MyBert.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "31223074-8217-4173-b148-61f548444cdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taken from https://medium.com/data-and-beyond/complete-guide-to-building-bert-model-from-sratch-3e6562228891\n",
    "import os\n",
    "from pathlib import Path\n",
    "import torch\n",
    "import re\n",
    "import random\n",
    "from tokenizers import BertWordPieceTokenizer\n",
    "import tqdm\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import itertools\n",
    "import math\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "from torch.optim import Adam\n",
    "import datasets\n",
    "import transformers\n",
    "from transformers import BertTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86b6c6df-9342-4b74-be52-e31d8a655a26",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_LEN = 64\n",
    "\n",
    "### loading all data into memory\n",
    "corpus_movie_conv = './datasets/movie_conversations.txt'\n",
    "corpus_movie_lines = './datasets/movie_lines.txt'\n",
    "with open(corpus_movie_conv, 'r', encoding='iso-8859-1') as c:\n",
    "    conv = c.readlines() \n",
    "with open(corpus_movie_lines, 'r', encoding='iso-8859-1') as l:\n",
    "    lines = l.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10d81ae1-639b-4caa-af83-f8728d12f0a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('L1045', 'They do not!\\n'),\n",
       " ('L1044', 'They do to!\\n'),\n",
       " ('L985', 'I hope so.\\n'),\n",
       " ('L984', 'She okay?\\n')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### splitting text using special lines\n",
    "lines_dic = {}\n",
    "for line in lines:\n",
    "    objects = line.split(\" +++$+++ \")\n",
    "    lines_dic[objects[0]] = objects[-1]\n",
    "list(lines_dic.items())[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3a53b24-cea4-49e5-b37e-dbc507bcd312",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "221616\n",
      "[['Can we make this quick? Roxanne Korrine and Andrew Barrett are having an incredibly horrendous public break- up on the quad. Again.', \"Well, I thought we'd start with pronunciation, if that's okay with you.\"], [\"Well, I thought we'd start with pronunciation, if that's okay with you.\", 'Not the hacking and gagging and spitting part. Please.'], ['Not the hacking and gagging and spitting part. Please.', \"Okay... then how 'bout we try out some French cuisine. Saturday? Night?\"]]\n"
     ]
    }
   ],
   "source": [
    "### generate question answer pairs\n",
    "pairs = []\n",
    "for con in conv:\n",
    "    ids = eval(con.split(\" +++$+++ \")[-1])\n",
    "    for i in range(len(ids)):\n",
    "        qa_pairs = []\n",
    "        \n",
    "        if i == len(ids) - 1:\n",
    "            break\n",
    "\n",
    "        first = lines_dic[ids[i]].strip()  \n",
    "        second = lines_dic[ids[i+1]].strip() \n",
    "\n",
    "        qa_pairs.append(' '.join(first.split()[:MAX_LEN]))\n",
    "        qa_pairs.append(' '.join(second.split()[:MAX_LEN]))\n",
    "        pairs.append(qa_pairs)\n",
    "print(len(pairs))\n",
    "print(pairs[0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17cef724-b901-44c7-95d1-e2dffdd49288",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 221616/221616 [00:00<00:00, 2307967.99it/s]\n"
     ]
    }
   ],
   "source": [
    "### save data as txt file\n",
    "os.mkdir('./data')\n",
    "text_data = []\n",
    "file_count = 0\n",
    "\n",
    "for sample in tqdm.tqdm([x[0] for x in pairs]):\n",
    "    text_data.append(sample)\n",
    "\n",
    "    # once we hit the 10K mark, save to file\n",
    "    if len(text_data) == 10000:\n",
    "        with open(f'./data/text_{file_count}.txt', 'w', encoding='utf-8') as fp:\n",
    "            fp.write('\\n'.join(text_data))\n",
    "        text_data = []\n",
    "        file_count += 1\n",
    "\n",
    "paths = [str(x) for x in Path('./data').glob('**/*.txt')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b12adbee-5cf7-4497-bc3d-5266c7cc48e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shanmugamr/python_tests/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1930: FutureWarning: Calling BertTokenizer.from_pretrained() with the path to a single file or url is deprecated and won't be possible anymore in v5. Use a model identifier or the path to a directory instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "### training own tokenizer\n",
    "tokenizer = BertWordPieceTokenizer(\n",
    "    clean_text=True,\n",
    "    handle_chinese_chars=False,\n",
    "    strip_accents=False,\n",
    "    lowercase=True\n",
    ")\n",
    "\n",
    "tokenizer.train( \n",
    "    files=paths,\n",
    "    vocab_size=30_000, \n",
    "    min_frequency=5,\n",
    "    limit_alphabet=1000, \n",
    "    wordpieces_prefix='##',\n",
    "    special_tokens=['[PAD]', '[CLS]', '[SEP]', '[MASK]', '[UNK]']\n",
    "    )\n",
    "\n",
    "os.mkdir('./bert-it-1')\n",
    "tokenizer.save_model('./bert-it-1', 'bert-it')\n",
    "tokenizer = BertTokenizer.from_pretrained('./bert-it-1/bert-it-vocab.txt', local_files_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8983d001-0acc-4daa-89d8-c1b33e825f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BERTDataset(Dataset):\n",
    "    def __init__(self, data_pair, tokenizer, seq_len=64):\n",
    "\n",
    "        self.tokenizer = tokenizer\n",
    "        self.seq_len = seq_len\n",
    "        self.corpus_lines = len(data_pair)\n",
    "        self.lines = data_pair\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.corpus_lines\n",
    "\n",
    "    def __getitem__(self, item):\n",
    "\n",
    "        # Step 1: get random sentence pair, either negative or positive (saved as is_next_label)\n",
    "        t1, t2, is_next_label = self.get_sent(item)    \n",
    "\n",
    "        # Step 2: replace random words in sentence with mask / random words\n",
    "        t1_random, t1_label = self.random_word(t1)\n",
    "        t2_random, t2_label = self.random_word(t2)\n",
    "\n",
    "        # Step 3: Adding CLS and SEP tokens to the start and end of sentences\n",
    "         # Adding PAD token for labels\n",
    "        t1 = [self.tokenizer.vocab['[CLS]']] + t1_random + [self.tokenizer.vocab['[SEP]']]\n",
    "\n",
    "        t2 = t2_random + [self.tokenizer.vocab['[SEP]']]\n",
    "        t1_label = [self.tokenizer.vocab['[PAD]']] + t1_label + [self.tokenizer.vocab['[PAD]']]\n",
    "\n",
    "        t2_label = t2_label + [self.tokenizer.vocab['[PAD]']]\n",
    "\n",
    "        # Step 4: combine sentence 1 and 2 as one input\n",
    "        # adding PAD tokens to make the sentence same length as seq_len\n",
    "        segment_label = ([1 for _ in range(len(t1))] + [2 for _ in range(len(t2))])[:self.seq_len]\n",
    "        bert_input = (t1 + t2)[:self.seq_len]\n",
    "        bert_label = (t1_label + t2_label)[:self.seq_len]\n",
    "        padding = [self.tokenizer.vocab['[PAD]'] for _ in range(self.seq_len - len(bert_input))]\n",
    "        bert_input.extend(padding), bert_label.extend(padding), segment_label.extend(padding)\n",
    "\n",
    "        output = {\"bert_input\": bert_input,\n",
    "                  \"bert_label\": bert_label,\n",
    "                  \"segment_label\": segment_label,\n",
    "                  \"is_next\": is_next_label}\n",
    "\n",
    "        return {key: torch.tensor(value) for key, value in output.items()}\n",
    "\n",
    "    def random_word(self, sentence):\n",
    "        tokens = sentence.split()\n",
    "        output_label = []\n",
    "        output = []\n",
    "\n",
    "        # 15% of the tokens would be replaced\n",
    "        for i, token in enumerate(tokens):\n",
    "            prob = random.random()\n",
    "\n",
    "            # remove cls and sep token\n",
    "            token_id = self.tokenizer(token)['input_ids'][1:-1]\n",
    "            \n",
    "            if prob < 0.15:\n",
    "                prob /= 0.15\n",
    "\n",
    "                # 80% chance change token to mask token\n",
    "                if prob < 0.8:\n",
    "                    for i in range(len(token_id)):\n",
    "                        output.append(self.tokenizer.vocab['[MASK]'])\n",
    "\n",
    "                # 10% chance change token to random token\n",
    "                elif prob < 0.9:\n",
    "                    for i in range(len(token_id)):\n",
    "                        output.append(random.randrange(len(self.tokenizer.vocab)))\n",
    "\n",
    "                # 10% chance change token to current token\n",
    "                else:\n",
    "                    output.append(token_id)\n",
    "\n",
    "                output_label.append(token_id)\n",
    "\n",
    "            else:\n",
    "                output.append(token_id)\n",
    "                for i in range(len(token_id)):\n",
    "                    output_label.append(0)\n",
    "\n",
    "        # flattening\n",
    "        output = list(itertools.chain(*[[x] if not isinstance(x, list) else x for x in output]))\n",
    "        output_label = list(itertools.chain(*[[x] if not isinstance(x, list) else x for x in output_label]))\n",
    "        assert len(output) == len(output_label)\n",
    "        return output, output_label\n",
    "\n",
    "    def get_sent(self, index):\n",
    "        '''return random sentence pair'''\n",
    "        t1, t2 = self.get_corpus_line(index)\n",
    "\n",
    "        # negative or positive pair, for next sentence prediction\n",
    "        if random.random() > 0.5:\n",
    "            return t1, t2, 1\n",
    "        else:\n",
    "            return t1, self.get_random_line(), 0\n",
    "\n",
    "    def get_corpus_line(self, item):\n",
    "        '''return sentence pair'''\n",
    "        return self.lines[item][0], self.lines[item][1]\n",
    "\n",
    "    def get_random_line(self):\n",
    "        '''return random single sentence'''\n",
    "        return self.lines[random.randrange(len(self.lines))][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79fff861-de63-41b3-9869-92f552a9ec57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bert_input': tensor([  1, 580,  17,   2,   3, 146,  15, 592,  17,   2,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0]), 'bert_label': tensor([  0,   0,   0,   0, 419,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "          0,   0,   0,   0,   0,   0,   0,   0]), 'segment_label': tensor([1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'is_next': tensor(0)}\n",
      "[CLS] hey. [SEP] [MASK] you, kid. [SEP] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD] [PAD]\n"
     ]
    }
   ],
   "source": [
    "train_data = BERTDataset(\n",
    "   pairs, seq_len=MAX_LEN, tokenizer=tokenizer)\n",
    "train_loader = DataLoader(\n",
    "   train_data, batch_size=32, shuffle=True, pin_memory=True)\n",
    "print_sample = train_data[random.randrange(len(train_data))]\n",
    "print(print_sample)\n",
    "print(tokenizer.decode(print_sample['bert_input']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2894ed19-477e-4b76-8a80-4880651408d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bert_input': tensor([    1,   230,   184,   432,   208,  1712,    34,   529,   119, 13336,\n",
       "             3,     3,     3,     3, 12824,   234,  1135,   160,  6991,  4996,\n",
       "         19495,   313,  1993,  1023,    16,     3,   192,   150,  8110,    17,\n",
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       "            11,    58,   459,   231,   146,    17,     2,     0,     0,     0,\n",
       "             0,     0,     0,     0]),\n",
       " 'bert_label': tensor([    0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "          8254, 10999,   179,  5189,     0,     0,     0,     0,     0,     0,\n",
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       "             0,     0,     0,     0,     0,     0,     0,     0,     0,     0,\n",
       "             0,     0,     0,     0]),\n",
       " 'segment_label': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "         2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0]),\n",
       " 'is_next': tensor(1)}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "id": "9ee5fa07-0e9d-4e19-9f06-daf4e1621f4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn\n",
    "class TokenTypelEmbeddings(nn.Module):\n",
    "    def __init__(self, num_token_types = 3, embedding_dim = 768):\n",
    "        super().__init__()\n",
    "        self.token_type_embeddings = nn.Embedding(num_token_types, embedding_dim, padding_idx=0) # TokenizEr pad id\n",
    "\n",
    "    def forward(self, input):\n",
    "        return self.token_type_embeddings(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "id": "3c024f49-675c-4209-abb0-94214b7c0c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "class WordEmbeddings(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim = 768):\n",
    "        super().__init__()\n",
    "        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)\n",
    "\n",
    "    def forward(self, input):\n",
    "        return self.word_embeddings(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "id": "ffa67f7a-12c0-4dd1-94d0-f56ba70147c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEmbeddings(nn.Module):\n",
    "    def __init__(self, seq_len, embedding_dim = 768):\n",
    "        super().__init__()\n",
    "        self.seq_len = seq_len\n",
    "        positional_embedding = torch.zeros((seq_len, embedding_dim))\n",
    "        positional_embedding.require_grad = False\n",
    "        \n",
    "        for pos in range(seq_len):\n",
    "            for i in range(0, embedding_dim, 2):\n",
    "                positional_embedding[pos, i] = math.sin(pos / (10000 ** ((2 * i)/embedding_dim)))\n",
    "                positional_embedding[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/embedding_dim)))\n",
    "            \n",
    "        self.positional_embeddings = positional_embedding.unsqueeze(0)\n",
    "\n",
    "    def forward(self, input):\n",
    "        return self.positional_embeddings\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "id": "72aea756-7dc8-4f97-9a5e-21130684be97",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BertEmbeddingLayer(nn.Module):\n",
    "    \n",
    "    def __init__(self, vocab_size, seq_len = 64, embedding_dim = 768, num_token_types = 3, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.positional_embeddings = PositionalEmbeddings(seq_len, embedding_dim)\n",
    "        self.token_type_embeddings = TokenTypelEmbeddings(num_token_types, embedding_dim)\n",
    "        self.word_embeddings = WordEmbeddings(vocab_size, embedding_dim)\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, input_tokens, input_token_types):\n",
    "        x = self.positional_embeddings(input_tokens) + self.word_embeddings(input_tokens) + self.token_type_embeddings(input_token_types)\n",
    "        return self.dropout(x)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "id": "a57219bb-e9fa-4e7c-ab16-02ebaa5d29cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Projection(nn.Module):\n",
    "    def __init__(self, embedding_dim = 768):\n",
    "        super().__init__()\n",
    "        self.queries = nn.Linear(embedding_dim, embedding_dim)\n",
    "        self.keys = nn.Linear(embedding_dim, embedding_dim)\n",
    "        self.values = nn.Linear(embedding_dim, embedding_dim)\n",
    "\n",
    "    def forward(self, input_embeddings):\n",
    "        return self.queries(input_embeddings), self.keys(input_embeddings), self.values(input_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "id": "39a5203d-3a68-4c31-8489-6ce216de1fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "class Attention(nn.Module):\n",
    "    def __init__(self, num_attention_heads = 8, embedding_dim = 768, dropout=0.1):\n",
    "        super().__init__()\n",
    "        self.num_attention_heads = num_attention_heads\n",
    "        self.dropout = torch.nn.Dropout(dropout)\n",
    "        self.output_linear = nn.Linear(embedding_dim, embedding_dim)\n",
    "\n",
    "    def forward(self, q, k, v, padding_mask):\n",
    "        bs, seq_len, embed_dim = q.shape\n",
    "        num_elem_per_attn_head = embed_dim//self.num_attention_heads\n",
    "\n",
    "        qr = q.view(bs, seq_len, self.num_attention_heads, -1).permute(0,2,1,3)\n",
    "        vr = v.view(bs, seq_len, self.num_attention_heads, -1).permute(0,2,1,3)\n",
    "        kr = k.view(bs, seq_len, self.num_attention_heads, -1).permute(0,2,1,3)\n",
    "\n",
    "        attn_scores = torch.matmul(qr, kr.permute(0,1,3,2))/math.sqrt(num_elem_per_attn_head)\n",
    "        #ATTN SCORES SHAPE torch.Size([16, 12, 64, 64]) , PADDING MASK SHAPE torch.Size([16, 1, 64, 64])\n",
    "        attn_scores_masked = attn_scores.masked_fill(padding_mask == 0, -1e9)\n",
    "        attn_scores_normalized = torch.nn.functional.softmax(attn_scores_masked, dim=-1)\n",
    "        attn_scores_regularized = self.dropout(attn_scores_normalized)\n",
    "                        \n",
    "        attn_output = torch.matmul(attn_scores_regularized, vr).permute(0,2,1,3).contiguous().view(bs, seq_len, -1)\n",
    "        return self.output_linear(attn_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "id": "244ab95a-a8ec-4132-86a9-67e1d4e18db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class FullyConnected(nn.Module):\n",
    "    def __init__(self, embedding_dim = 768, expansion_factor = 4, dropout = 0.1):\n",
    "        super().__init__()\n",
    "        intermediate_dim = embedding_dim * expansion_factor \n",
    "        self.fc1 = nn.Linear(embedding_dim, intermediate_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.fc2 = nn.Linear(intermediate_dim, embedding_dim)\n",
    "        self.activation = nn.GELU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.activation(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.activation(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "id": "16cb03b7-603d-437a-8728-c50e155f857c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerLayer(nn.Module):\n",
    "    def __init__(self, embedding_dim = 768, num_attention_heads = 8, expansion_factor = 4, dropout = 0.1):\n",
    "        super().__init__()\n",
    "        self.projection = Projection(embedding_dim)\n",
    "        self.attention = Attention(num_attention_heads, embedding_dim)\n",
    "        self.layernorm1 = nn.LayerNorm(embedding_dim)\n",
    "        self.fully_connected = FullyConnected(embedding_dim, expansion_factor)\n",
    "        self.layernorm2 = nn.LayerNorm(embedding_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x, padding_mask):\n",
    "        queries, keys, values = self.projection(x)\n",
    "        attention_ouptut = self.attention(queries, keys, values, padding_mask)\n",
    "        attention_output_regularized = self.dropout(attention_ouptut)\n",
    "        residual_1 = x + attention_output_regularized\n",
    "        layer_norm_1 = self.layernorm1(residual_1)\n",
    "        fully_connected_output = self.fully_connected(layer_norm_1)\n",
    "        fully_connected_output_regularized = self.dropout(fully_connected_output)\n",
    "        residual_2 = layer_norm_1 + fully_connected_output_regularized\n",
    "        final_output = self.layernorm2(residual_2)\n",
    "        return final_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "id": "e2aed7a6-4ade-4a6c-85b2-96d1ff9f0c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, num_layers = 12, embedding_dim = 768, num_attention_heads = 12, expansion_factor = 4):\n",
    "        super().__init__()\n",
    "        self.layers = nn.ModuleList([\n",
    "            TransformerLayer(embedding_dim, num_attention_heads, expansion_factor) for _ in range(num_layers)\n",
    "                       ])\n",
    "        \n",
    "    def forward(self, x, padding_mask):\n",
    "        for l in range(1, num_layers):\n",
    "            x = self.layers[l](x, padding_mask)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 863,
   "id": "66499074-68b8-44f9-9934-d656adc10de5",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, vocab_size, seq_len = 64, num_token_types = 3, num_layers = 12, embedding_dim = 768, num_attention_heads = 12, expansion_factor = 4):\n",
    "        super().__init__()\n",
    "        self.embedding_layer = BertEmbeddingLayer(vocab_size, seq_len, embedding_dim, num_token_types)\n",
    "        self.transformer_blocks = TransformerBlock(num_layers, embedding_dim, num_attention_heads, expansion_factor)\n",
    "\n",
    "    def forward(self, inp_tokens, input_token_types):\n",
    "        seq_len = inp_tokens.size(1)\n",
    "        padding_mask = (inp_tokens > 0).unsqueeze(1).repeat(1, seq_len, 1).unsqueeze(1)\n",
    "        embeddings = self.embedding_layer(inp_tokens, input_token_types)\n",
    "        encoded_representation = self.transformer_blocks(embeddings, padding_mask)\n",
    "        return encoded_representation\n",
    "\n",
    "\n",
    "e = Encoder(tokenizer.vocab_size)\n",
    "op = e(inp['bert_input'], inp['segment_label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 882,
   "id": "63076728-4eb0-43d4-bf5a-495e8de49d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NextSentancePrediction(nn.Module):\n",
    "    def __init__(self, embedding_dim = 768):\n",
    "        super().__init__()\n",
    "        self.linear_layer = nn.Linear(embedding_dim, 2)\n",
    "\n",
    "    def forward(self, encoded_representation, target_labels):\n",
    "        cls_token_representation = encoded_representation[:,0,:]\n",
    "        logits = self.linear_layer(cls_token_representation)\n",
    "        nsp_loss = torch.nn.CrossEntropyLoss()(logits, target_labels)\n",
    "        return nsp_loss\n",
    "\n",
    "class MaskedLanguageModel(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim = 768):\n",
    "        super().__init__()\n",
    "        self.linear_layer = nn.Linear(embedding_dim, vocab_size)\n",
    "\n",
    "    def forward(self, encoded_representation, target_labels):\n",
    "        bs, seq_len , embedding_dim = encoded_representation.shape\n",
    "        target_labels_flattened = target_labels.view(bs*seq_len)\n",
    "        logits = self.linear_layer(encoded_representation)\n",
    "        logits_flattenend = logits.view((bs*seq_len, -1))\n",
    "        all_loss = nn.CrossEntropyLoss(ignore_index=0)(logits_flattenend, target_labels_flattened) #reduction='none', \n",
    "        #target_labels_flattened[target_labels_flattened > 0] = 1         \n",
    "        #mlm_loss = torch.dot(target_labels_flattened.float(), all_loss) / torch.sum(target_labels_flattened)\n",
    "        return all_loss #mlm_loss\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 883,
   "id": "74651c6d-c5f5-41b8-9f29-9c16a7ab109f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(10.8747, grad_fn=<AddBackward0>)\n"
     ]
    }
   ],
   "source": [
    "vocab_size = 200\n",
    "seq_len = 64\n",
    "num_token_types = 3\n",
    "num_layers = 12\n",
    "embedding_dim = 768\n",
    "num_attention_heads = 8\n",
    "expansion_factor = 4\n",
    "vocab_size = tokenizer.vocab_size\n",
    "batch_size = 16\n",
    "\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n",
    "train_data_iterator  = iter(train_dataloader)\n",
    "\n",
    "inp = next(train_data_iterator)\n",
    "\n",
    "\n",
    "class BertModel(nn.Module):\n",
    "    def __init__(self, vocab_size, seq_len = 64, num_token_types=3, num_layers=12, embedding_dim = 768, num_attention_heads = 12, expansion_factor=4):\n",
    "        super().__init__()\n",
    "        self.encoder = Encoder(vocab_size, seq_len, num_token_types, num_layers, embedding_dim, num_attention_heads, expansion_factor)\n",
    "        self.masked_language_model = MaskedLanguageModel(vocab_size, embedding_dim) \n",
    "        self.next_sentance_prediction = NextSentancePrediction(embedding_dim)\n",
    "\n",
    "    def forward(self, input_tokens, input_token_types, target_mlm_labels, target_nsp_labels):\n",
    "        encoded_representation = self.encoder(input_tokens, input_token_types)\n",
    "        nsp_loss = self.next_sentance_prediction(encoded_representation, target_nsp_labels)\n",
    "        mlm_loss = self.masked_language_model(encoded_representation, target_mlm_labels)\n",
    "        total_loss = mlm_loss + nsp_loss\n",
    "        return total_loss\n",
    "        \n",
    "loss = BertModel(tokenizer.vocab_size)(inp['bert_input'], inp['segment_label'], inp['bert_label'].clone(), inp['is_next'])\n",
    "print(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 899,
   "id": "e5e2a083-79a1-42f2-9f9a-bc07b7964ddd",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'profile_macs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [899]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m macs \u001b[38;5;241m=\u001b[39m \u001b[43mprofile_macs\u001b[49m( BertModel(tokenizer\u001b[38;5;241m.\u001b[39mvocab_size), (inp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbert_input\u001b[39m\u001b[38;5;124m'\u001b[39m], inp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msegment_label\u001b[39m\u001b[38;5;124m'\u001b[39m], inp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbert_label\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mclone(), inp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis_next\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n",
      "\u001b[0;31mNameError\u001b[0m: name 'profile_macs' is not defined"
     ]
    }
   ],
   "source": [
    "macs = profile_macs( BertModel(tokenizer.vocab_size), (inp['bert_input'], inp['segment_label'], inp['bert_label'].clone(), inp['is_next']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 884,
   "id": "8f330c22-e30c-46b4-9a0b-387e68a2708a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ScheduledOptim():\n",
    "    '''A simple wrapper class for learning rate scheduling'''\n",
    "\n",
    "    def __init__(self, optimizer, d_model, n_warmup_steps):\n",
    "        self._optimizer = optimizer\n",
    "        self.n_warmup_steps = n_warmup_steps\n",
    "        self.n_current_steps = 0\n",
    "        self.init_lr = np.power(d_model, -0.5)\n",
    "\n",
    "    def step_and_update_lr(self):\n",
    "        \"Step with the inner optimizer\"\n",
    "        self._update_learning_rate()\n",
    "        self._optimizer.step()\n",
    "\n",
    "    def zero_grad(self):\n",
    "        \"Zero out the gradients by the inner optimizer\"\n",
    "        self._optimizer.zero_grad()\n",
    "\n",
    "    def _get_lr_scale(self):\n",
    "        return np.min([\n",
    "            np.power(self.n_current_steps, -0.5),\n",
    "            np.power(self.n_warmup_steps, -1.5) * self.n_current_steps])\n",
    "\n",
    "    def _update_learning_rate(self):\n",
    "        ''' Learning rate scheduling per step '''\n",
    "\n",
    "        self.n_current_steps += 1\n",
    "        lr = self.init_lr * self._get_lr_scale()\n",
    "\n",
    "        for param_group in self._optimizer.param_groups:\n",
    "            param_group['lr'] = lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 887,
   "id": "a8719525-784e-4201-814c-46ff30381759",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "class BERTTrainer:\n",
    "    def __init__(\n",
    "        self, \n",
    "        model, \n",
    "        train_dataloader, \n",
    "        test_dataloader=None, \n",
    "        lr= 1e-4,\n",
    "        weight_decay=0.01,\n",
    "        betas=(0.9, 0.999),\n",
    "        warmup_steps=10000,\n",
    "        log_freq=10,\n",
    "        device='cuda',\n",
    "        embedding_dim = 768\n",
    "        ):\n",
    "\n",
    "        self.device = device\n",
    "        self.model = model\n",
    "        self.train_data = train_dataloader\n",
    "        self.test_data = test_dataloader\n",
    "\n",
    "        # Setting the Adam optimizer with hyper-param\n",
    "        self.optim = Adam(self.model.parameters(), lr=lr, betas=betas, weight_decay=weight_decay)\n",
    "        self.optim_schedule = ScheduledOptim(\n",
    "            self.optim, embedding_dim, n_warmup_steps=warmup_steps\n",
    "            )\n",
    "\n",
    "        # Using Negative Log Likelihood Loss function for predicting the masked_token\n",
    "        self.criterion = torch.nn.NLLLoss(ignore_index=0)\n",
    "        self.log_freq = log_freq\n",
    "        print(\"Total Parameters:\", sum([p.nelement() for p in self.model.parameters()]))\n",
    "    \n",
    "    def train(self, epoch):\n",
    "        self.iteration(epoch, self.train_data)\n",
    "\n",
    "    def test(self, epoch):\n",
    "        self.iteration(epoch, self.test_data, train=False)\n",
    "\n",
    "    def iteration(self, epoch, data_loader, train=True):\n",
    "        \n",
    "        avg_loss = 0.0\n",
    "        total_correct = 0\n",
    "        total_element = 0\n",
    "        \n",
    "        mode = \"train\" if train else \"test\"\n",
    "\n",
    "        # progress bar\n",
    "        data_iter = tqdm.tqdm(\n",
    "            enumerate(data_loader),\n",
    "            desc=\"EP_%s:%d\" % (mode, epoch),\n",
    "            total=len(data_loader),\n",
    "            bar_format=\"{l_bar}{r_bar}\"\n",
    "        )\n",
    "\n",
    "        for i, data in data_iter:\n",
    "\n",
    "            # 0. batch_data will be sent into the device(GPU or cpu)\n",
    "            data = {key: value.to(self.device) for key, value in data.items()}\n",
    "\n",
    "            loss = self.model(data['bert_input'], data['segment_label'], data['bert_label'].clone(), data['is_next'])\n",
    "\n",
    "            # 3. backward and optimization only in train\n",
    "            if train:\n",
    "                self.optim_schedule.zero_grad()\n",
    "                loss.backward()\n",
    "                self.optim_schedule.step_and_update_lr()\n",
    "\n",
    "            # next sentence prediction accuracy\n",
    "           #correct = next_sent_output.argmax(dim=-1).eq(data[\"is_next\"]).sum().item()\n",
    "            #avg_loss += loss.item()\n",
    "            #total_correct += correct\n",
    "            #total_element += data[\"is_next\"].nelement()\n",
    "\n",
    "            post_fix = {\n",
    "                \"epoch\": epoch,\n",
    "                \"iter\": i,\n",
    "              #  \"avg_loss\": avg_loss / (i + 1),\n",
    "              #  \"avg_acc\": total_correct / total_element * 100,\n",
    "                \"loss\": loss.item()\n",
    "            }\n",
    "\n",
    "            if i % self.log_freq == 0:\n",
    "                data_iter.write(str(post_fix))\n",
    "        print(f\"EP{epoch}, {mode}: \\\n",
    "            avg_loss={avg_loss / len(data_iter)}, \\\n",
    "            total_acc={total_correct * 100.0 / total_element}\"\n",
    "        ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 888,
   "id": "53605c20-a436-4ae0-b1bf-96dee1855c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Parameters: 117581226\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   0%|| 1/6926 [00:03<6:24:37,  3.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 0, 'loss': 10.980493545532227}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   0%|| 11/6926 [00:30<5:12:06,  2.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 10, 'loss': 10.817377090454102}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   0%|| 21/6926 [00:57<5:01:25,  2.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 20, 'loss': 10.684929847717285}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   0%|| 31/6926 [01:24<5:25:23,  2.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 30, 'loss': 10.592150688171387}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   1%|| 41/6926 [01:52<5:26:45,  2.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 40, 'loss': 10.368402481079102}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   1%|| 51/6926 [02:21<5:26:54,  2.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 50, 'loss': 10.154437065124512}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   1%|| 61/6926 [02:50<5:26:21,  2.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 60, 'loss': 10.01576042175293}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   1%|| 71/6926 [03:19<5:29:36,  2.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 70, 'loss': 9.73730182647705}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   1%|| 81/6926 [03:47<5:28:58,  2.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 80, 'loss': 9.776338577270508}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   1%|| 91/6926 [04:16<5:28:32,  2.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 90, 'loss': 9.896708488464355}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   1%|| 101/6926 [04:45<5:22:11,  2.83s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 100, 'loss': 9.029985427856445}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   2%|| 111/6926 [05:14<5:32:10,  2.92s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 110, 'loss': 9.456299781799316}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EP_train:0:   2%|| 121/6926 [05:43<5:19:44,  2.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epoch': 0, 'iter': 120, 'loss': 9.487717628479004}\n"
     ]
    },
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     "text": [
      "{'epoch': 0, 'iter': 6760, 'loss': 6.662722110748291}\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
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    },
    {
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      "EP_train:0: 100%|| 6926/6926 [7:32:48<00:00,  3.92s/it]\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "float division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [888]\u001b[0m, in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20\u001b[39m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs):\n\u001b[0;32m---> 16\u001b[0m   \u001b[43mbert_trainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [887]\u001b[0m, in \u001b[0;36mBERTTrainer.train\u001b[0;34m(self, epoch)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(\u001b[38;5;28mself\u001b[39m, epoch):\n\u001b[0;32m---> 34\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miteration\u001b[49m\u001b[43m(\u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_data\u001b[49m\u001b[43m)\u001b[49m\n",
      "Input \u001b[0;32mIn [887]\u001b[0m, in \u001b[0;36mBERTTrainer.iteration\u001b[0;34m(self, epoch, data_loader, train)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_freq \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     83\u001b[0m         data_iter\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;28mstr\u001b[39m(post_fix))\n\u001b[1;32m     84\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEP\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmode\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;124m    avg_loss=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mavg_loss \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(data_iter)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;130;01m\\\u001b[39;00m\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;124m    total_acc=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtotal_correct \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m100.0\u001b[39m \u001b[38;5;241m/\u001b[39m total_element\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     87\u001b[0m )\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero"
     ]
    }
   ],
   "source": [
    "'''test run'''\n",
    "\n",
    "train_data = BERTDataset(\n",
    "   pairs, seq_len=MAX_LEN, tokenizer=tokenizer)\n",
    "\n",
    "train_loader = DataLoader(\n",
    "   train_data, batch_size=32, shuffle=True, pin_memory=True)\n",
    "\n",
    "bert_model =  BertModel(tokenizer.vocab_size)\n",
    "\n",
    "bert_trainer = BERTTrainer(bert_model, train_loader, device='cpu')\n",
    "epochs = 20\n",
    "\n",
    "for epoch in range(epochs):\n",
    "  bert_trainer.train(epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 889,
   "id": "d0a93220-89c2-419d-9b4c-e6cfb8961aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Counter:\n",
    "\n",
    "    def __init__(self, start: int = 0) -> None:\n",
    "        self.counter = start\n",
    "\n",
    "    def __next__(self) -> int:\n",
    "        i = self.counter\n",
    "        self.counter += 1\n",
    "        return i\n",
    "\n",
    "    def reset(self) -> None:\n",
    "        self.counter = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 890,
   "id": "f0453ba7-d28e-46b9-bccc-9aa9b629e8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 891,
   "id": "229fec40-5bfd-4b0b-9f76-bd84b8c4fee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 891,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 892,
   "id": "44f1a849-e3d6-4c7b-93b4-5b164c3f73f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 892,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 893,
   "id": "c081ac67-045a-45da-953e-29aa7da6311b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 893,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 894,
   "id": "0b1483fb-347b-429a-bf9d-d559c97c03fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once deleted, variables cannot be recovered. Proceed (y/[n])?  y\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Don't know how to reset  (c), please run `%reset?` for details\n"
     ]
    }
   ],
   "source": [
    "reset(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 895,
   "id": "707351a9-4ecc-4076-a0a2-dea67b4b75f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Counter at 0x34fdec970>"
      ]
     },
     "execution_count": 895,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 896,
   "id": "a62595fa-27fe-4c1c-85ba-60262ab61cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 896,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 897,
   "id": "19ab018c-ce10-4723-8633-4a6d05b13cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "c.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a4e3077-1408-4c45-8c9b-62b6ceda4ac4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 898,
   "id": "27631f7c-43c5-49c4-943f-91a83946b9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 898,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff94676f-f10b-4a61-b081-22af42d7f41d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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