size = self.embeddings.embedding_dim\n",
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" self.tokenizer.add_special_tokens({'additional_special_tokens': [pseudo_token]})\n",
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" self.pseudo_token_id = self.tokenizer.get_vocab()[pseudo_token]\n",
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" self.pad_token_id = self.tokenizer.pad_token_id\n",
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" \n",
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" self.prompt_encoder = PromptEncoder(self.template, self.hidden_size)\n",
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"\n",
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" self.loss_fn = nn.CrossEntropyLoss()\n",
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"\n",
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" def get_query(self, query):\n",
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" device = query.device\n",
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" return torch.cat([torch.tensor([self.tokenizer.cls_token_id]).to(device), # [CLS]\n",
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" torch.tensor([self.pseudo_token_id] * self.template[0]).to(device), # [PROMPT]\n",
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" torch.tensor([self.tokenizer.mask_token_id]).to(device), # [MASK] \n",
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" torch.tensor([self.pseudo_token_id] * self.template[1]).to(device), # [PROMPT]\n",
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" query, \n",
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" torch.tensor([self.tokenizer.sep_token_id]).to(device)], dim=0) # [SEP]\n",
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"\n",
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" def forward(self, input_ids):\n",
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" input_ids = torch.stack([self.get_query(input_ids[i]) for i in range(len(input_ids))])\n",
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" attention_mask = input_ids != self.pad_token_id\n",
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" \n",
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" bz = input_ids.shape[0]\n",
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" inputs_embeds = input_ids.clone()\n",
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" inputs_embeds[(input_ids == self.pseudo_token_id)] = self.tokenizer.unk_token_id\n",
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" inputs_embeds = self.embeddings(inputs_embeds)\n",
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"\n",
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" blocked_indices = (input_ids == self.pseudo_token_id).nonzero().reshape((bz, self.spell_length, 2))[:, :, 1] # bz\n",
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" replace_embeds = self.prompt_encoder(input_ids.device)\n",
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" for bidx in range(bz):\n",
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" for i in range(self.spell_length):\n",
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" inputs_embeds[bidx, blocked_indices[bidx, i], :] = replace_embeds[i, :]\n",
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" \n",
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" return self.back_bone(inputs_embeds=inputs_embeds, attention_mask=attention_mask)\n",
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"\n",
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" def train_step(self, input_ids, attention_mask, labels):\n",
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" pred = self(input_ids).logits\n",
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" return {\"loss\": self.loss_fn(pred, labels)}\n",
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"\n",
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" def evaluate_step(self, input_ids, attention_mask, labels):\n",
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" pred = self(input_ids).logits\n",
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" pred = torch.max(pred, dim=-1)[1]\n",
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" return {\"pred\": pred, \"target\": labels}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"- 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",
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"- 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",
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"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",
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"init prompt encoder...\n"
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]
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}
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],
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"source": [
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"num_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\n",
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"\n",
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"model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
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"\n",
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"optimizers = AdamW(params=model.parameters(), lr=5e-4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"scrolled": false
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},
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@ -77,14 +209,13 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "253d79d7a67e4dc88338448b5bcb3fb9",
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"model_id": "f82d2ccee863492582f94552654482f9",
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"version_major": 2,
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"version_minor": 0
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},
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@ -102,88 +233,57 @@
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"dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'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"
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]
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}
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],
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"source": [
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"tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n",
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"\n",
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"print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"task_to_keys = {\n",
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" \"cola\": (\"sentence\", None),\n",
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" \"mnli\": (\"premise\", \"hypothesis\"),\n",
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" \"mnli-mm\": (\"premise\", \"hypothesis\"),\n",
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" \"mrpc\": (\"sentence1\", \"sentence2\"),\n",
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" \"qnli\": (\"question\", \"sentence\"),\n",
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" \"qqp\": (\"question1\", \"question2\"),\n",
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" \"rte\": (\"sentence1\", \"sentence2\"),\n",
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" \"sst2\": (\"sentence\", None),\n",
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" \"stsb\": (\"sentence1\", \"sentence2\"),\n",
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" \"wnli\": (\"sentence1\", \"sentence2\"),\n",
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"}\n",
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"\n",
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"sentence1_key, sentence2_key = task_to_keys[task]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Sentence: hide new secretions from the parental units \n"
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]
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}
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],
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"source": [
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"if sentence2_key is None:\n",
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" print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\n",
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"else:\n",
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" print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\n",
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" print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ca1fbe5e8eb059f3.arrow\n",
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"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-03661263fbf302f5.arrow\n",
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"Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-fbe8e7a4e4f18f45.arrow\n"
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]
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "cf324902e7b94ea9be709b979b425c96",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/68 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "21eb6203ec6f4592b8cb8530a59eda49",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/1 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "05b83c4b1a9f44aea805788e1e52db78",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/2 [00:00<?, ?ba/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"def preprocess_function(examples):\n",
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" if sentence2_key is None:\n",
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" return tokenizer(examples[sentence1_key], truncation=True)\n",
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" return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\n",
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" return model.tokenizer(examples['sentence'], truncation=True)\n",
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"\n",
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"encoded_dataset = dataset.map(preprocess_function, batched=True)"
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]
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@ -193,58 +293,6 @@
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"class ClassModel(nn.Module):\n",
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" def __init__(self, num_labels, model_checkpoint):\n",
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" nn.Module.__init__(self)\n",
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" self.num_labels = num_labels\n",
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" self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
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" num_labels=num_labels)\n",
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" self.loss_fn = nn.CrossEntropyLoss()\n",
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"\n",
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" def forward(self, input_ids, attention_mask):\n",
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" return self.back_bone(input_ids, attention_mask)\n",
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"\n",
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" def train_step(self, input_ids, attention_mask, labels):\n",
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" pred = self(input_ids, attention_mask).logits\n",
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" return {\"loss\": self.loss_fn(pred, labels)}\n",
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"\n",
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" def evaluate_step(self, input_ids, attention_mask, labels):\n",
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" pred = self(input_ids, attention_mask).logits\n",
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" pred = torch.max(pred, dim=-1)[1]\n",
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" return {\"pred\": pred, \"target\": labels}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"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",
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"- 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",
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"- 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",
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"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",
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"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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]
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}
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],
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"source": [
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"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-5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class TestDistilBertDataset(Dataset):\n",
|
||||
" def __init__(self, dataset):\n",
|
||||
@ -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": {},
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||||
"outputs": [],
|
||||
"source": [
|
||||
@ -319,511 +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": [
|
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||||
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@ -856,6 +408,46 @@
|
||||
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|
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|
||||
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|
||||
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|
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|
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|
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@ -881,6 +473,15 @@
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},
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"nbformat": 4,
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|
||||
Loading…
Reference in New Issue
Block a user