{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a825ba6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import os\n",
    "\n",
    "import torch\n",
    "from torch.optim import AdamW\n",
    "from torch.utils.data import DataLoader\n",
    "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, PeftType, \\\n",
    "PrefixTuningConfig, PromptEncoderConfig\n",
    "\n",
    "import evaluate\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2bd7cbb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "model_name_or_path = \"roberta-large\"\n",
    "task = \"mrpc\"\n",
    "peft_type = PeftType.P_TUNING\n",
    "device = \"cuda\"\n",
    "num_epochs = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "33d9b62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "peft_config = PromptEncoderConfig(\n",
    "    task_type=\"SEQ_CLS\",\n",
    "    num_virtual_tokens=20,\n",
    "    encoder_hidden_size=128\n",
    ")\n",
    "lr = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "152b6177",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset glue (/home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2e4639741674cd199ba4cd553c3fabe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-121b991f592093a4.arrow\n",
      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-6c55b9fb8fbb12c7.arrow\n",
      "Loading cached processed dataset at /home/sourab/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-b9740d82185f93e5.arrow\n"
     ]
    }
   ],
   "source": [
    "if any(k in model_name_or_path for k in (\"gpt\", \"opt\", \"bloom\")):\n",
    "    padding_side = \"left\"\n",
    "else:\n",
    "    padding_side = \"right\"\n",
    "    \n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, padding_side=padding_side)\n",
    "if getattr(tokenizer, \"pad_token_id\") is None:\n",
    "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
    "    \n",
    "datasets = load_dataset(\"glue\", task)\n",
    "metric = evaluate.load(\"glue\", task)\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    # max_length=None => use the model max length (it's actually the default)\n",
    "    outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n",
    "    return outputs\n",
    "\n",
    "tokenized_datasets = datasets.map(\n",
    "    tokenize_function,\n",
    "    batched=True,\n",
    "    remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n",
    ")\n",
    "\n",
    "# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n",
    "# transformers library\n",
    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
    "\n",
    "def collate_fn(examples):\n",
    "    return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n",
    "\n",
    "# Instantiate dataloaders.\n",
    "train_dataloader = DataLoader(\n",
    "    tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size\n",
    ")\n",
    "eval_dataloader = DataLoader(\n",
    "    tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=batch_size\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6bc8144",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at roberta-large were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'lm_head.dense.bias', 'lm_head.dense.weight', 'roberta.pooler.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.weight', 'lm_head.layer_norm.bias', 'lm_head.decoder.weight']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification 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 RobertaForSequenceClassification 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 RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-large and are newly initialized: ['classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.dense.weight']\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": [
      "trainable params: 1351938 || all params: 355662082 || trainable%: 0.38011867680626127\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PETModelForSequenceClassification(\n",
       "  (base_model): RobertaForSequenceClassification(\n",
       "    (roberta): RobertaModel(\n",
       "      (embeddings): RobertaEmbeddings(\n",
       "        (word_embeddings): Embedding(50265, 1024, padding_idx=1)\n",
       "        (position_embeddings): Embedding(514, 1024, padding_idx=1)\n",
       "        (token_type_embeddings): Embedding(1, 1024)\n",
       "        (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout): Dropout(p=0.1, inplace=False)\n",
       "      )\n",
       "      (encoder): RobertaEncoder(\n",
       "        (layer): ModuleList(\n",
       "          (0): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (1): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (2): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (3): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (4): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (5): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (6): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (7): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (8): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (9): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (10): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (11): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (12): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (13): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (14): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (15): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (16): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (17): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (18): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (19): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (20): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (21): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (22): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (23): RobertaLayer(\n",
       "            (attention): RobertaAttention(\n",
       "              (self): RobertaSelfAttention(\n",
       "                (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "              (output): RobertaSelfOutput(\n",
       "                (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "                (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "                (dropout): Dropout(p=0.1, inplace=False)\n",
       "              )\n",
       "            )\n",
       "            (intermediate): RobertaIntermediate(\n",
       "              (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "              (intermediate_act_fn): GELUActivation()\n",
       "            )\n",
       "            (output): RobertaOutput(\n",
       "              (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (classifier): RobertaClassificationHead(\n",
       "      (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "      (out_proj): Linear(in_features=1024, out_features=2, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (word_embeddings): Embedding(50265, 1024, padding_idx=1)\n",
       "  (prompt_encoder): PromptEncoder(\n",
       "    (embedding): Embedding(20, 1024)\n",
       "    (mlp_head): Sequential(\n",
       "      (0): Linear(in_features=1024, out_features=128, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): Linear(in_features=128, out_features=128, bias=True)\n",
       "      (3): ReLU()\n",
       "      (4): Linear(in_features=128, out_features=1024, bias=True)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, return_dict=True)\n",
    "model = get_peft_model(model, peft_config)\n",
    "model.print_trainable_parameters()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af41c571",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = AdamW(params=model.parameters(), lr=lr)\n",
    "\n",
    "# Instantiate scheduler\n",
    "lr_scheduler = get_linear_schedule_with_warmup(\n",
    "    optimizer=optimizer,\n",
    "    num_warmup_steps=0,#0.06*(len(train_dataloader) * num_epochs),\n",
    "    num_training_steps=(len(train_dataloader) * num_epochs),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90993c93",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                       | 0/115 [00:00<?, ?it/s]You're using a RobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.62it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1: {'accuracy': 0.6911764705882353, 'f1': 0.7993630573248407}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 2: {'accuracy': 0.7230392156862745, 'f1': 0.8138385502471169}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.62it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 3: {'accuracy': 0.6985294117647058, 'f1': 0.8177777777777778}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.63it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 4: {'accuracy': 0.6862745098039216, 'f1': 0.8128654970760235}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████| 115/115 [00:31<00:00,  3.62it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 5: {'accuracy': 0.7034313725490197, 'f1': 0.7986688851913478}\n"
     ]
    },
    {
     "name": "stderr",
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    },
    {
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     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model.to(device)\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "    for step, batch in enumerate(tqdm(train_dataloader)):\n",
    "        batch.to(device)\n",
    "        outputs = model(**batch)\n",
    "        loss = outputs.loss\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        lr_scheduler.step()\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "    model.eval()\n",
    "    for step, batch in enumerate(tqdm(eval_dataloader)):\n",
    "        batch.to(device)\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**batch)\n",
    "        predictions = outputs.logits.argmax(dim=-1)\n",
    "        predictions, references = predictions, batch[\"labels\"]\n",
    "        metric.add_batch(\n",
    "            predictions=predictions,\n",
    "            references=references,\n",
    "        )\n",
    "\n",
    "    eval_metric = metric.compute()\n",
    "    print(f\"epoch {epoch}:\", eval_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afaf42dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "234abda3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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