roberta-base-from-scratch-pytorch.ipynb

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   "source": [
    "import gc\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import pickle\n",
    "import random\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.metrics import log_loss\n",
    "import math\n",
    "import torch\n",
    "import transformers\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.cuda.amp import GradScaler, autocast\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from transformers import AutoModel, AutoTokenizer, AdamW, get_linear_schedule_with_warmup\n",
    "from transformers import AutoConfig\n",
    "os.environ['TOKENIZERS_PARALLELISM'] = 'false'\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
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     "text": [
      "Sat Feb 25 10:29:42 2023       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 470.82.01    Driver Version: 470.82.01    CUDA Version: 11.4     |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                               |                      |               MIG M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\r\n",
      "| N/A   31C    P0    25W / 250W |      0MiB / 16280MiB |      0%      Default |\r\n",
      "|                               |                      |                  N/A |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                  |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\r\n",
      "|        ID   ID                                                   Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|  No running processes found                                                 |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "! nvidia-smi"
   ]
  },
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   "source": [
    "device = torch.device('cuda') if torch.cuda.is_available() else 'cpu'"
   ]
  },
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   "source": [
    "def seed_everything(seed):\n",
    "    random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True"
   ]
  },
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   "source": [
    "EPOCHS = 12\n",
    "lr = 2e-5\n",
    "SEED = 42\n",
    "MAX_LEN = 128\n",
    "BATCH_SIZE = 24\n",
    "accumulation_steps = 4\n",
    "seed_everything(SEED)"
   ]
  },
  {
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Target</th>\n",
       "      <th>intent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>what expression would i use to say i love you ...</td>\n",
       "      <td>61</td>\n",
       "      <td>translate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>can you tell me how to say 'i do not speak muc...</td>\n",
       "      <td>61</td>\n",
       "      <td>translate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text  Target     intent\n",
       "0  what expression would i use to say i love you ...      61  translate\n",
       "1  can you tell me how to say 'i do not speak muc...      61  translate"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = '../input/data-for-distilation' \n",
    "train = pd.read_csv('../input/data-for-distilation/Clinc_Train.csv')\n",
    "valid = pd.read_csv('../input/data-for-distilation/Clinc_valid.csv')\n",
    "n_classes = np.unique(train.Target).shape[0]\n",
    "train.head(2)"
   ]
  },
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   "source": [
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self,config,device):\n",
    "        super(MultiHeadAttention,self).__init__()\n",
    "        self.n_heads = config.num_attention_heads\n",
    "        self.hidden_size = config.hidden_size\n",
    "        self.head_dim = self.hidden_size//self.n_heads\n",
    "        self.q = nn.Linear(self.hidden_size,self.hidden_size)\n",
    "        self.k = nn.Linear(self.hidden_size,self.hidden_size)\n",
    "        self.v = nn.Linear(self.hidden_size,self.hidden_size)\n",
    "        self.device = device\n",
    "\n",
    "        self.fc = nn.Linear(self.hidden_size,self.hidden_size)\n",
    "        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)\n",
    "        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(self.device)\n",
    "\n",
    "    def forward(self, query, key, value, mask = None):\n",
    "        batch_size = query.shape[0]\n",
    "        Q = self.q(query)\n",
    "        K = self.k(key)\n",
    "        V = self.v(value)\n",
    "        # [batch size, query len, hid dim]\n",
    "        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        # [batch size, n heads, query len, head dim]\n",
    "        score = torch.matmul(Q, K.permute(0, 1, 3, 2)) /self.scale\n",
    "        if mask is not None:\n",
    "            score = score.masked_fill(mask == 0, -1e10)\n",
    "        attention = torch.softmax(score, dim = -1)\n",
    "        x = torch.matmul(self.dropout(attention), V)\n",
    "        x = x.permute(0, 2, 1, 3).contiguous()\n",
    "        \n",
    "        #x = [batch size, query len, n heads, head dim]\n",
    "        \n",
    "        x = x.view(batch_size, -1, self.hidden_size)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        x = self.fc(x)\n",
    "        return x, attention\n",
    "    \n",
    "class PositionwiseFeedforwardLayer(nn.Module):\n",
    "    def __init__(self,config):\n",
    "        super(PositionwiseFeedforwardLayer,self).__init__()\n",
    "        self.pf_dim = config.intermediate_size\n",
    "        self.hid_dim = config.hidden_size\n",
    "        self.fc_1 = nn.Linear(self.hid_dim, self.pf_dim)\n",
    "        self.fc_2 = nn.Linear(self.pf_dim, self.hid_dim)\n",
    "        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        x = self.dropout(nn.GELU()(self.fc_1(x)))\n",
    "        \n",
    "        #x = [batch size, seq len, pf dim]\n",
    "        \n",
    "        x = self.fc_2(x)\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        return x\n",
    "    \n",
    "class EncoderLayer(nn.Module):\n",
    "    def __init__(self,config,device):\n",
    "        super(EncoderLayer,self).__init__()\n",
    "        self.hid_dim = config.hidden_size\n",
    "        self.self_attn_layer_norm = nn.LayerNorm(self.hid_dim)\n",
    "        self.ff_layer_norm = nn.LayerNorm(self.hid_dim)\n",
    "        self.device = device\n",
    "        self.self_attention =  MultiHeadAttention(config, device)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(config)\n",
    "        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
    "        \n",
    "    def forward(self, x,mask):\n",
    "        \n",
    "        #x = [batch size, x len, hid dim]\n",
    "        #x_mask = [batch size, 1, 1, x len] \n",
    "                \n",
    "        #self attention\n",
    "        _x, _ = self.self_attention(x,x,x,mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        x = self.self_attn_layer_norm(x + self.dropout(_x))\n",
    "        \n",
    "        #x = [batch size, x len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _x = self.positionwise_feedforward(_x)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        x = self.ff_layer_norm(x + self.dropout(_x))\n",
    "        \n",
    "        #x = [batch size, x len, hid dim]\n",
    "        \n",
    "        return x\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self,config,device):\n",
    "        super(Encoder,self).__init__()\n",
    "        self.vocab = config.vocab_size\n",
    "        self.hid_dim = config.hidden_size\n",
    "        self.max_length = config.max_position_embeddings\n",
    "        self.tok_embedding = nn.Embedding(self.vocab, self.hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(self.max_length, self.hid_dim)\n",
    "        self.n_layers = config.num_hidden_layers\n",
    "        self.layers = nn.ModuleList([EncoderLayer(config,device) \n",
    "                                     for _ in range(self.n_layers)])\n",
    "        \n",
    "        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([config.hidden_size])).to(device)\n",
    "        self.device = device\n",
    "    def forward(self, x,mask):\n",
    "        \n",
    "        #x = [batch size, x len]\n",
    "        #x_mask = [batch size, 1, 1, x len]\n",
    "        \n",
    "        batch_size = x.shape[0]\n",
    "        x_len = x.shape[1]\n",
    "        \n",
    "        pos = torch.arange(0, x_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "        \n",
    "        #pos = [batch size, x len]\n",
    "        \n",
    "        x = self.dropout((self.tok_embedding(x) * self.scale) + self.pos_embedding(pos))\n",
    "        \n",
    "        #x = [batch size, x len, hid dim]\n",
    "        mask = mask.unsqueeze(1).unsqueeze(2)\n",
    "        # mask = [batch size, 1, 1, x len]\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, mask)\n",
    "            \n",
    "        #x = [batch size, x len, hid dim]\n",
    "            \n",
    "        return x"
   ]
  },
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   ],
   "source": [
    "model_ckpt = 'roberta-base'\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
    "config = AutoConfig.from_pretrained(model_ckpt)"
   ]
  },
  {
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   "source": [
    "class callback:\n",
    "    def __init__(self):\n",
    "        self.loss = list()\n",
    "        self.model = list()\n",
    "    \n",
    "    def put(self, model, loss):\n",
    "        self.loss.append(loss)\n",
    "        self.model.append(model)\n",
    "\n",
    "    def get_model(self):\n",
    "        ind = np.argmin(self.loss)\n",
    "        return self.model[ind]\n",
    "\n",
    "class ClinicModel(nn.Module):\n",
    "    def __init__(self, config,device):\n",
    "        super(ClinicModel, self).__init__()\n",
    "        self.model = Encoder(config,device)\n",
    "        self.output = nn.Linear(768, n_classes)\n",
    "\n",
    "    def forward(self, ids, mask):\n",
    "        sequence_output = self.model(ids, mask)[:, 0, :]\n",
    "        logits = self.output(sequence_output)\n",
    "        return logits\n",
    "\n",
    "\n",
    "class ClinicDataset(Dataset):\n",
    "    def __init__(self, data,is_test=False):\n",
    "        self.X = data['Text'].values\n",
    "        self.Y = data['Target'].values\n",
    "        self.is_test = is_test\n",
    "        self.tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
    "        self.MAX_LEN = 128\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        inputs = self.tokenizer.encode_plus(self.X[idx],\n",
    "            add_special_tokens=True,\n",
    "            truncation=True,\n",
    "            max_length=self.MAX_LEN\n",
    "        )['input_ids'] \n",
    "\n",
    "        if not self.is_test:\n",
    "            target_value = self.Y[idx]\n",
    "      \n",
    "        mask = [1]*len(inputs) + [0] * (self.MAX_LEN - len(inputs)) \n",
    "        mask = torch.tensor(mask, dtype=torch.long)\n",
    "        \n",
    "        if len(inputs) != self.MAX_LEN:\n",
    "            inputs = inputs + [self.tokenizer.pad_token_id] * (self.MAX_LEN - len(inputs)) \n",
    "        ids = torch.tensor(inputs, dtype=torch.long)\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        if self.is_test:\n",
    "            return {\n",
    "                'ids': ids,\n",
    "                'mask': mask,\n",
    "            }\n",
    "        \n",
    "        else:\n",
    "            targets = torch.FloatTensor(target_value)\n",
    "            return {\n",
    "                'ids': ids,\n",
    "                'mask': mask,\n",
    "                'targets': targets\n",
    "            }\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a9ece4fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T10:30:01.571789Z",
     "iopub.status.busy": "2023-02-25T10:30:01.570364Z",
     "iopub.status.idle": "2023-02-25T10:30:02.816406Z",
     "shell.execute_reply": "2023-02-25T10:30:02.815155Z"
    },
    "papermill": {
     "duration": 1.256817,
     "end_time": "2023-02-25T10:30:02.819481",
     "exception": false,
     "start_time": "2023-02-25T10:30:01.562664",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_texts = train['Text'].values.tolist()\n",
    "val_texts = valid['Text'].values.tolist()\n",
    "train_labels = train['Target'].values.tolist()\n",
    "val_labels = valid['Target'].values.tolist()\n",
    "train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=128)\n",
    "val_encodings = tokenizer(val_texts, truncation=True, padding=True, max_length=128)\n",
    "\n",
    "class ClinicDatasetV2(torch.utils.data.Dataset):\n",
    "    def __init__(self, encodings, labels):\n",
    "        self.encodings = encodings\n",
    "        self.labels = labels\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
    "        item['labels'] = torch.tensor(self.labels[idx])\n",
    "        return {'ids':item.get('input_ids'),'mask':item.get('attention_mask'),'labels':item.get('labels')}\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.labels)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(ClinicDatasetV2(train_encodings, train_labels),batch_size=BATCH_SIZE, shuffle=True, num_workers=2)\n",
    "val_loader = torch.utils.data.DataLoader(ClinicDatasetV2(val_encodings, val_labels),batch_size=BATCH_SIZE, shuffle=False, num_workers=2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30a65d8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T10:30:02.834680Z",
     "iopub.status.busy": "2023-02-25T10:30:02.832335Z",
     "iopub.status.idle": "2023-02-25T10:30:02.845771Z",
     "shell.execute_reply": "2023-02-25T10:30:02.844410Z"
    },
    "papermill": {
     "duration": 0.024008,
     "end_time": "2023-02-25T10:30:02.849154",
     "exception": false,
     "start_time": "2023-02-25T10:30:02.825146",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def valid_func(model,valid_loader):\n",
    "    model.eval()\n",
    "    bar = tqdm(valid_loader,file=sys.stdout)\n",
    "    loss_fn = torch.nn.CrossEntropyLoss()\n",
    "    PROB = []\n",
    "    TARGETS = []\n",
    "    losses = []\n",
    "    PREDS = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (data) in enumerate(bar):\n",
    "            input_ids = data['ids'].cuda()\n",
    "            input_masks = data['mask'].cuda()\n",
    "            targets = data['labels'].long().view(-1).cuda()\n",
    "\n",
    "            logits = model(input_ids,input_masks)\n",
    "\n",
    "            PREDS += [torch.argmax(logits, 1).detach().cpu()]\n",
    "            TARGETS += [targets.detach().cpu()]\n",
    "\n",
    "            loss = loss_fn (logits, targets)\n",
    "            losses.append(loss.item())\n",
    "           \n",
    "            bar.set_description(f'loss: {loss.item():.5f}')\n",
    "\n",
    "    PREDS = torch.cat(PREDS).cpu().numpy()\n",
    "    TARGETS = torch.cat(TARGETS).cpu().numpy()\n",
    "    accuracy = (PREDS==TARGETS).mean()\n",
    "   \n",
    "    loss_valid = np.mean(losses)\n",
    "    return loss_valid, accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d3d19ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T10:30:02.863183Z",
     "iopub.status.busy": "2023-02-25T10:30:02.862718Z",
     "iopub.status.idle": "2023-02-25T10:44:15.549053Z",
     "shell.execute_reply": "2023-02-25T10:44:15.546994Z"
    },
    "papermill": {
     "duration": 852.697698,
     "end_time": "2023-02-25T10:44:15.552688",
     "exception": false,
     "start_time": "2023-02-25T10:30:02.854990",
     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
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    },
    {
     "data": {
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--1 ### Train loss---3.1896 ### Valid_Loss---3.2159992612325228 ### Valid_Acc---0.42129032258064514\n",
      "Model Saved--epoch--1\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "version_major": 2,
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--2 ### Train loss---1.4775 ### Valid_Loss---1.969340900045175 ### Valid_Acc---0.632258064516129\n",
      "Model Saved--epoch--2\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
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       "  0%|          | 0/130 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--3 ### Train loss---1.8931 ### Valid_Loss---1.4984107043880683 ### Valid_Acc---0.7090322580645161\n",
      "Model Saved--epoch--3\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--4 ### Train loss---1.4479 ### Valid_Loss---1.2553657522568336 ### Valid_Acc---0.75\n",
      "Model Saved--epoch--4\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--5 ### Train loss---0.733 ### Valid_Loss---1.101968218271549 ### Valid_Acc---0.7719354838709678\n",
      "Model Saved--epoch--5\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--6 ### Train loss---0.8781 ### Valid_Loss---1.0187745079111594 ### Valid_Acc---0.7883870967741935\n",
      "Model Saved--epoch--6\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--7 ### Train loss---0.5984 ### Valid_Loss---0.9503974425391509 ### Valid_Acc---0.8038709677419354\n",
      "Model Saved--epoch--7\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--8 ### Train loss---0.3018 ### Valid_Loss---0.9061386542824599 ### Valid_Acc---0.8067741935483871\n",
      "Model Saved--epoch--8\n"
     ]
    },
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--9 ### Train loss---0.9724 ### Valid_Loss---0.8830602332089956 ### Valid_Acc---0.8096774193548387\n",
      "Model Saved--epoch--9\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--10 ### Train loss---0.6607 ### Valid_Loss---0.8642050722184089 ### Valid_Acc---0.8103225806451613\n",
      "Model Saved--epoch--10\n"
     ]
    },
    {
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       "version_minor": 0
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--11 ### Train loss---0.6467 ### Valid_Loss---0.8601169919308561 ### Valid_Acc---0.8145161290322581\n",
      "Model Saved--epoch--11\n"
     ]
    },
    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch--12 ### Train loss---0.3309 ### Valid_Loss---0.8474599173005957 ### Valid_Acc---0.8154838709677419\n",
      "Model Saved--epoch--12\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "164"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_amp = True\n",
    "debug = False\n",
    "gc.collect()\n",
    "best_epoch_loss = np.inf\n",
    "\n",
    "net = ClinicModel(config,device)\n",
    "net.cuda()\n",
    "log_df = pd.DataFrame(columns = ['Epoch','Train_Loss','Valid_Loss','Valid_Accuracy'])\n",
    "\n",
    "loss_fn = torch.nn.CrossEntropyLoss()\n",
    "optimizer = AdamW(net.parameters(), lr = lr)    \n",
    "num_train_optimization_steps = int(EPOCHS * len(train_loader) / accumulation_steps)\n",
    "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0.05 * num_train_optimization_steps,\n",
    "                                    num_training_steps=num_train_optimization_steps)# PyTorch scheduler\n",
    "if use_amp:\n",
    "        scaler = torch.cuda.amp.GradScaler()\n",
    "for epoch in range(EPOCHS):\n",
    "    start_time = time.time()\n",
    "    avg_loss = 0.0\n",
    "    net.train()\n",
    "    tbar = tqdm(train_loader, file=sys.stdout)\n",
    "    loss_list = []\n",
    "    val_loss_list = []\n",
    "    for step, data in enumerate(tbar):\n",
    "        if debug and step == 10:\n",
    "            print('Debug Mode. Only train on first 100 batches.')\n",
    "            break\n",
    "        input_ids = data['ids'].cuda()\n",
    "        input_masks = data['mask'].cuda()\n",
    "        targets = data['labels'].long().view(-1).cuda()\n",
    "        if use_amp:\n",
    "                with torch.cuda.amp.autocast():\n",
    "                     pred = net(input_ids,input_masks)\n",
    "                     loss = loss_fn(pred, targets)\n",
    "                scaler.scale(loss).backward()\n",
    "                if step % accumulation_steps == 0 or step == len(tbar) - 1:\n",
    "                    scaler.step(optimizer)\n",
    "                    scaler.update()\n",
    "                    optimizer.zero_grad()\n",
    "                    scheduler.step()\n",
    "        else:\n",
    "            pred = net(input_ids,input_masks)\n",
    "            loss = loss_fn(pred, targets)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            optimizer.zero_grad()\n",
    "       \n",
    "\n",
    "    loss_list.append(loss.detach().cpu().item())\n",
    "    avg_loss = np.round(np.mean(loss_list), 4)\n",
    "    tbar.set_description(f\"Epoch {epoch + 1} Loss: {avg_loss} lr: {scheduler.get_last_lr()}\")\n",
    "    vloss,vaccuracy = valid_func(net,val_loader )\n",
    "    log_df.loc[len(log_df.index)] = [epoch+1,avg_loss,vloss,vaccuracy]\n",
    "    print(f'Epoch--{epoch+1} ### Train loss---{avg_loss} ### Valid_Loss---{vloss} ### Valid_Acc---{vaccuracy}')\n",
    "    if vloss<best_epoch_loss:\n",
    "        best_epoch_loss = vloss\n",
    "        PATH = f\"debertav3-Base_epoch__{epoch}.pth\"\n",
    "        torch.save(net.state_dict(), PATH)\n",
    "        print(f'Model Saved--epoch--{epoch+1}')\n",
    "        \n",
    "    \n",
    "del train_loader\n",
    "del net\n",
    "del val_loader\n",
    "gc.collect()\n"
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